coronavirus_timeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Infection Maps and Timeline\n",
    "The purpose of this notebook is to explore the covid-19 dataset and routes dataset and utilizing the ipyleaflet maps library to produce visual interactiable map graphs.\n",
    "### Installing the libraries and the map\n",
    "Below will import libraries and install the map. If the map does not show after installation, try refreshing the page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math, random, requests, io\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After importing, refresh the page to show the maps\n",
    "from ipyleaflet import (\n",
    "    Map, basemaps, basemap_to_tiles, Circle, \n",
    "    Marker, FullScreenControl, LayersControl, \n",
    "    LayerGroup, CircleMarker, Polyline\n",
    ")\n",
    "# If error received: No moduled named 'ipyleaflet'. You need to install the ipyleaflet from the environment.yml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07952fc2bb874164873528e610b5dd17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[0, 0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "center = (0, 0)\n",
    "zoom=1\n",
    "\n",
    "map_ = Map(basemap=basemaps.OpenStreetMap.Mapnik, center=center, zoom=zoom)\n",
    "map_.add_control(FullScreenControl())\n",
    "map_.add_control(LayersControl())\n",
    "display(map_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the map doesn't show above, try refreshing the page and ensuring that ipyleaflet has installed properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22679c3e169f428c9d837690e8e7d5a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Box(children=(IntSlider(value=1, description='zoom', max=10, min=1),))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map_.interact(zoom=(1,10,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'covid_confirmed':     Province/State         Country/Region        Lat       Long  1/22/20  \\\n",
      "0              NaN            Afghanistan  33.000000  65.000000        0   \n",
      "1              NaN                Albania  41.153300  20.168300        0   \n",
      "2              NaN                Algeria  28.033900   1.659600        0   \n",
      "3              NaN                Andorra  42.506300   1.521800        0   \n",
      "4              NaN                 Angola -11.202700  17.873900        0   \n",
      "..             ...                    ...        ...        ...      ...   \n",
      "261            NaN  Sao Tome and Principe   0.186360   6.613081        0   \n",
      "262            NaN                  Yemen  15.552727  48.516388        0   \n",
      "263            NaN                Comoros -11.645500  43.333300        0   \n",
      "264            NaN             Tajikistan  38.861034  71.276093        0   \n",
      "265            NaN                Lesotho -29.609988  28.233608        0   \n",
      "\n",
      "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  5/15/20  5/16/20  \\\n",
      "0          0        0        0        0        0  ...     6053     6402   \n",
      "1          0        0        0        0        0  ...      916      933   \n",
      "2          0        0        0        0        0  ...     6629     6821   \n",
      "3          0        0        0        0        0  ...      761      761   \n",
      "4          0        0        0        0        0  ...       48       48   \n",
      "..       ...      ...      ...      ...      ...  ...      ...      ...   \n",
      "261        0        0        0        0        0  ...      235      235   \n",
      "262        0        0        0        0        0  ...      106      122   \n",
      "263        0        0        0        0        0  ...       11       11   \n",
      "264        0        0        0        0        0  ...     1118     1322   \n",
      "265        0        0        0        0        0  ...        1        1   \n",
      "\n",
      "     5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  5/24/20  \n",
      "0       6664     7072     7653     8145     8676     9216     9998    10582  \n",
      "1        946      948      949      964      969      981      989      998  \n",
      "2       7019     7201     7377     7542     7728     7918     8113     8306  \n",
      "3        761      761      761      762      762      762      762      762  \n",
      "4         48       50       52       52       58       60       61       69  \n",
      "..       ...      ...      ...      ...      ...      ...      ...      ...  \n",
      "261      235      246      251      251      251      251      251      251  \n",
      "262      128      130      167      184      197      209      212      222  \n",
      "263       11       11       11       34       34       78       78       87  \n",
      "264     1524     1729     1936     2140     2350     2551     2738     2929  \n",
      "265        1        1        1        1        1        2        2        2  \n",
      "\n",
      "[266 rows x 128 columns], 'covid_deaths':     Province/State         Country/Region        Lat       Long  1/22/20  \\\n",
      "0              NaN            Afghanistan  33.000000  65.000000        0   \n",
      "1              NaN                Albania  41.153300  20.168300        0   \n",
      "2              NaN                Algeria  28.033900   1.659600        0   \n",
      "3              NaN                Andorra  42.506300   1.521800        0   \n",
      "4              NaN                 Angola -11.202700  17.873900        0   \n",
      "..             ...                    ...        ...        ...      ...   \n",
      "261            NaN  Sao Tome and Principe   0.186360   6.613081        0   \n",
      "262            NaN                  Yemen  15.552727  48.516388        0   \n",
      "263            NaN                Comoros -11.645500  43.333300        0   \n",
      "264            NaN             Tajikistan  38.861034  71.276093        0   \n",
      "265            NaN                Lesotho -29.609988  28.233608        0   \n",
      "\n",
      "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  5/15/20  5/16/20  \\\n",
      "0          0        0        0        0        0  ...      153      168   \n",
      "1          0        0        0        0        0  ...       31       31   \n",
      "2          0        0        0        0        0  ...      536      542   \n",
      "3          0        0        0        0        0  ...       49       51   \n",
      "4          0        0        0        0        0  ...        2        2   \n",
      "..       ...      ...      ...      ...      ...  ...      ...      ...   \n",
      "261        0        0        0        0        0  ...        7        7   \n",
      "262        0        0        0        0        0  ...       15       18   \n",
      "263        0        0        0        0        0  ...        1        1   \n",
      "264        0        0        0        0        0  ...       33       36   \n",
      "265        0        0        0        0        0  ...        0        0   \n",
      "\n",
      "     5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  5/24/20  \n",
      "0        169      173      178      187      193      205      216      218  \n",
      "1         31       31       31       31       31       31       31       32  \n",
      "2        548      555      561      568      575      582      592      600  \n",
      "3         51       51       51       51       51       51       51       51  \n",
      "4          2        3        3        3        3        3        4        4  \n",
      "..       ...      ...      ...      ...      ...      ...      ...      ...  \n",
      "261        7        7        8        8        8        8        8        8  \n",
      "262       20       20       28       30       33       33       39       42  \n",
      "263        1        1        1        1        1        1        1        1  \n",
      "264       39       41       41       41       44       44       44       46  \n",
      "265        0        0        0        0        0        0        0        0  \n",
      "\n",
      "[266 rows x 128 columns], 'covid_recovered':     Province/State         Country/Region        Lat       Long  1/22/20  \\\n",
      "0              NaN            Afghanistan  33.000000  65.000000        0   \n",
      "1              NaN                Albania  41.153300  20.168300        0   \n",
      "2              NaN                Algeria  28.033900   1.659600        0   \n",
      "3              NaN                Andorra  42.506300   1.521800        0   \n",
      "4              NaN                 Angola -11.202700  17.873900        0   \n",
      "..             ...                    ...        ...        ...      ...   \n",
      "248            NaN  Sao Tome and Principe   0.186360   6.613081        0   \n",
      "249            NaN                  Yemen  15.552727  48.516388        0   \n",
      "250            NaN                Comoros -11.645500  43.333300        0   \n",
      "251            NaN             Tajikistan  38.861034  71.276093        0   \n",
      "252            NaN                Lesotho -29.609988  28.233608        0   \n",
      "\n",
      "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  5/15/20  5/16/20  \\\n",
      "0          0        0        0        0        0  ...      745      745   \n",
      "1          0        0        0        0        0  ...      705      714   \n",
      "2          0        0        0        0        0  ...     3271     3409   \n",
      "3          0        0        0        0        0  ...      604      615   \n",
      "4          0        0        0        0        0  ...       17       17   \n",
      "..       ...      ...      ...      ...      ...  ...      ...      ...   \n",
      "248        0        0        0        0        0  ...        4        4   \n",
      "249        0        0        0        0        0  ...        1        1   \n",
      "250        0        0        0        0        0  ...        3        3   \n",
      "251        0        0        0        0        0  ...        0        0   \n",
      "252        0        0        0        0        0  ...        0        0   \n",
      "\n",
      "     5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  5/24/20  \n",
      "0        778      801      850      930      938      996     1040     1075  \n",
      "1        715      727      742      758      771      777      783      789  \n",
      "2       3507     3625     3746     3968     4062     4256     4426     4784  \n",
      "3        617      624      628      639      639      652      653      653  \n",
      "4         17       17       17       17       17       17       18       18  \n",
      "..       ...      ...      ...      ...      ...      ...      ...      ...  \n",
      "248        4        4        4        4        4        4        4        4  \n",
      "249        1        1        5        5        5       11       11       10  \n",
      "250        3        3        3        8        8       18       18       21  \n",
      "251        0        0      641      470      470     1089     1223     1301  \n",
      "252        0        0        0        0        0        0        0        0  \n",
      "\n",
      "[253 rows x 128 columns], 'covid_us_confirmed':            UID iso2 iso3  code3  FIPS          County  \\\n",
      "0           16   AS  ASM     16  60.0             NaN   \n",
      "1          316   GU  GUM    316  66.0             NaN   \n",
      "2          580   MP  MNP    580  69.0             NaN   \n",
      "3          630   PR  PRI    630  72.0             NaN   \n",
      "4          850   VI  VIR    850  78.0             NaN   \n",
      "...        ...  ...  ...    ...   ...             ...   \n",
      "3150  84070016   US  USA    840   NaN    Central Utah   \n",
      "3151  84070017   US  USA    840   NaN  Southeast Utah   \n",
      "3152  84070018   US  USA    840   NaN  Southwest Utah   \n",
      "3153  84070019   US  USA    840   NaN       TriCounty   \n",
      "3154  84070020   US  USA    840   NaN    Weber-Morgan   \n",
      "\n",
      "                Province/State Country/Region        Lat        Long  ...  \\\n",
      "0               American Samoa             US -14.271000 -170.132000  ...   \n",
      "1                         Guam             US  13.444300  144.793700  ...   \n",
      "2     Northern Mariana Islands             US  15.097900  145.673900  ...   \n",
      "3                  Puerto Rico             US  18.220800  -66.590100  ...   \n",
      "4               Virgin Islands             US  18.335800  -64.896300  ...   \n",
      "...                        ...            ...        ...         ...  ...   \n",
      "3150                      Utah             US  39.372319 -111.575868  ...   \n",
      "3151                      Utah             US  38.996171 -110.701396  ...   \n",
      "3152                      Utah             US  37.854472 -111.441876  ...   \n",
      "3153                      Utah             US  40.124915 -109.517442  ...   \n",
      "3154                      Utah             US  41.271160 -111.914512  ...   \n",
      "\n",
      "     5/15/20  5/16/20  5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  \\\n",
      "0          0        0        0        0        0        0        0        0   \n",
      "1        154      154      154      154      154      154      165      165   \n",
      "2         19       21       21       21       21       21       22       22   \n",
      "3       2542     2589     2646     2710     2805     2866     2913     3030   \n",
      "4         69       69       69       69       69       69       69       69   \n",
      "...      ...      ...      ...      ...      ...      ...      ...      ...   \n",
      "3150      29       29       30       30       30       31       32       34   \n",
      "3151      13       14       14       15       16       16       17       18   \n",
      "3152     200      209      219      236      245      260      271      281   \n",
      "3153      16       19       19       22       19       20       20       21   \n",
      "3154     216      221      226      234      234      242      251      252   \n",
      "\n",
      "      5/23/20  5/24/20  \n",
      "0           0        0  \n",
      "1         165      166  \n",
      "2          22       22  \n",
      "3        3100     3189  \n",
      "4          69       69  \n",
      "...       ...      ...  \n",
      "3150       33       33  \n",
      "3151       18       20  \n",
      "3152      294      305  \n",
      "3153       20       20  \n",
      "3154      256      260  \n",
      "\n",
      "[3155 rows x 135 columns], 'covid_us_deaths':            UID iso2 iso3  code3  FIPS          County  \\\n",
      "0           16   AS  ASM     16  60.0             NaN   \n",
      "1          316   GU  GUM    316  66.0             NaN   \n",
      "2          580   MP  MNP    580  69.0             NaN   \n",
      "3          630   PR  PRI    630  72.0             NaN   \n",
      "4          850   VI  VIR    850  78.0             NaN   \n",
      "...        ...  ...  ...    ...   ...             ...   \n",
      "3150  84070016   US  USA    840   NaN    Central Utah   \n",
      "3151  84070017   US  USA    840   NaN  Southeast Utah   \n",
      "3152  84070018   US  USA    840   NaN  Southwest Utah   \n",
      "3153  84070019   US  USA    840   NaN       TriCounty   \n",
      "3154  84070020   US  USA    840   NaN    Weber-Morgan   \n",
      "\n",
      "                Province/State Country/Region        Lat        Long  ...  \\\n",
      "0               American Samoa             US -14.271000 -170.132000  ...   \n",
      "1                         Guam             US  13.444300  144.793700  ...   \n",
      "2     Northern Mariana Islands             US  15.097900  145.673900  ...   \n",
      "3                  Puerto Rico             US  18.220800  -66.590100  ...   \n",
      "4               Virgin Islands             US  18.335800  -64.896300  ...   \n",
      "...                        ...            ...        ...         ...  ...   \n",
      "3150                      Utah             US  39.372319 -111.575868  ...   \n",
      "3151                      Utah             US  38.996171 -110.701396  ...   \n",
      "3152                      Utah             US  37.854472 -111.441876  ...   \n",
      "3153                      Utah             US  40.124915 -109.517442  ...   \n",
      "3154                      Utah             US  41.271160 -111.914512  ...   \n",
      "\n",
      "     5/15/20  5/16/20  5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  \\\n",
      "0          0        0        0        0        0        0        0        0   \n",
      "1          5        5        5        5        5        5        5        5   \n",
      "2          2        2        2        2        2        2        2        2   \n",
      "3        122      122      123      124      124      125      126      126   \n",
      "4          6        6        6        6        6        6        6        6   \n",
      "...      ...      ...      ...      ...      ...      ...      ...      ...   \n",
      "3150       0        0        0        0        0        0        0        0   \n",
      "3151       0        0        0        0        0        0        0        0   \n",
      "3152       3        3        3        3        4        4        4        4   \n",
      "3153       0        0        0        0        0        0        0        0   \n",
      "3154       3        3        3        3        3        3        3        4   \n",
      "\n",
      "      5/23/20  5/24/20  \n",
      "0           0        0  \n",
      "1           5        5  \n",
      "2           2        2  \n",
      "3         127      127  \n",
      "4           6        6  \n",
      "...       ...      ...  \n",
      "3150        0        0  \n",
      "3151        0        0  \n",
      "3152        4        4  \n",
      "3153        0        0  \n",
      "3154        4        4  \n",
      "\n",
      "[3155 rows x 136 columns], 'iso_table':            UID iso2 iso3  code3     FIPS      County Province/State  \\\n",
      "0            4   AF  AFG    4.0      NaN         NaN            NaN   \n",
      "1            8   AL  ALB    8.0      NaN         NaN            NaN   \n",
      "2           12   DZ  DZA   12.0      NaN         NaN            NaN   \n",
      "3           20   AD  AND   20.0      NaN         NaN            NaN   \n",
      "4           24   AO  AGO   24.0      NaN         NaN            NaN   \n",
      "...        ...  ...  ...    ...      ...         ...            ...   \n",
      "3775  84056037   US  USA  840.0  56037.0  Sweetwater        Wyoming   \n",
      "3776  84056039   US  USA  840.0  56039.0       Teton        Wyoming   \n",
      "3777  84056041   US  USA  840.0  56041.0       Uinta        Wyoming   \n",
      "3778  84056043   US  USA  840.0  56043.0    Washakie        Wyoming   \n",
      "3779  84056045   US  USA  840.0  56045.0      Weston        Wyoming   \n",
      "\n",
      "     Country/Region        Lat        Long             Combined_Key  \\\n",
      "0       Afghanistan  33.939110   67.709953              Afghanistan   \n",
      "1           Albania  41.153300   20.168300                  Albania   \n",
      "2           Algeria  28.033900    1.659600                  Algeria   \n",
      "3           Andorra  42.506300    1.521800                  Andorra   \n",
      "4            Angola -11.202700   17.873900                   Angola   \n",
      "...             ...        ...         ...                      ...   \n",
      "3775             US  41.659439 -108.882788  Sweetwater, Wyoming, US   \n",
      "3776             US  43.935225 -110.589080       Teton, Wyoming, US   \n",
      "3777             US  41.287818 -110.547578       Uinta, Wyoming, US   \n",
      "3778             US  43.904516 -107.680187    Washakie, Wyoming, US   \n",
      "3779             US  43.839612 -104.567488      Weston, Wyoming, US   \n",
      "\n",
      "      Population  \n",
      "0     38928341.0  \n",
      "1      2877800.0  \n",
      "2     43851043.0  \n",
      "3        77265.0  \n",
      "4     32866268.0  \n",
      "...          ...  \n",
      "3775     42343.0  \n",
      "3776     23464.0  \n",
      "3777     20226.0  \n",
      "3778      7805.0  \n",
      "3779      6927.0  \n",
      "\n",
      "[3780 rows x 12 columns], 'full': {'confirmed':               County Province/State Country/Region        Lat        Long  \\\n",
      "0                NaN            NaN    Afghanistan  33.000000   65.000000   \n",
      "1                NaN            NaN        Albania  41.153300   20.168300   \n",
      "2                NaN            NaN        Algeria  28.033900    1.659600   \n",
      "3                NaN            NaN        Andorra  42.506300    1.521800   \n",
      "4                NaN            NaN         Angola -11.202700   17.873900   \n",
      "...              ...            ...            ...        ...         ...   \n",
      "3415    Central Utah           Utah             US  39.372319 -111.575868   \n",
      "3416  Southeast Utah           Utah             US  38.996171 -110.701396   \n",
      "3417  Southwest Utah           Utah             US  37.854472 -111.441876   \n",
      "3418       TriCounty           Utah             US  40.124915 -109.517442   \n",
      "3419    Weber-Morgan           Utah             US  41.271160 -111.914512   \n",
      "\n",
      "      1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  ...  5/15/20  5/16/20  \\\n",
      "0           0        0        0        0        0  ...     6053     6402   \n",
      "1           0        0        0        0        0  ...      916      933   \n",
      "2           0        0        0        0        0  ...     6629     6821   \n",
      "3           0        0        0        0        0  ...      761      761   \n",
      "4           0        0        0        0        0  ...       48       48   \n",
      "...       ...      ...      ...      ...      ...  ...      ...      ...   \n",
      "3415        0        0        0        0        0  ...       29       29   \n",
      "3416        0        0        0        0        0  ...       13       14   \n",
      "3417        0        0        0        0        0  ...      200      209   \n",
      "3418        0        0        0        0        0  ...       16       19   \n",
      "3419        0        0        0        0        0  ...      216      221   \n",
      "\n",
      "      5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  5/24/20  \n",
      "0        6664     7072     7653     8145     8676     9216     9998    10582  \n",
      "1         946      948      949      964      969      981      989      998  \n",
      "2        7019     7201     7377     7542     7728     7918     8113     8306  \n",
      "3         761      761      761      762      762      762      762      762  \n",
      "4          48       50       52       52       58       60       61       69  \n",
      "...       ...      ...      ...      ...      ...      ...      ...      ...  \n",
      "3415       30       30       30       31       32       34       33       33  \n",
      "3416       14       15       16       16       17       18       18       20  \n",
      "3417      219      236      245      260      271      281      294      305  \n",
      "3418       19       22       19       20       20       21       20       20  \n",
      "3419      226      234      234      242      251      252      256      260  \n",
      "\n",
      "[3420 rows x 129 columns], 'deaths':               County Province/State Country/Region        Lat        Long  \\\n",
      "0                NaN            NaN    Afghanistan  33.000000   65.000000   \n",
      "1                NaN            NaN        Albania  41.153300   20.168300   \n",
      "2                NaN            NaN        Algeria  28.033900    1.659600   \n",
      "3                NaN            NaN        Andorra  42.506300    1.521800   \n",
      "4                NaN            NaN         Angola -11.202700   17.873900   \n",
      "...              ...            ...            ...        ...         ...   \n",
      "3415    Central Utah           Utah             US  39.372319 -111.575868   \n",
      "3416  Southeast Utah           Utah             US  38.996171 -110.701396   \n",
      "3417  Southwest Utah           Utah             US  37.854472 -111.441876   \n",
      "3418       TriCounty           Utah             US  40.124915 -109.517442   \n",
      "3419    Weber-Morgan           Utah             US  41.271160 -111.914512   \n",
      "\n",
      "      1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  ...  5/15/20  5/16/20  \\\n",
      "0           0        0        0        0        0  ...      153      168   \n",
      "1           0        0        0        0        0  ...       31       31   \n",
      "2           0        0        0        0        0  ...      536      542   \n",
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      "4           0        0        0        0        0  ...        2        2   \n",
      "...       ...      ...      ...      ...      ...  ...      ...      ...   \n",
      "3415        0        0        0        0        0  ...        0        0   \n",
      "3416        0        0        0        0        0  ...        0        0   \n",
      "3417        0        0        0        0        0  ...        3        3   \n",
      "3418        0        0        0        0        0  ...        0        0   \n",
      "3419        0        0        0        0        0  ...        3        3   \n",
      "\n",
      "      5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  5/24/20  \n",
      "0         169      173      178      187      193      205      216      218  \n",
      "1          31       31       31       31       31       31       31       32  \n",
      "2         548      555      561      568      575      582      592      600  \n",
      "3          51       51       51       51       51       51       51       51  \n",
      "4           2        3        3        3        3        3        4        4  \n",
      "...       ...      ...      ...      ...      ...      ...      ...      ...  \n",
      "3415        0        0        0        0        0        0        0        0  \n",
      "3416        0        0        0        0        0        0        0        0  \n",
      "3417        3        3        4        4        4        4        4        4  \n",
      "3418        0        0        0        0        0        0        0        0  \n",
      "3419        3        3        3        3        3        4        4        4  \n",
      "\n",
      "[3420 rows x 129 columns]}}\n"
     ]
    }
   ],
   "source": [
    "# Needed for when editing external modules\n",
    "%load_ext autoreload\n",
    "%autoreload\n",
    "from datasetmanager import CovidManager\n",
    "\n",
    "covid_manager = CovidManager()\n",
    "\n",
    "datasets = covid_manager.getDatasets()\n",
    "\n",
    "print(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data analysis\n",
    "A simple exploration of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>630</td>\n",
       "      <td>PR</td>\n",
       "      <td>PRI</td>\n",
       "      <td>630</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Puerto Rico</td>\n",
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       "      <td>-66.590100</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Utah</td>\n",
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       "      <td>84070020</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Weber-Morgan</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>41.271160</td>\n",
       "      <td>-111.914512</td>\n",
       "      <td>...</td>\n",
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       "      <td>3</td>\n",
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       "      <td>4</td>\n",
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       "</table>\n",
       "<p>3155 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           UID iso2 iso3  code3  FIPS          County  \\\n",
       "0           16   AS  ASM     16  60.0             NaN   \n",
       "1          316   GU  GUM    316  66.0             NaN   \n",
       "2          580   MP  MNP    580  69.0             NaN   \n",
       "3          630   PR  PRI    630  72.0             NaN   \n",
       "4          850   VI  VIR    850  78.0             NaN   \n",
       "...        ...  ...  ...    ...   ...             ...   \n",
       "3150  84070016   US  USA    840   NaN    Central Utah   \n",
       "3151  84070017   US  USA    840   NaN  Southeast Utah   \n",
       "3152  84070018   US  USA    840   NaN  Southwest Utah   \n",
       "3153  84070019   US  USA    840   NaN       TriCounty   \n",
       "3154  84070020   US  USA    840   NaN    Weber-Morgan   \n",
       "\n",
       "                Province/State Country/Region        Lat        Long  ...  \\\n",
       "0               American Samoa             US -14.271000 -170.132000  ...   \n",
       "1                         Guam             US  13.444300  144.793700  ...   \n",
       "2     Northern Mariana Islands             US  15.097900  145.673900  ...   \n",
       "3                  Puerto Rico             US  18.220800  -66.590100  ...   \n",
       "4               Virgin Islands             US  18.335800  -64.896300  ...   \n",
       "...                        ...            ...        ...         ...  ...   \n",
       "3150                      Utah             US  39.372319 -111.575868  ...   \n",
       "3151                      Utah             US  38.996171 -110.701396  ...   \n",
       "3152                      Utah             US  37.854472 -111.441876  ...   \n",
       "3153                      Utah             US  40.124915 -109.517442  ...   \n",
       "3154                      Utah             US  41.271160 -111.914512  ...   \n",
       "\n",
       "     5/15/20  5/16/20  5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  \\\n",
       "0          0        0        0        0        0        0        0        0   \n",
       "1          5        5        5        5        5        5        5        5   \n",
       "2          2        2        2        2        2        2        2        2   \n",
       "3        122      122      123      124      124      125      126      126   \n",
       "4          6        6        6        6        6        6        6        6   \n",
       "...      ...      ...      ...      ...      ...      ...      ...      ...   \n",
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       "3153       0        0        0        0        0        0        0        0   \n",
       "3154       3        3        3        3        3        3        3        4   \n",
       "\n",
       "      5/23/20  5/24/20  \n",
       "0           0        0  \n",
       "1           5        5  \n",
       "2           2        2  \n",
       "3         127      127  \n",
       "4           6        6  \n",
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       "3151        0        0  \n",
       "3152        4        4  \n",
       "3153        0        0  \n",
       "3154        4        4  \n",
       "\n",
       "[3155 rows x 136 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shows the dataset for deaths in the US\n",
    "datasets['covid_us_deaths']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6053</td>\n",
       "      <td>6402</td>\n",
       "      <td>6664</td>\n",
       "      <td>7072</td>\n",
       "      <td>7653</td>\n",
       "      <td>8145</td>\n",
       "      <td>8676</td>\n",
       "      <td>9216</td>\n",
       "      <td>9998</td>\n",
       "      <td>10582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>916</td>\n",
       "      <td>933</td>\n",
       "      <td>946</td>\n",
       "      <td>948</td>\n",
       "      <td>949</td>\n",
       "      <td>964</td>\n",
       "      <td>969</td>\n",
       "      <td>981</td>\n",
       "      <td>989</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6629</td>\n",
       "      <td>6821</td>\n",
       "      <td>7019</td>\n",
       "      <td>7201</td>\n",
       "      <td>7377</td>\n",
       "      <td>7542</td>\n",
       "      <td>7728</td>\n",
       "      <td>7918</td>\n",
       "      <td>8113</td>\n",
       "      <td>8306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>50</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>61</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>17.0608</td>\n",
       "      <td>-61.7964</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-38.4161</td>\n",
       "      <td>-63.6167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7479</td>\n",
       "      <td>7805</td>\n",
       "      <td>8068</td>\n",
       "      <td>8371</td>\n",
       "      <td>8809</td>\n",
       "      <td>9283</td>\n",
       "      <td>9931</td>\n",
       "      <td>10649</td>\n",
       "      <td>11353</td>\n",
       "      <td>12076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>40.0691</td>\n",
       "      <td>45.0382</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4044</td>\n",
       "      <td>4283</td>\n",
       "      <td>4472</td>\n",
       "      <td>4823</td>\n",
       "      <td>5041</td>\n",
       "      <td>5271</td>\n",
       "      <td>5606</td>\n",
       "      <td>5928</td>\n",
       "      <td>6302</td>\n",
       "      <td>6661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-35.4735</td>\n",
       "      <td>149.0124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.8688</td>\n",
       "      <td>151.2093</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3074</td>\n",
       "      <td>3075</td>\n",
       "      <td>3076</td>\n",
       "      <td>3078</td>\n",
       "      <td>3081</td>\n",
       "      <td>3082</td>\n",
       "      <td>3084</td>\n",
       "      <td>3086</td>\n",
       "      <td>3087</td>\n",
       "      <td>3090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-12.4634</td>\n",
       "      <td>130.8456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-28.0167</td>\n",
       "      <td>153.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1055</td>\n",
       "      <td>1055</td>\n",
       "      <td>1057</td>\n",
       "      <td>1057</td>\n",
       "      <td>1058</td>\n",
       "      <td>1058</td>\n",
       "      <td>1058</td>\n",
       "      <td>1060</td>\n",
       "      <td>1061</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-34.9285</td>\n",
       "      <td>138.6007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-41.4545</td>\n",
       "      <td>145.9707</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8136</td>\n",
       "      <td>144.9631</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1551</td>\n",
       "      <td>1558</td>\n",
       "      <td>1564</td>\n",
       "      <td>1573</td>\n",
       "      <td>1573</td>\n",
       "      <td>1581</td>\n",
       "      <td>1593</td>\n",
       "      <td>1593</td>\n",
       "      <td>1603</td>\n",
       "      <td>1605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>552</td>\n",
       "      <td>553</td>\n",
       "      <td>554</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>560</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austria</td>\n",
       "      <td>47.5162</td>\n",
       "      <td>14.5501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16109</td>\n",
       "      <td>16201</td>\n",
       "      <td>16242</td>\n",
       "      <td>16269</td>\n",
       "      <td>16321</td>\n",
       "      <td>16353</td>\n",
       "      <td>16404</td>\n",
       "      <td>16436</td>\n",
       "      <td>16486</td>\n",
       "      <td>16503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>40.1431</td>\n",
       "      <td>47.5769</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2980</td>\n",
       "      <td>3138</td>\n",
       "      <td>3274</td>\n",
       "      <td>3387</td>\n",
       "      <td>3518</td>\n",
       "      <td>3631</td>\n",
       "      <td>3749</td>\n",
       "      <td>3855</td>\n",
       "      <td>3982</td>\n",
       "      <td>4122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahamas</td>\n",
       "      <td>25.0343</td>\n",
       "      <td>-77.3963</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>26.0275</td>\n",
       "      <td>50.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6583</td>\n",
       "      <td>6747</td>\n",
       "      <td>6956</td>\n",
       "      <td>7184</td>\n",
       "      <td>7532</td>\n",
       "      <td>7888</td>\n",
       "      <td>8174</td>\n",
       "      <td>8414</td>\n",
       "      <td>8802</td>\n",
       "      <td>9138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Province/State       Country/Region      Lat      Long  \\\n",
       "0                            NaN          Afghanistan  33.0000   65.0000   \n",
       "1                            NaN              Albania  41.1533   20.1683   \n",
       "2                            NaN              Algeria  28.0339    1.6596   \n",
       "3                            NaN              Andorra  42.5063    1.5218   \n",
       "4                            NaN               Angola -11.2027   17.8739   \n",
       "5                            NaN  Antigua and Barbuda  17.0608  -61.7964   \n",
       "6                            NaN            Argentina -38.4161  -63.6167   \n",
       "7                            NaN              Armenia  40.0691   45.0382   \n",
       "8   Australian Capital Territory            Australia -35.4735  149.0124   \n",
       "9                New South Wales            Australia -33.8688  151.2093   \n",
       "10            Northern Territory            Australia -12.4634  130.8456   \n",
       "11                    Queensland            Australia -28.0167  153.4000   \n",
       "12               South Australia            Australia -34.9285  138.6007   \n",
       "13                      Tasmania            Australia -41.4545  145.9707   \n",
       "14                      Victoria            Australia -37.8136  144.9631   \n",
       "15             Western Australia            Australia -31.9505  115.8605   \n",
       "16                           NaN              Austria  47.5162   14.5501   \n",
       "17                           NaN           Azerbaijan  40.1431   47.5769   \n",
       "18                           NaN              Bahamas  25.0343  -77.3963   \n",
       "19                           NaN              Bahrain  26.0275   50.5500   \n",
       "\n",
       "    1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  5/15/20  \\\n",
       "0         0        0        0        0        0        0  ...     6053   \n",
       "1         0        0        0        0        0        0  ...      916   \n",
       "2         0        0        0        0        0        0  ...     6629   \n",
       "3         0        0        0        0        0        0  ...      761   \n",
       "4         0        0        0        0        0        0  ...       48   \n",
       "5         0        0        0        0        0        0  ...       25   \n",
       "6         0        0        0        0        0        0  ...     7479   \n",
       "7         0        0        0        0        0        0  ...     4044   \n",
       "8         0        0        0        0        0        0  ...      107   \n",
       "9         0        0        0        0        3        4  ...     3074   \n",
       "10        0        0        0        0        0        0  ...       29   \n",
       "11        0        0        0        0        0        0  ...     1055   \n",
       "12        0        0        0        0        0        0  ...      439   \n",
       "13        0        0        0        0        0        0  ...      228   \n",
       "14        0        0        0        0        1        1  ...     1551   \n",
       "15        0        0        0        0        0        0  ...      552   \n",
       "16        0        0        0        0        0        0  ...    16109   \n",
       "17        0        0        0        0        0        0  ...     2980   \n",
       "18        0        0        0        0        0        0  ...       96   \n",
       "19        0        0        0        0        0        0  ...     6583   \n",
       "\n",
       "    5/16/20  5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  \\\n",
       "0      6402     6664     7072     7653     8145     8676     9216     9998   \n",
       "1       933      946      948      949      964      969      981      989   \n",
       "2      6821     7019     7201     7377     7542     7728     7918     8113   \n",
       "3       761      761      761      761      762      762      762      762   \n",
       "4        48       48       50       52       52       58       60       61   \n",
       "5        25       25       25       25       25       25       25       25   \n",
       "6      7805     8068     8371     8809     9283     9931    10649    11353   \n",
       "7      4283     4472     4823     5041     5271     5606     5928     6302   \n",
       "8       107      107      107      107      107      107      107      107   \n",
       "9      3075     3076     3078     3081     3082     3084     3086     3087   \n",
       "10       29       29       29       29       29       29       29       29   \n",
       "11     1055     1057     1057     1058     1058     1058     1060     1061   \n",
       "12      439      439      439      439      439      439      439      439   \n",
       "13      228      228      228      228      228      228      228      228   \n",
       "14     1558     1564     1573     1573     1581     1593     1593     1603   \n",
       "15      553      554      557      557      557      557      557      560   \n",
       "16    16201    16242    16269    16321    16353    16404    16436    16486   \n",
       "17     3138     3274     3387     3518     3631     3749     3855     3982   \n",
       "18       96       96       96       96       97       97       97      100   \n",
       "19     6747     6956     7184     7532     7888     8174     8414     8802   \n",
       "\n",
       "    5/24/20  \n",
       "0     10582  \n",
       "1       998  \n",
       "2      8306  \n",
       "3       762  \n",
       "4        69  \n",
       "5        25  \n",
       "6     12076  \n",
       "7      6661  \n",
       "8       107  \n",
       "9      3090  \n",
       "10       29  \n",
       "11     1056  \n",
       "12      439  \n",
       "13      228  \n",
       "14     1605  \n",
       "15      560  \n",
       "16    16503  \n",
       "17     4122  \n",
       "18      100  \n",
       "19     9138  \n",
       "\n",
       "[20 rows x 128 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset for the number of confirmed cases\n",
    "datasets['covid_confirmed'].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Number of days since first recorded case**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of days since first recorded confirmed case: 124\n"
     ]
    }
   ],
   "source": [
    "# Outputs the number of days since the first recorded confirmed date\n",
    "number_of_days = len(datasets['covid_confirmed'].count(axis='rows')) - 4\n",
    "print(\"Number of days since first recorded confirmed case:\", number_of_days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Latest Date**\n",
    "\n",
    "The below block will find the latest date and create a dataset that only contains the current number of confirmed cases for each country + state/region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latest date:  5/24/20\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>10582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>8306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Angola</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>260</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.215500</td>\n",
       "      <td>-12.885800</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>261</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.186360</td>\n",
       "      <td>6.613081</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>262</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>263</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>264</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.861034</td>\n",
       "      <td>71.276093</td>\n",
       "      <td>2929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>265 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country/Region Province/State        Lat       Long  5/24/20\n",
       "0              Afghanistan            NaN  33.000000  65.000000    10582\n",
       "1                  Albania            NaN  41.153300  20.168300      998\n",
       "2                  Algeria            NaN  28.033900   1.659600     8306\n",
       "3                  Andorra            NaN  42.506300   1.521800      762\n",
       "4                   Angola            NaN -11.202700  17.873900       69\n",
       "..                     ...            ...        ...        ...      ...\n",
       "260         Western Sahara            NaN  24.215500 -12.885800        9\n",
       "261  Sao Tome and Principe            NaN   0.186360   6.613081      251\n",
       "262                  Yemen            NaN  15.552727  48.516388      222\n",
       "263                Comoros            NaN -11.645500  43.333300       87\n",
       "264             Tajikistan            NaN  38.861034  71.276093     2929\n",
       "\n",
       "[265 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_date = datasets['covid_confirmed'].columns[-1] # number of confirmed cases to date\n",
    "print(\"Latest date: \", latest_date)\n",
    "df = datasets['covid_confirmed'].head(265)[['Country/Region', 'Province/State', 'Lat', 'Long', latest_date]]\n",
    "df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confirmed cases by country sorted**\n",
    "\n",
    "We can then show the countries ordered by the number of cases. The US has significantly more (x4 at the time) cases they Spain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>225</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0902</td>\n",
       "      <td>-95.7129</td>\n",
       "      <td>1643246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.2350</td>\n",
       "      <td>-51.9253</td>\n",
       "      <td>363211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>344481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>259559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>201</td>\n",
       "      <td>Spain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>235772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>Italy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.0000</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>229858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>180328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.2276</td>\n",
       "      <td>2.2137</td>\n",
       "      <td>179859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.9637</td>\n",
       "      <td>35.2433</td>\n",
       "      <td>156827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>78.0000</td>\n",
       "      <td>138536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>Iran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>53.0000</td>\n",
       "      <td>135701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>Peru</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.1900</td>\n",
       "      <td>-75.0152</td>\n",
       "      <td>119959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>45.0000</td>\n",
       "      <td>72560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>Chile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-35.6751</td>\n",
       "      <td>-71.5430</td>\n",
       "      <td>69102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>68620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>China</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>68135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.8333</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>57092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.3753</td>\n",
       "      <td>69.3451</td>\n",
       "      <td>54601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>52.9399</td>\n",
       "      <td>-73.5491</td>\n",
       "      <td>47420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.1326</td>\n",
       "      <td>5.2913</td>\n",
       "      <td>45236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country/Region Province/State      Lat      Long  5/24/20\n",
       "225              US            NaN  37.0902  -95.7129  1643246\n",
       "28           Brazil            NaN -14.2350  -51.9253   363211\n",
       "187          Russia            NaN  60.0000   90.0000   344481\n",
       "223  United Kingdom            NaN  55.3781   -3.4360   259559\n",
       "201           Spain            NaN  40.0000   -4.0000   235772\n",
       "137           Italy            NaN  43.0000   12.0000   229858\n",
       "120         Germany            NaN  51.0000    9.0000   180328\n",
       "116          France            NaN  46.2276    2.2137   179859\n",
       "213          Turkey            NaN  38.9637   35.2433   156827\n",
       "131           India            NaN  21.0000   78.0000   138536\n",
       "133            Iran            NaN  32.0000   53.0000   135701\n",
       "181            Peru            NaN  -9.1900  -75.0152   119959\n",
       "192    Saudi Arabia            NaN  24.0000   45.0000    72560\n",
       "48            Chile            NaN -35.6751  -71.5430    69102\n",
       "158          Mexico            NaN  23.6345 -102.5528    68620\n",
       "62            China          Hubei  30.9756  112.2707    68135\n",
       "23          Belgium            NaN  50.8333    4.0000    57092\n",
       "177        Pakistan            NaN  30.3753   69.3451    54601\n",
       "44           Canada         Quebec  52.9399  -73.5491    47420\n",
       "169     Netherlands            NaN  52.1326    5.2913    45236"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsorted = df.sort_values(latest_date, ascending=False)\n",
    "dfsorted.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Australia Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Province/State  5/24/20\n",
       "8   Australian Capital Territory        3\n",
       "9                New South Wales       48\n",
       "10            Northern Territory        0\n",
       "11                    Queensland        6\n",
       "12               South Australia        4\n",
       "13                      Tasmania       13\n",
       "14                      Victoria       19\n",
       "15             Western Australia        9"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recorded Deaths (RD)\n",
    "covid_deaths_df = datasets['covid_deaths']\n",
    "ausRD = covid_deaths_df[['Province/State', latest_date]].loc[covid_deaths_df['Country/Region'] == 'Australia']\n",
    "ausRD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5/24/20    102\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of confirmed cases in Australia\n",
    "ausRD.sum(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combined Data**\n",
    "\n",
    "Below shows the combined datasets of Comfirmed Cases (CC), Recorded Deaths (RD), Recorded Recoveries (RR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>CC</th>\n",
       "      <th>RD</th>\n",
       "      <th>RR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>-12.4634</td>\n",
       "      <td>130.8456</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>-35.4735</td>\n",
       "      <td>149.0124</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>-41.4545</td>\n",
       "      <td>145.9707</td>\n",
       "      <td>228</td>\n",
       "      <td>13</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>-34.9285</td>\n",
       "      <td>138.6007</td>\n",
       "      <td>439</td>\n",
       "      <td>4</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>560</td>\n",
       "      <td>9</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>-28.0167</td>\n",
       "      <td>153.4000</td>\n",
       "      <td>1056</td>\n",
       "      <td>6</td>\n",
       "      <td>1038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>-37.8136</td>\n",
       "      <td>144.9631</td>\n",
       "      <td>1605</td>\n",
       "      <td>19</td>\n",
       "      <td>1520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>-33.8688</td>\n",
       "      <td>151.2093</td>\n",
       "      <td>3090</td>\n",
       "      <td>48</td>\n",
       "      <td>2656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Region                Province/State      Lat      Long    CC  RD  \\\n",
       "2      Australia            Northern Territory -12.4634  130.8456    29   0   \n",
       "0      Australia  Australian Capital Territory -35.4735  149.0124   107   3   \n",
       "5      Australia                      Tasmania -41.4545  145.9707   228  13   \n",
       "4      Australia               South Australia -34.9285  138.6007   439   4   \n",
       "7      Australia             Western Australia -31.9505  115.8605   560   9   \n",
       "3      Australia                    Queensland -28.0167  153.4000  1056   6   \n",
       "6      Australia                      Victoria -37.8136  144.9631  1605  19   \n",
       "1      Australia               New South Wales -33.8688  151.2093  3090  48   \n",
       "\n",
       "     RR  \n",
       "2    29  \n",
       "0   104  \n",
       "5   200  \n",
       "4   435  \n",
       "7   549  \n",
       "3  1038  \n",
       "6  1520  \n",
       "1  2656  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the histogram of cases in Australia, confirmed, recovered and deaths\n",
    "ausCC = df.loc[df['Country/Region'] == 'Australia'] # confirmed cases\n",
    "ausRR = datasets['covid_recovered'][['Province/State', latest_date]].loc[df['Country/Region'] == 'Australia'] # rec. recovery\n",
    "\n",
    "# Rename the confirmed cases\n",
    "ausCC = ausCC.rename(columns={latest_date: \"CC\"})\n",
    "ausRD = ausRD.rename(columns={latest_date: \"RD\"})\n",
    "ausRR = ausRR.rename(columns={latest_date: \"RR\"})\n",
    "\n",
    "# Merge the tables together\n",
    "ausMerged = pd.merge(ausCC, ausRD, on='Province/State')\n",
    "ausMerged = pd.merge(ausMerged, ausRR, on='Province/State')\n",
    "ausMerged = ausMerged.sort_values('CC')\n",
    "ausMerged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df1eae7208>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bTXo/6vfSXdL8yXT7qU9W90TiX0rXGj+d6/q6PSrJyem20wmPTfKUiWXcL+eXJPm5dNvQgky9fW2fkXXc7/OuyfTb1/ZJrm2ttZGyydvXH06qx079cLMNO5WL+nneMl1r9S9PmtYLJk3raen2Dzulu3LitjnM853p9hs79HU7M90JnCR5cboTDxPT237S9N6QbluZMNW+fdb6ttZ+nG5f86p0n81zRh+ANMl02+5Rrbt3/KB0J1Amf/+9rV+WC9N9n0y+33linCtst7Ps61dQVc+pqov6hzndnu6E7kzf4zPtG5PZ71d9bJJ3jgx/a7r9wEz7yenKpjOX44LJ43t/ksP7h129MN0JpetXYpqw0oRV1mtVtUm6HerT+ycK3pDukq19qmqfvrcfpzv4nPBzo+NorZ3cWntyusvsHp/uvo2kO5ibygPlVfXYdGeNX5Nkq/6L9Yp0Xzqz+WC6g5md+rDw9zMM959JDqmqTafpfl2SnWr5J5runK7FYRymWxZz8YN0Bxp7tda26F+bt+7BGUl3H+JOI/3vPMO4vpXuy/XXZ+jnrX19n9hae1S61sYHlmtr7YOttaelO1hoSf5v3+madJc/bTHyWtBau7a1dm9r7U9ba3umO0D/tUz/sKOvpduOJsy27v47XQvCUTPMU9JdLvbVabptmK5FJ617GNMR6R60dHG6S5CnOgBeTmvt8nSXc59SVRPLa9pl0nfbdYpR/SBdC8NjR8omb4uTt6eZtoFr0rXMbT1Sh0e11vaaw7BzUt3TcXdIF+pn217/MN1B81P67etXJkYzzbzNqLV2Q2vtt1tr26drgf67mvpnbq7qqlo7TNEt/cm6VyY5oWZ/ovPzkny5DxwT5rp9fSLJHyT5j3QtiSfNMq207onDr0vyhKqa2M6vSfJfk7atR7bW/me6dXB3pt6+rsvIttVvqztl+u3r+iQ7jGzTyYrb11sm1eMRrbUPzWHYmeb5znS3pry0HvypoWvStVSOTmvT1tqJfbdHT/MwpsnzvGm6y9En5vlDSX6j/z56Srp7VCem991J09ustXbYaFVnmI2Z6pvW2nmttYPThe0r030XTmXyPnE5rbX/StfKN+XD3Vpr3093wuSd/Xf+hD2SLG2t/XCa4abb1y83z/2DxT7ST3/b/nv83Mz8mZ5231hVP5dumXx5luFfOWn4TVprXxidhalma6p5nabbXI4Llhum37f/d7p9xEuz4gPdYOyEVdZ3R6U7m7lnusvxFqX7AvtsHgwTlyV5ft8isluSV0wMXFW/2LdwbpQu1N7djy/pWg5n+/2yTdPt7G/ux3dMupbVudgs3Zn0u6tq/3RnxKfzT+m+3D5SVb9QVQ+rqq2q+028w9I9MOnHSf64qjaqqoPSPR133n+Hrj+r+54kb6/+p1eqaod68Kcvzkry8up+h+8R6S4DnG5cLd2B8puq6pjqfhLmYVX1tKp6d9/bZukuL7u9P7CfOPmQqvr5qnpmf3Byd7pQMrG+/z7JW/oDvlTVNlV1ZP//M6rqCX3r2Q/ThbGJ4SY7N90lhBNmXHettTvS3VN2SnW/cfuIfh0+p6r+amQ8T0936dhUPpdkQVX9WX8w97B0924+Pt0ldHN1eroWy4knak+7TNK13vxqVb2wqjbs52lRa+3+dOv0LVW1WT/sH6Q7Yz+ds5L8blXtWFVbpntwSZKkP6v/H0n+emR971pVT59t2Nn04/u1dJ+T97fWLp/D9rpZuu3m9qp6dFbcXuey3xitwwuqaqI1+7Z0+5MVtq3W2r3pTnw8fXK3kX6uTHcFwB9PMZ3q5+PN6R6k84aRbjuku+TvomlG/S9J/qSq9ukPfL+dbhlsmq4FdFattXvS3ff6J33RJ5I8vqpe2m/vG/X74z36dXBakpOqavuq2qCqDuw/t2cleW5VPavfb/9hupMZX5hiskl34H1fum1kw6p6fpZ/gvp7kryq/x6oqtq0qp5bVZvNYdjZ5vmWJO8dmeeJVqtD+nlaUN3PouzYb+f/nu5kxZb98pg4EfLBJMdU1aJ+Gfxlki+21pb20/lKuu+g9yY5r7U20dL4pSQ/rKrXVdUm/TT3rpGfLprFtPWtqm2r+63TTdMt/zsz/T7x/CRPqqqZtpV3JDm4qhZN1bG1dn668DX69N9p94mz7OtvTLKwHgxxD0933+nNSe6rqueku50gI/1vVVWbj5TNtG88LMknR1rkb063H37cpOFfX1V79cNvXlUvmGpeVsLkfc+qHheckW4f8oR096zCGiWssr5bnO7+m+/3LRQ3tNZuSHdp70uqu7Ty7enu67ox3cH4B0aGf1S6g5Xb0l0ec0sePLt7apI9q7tMZ/Ij95MkrbVvpDsA++9+/E9Id4/gXPxOkj+rqh+lO5g5a7oe+5aJX0139vr8dIHpS+kuU/pifyB4RJLnpGuV+LskL+sPXofgdeke9HBRdZdO/mf6S7paa/+e7kDlU30/n5ppRK21D6e7/OzYdAcvN6ZrEfxY38ufpnsQyR3pHljyryODb5wHH0JzQ7pgNnHQ/s50Ld3/0a+Ti9K1UiRda/yH0y33b6Z7eut04euMJIf1oXHWddf3c1K6QPd/0h3YXJOutf7fkqQ/yDss3fY71TK5I93B1QH9MvlauqsJnpTk2Kr67WnqOnk896S7XPNNsy2TvrXjsHRh4dZ0J4UmrmZ4bbqDpKvTBekPpgsf03lPupD11XStEf86qfvL0h1QfiPdZ/XDefDS6tmGncrH+/m5Jt19qiele9DPhGm313Tb6ibptqGL0l0iPOqd6Vq5bquqk+dQl19M8sWqujPdsv691tp3p+n3H9K1dszk/yU5rh78Td7t+3Hfma6l/Qnp7u/8j5FhXpyuBf6n04zzbenW30fTreuT013+eXqScyYdxM/ktCQ7V9XhrbUfpdtmj063zd6QruVr4udz/ijdfdgX99P8v+nuvftWuqsl/ibdOjg83T3Z90w1wb78+elagW9Lt+/415Hul6S7z/Nv++5L+n5nHXaO3pFuf/DE1to16X6O6g158HP+v/PgsdpL050IuzLd/Za/39fjgnSfyY+ka+3dNd1yG/WhdPuZD47M2/3pls+idPd8/iBdoJ3T+pqlvg9L99m/Lt36eXq677SpxnNjuv36kVN17/u5Od2+803T9ZNu2/7jevAnll6U6X+ia6Z9/cTPO91SVV/ut8XfTfcdfFu6z8PZI3W7Mt3yvbo/Htg+M39fLHcJcGvtJ+meFP75fvgDWmsfTbdNn9nvY65I9/29OpY7ZlmN44KPpr/MedLVF7BGTDxgA4C1pKr+MslNrbV3jGl8r013ufgKLWY8tFTV55K8tm9NG8f4Nk4X9H+ltXbTOMYJk1XVnulObuzfxnBgWlWHJ3lpa+2Fq125MepPkN+Q7gFt0z1jYPCq6jvpLlOe6Z50GAthFQAA1rD+ioZfb629a77rsqqq6tfTtfo+vi3/cCZYI4RVAABgRlX16XTPAHlpa+28ea4ODxHCKgAAAIPjAUsAAAAMjrAKAADA4Gw43xWYydZbb90WLlw439UAAABgDbj00kt/0FrbZqpugw6rCxcuzCWXXDLf1QAAAGANqKrvTdfNZcAAAAAMjrAKAADA4AirAAAADM6g71kFAAB4qLv33nuzbNmy3H333fNdlVW2YMGC7Ljjjtloo43mPIywCgAAMGDLli3LZpttloULF6aq5rs6K621lltuuSXLli3LLrvsMufhXAYMAAAwYHfffXe22mqrdTKoJklVZauttlrplmFhFQAAYODW1aA6YVXqL6wCAAAwoxtuuCFHH310dt111+y555457LDD8u1vfzvf/va3c9hhh2W33XbLHnvskRe+8IW58cYbxzJN96wCAACsQxYef85Yx7f0xOfO2L21luc973lZvHhxzjzzzCTJZZddlhtvvDHHHntsTjrppBx++OFJkgsvvDA333xztt1229Wul5ZVAAAApnXhhRdmo402yqte9aoHyhYtWpSrrroqBx544ANBNUme8YxnZO+99x7LdIVVAAAApnXFFVfkyU9+8pzLx0VYBQAAYHCEVQAAAKa111575dJLL51z+bgIqwAAAEzrmc98Zn7605/mPe95zwNlF198cXbbbbd84QtfyDnnPPjAp09+8pO5/PLLxzJdYRUAAIBpVVU++tGP5vzzz8+uu+6avfbaKyeccEK23377fOITn8jf/M3fZPfdd8+ee+6Z973vfXnMYx4zlun66RoAAIB1yGw/NbMmbL/99jnrrLOm7PbJT35yjUxTyyoAAACDI6wCAAAwOC4DBgAAWAkLjz9n9p7mYD4u512XaFkFAABgcIRVAAAABkdYBQAAYHCEVQAAAGa0wQYbZNGiRdl7771z+OGH5/bbb0+SLF26NJtsskkWLVqUPffcMy972cty7733jmWaHrAEAACwLjlh8zGP745Ze9lkk01y2WWXJUkWL16cU045JW984xuTJLvuumsuu+yy3H///Tn44INz1lln5SUveclqV0vLKgAAAHN24IEH5tprr12hfIMNNsj+++8/ZbdVIawCAAAwJ/fff38uuOCCHHHEESt0u/vuu/PFL34xhx566FimJawCAAAwo7vuuiuLFi3KVlttlVtvvTUHH3zwA92+853vPNBt5513zhOf+MSxTHPWsFpVC6rqS1X11ar6elX9aV++S1V9saquqqp/rqqH9+Ub9++X9N0Xjozr9X35t6rqkLHMAQAAAGvUxD2r3/ve93LPPffklFNOeaDbxD2rS5YsyUUXXZSzzz57LNOcS8vqT5M8s7W2T5JFSQ6tqgOS/N8kb2+t7Z7ktiSv6Pt/RZLbWmu7JXl731+qas8kRyfZK8mhSf6uqjYYy1wAAACwxm2++eY5+eST87a3vW2Fp/5ut912OfHEE/PWt751LNOaNay2zp392436V0vyzCQf7stPT3JU//+R/fv03Z9VVdWXn9la+2lr7btJliTZfyxzAQAAwFqx7777Zp999smZZ565QrejjjoqP/nJT/LZz352taczp5+u6VtAL02yW5JTknwnye2ttfv6XpYl2aH/f4ck1yRJa+2+qrojyVZ9+UUjox0dZnRaxyU5Lkl23nnnlZwdAACA9dwcfmpm3O68887l3n/84x9/4P8rrrjigf+rKl/96lfHMs05PWCptXZ/a21Rkh3TtYbuMVVvE/Wbptt05ZOn9e7W2n6ttf222WabuVQPAACA9cxKPQ24tXZ7kk8nOSDJFlU10TK7Y5Lr+v+XJdkpSfrumye5dbR8imEAAADgAXN5GvA2VbVF//8mSX41yTeTXJjkN/reFif5WP//2f379N0/1VprffnR/dOCd0mye5IvjWtGAAAAWH/M5Z7V7ZKc3t+3+rAkZ7XWPlFV30hyZlX9RZKvJDm17//UJP9UVUvStagenSStta9X1VlJvpHkviSvbq3dP97ZAQAAYH0wa1htrX0tyb5TlF+dKZ7m21q7O8kLphnXW5K8ZeWrCQAAwEPJSt2zCgAAAGuDsAoAAMCMNthggyxatCh77713Dj/88Nx+++1JkqVLl2aTTTbJvvvumz322CP7779/Tj/99LFMc06/swoAAMAwPOH0J4x1fJcvvnzWfjbZZJNcdtllSZLFixfnlFNOyRvf+MYkya677pqvfOUrSZKrr746z3/+8/Ozn/0sxxxzzGrVS8sqAAAAc3bggQfm2muvnbLb4x73uJx00kk5+eSTV3s6wioAAABzcv/99+eCCy7IEUccMW0/T3rSk3LllVeu9rSEVQAAAGZ01113ZdGiRdlqq61y66235uCDD56239baWKYprAIAADCjiXtWv/e97+Wee+7JKaecMm2/X/nKV7LHHnus9jSFVQAAAOZk8803z8knn5y3ve1tuffee1fovnTp0vzRH/1RXvva1672tDwNGAAAgDnbd999s88+++TMM8/ML//yL+c73/lO9t1339x9993ZbLPN8trXvna1nwScCKsAAADrlLn81My43Xnnncu9//jHP/7A/3fdddcamabLgAEAABgcYRUAAIDBEVYBAAAYHGEVAABg4Mb126XzZVXqL6wCAAAM2IIFC3LLLbess4G1tZZbbp9iaqoAACAASURBVLklCxYsWKnhPA0YAABgwHbccccsW7YsN99883xXZZUtWLAgO+6440oNI6wCAAAM2EYbbZRddtllvqux1rkMGAAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGJwN57sCAAAAD0knbD7Gcd0xvnENhJZVAAAABkdYBQAAYHCEVQAAAAZHWAUAAGBwhFUAAAAGR1gFAABgcIRVAAAABkdYBQAAYHCEVQAAAAZHWAUAAGBwhFUAAAAGR1gFAABgcIRVAAAABkdYBQAAYHCEVQAAAAZHWAUAAGBwZg2rVbVTVV1YVd+sqq9X1e/15SdU1bVVdVn/OmxkmNdX1ZKq+lZVHTJSfmhftqSqjl8zswQAAMC6bsM59HNfkj9srX25qjZLcmlVnd93e3tr7W2jPVfVnkmOTrJXku2T/GdVPb7vfEqSg5MsS3JxVZ3dWvvGOGYEAACA9cesYbW1dn2S6/v/f1RV30yywwyDHJnkzNbaT5N8t6qWJNm/77aktXZ1klTVmX2/wioAAADLWal7VqtqYZJ9k3yxL3pNVX2tqk6rqi37sh2SXDMy2LK+bLryydM4rqouqapLbr755pWpHgAAAOuJOYfVqnpkko8k+f3W2g+TvCvJrkkWpWt5/euJXqcYvM1QvnxBa+9ure3XWttvm222mWv1AAAAWI/M5Z7VVNVG6YLqB1pr/5okrbUbR7q/J8kn+rfLkuw0MviOSa7r/5+uHAAAAB4wl6cBV5JTk3yztXbSSPl2I709L8kV/f9nJzm6qjauql2S7J7kS0kuTrJ7Ve1SVQ9P9xCms8czGwAAAKxP5tKy+tQkL01yeVVd1pe9IcmLqmpRukt5lyZ5ZZK01r5eVWele3DSfUle3Vq7P0mq6jVJzkuyQZLTWmtfH+O8AAAAsJ6Yy9OAP5ep7zc9d4Zh3pLkLVOUnzvTcAAAAJCs5NOAAQAAYG0QVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYnFnDalXtVFUXVtU3q+rrVfV7ffmjq+r8qrqq/7tlX15VdXJVLamqr1XVk0bGtbjv/6qqWrzmZgsAAIB12VxaVu9L8oettT2SHJDk1VW1Z5Ljk1zQWts9yQX9+yR5TpLd+9dxSd6VdOE2yZuTPCXJ/knePBFwAQAAYNSsYbW1dn1r7cv9/z9K8s0kOyQ5MsnpfW+nJzmq///IJGe0zkVJtqiq7ZIckuT81tqtrbXbkpyf5NCxzg0AAADrhZW6Z7WqFibZN8kXk2zbWrs+6QJtksf0ve2Q5JqRwZb1ZdOVT57GcVV1SVVdcvPNN69M9QAAAFhPzDmsVtUjk3wkye+31n44U69TlLUZypcvaO3drbX9Wmv7bbPNNnOtHgAAAOuROYXVqtooXVD9QGvtX/viG/vLe9P/vakvX5Zkp5HBd0xy3QzlAAAAsJy5PA24kpya5JuttZNGOp2dZOKJvouTfGyk/GX9U4EPSHJHf5nweUmeXVVb9g9WenZfBgAAAMvZcA79PDXJS5NcXlWX9WVvSHJikrOq6hVJvp/kBX23c5MclmRJkp8kOSZJWmu3VtWfJ7m47+/PWmu3jmUuAAAAWK/MGlZba5/L1PebJsmzpui/JXn1NOM6LclpK1NBAAAAHnpW6mnAAAAAsDYIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4Gw43xUAAICHmoXHnzO2cS098bljGxcMiZZVAAAABkdYBQAAYHCEVQAAAAZHWAUAAGBwhFUAAAAGR1gFAABgcIRVAAAABkdYBQAAYHCEVQAAAAZHWAUAAGBwZg2rVXVaVd1UVVeMlJ1QVddW1WX967CRbq+vqiVV9a2qOmSk/NC+bElVHT/+WQEAAGB9seEc+nlfkr9Ncsak8re31t42WlBVeyY5OsleSbZP8p9V9fi+8ylJDk6yLMnFVXV2a+0bq1F3AADghM3HNJ47xjMeGJNZw2pr7TNVtXCO4zsyyZmttZ8m+W5VLUmyf99tSWvt6iSpqjP7foVVAAAAVrA696y+pqq+1l8mvGVftkOSa0b6WdaXTVcOAAAAK1jVsPquJLsmWZTk+iR/3ZfXFP22GcpXUFXHVdUlVXXJzTffvIrVAwAAYF22SmG1tXZja+3+1trPkrwnD17quyzJTiO97pjkuhnKpxr3u1tr+7XW9ttmm21WpXoAAACs41YprFbVdiNvn5dk4knBZyc5uqo2rqpdkuye5EtJLk6ye1XtUlUPT/cQprNXvdoAAACsz2Z9wFJVfSjJQUm2rqplSd6c5KCqWpTuUt6lSV6ZJK21r1fVWekenHRfkle31u7vx/OaJOcl2SDJaa21r499bgAAAFgvzOVpwC+aovjUGfp/S5K3TFF+bpJzV6p2AAAAPCStztOAAQAAYI0QVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDBEVYBAAAYHGEVAACAwRFWAQAAGJwN57sCAACsvIXHnzO2cS098bljGxfAuGhZBQAAYHC0rAIAPNSdsPkYx3XH+MYFPKRpWQUAAGBwZg2rVXVaVd1UVVeMlD26qs6vqqv6v1v25VVVJ1fVkqr6WlU9aWSYxX3/V1XV4jUzOwAAAKwP5tKy+r4kh04qOz7JBa213ZNc0L9Pkuck2b1/HZfkXUkXbpO8OclTkuyf5M0TARcAAAAmmzWsttY+k+TWScVHJjm9///0JEeNlJ/ROhcl2aKqtktySJLzW2u3ttZuS3J+VgzAAAAAkGTV71ndtrV2fZL0fx/Tl++Q5JqR/pb1ZdOVAwAAwArG/YClmqKszVC+4giqjquqS6rqkptvvnmslQMAAGDdsKph9cb+8t70f2/qy5cl2Wmkvx2TXDdD+Qpaa+9ure3XWttvm222WcXqAQAAsC5b1bB6dpKJJ/ouTvKxkfKX9U8FPiDJHf1lwucleXZVbdk/WOnZfRkAAACsYMPZeqiqDyU5KMnWVbUs3VN9T0xyVlW9Isn3k7yg7/3cJIclWZLkJ0mOSZLW2q1V9edJLu77+7PW2uSHNgEAAECSOYTV1tqLpun0rCn6bUlePc14Tkty2krVDgAAgIekcT9gCQAAAFabsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOBsON8VAADmz8LjzxnLeJae+NyxjAcAJgirAMDqO2HzMY7rjvGNC4B1lsuAAQAAGBxhFQAAgMFZrbBaVUur6vKquqyqLunLHl1V51fVVf3fLfvyqqqTq2pJVX2tqp40jhkAAABg/TOOltVntNYWtdb2698fn+SC1truSS7o3yfJc5Ls3r+OS/KuMUwbAACA9dCauAz4yCSn9/+fnuSokfIzWueiJFtU1XZrYPoAAACs41Y3rLYk/1FVl1bVcX3Ztq2165Ok//uYvnyHJNeMDLusLwMAAIDlrO5P1zy1tXZdVT0myflVdeUM/dYUZW2FnrrQe1yS7LzzzqtZPQAAANZFq9Wy2lq7rv97U5KPJtk/yY0Tl/f2f2/qe1+WZKeRwXdMct0U43x3a22/1tp+22yzzepUDwAAgHXUKofVqtq0qjab+D/Js5NckeTsJIv73hYn+Vj//9lJXtY/FfiAJHdMXC4MAAAAo1bnMuBtk3y0qibG88HW2ier6uIkZ1XVK5J8P8kL+v7PTXJYkiVJfpLkmNWYNgAAAOuxVQ6rrbWrk+wzRfktSZ41RXlL8upVnR4AAAAPHWvip2sAAABgtQirAAAADI6wCgAAwOCs7u+sAvAQt/D4c8Y2rqULXjyeEZ1wx3jGAwDMGy2rAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAyOsAoAAMDgCKsAAAAMjrAKAADA4AirAAAADI6wCgAAwOAIqwAAAAzOhvNdAYAkWXj8OWMZz9ITnzuW8QAAML+0rAIAADA4wioAAACDI6wCAAAwOO5ZBdYvJ2w+xnHdMb5xAQCwUrSsAgAAMDjCKgAAAIMjrAIAADA4wioAAACDI6wCAAAwOMIqAAAAgyOsAgAAMDjCKgAAAIMjrAIAADA4wioAAACDI6wCAAAwOBvOdwVgXBYef87YxrX0xOeObVwAAMDKE1ZhKidsPqbx3DGe8QAAwEOMy4ABAAAYHGEVAACAwRFWAQAAGBxhFQAAgMERVgEAABgcYRUAAIDB8dM1AAAA67gnnP6EsYzn8sWXj2U846BlFQAAgMERVgEAABictX4ZcFUdmuSdSTZI8t7W2olruw7TWXj8OWMb19IFLx7buHLCHeMbFwAAwDpgrYbVqtogySlJDk6yLMnFVXV2a+0ba7MeAADA8sZ1z2MyrPseWXet7ZbV/ZMsaa1dnSRVdWaSI5MIq8DgrI8PKoB1gQNmAJK1H1Z3SHLNyPtlSZ6ylusAa40DLpgfPnswf5zoA8alWmtrb2JVL0hySGvtt/r3L02yf2vttSP9HJfkuP7tzyf51lqr4Nq3dZIfzHclWGXW37rLulu3WX/rNutv3WXdrdusv3XX+r7uHtta22aqDmu7ZXVZkp1G3u+Y5LrRHlpr707y7rVZqflSVZe01vab73qwaqy/dZd1t26z/tZt1t+6y7pbt1l/666H8rpb2z9dc3GS3atql6p6eJKjk5y9lusAAADAwK3VltXW2n1V9Zok56X76ZrTWmtfX5t1AAAAYPjW+u+sttbOTXLu2p7uQD0kLndej1l/6y7rbt1m/a3brL91l3W3brP+1l0P2XW3Vh+wBAAAAHOxtu9ZBQAAgFkJqwAAAAzOWr9n9aGsqg5Ncl5z7fU6qaq+mOS0JB9qrf1wvusDsK6oqucm2SvJgomy1tqfzV+NAIarqp6a5LLW2o+r6jeTPCnJO1tr35vnqq117lldi6rqzCT7JTkryT+21q6a5yqxEqrqF5Ick+QFSb6Qbh1eML+1Yi6qauMkL8+KB8vHzVedmLuqWpDkFVlx/R07b5Vizqrq75M8Iskzkrw3yW8k+VJr7RXzWjF4CKiqvZPsmeX3nWfMX42Yi6r6WpJ9kjwxyT8lOTXJ81trT5/Xis0DlwGvRa21o9OF1WuTfKiqPltVx1bVpvNcNeagtXZla+11SXZP8pEkZ1TVd6vqTVW1xTxXj5mdkWRhkl9L8sUkuya5ez4rxEr5pyQ/l+SQJP+VZMckP5rXGrEyfqm19rIkt7XW/jTJgUl2muc6MYuquryqvjbda77rx+yq6s1J/qZ/PSPJXyU5Yl4rxVzd11+JeWS6FtV3Jtlsnus0L4TVtay1dnuSDyZ5X5Kdk7woyVer6nfms17MTVXtmeTEJG9N8rEkv5nkniSfms96MavHt9Zen+TO1tqpSQ5Nsvc814m526219qYkP26tnZ7kuUmeMM91Yu7u6v/+pKq2T3Jvkl3msT7Mza8lOTzJJ/vXS/rXuUk+PI/1Yu5+I8mzktzQWjsmXUvdxvNbJeboR1X1+iQvTXJOVW2QZKN5rtO8cM/qWlRVh6W7jHSPJB9IckBr7fq+ZfUbSf5uPuvHzPp7Vu9Kd9/qn7TWJg7APt/fW8Bw3dv/vb2q9khyY5LHzmN9WDmj62/vJDekayln3fCJ/uqT/5fky0lausuBGbCJe+Oq6qmttdHvuOOr6vNJ3HM8fHe11n5WVfdV1aOS3JTkcfNdKebkfyR5cZJjW2s3VNXO6fahDznC6tr1kiTvaq0t1wrX3zz92/NUJ+agP6P1kdbaX03VvbXmspphO7Wqtkzy5iTnpbt/7s3zWyVWwrv79femJGcneWSSP5nfKjFXrbU/7//9SFV9IsmC1tod81knVsqmVfW01trnkqSqfimJ25fWDZf0J4rek+TSJHcm+dL8Vom56APqR9LdepYkP0jy/9u7+3jP5zr/44/nDBpXM9i05fqaRQyZclEuN1KxqGiiErG1Npe1ViXV1hal3ZANtUKSi+jCr0I1LhpEM64K7VoXRSSECbkYz98f788xX9OZOd9zzHzf34vn/XY7t/P9vj/nHE9zZs75vj7v9/v1vqhipGrSYKlDmmLnh7Z3qp0lxkbSVbbfUDtHREQvkLS97Z9J2mO467Yv7HSmGD1Jr6GsKJrUDD1Kme2ZWS9VjJak1YCJtrPfuAc0k1gHAsvZXlPS2sBXbe9QOVrHZWa1Q2zPlvSMpIk59qRnXSLpUOBc4ImhwXw/u5ekqbbPkXTwcNdtn9DpTNE+SfvY/qakw4e7bvtLnc4Uo7INZT//LsNcM5BitQfYngFs3CwjVWbFu5+k9WzfLmnTYa5tmhsNPeEg4LWUppDY/l9Jr6gbqY4Uq531Z0ozpUt5cbEz7Aux6Dr/2Lw/omXMlEZZ0Z2Wbd4vXzVFjNXQUsOB7IDY62wf07x/X+0sMXbN0V9vo+wTX0QSkHNyu9zhlFm544e5ZmD7zsaJMXja9jND/94kLUL53g2cLAPuIEnDninXdCeNiIjoG/OaER+SmfHeIOnHwGOUPY+zh8ZtD1cIRZeQNA7Ywvb02lli9CQdR1ly/x7gQ8A/Abfa/ljVYBWkWO2w5s7IWs3TO2w/VzNPtK/53h0IbN0MXQ58Ld/D7ifp5cB+NDMDQ+O2D6yVKUYmab7LtG0Pu7w7ukNzxuM8NWeuRpeT9CvbOeqrB0m6xvYWtXPE6DU3G/YHdgREaQ75NQ9g4ZZitYMkvYFyuP19lL94rwTenbtevUHSKZRliWc2Q/sAf0nB0/2aYxau5a9nBs6tFipGJOm987venLkaEQuRpFOBE23fUjtLjI6kTwE3AxcOYpET/SHFagdJ+iXwHtu3Ns//DjjL9mZ1k0U7JN1ke+ORxqL7SLrR9uTaOSIGkaQJlBmCDYAJQ+O296sWKtom6VbKirC7gKcpN9tte6OqwWJEkmZRbrLPppwTP/S9m1g1WMyTpFuYz97UQfx3lwZLnbXYUKEKYPs2SYvVDBSj8ryk1WzfDS+0gX++ZqBo248k7Wj70tpBYvQkLQ8cCazPi4udNAnpDWcBtwM7AZ+mnDl+W9VEMRo71w4QY2M7zel6z1trB+g2mVntIEnfoNyVPKsZ2htYwvZ8l7pFd5D0RuDrwP9Q7k6uBbzf9mVVg8WIJP2Jckbgk8AzzLm7vFzVYNGWpoP6ucCHgQ8A7wX+aPvIqsGiLZJusL2JpJttbyRpUeCS3GzoLc2xGa03i35bMU60SdKutPTasH1xzTwRo5VitYOapVAHA6+nvFi+EjjB9tNVg0VbmgZLiwJ/R/n+3Qo8mwZL3U/S+OHGbc8ebjy6i6QZtl8zVOw0Y1fY3qZ2thiZpOtsv1bSlZSOlg8A19leo3K0aENT7BwPrAA8CKwK3GZ7g6rBYkSSPg9MAc5uhqYCM2z/a71U0Q5JmwMnUl5zLgaMB54YxCXcWQbcWe+3fRxw3NCApH8GTqoXKUbhOtubAi8cpi1pJvBXh25Hd7E9W9IkYE1aZgaAqytFitF5tnl/v6S3AL8HVqqYJ0bnVEnLAh8Hvg8sBRxdN1KMwr8BmwM/aWbIt6MUPdH93gxMtv08gKQzgBuAFKvd7yTgncD5wGaUI2zWmu9n9KkUq521H39dmO4/zFh0kWbp06uAxSW9mjKrCjARWKJasGhbc8bx4cCKwC2UO83XAttWjBXt+0xzs+EIyp3micBhdSNFO5rjFx63/SfKaqLMpvaeZ20/LGmcpHG2p0k6tnaoaNsywCPN40k1g8To2L5D0vhmFdjpkgbyBnuK1Q6QtBfl7sjqki5suTSRcuBvdLe3UG40rASc3DI+i8wO9IpDKXcmr7H9BkkbUGZ5oss1S7jXbvZZPQZsVzlSjILt55sVROfVzhJj9qikpSg3G86W9CCQ7S+94XPADZKmUW60bw18tG6kaNOTTRPWGyUdB9xP6ew8cLJntQMkrU5Zfvg5Xrz0YhZwg+1nh/3E6CqS9rSdF1w9SNL1tqdIuhF4re1nhpq+1M4WI5M0zXaK1B4l6WjKsRnnAk8Mjdt+ZJ6fFF1D0pLAXyjFzt6U2bmzbT9cNVi0RdKrKKuJBPzC9gOVI0UbJK0K/IGyX/Uwyr+7k23fUTVYBSlWI0YgaartcyQdwjBnX9k+oUKsGAVJ36fs9ziC0uDsEWBJ22+qGizaIumzlF/Ucxc7M+f5SdE1JN01zLDTYCli4ZL0U9s7jDQW3UPSbsDVth+snaVbZBlwBwx1rWyOz2gtdnJ8Rm9Ytnn/8qopYsxs79o8PFrSDpTC5/9VjBSjs2Xz/tMtYwZy9Elv+Dvbf2kdaLrjRxeTNIvy70wM/9pl4LqS9orm39cSwMub5matvTZWqBYs2rEP8BVJTwLTKY0gp9v+dd1Y9WRmtQOahgTP5/iM3tV87w7KLGrvkjSRsu/4hZt0tm+ulyjaJWkN23eONBbdSdLMppP6fMciYsFoVoIdSilM72NOsfo4cJrtNPbscpJWo9yo3RLYAlgFuN72myvGqiIzqx3QUqjOtL1x7Twxes3RJ3sAKVZ7kKRjgAOBu4Hnm2Ez56D06G4X8NdHRJ0PvKZClmiTpFdSOnAvLmkT0km9J0laE7jX9tOStgU2As60nQaRXcr2l4EvS/qQ7RNr54nRs313M0O+ePM29HjgpFjtkKbYuVXSirbvq50nxuTnkr4MfJsX75vL7Fz3exewhu2naweJ9klaD9gAmNTcLBoykReflxvdaSdgX8qKhuOZU6zOIh1Je8l3gM0krQV8nXJW7rcoZ3hGd3te0jJDNxaaJcFTbZ88wudFJZI+SplJXR74DeWYvZOAAwd1JWaWAXeQpMuA1wHX8OJiZ495flJ0DUlXDTNs25md63LNkVEH2n6odpZon6R/AHYDdqW8QB4yC/i27YE8c67XSHqb7e/UzhFjM7RkW9JHgL/YPjHd1HuDpBttT55rLN+7LibpduDPwMWU/aq/sP1Y3VR1ZWa1sz5fO0CMTbOM+8u2L6idJcbks5Sz5m4GXphdzY2i7mb7e8D3JG1h+5raeWLMVmr2jM8CTqMs6f5X25fWjRVtelbSVOC9wC7N2KIVV5WzMQAAG9NJREFU80T7xkmSm5mp5rXMYpUzxXzYXk/ScpS9qtsC/9qcc3wTpUvw6TXz1ZCZ1Q5pfkD80PZOtbPE2Ei6yvYbaueI0ZP0K+C/gVuYs2cV2z+tFira1hyI/hnKWZ0/BjYGDrX9zarBoi2SbrK9saSdgIOAo4HT02CpN0haH/gAcE1zjNvqwF62cwO+y0n6ArAa8FVKn4YPAL+zfUTNXNEeSYtQejNsDfwjsLrtYZu19rMUqx0k6QfA3rYfr50lRk/SxylLM+Y+6zHfzy4n6cos1+5dQ0vZJO1OWRZ8GDAtDet6g6SbbW/U7Pm/3PZFWYoYsfBJGkcpcnag7Bm/FPjaoO597AWSdqXMqm5F6dnwa8py4KspM6t/rBivihSrHSTpHGBzyg+L1mLn8Gqhom2Sftfy9IWz52yvUilStEnS8cCTlH2PrcuA0xyrB0j6te0NJJ0GfMf2j4dm62pni5FJOp3SFXh1yqz4eErRmm7OPUDSVsAngVUp28eGfvetUTNXRD9qemxcTTljdYbtZypHqi7FagdJ2n+4cdtf73SWiEGS5li9TdLnKTOqTwGvBZYBLrb9uqrBoi3N7M5k4E7bj0r6G2DF3CzqDU3Dl8OAGcALM3K2H64WKuZL0nm295R0C+Xm+ovY3qhCrIgxSbHaYZIWA1axfUftLDE6khYHDgFWtf3Bpo3/2rZ/VDlaRN9rjlx4vDkGbAlgou0HaueKkUka9qaQ7Ss7nSVGT9IvcmOot0j6OqVPw30MX6ze0/FQEWOUYrWDJL0F+BKwmO3VJU0GjrG9e+Vo0YZmGfctwLtsb9i8YJ6efVfdT9I/Uw6xf1zSVyndSI9Kg6XeIOk9w43bPrPTWWL0mn4NQyZQZsdn2N6+UqQYhWZlw3jgQl68jWJmtVAxX5IOAd4JvIrSZ+Mc2zfWTRUxNilWO0jSDMom92lDBY6kW2y/um6yaIekX9rerLUxyHBnmEX3aWnwsiNwMHAMcGr2zPUGSSe2PJ1A+Tk60/bbK0WKl0DSysBxtqfWzhIjkzRtmGHnZkP3k7QqpWh9J+Vn5zmUM6r/p2qwiFHIOaud9WyzX6d1LHcLesczkibQfM+a9v0Dv/G9Rwz9O9uZcmTGjGYfXfQA2x9qfS5pEnBWpTjx0t0LbFg7RLTH9na1M8TYNMt9jwWOlbQJZWnwMZSZ8uhiaWw2R4rVzrpN0p6UQ5pXp+x/vLZypmjfpylnPK4k6QxgG2DYplnRdW6S9ENgHeBjzQHbuVHUu54E1q4dItrTzIwP/XsbarZ0U71EMRqS/hb4d2AF2zs3565ukeaQ3U/SosCbKDOrOwBXAJ+qGira9XWGaWw2iLIMuIMkLQl8AtixGboE+LTtJ+ulipFIWsX2b5vHy1POvxLlvKsHq4aLtkgaTzlY+w7bj0h6ObCy7RsqR4s2NHseW4ud9YHzbR9ZL1W0S9J7W54+B9xte3qtPDE6kn4EnA58zPbGkhYBbsgWpu4l6Y3AVOAtwHXAt4Hv2n5ivp8YXSONzeZIsdoBkv7d9kdr54ixkTTT9qa1c8RL0ywdXZOybwcA21fXSxTtkrRNy9PngHts31srT7w0zZ7Vd9r+Qu0sMTJJ19uekn4NvaPZZ/wtyrnUj9TOE+2TNPR6c0/S2AzIMuBOeROQYrV3aeQPiW4maT/gCGBFSkfnKZQl+NtWjBVtsn1F63NJW0k6yvZBtTLF6DSrGd5Bme1ZEbiobqIYhSeas3GH+jVsDjxWN1LMT/YZ97Tj53q+WctjAwPX2CzFameMb84IHLboyV2vrreipBPmddH2wZ0ME2NyGOUH/jW23yBpA+DjlTPFKDRHfb2Lcrf5Lsrd5uhikpYGdqd839ahFKhr2F6parAYrcOB7wNrSpoOLA+kE3fEQjB0o0HSGrbvbL0maeCaK0GK1U5Zj7JBerhi1cBA/uXrIU9Rvn/Ru/5i+ylJSFrM9q8lrVc7VMyfpHUojUGmAg9TzgtUZg16xoOU/XIfB35u25JyrniPsT2zWYq/LuV1zG9sP1s5VkS/u4ByJnyr8yn9NwZKitXOuHVon0f0pIdtn1E7RIyepEVsPwfcL2kZ4AfAJZIeAf5QN1204XbgKmAX23cASDqsbqQYhY9Sbjb8F/AtSedWzhNjIGkJyuzqqrYPkLS2pHVtX1w7W0S/aW6kbwBMkrRHy6WJtPTcGCQ5ZzBiZDlLtXddB2B7V9uP2j4a+AxwNvAPVZNFO94GPABMk3SapB3IHvKeYfs/mm6Wu1K+b98FVpB0ZDNrHr3hdMrvwS2a5/dSfo5GxIK3LvBWYBlgl5a3TYEDKuaqJt2AO0DSvra/UTtHxKBp7V4Zvas59ms3ynLg7YEzgItsX1o1WIyapFdTvo972V6zdp4YmaRf2t5srm7AN9neuHa2iH4laQvb19TO0Q1SrEZE35J0L/CleV23Pc9r0Z0kLUfpKruX7YHrihjRaZKuBnYAptveVNKawDm2X1s5WkTfknQ6c84Xf4Ht/SrEqSp7ViOin40HliJLR/tG0z39lOYtIha+Y4AfAytLOhvYCti3aqKI/te6J3wCpbP67ytlqSozqxEjaGZy5ilHD3UvSTNtz91NLyIiRqE5Z3Vzyo2/a20/VDlSxECRNA74ySCuKMrMagdJWp6yOXo1Wv7sB3FKv8fMoCzFyNFDvSczqhERL4GkrZuHs5r360vC9pW1MkUMoLWBVWqHqCHFamd9j3IMw0+A2ZWzRJtsr147Q4zZDrUDRAw6SVsBnwRWpbzuEGDbudHXGz7S8ngC8FrKTdyBm+GJ6BRJs5gzUWJKZ/wjq4aqJMuAO0jSjbYn184RYydpWcrdrRfOusrd5YiFqzlr7ljgFZRf3EPFzsSqwaItkm4HDqMUOC/cqLX9cLVQMWaSVgaOsz21dpaI6H+ZWe2siyW92fYPaweJ0ZP0fuAQYCXgRsr+nWvI3eWIhe04YBfbt9UOEmPymO0f1Q4RC8y9wIa1Q0T0O0m7AkPL8C+3ffH8Pr5fZWa1g5op/SWBp4FnyexAT5F0CzCF0lxisqT1gE/Z3qtytIi+Jmm67a1q54ixkfR5SmfuCym//wCwPbNaqGibpBOZc4TGOGAT4C7b+9RLFdHfmp+bU4Czm6GpwC9tH1UvVR0pVjtEkoCVbf+2dpYYG0nX254i6UbgdbafztLuiIWnWf4LsA3wSuC7vLjYubBGrhgdSdOGGfYgdrXsRZIOotxsAHgYuNv29IqRIvqepJuBybafb56PB26wvVHdZJ2XZcAdYtuSLgJeUztLjNm9kpahvGC+TNKfGNAzryI6ZJeWx08CO7Y8N2WmLrpYc9zCf9k+r3aWGB1JiwJfAN4D3E1ZDfYK4ERguqRNbN9QL2FE31sGGDoecVLNIDVlZrWDJH0F+Ibt62tniZdG0jaUHxw/sv1s7TwR/UzSVnPP5Aw3Ft1J0pW2tx75I6ObSDoBWAI4zPasZmwi8EVKo6w3pVt+xMIhaSrweWAa5UbR1sBRtr9dNVgFKVY7SNKtwLqUO5RPMGfP6sBN6fciSWfZfvdIYxGxYEmaaXvTkcaiO0k6GngKOJfyuw8A24/M85OiOkl3AGt7rheKzXLEh4CdbV9bJVzEAJD0Ksq+VYDrbD9QM08tWQbcWTvXDhAvyQatT5pf2FnWHbGQSNoC2BJYXtLhLZcmMmcPXXS//Zr3B7WMGcg5q93t+bkLVQDbsyX9MYVqxIInaVXgUduP2b6/ac66G7CWpJNsP1M5YseNqx1gkNi+B1gZ2L55/CT5HnQ9SUc1Pyw2kvR48zYLeBD4XuV4Ef1sMWApyo3VpVveHgfeXjFXjILt1Yd5S6Ha/W6V9J65ByXtA+QYqYiF4zzKySFImgycD/wW2Bg4uWKuarIMuIMkHQNsBqxrex1JKwDn50iG3iDpc4PYMjyiNkmrNjf4ogdJWgI4HFjF9oGS1qb8HhzIMwN7haQVKU3MngJmUGbDpwCLA7vbvq9ivIi+JOnmoe2Bkr5IWeHwL02zuhsHcetglgF31u6U88lmAtj+vaSl60aKkUhaz/btwPmS/mqPXM4KjFjoviFpuOWIOfqkN5xOKXa2bJ7fS5ktSLHaxZpi9HWStqdsgxGlqeBP6yaL6Gtqebw9cBSA7efLKZiDJ8VqZz3THGFjAElL1g4UbTkcOBA4fphrpvwwiYiF58MtjycAbwOeq5QlRm9N23s13S2x/ZQG9VVXD7L9M+BntXNEDIifSToPuB9YlubfXtNsaeD2q0KK1U47T9IpwDKSDqA0nTitcqYYge0Dm/fb1c4SMYhsz5hraLqkK6qEibF4RtLilJt7SFoTeLpupIiIrnQosBfwKuD1LccjvhL4WLVUFWXPaodJeiPlYHsBl9i+rHKkaJOkCcA/Aa+nvOi6Cviq7b9UDRbR5yQt1/J0HKUL9wm2160UKUZB0o6UF1nrA5cCWwHvsz2tarCIiOh6KVYj2tQsy5gFfLMZmgosa/sd9VJF9D9Jd1FuEImy/Pcu4NO2f141WLRN0t8Am1O+h9fafqhypIiI6AEpVjtI0h7AscArKL+wBdj2xKrBoi2SbrK98UhjERExh6Sf2t5hpLGIiIi5Zc9qZx0H7GI755P1phskbT50ELqk1wHTK2eK6HuSFgU+CGzdDF0OnNKylye6ULN1Ygng5ZKWZU6Xy4nACtWCRUR0uaYL97W2n6ydpbbMrHaQpOk5U7V3SboNWJdyODPAKpSD0Z+nzJAP3NlXEZ0g6WvAosAZzdC7gdm2318vVYxE0iGUZiErAPcxp1h9HDjN9km1skVEdDNJZ1K2TjxM6ZFyFfBz23+qGqyCFKsd0Cz/BdiG0s3ru7R0QrR9YY1cMTqSVp3fddv3dCpLxCDJEvzeJulDtk+snSMiotdIWgF4O+UItxVsD9yq2IH7H65kl5bHT1K6AQ8xkGK1BwwVo5JeQTnrcWj8t/P8pIhYEGZLWtP2/wFIWgOYXTlTtO8BSUvbniXp48CmwGdsz6wdLCKiG0naB3gD8GrgIeAkyuzqwMnMagdJ2sr29JHGojtJ2hU4nrKk7UFgVeA22xtUDRbR5yTtAJwO3ElZSroqOfqkZ0i62fZGkl4PfA74IvBR26+rHC0ioitJegj4P+CrwDTbd9dNVE+K1Q6SNNP2piONRXeSdBOwPfAT25tI2g6YavvAytEi+p6kl1H2jAu43fbTI3xKdAlJNzQ/Mz8H3GL7W0NjtbNFRHQrSRtQGgu+Hlgb+I3td9dN1XlZBtwBkrYAtgSWl3R4y6WJwPg6qWIMnrX9sKRxksbZnibp2NqhIvqVpCnA72w/YPtpSZOBtwH3SPqk7UcqR4z23CfpFODvgWObGw/jKmeKiOhakiZSGnmuCqwGTKI09Bw4+WXRGYsBS1FuDizd8vY4ZdN09IZHJS0FXAmcLenLwHOVM0X0s1OAZwAkbQ18HjgTeAw4tWKuGJ09gUuAN9l+FFgO+EjdSBERXe3nlJ43NwN72V7X9nsrZ6oiy4A7RNJ44FzbKU57lKQlgacoN3n2ptzlOtv2w1WDRfSp1o6/kr4C/NH2J5vnN9qeXDNftK/Zr7q27dMlLQ8sZfuu2rkiIrqZpCVtP1E7R02ZWe0Q27Mpd5Ojx0haq2mE9YTt520/Z/sM4EZgmdr5IvrYeElD21V2AH7Wci3bWHqEpGOAI4GjmqFFgW/WSxQR0d0kbSHpVuC25vnGkk6uHKuKFKuddYOk70t6t6Q9ht5qh4oR/Scwa5jxJ5trEbFwnANcIel7lFUNV0G5gURZChy9YXdgV+AJANu/p2yFiYiI4f0nsBPwMIDtmyjNlgZO7kx31nKUv3Tbt4zlnNXut5rtm+cetP1LSat1Pk7EYLD9WUk/BV4FXOo5+1bGAR+qlyxG6RnblmR4YUtFRETMh+3fSWodGsjzxVOsdpDt99XOEGMyYT7XFu9YiogBZPvaYcb+p0aWGLPzmm7Ay0g6ANgPOK1ypoiIbvY7SVsClrQYcDDNkuBBk2XAHSRpJUkXSXpQ0h8kfUfSSrVzxYiub15gvYik/YEZFfJERPQM218ELgC+Qzkr9xO2T6ybKiKiq30AOAhYEbgXmNw8HzjpBtxBki4DvgWc1QztA+xt+431UsVIJP0tcBHlCI2h4nQzypFEu9t+oFa2iIhuJelQYDpwg+0c8xUREaOWYrWDhjtqIccv9A5J2wEbNk9/bftn8/v4iIhBJumLwJbAepSzAq+mFK/X2H6kZraIiG4k6RPzuWzb/9axMF0ixWoHSfoJ8A1Kh0uAqcD7bO9QLVRERMRC1Oy32oxSuG7RvD1qe/2qwSIiuoykI4YZXhLYH/gb20t1OFJ1abDUWfsBJwH/QekCfHUzFhER0a8WByYCk5q33wO3VE0UEdGFbB8/9FjS0sAhwPuAbwPHz+vz+llmViMiImKBk3QqsAHlnOpfANcC19r+U9VgERFdTNJywOHA3sAZwJcH+edmZlY7IOvPIyJiAK0CvAz4X+A+SkfLR6smiojoYpK+AOwBnAq82vafK0eqLjOrHZD15/1B0h7AscArADVvtj2xarCIiC6lcqL9BpT9qltSmtQ9QmmydEzNbBER3UbS88DTwHOULYMvXGJAX3OmWO2wlvXn+wPnAcfbfrBuqmiHpDuAXWwP5KHMERFj1ZwpvhWlYH0r5UbtMnVTRUREt8sy4A4ZZv35poO8/rxH/SGFakREeyQdTClOtwKepTm2Bvhv0mApIiLakGK1A7L+vG/8UtK5wHcpSzQAsH1hvUgREV1rNeAC4DDb91fOEhERPSjLgDsg68/7g6TThxm27Rw/FBERERGxgKVYjYiIiIiIiK6TZcARbZI0gdIYawNgwtB4ZlYjIiIiIha8cbUDRPSQs4BXAjsBVwArUQ67j4iIiIiIBSzLgCPaJOkG25tIutn2RpIWBS6xvX3tbBERERER/SYzqxHte7Z5/6ikDYFJlG6XERERERGxgGXPakT7TpW0LHA08H1gKeATdSNFRERERPSnLAOOiIiIiIiIrpOZ1YgRSNrH9jclHT7cddtf6nSmiIiIiIh+l2I1YmRLNu+XrpoiIiIiImKAZBlwREREREREdJ3MrEaMQNIJ87tu++BOZYmIiIiIGBQpViNGNqN2gIiIiIiIQZNlwBEREREREdF1MrMa0SZJywNHAusDE4bGbW9fLVRERERERJ8aVztARA85G7gNWB34FHA3cH3NQBERERER/SrLgCPaJGmG7ddIutn2Rs3YFba3qZ0tIiIiIqLfZBlwRPuebd7fL+ktwO+BlSrmiYiIiIjoWylWI9r3GUmTgCOAE4GJwGF1I0VERERE9KcUqxFtkDQeWNv2xcBjwHaVI0VERERE9LU0WIpog+3ZwK61c0REREREDIo0WIpok6TPApOAc4EnhsZtz6wWKiIiIiKiT6VYjWiTpGnDDDvnrEZERERELHgpViPaJGkN23eONBYRERERES9d9qxGtO+CYcbO73iKiIiIiIgBkG7AESOQtB6wATBJ0h4tlyYCE+qkioiIiIjobylWI0a2LvBWYBlgl5bxWcABVRJFRERERPS57FmNaJOkLWxfUztHRERERMQgyJ7ViPbtLmmipEUl/VTSQ5L2qR0qIiIiIqIfpViNaN+Oth+nLAm+F1gH+EjdSBERERER/SnFakT7Fm3evxk4x/YjNcNERERERPSzNFiKaN8PJN0OPAX8k6Tlgb9UzhQRERER0ZfSYCliFCQtCzxue7akJYCJth+onSsiIiIiot9kZjWiTZLe0/K49dKZnU8TEREREdHfUqxGtG9Ky+MJwA7ATFKsRkREREQscFkGHDFGkiYBZ9netXaWiIiIiIh+k27AEWP3JLB27RAREREREf0oy4Aj2iTpB8DQUoRxwPrA+fUSRURERET0rywDjmiTpG1anj4H3GP73lp5IiIiIiL6WYrViDGStBXwLtsH1c4SEREREdFvsgw4YhQkTQbeBewJ3AVcWDdRRERERER/SrEaMQJJ6wDvBKYCDwPnUlYlbFc1WEREREREH8sy4IgRSHoeuArY3/YdzdidtteomywiIiIion/l6JqIkb0NeACYJuk0STsAqpwpIiIiIqKvZWY1ok2SlgR2oywH3h44A7jI9qVVg0VERERE9KEUqxFjIGk54B3AXra3r50nIiIiIqLfpFiNiIiIiIiIrpM9qxEREREREdF1UqxGRERERERE10mxGhERA0/SbEk3SvqVpPMlLbGAvu7VC+LrDPN1fyxpRUlvlXSDpJsk3SrpH5vru0lav42v09bHRURE1JBiNSIiAp6yPdn2hsAzwAdaL6oY9e9M21suqIAtWRYHlgMeBE4FdrG9MbAJcHnzYbsB7RSh7X5cREREx6VYjYiIeLGrgLUkrSbpNkknAzOBlSVNlXRLMwN7LICkD0o6buiTJe0r6cTm8Z+b99tKulzSBZJul3S2JDXXpki6upkdvU7S0pLGS/qCpOsl3Tw0Y9rYllKULg0sAjwMYPtp27+RtCWwK/CFZrZ4TUkHNF/rJknfkbTEPD5uzWbWdoakqySttzD/oCMiIuYnxWpERERD0iLAzsAtzdC6wJm2NwGeBY6lnLM8GZgiaTfgAmCPli+zF3DuMF9+E+BQykzmGsBWkhZrPvaQZnb074GngP2Bx2xPAaYAB0havfk6OwM/tv0I8H3gHknnSNpb0jjbVzfjH2lmi/8PuND2lOa/cRuw/zw+7lTgQ7ZfA3wYOHnMf5gREREv0SK1A0RERHSBxSXd2Dy+Cvg6sAJwj+1rm/EpwOW2/wgg6Wxga9vflXSnpM2B/6UUuNOH+W9cZ/ve5nNvBFYDHgPut309gO3Hm+s7AhtJenvzuZOAtYG7gK0ohSS23y/p1ZQi98PAG4F9h/lvbyjpM8AywFLAJXN/gKSlgC2B85tJX4CXzesPLCIiYmFLsRoREdHsWW0daAq2J1qH5vP55wJ7ArcDF3n4Q8yfbnk8m/I7WMBwHyvKDOeLikpJawC/s/3M0JjtW4BbJJ1FKWb3HebrfQPYzfZNkvalLCWe2zjg0bn/HCIiImrJMuCIiIj2/ALYRtLLJY0HpgJXNNcupDQrmsrwS4Dn5XZgBUlTAJr9qotQZj4/KGnRZnwdSUvSLAFuxpaStG3L15oM3NM8nkXZ0zpkaeD+5uvt3TL+wsc1s7p3SXpH8/UlaeNR/L9EREQsUClWIyIi2mD7fuAoYBpwEzDT9veaa38CbgVWtX3dKL7mM5Q9ridKugm4DJgAfK35ejMl/Qo4hTIT+yaaYpUy+/ovkn7TLCv+FHNmVb8NfKQ51mZN4GhKsX0ZpUBmHh+3N7B/k+XXwD+0+/8SERGxoGn4lUoRERHRTSS9DJhue7PaWSIiIjohxWpERERERER0nSwDjoiIiIiIiK6TYjUiIiIiIiK6TorViIiIiIiI6DopViMiIiIiIqLrpFiNiIiIiIiIrpNiNSIiIiIiIrpOitWIiIiIiIjoOv8f0qk3/vPI+f0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows the bar graph for each state\n",
    "title = 'Australia Confirmed Cases (CC) & Recorded Deaths (RD) & Recorded Recoveries (RR) state/territory'\n",
    "ausMerged.plot.bar(x='Province/State', y=['CC', 'RR', 'RD'], figsize=(16, 8), title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the above graph that NSW, Victoria and Queensland are the top 3 states. (Data may change over time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>County</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>...</th>\n",
       "      <th>5/15/20</th>\n",
       "      <th>5/16/20</th>\n",
       "      <th>5/17/20</th>\n",
       "      <th>5/18/20</th>\n",
       "      <th>5/19/20</th>\n",
       "      <th>5/20/20</th>\n",
       "      <th>5/21/20</th>\n",
       "      <th>5/22/20</th>\n",
       "      <th>5/23/20</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>AS</td>\n",
       "      <td>ASM</td>\n",
       "      <td>16</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>US</td>\n",
       "      <td>-14.271000</td>\n",
       "      <td>-170.132000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>316</td>\n",
       "      <td>GU</td>\n",
       "      <td>GUM</td>\n",
       "      <td>316</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Guam</td>\n",
       "      <td>US</td>\n",
       "      <td>13.444300</td>\n",
       "      <td>144.793700</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>580</td>\n",
       "      <td>MP</td>\n",
       "      <td>MNP</td>\n",
       "      <td>580</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>15.097900</td>\n",
       "      <td>145.673900</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>630</td>\n",
       "      <td>PR</td>\n",
       "      <td>PRI</td>\n",
       "      <td>630</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>US</td>\n",
       "      <td>18.220800</td>\n",
       "      <td>-66.590100</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>123</td>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
       "      <td>125</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>850</td>\n",
       "      <td>VI</td>\n",
       "      <td>VIR</td>\n",
       "      <td>850</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>18.335800</td>\n",
       "      <td>-64.896300</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3150</td>\n",
       "      <td>84070016</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>39.372319</td>\n",
       "      <td>-111.575868</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3151</td>\n",
       "      <td>84070017</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southeast Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>38.996171</td>\n",
       "      <td>-110.701396</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3152</td>\n",
       "      <td>84070018</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southwest Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>37.854472</td>\n",
       "      <td>-111.441876</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3153</td>\n",
       "      <td>84070019</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TriCounty</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>40.124915</td>\n",
       "      <td>-109.517442</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3154</td>\n",
       "      <td>84070020</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Weber-Morgan</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>41.271160</td>\n",
       "      <td>-111.914512</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3155 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           UID iso2 iso3  code3  FIPS          County  \\\n",
       "0           16   AS  ASM     16  60.0             NaN   \n",
       "1          316   GU  GUM    316  66.0             NaN   \n",
       "2          580   MP  MNP    580  69.0             NaN   \n",
       "3          630   PR  PRI    630  72.0             NaN   \n",
       "4          850   VI  VIR    850  78.0             NaN   \n",
       "...        ...  ...  ...    ...   ...             ...   \n",
       "3150  84070016   US  USA    840   NaN    Central Utah   \n",
       "3151  84070017   US  USA    840   NaN  Southeast Utah   \n",
       "3152  84070018   US  USA    840   NaN  Southwest Utah   \n",
       "3153  84070019   US  USA    840   NaN       TriCounty   \n",
       "3154  84070020   US  USA    840   NaN    Weber-Morgan   \n",
       "\n",
       "                Province/State Country/Region        Lat        Long  ...  \\\n",
       "0               American Samoa             US -14.271000 -170.132000  ...   \n",
       "1                         Guam             US  13.444300  144.793700  ...   \n",
       "2     Northern Mariana Islands             US  15.097900  145.673900  ...   \n",
       "3                  Puerto Rico             US  18.220800  -66.590100  ...   \n",
       "4               Virgin Islands             US  18.335800  -64.896300  ...   \n",
       "...                        ...            ...        ...         ...  ...   \n",
       "3150                      Utah             US  39.372319 -111.575868  ...   \n",
       "3151                      Utah             US  38.996171 -110.701396  ...   \n",
       "3152                      Utah             US  37.854472 -111.441876  ...   \n",
       "3153                      Utah             US  40.124915 -109.517442  ...   \n",
       "3154                      Utah             US  41.271160 -111.914512  ...   \n",
       "\n",
       "     5/15/20  5/16/20  5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  \\\n",
       "0          0        0        0        0        0        0        0        0   \n",
       "1          5        5        5        5        5        5        5        5   \n",
       "2          2        2        2        2        2        2        2        2   \n",
       "3        122      122      123      124      124      125      126      126   \n",
       "4          6        6        6        6        6        6        6        6   \n",
       "...      ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "3150       0        0        0        0        0        0        0        0   \n",
       "3151       0        0        0        0        0        0        0        0   \n",
       "3152       3        3        3        3        4        4        4        4   \n",
       "3153       0        0        0        0        0        0        0        0   \n",
       "3154       3        3        3        3        3        3        3        4   \n",
       "\n",
       "      5/23/20  5/24/20  \n",
       "0           0        0  \n",
       "1           5        5  \n",
       "2           2        2  \n",
       "3         127      127  \n",
       "4           6        6  \n",
       "...       ...      ...  \n",
       "3150        0        0  \n",
       "3151        0        0  \n",
       "3152        4        4  \n",
       "3153        0        0  \n",
       "3154        4        4  \n",
       "\n",
       "[3155 rows x 136 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets['covid_us_deaths']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US bar graphs\n",
    "Below will do the same with the US states data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>CC</th>\n",
       "      <th>RD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>New York</td>\n",
       "      <td>361515</td>\n",
       "      <td>29141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>153928</td>\n",
       "      <td>11138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>110050</td>\n",
       "      <td>4855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>California</td>\n",
       "      <td>94020</td>\n",
       "      <td>3754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>92367</td>\n",
       "      <td>6368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>71563</td>\n",
       "      <td>5136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>Texas</td>\n",
       "      <td>55861</td>\n",
       "      <td>1528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>51177</td>\n",
       "      <td>5160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Florida</td>\n",
       "      <td>50782</td>\n",
       "      <td>2237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>46313</td>\n",
       "      <td>2212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>40258</td>\n",
       "      <td>3693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>38332</td>\n",
       "      <td>1799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>37091</td>\n",
       "      <td>2568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>36244</td>\n",
       "      <td>1171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>31911</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>31376</td>\n",
       "      <td>1824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>24049</td>\n",
       "      <td>1332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>23365</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>20554</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>Washington</td>\n",
       "      <td>19756</td>\n",
       "      <td>1061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>19697</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>17248</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>16377</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>15277</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>14478</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>13260</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>12547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>12149</td>\n",
       "      <td>686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>11945</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>10096</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>9004</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>8752</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8542</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>Utah</td>\n",
       "      <td>8392</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>8110</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>7881</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>6755</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>6037</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>5772</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>4563</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4135</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>3927</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>3189</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>2626</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>2418</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Maine</td>\n",
       "      <td>2054</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>1759</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>951</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>838</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>633</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Montana</td>\n",
       "      <td>479</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>407</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Guam</td>\n",
       "      <td>166</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Province/State      CC     RD\n",
       "34                  New York  361515  29141\n",
       "32                New Jersey  153928  11138\n",
       "15                  Illinois  110050   4855\n",
       "5                 California   94020   3754\n",
       "23             Massachusetts   92367   6368\n",
       "41              Pennsylvania   71563   5136\n",
       "47                     Texas   55861   1528\n",
       "24                  Michigan   51177   5160\n",
       "10                   Florida   50782   2237\n",
       "22                  Maryland   46313   2212\n",
       "7                Connecticut   40258   3693\n",
       "11                   Georgia   38332   1799\n",
       "20                 Louisiana   37091   2568\n",
       "51                  Virginia   36244   1171\n",
       "38                      Ohio   31911   1969\n",
       "16                   Indiana   31376   1824\n",
       "6                   Colorado   24049   1332\n",
       "35            North Carolina   23365    784\n",
       "25                 Minnesota   20554    869\n",
       "52                Washington   19756   1061\n",
       "46                 Tennessee   19697    332\n",
       "17                      Iowa   17248    456\n",
       "3                    Arizona   16377    799\n",
       "54                 Wisconsin   15277    510\n",
       "0                    Alabama   14478    551\n",
       "26               Mississippi   13260    625\n",
       "43              Rhode Island   12547      0\n",
       "27                  Missouri   12149    686\n",
       "29                  Nebraska   11945    134\n",
       "44            South Carolina   10096    435\n",
       "18                    Kansas    9004    205\n",
       "8                   Delaware    8752    325\n",
       "19                  Kentucky    8542    391\n",
       "48                      Utah    8392     97\n",
       "9       District of Columbia    8110    432\n",
       "30                    Nevada    7881    380\n",
       "33                New Mexico    6755    317\n",
       "39                  Oklahoma    6037    311\n",
       "4                   Arkansas    5772    116\n",
       "45              South Dakota    4563     50\n",
       "31             New Hampshire    4135    172\n",
       "40                    Oregon    3927    148\n",
       "42               Puerto Rico    3189    127\n",
       "14                     Idaho    2626     79\n",
       "36              North Dakota    2418     47\n",
       "21                     Maine    2054     78\n",
       "53             West Virginia    1759     72\n",
       "49                   Vermont     951     54\n",
       "55                   Wyoming     838      1\n",
       "13                    Hawaii     633     17\n",
       "28                   Montana     479     16\n",
       "1                     Alaska     407     10\n",
       "12                      Guam     166      5\n",
       "50            Virgin Islands      69      6\n",
       "37  Northern Mariana Islands      22      2\n",
       "2             American Samoa       0      0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the histogram of cases in Australia, confirmed, recovered and deaths\n",
    "usCC = datasets['covid_us_confirmed'][['Province/State', latest_date]].groupby(['Province/State']).sum() # confirmed cases\n",
    "usRD = datasets['covid_us_deaths'][['Province/State', latest_date]].groupby(['Province/State']).sum() # Recorded deaths\n",
    "\n",
    "# Rename the confirmed cases\n",
    "usCC = usCC.rename(columns={latest_date: \"CC\"}).reset_index()\n",
    "usRD = usRD.rename(columns={latest_date: \"RD\"}).reset_index()\n",
    "\n",
    "# Merge the tables together\n",
    "usMerged = pd.merge(usCC, usRD, on='Province/State')\n",
    "usMerged = usMerged.sort_values('CC', ascending=False)\n",
    "usMerged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df200b78c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'US Confirmed Cases (CC) & Recorded Deaths(RD) for each US state/territory'\n",
    "usMerged.plot.bar(x='Province/State', y=['CC', 'RD'], figsize=(16, 8), title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the above graph that New York is the recorded top number of confirmed cases. (Note that data may change over time and these are US states and not cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maps\n",
    "## Generating Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function will generate the map with preset settings, given the map's starting coordinates (tuple) and zoom\n",
    "# coords - (lat, long) of the starting coordinates, zoom - zoom of the map\n",
    "def generateMap(coords, zoom):\n",
    "    basemap = Map(basemap=basemaps.OpenStreetMap.Mapnik, center=(coords), zoom=zoom)\n",
    "    basemap.add_control(FullScreenControl())\n",
    "    basemap.add_control(LayersControl())\n",
    "    \n",
    "    marker_layer = LayerGroup(name='markers')\n",
    "    circle_layer = LayerGroup(name='circles')\n",
    "    return basemap, marker_layer, circle_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function adds the data to the map, the circles and markers are added to the pymap\n",
    "# df - dataframe containing the confirmed cases, pymap - map to add data and circles to, \n",
    "# G - graph to perform analysis on, scale - scale the radius of the number of cases\n",
    "# mopacity - opacity of the marker (default 0)\n",
    "# \n",
    "def generateMapData(df, date, coords, zoom, scale=1, mopacity=0):\n",
    "    basemap, marker_layer, circle_layer = generateMap(coords, zoom)\n",
    "    pos = {}\n",
    "    \n",
    "    for index, row in df.iterrows():\n",
    "        coords = (row['Lat'], row['Long'])\n",
    "        name = row['Country/Region']\n",
    "        \n",
    "        '''\n",
    "        # Adds the state or province if the point has one\n",
    "        if not pd.isnull(row['Province/State']):\n",
    "            name += ' - ' + row['Province/State']\n",
    "            if None != G:\n",
    "                pos[name] = coords # Add to pos dict for drawing\n",
    "                G.add_node(row['Province/State'], pos=coords) \n",
    "        elif None != G:\n",
    "            pos[name] = coords # Add to pos dict for drawing\n",
    "            G.add_node(name, pos=coords) \n",
    "        '''\n",
    "            \n",
    "        # Add number of cases in the title\n",
    "        name += ' - Confirmed: ' + str(row[date])\n",
    "        \n",
    "        # Marker\n",
    "        if (row[date] > 0):\n",
    "            marker = Marker(location=coords, draggable=False, title=name, opacity=mopacity)\n",
    "            marker_layer.add_layer(marker)\n",
    "        \n",
    "        # Circle\n",
    "        if (row[date] > 0):\n",
    "            circle = Circle(location=coords, radius=round(row[date]*scale), color='red', fill_color='red')\n",
    "            circle_layer.add_layer(circle)\n",
    "        \n",
    "    basemap.add_layer(marker_layer)\n",
    "    basemap.add_layer(circle_layer)\n",
    "    return basemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate map for the confirmed cases\n",
    "confirmedMap = generateMapData(df, latest_date, center, zoom, scale=1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb26f24e387e4c509c9337ca0b8cc18b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[0, 0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confirmedMap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this map you can see the dots represent the number of confirmed cases with the circle's radius representing the number of cases. The larger the circle, the higher number of cases the country/state/provice has. You can hover your mouse over the centre of a circle and it will display the country/state and number of confirmed cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Australia\n",
    "Below will show only Australia's Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>-35.4735</td>\n",
       "      <td>149.0124</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>-33.8688</td>\n",
       "      <td>151.2093</td>\n",
       "      <td>3090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>-12.4634</td>\n",
       "      <td>130.8456</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>-28.0167</td>\n",
       "      <td>153.4000</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>-34.9285</td>\n",
       "      <td>138.6007</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>-41.4545</td>\n",
       "      <td>145.9707</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>-37.8136</td>\n",
       "      <td>144.9631</td>\n",
       "      <td>1605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country/Region                Province/State      Lat      Long  5/24/20\n",
       "8       Australia  Australian Capital Territory -35.4735  149.0124      107\n",
       "9       Australia               New South Wales -33.8688  151.2093     3090\n",
       "10      Australia            Northern Territory -12.4634  130.8456       29\n",
       "11      Australia                    Queensland -28.0167  153.4000     1056\n",
       "12      Australia               South Australia -34.9285  138.6007      439\n",
       "13      Australia                      Tasmania -41.4545  145.9707      228\n",
       "14      Australia                      Victoria -37.8136  144.9631     1605\n",
       "15      Australia             Western Australia -31.9505  115.8605      560"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a separate dataframe for australian cities\n",
    "ausCities = df.loc[df['Country/Region'] == 'Australia']\n",
    "ausCities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb550e155f8d49088987b4dbdee1ec97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[-28, 138], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ausCoords = (-28, 138)\n",
    "ausZoom = 4\n",
    "ausMap = generateMapData(ausCities, latest_date, ausCoords, ausZoom, scale=50)\n",
    "ausMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5238377a89294d4e9b5190a1cd29d8a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Box(children=(IntSlider(value=4, description='zoom', max=10, min=1),))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ausMap.interact(zoom=(1,10,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeline\n",
    "Below will show code to show the progression of cases in Australia with time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = datasets['covid_confirmed']\n",
    "dfaus = df.loc[df['Country/Region'] == 'Australia']\n",
    "\n",
    "ausMapTimeline, marker_layer_aus, circle_layer_aus = generateMap((-28, 138), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function generates a graph given the dataframe, this is optimized for the timeline display\n",
    "# df - dataframe containing the data, pymap - map to be given to, scale - scale of the number of cases (circle size)\n",
    "# output - circle_layer - to delete this later\n",
    "def generateGraphTimeline(df, pymap, date, circle_layer_aus, marker_layer_aus, showMarkers, scale=1):\n",
    "    for index, row in df.iterrows():\n",
    "        coords = (row['Lat'], row['Long'])\n",
    "        \n",
    "        # Everything to do with markers, can be selected to be off\n",
    "        if showMarkers:\n",
    "            name = row['Country/Region']\n",
    "\n",
    "            # Adds the state or province if the point has one\n",
    "            if not pd.isnull(row['Province/State']):\n",
    "                name += ' - ' + row['Province/State']\n",
    "\n",
    "            # Add number of cases in the title\n",
    "            name += ' - Confirmed: ' + str(row[date])\n",
    "\n",
    "            # Marker\n",
    "            marker = Marker(location=coords, draggable=False, title=name, opacity=0.1)\n",
    "            marker_layer_aus.add_layer(marker)\n",
    "        \n",
    "        # Circle\n",
    "        if (row[date] > 0):\n",
    "            circle = CircleMarker(location=coords, radius=round(row[date]*scale), color='red', fill_color='red')\n",
    "            circle_layer_aus.add_layer(circle)\n",
    "            \n",
    "    pymap.add_layer(circle_layer_aus)\n",
    "    if showMarkers:\n",
    "        marker_layer_aus.name = 'markers'\n",
    "        pymap.add_layer(marker_layer_aus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(date, showMarkers):\n",
    "    # Gets the data from the selected date\n",
    "    date = dfaus.columns[date+3] # number of confirmed cases to date\n",
    "    ausCities = dfaus[['Country/Region', 'Province/State', 'Lat', 'Long', date]]\n",
    "    \n",
    "    # Removes the old circle/marker layer\n",
    "    if circle_layer_aus in ausMapTimeline.layers:\n",
    "        ausMapTimeline.remove_layer(circle_layer_aus)\n",
    "        circle_layer_aus.clear_layers()\n",
    "    \n",
    "    if marker_layer_aus in ausMapTimeline.layers:\n",
    "        ausMapTimeline.remove_layer(marker_layer_aus)\n",
    "        marker_layer_aus.clear_layers()\n",
    "    \n",
    "    # Generates and adds the new layer\n",
    "    generateGraphTimeline(ausCities, ausMapTimeline, date, circle_layer_aus, marker_layer_aus, showMarkers, 0.02)\n",
    "\n",
    "    return date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timeline Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5061fd10bfb84e1d92e444e1550816c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[-28, 138], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ausMapTimeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07089445e8d3493bad3cfb9eb59cf326",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=62, description='date', max=124, min=1), Checkbox(value=False, descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Slider: days since first confirmed case\n",
    "interact(f, date=(1, number_of_days), showMarkers=widgets.Checkbox());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usage of map above: Move the slider to see the number of confirmed cases represented with the radius of the circle with respect to time. Move the slider to the very right to see the latest number of cases. You can select to show markers which will show the number of confirmed cases when you hover your mouse over the marker."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## US Timeline Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>County</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>...</th>\n",
       "      <th>5/15/20</th>\n",
       "      <th>5/16/20</th>\n",
       "      <th>5/17/20</th>\n",
       "      <th>5/18/20</th>\n",
       "      <th>5/19/20</th>\n",
       "      <th>5/20/20</th>\n",
       "      <th>5/21/20</th>\n",
       "      <th>5/22/20</th>\n",
       "      <th>5/23/20</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>AS</td>\n",
       "      <td>ASM</td>\n",
       "      <td>16</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>US</td>\n",
       "      <td>-14.271000</td>\n",
       "      <td>-170.132000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>316</td>\n",
       "      <td>GU</td>\n",
       "      <td>GUM</td>\n",
       "      <td>316</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Guam</td>\n",
       "      <td>US</td>\n",
       "      <td>13.444300</td>\n",
       "      <td>144.793700</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>580</td>\n",
       "      <td>MP</td>\n",
       "      <td>MNP</td>\n",
       "      <td>580</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>15.097900</td>\n",
       "      <td>145.673900</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>630</td>\n",
       "      <td>PR</td>\n",
       "      <td>PRI</td>\n",
       "      <td>630</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>US</td>\n",
       "      <td>18.220800</td>\n",
       "      <td>-66.590100</td>\n",
       "      <td>...</td>\n",
       "      <td>2542</td>\n",
       "      <td>2589</td>\n",
       "      <td>2646</td>\n",
       "      <td>2710</td>\n",
       "      <td>2805</td>\n",
       "      <td>2866</td>\n",
       "      <td>2913</td>\n",
       "      <td>3030</td>\n",
       "      <td>3100</td>\n",
       "      <td>3189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>850</td>\n",
       "      <td>VI</td>\n",
       "      <td>VIR</td>\n",
       "      <td>850</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>18.335800</td>\n",
       "      <td>-64.896300</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3150</td>\n",
       "      <td>84070016</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>39.372319</td>\n",
       "      <td>-111.575868</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3151</td>\n",
       "      <td>84070017</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southeast Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>38.996171</td>\n",
       "      <td>-110.701396</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3152</td>\n",
       "      <td>84070018</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southwest Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>37.854472</td>\n",
       "      <td>-111.441876</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>209</td>\n",
       "      <td>219</td>\n",
       "      <td>236</td>\n",
       "      <td>245</td>\n",
       "      <td>260</td>\n",
       "      <td>271</td>\n",
       "      <td>281</td>\n",
       "      <td>294</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3153</td>\n",
       "      <td>84070019</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TriCounty</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>40.124915</td>\n",
       "      <td>-109.517442</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3154</td>\n",
       "      <td>84070020</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Weber-Morgan</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>41.271160</td>\n",
       "      <td>-111.914512</td>\n",
       "      <td>...</td>\n",
       "      <td>216</td>\n",
       "      <td>221</td>\n",
       "      <td>226</td>\n",
       "      <td>234</td>\n",
       "      <td>234</td>\n",
       "      <td>242</td>\n",
       "      <td>251</td>\n",
       "      <td>252</td>\n",
       "      <td>256</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3155 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           UID iso2 iso3  code3  FIPS          County  \\\n",
       "0           16   AS  ASM     16  60.0             NaN   \n",
       "1          316   GU  GUM    316  66.0             NaN   \n",
       "2          580   MP  MNP    580  69.0             NaN   \n",
       "3          630   PR  PRI    630  72.0             NaN   \n",
       "4          850   VI  VIR    850  78.0             NaN   \n",
       "...        ...  ...  ...    ...   ...             ...   \n",
       "3150  84070016   US  USA    840   NaN    Central Utah   \n",
       "3151  84070017   US  USA    840   NaN  Southeast Utah   \n",
       "3152  84070018   US  USA    840   NaN  Southwest Utah   \n",
       "3153  84070019   US  USA    840   NaN       TriCounty   \n",
       "3154  84070020   US  USA    840   NaN    Weber-Morgan   \n",
       "\n",
       "                Province/State Country/Region        Lat        Long  ...  \\\n",
       "0               American Samoa             US -14.271000 -170.132000  ...   \n",
       "1                         Guam             US  13.444300  144.793700  ...   \n",
       "2     Northern Mariana Islands             US  15.097900  145.673900  ...   \n",
       "3                  Puerto Rico             US  18.220800  -66.590100  ...   \n",
       "4               Virgin Islands             US  18.335800  -64.896300  ...   \n",
       "...                        ...            ...        ...         ...  ...   \n",
       "3150                      Utah             US  39.372319 -111.575868  ...   \n",
       "3151                      Utah             US  38.996171 -110.701396  ...   \n",
       "3152                      Utah             US  37.854472 -111.441876  ...   \n",
       "3153                      Utah             US  40.124915 -109.517442  ...   \n",
       "3154                      Utah             US  41.271160 -111.914512  ...   \n",
       "\n",
       "     5/15/20  5/16/20  5/17/20  5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  \\\n",
       "0          0        0        0        0        0        0        0        0   \n",
       "1        154      154      154      154      154      154      165      165   \n",
       "2         19       21       21       21       21       21       22       22   \n",
       "3       2542     2589     2646     2710     2805     2866     2913     3030   \n",
       "4         69       69       69       69       69       69       69       69   \n",
       "...      ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "3150      29       29       30       30       30       31       32       34   \n",
       "3151      13       14       14       15       16       16       17       18   \n",
       "3152     200      209      219      236      245      260      271      281   \n",
       "3153      16       19       19       22       19       20       20       21   \n",
       "3154     216      221      226      234      234      242      251      252   \n",
       "\n",
       "      5/23/20  5/24/20  \n",
       "0           0        0  \n",
       "1         165      166  \n",
       "2          22       22  \n",
       "3        3100     3189  \n",
       "4          69       69  \n",
       "...       ...      ...  \n",
       "3150       33       33  \n",
       "3151       18       20  \n",
       "3152      294      305  \n",
       "3153       20       20  \n",
       "3154      256      260  \n",
       "\n",
       "[3155 rows x 135 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets['covid_us_confirmed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfus = datasets['covid_us_confirmed']\n",
    "usCoords = (38.451217, -102.675199)\n",
    "usZoom = 4\n",
    "usMapTimeline, marker_layer_us, circle_layer_us = generateMap(usCoords, usZoom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function generates a graph given the dataframe, this is optimized for the timeline display for US data\n",
    "def usf(date, showMarkers):\n",
    "    # Gets the data from the selected date\n",
    "    date = dfus.columns[date+3] # number of confirmed cases to date\n",
    "    uStates = dfus[['Province/State', 'Lat', 'Long', date]]\n",
    "    \n",
    "    # Removes the old circle/marker layer\n",
    "    if circle_layer_us in usMapTimeline.layers:\n",
    "        usMapTimeline.remove_layer(circle_layer_us)\n",
    "        circle_layer_us.clear_layers()\n",
    "    \n",
    "    if marker_layer_us in usMapTimeline.layers:\n",
    "        usMapTimeline.remove_layer(marker_layer_us)\n",
    "        marker_layer_us.clear_layers()\n",
    "    \n",
    "    # Generates and adds the new layer\n",
    "    generateGraphTimeline(uStates, usMapTimeline, date, circle_layer_us, marker_layer_us, showMarkers, 0.001)\n",
    "\n",
    "    return date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6201bb2acc9049fab631aa5ab2a7d667",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[38.451217, -102.675199], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(usMapTimeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a77c3e9d78047e6ad6e78a1ff002e19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=62, description='date', max=124, min=1), Checkbox(value=False, descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Slider: can be quite slow, click 'run Interact' to load the data\n",
    "interact_manual(usf, date=(1, number_of_days), showMarkers=widgets.Checkbox());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Percentage Growth of cases\n",
    " The below graphs shows the percentage changes which show the growth of cases for countries and us states. This data is date dependent and may change over time as countries implement border closures, social distancing measures and lockdowns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from covid_data import CovidData\n",
    "from dateutil.parser import parse\n",
    "\n",
    "def plot_max_percent_changes(data_point, title, bin_region_column='country', country=None, limit=None, thresh=50):\n",
    "    covid_data_obj = CovidData()\n",
    "    data, routes = covid_data_obj.getData(bin_region_column=bin_region_column, country=country)\n",
    "    \n",
    "    locations = []\n",
    "    max_percent_changes = []\n",
    "    \n",
    "    df = data[data_point].copy(deep=True)\n",
    "    columns = df.columns\n",
    "    dates = []\n",
    "    \n",
    "    for ii in range(len(columns)):\n",
    "        try:\n",
    "            parse(columns[ii])\n",
    "            dates = columns[ii:]\n",
    "            break\n",
    "        except:\n",
    "            pass\n",
    "    \n",
    "    for _index, row in df.iterrows():\n",
    "        percent_changes = []\n",
    "        for ii in range(len(dates)-1):\n",
    "            if row[dates[ii]] < thresh:\n",
    "                # Ignore small numbers, would show an artificial percent spike in change\n",
    "                percent_changes.append(0)\n",
    "                continue\n",
    "            percent_changes.append(row[dates[ii + 1]]/row[dates[ii]])\n",
    "        \n",
    "        max_pc = max(percent_changes)\n",
    "        # Ignore the location if there isn't sufficient data (max percent change is 0)\n",
    "        if max_pc == 0: continue\n",
    "            \n",
    "        location_str = \"\"\n",
    "        \n",
    "        \n",
    "        if bin_region_column == 'county':\n",
    "            county = row['County']\n",
    "            if not county == 'none':\n",
    "                location_str = county\n",
    "            location_str = location_str + \" \" + row['Province/State']\n",
    "        \n",
    "        if bin_region_column == 'state':\n",
    "            state = row['Province/State']\n",
    "            if not state == 'none':\n",
    "                location_str = location_str + \" \" + state\n",
    "                \n",
    "        curr_country = row['Country/Region']\n",
    "        location_str = location_str + \" \" + curr_country\n",
    "        locations.append(location_str)\n",
    "        max_percent_changes.append(max(percent_changes))\n",
    "        \n",
    "    new_df = pd.DataFrame({'Locations' : locations, 'Max Percent Change': max_percent_changes})\n",
    "    new_df = new_df.sort_values('Max Percent Change')\n",
    "    \n",
    "    if not limit == None:\n",
    "        new_df = new_df.tail(limit)\n",
    "    \n",
    "    new_df.plot.bar(x='Locations', y='Max Percent Change', figsize=(16,8), title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph below shows the percentage growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_max_percent_changes('confirmed', \"Max Percent Changes for Confirmed\", limit=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_max_percent_changes('deaths', \"Max Percent Changes for Deaths (US County)\", bin_region_column='county', country='US', limit=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_max_percent_changes('deaths', \"Max Percent Changes for Deaths (US County)\", bin_region_column='state', country='US', limit=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flights Data\n",
    "An Analysis of Route data from airport to airport to see if flights effect the rate of spread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from covid_data import CovidData\n",
    "covid_data = CovidData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_cases_aus, ausRoutes = covid_data.getData(bin_region_column='state', country='Australia', specific_date=latest_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'confirmed':                  Province/State Country/Region      Lat      Long  5/24/20\n",
       " 0  Australian Capital Territory      Australia -35.4735  149.0124      107\n",
       " 1               New South Wales      Australia -33.8688  151.2093     3090\n",
       " 2            Northern Territory      Australia -12.4634  130.8456       29\n",
       " 3                    Queensland      Australia -28.0167  153.4000     1056\n",
       " 4               South Australia      Australia -34.9285  138.6007      439\n",
       " 5                      Tasmania      Australia -41.4545  145.9707      228\n",
       " 6                      Victoria      Australia -37.8136  144.9631     1605\n",
       " 7             Western Australia      Australia -31.9505  115.8605      560,\n",
       " 'deaths':                  Province/State Country/Region      Lat      Long  5/24/20\n",
       " 0  Australian Capital Territory      Australia -35.4735  149.0124        3\n",
       " 1               New South Wales      Australia -33.8688  151.2093       48\n",
       " 2            Northern Territory      Australia -12.4634  130.8456        0\n",
       " 3                    Queensland      Australia -28.0167  153.4000        6\n",
       " 4               South Australia      Australia -34.9285  138.6007        4\n",
       " 5                      Tasmania      Australia -41.4545  145.9707       13\n",
       " 6                      Victoria      Australia -37.8136  144.9631       19\n",
       " 7             Western Australia      Australia -31.9505  115.8605        9}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the cases data, Australian states and confirmed cases from the latest date\n",
    "covid_cases_aus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-35.4735</td>\n",
       "      <td>149.0124</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.8688</td>\n",
       "      <td>151.2093</td>\n",
       "      <td>3090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-12.4634</td>\n",
       "      <td>130.8456</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-28.0167</td>\n",
       "      <td>153.4000</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-34.9285</td>\n",
       "      <td>138.6007</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-41.4545</td>\n",
       "      <td>145.9707</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8136</td>\n",
       "      <td>144.9631</td>\n",
       "      <td>1605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Province/State Country/Region      Lat      Long  5/24/20\n",
       "0  Australian Capital Territory      Australia -35.4735  149.0124      107\n",
       "1               New South Wales      Australia -33.8688  151.2093     3090\n",
       "2            Northern Territory      Australia -12.4634  130.8456       29\n",
       "3                    Queensland      Australia -28.0167  153.4000     1056\n",
       "4               South Australia      Australia -34.9285  138.6007      439\n",
       "5                      Tasmania      Australia -41.4545  145.9707      228\n",
       "6                      Victoria      Australia -37.8136  144.9631     1605\n",
       "7             Western Australia      Australia -31.9505  115.8605      560"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cases data in a dataframe\n",
    "ausCases = covid_cases_aus['confirmed']\n",
    "ausCases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DepartProvince/State</th>\n",
       "      <th>DepartCountry/Region</th>\n",
       "      <th>ArrivalProvince/State</th>\n",
       "      <th>ArrivalCountry/Region</th>\n",
       "      <th>NumberOfRoutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DepartProvince/State DepartCountry/Region  \\\n",
       "0   Australian Capital Territory            Australia   \n",
       "1   Australian Capital Territory            Australia   \n",
       "2   Australian Capital Territory            Australia   \n",
       "3   Australian Capital Territory            Australia   \n",
       "4   Australian Capital Territory            Australia   \n",
       "5   Australian Capital Territory            Australia   \n",
       "6                New South Wales            Australia   \n",
       "7                New South Wales            Australia   \n",
       "8                New South Wales            Australia   \n",
       "9                New South Wales            Australia   \n",
       "10               New South Wales            Australia   \n",
       "11               New South Wales            Australia   \n",
       "12               New South Wales            Australia   \n",
       "13               New South Wales            Australia   \n",
       "14            Northern Territory            Australia   \n",
       "15            Northern Territory            Australia   \n",
       "16            Northern Territory            Australia   \n",
       "17            Northern Territory            Australia   \n",
       "18            Northern Territory            Australia   \n",
       "19            Northern Territory            Australia   \n",
       "20                    Queensland            Australia   \n",
       "21                    Queensland            Australia   \n",
       "22                    Queensland            Australia   \n",
       "23                    Queensland            Australia   \n",
       "24                    Queensland            Australia   \n",
       "25                    Queensland            Australia   \n",
       "26                    Queensland            Australia   \n",
       "27                    Queensland            Australia   \n",
       "28               South Australia            Australia   \n",
       "29               South Australia            Australia   \n",
       "30               South Australia            Australia   \n",
       "31               South Australia            Australia   \n",
       "32               South Australia            Australia   \n",
       "33               South Australia            Australia   \n",
       "34               South Australia            Australia   \n",
       "35                      Tasmania            Australia   \n",
       "36                      Tasmania            Australia   \n",
       "37                      Tasmania            Australia   \n",
       "38                      Tasmania            Australia   \n",
       "39                      Tasmania            Australia   \n",
       "40                      Victoria            Australia   \n",
       "41                      Victoria            Australia   \n",
       "42                      Victoria            Australia   \n",
       "43                      Victoria            Australia   \n",
       "44                      Victoria            Australia   \n",
       "45                      Victoria            Australia   \n",
       "46                      Victoria            Australia   \n",
       "47                      Victoria            Australia   \n",
       "48             Western Australia            Australia   \n",
       "49             Western Australia            Australia   \n",
       "50             Western Australia            Australia   \n",
       "51             Western Australia            Australia   \n",
       "52             Western Australia            Australia   \n",
       "53             Western Australia            Australia   \n",
       "54             Western Australia            Australia   \n",
       "\n",
       "           ArrivalProvince/State ArrivalCountry/Region  NumberOfRoutes  \n",
       "0   Australian Capital Territory             Australia               1  \n",
       "1                New South Wales             Australia             124  \n",
       "2                     Queensland             Australia              23  \n",
       "3                South Australia             Australia               3  \n",
       "4                       Victoria             Australia              51  \n",
       "5              Western Australia             Australia               2  \n",
       "6   Australian Capital Territory             Australia             107  \n",
       "7                New South Wales             Australia             127  \n",
       "8             Northern Territory             Australia               6  \n",
       "9                     Queensland             Australia             239  \n",
       "10               South Australia             Australia              37  \n",
       "11                      Tasmania             Australia              18  \n",
       "12                      Victoria             Australia             147  \n",
       "13             Western Australia             Australia              22  \n",
       "14               New South Wales             Australia               6  \n",
       "15            Northern Territory             Australia              44  \n",
       "16                    Queensland             Australia              58  \n",
       "17               South Australia             Australia               6  \n",
       "18                      Victoria             Australia               7  \n",
       "19             Western Australia             Australia              19  \n",
       "20  Australian Capital Territory             Australia              21  \n",
       "21               New South Wales             Australia             259  \n",
       "22            Northern Territory             Australia              56  \n",
       "23                    Queensland             Australia             467  \n",
       "24               South Australia             Australia              26  \n",
       "25                      Tasmania             Australia               6  \n",
       "26                      Victoria             Australia             129  \n",
       "27             Western Australia             Australia              29  \n",
       "28  Australian Capital Territory             Australia               3  \n",
       "29               New South Wales             Australia              35  \n",
       "30            Northern Territory             Australia               6  \n",
       "31                    Queensland             Australia              23  \n",
       "32               South Australia             Australia              90  \n",
       "33                      Victoria             Australia              56  \n",
       "34             Western Australia             Australia              10  \n",
       "35               New South Wales             Australia              16  \n",
       "36                    Queensland             Australia               7  \n",
       "37                      Tasmania             Australia              14  \n",
       "38                      Victoria             Australia              59  \n",
       "39             Western Australia             Australia               1  \n",
       "40  Australian Capital Territory             Australia              42  \n",
       "41               New South Wales             Australia             152  \n",
       "42            Northern Territory             Australia               7  \n",
       "43                    Queensland             Australia             117  \n",
       "44               South Australia             Australia              56  \n",
       "45                      Tasmania             Australia              51  \n",
       "46                      Victoria             Australia              24  \n",
       "47             Western Australia             Australia              29  \n",
       "48  Australian Capital Territory             Australia               1  \n",
       "49               New South Wales             Australia              21  \n",
       "50            Northern Territory             Australia              22  \n",
       "51                    Queensland             Australia              29  \n",
       "52               South Australia             Australia              12  \n",
       "53                      Victoria             Australia              36  \n",
       "54             Western Australia             Australia             301  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the routes data, shows depart and arrival country and state, with number of routes\n",
    "ausRoutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gets the coordinates of the state/country given state/country\n",
    "def getCoordinates(place, cases):\n",
    "    df = None\n",
    "    df = cases.loc[cases['Province/State'] == place]\n",
    "    # If there's no state, it will just take the country coordinates\n",
    "    if df.empty:\n",
    "        df = cases.loc[cases['Country/Region'] == place]\n",
    "    return df.iloc[0]['Lat'], df.iloc[0]['Long']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-41.4545, 145.9707)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of function usages\n",
    "getCoordinates('Tasmania', ausCases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generates the line layer for the routes\n",
    "def generateRoutes(routes, cases, Graph=None, showOpacity=True, opacity=0.2):\n",
    "    locations = []\n",
    "    maxRoute = max(routes['NumberOfRoutes']) # Route with most flights, for calculating opacity\n",
    "    line_layer = LayerGroup(name='Lines')\n",
    "\n",
    "    for index, row in routes.iterrows():\n",
    "\n",
    "        # higher opacity means higher freqency route (on my default)\n",
    "        if showOpacity:\n",
    "            opacity = row[4]/maxRoute\n",
    "        \n",
    "        # If its a state, the dept & ariv are the state, otherwise its the country\n",
    "        if row[0] != 'none':\n",
    "            dept = row[0]\n",
    "        else:\n",
    "            dept = row[1]\n",
    "            \n",
    "        if row[2] != 'none':\n",
    "            ariv = row[2]\n",
    "        else:\n",
    "            ariv = row[3]\n",
    "        \n",
    "        # Add line that represents the route\n",
    "        line = Polyline(locations=[getCoordinates(dept, cases), getCoordinates(ariv, cases)], color='blue', opacity=opacity)\n",
    "        line_layer.add_layer(line)\n",
    "        \n",
    "        # Add edge to the graph\n",
    "        if Graph != None:\n",
    "            Graph.add_edge(row[0], row[2], weight=row[4])\n",
    "    \n",
    "    return line_layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map of Australia with routes data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the Map\n",
    "ausMapFlights = generateMapData(ausCities, latest_date, ausCoords, ausZoom, scale=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adds the routes layer to the map\n",
    "ausMapFlights.add_layer(generateRoutes(ausRoutes, ausCases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc564ea068a34841a8428e4c6c89b179",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[-28, 138], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ausMapFlights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map of US with flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>32.884280</td>\n",
       "      <td>-86.710122</td>\n",
       "      <td>14478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>US</td>\n",
       "      <td>60.261375</td>\n",
       "      <td>-145.838598</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>US</td>\n",
       "      <td>-14.271000</td>\n",
       "      <td>-170.132000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>US</td>\n",
       "      <td>33.675904</td>\n",
       "      <td>-111.463232</td>\n",
       "      <td>16377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>US</td>\n",
       "      <td>34.911889</td>\n",
       "      <td>-92.433978</td>\n",
       "      <td>5772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>37.843962</td>\n",
       "      <td>-120.728594</td>\n",
       "      <td>94020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>US</td>\n",
       "      <td>38.935474</td>\n",
       "      <td>-105.486240</td>\n",
       "      <td>24049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>US</td>\n",
       "      <td>41.613680</td>\n",
       "      <td>-72.656830</td>\n",
       "      <td>40258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>US</td>\n",
       "      <td>39.109277</td>\n",
       "      <td>-75.533237</td>\n",
       "      <td>8752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>US</td>\n",
       "      <td>38.904178</td>\n",
       "      <td>-77.016560</td>\n",
       "      <td>8110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Florida</td>\n",
       "      <td>US</td>\n",
       "      <td>28.940755</td>\n",
       "      <td>-82.700744</td>\n",
       "      <td>50782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>US</td>\n",
       "      <td>32.808532</td>\n",
       "      <td>-83.577430</td>\n",
       "      <td>38332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Guam</td>\n",
       "      <td>US</td>\n",
       "      <td>13.444300</td>\n",
       "      <td>144.793700</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>US</td>\n",
       "      <td>21.028283</td>\n",
       "      <td>-157.323236</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>US</td>\n",
       "      <td>44.263401</td>\n",
       "      <td>-114.460125</td>\n",
       "      <td>2626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>US</td>\n",
       "      <td>39.843618</td>\n",
       "      <td>-89.178330</td>\n",
       "      <td>110050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>US</td>\n",
       "      <td>39.837353</td>\n",
       "      <td>-86.218568</td>\n",
       "      <td>31376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>US</td>\n",
       "      <td>42.029910</td>\n",
       "      <td>-93.465519</td>\n",
       "      <td>17248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>US</td>\n",
       "      <td>38.485379</td>\n",
       "      <td>-98.084552</td>\n",
       "      <td>9004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>US</td>\n",
       "      <td>37.622144</td>\n",
       "      <td>-85.199891</td>\n",
       "      <td>8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>US</td>\n",
       "      <td>31.092427</td>\n",
       "      <td>-91.824187</td>\n",
       "      <td>37091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Maine</td>\n",
       "      <td>US</td>\n",
       "      <td>44.687688</td>\n",
       "      <td>-69.523769</td>\n",
       "      <td>2054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>US</td>\n",
       "      <td>39.014921</td>\n",
       "      <td>-76.675468</td>\n",
       "      <td>46313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>US</td>\n",
       "      <td>42.066277</td>\n",
       "      <td>-71.418469</td>\n",
       "      <td>92367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>US</td>\n",
       "      <td>44.043620</td>\n",
       "      <td>-85.181696</td>\n",
       "      <td>51177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>US</td>\n",
       "      <td>45.552013</td>\n",
       "      <td>-94.430953</td>\n",
       "      <td>20554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>US</td>\n",
       "      <td>32.792727</td>\n",
       "      <td>-89.645726</td>\n",
       "      <td>13260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>US</td>\n",
       "      <td>38.456765</td>\n",
       "      <td>-92.499487</td>\n",
       "      <td>12149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Montana</td>\n",
       "      <td>US</td>\n",
       "      <td>46.974691</td>\n",
       "      <td>-109.700395</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>US</td>\n",
       "      <td>41.321795</td>\n",
       "      <td>-99.039012</td>\n",
       "      <td>11945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>US</td>\n",
       "      <td>39.255997</td>\n",
       "      <td>-117.563994</td>\n",
       "      <td>7881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>US</td>\n",
       "      <td>43.478973</td>\n",
       "      <td>-71.577657</td>\n",
       "      <td>4135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>US</td>\n",
       "      <td>40.286441</td>\n",
       "      <td>-74.622450</td>\n",
       "      <td>153928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>US</td>\n",
       "      <td>34.549359</td>\n",
       "      <td>-105.887107</td>\n",
       "      <td>6755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>New York</td>\n",
       "      <td>US</td>\n",
       "      <td>42.544151</td>\n",
       "      <td>-75.474183</td>\n",
       "      <td>361515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>US</td>\n",
       "      <td>35.624210</td>\n",
       "      <td>-79.501099</td>\n",
       "      <td>23365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>US</td>\n",
       "      <td>47.393507</td>\n",
       "      <td>-100.249393</td>\n",
       "      <td>2418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>15.097900</td>\n",
       "      <td>145.673900</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>US</td>\n",
       "      <td>40.310670</td>\n",
       "      <td>-82.835408</td>\n",
       "      <td>31911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>US</td>\n",
       "      <td>35.501914</td>\n",
       "      <td>-97.251400</td>\n",
       "      <td>6037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>US</td>\n",
       "      <td>44.492401</td>\n",
       "      <td>-121.476920</td>\n",
       "      <td>3927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>US</td>\n",
       "      <td>40.818078</td>\n",
       "      <td>-77.615318</td>\n",
       "      <td>71563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>US</td>\n",
       "      <td>18.220800</td>\n",
       "      <td>-66.590100</td>\n",
       "      <td>3189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>US</td>\n",
       "      <td>41.655142</td>\n",
       "      <td>-71.464105</td>\n",
       "      <td>12547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>US</td>\n",
       "      <td>33.957472</td>\n",
       "      <td>-80.983400</td>\n",
       "      <td>10096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>US</td>\n",
       "      <td>44.272695</td>\n",
       "      <td>-99.310636</td>\n",
       "      <td>4563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>US</td>\n",
       "      <td>35.885102</td>\n",
       "      <td>-86.108723</td>\n",
       "      <td>19697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>Texas</td>\n",
       "      <td>US</td>\n",
       "      <td>31.660643</td>\n",
       "      <td>-98.653069</td>\n",
       "      <td>55861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>39.672885</td>\n",
       "      <td>-111.557713</td>\n",
       "      <td>8392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>US</td>\n",
       "      <td>44.159496</td>\n",
       "      <td>-72.684381</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>18.335800</td>\n",
       "      <td>-64.896300</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>US</td>\n",
       "      <td>37.566150</td>\n",
       "      <td>-78.513712</td>\n",
       "      <td>36244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>Washington</td>\n",
       "      <td>US</td>\n",
       "      <td>47.233786</td>\n",
       "      <td>-120.719220</td>\n",
       "      <td>19756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>US</td>\n",
       "      <td>38.814254</td>\n",
       "      <td>-80.617455</td>\n",
       "      <td>1759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>US</td>\n",
       "      <td>44.395143</td>\n",
       "      <td>-89.797980</td>\n",
       "      <td>15277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.086886</td>\n",
       "      <td>-107.153242</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Province/State Country/Region        Lat        Long  5/24/20\n",
       "0                    Alabama             US  32.884280  -86.710122    14478\n",
       "1                     Alaska             US  60.261375 -145.838598      407\n",
       "2             American Samoa             US -14.271000 -170.132000        0\n",
       "3                    Arizona             US  33.675904 -111.463232    16377\n",
       "4                   Arkansas             US  34.911889  -92.433978     5772\n",
       "5                 California             US  37.843962 -120.728594    94020\n",
       "6                   Colorado             US  38.935474 -105.486240    24049\n",
       "7                Connecticut             US  41.613680  -72.656830    40258\n",
       "8                   Delaware             US  39.109277  -75.533237     8752\n",
       "9       District of Columbia             US  38.904178  -77.016560     8110\n",
       "10                   Florida             US  28.940755  -82.700744    50782\n",
       "11                   Georgia             US  32.808532  -83.577430    38332\n",
       "12                      Guam             US  13.444300  144.793700      166\n",
       "13                    Hawaii             US  21.028283 -157.323236      633\n",
       "14                     Idaho             US  44.263401 -114.460125     2626\n",
       "15                  Illinois             US  39.843618  -89.178330   110050\n",
       "16                   Indiana             US  39.837353  -86.218568    31376\n",
       "17                      Iowa             US  42.029910  -93.465519    17248\n",
       "18                    Kansas             US  38.485379  -98.084552     9004\n",
       "19                  Kentucky             US  37.622144  -85.199891     8542\n",
       "20                 Louisiana             US  31.092427  -91.824187    37091\n",
       "21                     Maine             US  44.687688  -69.523769     2054\n",
       "22                  Maryland             US  39.014921  -76.675468    46313\n",
       "23             Massachusetts             US  42.066277  -71.418469    92367\n",
       "24                  Michigan             US  44.043620  -85.181696    51177\n",
       "25                 Minnesota             US  45.552013  -94.430953    20554\n",
       "26               Mississippi             US  32.792727  -89.645726    13260\n",
       "27                  Missouri             US  38.456765  -92.499487    12149\n",
       "28                   Montana             US  46.974691 -109.700395      479\n",
       "29                  Nebraska             US  41.321795  -99.039012    11945\n",
       "30                    Nevada             US  39.255997 -117.563994     7881\n",
       "31             New Hampshire             US  43.478973  -71.577657     4135\n",
       "32                New Jersey             US  40.286441  -74.622450   153928\n",
       "33                New Mexico             US  34.549359 -105.887107     6755\n",
       "34                  New York             US  42.544151  -75.474183   361515\n",
       "35            North Carolina             US  35.624210  -79.501099    23365\n",
       "36              North Dakota             US  47.393507 -100.249393     2418\n",
       "37  Northern Mariana Islands             US  15.097900  145.673900       22\n",
       "38                      Ohio             US  40.310670  -82.835408    31911\n",
       "39                  Oklahoma             US  35.501914  -97.251400     6037\n",
       "40                    Oregon             US  44.492401 -121.476920     3927\n",
       "41              Pennsylvania             US  40.818078  -77.615318    71563\n",
       "42               Puerto Rico             US  18.220800  -66.590100     3189\n",
       "43              Rhode Island             US  41.655142  -71.464105    12547\n",
       "44            South Carolina             US  33.957472  -80.983400    10096\n",
       "45              South Dakota             US  44.272695  -99.310636     4563\n",
       "46                 Tennessee             US  35.885102  -86.108723    19697\n",
       "47                     Texas             US  31.660643  -98.653069    55861\n",
       "48                      Utah             US  39.672885 -111.557713     8392\n",
       "49                   Vermont             US  44.159496  -72.684381      951\n",
       "50            Virgin Islands             US  18.335800  -64.896300       69\n",
       "51                  Virginia             US  37.566150  -78.513712    36244\n",
       "52                Washington             US  47.233786 -120.719220    19756\n",
       "53             West Virginia             US  38.814254  -80.617455     1759\n",
       "54                 Wisconsin             US  44.395143  -89.797980    15277\n",
       "55                   Wyoming             US  43.086886 -107.153242      838"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_cases_us, usRoutes = covid_data.getData(bin_region_column='state', country='US', specific_date=latest_date)\n",
    "usCases = covid_cases_us['confirmed']\n",
    "usCases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the Map\n",
    "usMapFlights = generateMapData(ausCities, latest_date, usCoords, usZoom, scale=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# May take some time to load\n",
    "usMapFlights.add_layer(generateRoutes(usRoutes, usCases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b55541e521545aab9ff993ba2a919f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[38.451217, -102.675199], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usMapFlights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incoming routes to Australia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Routes to and from each city in Australia\n",
    "covid_cases, allRoutes = covid_data.getData(bin_region_column='state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get all the incoming routes to Australia\n",
    "incAusRoutes = allRoutes.loc[allRoutes['ArrivalCountry/Region'] == 'Australia']\n",
    "\n",
    "# This will combine the Country and the state\n",
    "incAusRoutesC = incAusRoutes.copy()\n",
    "incAusRoutesC['Depart'] = incAusRoutes['DepartProvince/State'] + '-' + incAusRoutes['DepartCountry/Region']\n",
    "incAusRoutesC['Arrival'] = incAusRoutes['ArrivalProvince/State'] + '-' + incAusRoutes['ArrivalCountry/Region']\n",
    "\n",
    "# removes the unwanted 'none' strings when entries don't have states/provinces\n",
    "incAusRoutesC['Depart'] = incAusRoutesC['Depart'].map(lambda x: x.lstrip('-none'))\n",
    "incAusRoutesC['Arrival'] = incAusRoutesC['Arrival'].map(lambda x: x.lstrip('-none'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Seperates depart state and country and remove dups\n",
    "depAusRoutes = incAusRoutes[['DepartProvince/State', 'DepartCountry/Region']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DepartProvince/State</th>\n",
       "      <th>DepartCountry/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>...</th>\n",
       "      <th>5/15/20</th>\n",
       "      <th>5/16/20</th>\n",
       "      <th>5/17/20</th>\n",
       "      <th>5/18/20</th>\n",
       "      <th>5/19/20</th>\n",
       "      <th>5/20/20</th>\n",
       "      <th>5/21/20</th>\n",
       "      <th>5/22/20</th>\n",
       "      <th>5/23/20</th>\n",
       "      <th>5/24/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-35.473500</td>\n",
       "      <td>149.012400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>40.182400</td>\n",
       "      <td>116.414200</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Canada</td>\n",
       "      <td>49.282700</td>\n",
       "      <td>-123.120700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2407</td>\n",
       "      <td>2428</td>\n",
       "      <td>2428</td>\n",
       "      <td>2444</td>\n",
       "      <td>2446</td>\n",
       "      <td>2467</td>\n",
       "      <td>2479</td>\n",
       "      <td>2507</td>\n",
       "      <td>2517</td>\n",
       "      <td>2517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>37.843962</td>\n",
       "      <td>-120.728594</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76693</td>\n",
       "      <td>78725</td>\n",
       "      <td>80166</td>\n",
       "      <td>81457</td>\n",
       "      <td>83752</td>\n",
       "      <td>85997</td>\n",
       "      <td>88031</td>\n",
       "      <td>90252</td>\n",
       "      <td>92539</td>\n",
       "      <td>94020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>China</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>China</td>\n",
       "      <td>30.057200</td>\n",
       "      <td>107.874000</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>France</td>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>France</td>\n",
       "      <td>-17.679700</td>\n",
       "      <td>149.406800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>China</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>China</td>\n",
       "      <td>26.078900</td>\n",
       "      <td>117.987400</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>23.341700</td>\n",
       "      <td>113.424400</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>53</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>1589</td>\n",
       "      <td>1590</td>\n",
       "      <td>1590</td>\n",
       "      <td>1590</td>\n",
       "      <td>1590</td>\n",
       "      <td>1590</td>\n",
       "      <td>1590</td>\n",
       "      <td>1591</td>\n",
       "      <td>1592</td>\n",
       "      <td>1592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Hainan</td>\n",
       "      <td>China</td>\n",
       "      <td>Hainan</td>\n",
       "      <td>China</td>\n",
       "      <td>19.195900</td>\n",
       "      <td>109.745300</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>US</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>US</td>\n",
       "      <td>21.028283</td>\n",
       "      <td>-157.323236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>628</td>\n",
       "      <td>629</td>\n",
       "      <td>630</td>\n",
       "      <td>630</td>\n",
       "      <td>631</td>\n",
       "      <td>633</td>\n",
       "      <td>637</td>\n",
       "      <td>632</td>\n",
       "      <td>633</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>33.882000</td>\n",
       "      <td>113.614000</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>China</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>China</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>114.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1052</td>\n",
       "      <td>1052</td>\n",
       "      <td>1055</td>\n",
       "      <td>1055</td>\n",
       "      <td>1055</td>\n",
       "      <td>1055</td>\n",
       "      <td>1055</td>\n",
       "      <td>1065</td>\n",
       "      <td>1065</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>30.975600</td>\n",
       "      <td>112.270700</td>\n",
       "      <td>444</td>\n",
       "      <td>444</td>\n",
       "      <td>549</td>\n",
       "      <td>761</td>\n",
       "      <td>...</td>\n",
       "      <td>68134</td>\n",
       "      <td>68134</td>\n",
       "      <td>68134</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>27.610400</td>\n",
       "      <td>111.708800</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>China</td>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>China</td>\n",
       "      <td>32.971100</td>\n",
       "      <td>119.455000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>France</td>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>France</td>\n",
       "      <td>-20.904300</td>\n",
       "      <td>165.618000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.868800</td>\n",
       "      <td>151.209300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3074</td>\n",
       "      <td>3075</td>\n",
       "      <td>3076</td>\n",
       "      <td>3078</td>\n",
       "      <td>3081</td>\n",
       "      <td>3082</td>\n",
       "      <td>3084</td>\n",
       "      <td>3086</td>\n",
       "      <td>3087</td>\n",
       "      <td>3090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-12.463400</td>\n",
       "      <td>130.845600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-28.016700</td>\n",
       "      <td>153.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1055</td>\n",
       "      <td>1055</td>\n",
       "      <td>1057</td>\n",
       "      <td>1057</td>\n",
       "      <td>1058</td>\n",
       "      <td>1058</td>\n",
       "      <td>1058</td>\n",
       "      <td>1060</td>\n",
       "      <td>1061</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Shaanxi</td>\n",
       "      <td>China</td>\n",
       "      <td>Shaanxi</td>\n",
       "      <td>China</td>\n",
       "      <td>35.191700</td>\n",
       "      <td>108.870100</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Shandong</td>\n",
       "      <td>China</td>\n",
       "      <td>Shandong</td>\n",
       "      <td>China</td>\n",
       "      <td>36.342700</td>\n",
       "      <td>118.149800</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "      <td>788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>31.202000</td>\n",
       "      <td>121.449100</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>665</td>\n",
       "      <td>665</td>\n",
       "      <td>666</td>\n",
       "      <td>666</td>\n",
       "      <td>666</td>\n",
       "      <td>666</td>\n",
       "      <td>666</td>\n",
       "      <td>667</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Sichuan</td>\n",
       "      <td>China</td>\n",
       "      <td>Sichuan</td>\n",
       "      <td>China</td>\n",
       "      <td>30.617100</td>\n",
       "      <td>102.710300</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>561</td>\n",
       "      <td>561</td>\n",
       "      <td>561</td>\n",
       "      <td>561</td>\n",
       "      <td>561</td>\n",
       "      <td>561</td>\n",
       "      <td>561</td>\n",
       "      <td>563</td>\n",
       "      <td>563</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-34.928500</td>\n",
       "      <td>138.600700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-41.454500</td>\n",
       "      <td>145.970700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Texas</td>\n",
       "      <td>US</td>\n",
       "      <td>Texas</td>\n",
       "      <td>US</td>\n",
       "      <td>31.660643</td>\n",
       "      <td>-98.653069</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>45721</td>\n",
       "      <td>47452</td>\n",
       "      <td>48396</td>\n",
       "      <td>49177</td>\n",
       "      <td>50552</td>\n",
       "      <td>51673</td>\n",
       "      <td>53053</td>\n",
       "      <td>53539</td>\n",
       "      <td>54776</td>\n",
       "      <td>55861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.813600</td>\n",
       "      <td>144.963100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1551</td>\n",
       "      <td>1558</td>\n",
       "      <td>1564</td>\n",
       "      <td>1573</td>\n",
       "      <td>1573</td>\n",
       "      <td>1581</td>\n",
       "      <td>1593</td>\n",
       "      <td>1593</td>\n",
       "      <td>1603</td>\n",
       "      <td>1605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.950500</td>\n",
       "      <td>115.860500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>552</td>\n",
       "      <td>553</td>\n",
       "      <td>554</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>560</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>29.183200</td>\n",
       "      <td>120.093400</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>none</td>\n",
       "      <td>Brunei</td>\n",
       "      <td>none</td>\n",
       "      <td>Brunei</td>\n",
       "      <td>4.535300</td>\n",
       "      <td>114.727700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>none</td>\n",
       "      <td>Chile</td>\n",
       "      <td>none</td>\n",
       "      <td>Chile</td>\n",
       "      <td>-35.675100</td>\n",
       "      <td>-71.543000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>39542</td>\n",
       "      <td>41428</td>\n",
       "      <td>43781</td>\n",
       "      <td>46059</td>\n",
       "      <td>49579</td>\n",
       "      <td>53617</td>\n",
       "      <td>57581</td>\n",
       "      <td>61857</td>\n",
       "      <td>65393</td>\n",
       "      <td>69102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>none</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>none</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>-17.713400</td>\n",
       "      <td>178.065000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>none</td>\n",
       "      <td>India</td>\n",
       "      <td>none</td>\n",
       "      <td>India</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>85784</td>\n",
       "      <td>90648</td>\n",
       "      <td>95698</td>\n",
       "      <td>100328</td>\n",
       "      <td>106475</td>\n",
       "      <td>112028</td>\n",
       "      <td>118226</td>\n",
       "      <td>124794</td>\n",
       "      <td>131423</td>\n",
       "      <td>138536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>none</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>none</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>-0.789300</td>\n",
       "      <td>113.921300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16496</td>\n",
       "      <td>17025</td>\n",
       "      <td>17514</td>\n",
       "      <td>18010</td>\n",
       "      <td>18496</td>\n",
       "      <td>19189</td>\n",
       "      <td>20162</td>\n",
       "      <td>20796</td>\n",
       "      <td>21745</td>\n",
       "      <td>22271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>none</td>\n",
       "      <td>Japan</td>\n",
       "      <td>none</td>\n",
       "      <td>Japan</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>16203</td>\n",
       "      <td>16237</td>\n",
       "      <td>16285</td>\n",
       "      <td>16305</td>\n",
       "      <td>16367</td>\n",
       "      <td>16367</td>\n",
       "      <td>16424</td>\n",
       "      <td>16513</td>\n",
       "      <td>16536</td>\n",
       "      <td>16550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>none</td>\n",
       "      <td>Korea, South</td>\n",
       "      <td>none</td>\n",
       "      <td>Korea, South</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>11037</td>\n",
       "      <td>11050</td>\n",
       "      <td>11065</td>\n",
       "      <td>11078</td>\n",
       "      <td>11110</td>\n",
       "      <td>11122</td>\n",
       "      <td>11142</td>\n",
       "      <td>11165</td>\n",
       "      <td>11190</td>\n",
       "      <td>11206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>none</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>none</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>112.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6855</td>\n",
       "      <td>6872</td>\n",
       "      <td>6894</td>\n",
       "      <td>6941</td>\n",
       "      <td>6978</td>\n",
       "      <td>7009</td>\n",
       "      <td>7059</td>\n",
       "      <td>7137</td>\n",
       "      <td>7185</td>\n",
       "      <td>7245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>none</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>none</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>-20.200000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>332</td>\n",
       "      <td>332</td>\n",
       "      <td>332</td>\n",
       "      <td>332</td>\n",
       "      <td>332</td>\n",
       "      <td>332</td>\n",
       "      <td>332</td>\n",
       "      <td>332</td>\n",
       "      <td>332</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>none</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>none</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>-40.900600</td>\n",
       "      <td>174.886000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1498</td>\n",
       "      <td>1499</td>\n",
       "      <td>1499</td>\n",
       "      <td>1499</td>\n",
       "      <td>1503</td>\n",
       "      <td>1503</td>\n",
       "      <td>1504</td>\n",
       "      <td>1504</td>\n",
       "      <td>1504</td>\n",
       "      <td>1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>none</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>none</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-6.315000</td>\n",
       "      <td>143.955500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>none</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>none</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12091</td>\n",
       "      <td>12305</td>\n",
       "      <td>12513</td>\n",
       "      <td>12718</td>\n",
       "      <td>12942</td>\n",
       "      <td>13221</td>\n",
       "      <td>13434</td>\n",
       "      <td>13597</td>\n",
       "      <td>13777</td>\n",
       "      <td>14035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>none</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>none</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>25.354800</td>\n",
       "      <td>51.183900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29425</td>\n",
       "      <td>30972</td>\n",
       "      <td>32604</td>\n",
       "      <td>33969</td>\n",
       "      <td>35606</td>\n",
       "      <td>37097</td>\n",
       "      <td>38651</td>\n",
       "      <td>40481</td>\n",
       "      <td>42213</td>\n",
       "      <td>43714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>none</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>none</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.283300</td>\n",
       "      <td>103.833300</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>26891</td>\n",
       "      <td>27356</td>\n",
       "      <td>28038</td>\n",
       "      <td>28343</td>\n",
       "      <td>28794</td>\n",
       "      <td>29364</td>\n",
       "      <td>29812</td>\n",
       "      <td>30426</td>\n",
       "      <td>31068</td>\n",
       "      <td>31616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>none</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>none</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>-30.559500</td>\n",
       "      <td>22.937500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13524</td>\n",
       "      <td>14355</td>\n",
       "      <td>15515</td>\n",
       "      <td>16433</td>\n",
       "      <td>17200</td>\n",
       "      <td>18003</td>\n",
       "      <td>19137</td>\n",
       "      <td>20125</td>\n",
       "      <td>21343</td>\n",
       "      <td>22583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>none</td>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>none</td>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>440</td>\n",
       "      <td>440</td>\n",
       "      <td>440</td>\n",
       "      <td>440</td>\n",
       "      <td>440</td>\n",
       "      <td>440</td>\n",
       "      <td>440</td>\n",
       "      <td>441</td>\n",
       "      <td>441</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>none</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3025</td>\n",
       "      <td>3025</td>\n",
       "      <td>3028</td>\n",
       "      <td>3031</td>\n",
       "      <td>3033</td>\n",
       "      <td>3034</td>\n",
       "      <td>3037</td>\n",
       "      <td>3037</td>\n",
       "      <td>3040</td>\n",
       "      <td>3040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>none</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>none</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>-8.874217</td>\n",
       "      <td>125.727539</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>none</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>none</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>21831</td>\n",
       "      <td>22627</td>\n",
       "      <td>23358</td>\n",
       "      <td>24190</td>\n",
       "      <td>25063</td>\n",
       "      <td>26004</td>\n",
       "      <td>26898</td>\n",
       "      <td>27892</td>\n",
       "      <td>28704</td>\n",
       "      <td>29485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>none</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>none</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>314</td>\n",
       "      <td>318</td>\n",
       "      <td>320</td>\n",
       "      <td>324</td>\n",
       "      <td>324</td>\n",
       "      <td>324</td>\n",
       "      <td>324</td>\n",
       "      <td>324</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            DepartProvince/State  DepartCountry/Region  \\\n",
       "0   Australian Capital Territory             Australia   \n",
       "1                        Beijing                 China   \n",
       "2               British Columbia                Canada   \n",
       "3                     California                    US   \n",
       "4                      Chongqing                 China   \n",
       "5               French Polynesia                France   \n",
       "6                         Fujian                 China   \n",
       "7                      Guangdong                 China   \n",
       "8                         Hainan                 China   \n",
       "9                         Hawaii                    US   \n",
       "10                         Henan                 China   \n",
       "11                     Hong Kong                 China   \n",
       "12                         Hubei                 China   \n",
       "13                         Hunan                 China   \n",
       "14                       Jiangsu                 China   \n",
       "15                 New Caledonia                France   \n",
       "16               New South Wales             Australia   \n",
       "17            Northern Territory             Australia   \n",
       "18                    Queensland             Australia   \n",
       "19                       Shaanxi                 China   \n",
       "20                      Shandong                 China   \n",
       "21                      Shanghai                 China   \n",
       "22                       Sichuan                 China   \n",
       "23               South Australia             Australia   \n",
       "24                      Tasmania             Australia   \n",
       "25                         Texas                    US   \n",
       "26                      Victoria             Australia   \n",
       "27             Western Australia             Australia   \n",
       "28                      Zhejiang                 China   \n",
       "29                          none                Brunei   \n",
       "30                          none                 Chile   \n",
       "31                          none                  Fiji   \n",
       "32                          none                 India   \n",
       "33                          none             Indonesia   \n",
       "34                          none                 Japan   \n",
       "35                          none          Korea, South   \n",
       "36                          none              Malaysia   \n",
       "37                          none             Mauritius   \n",
       "38                          none           New Zealand   \n",
       "39                          none      Papua New Guinea   \n",
       "40                          none           Philippines   \n",
       "41                          none                 Qatar   \n",
       "42                          none             Singapore   \n",
       "43                          none          South Africa   \n",
       "44                          none               Taiwan*   \n",
       "45                          none              Thailand   \n",
       "46                          none           Timor-Leste   \n",
       "47                          none  United Arab Emirates   \n",
       "48                          none               Vietnam   \n",
       "\n",
       "                  Province/State        Country/Region        Lat        Long  \\\n",
       "0   Australian Capital Territory             Australia -35.473500  149.012400   \n",
       "1                        Beijing                 China  40.182400  116.414200   \n",
       "2               British Columbia                Canada  49.282700 -123.120700   \n",
       "3                     California                    US  37.843962 -120.728594   \n",
       "4                      Chongqing                 China  30.057200  107.874000   \n",
       "5               French Polynesia                France -17.679700  149.406800   \n",
       "6                         Fujian                 China  26.078900  117.987400   \n",
       "7                      Guangdong                 China  23.341700  113.424400   \n",
       "8                         Hainan                 China  19.195900  109.745300   \n",
       "9                         Hawaii                    US  21.028283 -157.323236   \n",
       "10                         Henan                 China  33.882000  113.614000   \n",
       "11                     Hong Kong                 China  22.300000  114.200000   \n",
       "12                         Hubei                 China  30.975600  112.270700   \n",
       "13                         Hunan                 China  27.610400  111.708800   \n",
       "14                       Jiangsu                 China  32.971100  119.455000   \n",
       "15                 New Caledonia                France -20.904300  165.618000   \n",
       "16               New South Wales             Australia -33.868800  151.209300   \n",
       "17            Northern Territory             Australia -12.463400  130.845600   \n",
       "18                    Queensland             Australia -28.016700  153.400000   \n",
       "19                       Shaanxi                 China  35.191700  108.870100   \n",
       "20                      Shandong                 China  36.342700  118.149800   \n",
       "21                      Shanghai                 China  31.202000  121.449100   \n",
       "22                       Sichuan                 China  30.617100  102.710300   \n",
       "23               South Australia             Australia -34.928500  138.600700   \n",
       "24                      Tasmania             Australia -41.454500  145.970700   \n",
       "25                         Texas                    US  31.660643  -98.653069   \n",
       "26                      Victoria             Australia -37.813600  144.963100   \n",
       "27             Western Australia             Australia -31.950500  115.860500   \n",
       "28                      Zhejiang                 China  29.183200  120.093400   \n",
       "29                          none                Brunei   4.535300  114.727700   \n",
       "30                          none                 Chile -35.675100  -71.543000   \n",
       "31                          none                  Fiji -17.713400  178.065000   \n",
       "32                          none                 India  21.000000   78.000000   \n",
       "33                          none             Indonesia  -0.789300  113.921300   \n",
       "34                          none                 Japan  36.000000  138.000000   \n",
       "35                          none          Korea, South  36.000000  128.000000   \n",
       "36                          none              Malaysia   2.500000  112.500000   \n",
       "37                          none             Mauritius -20.200000   57.500000   \n",
       "38                          none           New Zealand -40.900600  174.886000   \n",
       "39                          none      Papua New Guinea  -6.315000  143.955500   \n",
       "40                          none           Philippines  13.000000  122.000000   \n",
       "41                          none                 Qatar  25.354800   51.183900   \n",
       "42                          none             Singapore   1.283300  103.833300   \n",
       "43                          none          South Africa -30.559500   22.937500   \n",
       "44                          none               Taiwan*  23.700000  121.000000   \n",
       "45                          none              Thailand  15.000000  101.000000   \n",
       "46                          none           Timor-Leste  -8.874217  125.727539   \n",
       "47                          none  United Arab Emirates  24.000000   54.000000   \n",
       "48                          none               Vietnam  16.000000  108.000000   \n",
       "\n",
       "    1/22/20  1/23/20  1/24/20  1/25/20  ...  5/15/20  5/16/20  5/17/20  \\\n",
       "0         0        0        0        0  ...      107      107      107   \n",
       "1        14       22       36       41  ...      593      593      593   \n",
       "2         0        0        0        0  ...     2407     2428     2428   \n",
       "3         0        0        0        0  ...    76693    78725    80166   \n",
       "4         6        9       27       57  ...      579      579      579   \n",
       "5         0        0        0        0  ...       60       60       60   \n",
       "6         1        5       10       18  ...      356      356      356   \n",
       "7        26       32       53       78  ...     1589     1590     1590   \n",
       "8         4        5        8       19  ...      169      169      169   \n",
       "9         0        0        0        0  ...      628      629      630   \n",
       "10        5        5        9       32  ...     1276     1276     1276   \n",
       "11        0        2        2        5  ...     1052     1052     1055   \n",
       "12      444      444      549      761  ...    68134    68134    68134   \n",
       "13        4        9       24       43  ...     1019     1019     1019   \n",
       "14        1        5        9       18  ...      653      653      653   \n",
       "15        0        0        0        0  ...       18       18       18   \n",
       "16        0        0        0        0  ...     3074     3075     3076   \n",
       "17        0        0        0        0  ...       29       29       29   \n",
       "18        0        0        0        0  ...     1055     1055     1057   \n",
       "19        0        3        5       15  ...      308      308      308   \n",
       "20        2        6       15       27  ...      788      788      788   \n",
       "21        9       16       20       33  ...      665      665      666   \n",
       "22        5        8       15       28  ...      561      561      561   \n",
       "23        0        0        0        0  ...      439      439      439   \n",
       "24        0        0        0        0  ...      228      228      228   \n",
       "25        0        0        0        0  ...    45721    47452    48396   \n",
       "26        0        0        0        0  ...     1551     1558     1564   \n",
       "27        0        0        0        0  ...      552      553      554   \n",
       "28       10       27       43       62  ...     1268     1268     1268   \n",
       "29        0        0        0        0  ...      141      141      141   \n",
       "30        0        0        0        0  ...    39542    41428    43781   \n",
       "31        0        0        0        0  ...       18       18       18   \n",
       "32        0        0        0        0  ...    85784    90648    95698   \n",
       "33        0        0        0        0  ...    16496    17025    17514   \n",
       "34        2        2        2        2  ...    16203    16237    16285   \n",
       "35        1        1        2        2  ...    11037    11050    11065   \n",
       "36        0        0        0        3  ...     6855     6872     6894   \n",
       "37        0        0        0        0  ...      332      332      332   \n",
       "38        0        0        0        0  ...     1498     1499     1499   \n",
       "39        0        0        0        0  ...        8        8        8   \n",
       "40        0        0        0        0  ...    12091    12305    12513   \n",
       "41        0        0        0        0  ...    29425    30972    32604   \n",
       "42        0        1        3        3  ...    26891    27356    28038   \n",
       "43        0        0        0        0  ...    13524    14355    15515   \n",
       "44        1        1        3        3  ...      440      440      440   \n",
       "45        2        3        5        7  ...     3025     3025     3028   \n",
       "46        0        0        0        0  ...       24       24       24   \n",
       "47        0        0        0        0  ...    21831    22627    23358   \n",
       "48        0        2        2        2  ...      314      318      320   \n",
       "\n",
       "    5/18/20  5/19/20  5/20/20  5/21/20  5/22/20  5/23/20  5/24/20  \n",
       "0       107      107      107      107      107      107      107  \n",
       "1       593      593      593      593      593      593      593  \n",
       "2      2444     2446     2467     2479     2507     2517     2517  \n",
       "3     81457    83752    85997    88031    90252    92539    94020  \n",
       "4       579      579      579      579      579      579      579  \n",
       "5        60       60       60       60       60       60       60  \n",
       "6       356      356      356      356      356      356      356  \n",
       "7      1590     1590     1590     1590     1591     1592     1592  \n",
       "8       169      169      169      169      169      169      169  \n",
       "9       630      631      633      637      632      633      633  \n",
       "10     1276     1276     1276     1276     1276     1276     1276  \n",
       "11     1055     1055     1055     1055     1065     1065     1065  \n",
       "12    68135    68135    68135    68135    68135    68135    68135  \n",
       "13     1019     1019     1019     1019     1019     1019     1019  \n",
       "14      653      653      653      653      653      653      653  \n",
       "15       18       18       18       18       18       18       18  \n",
       "16     3078     3081     3082     3084     3086     3087     3090  \n",
       "17       29       29       29       29       29       29       29  \n",
       "18     1057     1058     1058     1058     1060     1061     1056  \n",
       "19      308      308      308      308      308      308      308  \n",
       "20      788      788      788      788      788      788      788  \n",
       "21      666      666      666      666      667      668      668  \n",
       "22      561      561      561      561      563      563      564  \n",
       "23      439      439      439      439      439      439      439  \n",
       "24      228      228      228      228      228      228      228  \n",
       "25    49177    50552    51673    53053    53539    54776    55861  \n",
       "26     1573     1573     1581     1593     1593     1603     1605  \n",
       "27      557      557      557      557      557      560      560  \n",
       "28     1268     1268     1268     1268     1268     1268     1268  \n",
       "29      141      141      141      141      141      141      141  \n",
       "30    46059    49579    53617    57581    61857    65393    69102  \n",
       "31       18       18       18       18       18       18       18  \n",
       "32   100328   106475   112028   118226   124794   131423   138536  \n",
       "33    18010    18496    19189    20162    20796    21745    22271  \n",
       "34    16305    16367    16367    16424    16513    16536    16550  \n",
       "35    11078    11110    11122    11142    11165    11190    11206  \n",
       "36     6941     6978     7009     7059     7137     7185     7245  \n",
       "37      332      332      332      332      332      332      334  \n",
       "38     1499     1503     1503     1504     1504     1504     1504  \n",
       "39        8        8        8        8        8        8        8  \n",
       "40    12718    12942    13221    13434    13597    13777    14035  \n",
       "41    33969    35606    37097    38651    40481    42213    43714  \n",
       "42    28343    28794    29364    29812    30426    31068    31616  \n",
       "43    16433    17200    18003    19137    20125    21343    22583  \n",
       "44      440      440      440      440      441      441      441  \n",
       "45     3031     3033     3034     3037     3037     3040     3040  \n",
       "46       24       24       24       24       24       24       24  \n",
       "47    24190    25063    26004    26898    27892    28704    29485  \n",
       "48      324      324      324      324      324      325      325  \n",
       "\n",
       "[49 rows x 130 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gets the lat and long and confirmed cases from the covid cases data\n",
    "incAusCities = pd.merge(depAusRoutes, covid_cases['confirmed'], how='left'\n",
    "                        , left_on=['DepartProvince/State','DepartCountry/Region']\n",
    "                        , right_on = ['Province/State','Country/Region'])\n",
    "# Combine the DepartProvince/State and DepartCountry/Region\n",
    "incAusCities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above shows the combination of the routes data and the number of confirmed cases into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generates the line layer for the routes (changed to work with combined country + state)\n",
    "def generateRoutesCombined(routes, cases, graph=None, showOpacity=True, opacity=0.2):\n",
    "    locations = []\n",
    "    maxRoute = max(routes['NumberOfRoutes']) # Route with most flights, for calculating opacity\n",
    "    line_layer = LayerGroup(name='Lines')\n",
    "    \n",
    "    for index, row in routes.iterrows():\n",
    "\n",
    "        # higher opacity means higher freqency route (on my default)\n",
    "        if showOpacity:\n",
    "            opacity = row[4]/maxRoute\n",
    "        \n",
    "        # If its a state, the dept & ariv are the state, otherwise its the country\n",
    "        if row[0] != 'none':\n",
    "            dept = row[0]\n",
    "        else:\n",
    "            dept = row[1]\n",
    "            \n",
    "        if row[2] != 'none':\n",
    "            ariv = row[2]\n",
    "        else:\n",
    "            ariv = row[3]\n",
    "        \n",
    "        # Add line that represents the route\n",
    "        line = Polyline(locations=[getCoordinates(dept, cases), getCoordinates(ariv, cases)], color='blue', opacity=opacity)\n",
    "        line_layer.add_layer(line)\n",
    "        \n",
    "        # Add edge to the graph\n",
    "        if graph != None:\n",
    "            graph.add_edge(row[5], row[6], weight=maxRoute-row[4]+1)\n",
    "    \n",
    "    return line_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the Map\n",
    "incAusMapFlights, marker_layer_incAus_flights, circle_layer_incAus_flights = generateMap(ausCoords, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# May take some time to load\n",
    "incAusGraphFlights = nx.Graph()\n",
    "incAusMapFlights.add_layer(generateRoutesCombined(incAusRoutesC, incAusCities, incAusGraphFlights, showOpacity=False, opacity=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flightsf(date, showMarkers):\n",
    "    # Gets the data from the selected date\n",
    "    date = dfaus.columns[date+3] # number of confirmed cases to date\n",
    "    ausCities = dfaus[['Country/Region', 'Province/State', 'Lat', 'Long', date]]\n",
    "    \n",
    "    # Removes the old circle/marker layer\n",
    "    if circle_layer_incAus_flights in incAusMapFlights.layers:\n",
    "        incAusMapFlights.remove_layer(circle_layer_incAus_flights)\n",
    "        circle_layer_incAus_flights.clear_layers()\n",
    "    \n",
    "    if marker_layer_incAus_flights in incAusMapFlights.layers:\n",
    "        incAusMapFlights.remove_layer(marker_layer_incAus_flights)\n",
    "        marker_layer_incAus_flights.clear_layers()\n",
    "    \n",
    "    # Generates and adds the new layer\n",
    "    generateGraphTimeline(incAusCities, incAusMapFlights, date, \n",
    "                          circle_layer_incAus_flights, marker_layer_incAus_flights, showMarkers, 0.001)\n",
    "\n",
    "    return date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "474e1ff0c23a49ce89b4924ed5cb411d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[-28, 138], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "incAusMapFlights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee2288abd4ce483db706a8aa7b4638b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=62, description='date', max=124, min=1), Checkbox(value=False, descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Slider: days since first confirmed case\n",
    "interact(flightsf, date=(1, number_of_days), showMarkers=widgets.Checkbox());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Map above is useful for finding which routes most likely contributed the spread of the virus to Australia. For example, 31 Jan, '1/31/20' (10) you can see, New South Walkes, Victoria, and Queensland show infected cases and have lots of routes coming from infected areas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of locations in the graph\n",
    "len(incAusGraphFlights.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of edges in the graph\n",
    "len(incAusGraphFlights.edges())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Border Closures\n",
    "Below we are going to investiage the effectiveness of the border closures in Australia for the months of Februrary, March, April, May and use degree distribution to determine the level of rubustness in the network as these closures are added.\n",
    "\n",
    "### Initial flights 31 January 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates the map and graph using the routes and node data (including confirmed cases)\n",
    "def renderMap(routes, nodes, latest_date, coords, zoom, showOpacity=False, scale=0.001):\n",
    "    incAusMapFlights, circles_incAus_flights, markers_incAus_flights = generateMap(coords, zoom)\n",
    "    incAusGraphFlights = nx.Graph()\n",
    "    incAusMapFlights.add_layer(generateRoutesCombined(routes, nodes, incAusGraphFlights, showOpacity=showOpacity))\n",
    "    generateGraphTimeline(nodes, incAusMapFlights, latest_date, \n",
    "                              circles_incAus_flights, markers_incAus_flights, True, scale)\n",
    "    return incAusMapFlights, incAusGraphFlights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "incAusMapFlights1, incAusGraphFlights1 = renderMap(incAusRoutesC, incAusCities, '1/31/20', (-28,138), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bf52507035b4e06bad03f6f165e8a52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[-28, 138], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "incAusMapFlights1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can find the number of routes for each state\n",
    "def getNumRoutes(routes):\n",
    "    return routes[['Arrival', 'NumberOfRoutes']].groupby(['Arrival']).sum().sort_values(by='NumberOfRoutes', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumberOfRoutes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        NumberOfRoutes\n",
       "Arrival                                               \n",
       "Queensland-Australia                              1063\n",
       "New South Wales-Australia                          917\n",
       "Victoria-Australia                                 615\n",
       "Western Australia-Australia                        460\n",
       "South Australia-Australia                          242\n",
       "Australian Capital Territory-Australia             175\n",
       "Northern Territory-Australia                       153\n",
       "Tasmania-Australia                                  89"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prints the number of routes\n",
    "getNumRoutes(incAusRoutesC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Degree distribution**\n",
    "\n",
    "Degree distribution tells us which nodes is connected to the most routes. We can use this to find which state is has the most distinct incoming traffic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Australian Capital Territory-Australia': 0.14583333333333331,\n",
       " 'New South Wales-Australia': 0.9375,\n",
       " 'Queensland-Australia': 0.6041666666666666,\n",
       " 'South Australia-Australia': 0.35416666666666663,\n",
       " 'Victoria-Australia': 0.7916666666666666,\n",
       " 'Western Australia-Australia': 0.41666666666666663,\n",
       " 'Beijing-China': 0.041666666666666664,\n",
       " 'British Columbia-Canada': 0.0625,\n",
       " 'California-US': 0.0625,\n",
       " 'Chongqing-China': 0.041666666666666664,\n",
       " 'French Polynesia-France': 0.020833333333333332,\n",
       " 'Fujian-China': 0.041666666666666664,\n",
       " 'Guangdong-China': 0.10416666666666666,\n",
       " 'Hainan-China': 0.020833333333333332,\n",
       " 'Hawaii-US': 0.0625,\n",
       " 'Henan-China': 0.020833333333333332,\n",
       " 'Hong Kong-China': 0.10416666666666666,\n",
       " 'Hubei-China': 0.020833333333333332,\n",
       " 'Hunan-China': 0.041666666666666664,\n",
       " 'Jiangsu-China': 0.020833333333333332,\n",
       " 'New Caledonia-France': 0.0625,\n",
       " 'Northern Territory-Australia': 0.2708333333333333,\n",
       " 'Tasmania-Australia': 0.125,\n",
       " 'Shaanxi-China': 0.041666666666666664,\n",
       " 'Shandong-China': 0.041666666666666664,\n",
       " 'Shanghai-China': 0.0625,\n",
       " 'Sichuan-China': 0.041666666666666664,\n",
       " 'Texas-US': 0.020833333333333332,\n",
       " 'Zhejiang-China': 0.041666666666666664,\n",
       " 'Brunei': 0.020833333333333332,\n",
       " 'Chile': 0.020833333333333332,\n",
       " 'Fiji': 0.0625,\n",
       " 'India': 0.041666666666666664,\n",
       " 'Indonesia': 0.125,\n",
       " 'Japan': 0.08333333333333333,\n",
       " 'Korea, South': 0.041666666666666664,\n",
       " 'Malaysia': 0.125,\n",
       " 'Mauritius': 0.020833333333333332,\n",
       " 'New Zealand': 0.125,\n",
       " 'Papua New Guinea': 0.041666666666666664,\n",
       " 'Philippines': 0.0625,\n",
       " 'Qatar': 0.08333333333333333,\n",
       " 'Singapore': 0.125,\n",
       " 'South Africa': 0.041666666666666664,\n",
       " 'Taiwan*': 0.0625,\n",
       " 'Thailand': 0.08333333333333333,\n",
       " 'Timor-Leste': 0.020833333333333332,\n",
       " 'United Arab Emirates': 0.10416666666666666,\n",
       " 'Vietnam': 0.041666666666666664}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the degree distribution for the nodes\n",
    "degrees = nx.degree_centrality(incAusGraphFlights1)\n",
    "degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Converts results from above into a pandas dataframe and print out only Australian cities\n",
    "def dictToDataframe(degrees):\n",
    "    dfdegrees = pd.DataFrame.from_dict(degrees, orient='index', columns=['Degrees'])\n",
    "    dfdegrees.reset_index(level=0, inplace=True) # key indexs to seperate column\n",
    "    dfdegrees = dfdegrees.loc[dfdegrees['index'].str.contains('Australia')] # get only australia\n",
    "    dfdegrees = dfdegrees.sort_values(by='Degrees', ascending=False) # sort by degrees\n",
    "    dfdegrees = dfdegrees.reset_index(drop=True) # fix indexing\n",
    "    return dfdegrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.604167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.354167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.270833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.145833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index   Degrees\n",
       "0               New South Wales-Australia  0.937500\n",
       "1                      Victoria-Australia  0.791667\n",
       "2                    Queensland-Australia  0.604167\n",
       "3             Western Australia-Australia  0.416667\n",
       "4               South Australia-Australia  0.354167\n",
       "5            Northern Territory-Australia  0.270833\n",
       "6  Australian Capital Territory-Australia  0.145833\n",
       "7                      Tasmania-Australia  0.125000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outputs the degree distribution\n",
    "dfdegrees1 = dictToDataframe(degrees)\n",
    "dfdegrees1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df2208f108>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows the bar graph for each state\n",
    "title = 'Degree Distribution in Australian cities using incoming routes for 31 Jan 2020'\n",
    "dfdegrees1.plot.bar(x='index', y=['Degrees'], figsize=(8, 4), title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Closeness**\n",
    "\n",
    "However, the degree distribution doesn't account for the frequency of flights so a closeness centrality can be used to identify the nodes closer to all other nodes which are the biggest airport hubs in the network.\n",
    "\n",
    "As the frequency of the routes was the weight, the inverse weight had to be used by using the max distance and subtracting it from the weight with adding one (so no 0 weights). Therefore, the closest values with the most frequent routes would have a low weight number, and the least frequent routes would have a higher weight value as if distance was further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.002038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.001842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.001776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.001358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.001329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.001250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.001249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.001151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index   Degrees\n",
       "0               New South Wales-Australia  0.002038\n",
       "1                      Victoria-Australia  0.001842\n",
       "2                    Queensland-Australia  0.001776\n",
       "3             Western Australia-Australia  0.001358\n",
       "4               South Australia-Australia  0.001329\n",
       "5            Northern Territory-Australia  0.001250\n",
       "6  Australian Capital Territory-Australia  0.001249\n",
       "7                      Tasmania-Australia  0.001151"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closeness1 = nx.closeness_centrality(incAusGraphFlights1, distance='weight')\n",
    "dfcloseness1 = dictToDataframe(closeness1)\n",
    "dfcloseness1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df22142a48>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/Uc7rH3l9bdfkO9Pd7TuU1NF1UV5+Oen2+DYfJQWr55A6Kb5I563Gba0dR5F+lBeRgprL21+kU18n9ae7U2nwssWSzi1Mn0Tqy/As6Vg5IPcBgfRZDQSmFZb9fWHZo0h9BeeSjvVPRyd9k7TiZW0zs9cPSZ8mBX4NdVbty15Pda2KpO2Am3Lny5a2SrY8mJn1BEkbS9o5N1G/hXRW3dBt4X3N66muq4J8afggUitIy1uVRxEzM2u2NYAfksYBeY50a+TZlZao57ye6roq+CfpktXHqy5Ib/BlCzMzMyvFly3MzMysFAcPZmZmVkpL93kYPHhwDB8+vOpimJmZ9Zq77rrr3xExpPM5u66lg4fhw4czffrrouOrmZkZAJLqPeywqXzZwszMzEpx8GBmZmalOHgwMzOzUlq6z4OZmfUNr7zyCnPmzGHJkoaenm3AgAEDGDZsGKuv3uhzuprHwYOZmVVuzpw5rLvuugwfPhypy0+tft2ICObPn8+cOXMYMWJEr6/fly3MzKxyS5YsYdCgQQ4cGiSJQYMGVdZS4+DBzMxWCQ4cyqlyezl4MDMzA/r168eYMWMYPXo022+/Pd/73vdYvnx51cVaJTXU50HSBOB0oB/w44j4Vs30NYGfATsA84GDI+KxPO144HBgGfCZiLhG0qZ5/o2A5cB5EXF6nn8D4DJgOPAYcFBEPKsUYp0O7A28ABwWEXd3ueYdGH7c1T2Rbbse+9Y+vbo+M7NVXbO/hxv5nl1rrbW45557AJg7dy6HHHIICxYs4OSTT+72+pctW0a/fv26nc+qotOWB0n9gLOAvYBRwGRJo2pmOxx4NiK2BE4DTs3LjgImAaOBCcDZOb+lwBciYhvgHcDRhTyPA66LiJHAdfk9ef0j898RwDldqrGZmVknNtxwQ8477zzOPPNMIoJly5Zx7LHHsuOOO7Lddtvxwx/+EIDly5dz1FFHMXr0aPbdd1/23ntvrrjiCiCNcnzKKaewyy678Mtf/pJHHnmECRMmsMMOO/Dud7+bhx56CIB58+ax//77s+OOO7Ljjjty6623AnDjjTcyZswYxowZw9ve9jYWLVpUzcaoo5GWh3HArIiYDSDpUmAi8EBhnonASfn1FcCZuaVgInBpRLwEPCppFjAuIv4CPAkQEYskPQgMzXlOBHbNeV0I3AB8Kaf/LNIzxG+TtL6kjSPiya5U3MzMrCNbbLEFy5cvZ+7cuVx11VUMHDiQO++8k5deeomdd96ZPffck7vuuovHHnuM+++/n7lz57LNNtvwiU984tU8BgwYwC233ALA7rvvzrnnnsvIkSO5/fbbOeqoo7j++uv5r//6Lz73uc+xyy678M9//pPx48fz4IMP8p3vfIezzjqLnXfemcWLFzNgwICqNsVKGgkehgKPF97PAXZqb56IWCppATAop99Ws+zQ4oKShgNvA27PSW9qCwgi4klJG3ZQjqHkIMTMzKzZ0vkq/OEPf+C+++57tVVhwYIFPPzww9xyyy0ceOCBrLbaamy00UbstttuKyx/8MEHA7B48WL+/Oc/c+CBB7467aWXXgLg2muv5YEHXjsfX7hwIYsWLWLnnXfm85//PB/+8If50Ic+xLBhw3q0rmU0EjzU684ZDc7T4bKS1gGuBD4bEQubUA4kHUG6rMFmm23WSZZmZmb1zZ49m379+rHhhhsSEZxxxhmMHz9+hXmuvrrjvhlrr702kC5vrL/++q/2qShavnw5f/nLX1hrrbVWSD/uuOPYZ599mDZtGu94xzu49tpr2XrrrbtZq+Zo5G6LOcCmhffDgCfam0dSf2Ag8ExHy0panRQ4XBwRvyrM87SkjfM8GwNzS5SDiDgvIsZGxNghQ3r0iaRmZtai5s2bx5FHHskxxxyDJMaPH88555zDK6+8AsDf//53nn/+eXbZZReuvPJKli9fztNPP80NN9xQN7/11luPESNG8Mtf/hJILRr33nsvAHvuuSdnnnnmq/O2BRiPPPIIb33rW/nSl77E2LFjX+0jsSpoJHi4ExgpaYSkNUgdIKfWzDMVODS/PgC4PvdNmApMkrSmpBGkzo535P4Q5wMPRsT3OsjrUOCqQvrHlLwDWOD+DmZm1iwvvvjiq7dq7rHHHuy5556ceOKJAHzyk59k1KhRvP3tb2fbbbflU5/6FEuXLmX//fdn2LBhr6bttNNODBw4sG7+F198Meeffz7bb789o0eP5qqr0s/bD37wA6ZPn852223HqFGjOPfccwH4/ve/z7bbbsv222/PWmutxV577dU7G6IBarue0+FM0t7A90m3al4QEVMknQJMj4ipkgYAF5H6LjwDTCp0sPwK8AnSHRafjYjfS9oFuBm4n3SrJsCXI2KapEHA5cBmwD+BAyPimRxwnEm6a+MF4OMRMb2jco8dOzamT+9wlrp8q6aZWe968MEH2WabbaouRpcsXryYddZZh/nz5zNu3DhuvfVWNtpoo15Zd73tJumuiBjbk+ttaJyHiJgGTKtJO6HweglwYO1yedoUYEpN2i3U78NARMwHdq+THsDRjZTXzMyst+y7774899xzvPzyy3zta1/rtcChSn4wlpmZWTe018+hlXl4ajMzMyvFwYOZma0SGumDZ6+pcns5eDAzs8oNGDCA+fPnO4BoUEQwf/78ykaddJ8HMzOr3LBhw5gzZw7z5s2ruih9xoABAyobddLBg5mZVW711VdnxIgRVRfDGuTLFmZmZlaKgwczMzMrxcGDmZmZleLgwczMzEpx8GBmZmal+G6L1yE/+MvMzLrDLQ9mZmZWioMHMzMzK8XBg5mZmZXi4MHMzMxKcfBgZmZmpTQUPEiaIGmmpFmSjqszfU1Jl+Xpt0saXph2fE6fKWl8If0CSXMl/a0mr8sk3ZP/HpN0T04fLunFwrRzu1ppMzMz67pOb9WU1A84C3gfMAe4U9LUiHigMNvhwLMRsaWkScCpwMGSRgGTgNHAJsC1kraKiGXAT4EzgZ8V1xcRBxfW/V1gQWHyIxExpnw17fXEt6KamfWsRloexgGzImJ2RLwMXApMrJlnInBhfn0FsLsk5fRLI+KliHgUmJXzIyJuAp5pb6V5+YOAS0rUx8zMzHpYI8HDUODxwvs5Oa3uPBGxlNRaMKjBZdvzbuDpiHi4kDZC0l8l3Sjp3Q3mY2ZmZk3UyAiTqpMWDc7TyLLtmcyKrQ5PAptFxHxJOwC/kTQ6IhauUBDpCOAIgM0226zBVZmZmVmjGml5mANsWng/DHiivXkk9QcGki5JNLLsSnIeHwIua0vLlz7m59d3AY8AW9UuGxHnRcTYiBg7ZMiQTitnZmZm5TQSPNwJjJQ0QtIapA6QU2vmmQocml8fAFwfEZHTJ+W7MUYAI4E7GljnHsBDETGnLUHSkNx5E0lb5LxmN5CXmZmZNVGnly0iYqmkY4BrgH7ABRExQ9IpwPSImAqcD1wkaRapxWFSXnaGpMuBB4ClwNH5TgskXQLsCgyWNAc4MSLOz6udxModJd8DnCJpKbAMODIi2u1waWZmZj2joadqRsQ0YFpN2gmF10uAA9tZdgowpU765A7Wd1idtCuBKxspr5mZmfUcjzBpZmZmpTh4MDMzs1IcPJiZmVkpDh7MzMysFAcPZmZmVoqDBzMzMyvFwYOZmZmV4uDBzMzMSnHwYGZmZqU4eDAzM7NSHDyYmZlZKQ4ezMzMrBQHD2ZmZlaKgwczMzMrxcGDmZmZleLgwczMzEppKHiQNEHSTEmzJB1XZ/qaki7L02+XNLww7ficPlPS+EL6BZLmSvpbTV4nSfqXpHvy396d5WVmZma9p9PgQVI/4CxgL2AUMFnSqJrZDgeejYgtgdOAU/Oyo4BJwGhgAnB2zg/gpzmtntMiYkz+m9ZAXmZmZtZLGml5GAfMiojZEfEycCkwsWaeicCF+fUVwO6SlNMvjYiXIuJRYFbOj4i4CXimRFnbzcvMzMx6TyPBw1Dg8cL7OTmt7jwRsRRYAAxqcNl6jpF0X7608cYS5TAzM7Me1kjwoDpp0eA8jSxb6xzgzcAY4EnguyXKgaQjJE2XNH3evHmdrMrMzMzKaiR4mANsWng/DHiivXkk9QcGki5JNLLsCiLi6YhYFhHLgR/x2qWJhvKKiPMiYmxEjB0yZEgnVTMzM7OyGgke7gRGShohaQ1Sp8WpNfNMBQ7Nrw8Aro+IyOmT8t0YI4CRwB0drUzSxoW3HwTa7sYonZeZmZk1X//OZoiIpZKOAa4B+gEXRMQMSacA0yNiKnA+cJGkWaQWh0l52RmSLgceAJYCR0fEMgBJlwC7AoMlzQFOjIjzgW9LGkO6JPEY8KnO8jIzM7Pe02nwAJBvl5xWk3ZC4fUS4MB2lp0CTKmTPrmd+T/aQTnq5mVmZma9xyNMmpmZWSkOHszMzKwUBw9mZmZWioMHMzMzK8XBg5mZmZXi4MHMzMxKcfBgZmZmpTh4MDMzs1IcPJiZmVkpDY0waWarjuHHXd2r63vsW/v06vrMbNXnlgczMzMrxcGDmZmZleLLFma2yvAlGbO+wS0PZmZmVoqDBzMzMyvFwYOZmZmV4uDBzMzMSmkoeJA0QdJMSbMkHVdn+pqSLsvTb5c0vDDt+Jw+U9L4QvoFkuZK+ltNXv8r6SFJ90n6taT1c/pwSS9Kuif/ndvVSpuZmVnXdRo8SOoHnAXsBYwCJksaVTPb4cCzEbElcBpwal52FDAJGA1MAM7O+QH8NKfV+iOwbURsB/wdOL4w7ZGIGJP/jmysimZmZtZMjbQ8jANmRcTsiHgZuBSYWDPPRODC/PoKYHdJyumXRsRLEfEoMCvnR0TcBDxTu7KI+ENELM1vbwOGlayTmZmZ9aBGxnkYCjxeeD8H2Km9eSJiqaQFwKCcflvNskNLlO8TwGWF9yMk/RVYCHw1Im6uXUDSEcARAJtttlmJVZmZ9SyPY2GtopHgQXXSosF5Glm2/kqlrwBLgYtz0pPAZhExX9IOwG8kjY6IhStkHnEecB7A2LFjG1qXmZl1n4Oj149GLlvMATYtvB8GPNHePJL6AwNJlyQaWXYlkg4F9gU+HBEBkC99zM+v7wIeAbZqoPxmZmbWRI0ED3cCIyWNkLQGqQPk1Jp5pgKH5tcHANfnH/2pwKR8N8YIYCRwR0crkzQB+BLwgYh4oZA+pK2zpaQtcl6zGyi/mZmZNVGnly1yH4ZjgGuAfsAFETFD0inA9IiYCpwPXCRpFqnFYVJedoaky4EHSJcgjo6IZQCSLgF2BQZLmgOcGBHnA2cCawJ/TH0uuS3fWfEe4BRJS4FlwJERsVKHSzMzM+tZDT0YKyKmAdNq0k4ovF4CHNjOslOAKXXSJ7cz/5btpF8JXNlIec3MzKzneIRJMzMzK8XBg5mZmZXi4MHMzMxKcfBgZmZmpTh4MDMzs1IautvCzMzs9c4jaL7GLQ9mZmZWioMHMzMzK8XBg5mZmZXi4MHMzMxKcfBgZmZmpTh4MDMzs1IcPJiZmVkpDh7MzMysFAcPZmZmVoqDBzMzMyuloeBB0gRJMyXNknRcnelrSrosT79d0vDCtONz+kxJ4wvpF0iaK+lvNXltIOmPkh7O/9+Y0yXpBzmv+yS9vauVNjMzs67rNHiQ1A84C9gLGAVMljSqZrbDgWcjYkvgNODUvOwoYBIwGpgAnJ3zA/hpTqt1HHBdRIwErsvvyesfmf+OAM5prIpmZmbWTI20PIwDZkXE7Ih4GbgUmFgzz0Tgwvz6CmB3Scrpl0bESxHxKDAr50dE3AQ8U2d9xbwuBPYrpP8sktuA9SVt3EglzczMrHkaCR6GAo8X3s/JaXXniYilwAJgUIPL1npTRDyZ83oS2LBEOczMzKyHNRI8qE5aNDhPI8s2qqG8JB0habqk6fPmzeviqszMzKw9jQQPc4BNC++HAU+0N4+k/sBA0iWJRpat9XTb5Yj8f26JchAR50XE2IgYO2TIkE5WZWZmZmU1EjzcCYyUNELSGqQOkFNr5pkKHJpfHwBcHxGR0yfluzFGkDo73tHJ+op5HQpcVUj/WL7r4h3AgrbLG2ZmZtZ7+nc2Q0QslXQMcA3QD7ggImZIOgWYHhFTgfOBiyTNIrU4TMrLzpB0OfAAsBQ4Ovcvn38AACAASURBVCKWAUi6BNgVGCxpDnBiRJwPfAu4XNLhwD+BA3NRpgF7kzpdvgB8vBkbwMzMzMrpNHgAiIhppB/vYtoJhddLeO1HvnbZKcCUOumT25l/PrB7nfQAjm6kvGZmZtZzPMKkmZmZleLgwczMzEpx8GBmZmalOHgwMzOzUhw8mJmZWSkOHszMzKwUBw9mZmZWioMHMzMzK8XBg5mZmZXi4MHMzMxKcfBgZmZmpTh4MDMzs1IcPJiZmVkpDh7MzMysFAcPZmZmVoqDBzMzMyuloeBB0gRJMyXNknRcnelrSrosT79d0vDCtONz+kxJ4zvLU9LNku7Jf09I+k1O31XSgsK0E7pTcTMzM+ua/p3NIKkfcBbwPmAOcKekqRHxQGG2w4FnI2JLSZOAU4GDJY0CJgGjgU2AayVtlZepm2dEvLuw7iuBqwrruTki9u1qZc3MzKz7Gml5GAfMiojZEfEycCkwsWaeicCF+fUVwO6SlNMvjYiXIuJRYFbOr9M8Ja0LvBf4TdeqZmZmZj2hkeBhKPB44f2cnFZ3nohYCiwABnWwbCN5fhC4LiIWFtLeKeleSb+XNLqBspuZmVmTNRI8qE5aNDhP2fSiycAlhfd3A5tHxPbAGbTTIiHpCEnTJU2fN29evVnMzMysGxoJHuYAmxbeDwOeaG8eSf2BgcAzHSzbYZ6SBpEubVzdlhYRCyNicX49DVhd0uDawkbEeRExNiLGDhkypIHqmZmZWRmNBA93AiMljZC0BqkD5NSaeaYCh+bXBwDXR0Tk9En5bowRwEjgjgbyPBD4XUQsaUuQtFHuR4Gkcbns88tV18zMzLqr07stImKppGOAa4B+wAURMUPSKcD0iJgKnA9cJGkWqcVhUl52hqTLgQeApcDREbEMoF6ehdVOAr5VU5QDgE9LWgq8CEzKAYqZmZn1ok6DB3j1MsG0mrQTCq+XkFoL6i07BZjSSJ6FabvWSTsTOLOR8pqZmVnP8QiTZmZmVoqDBzMzMyvFwYOZmZmV4uDBzMzMSnHwYGZmZqU4eDAzM7NSHDyYmZlZKQ4ezMzMrBQHD2ZmZlaKgwczMzMrxcGDmZmZleLgwczMzEpx8GBmZmalOHgwMzOzUhw8mJmZWSkOHszMzKwUBw9mZmZWSkPBg6QJkmZKmiXpuDrT15R0WZ5+u6ThhWnH5/SZksZ3lqekn0p6VNI9+W9MTpekH+T575P09u5U3MzMzLqm0+BBUj/gLGAvYBQwWdKomtkOB56NiC2B04BT87KjgEnAaGACcLakfg3keWxEjMl/9+S0vYCR+e8I4JyuVNjMzMy6p5GWh3HArIiYHREvA5cCE2vmmQhcmF9fAewuSTn90oh4KSIeBWbl/BrJs9ZE4GeR3AasL2njBspvZmZmTdRI8DAUeLzwfk5OqztPRCwFFgCDOli2szyn5EsTp0las0Q5zMzMrIc1EjyoTlo0OE/ZdIDjga2BHYENgC+VKAeSjpA0XdL0efPm1VnEzMzMuqOR4GEOsGnh/TDgifbmkdQfGAg808Gy7eYZEU/mSxMvAT8hXeJotBxExHkRMTYixg4ZMqSB6pmZmVkZjQQPdwIjJY2QtAapA+TUmnmmAofm1wcA10dE5PRJ+W6MEaTOjnd0lGdbP4bcZ2I/4G+FdXws33XxDmBBRDzZpVqbmZlZl/XvbIaIWCrpGOAaoB9wQUTMkHQKMD0ipgLnAxdJmkVqcZiUl50h6XLgAWApcHRELAOol2de5cWShpAuU9wDHJnTpwF7kzpdvgB8vNu1NzMzs9I6DR4AImIa6ce7mHZC4fUS4MB2lp0CTGkkz5z+3nbyCeDoRsprZmZmPccjTJqZmVkpDh7MzMysFAcPZmZmVoqDBzMzMyvFwYOZmZmV4uDBzMzMSnHwYGZmZqU4eDAzM7NSHDyYmZlZKQ4ezMzMrBQHD2ZmZlaKgwczMzMrxcGDmZmZleLgwczMzEpx8GBmZmalOHgwMzOzUhoKHiRNkDRT0ixJx9WZvqaky/L02yUNL0w7PqfPlDS+szwlXZzT/ybpAkmr5/RdJS2QdE/+O6E7FTczM7Ou6TR4kNQPOAvYCxgFTJY0qma2w4FnI2JL4DTg1LzsKGASMBqYAJwtqV8neV4MbA28FVgL+GRhPTdHxJj8d0pXKmxmZmbd00jLwzhgVkTMjoiXgUuBiTXzTAQuzK+vAHaXpJx+aUS8FBGPArNyfu3mGRHTIgPuAIZ1r4pmZmbWTI0ED0OBxwvv5+S0uvNExFJgATCog2U7zTNfrvgo8H+F5HdKulfS7yWNbqDsZmZm1mT9G5hHddKiwXnaS68XtNTmeTZwU0TcnN/fDWweEYsl7Q38Bhi5UmGlI4AjADbbbLM6qzEzM7PuaKTlYQ6waeH9MOCJ9uaR1B8YCDzTwbId5inpRGAI8Pm2tIhYGBGL8+tpwOqSBtcWNiLOi4ixETF2yJAhDVTPzMzMymgkeLgTGClphKQ1SB0gp9bMMxU4NL8+ALg+91mYCkzKd2OMILUU3NFRnpI+CYwHJkfE8rYVSNoo96NA0rhc9vldqbSZmZl1XaeXLSJiqaRjgGuAfsAFETFD0inA9IiYCpwPXCRpFqnFYVJedoaky4EHgKXA0RGxDKBennmV5wL/AP6SY4Vf5TsrDgA+LWkp8CIwKQcoZmZm1osa6fPQdplgWk3aCYXXS4AD21l2CjClkTxzet0yRcSZwJmNlNfMzMx6jkeYNDMzs1IcPJiZmVkpDh7MzMysFAcPZmZmVoqDBzMzMyvFwYOZmZmV4uDBzMzMSnHwYGZmZqU4eDAzM7NSHDyYmZlZKQ4ezMzMrBQHD2ZmZlaKgwczMzMrxcGDmZmZleLgwczMzEpx8GBmZmalOHgwMzOzUhoKHiRNkDRT0ixJx9WZvqaky/L02yUNL0w7PqfPlDS+szwljch5PJzzXKOzdZiZmVnv6TR4kNQPOAvYCxgFTJY0qma2w4FnI2JL4DTg1LzsKGASMBqYAJwtqV8neZ4KnBYRI4Fnc97trsPMzMx6VyMtD+OAWRExOyJeBi4FJtbMMxG4ML++AthdknL6pRHxUkQ8CszK+dXNMy/z3pwHOc/9OlmHmZmZ9aL+DcwzFHi88H4OsFN780TEUkkLgEE5/baaZYfm1/XyHAQ8FxFL68zf3jr+XSyIpCOAI/LbxZJmNlDHZhlcW55GqO+0obh+dbh+q4RWrhu4fnW5fu3avMtLNqiR4KHe2X00OE976fVaPDqav9FyEBHnAefVmbfHSZoeEWOrWHdvcP36tlauXyvXDVy/vq4V69fIZYs5wKaF98OAJ9qbR1J/YCDwTAfLtpf+b2D9nEftutpbh5mZmfWiRoKHO4GR+S6INUgdIKfWzDMVODS/PgC4PiIip0/Kd0qMAEYCd7SXZ17mTzkPcp5XdbIOMzMz60WdXrbI/QuOAa4B+gEXRMQMSacA0yNiKnA+cJGkWaTWgEl52RmSLgceAJYCR0fEMoB6eeZVfgm4VNLXgb/mvGlvHauYSi6X9CLXr29r5fq1ct3A9evrWq5+8sm7mZmZleERJs3MzKwUBw9mZmZWioMHMzMzK6WRcR6sA5K2JQ2xPaAtLSJ+Vl2JzBJJA0jDuo9mxf3zE5UVqkkkDSF1rq499t5bWaGaSNKOwBnANsCapHFuXoqI9SotWBPlZx3V7pvfqK5EzSNpMPBFVt4/96ysUE3m4KEbJJ0I7EraQaaRntVxC9AywYOkfVj5AD+luhJZCRcBDwHjgVOADwMPVlqi5rkYuAzYBziSdBv3vEpL1FxnAx8hDd0/DjiMFcfG6dMknQ2sD7wH+AmwPyuORtzX/Rz4NenxCkeT9s+nKi1Rk/myRfccAOwOPBURHwe2J50ltARJ5wIHA/9JOvM5kF4Y9rS3SHqHpDslLZb0sqRlkhZWXa4m2jIivgY8HxEXkn5o31pxmZplUEScD7wSETfm1pR3VF2oJlotImYC/SPilYj4EbBH1YVqol0i4hBgft5HdyINCtgqhkTED4GXI+I6UvAwruIyNZWDh+55MSKWA0slrQfMBbaouEzN9K6I+BjpaaYnA++khc5+gDOBycDDwFrAJ0lNxa3ilfz/uXx5bSAwvLriNFVb3Z6UtI+kt9FaPz7P5wH07pX0DUn/CaxTdaGa6MX8f4mkjYAltM6+Ca/tn0/lyzPb0lrfnb5s0U3TJa0P/Ai4C1hMGkGzVbQd4C9I2gSYD4yosDxNFxGzJPXLg5f9RNKfqy5TE50n6Y3A10gjtK4DnFBtkZrm65IGAl8gBXzrAZ+rtkhNdRjp5O4YUh1H8trIu63g9/m78zvAPcAyWuhyL/CNvH9+ETiLtH8eW22RmsuDRDWJpOHAehFxX8VFaRpJXyN9Me9OOgAC+HFuZuzzJN1Eagr+Mel65JPAYRGxfaUFM3sdkbQWsFZE+FlFfYiDhy6QtHVEPCTp7fWmR8TdvV2mniZpTWBARCyouizNImlz0qWm1UlnrQOBsyNiVqUF6yZJH4mIn0v6fL3pEfG93i5Ts0j674j4tqQzqP9U3c9UUKymkXRJREyW9Ffq16/ud05fIek/IuJGSR+oNz0/7qDPkvSFiPiupNOo//nVPSb7Il+26JovAP8P+G6daQH06dvFJL03Iq6X9KE604iIX1VRrmaLiH/kly8CJ1dZliZbO/9ft9JS9Iy2u0WmV1qKntPWtN1KlyiK3gfcSOp8XavtYYp92SP5/98qLUUvcMuDrUTSyRFxoqSf1JkcfX2cAEmXR8RBku6n/tnBdhUUy8ysz3Dw0AX1zsiLWuXMvFVJ2jginsyXLVZSaJHokyT9oKPpfblpX9JvqRPwtYmIus3hfYWkZ6lfP5EC9w16uUhNJanDfS8iOtx3V3WSfk3H+2eHvx19iS9bdM37O5gWQJ8OHtq7Vt6mL18zB4iIJ/P/Ph0kdOCuqgvQg75TdQF62OCqC9DDhlRdgB52ZtUF6C1uebCV5JEz25XHfOizJC2i47O7lhkC2Po2SRuw4uiuT1RYHLNXOXjoJg/fbKuqVn7+g6SRwDdZuW4tMUhb/l45jTTw1XxgKPD3iNi60oI1Sb576zBW/u48oqoyNZOkNwNTWHn/3KqyQjWZR5jshtfB8M0DJB0t6WxJF7T9VV2uZpO0oaTN2v6qLk8TXUy6O2EE6W6Sx4A7qyxQE/0EOAdYCuxGGmDookpL1FxTgJ2BmRGxKen5JDdUWqLm+hlpRMl9gduBN5NGmWwVPyXtoyI98+hy0nNKWoaDh+5p9eGbLwI2In1x3Ug6C1pUaYmaSNIHJD0MPEqq32PA7ystVHO18vMf1srPDFBE/CMiTqKP3yJdY2lEzANWk6SI+CPQp8d4qLFVRBwPLM776ATSEM6t4g0RcQ1ARDwSEV8lBbktwx0mu6ctUm7V4Zu3jIgDJU2MiAsl/QK4pupCNdH/kH5Mr42It0najfSsi1axwvMfgCdonec/LJG0GvCwpGOAfwEbVlymZlogaW3yU3olzQWWV1ymZio+d2Ub4GlaqNUWeEmSgEckHUnr7Z9ueeim3+bx2f8XuJt05npJpSVqrlZ+sBKkM/L5pLO71SLiT8CYqgvVRMXnP3yRNAx3qzz/4bPAG4DPADuQHl99aKUlaq79SCcnnyVdrvgXHd/l1decn5+7ciLphOTv1B90r6/6HOlZMp8hXX76JNCnx8ep5ZaHLspnPddFxHPAlZJ+R4sN38xrD1b6Kq89WKklnmuRPSdpHeAm4OJ8dre04jI1haR+wMiI+B2wgBZqMs11OygijiU9jO7jFRepqXL9roiI8aQHRp1fcZGaKtfv3xHxLPAnoJX6GbXV74MRcTvpMu9HKy5Sj3DLQxflR3F/t/D+pVYKHHJwtDAino2ImyJii4jYMD+jvlVMBF4gnSX8H2lo2ZY4u8tPCe3TAya1J9dth9ws3HJy/V6W1JK3DOf6fbbqcvSUXL9xVZejp/lWzW6QdDJwH/CraMENKemmiHhP1eXoCfns4JqI2KPqsvQUSVNIl5ouA55vS2+FB7dJ+i7pMdW/ZMW69ekB2tpIuoTUH+cPrFi/lniwkqSvklqNavfNhZUVqokkfQfYgpX3z77+7I5XOXjohjzY0Nqkpu4ltNggQ/mR3C+y8gHeEo/OlTQV+GgrtRgVSfpTneRokXEeWvK5K20kHV4nOSKiJW6VlvR44W3w2ndnS1zCkFTvtuHId+e1BPd56IaIaMWnFha1fREfXUgLUkTdCpYA90v6IysGR3322Q81Do+I2cUESa3y2f04Im4tJkjauarC9IC1ImKFoY7zXSWtYouIeKWYIGn1qgrTA86KiNuKCZJa5TZpwC0P3SLpuojYvbO0vkrSgIhY0llaXyWpXu/8iIif9XpheoCkuyPi7TVpd0XEDlWVqVnaqdtKaX1VO/X7a0S8raoyNdPr9PNriWOvjVseukDSANJtYoPz3QhtHbfWAzaprGDN92dWHpimXlpftX5EnF5MkPRfVRWmWSRtTRr2d2DNE2DXozBUbl8k6Z3Au4AhNQ9wWw/oV02pmkfSwcAkYISkYv+N9YDnqilV80jaENgYWEvSW1nxu/MNlRWsSSSNIw0WOKTmCaLrAa3UsuLgoYs+ReotvAnpCYZtB8BC4KyqCtUskjYijaW/lqS30WIHeMGhwOk1aYfVSetr3kIa9nd9Vrx7ZBHw/yopUfOsQbpluD9QvGy4EDigkhI11x2kweaGseJ3ySLgr5WUqLn2IV0Obatf23fLIlrjNvC1SU9G7c+KTxBdRHp8QcvwZYtukPSfEXFG1eVottycfxgwlvQshOIB/tO+3qNd0mTgEGAX4ObCpPVIwwK3xB0Ykt4ZEX+puhw9QdLmbY9Uz7cVr9MqPfUBJL0BeDEiIj9k6S3AHyKiVcYhOSgiLq+6HD1F0hZt/Y3yLcVviIjnO1msT/E4D93zlKR1Id16JOlXkvp8k35EXBgRuwGHRcR7I2K3/PeBvh44ZH8mjdHxUP7f9vd50hj7reKDktaTtLqk6yT9W9JHqi5Uk3wz121t4AFgpqRjqy5UE91EavnbmPTclU8DLXGnRbZh2zgWks6VdIeklugrlp2U9883ADOAR2sus/V5Dh6652sRsUjSLqSHR11IetJfqxiWDwBJ+rGkuyXtWXWhuis/SOkGYA/g5oi4EXiS1JTaSgMP7ZnPxvcF5gBbAa3yAzsq120/YBpplMJWGslvtYh4AdgfODMi3g9sV3GZmumIiFiYv0+GkYKjb1dcpmZ6a2H//AOpjodVWqImc/DQPcvy/32AcyLiKtI12VbxiXwA7El6qMvHgW9VW6SmugkYIGkocB2pfj+ttETN1dZBa2/gklYZnyNbPd/atx9wVb7tr5Wuwa4maUfS5bXf5bQ+3yG0oO2z2gv4SUTcRWv9Hq0hqT9pFNvfRMTLtNaDzVrqw6rCvyT9EDgImCZpTVprm7adhe9NOsDvpbXOzJXP7j4EnBERHwRGVVymZvqtpIdIfVeukzSE154E29f9kPQgurWBmyRtTuo02So+D5wMXB0Rf8vjc9zcyTJ9yb2SppE69P4+P2OmlYK/HwP/BN4I3ChpM9KImi3DHSa7IV/PmgDcHxEP5+uTb42IP1RctKbIo/gNJT1mfHvSmc8NrXKvsqS/AkcBp5EGVJoh6f6IeGvFRWuafCvxwohYlvfX9SLiqarL1RMk9W+VDoWtLg8PvwMwKyKekTQY2DQiWuGOkpXkTpOr5xaIluBbNbtnMDAdIEeWkDrhtYrDSY+onh0RL0gaRGs9wfCzwPHAr3PgsAXpKX8tQdLHCq+Lk/r8IFiSTmhn0im9WpAekkc9XenMLiL6fJ+jbKf8f2u14PPNJH25nUnf6NWC9CAHD91zNa+Nyz6AdIY+kzRATyvYJf/frhUP8NxR8sbC+9lAqwxNDbBj4fUAYHfgblogeKAwnDipbvsCD1ZUlp7w1cLrAaSOky9VVJaeUBzTYQCpFeKvwH9UU5ymW1Z4PYDUL25GRWXpEb5s0UT5Ns1PRcSnqi5LM0j6beHtANJjZu9qhQcrwasPjqp3dtcS9aslaSBwUUS03KO6c3+jqRExvuqy9BRJN0ZEq/y4rkDScOAbEXFIxUXpEXlU4t9ERMvcCu6WhyaKiLtzD+mWkG8Pe5WkTWmt26m+WHjddnbXytfMXyA9xroVvYHWeWAbbWMgZKuRzsw3rqg4PS4iHpO0bdXl6EFrAm+uuhDN5OChG2oG/ViN9MyHeRUVpzfMAVrmAM+3hxXdKunGujP3QbnlqK1lZTXSnSS/rK5EzSPpfl6rWz/SUMD/U12Jmm4Gr10SXQo8St8fWvxVkk5jxX3zbbRQs37ujF3cPzcGvlldiZrPwUP3FMfWX0rqA3FlRWVpOklnsOIBPga4t7oSNZekDQpv287uNqqoOD3hO4XXS4F/RMScqgrTZPsWXi8Fnm6lOy0iYtPatHyHQqv4W+H1UlKn5ZYJ3FnxOStLgaciopX6rLjPQzPl61rvj4hWObsrPrJ6KfBYRNxaVXmaTdKjrHx2d0pE3FJpwXqIpJ2BQyLi6KrL0kx5iOr9SHXbp+ryNJuk95AGi9ovIlopuH1Vvs19UkScVnVZmk3SWsAHSPvnxKrL0yxueeimfDawJzCZNET1zbRI03BEXFh8L2lTScdGxP9WVaZmiogRtWl51MKWIWkM6YfnIFJw1ArPJkHSGqTByw4hjbVyJXBupYVqIkk7kOq2P+mSzGdY8Q6MPi+PQXIA6btzC+A31ZaoefLokhNIn+HewG9prdFrHTx0VeFsYB/SY3R3BkbkEQtbRh685UDSAT4U+HW1JWq+PIDLbqTP8/3Am6otUfdI2gqYRPrM5gOXkVoZd6u0YE0g6X28Fqj/CbgIGBcRLTH+iKSTgYOBp4FLSLfb3hER51dasCbJrUQTScfatsBVwFsiYmilBWsSSbuR9s+9gVtIx97OEdFKz10BfNmiSyTNIQ09eg7p9ptFkh6tdybbF+UnhX6QdIBvRQoYDo6IYZUWrMkk7USq4weBDYCjSbf7PVtpwbpJ0nJSC9jhETErp82OiD5/N0KhbodFxKM5rSXqBiBpPqnj4PeAaRHxcovV73nSwHonkUarjRb77mzbPw+NiMdyWst8fkWt9ByG3nQl6Sz8YOD9OZpupShsLml0ySnAmyPiC0DLDKsqaYqkh0mjvd1P6uk9Lz+KvE8HDtn+wFPAnyT9KD/quFVG+doBuA24VtIfJR1Oaz0waiPgf0mXmWbnIeLXktQq39UnAeuQgqNj8zNJWum7cydScHSDpN/nfmOttH++yi0PXVRo6m5rolqP9IM7LSL69ANQJH2O1Oy9NvALUtPbH1slepY0jzQS6PeB30XEklY8Oyh0JJwMvJf0yPhft9CzV3Ym1W1/4B5S3c6rtlTNk59F8gFSHceRjsGPdbxU35AvrU0mfc+MAL5C+vxmV1qwJsm/D+8h1fGDpEvbv46ICyotWBM5eGiC3MluAmlH2TMiBldcpKbIz3poO8BHAieSDoC/V1qwbqrp5Ppe0rXzPUgP5mmZ2/2K8m2pB5IuP7XUCJr5rPx9pN76LdH3oVbuXPihVun7UCTpbaRj8aCIGF5xcZoud54cT9o/W6bvg4OHJpH09jzC5FoR8WLV5Wk2SW8lHeAHR0TLjJSWb6/dl9T3YWfgulYcIlfSEa10Vl4k6aSIOKnqcvQUSb+JiP2qLkdPkTQhIv6v6nL0FElfjYivV12OZnPw0CSS7o6It1ddjp4iad+I+F3V5ehJeUjgD9beotoKWnn/bOW6QRqtMCLeVnU5esrr4PNryfq1SiecVUGrdEhrT0s86rg9kn4XEQtbMXDIWnn/bOW6AdxXdQF6WKt/fi1ZPwcPzXNy1QXoYS15ABS0xH3mHXh/57P0WTtUXYBmkzQhd7ojIg7tbP4+7qiqC9DDxlVdgJ7gQaK6Iff2vicingfWkfQ94PSI+EfFResJLfGY8Q78teoC9ARJ+wCjgQH5t4iI6POtSLmvyuGsXLdPVFmuJjoMOFPS5cBPIuLhisvTVJJuBy4ALomIv1RdnmbLj4g/jJX3zyMqLFZTueWhe84BXpC0PXAs8A/gZ9UWqbkkbSvpIGBrSR+T1BK3itVqoR+dV0k6lzQWyX+SWo4OBDavtFDNcxFpTITxwI3AMGBRpSVqooiYBIwF/gVcIulmSZ/It9+2gkNJQ1LfI+nneSySVvIzYDipM/btpMdxL6myQM3mDpPd0NYRRtIJwL8i4vxW6hwj6URgV9KjnKcBewG3RMQBHS23qqt5nPNKImK7XixOj5F0X0RsV/i/Dv+/vfuOsrOq9z/+/tBuAEkAAREw1AASUeql92WlKKBgBPmJCBZU2vUKKqCoV1RAEQQBJReQi3QELCAQkS6GeikqlyIdAYHQ2+f3x96HnIRJmTnPnJ1n5/taa9bMec7MWp+TTNnPLt8vnGP7faWz9aqzibDrtc0NXFThMdSFgJ1INyd/I9VEOML2MUWDNSQfm94GOJpUiO5E4CjbTxUN1qPZ4fszli16M0nSAcAngY3yD0JNjZU+CrwHuNH2rpLeBvy8cKYmdNo5d7pLnpLf7wTU1Jukc2T4eUlLkPpcVFEGGHglv39K0rtIFTWXKRenWZI+BOwKvBM4FVjX9sN55uF2oPWDB0mrkF7j1qQeF6cCGwKXAW2/Aev+/nwnqVdJLbN+QAweerUjqT7Ap20/Imk0qbRsLV6w/bqkV/MxxsdIU42t1tmTImkD2xt0PbW/pKuo52TJhZIWJH1P3kCabalh8AdwfL4rPxA4n1Ty+KCykRq1E3Cs7cu6L9p+TtLuhTI1Ju95eIE003BQV22cq/Jesrb7Rf7+PBi4CJgvf1yNWLboUa7NPsb2Jbmc7Jy2q1h7lXQM8DVShcn9gGdJG0Srxm6Z8gAAH6FJREFUqOIn6Sbgi7avzI/XB46xvVrZZM3LG7hG2H66dJYwfXkG87e23186y3DIr28/2z8onSUMXQweepDvAPYAFra9vKQxwM9s17b5B0nLACNtV3PmXNKapDufUfnSU6RZpBvKpeqdpM1tXyZpu4Get31OvzM1RdLOtn8pad+Bnrd9RL8zDQdJFwA72X6mdJbhIOkK2xuVztE0SeNsnybpywM9b/sn/c40XGLZojd7ks7wXgdg+++SFisbqXeSVrZ9p6Q3rTt2ynCXyNU02xOB9+QlGVV0V74Jad14oNoOBlo7eCA1awNYoGiK4fcscLOki4HnOhdtDzhoaqGLJO1NarrX/fraPlhaKL9ftGiKPoiZhx5Ius72Ol07a+cCbmj7bn1Jx9veQ9KEAZ52LTuG81T+9qSNdm8MpGuogxDaTanV+JvU0hhL0v0DXLbt0X0PE4YkBg89kPQD0lT3LqSz9F8Abrf99aLBGpA7Fa5n+6rSWYaLpN8DTwMTgdc6120fXixUA6Y1pd/R5ql9SdOd9rU94HRxG+WbkRXyw7tq7fhaI0mLAJ/mzTcm1RSJimWL3uxPqnJ3K6kC42+pZDd7PmVxGLBe6SzDaCnbHygdYhjUPKU/sXSAfpC0EekI8YOkAl+LS/pkLYP5PDDaA9g4X/oj8POKBki/Bq4FrqTrxqQmMfMQpknSt0hNec5xhd8oko4nFaS5tXSWELpJ+guwi+3b8+N3AqfYXqtssmZIOo60f6VTkXdn4MVa7swl3VTjqa1uMXgYgtmoQuEk0g/4a6Qz2SKtS44sGqwhkm4nTQvfA7zE5NdXy//fFP0fOtdrKMUtaVHgq6Tqp92vrZb9OLdM/X040LW2knSz7ffM6FpbSfoeMMH2xaWzDJdYthiarWb8Ke1nu+bpb0jltmt2CnAnqf/DIaTCQ3cUTdScU0k79bcEPkfqlfDPoomadUO+O++uflpT87bXJS1j+1544yj46yUDNexzwFclPU8qu925MVm4bKzmxMxDmC5J29C1Lmn7wpJ5hkM+Xtt99/qPgnEaU3N9fUkTba/ZfTcu6XLbm5TO1oQ8a/RlUrlmAX8CfmL7paLBGiLpvcAvSP06RJoB/IztPxQN1pBcCOtNbFez/yFmHnogaV3gKFL9+XmAOYHnKprWPxRYm3SXB7CXpA1t718wVmPywOhwYAlS6e2lSXfmY0vmalDN/R86r+1hpbbjD5E6a9biM7kC4xtVGCV9kdRAqgYTgJVIvztF6tfxynS/okVsvyZpFKmb5oiup64uFKlxMfPQg7yp6ePAmaT2ubsAK9RwVBPSGiuwmu3X8+M5SU2yqll3BTYHLsl36JsB4yratPUZ4GxgVeC/Sf0fDrR9XMlcTZC0FXAF8A7SAH4k8C3b5xcN1pCBuvN2ZpJKZWrSNF5fTR2JdwP2BZYkncZbG7jW9qYlczUpZh56ZPsuSXPm6ajxkqoZWWYLAk/mj0dN7xNb6BXbT0iaQ9IctidI+n7pUE3IdTqesf0v0pR36xuadeRB7Ji8hPY0sFnhSI2RtCPphmRZSd2VQEeSasq0Wl4ifDswr6RVSbMOkF7ffMWCNW9v0g3lNbY3kjQW+EbhTI2KwUNvnpc0D3BTLhj1MJPL59bge8CNudKkSHsfvlY2UqOekvQW0h/XUyU9BlRxzjzX6fgicEbpLE3LU8LbAD8qnWUY/JnUOn0p4Kdd1ydRx4bJLUnFk5Ziyrbik0gdUmvxou0XJCFpHtu3SVq5dKgmxbJFD3JHzUdJ+x32Id2ZH2P7rqLBGiTp7aQpNwHX2X6kcKTGSJofeJH02nYi/f+davuJosEaIulA0hHbqfsHPDnNL2oJSd8l/X9N/dqq6LtSO0k72K5uYNsh6XzSMvZ+pE2vTwLz11SULgYPQyDpI8DVth8rnWU4Sbp06g6hA10LsyZJ9wxw2bZbv4RRa9+VzokRSf9iyloyVRz16+o6uRcD1Mqpqetkh6QtSAPd39RyWgZi2WKodgZ+ms/wXkXaQXuV7dvKxmpGPiY2H7CIpIWYcl1yiWLBGpKLX5n8C7n7KSoqggW80/aL3Rfy/20NdrN9d/cFSa0fFDF5/8YiRVMMn07XyVpf3xtyt96lSPVH/kk6XXJL0VANipmHHuTCJuvnt/WA0cD1tj9UMFbP8l3B3qSBQqe2PsAzwAm2azkuVrWad7RP47VNtL1mqUxNyRtCb6il2uLU8uvbs8ZZhg5JB5N6d9zL5OJXtr3xNL+oZWLmoQe27813cvPmt87HrWb7SOBISV+yfVTpPMNF0vLAA7ZfkrQp8G7gZNut3tUuaXHSEbF5Ja1ORTva86azscAoSdt1PTWSKc/Tt1beEHq7pCVtP1g6T9Py69sOqHbwAHwCWK6mZYqpxeBhCCR9jTTTsCjwV1L3tKOBPWqqIEYqIbtg549pXsIYZ/uYGXxdW5wNrCVpBVK1u/OB/wFaPXNEKkf9KdKU6eFMHjxMov2nZVYilYdfENi66/okYPciiYbHIsAdkq5hyg2h2037S1rlSklHAr9iytdXy7T+baTuttUOHmLZYggk3Qk8C1xI2u9wne2ny6Zq3kCd4WosVCPpK6SjVUdV9vq2t3126RzDQdJ6tq8pnWO45E12b2L70n5nGQ6SrhjgcjXT+pLWBM4j7XF4YwBR0eAvZh6GwvbKkhYm7XXYFNg/1wu4mXQKY3zJfA2aQ5KcR5h5rXKewpma9IqkcaSmSp272LkL5mnaUnnT1iTgBGANYP9KOv1tK+k20lHU3wPvAfa2/cuysXqXf87+0/b7S2cZDvn1HWn7rNJZhtFJpDokt1JXw683xMxDjyTNBaxJKqD0WWBZ2wM2RWkbST8k9UL4GelUwueA+23vVzJXUyStQnpN1+TjY8sCO9o+tHC0Rii3OJb0fmBPUhGe8ZVsmLzJ9mqStgU+QqqzMqGWTYaSLgB2sv1M6SzDQdIVtjcqnWO4SPpTLbMo0xKDhyHI1e3WBzYgbd66jbR8cTVp5qGK1sC5xPFngS1I6+YXAz+vbF9HtTS5m+aRpI6o59ayLCPpNttjJZ0AnG37953BUulsTZB0GrAu6Weue0/AvsVCNUjSN0hLv1MX+apisCTpcOB50j6q7mWLWvZ0xOBhKHLN+atJNR4m2n65cKQwBJI2AL5J6qY5F5PrPNRQLwBJ40mnLpYlTevPSRpE1HCc8VDSjMMLwL+TNlBeaHudosEakhsrvYntX/Q7y3CQdH/XwzdqrtgeXShSo2rf0wExeAgDkHSG7R0k3crAVeBq6ap5J2m6eyLwxmxKReWp5wBWA+62/ZSktwJL1nL3k0//PJOP/s0HjKysfPo8wOiayt2HesSGyTCQSfmufGsGGDxU5GnbvysdYhhtmN+/W9J0P7FtJO3S9XH3Uyf3P03zJG0JHEHaoLyspNWAg21vWzZZMyTNC+wFLG378/m49Jhafh5zU7qTbT8j6WekzcoH1HJaBmLwEAZ2C3AYqXXu6cBptm8qG2lYTMibQs9hynXJWporfaXr4xGk6f2JQKv7P2Rrd308grQv5wYqGTwAhwDrABMAbN+U/8DW4kTSSYTOpsmHgDOBKgYPpJo/R0t6H6neyueB40mb66sQg4fwJl0VJpcGPg6Mz5U0TwN+ZftvRQM2p7M+vlbXNVPHH1dsdxdRQtI7gB8UitMo21/qfixpFHBKoTjD4ZW81NR9raZZwDG2x0n6GIDt51XX9Fjn/+qDpBNOE/MyYjVi8NCD2jfc2b4P+D7w/Vzm+ETgYNLGu9azvdmMP6sqDwDvKh1imDwPjCkdokF3SNqBVGtlWdIU/7WFMzXp5XxD0qkhsyxQ08bzmyX9FlgR+HquA1TT4C8GDz36BQNsuKuFpLmBD5BmH7YALge+VTRUgyS9DfgvYAnbH8x1H9araEf7UUz+hdXZPHlzuUTNyXUQul/bKqRp71p8ETiIVGDoHOAi2l9avNshpOJeS0k6CdgEGPCESUvtSlqiuCvPqixCXa8vTlv0QtJ1tRwN6ybpvcA4YEvgz6T68+fZfm66X9gykn4HjAe+nospzQXcaHvVwtEaIen/dT18FbjX9lWl8jRJ0iZdD18F7rP9QKk8TZH0X7ZrGiRMQdJo2//IHy9KqpcjUn2cx4qGa1heSlueroZttq8ul6hZMXgYAkmdCn07kKbwq9pwJ2kCqUHU2bafLJ1nuEi63vba3YWTBurnUYu85+Hjtn9YOkvT8hLiJ2zvWTpLL1RJy/Rpqf31dUj6NLAfqc7KraQNvtfa3rRkribFssXQHD7V46o23M1GewGey7UPOuuu6wJVNTjL06UfI80kLQmcWzZRc/LxxU+QBvH3kAbxbTdnrl8x4ObBCgbzNW2KnJ59SH8XrrG9kaSxwDcKZ2pUDB6GoPPHVdJytu/ufk5SFZslZxP7ksrHLi/pKlKL9Y+WjdQ7SQsA25L+sK5IGjAsZ3uposEaIGlF0h6cccATpKPEqmjAuzJpD9VAf2QNtP33y5KSfjKtJ21/uZ9hhtGLtl+QhKR5bN8maeXSoZoUg4fenEUq/tHtTCo6y1sz2zfktfOVSL+s/2r7lcKxmvAYaa/KN4ArbTs3kKrBncAVwNadyouS9ikbqVG319B7ZDpeIA2OqiRpLtuvAg9LWhC4ALhI0pPAo2XTNSsGD0OQR5BjgVGSuvuzj6Rrc0yYteWSxvuSqtztLmmMpJVsX1g6W4++Rro7Pxb4H0mnF87TpO1Jr22CpN+TNvPOLlPhNXjC9kmlQwyjPwNr2N4mPz5Q0hbAKOA35WI1r6qiFX20ErAVqRnP1l1vawC7F8wVBmc86Wz5evnxA8B3ysVphu0f5VNA25D+sJ4HLCHpq3nav7Vsn2t7R9L0/h9Ja8tvk3RsrubXdkeWDjDMaqrlMJA3DWRtX2r7HNsvDfQFbRWnLXogaT3b15TOEYZG0l9srzXVaYtq2jp3k7QqaZ/AjraXL52nSZIWJm0K3dF2qzcrh3aT9ACpJ8mAbE/zubaJZYve7CHpTTMNtj9dIkwYtJdzg57OaYvl6TpyWxPbt5KOjFVXQyCfQDguv4VQ0pzAW5gNltJi8NCb7rXxEaQd7g8VyhIG72BSlbt3SDoV2AD4VNFEIYQ2e9j2IaVD9EMsWzQoNz65JKZO2yPXeViXdKdwre3HC0cKoVN9cXdgGbpu8to+q5mXmKap7XUsupdAaxczD80aA4wuHSLMHEkb5w8n5ferSML2n0plCiH7NelI6iXU1TdnImmZsNY6FluUDtAvMfPQA0mTmPyDYOAR4ADbZxcNFmZKbq7UMQL4d2BiLTNHNXd9zUekvw8sRnpdndc2smiwhtRcJj3UIQYPIWS598MPbI8rnaUJku5kgK6vtp8oFqohku4iFYq6o3SW4SDpO6RmUb8tnWW45DLcY5iycVTM+rVEDB56JGkboDP9/ccKCgzNtiQJuKWirppVdn0FkHSV7Q1K5xgueVZzftLpn1eob2blM8BewFLATaR9R9fUMus3O4jBQw8kHUrqlnZqvjQO+IvtA8qlCjNL0lHkY5qkgmmrA/fY3rlcqubk78+qur52VXTdBFicVACr+7W1vjlWHsS+o9O6ukaSujtNrpar9n4rFwALLRCDhx5IugVYzfbr+fGcwI223102WZgZkvYk/XGF1GTpXttXFYzUqNxafWpu892dpPHTedptP43QIWmi7Wp75Ei63vbakm4C1rH9UuzzaJc4bdG7BYHO8aJRJYOEmSNpbuCHwC7AvaQp4cWAo4CrJK1u+8ZyCXuXjw0fa/uM0lmaZHtXSJtBpx7o5Q2itbhW0tq2ry8dZJg8kBtHnQf8QdK/iBo5rRIzDz2QNA44FJhA+gO0Mem0xa+KBgvTlVsCzwfsY3tSvjYSOIy0sfADtpctGLERkv5ke+MZf2b7SLrB9hozutZWkm4n9dC5F3iOyXseqpvVzJ1tRwG/q6Sr7WwhBg89kvR20todwJ9tP1IyT5ixvFN/jKf65s/LTo8DH7R9bZFwDZJ0IKkF8umkP0BAuwvxSFoPWB/YG/hR11MjgW1r6UsiaemBrtu+r99ZhoOkU2x/ckbXwqwrli2GIP9gP2X7adsP553RHwFWkHS07do7x7Xd61MPHABsvybpnzUMHLLO+v+eXdfaXohnHlLvgLmABbquPwN8tEiiYWD7Pkkbkga543PFybeUztWgsd0P8sC92j0eNYqZhyGQdB3pLuchSauRqsB9D3g38IrtzxQNGKZL0nnAObZPnur6zsDHbH+4TLIwsyQtXctd+EAkHQysBaxke0VJSwBntv14qqQDSM3Z5gWe71wmteo+Pk6qtUcMHoZA0i2dtUdJh5HuZP8zb1K7qcZ1yZpIWpJ0fPEFJpfLXZv0C21b2w8WjNcYSfMB+wKjbe8haQzpj1Hra5HkkyQDzR619iRJt3wKYXXghq528bfU8rtF0vdioNBusWwxNN112TcHDgCw/Xo6oh1mZXlwsI6kzUnTpyJt1rq0bLLGjScNjtbPjx8AzmTKbrBt9R9dH48AtgdeLZRlOLxs25I67eLnLx2oCZJWtn0ncKakN21ubXMNktlNDB6G5jJJZwAPAwsBl8Ebmydjv0NL2L6M/H9XqeVt75hPBWH7BVUyurU9capLV0m6vEiY4XGGpOOABSXtTtq/ckLhTE3YF9gDOHyA50y6GQstEIOHodkb2BF4O7Bh1/GixYGvF0sVwpReljQveXpf0vJ0VWNss6laO89B2my3eKE4jbN9mKT3kjaCrgQcZPsPhWP1zPYe+f1mpbOE3sSehxAqJel9pMHsKsDFwAbArrYHqjzZKpLuYXJH21eBe4BDbF9ZNFiYKZJGAF8ANiT9P14B/Mz2i0WDhZkWg4cQKibpraSmQyL1EXi8cKQwE2aDluNnAJOAX+ZL44CFbH+sXKowGDF4CKFSki61vcWMrrVRLjH+ebo62gLH1VKhcDZoOX7z1AW9BroWZl1zlA7QZpI2z8fhQphlSBqR9wQsImkhSQvnt2WAJcqma8yxpH0Ox+S3NfO1Wjxa68Ahu1HSup0HktYBqmlKNzuImYceSDqZNCX8BGnN7grgStv/KhoszNYk7UXa1LsE8CCTjxY/A5xg++hS2ZpS653r7NByHEDSHaSNoJ2246OBO4DXqbSHR21i8NCAXP3to6Sz50vYjlMsoThJX7J9VOkcw0HSDaRqoP+XHy8HnNX2xlizUcvxAXt3dNRcPbQW8UeuB7mc8UbAqqSGSkeTZh9CmBU8ImkB25MkfQNYA/hOJYV4vgJMkHQ3aWZlaWDXspF6N7u0HO8MDiQtRiry1bn+j2l+UZilxMxDDyQ9Dvwf8DNggu17yyYKYbJOOePcYOl7pJbjX7O9TuFojZD0b6SpbwF32q6ihgXMFi3HtyEViloCeIw0+LvD9tjpfmGYZcTMQw9sLyJpLGnH93dz74C/RlvZMIt4Lb/fEjjW9q8lfbNgnp5JWhu43/Yjtl/Kjem2B+6T9M02txuHKVqOLypp366nRgJzlkk1LL5N2i92ie3VJW1GOq4ZWiJOW/RA0kjSRp+lgWWAUaQNPyHMCh7MJY53AH6b79Tb/jN/HLkEvKSNgUOBk4GngeML5mrK1C3HO29VtRwndR9+AphD0hy5cNlqpUOFmRfLFj2QdAtwZX77k+0HCkcK4Q35GPEHgFtt/z33XlnV9sWFow1Z94kKST8F/mn7m/nxTbZb/wdI0pzA6bZrGixMQdIlwEdIy2mLkJYu1ra9/nS/MMwyYtmiB11tuee3/VzpPCF0s/28pMdIJYD/Tirj/PeyqXo2p6S5bL8KbEFqstRRxe8z269N1bujRh8GXgD2AXYizdoeUjRRGJQqfthKyeuTvyBNM46W9B7gs7a/UDZZCCDpYGAt0qbC8cDcpHLAbd61fxpwed6s/AL5dJOkFUhLF7W4UdL5pBbqb9yYtL3OQ/5/elvXSZLXgZPyEtSCpJo5oQXavv5Z2o+B95O/4W3fzORyuSGUti2wDfmPj+2HSOvnrWX7u8B+wH+TOtp21l3nAL5UKtcwWJj0e2VzYOv8tlXRRM34MamnxdSez8+FloiZhx7Zvl9S96XXpvW5IfTZy7YtqdOSe/7SgZpg+9oBrv2tRJbh0qn3UKFlbN8y9UXbf8nl00NLxMxDb+6XtD5gSfNI+g9SidUQZgVn5NMWC0raHbgEOKFwpjATJC0l6VxJj0l6VNLZkpYqnasBI6bz3Lx9SxF6FoOH3nwO2BNYEniAdNRoz6KJQshsHwacBZxN2vdwUK3lqis0HjifVERpSeCCfK3trs8D2SlI2g2YWCBPGKI4qhlCZSTtTepQeGM+lRBaZqBjpzUcRZX0NuBcUq2OzmBhLVJ9i21tP1IqWxic2PMwBJIOms7Ttv3tvoUJ4c2WAo4EVs61SK4mDSauaXsFxtnI47l3zmn58TgqOIlg+1Fg/VxR8l358m9sX1YwVhiCmHkYAkn7DXB5fmA34K2239LnSCG8iaR5SHd16wPr5benbK9SNFiYIUmjSY321gNMGgDuFd0mw6wiZh6GwPbhnY8lLQDsRero9ytSs5cQZgXzknoijMpvDwG3Fk0UZkruLrlN6RwhTEsMHoYoV4Dbl1Qd7SRgDdv/KpsqBJB0PDCWdJ7+OtJd6xHx/TnriyXR0BYxeBgCST8EtiM14lnV9rOFI4XQbTTwb6RS1A+STgI9VTRRmFkDlbl/Y0mU1I0yhOJiz8MQSHodeInUK6D7H1Cku4ORRYKFkClVLhtL2u+wPmlz2pOkTZMHl8wWZk7XkuhuwBnA4bYfK5uqGZK2A74PLEb6vRm/O1smBg8hVCwXFtqANIDYirShd8GyqcL0DLAkemRtS06S7gK2th1F9Voqli1CqIykL5MGCxsAr5CPaQInEhsmZ2mz0ZLoozFwaLeYeQihMpKOINd2sP1w6Txh5s0uS6KSjgQWB84jvV6g/V1DZycxeAghhNBXkgYqtW3bn+57mDAkMXgIIYQQwqDEnocQQgh9JWkE6RTJWLo6bcbMQ3tEV80QQgj9dgppz8P7gctJ/VgmFU0UBiWWLUIIIfSVpBttry7pFtvvljQ3cJHtzUtnCzMnZh5CCCH02yv5/VOS3kXqvbJMuThhsGLPQwghhH47XtJCwIHA+cBbgOn19QizmFi2CCGEEMKgxMxDCCGEvpC0s+1fStp3oOdtH9HvTGFoYvAQQgihX+bP7xcomiL0LJYtQgghhDAoMfMQQgihLyT9ZHrP2/5yv7KE3sTgIYQQQr9MLB0gNCOWLUIIIYQwKDHzEEIIoa8kLQp8FViFKXtbRIXJlogKkyGEEPrtVOAOYFngW8C9wPUlA4XBiWWLEEIIfSVpou01O70t8rXLbW9SOluYObFsEUIIod86vS0elrQl8BCps2ZoiRg8hBBC6LfvSBoF7AccBYwE9ikbKQxGDB5CCCH0jaQ5gTG2LwSeBjYrHCkMQWyYDCGE0De2XwO2KZ0j9CY2TIYQQugrSd8FRgGnA891rtu+oVioMCgxeAghhNBXkiYMcNlR56E9YvAQQgihryQtZ/vuGV0Ls67Y8xBCCKHfzhrg2pl9TxGGLE5bhBBC6AtJKwNjgVGStut6aiRdZarDrC8GDyGEEPplJWArYEFg667rk4DdiyQKQxJ7HkIIIfSVpPVsX1M6Rxi62PMQQgih37aVNFLS3JIulfS4pJ1LhwozLwYPIYQQ+u19tp8hLWE8AKwIfKVspDAYMXgIIYTQb3Pn9x8CTrP9ZMkwYfBiw2QIIYR+u0DSncALwBckLQq8WDhTGITYMBlCCKHvJC0EPGP7NUnzASNtP1I6V5g5MfMQQgihryTt0vVx91Mn9z9NGIoYPIQQQui3tbs+HgFsAdxADB5aI5YtQgghFCVpFHCK7WjV3RJx2iKEEEJpzwNjSocIMy+WLUIIIfSVpAuAzrT3HMAqRGOsVollixBCCH0laZOuh68C99l+oFSeMHgxeAghhFCUpA2AT9jes3SWMHNi2SKEEELfSVoN+ASwA3APcE7ZRGEwYvAQQgihLyStCHwcGAc8AZxOmgHfrGiwMGixbBFCCKEvJL0OXAHsZvuufO1u28uVTRYGK45qhhBC6JftgUeACZJOkLQFoBl8TZgFxcxDCCGEvpI0P/AR0vLF5sBJwLm2Ly4aLMy0GDyEEEIoRtLCwMeAHW1vXjpPmDkxeAghhBDCoMSehxBCCCEMSgweQgghhDAoMXgIIcyQpKsH+fmbSrpwuPKEEMqKwUMIYYZsr186Qwhh1hGDhxDCDEl6Nr/fVNIfJZ0l6U5Jp0pSfu4D+dqVwHZdXzu/pBMlXS/pRkkfztf3lXRi/nhVSf8rab4CLy+EMEgxeAghDNbqwN6kNsrLARtIGgGcAGwNbAQs3vX5Xwcus702sBnww3zO/8fACpK2BcYDn7X9fP9eRghhqGLwEEIYrD/bfsD268BNwDLAysA9tv/udP77l12f/z5gf0k3AX8ERgCj89d/CjgFuNz2Vf17CSGEXkRjrBDCYL3U9fFrTP49Mq2iMQK2t/3XAZ4bAzwLLNFcvBDCcIuZhxBCE+4ElpW0fH48ruu5i4Avde2NWD2/HwUcCWwMvFXSR/uYN4TQgxg8hBB6ZvtFYA/gN3nD5H1dT38bmBu4RdL/5scAPwKOsf03YDfgUEmL9TF2CGGIojx1CCGEEAYlZh5CCCGEMCgxeAghhBDCoMTgIYQQQgiDEoOHEEIIIQxKDB5CCCGEMCgxeAghhBDCoMTgIYQQQgiDEoOHEEIIIQzK/wdFBngAGzL7EwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows the bar graph for each state\n",
    "title = 'Closeness Distribution in Australian cities using incoming routes for 31 Jan 2020'\n",
    "dfcloseness1.plot.bar(x='index', y=['Degrees'], figsize=(8, 4), title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar graph above shows that NSW, Victoria and Queensland have the top 3 highest closeness distribution showing they are the major hubs in Australia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Australia closes borders with China 1 Feb 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove flights from china\n",
    "incAusRoutes2 = incAusRoutesC.loc[incAusRoutesC['DepartCountry/Region'] != 'China']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20b4722609b14948816e9e53015c8571",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[-28, 138], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "incAusMapFlights2, incAusGraphFlights2 = renderMap(incAusRoutes2, incAusCities, '2/1/20', (-28,138), 2)\n",
    "incAusMapFlights2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Australian Capital Territory-Australia': 0.21212121212121213,\n",
       " 'New South Wales-Australia': 0.9393939393939394,\n",
       " 'Queensland-Australia': 0.7575757575757576,\n",
       " 'South Australia-Australia': 0.4545454545454546,\n",
       " 'Victoria-Australia': 0.8181818181818182,\n",
       " 'Western Australia-Australia': 0.5454545454545454,\n",
       " 'British Columbia-Canada': 0.09090909090909091,\n",
       " 'California-US': 0.09090909090909091,\n",
       " 'French Polynesia-France': 0.030303030303030304,\n",
       " 'Hawaii-US': 0.09090909090909091,\n",
       " 'New Caledonia-France': 0.09090909090909091,\n",
       " 'Northern Territory-Australia': 0.3939393939393939,\n",
       " 'Tasmania-Australia': 0.18181818181818182,\n",
       " 'Texas-US': 0.030303030303030304,\n",
       " 'Brunei': 0.030303030303030304,\n",
       " 'Chile': 0.030303030303030304,\n",
       " 'Fiji': 0.09090909090909091,\n",
       " 'India': 0.06060606060606061,\n",
       " 'Indonesia': 0.18181818181818182,\n",
       " 'Japan': 0.12121212121212122,\n",
       " 'Korea, South': 0.06060606060606061,\n",
       " 'Malaysia': 0.18181818181818182,\n",
       " 'Mauritius': 0.030303030303030304,\n",
       " 'New Zealand': 0.18181818181818182,\n",
       " 'Papua New Guinea': 0.06060606060606061,\n",
       " 'Philippines': 0.09090909090909091,\n",
       " 'Qatar': 0.12121212121212122,\n",
       " 'Singapore': 0.18181818181818182,\n",
       " 'South Africa': 0.06060606060606061,\n",
       " 'Taiwan*': 0.09090909090909091,\n",
       " 'Thailand': 0.12121212121212122,\n",
       " 'Timor-Leste': 0.030303030303030304,\n",
       " 'United Arab Emirates': 0.15151515151515152,\n",
       " 'Vietnam': 0.06060606060606061}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the degree distribution for the nodes\n",
    "degrees2 = nx.degree_centrality(incAusGraphFlights2)\n",
    "degrees2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.939394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.393939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index   Degrees\n",
       "0               New South Wales-Australia  0.939394\n",
       "1                      Victoria-Australia  0.818182\n",
       "2                    Queensland-Australia  0.757576\n",
       "3             Western Australia-Australia  0.545455\n",
       "4               South Australia-Australia  0.454545\n",
       "5            Northern Territory-Australia  0.393939\n",
       "6  Australian Capital Territory-Australia  0.212121\n",
       "7                      Tasmania-Australia  0.181818"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfdegrees2 = dictToDataframe(degrees2)\n",
    "dfdegrees2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.002018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.001862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.001857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.001456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.001395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.001336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.001272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.001165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index   Degrees\n",
       "0               New South Wales-Australia  0.002018\n",
       "1                    Queensland-Australia  0.001862\n",
       "2                      Victoria-Australia  0.001857\n",
       "3             Western Australia-Australia  0.001456\n",
       "4               South Australia-Australia  0.001395\n",
       "5            Northern Territory-Australia  0.001336\n",
       "6  Australian Capital Territory-Australia  0.001272\n",
       "7                      Tasmania-Australia  0.001165"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closeness2 = nx.closeness_centrality(incAusGraphFlights2, distance='weight')\n",
    "dfcloseness2 = dictToDataframe(closeness2)\n",
    "dfcloseness2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Australia closes borders with Iran and South Korea\n",
    "Iran: 1 March 2020, South Korea: 5 March 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove flights from Iran and South Korea\n",
    "incAusRoutes3 = incAusRoutes2.loc[incAusRoutesC['DepartCountry/Region'] != 'Iran']\n",
    "incAusRoutes3 = incAusRoutes3.loc[incAusRoutesC['DepartCountry/Region'] != 'Korea, South']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "597c3d442f504bae9e036ce0267bc924",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[-28, 138], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "incAusMapFlights3, incAusGraphFlights3 = renderMap(incAusRoutes3, incAusCities, '3/5/20', (-28,138), 2)\n",
    "incAusMapFlights3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Australian Capital Territory-Australia': 0.21875,\n",
       " 'New South Wales-Australia': 0.9375,\n",
       " 'Queensland-Australia': 0.75,\n",
       " 'South Australia-Australia': 0.46875,\n",
       " 'Victoria-Australia': 0.84375,\n",
       " 'Western Australia-Australia': 0.5625,\n",
       " 'British Columbia-Canada': 0.09375,\n",
       " 'California-US': 0.09375,\n",
       " 'French Polynesia-France': 0.03125,\n",
       " 'Hawaii-US': 0.09375,\n",
       " 'New Caledonia-France': 0.09375,\n",
       " 'Northern Territory-Australia': 0.40625,\n",
       " 'Tasmania-Australia': 0.1875,\n",
       " 'Texas-US': 0.03125,\n",
       " 'Brunei': 0.03125,\n",
       " 'Chile': 0.03125,\n",
       " 'Fiji': 0.09375,\n",
       " 'India': 0.0625,\n",
       " 'Indonesia': 0.1875,\n",
       " 'Japan': 0.125,\n",
       " 'Malaysia': 0.1875,\n",
       " 'Mauritius': 0.03125,\n",
       " 'New Zealand': 0.1875,\n",
       " 'Papua New Guinea': 0.0625,\n",
       " 'Philippines': 0.09375,\n",
       " 'Qatar': 0.125,\n",
       " 'Singapore': 0.1875,\n",
       " 'South Africa': 0.0625,\n",
       " 'Taiwan*': 0.09375,\n",
       " 'Thailand': 0.125,\n",
       " 'Timor-Leste': 0.03125,\n",
       " 'United Arab Emirates': 0.15625,\n",
       " 'Vietnam': 0.0625}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the degree distribution for the nodes\n",
    "degrees3 = nx.degree_centrality(incAusGraphFlights3)\n",
    "degrees3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.93750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.84375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.56250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.46875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.40625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.21875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.18750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index  Degrees\n",
       "0               New South Wales-Australia  0.93750\n",
       "1                      Victoria-Australia  0.84375\n",
       "2                    Queensland-Australia  0.75000\n",
       "3             Western Australia-Australia  0.56250\n",
       "4               South Australia-Australia  0.46875\n",
       "5            Northern Territory-Australia  0.40625\n",
       "6  Australian Capital Territory-Australia  0.21875\n",
       "7                      Tasmania-Australia  0.18750"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfdegrees3 = dictToDataframe(degrees3)\n",
    "dfdegrees3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.002014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.001884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.001854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.001471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.001407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.001344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.001274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.001168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index   Degrees\n",
       "0               New South Wales-Australia  0.002014\n",
       "1                      Victoria-Australia  0.001884\n",
       "2                    Queensland-Australia  0.001854\n",
       "3             Western Australia-Australia  0.001471\n",
       "4               South Australia-Australia  0.001407\n",
       "5            Northern Territory-Australia  0.001344\n",
       "6  Australian Capital Territory-Australia  0.001274\n",
       "7                      Tasmania-Australia  0.001168"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closeness3 = nx.closeness_centrality(incAusGraphFlights3, distance='weight')\n",
    "dfcloseness3 = dictToDataframe(closeness3)\n",
    "dfcloseness3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Australia closes all borders 20 March 2020\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove all international flights\n",
    "incAusRoutes4 = incAusRoutes3.loc[incAusRoutesC['DepartCountry/Region'] == 'Australia']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c92d186aba274ea88aa40eb8b9ec0e5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[-28, 138], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "incAusMapFlights4, incAusGraphFlights4 = renderMap(incAusRoutes4, incAusCities, '3/20/20', (-28,138), 2)\n",
    "incAusMapFlights4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Australian Capital Territory-Australia': 1.0,\n",
       " 'New South Wales-Australia': 1.2857142857142856,\n",
       " 'Queensland-Australia': 1.2857142857142856,\n",
       " 'South Australia-Australia': 1.1428571428571428,\n",
       " 'Victoria-Australia': 1.2857142857142856,\n",
       " 'Western Australia-Australia': 1.2857142857142856,\n",
       " 'Northern Territory-Australia': 1.0,\n",
       " 'Tasmania-Australia': 0.8571428571428571}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the degree distribution for the nodes\n",
    "degrees4 = nx.degree_centrality(incAusGraphFlights4)\n",
    "degrees4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>1.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index   Degrees\n",
       "0               New South Wales-Australia  1.285714\n",
       "1                    Queensland-Australia  1.285714\n",
       "2                      Victoria-Australia  1.285714\n",
       "3             Western Australia-Australia  1.285714\n",
       "4               South Australia-Australia  1.142857\n",
       "5  Australian Capital Territory-Australia  1.000000\n",
       "6            Northern Territory-Australia  1.000000\n",
       "7                      Tasmania-Australia  0.857143"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfdegrees4 = dictToDataframe(degrees4)\n",
    "dfdegrees4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.002612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.002533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.002487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.002219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.001999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.001841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.001777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.001623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index   Degrees\n",
       "0               New South Wales-Australia  0.002612\n",
       "1                    Queensland-Australia  0.002533\n",
       "2                      Victoria-Australia  0.002487\n",
       "3             Western Australia-Australia  0.002219\n",
       "4               South Australia-Australia  0.001999\n",
       "5  Australian Capital Territory-Australia  0.001841\n",
       "6            Northern Territory-Australia  0.001777\n",
       "7                      Tasmania-Australia  0.001623"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closeness4 = nx.closeness_centrality(incAusGraphFlights4, distance='weight')\n",
    "dfcloseness4 = dictToDataframe(closeness4)\n",
    "dfcloseness4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>31 Jan</th>\n",
       "      <th>1  Feb</th>\n",
       "      <th>5  Mar</th>\n",
       "      <th>20 Mar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.93750</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.84375</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.604167</td>\n",
       "      <td>0.757576</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.56250</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.46875</td>\n",
       "      <td>1.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.40625</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.21875</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index    31 Jan    1  Feb   5  Mar  \\\n",
       "0               New South Wales-Australia  0.937500  0.939394  0.93750   \n",
       "1                      Victoria-Australia  0.791667  0.818182  0.84375   \n",
       "2                    Queensland-Australia  0.604167  0.757576  0.75000   \n",
       "3             Western Australia-Australia  0.416667  0.545455  0.56250   \n",
       "4               South Australia-Australia  0.354167  0.454545  0.46875   \n",
       "5            Northern Territory-Australia  0.270833  0.393939  0.40625   \n",
       "6  Australian Capital Territory-Australia  0.145833  0.212121  0.21875   \n",
       "7                      Tasmania-Australia  0.125000  0.181818  0.18750   \n",
       "\n",
       "     20 Mar  \n",
       "0  1.285714  \n",
       "1  1.285714  \n",
       "2  1.285714  \n",
       "3  1.285714  \n",
       "4  1.142857  \n",
       "5  1.000000  \n",
       "6  1.000000  \n",
       "7  0.857143  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the confirmed cases\n",
    "dfdegrees1 = dfdegrees1.rename(columns={'Degrees': \"31 Jan\"})\n",
    "dfdegrees2 = dfdegrees2.rename(columns={'Degrees': \"1  Feb\"})\n",
    "dfdegrees3 = dfdegrees3.rename(columns={'Degrees': \"5  Mar\"})\n",
    "dfdegrees4 = dfdegrees4.rename(columns={'Degrees': \"20 Mar\"})\n",
    "\n",
    "# Combination of all the graphs\n",
    "dfdegreesAll = pd.merge(dfdegrees1, dfdegrees2, on='index')\n",
    "dfdegreesAll = pd.merge(dfdegreesAll, dfdegrees3, on='index')\n",
    "dfdegreesAll = pd.merge(dfdegreesAll, dfdegrees4, on='index')\n",
    "dfdegreesAll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df22554388>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows the bar graph for each state\n",
    "title = 'Combined degree distribution bar graph'\n",
    "dfdegreesAll.plot.bar(x='index', y=['31 Jan', '1  Feb', '5  Mar', '20 Mar'], figsize=(16, 8), title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected as nodes are removed from the graph, nsw is still the most connected but the other states become more connected as there are less nodes in the graph.\n",
    "\n",
    "As seen in the above graph, the bar graph in NSW is the highest and also changes the slowest as more borders are closed which shows that NSW is the main hub in Australia with Victoria as the second. These hubs mean its suspected to have higher infection rates which is true as seen in the below graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>31 Jan</th>\n",
       "      <th>1  Feb</th>\n",
       "      <th>5  Mar</th>\n",
       "      <th>20 Mar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>New South Wales-Australia</td>\n",
       "      <td>0.002038</td>\n",
       "      <td>0.002018</td>\n",
       "      <td>0.002014</td>\n",
       "      <td>0.002612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Victoria-Australia</td>\n",
       "      <td>0.001842</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.001884</td>\n",
       "      <td>0.002487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Queensland-Australia</td>\n",
       "      <td>0.001776</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>0.001854</td>\n",
       "      <td>0.002533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Australia-Australia</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>0.002219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>South Australia-Australia</td>\n",
       "      <td>0.001329</td>\n",
       "      <td>0.001395</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.001999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Territory-Australia</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.001777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Australian Capital Territory-Australia</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.001272</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.001841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tasmania-Australia</td>\n",
       "      <td>0.001151</td>\n",
       "      <td>0.001165</td>\n",
       "      <td>0.001168</td>\n",
       "      <td>0.001623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index    31 Jan    1  Feb    5  Mar  \\\n",
       "0               New South Wales-Australia  0.002038  0.002018  0.002014   \n",
       "1                      Victoria-Australia  0.001842  0.001857  0.001884   \n",
       "2                    Queensland-Australia  0.001776  0.001862  0.001854   \n",
       "3             Western Australia-Australia  0.001358  0.001456  0.001471   \n",
       "4               South Australia-Australia  0.001329  0.001395  0.001407   \n",
       "5            Northern Territory-Australia  0.001250  0.001336  0.001344   \n",
       "6  Australian Capital Territory-Australia  0.001249  0.001272  0.001274   \n",
       "7                      Tasmania-Australia  0.001151  0.001165  0.001168   \n",
       "\n",
       "     20 Mar  \n",
       "0  0.002612  \n",
       "1  0.002487  \n",
       "2  0.002533  \n",
       "3  0.002219  \n",
       "4  0.001999  \n",
       "5  0.001777  \n",
       "6  0.001841  \n",
       "7  0.001623  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the confirmed cases\n",
    "dfcloseness1 = dfcloseness1.rename(columns={'Degrees': \"31 Jan\"})\n",
    "dfcloseness2 = dfcloseness2.rename(columns={'Degrees': \"1  Feb\"})\n",
    "dfcloseness3 = dfcloseness3.rename(columns={'Degrees': \"5  Mar\"})\n",
    "dfcloseness4 = dfcloseness4.rename(columns={'Degrees': \"20 Mar\"})\n",
    "\n",
    "# Combination of all the graphs\n",
    "dfclosenessAll = pd.merge(dfcloseness1, dfcloseness2, on='index')\n",
    "dfclosenessAll = pd.merge(dfclosenessAll, dfcloseness3, on='index')\n",
    "dfclosenessAll = pd.merge(dfclosenessAll, dfcloseness4, on='index')\n",
    "dfclosenessAll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df22c109c8>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows the bar graph for each state\n",
    "title = 'Combined closeness distribution bar graph'\n",
    "dfclosenessAll.plot.bar(x='index', y=['31 Jan', '1  Feb', '5  Mar', '20 Mar'], figsize=(16, 8), title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df22cc3608>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confirm cases in comparison\n",
    "ausCC = ausCC.sort_values(by='CC', ascending=False)\n",
    "ausCC.plot.bar(x='Province/State', y='CC', figsize=(18,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# International centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_cases1, allRoutes = covid_data.getData(bin_region_column='state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DepartProvince/State</th>\n",
       "      <th>DepartCountry/Region</th>\n",
       "      <th>ArrivalProvince/State</th>\n",
       "      <th>ArrivalCountry/Region</th>\n",
       "      <th>NumberOfRoutes</th>\n",
       "      <th>Depart</th>\n",
       "      <th>Arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>2</td>\n",
       "      <td>Alabama-US</td>\n",
       "      <td>Alabama-US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama-US</td>\n",
       "      <td>Arizona-US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama-US</td>\n",
       "      <td>Arkansas-US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama-US</td>\n",
       "      <td>California-US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>US</td>\n",
       "      <td>2</td>\n",
       "      <td>Alabama-US</td>\n",
       "      <td>Colorado-US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9979</td>\n",
       "      <td>none</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>none</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>2</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Kenya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9980</td>\n",
       "      <td>none</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>none</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>32</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9981</td>\n",
       "      <td>none</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>none</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Tanzania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9982</td>\n",
       "      <td>none</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>none</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>5</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9983</td>\n",
       "      <td>none</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>none</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9984 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     DepartProvince/State DepartCountry/Region ArrivalProvince/State  \\\n",
       "0                 Alabama                   US               Alabama   \n",
       "1                 Alabama                   US               Arizona   \n",
       "2                 Alabama                   US              Arkansas   \n",
       "3                 Alabama                   US            California   \n",
       "4                 Alabama                   US              Colorado   \n",
       "...                   ...                  ...                   ...   \n",
       "9979                 none             Zimbabwe                  none   \n",
       "9980                 none             Zimbabwe                  none   \n",
       "9981                 none             Zimbabwe                  none   \n",
       "9982                 none             Zimbabwe                  none   \n",
       "9983                 none             Zimbabwe                  none   \n",
       "\n",
       "     ArrivalCountry/Region  NumberOfRoutes      Depart        Arrival  \n",
       "0                       US               2  Alabama-US     Alabama-US  \n",
       "1                       US               1  Alabama-US     Arizona-US  \n",
       "2                       US               1  Alabama-US    Arkansas-US  \n",
       "3                       US               1  Alabama-US  California-US  \n",
       "4                       US               2  Alabama-US    Colorado-US  \n",
       "...                    ...             ...         ...            ...  \n",
       "9979                 Kenya               2    Zimbabwe          Kenya  \n",
       "9980          South Africa              32    Zimbabwe   South Africa  \n",
       "9981              Tanzania               1    Zimbabwe       Tanzania  \n",
       "9982                Zambia               5    Zimbabwe         Zambia  \n",
       "9983              Zimbabwe              14    Zimbabwe       Zimbabwe  \n",
       "\n",
       "[9984 rows x 7 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will combine the Country and the state\n",
    "allRoutesC = allRoutes.copy()\n",
    "allRoutesC['Depart'] = allRoutes['DepartProvince/State'] + '-' + allRoutes['DepartCountry/Region']\n",
    "allRoutesC['Arrival'] = allRoutes['ArrivalProvince/State'] + '-' + allRoutes['ArrivalCountry/Region']\n",
    "\n",
    "# removes the unwanted 'none' strings when entries don't have states/provinces\n",
    "allRoutesC['Depart'] = allRoutesC['Depart'].map(lambda x: x.lstrip('-none'))\n",
    "allRoutesC['Arrival'] = allRoutesC['Arrival'].map(lambda x: x.lstrip('-none'))\n",
    "\n",
    "allRoutesC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GlobalMapFlights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renders the global map - estimated 10 minutes execution time\n",
    "GlobalMapFlights, GlobalGraphFlights = renderMap(allRoutesC, covid_cases1['confirmed'], \n",
    "                                                 latest_date, (0,0), 1, showOpacity=True, scale=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DepartProvince/State</th>\n",
       "      <th>DepartCountry/Region</th>\n",
       "      <th>ArrivalProvince/State</th>\n",
       "      <th>ArrivalCountry/Region</th>\n",
       "      <th>NumberOfRoutes</th>\n",
       "      <th>Depart</th>\n",
       "      <th>Arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9857</td>\n",
       "      <td>none</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>none</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1412</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6295</td>\n",
       "      <td>none</td>\n",
       "      <td>Germany</td>\n",
       "      <td>none</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1342</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9008</td>\n",
       "      <td>none</td>\n",
       "      <td>Spain</td>\n",
       "      <td>none</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1242</td>\n",
       "      <td>Spain</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8958</td>\n",
       "      <td>none</td>\n",
       "      <td>Spain</td>\n",
       "      <td>none</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1136</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6304</td>\n",
       "      <td>none</td>\n",
       "      <td>Germany</td>\n",
       "      <td>none</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>683</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2367</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>US</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>Mississippi-US</td>\n",
       "      <td>Missouri-US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2363</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>US</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>Mississippi-US</td>\n",
       "      <td>Kentucky-US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2362</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>US</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>Mississippi-US</td>\n",
       "      <td>Indiana-US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2358</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>US</td>\n",
       "      <td>Curacao</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1</td>\n",
       "      <td>Mississippi-US</td>\n",
       "      <td>Curacao-Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama-US</td>\n",
       "      <td>Arizona-US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9766 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     DepartProvince/State DepartCountry/Region ArrivalProvince/State  \\\n",
       "9857                 none       United Kingdom                  none   \n",
       "6295                 none              Germany                  none   \n",
       "9008                 none                Spain                  none   \n",
       "8958                 none                Spain                  none   \n",
       "6304                 none              Germany                  none   \n",
       "...                   ...                  ...                   ...   \n",
       "2367          Mississippi                   US              Missouri   \n",
       "2363          Mississippi                   US              Kentucky   \n",
       "2362          Mississippi                   US               Indiana   \n",
       "2358          Mississippi                   US               Curacao   \n",
       "1                 Alabama                   US               Arizona   \n",
       "\n",
       "     ArrivalCountry/Region  NumberOfRoutes          Depart  \\\n",
       "9857                 Spain            1412  United Kingdom   \n",
       "6295                 Spain            1342         Germany   \n",
       "9008        United Kingdom            1242           Spain   \n",
       "8958               Germany            1136           Spain   \n",
       "6304                Turkey             683         Germany   \n",
       "...                    ...             ...             ...   \n",
       "2367                    US               1  Mississippi-US   \n",
       "2363                    US               1  Mississippi-US   \n",
       "2362                    US               1  Mississippi-US   \n",
       "2358           Netherlands               1  Mississippi-US   \n",
       "1                       US               1      Alabama-US   \n",
       "\n",
       "                  Arrival  \n",
       "9857                Spain  \n",
       "6295                Spain  \n",
       "9008       United Kingdom  \n",
       "8958              Germany  \n",
       "6304               Turkey  \n",
       "...                   ...  \n",
       "2367          Missouri-US  \n",
       "2363          Kentucky-US  \n",
       "2362           Indiana-US  \n",
       "2358  Curacao-Netherlands  \n",
       "1              Arizona-US  \n",
       "\n",
       "[9766 rows x 7 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allRoutesC = allRoutesC.loc[allRoutesC['Depart'] != allRoutesC['Arrival']]\n",
    "allRoutesC.sort_values(by='NumberOfRoutes', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converts results from above into a pandas dataframe\n",
    "def dictToDataframeGlobal(degrees):\n",
    "    dfdegrees = pd.DataFrame.from_dict(degrees, orient='index', columns=['Degrees'])\n",
    "    dfdegrees.reset_index(level=0, inplace=True) # key indexs to seperate column\n",
    "    dfdegrees = dfdegrees.sort_values(by='Degrees', ascending=False) # sort by degrees\n",
    "    dfdegrees = dfdegrees.reset_index(drop=True) # fix indexing\n",
    "    return dfdegrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.000152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0.000149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>France</td>\n",
       "      <td>0.000148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>New York-US</td>\n",
       "      <td>0.000146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0.000142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>0.000140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.000140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Florida-US</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Italy</td>\n",
       "      <td>0.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>California-US</td>\n",
       "      <td>0.000136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Beijing-China</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Ontario-Canada</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Texas-US</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Illinois-US</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Russia</td>\n",
       "      <td>0.000131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>New Jersey-US</td>\n",
       "      <td>0.000131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Georgia-US</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   index   Degrees\n",
       "0         United Kingdom  0.000152\n",
       "1                Germany  0.000149\n",
       "2                 France  0.000148\n",
       "3            New York-US  0.000146\n",
       "4                  Spain  0.000142\n",
       "5                 Turkey  0.000140\n",
       "6            Netherlands  0.000140\n",
       "7             Florida-US  0.000139\n",
       "8   United Arab Emirates  0.000138\n",
       "9                  Italy  0.000138\n",
       "10         California-US  0.000136\n",
       "11         Beijing-China  0.000134\n",
       "12        Ontario-Canada  0.000134\n",
       "13              Texas-US  0.000134\n",
       "14           Illinois-US  0.000133\n",
       "15                Russia  0.000131\n",
       "16         New Jersey-US  0.000131\n",
       "17                 Qatar  0.000130\n",
       "18            Georgia-US  0.000130\n",
       "19           Switzerland  0.000130"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate closeness centrality\n",
    "globalcloseness = nx.closeness_centrality(GlobalGraphFlights, distance='weight')\n",
    "globalcloseness = dictToDataframeGlobal(globalcloseness).head(20)\n",
    "globalcloseness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Closeness Centrality\n",
    "The closeness shows how close a node is to the rest of the network. This will show the major flight hubs in the network which are more likely to get infected early on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df2a1b3308>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aJPnwhz+cBz3oQXnQgx6UBz/4wbn22mt3aE7OqAAAAIAl7Cd/8idzyy235PLLL8/ZZ5+dPfbYI+eff35uuOGGHHzwwXnMYx6TT33qU7n00kvz7//+77n88stzv/vdL89+9rO/W2P58uX5yEc+kiQ59NBD87rXvS6rV6/Oeeedl+c///k599xz85u/+Zv57d/+7RxyyCH5yle+ksMOOyyf/exn86pXvSqnnHJKDj744Fx33XVZvnz5Ds1HUAEAAABLXHcnSd73vvflggsu+O7ZEtdcc02+8IUv5CMf+UiOOuqo3OEOd8jd7373PPKRj/y+7Z/61KcmSa677rp87GMfy1FHHfXddTfccEOS5P3vf38uuuii77Z/85vfzLXXXpuDDz44xx13XJ7+9KfnSU96UvbZZ58dmougAgAAAJawSy65JMuWLcvd7na3dHf+4i/+Iocddtj39Xn3u7d+08873elOSSaXiOy5557fvQfGTLfccks+/vGPZ/fdd/++9uOPPz6Pe9zjcs455+ThD3943v/+9+e+973vds/HPSoAAABgidq0aVOe97zn5dhjj01V5bDDDstf/uVf5qabbkqSfP7zn8+3vvWtHHLIIXnnO9+ZW265JV//+tfzoQ99aM56d73rXbNq1aq84x3vSDI5U+Mzn/lMkuQxj3lMXvva136375Yw44tf/GIe8IAH5EUvelEOPPDA797TYns5owIAAAC20zQfJ3pr+853vpMHPehB3/140mc+85k57rjjkiTPec5zcumll+YhD3lIujsrVqzIu971rjz5yU/OBz7wgdz//vfPfe5znzzsYQ/LHnvsMWf9t73tbfn1X//1vPKVr8xNN92UtWvX5oEPfGBOPvnkvOAFL8gBBxyQzZs352d/9mfzute9Lq95zWvywQ9+MMuWLcuaNWtyxBFH7ND8ast1LDujAw88sNevX7/YwwAAAGAn8dnPfjb3u9/9FnsY2+W6667Lne9851x55ZV56EMfmo9+9KO5+93vfrvse67nrao+1d0Hzu7rjIpZVh6/9et2bg2LkbgBAADww+3xj398rr766tx444156UtferuFFNtKUAEAAAA/BOa7L8XYuJkmAAAAbIOd+RYKt4Vtfb4EFQAAADCl5cuX58orrxRWTKm7c+WVV2b58uVTb+PSDwAAAJjSPvvsk40bN2bTpk2LPZQlY/ny5dlnn32m7i+o2Em5KSgAAMCtb9ddd82qVasWexg7NZd+AAAAAKMhqAAAAABGQ1ABAAAAjIZ7VDBq7rUBAADww8UZFQAAAMBoCCoAAACA0RBUAAAAAKPhHhVwO7g97rWRuN8GAACw9DmjAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjMYuiz0AYGlZefy7b/N9XPrHj7vN9wEAAIyTMyoAAACA0ZgqqKiqw6vq4qraUFXHz7H+jlX19mH9eVW1csa6Fw/tF1fVYQvVrKpjh7auqr3n2NdBVXVzVT1lWycLAAAAjNuCQUVVLUtySpIjkqxJ8rSqWjOr2zFJruru/ZK8OslJw7ZrkqxNsn+Sw5OcWlXLFqj50SSPTvLlecZyUpL3buM8AQAAgCVgmntUPDTJhu6+JEmq6owkRya5aEafI5O8fFg+K8lrq6qG9jO6+4YkX6qqDUO9zFezuz89tM01lt9I8s4kB007QYC5uNcGAACM0zRBxT2TfHXG441JHjZfn+7eXFXXJNlraP/ErG3vOSwvVPP7VNU9k/zPJI/KVoKKqnpukucmyb3uda+tlQRY8gQuAADsbKa5R8Vcpzb0lH22tX1rXpPkRd1989Y6dfdp3X1gdx+4YsWKBUoCAAAAYzLNGRUbk+w74/E+SS6bp8/GqtolyR5JvrHAtgvVnO3AJGcMl4TsneSxVbW5u981xRwAGDFnhgAAsMU0Z1Scn2R1Va2qqt0yuTnmull91iU5elh+SpJzu7uH9rXDp4KsSrI6ySenrPl9untVd6/s7pWZ3Afj+UIKAAAA2LksGFR09+Ykx2bySRufTXJmd19YVSdU1ROGbqcn2Wu4WeZxSY4ftr0wyZmZ3Hjzn5K8oLtvnq9mklTVC6tqYyZnWVxQVW+49aYLAAAAjNk0l36ku89Jcs6stpfNWL4+yVHzbHtikhOnqTm0n5zk5AXG86xpxg0AtzeXsQAA7JipggoA4IeLwAUAWCzT3KMCAAAA4HYhqAAAAABGw6UfAMBOyyUsALD0OKMCAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGi4mSYAwMi5KSgAP0wEFQAA3C5uj8AluX1CF+ERwG3HpR8AAADAaAgqAAAAgNFw6QcAAPyQcgkLMEbOqAAAAABGQ1ABAAAAjIZLPwAAgCVtZ7mEZWeZB+woZ1QAAAAAo+GMCgAAAG41t8eZIYmzQ3ZmggoAAACYw85yOc5Sm4dLPwAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMxlRBRVUdXlUXV9WGqjp+jvV3rKq3D+vPq6qVM9a9eGi/uKoOW6hmVR07tHVV7T2j/elVdcHw9bGqeuD2ThoAAAAYpwWDiqpaluSUJEckWZPkaVW1Zla3Y5Jc1d37JXl1kpOGbdckWZtk/ySHJzm1qpYtUPOjSR6d5Muz9vGlJI/o7gOS/FGS07ZxrgAAAMDITXNGxUOTbOjuS7r7xiRnJDlyVp8jk7x5WD4ryaFVVUP7Gd19Q3d/KcmGod68Nbv709196exBdPfHuvuq4eEnkuyzDfMEAAAAloBpgop7JvnqjMcbh7Y5+3T35iTXJNlrK9tOU3NrjknynrlWVNVzq2p9Va3ftGnTNpQEAAAAFts0QUXN0dZT9tnW9oUHU/XITIKKF821vrtP6+4Du/vAFStWTFMSAAAAGIldpuizMcm+Mx7vk+SyefpsrKpdkuyR5BsLbLtQzR9QVQckeUOSI7r7yinGDgAAACwh05xRcX6S1VW1qqp2y+TmmOtm9VmX5Ohh+SlJzu3uHtrXDp8KsirJ6iSfnLLm96mqeyX5uyTP7O7PTzc9AAAAYClZ8IyK7t5cVccmeW+SZUne2N0XVtUJSdZ397okpyd5S1VtyORMirXDthdW1ZlJLkqyOckLuvvmZPIxpLNrDu0vTPJ7Se6e5IKqOqe7n5PkZZnc9+LUyX06s7m7D7y1nggAAABg8U1z6Ue6+5wk58xqe9mM5euTHDXPticmOXGamkP7yUlOnqP9OUmeM814AQAAgKVpmks/AAAAAG4XggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMxlRBRVUdXlUXV9WGqjp+jvV3rKq3D+vPq6qVM9a9eGi/uKoOW6hmVR07tHVV7T2jvarq5GHdBVX1kO2dNAAAADBOCwYVVbUsySlJjkiyJsnTqmrNrG7HJLmqu/dL8uokJw3brkmyNsn+SQ5PcmpVLVug5keTPDrJl2ft44gkq4ev5yb5y22bKgAAADB205xR8dAkG7r7ku6+MckZSY6c1efIJG8els9KcmhV1dB+Rnff0N1fSrJhqDdvze7+dHdfOsc4jkzy1z3xiSR7VtVPbMtkAQAAgHGbJqi4Z5Kvzni8cWibs093b05yTZK9trLtNDW3ZxypqudW1fqqWr9p06YFSgIAAABjMk1QUXO09ZR9trV9R8eR7j6tuw/s7gNXrFixQEkAAABgTKYJKjYm2XfG432SXDZfn6raJckeSb6xlW2nqbk94wAAAACWsGmCivOTrK6qVVW1WyY3x1w3q8+6JEcPy09Jcm5399C+dvhUkFWZ3Ajzk1PWnG1dkl8ePv3j4Umu6e6vTTF+AAAAYInYZaEO3b25qo5N8t4ky5K8sbsvrKoTkqzv7nVJTk/ylqrakMmZFGuHbS+sqjOTXJRkc5IXdPfNyeRjSGfXHNpfmOT3ktw9yQVVdU53PyfJOUkem8kNOb+d5FdurScBAAAAGIcFg4ok6e5zMgkKZra9bMby9UmOmmfbE5OcOE3Nof3kJCfP0d5JXjDNeAEAAIClaZpLPwAAAABuF4IKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGY6qgoqoOr6qLq2pDVR0/x/o7VtXbh/XnVdXKGetePLRfXFWHLVSzqlYNNb4w1NxtaL9XVX2wqj5dVRdU1WN3ZOIAAADA+CwYVFTVsiSnJDkiyZokT6uqNbO6HZPkqu7eL8mrk5w0bLsmydok+yc5PMmpVbVsgZonJXl1d69OctVQO0lekuTM7n7wUPPU7ZsyAAAAMFbTnFHx0CQbuvuS7r4xyRlJjpzV58gkbx6Wz0pyaFXV0H5Gd9/Q3V9KsmGoN2fNYZtHDTUy1HzisNxJ7jos75Hksm2bKgAAADB20wQV90zy1RmPNw5tc/bp7s1Jrkmy11a2na99ryRXDzVm7+vlSZ5RVRuTnJPkN6YYOwAAALCETBNU1BxtPWWfW6s9SZ6W5K+6e58kj03ylqr6gfFX1XOran1Vrd+0adMc5QAAAICxmiao2Jhk3xmP98kPXnbx3T5VtUsml2Z8Yyvbztd+RZI9hxqz93VMkjOTpLs/nmR5kr1nD7a7T+vuA7v7wBUrVkwxPQAAAGAspgkqzk+yevg0jt0yuZHlull91iU5elh+SpJzu7uH9rXDp4KsSrI6ySfnqzls88GhRoaaZw/LX0lyaJJU1f0yCSqcMgEAAAA7kV0W6tDdm6vq2CTvTbIsyRu7+8KqOiHJ+u5el+T0TC7F2JDJmRRrh20vrKozk1yUZHOSF3T3zUkyV81hly9KckZVvTLJp4faSfI7Sf5vVf12JpeDPGsINgAAAICdxIJBRZJ09zmZ3MByZtvLZixfn+SoebY9McmJ09Qc2i/J5FNBZrdflOTgacYLAAAALE3TXPoBAAAAcLsQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaAgqAAAAgNEQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaAgqAAAAgNEQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaAgqAAAAgNEQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaAgqAAAAgNEQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0pgoqqurwqrq4qjZU1fFzrL9jVb19WEj2prUAACAASURBVH9eVa2cse7FQ/vFVXXYQjWratVQ4wtDzd1mrPvFqrqoqi6sqr/Z3kkDAAAA47RgUFFVy5KckuSIJGuSPK2q1szqdkySq7p7vySvTnLSsO2aJGuT7J/k8CSnVtWyBWqelOTV3b06yVVD7VTV6iQvTnJwd++f5Le2e9YAAADAKE1zRsVDk2zo7ku6+8YkZyQ5clafI5O8eVg+K8mhVVVD+xndfUN3fynJhqHenDWHbR411MhQ84nD8q8mOaW7r0qS7r5826cLAAAAjNk0QcU9k3x1xuONQ9ucfbp7c5Jrkuy1lW3na98rydVDjdn7uk+S+1TVR6vqE1V1+FyDrarnVtX6qlq/adOmKaYHAAAAjMU0QUXN0dZT9rm12pNklySrk/xckqcleUNV7fkDnbtP6+4Du/vAFStWzFEOAAAAGKtpgoqNSfad8XifJJfN16eqdkmyR5JvbGXb+dqvSLLnUGP2vjYmObu7bxouI7k4k+ACAAAA2ElME1Scn2T18Gkcu2Vyc8x1s/qsS3L0sPyUJOd2dw/ta4dPBVmVSbDwyflqDtt8cKiRoebZw/K7kjwySapq70wuBblkWycMAAAAjNcuC3Xo7s1VdWyS9yZZluSN3X1hVZ2QZH13r0tyepK3VNWGTM6kWDtse2FVnZnkoiSbk7ygu29OkrlqDrt8UZIzquqVST491M7Q9zFVdVGSm5P8bndfueNPAQAAADAWCwYVSdLd5yQ5Z1bby2YsX5/kqHm2PTHJidPUHNovyeRTQWa3d5Ljhi8AAABgJzTNpR8AAAAAtwtBBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZDUAEAAACMhqACAAAAGA1BBQAAADAaggoAAABgNAQVAAAAwGgIKgAAAIDREFQAAAAAoyGoAAAAAEZjqqCiqg6vqourakNVHT/H+jtW1duH9edV1coZ6148tF9cVYctVLOqVg01vjDU3G3Wvp5SVV1VB27PhAEAAIDxWjCoqKplSU5JckSSNUmeVlVrZnU7JslV3b1fklcnOWnYdk2StUn2T3J4klOratkCNU9K8uruXp3kqqH2lrHcJckLk5y3fdMFAAAAxmyaMyoemmRDd1/S3TcmOSPJkbP6HJnkzcPyWUkOraoa2s/o7hu6+0tJNgz15qw5bPOooUaGmk+csZ8/SvInSa7fxnkCAAAAS8A0QcU9k3x1xuONQ9ucfbp7c5Jrkuy1lW3na98rydVDje/bV1U9OMm+3f2PWxtsVT23qtZX1fpNmzZNMT0AAABgLKYJKmqOtp6yz63SXlV3yOSSkt/ZyjgnnbtP6+4Du/vAFStWLNQdAAAAGJFpgoqNSfad8XifJJfN16eqdkmyR5JvbGXb+dqvSLLnUGNm+12S3D/Jh6rq0iQPT7LODTUBAABg5zJNUHF+ktXDp3HslsnNMdfN6rMuydHD8lOSnNvdPbSvHT4VZFWS1Uk+OV/NYZsPDjUy1Dy7u6/p7r27e2V3r0zyiSRP6O712zlvAAAAYIR2WahDd2+uqmOTvDfJsiRv7O4Lq+qEJOu7e12S05O8pao2ZHImxdph2wur6swkFyXZnOQF3X1zksxVc9jli5KcUVWvTPLpoTYAAADwQ2DBoCJJuvucJOfManvZjOXrkxw1z7YnJjlxmppD+yWZfCrI1sbzc9OMGwAAAFhaprn0AwAAAOB2IagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaAgqAAAAgNEQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaAgqAAAAgNEQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaAgqAAAAgNEQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaAgqAAAAgNEQVAAAAACjIagAAAAARkNQAQAAAIyGoAIAAAAYDUEFAAAAMBqCCgAAAGA0BBUAAADAaEwVVFTV4VV1cVVtqKrj51h/x6p6+7D+vKpaOWPdi4f2i6vqsIVqVtWqocYXhpq7De3HVdVFVXVBVX2gqu69IxMHAAAAxmfBoKKqliU5JckRSdYkeVpVrZnV7ZgkV3X3fkleneSkYds1SdYm2T/J4UlOraplC9Q8Kcmru3t1kquG2kny6SQHdvcBSc5K8ifbN2UAAABgrKY5o+KhSTZ09yXdfWOSM5IcOavPkUnePCyfleTQqqqh/YzuvqG7v5Rkw1BvzprDNo8aamSo+cQk6e4Pdve3h/ZPJNln26cLAAAAjNk0QcU9k3x1xuONQ9ucfbp7c5Jrkuy1lW3na98rydVDjfn2lUzOsnjPXIOtqudW1fqqWr9p06YFJwcAAACMxzRBRc3R1lP2ubXav7ejqmckOTDJn87RN919Wncf2N0HrlixYq4uAAAAwEjtMkWfjUn2nfF4nySXzdNnY1XtkmSPJN9YYNu52q9IsmdV7TKcVfF9+6qqRyf5gySP6O4bphg7AAAAsIRMc0bF+UlWD5/GsVsmN8dcN6vPuiRHD8tPSXJud/fQvnb4VJBVSVYn+eR8NYdtPjjUyFDz7CSpqgcneX2SJ3T35ds3XQAAAGDMFjyjors3V9WxSd6bZFmSN3b3hVV1QpL13b0uyelJ3lJVGzI5k2LtsO2FVXVmkouSbE7ygu6+OUnmqjns8kVJzqiqV2bySR+nD+1/muTOSd4xuedmvtLdT9jhZwAAAAAYjWku/Uh3n5PknFltL5uxfH2So+bZ9sQkJ05Tc2i/JJNPBZnd/uhpxgoAAAAsXdNc+gEAAABwuxBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDQEFQAAAMBoCCoAAACA0RBUAAAAAKMhqAAAAABGQ1ABAAAAjIagAgAAABgNQQUAAAAwGoIKAAAAYDSmCiqq6vCquriqNlTV8XOsv2NVvX1Yf15VrZyx7sVD+8VVddhCNatq1VDjC0PN3RbaBwAAALBzWDCoqKplSU5JckSSNUmeVlVrZnU7JslV3b1fklcnOWnYdk2StUn2T3J4klOratkCNU9K8uruXp3kqqH2vPsAAAAAdh7TnFHx0CQbuvuS7r4xyRlJjpzV58gkbx6Wz0pyaFXV0H5Gd9/Q3V9KsmGoN2fNYZtHDTUy1HziAvsAAAAAdhLV3VvvUPWUJId393OGx89M8rDuPnZGn/8Y+mwcHn8xycOSvDzJJ7r7rUP76UneM2z2AzVn9N9vaN83yXu6+/7z7aO7r5g13ucmee7w8KeTXLytT8o22jvJFQv2Whp2lrmYx7iYx/jsLHMxj3HZWeaR7DxzMY9xMY/x2VnmYh7jYh7b5t7dvWJ24y5TbDjXWQuz0435+szXPteZHFvrP+040t2nJTltjr63iapa390H3l77uy3tLHMxj3Exj/HZWeZiHuOys8wj2XnmYh7jYh7js7PMxTzGxTxuHdNc+rExyb4zHu+T5LL5+lTVLkn2SPKNrWw7X/sVSfYcasze13z7AAAAAHYS0wQV5ydZPXwax26Z3Bxz3aw+65IcPSw/Jcm5PbmmZF2StcMndqxKsjrJJ+erOWzzwaFGhppnL7APAAAAYCex4KUf3b25qo5N8t4ky5K8sbsvrKoTkqzv7nVJTk/ylqrakMlZDmuHbS+sqjOTXJRkc5IXdPfNSTJXzWGXL0pyRlW9Msmnh9qZbx8jcLtdZnI72FnmYh7jYh7js7PMxTzGZWeZR7LzzMU8xsU8xmdnmYt5jIt53AoWvJkmAAAAwO1lmks/AAAAAG4XggoAAABgNAQVAABwG6qquyz2GACWEkHFNqqqZVX1hKp6YVUdt+VrscfF0lVVy6tq2YzH+1XVb1TVExZzXD/squq8qvq1qrrrYo+FnUtV3amq7jAs32f4P2XXxR7X9qiq+y/2GGBsquoPquq+w/JuVfXPSTZW1der6lGLPDyWsKr61apaPSxXVb2pqr5ZVRdU1UMWe3xMVNWPVlUt9jiWOkHFtvuHJM9KsleSu8z4WnKq6lVVtf9ij+PWUlWHVNWvDMsrho/EXQrem+SnkqSqfiqTj/Bdk+S4qjpxMQf2Q+7oJD+Z5N+q6q1VdehiD2hHVNXBVXWnYfkZVfVnVXXvxR7XtKrqF2aOt6peVlWfqap1S+hnfYt/SbK8qu6Z5ANJfiXJXy3qiLbf66rqk1X1/Krac7EHs62q6t5VtceMx4+sqj8f3oTYbTHHtr2G//9eVVXnVNW5W74We1zboqpWV9VZVXVRVV2y5Wuxx7UNfinJxcPyLyfZLZO/Gx+V5P8s1qC2R1UdVFV3n/H4l6vq7Ko6uap+bDHHtr2W+OvrN5NcOiw/LckBSVYlOS7Jny/SmLZbVd2hqv5jscexI4a/R7YEk3esqg8m+WKSr1fVoxd3dNOrqidt7WsxxiSo2Hb7dPeTuvsPu/sVW74We1Db6XNJThveOX7ezD/Wlpqq+sNMPtr2xUPTrkneungj2iY/1t2fH5aPTnJGd/96ksOSLMmzKoZfal+oqmuGpP/aqvrmYo9rW3T357r7RUlWJ3lnkr+uqi9V1UuX4gFZkr9M8u2qemCS30vy5SR/vbhD2iYnJtmUJFX1+CTPSPLsJOuSvG4Rx7U9qru/neRJSf6iu/9nJuHkktPdhyR5epJ9k6yvqr+pqp9f5GFtizOTbAnwHpTkHUm+kuSBSU5dxHHtiLcl+WwmBy+vyOSg5vzFHNB2eFMmv7M2J3lkJr+r3rKoI9o2N/b3Plbv8CR/292bu/vCTP4+WUpen+TGJKmqn03yx5l8P67J0v0IxqX8+trc3TcNy49P8tfdfWV3vz/D77KlpLtvSfKZqrrXYo9lBzw13wsmjx7+XZHkEUn+96KMaPv8wvB1TJLTM/m//elJ3pDJ31y3O0HFtntPVT1msQdxa+juN3T3wZmk/SuTXDD8kfnIxR3ZdvmfmRzUfytJuvuyLJ0zXWZ+RvCjkvxzknT3DUluWZQR7bg/SfKE7t6ju+/a3Xfp7iV3CUVVrcnkj7L/k+TsTH5R35hkSb07Odg8/OF8ZJI/7+4/z9L5GUmSHg7uk8kB/und/anufkMmfxAsJVVV/z2TPwDePbTtsojj2SHd/YUkL8kkLH5EkpOr6nOL9Q7MNtp9+P8imfx8v7G7/79MznJ56OINa4fs1d2nJ7mpuz/c3c9O8vDFHtQ22r27P5BJqPfl7n55Jv8/LhU3VNX9qmrLWRTvm7HuRxZpTNtrWXd/Y1h+apLTuvud3f3SJPst4rh2xFJ+fd1SVT9RVcuTHJrk/TPW7b5IY9pRP5Hkwqr6wHCW5LqqWrfYg9oGM4PJwzJ5w/Hm7v5sltD/7d39K939K5kcl6zp7id395OTLNrZ90vmyRuRTyT5+5pcX3xTksrkD+gldxCWTO65keS+w9cVST6TySUHv9bdaxd1cNvmxu7uqupkcg34Yg9oG1xYVX+c5D+T3CfDHzTDGS5L9fq2rw+/oJesqjovyXeSvDHJy7r7O8Oqj1bVwYs3su12bVW9OJODsZ8dfvaX0jt7VVV3TvLtTP44m/lu9/LFGdJ2+61Mzv76++6+8P9v776j7Kzq/Y+/PwlVQsBIEVSaUqRIC4qAtKsoRYqIghQFFPxdhQAqKngFbHgFFC5y5YKIqIiCFGkKAoEQBIFAkGoBRBAUKUpv4fP7Y++TnDmZSebMTM4++8z3tVbWzPOcmbW+SU55nr2/RdJKwOTCMQ2JpLeRbuq3JS2yvt/2LZKWBa4HzisZ3yA0v8duSc7Ks/1qxeXFjd3WRyRtCzwMvLFgPEPxQr7O+pOkT5M+H5cqHFM7PkPK9lqCtDB8H4CkbYDflwxsCMZKms/2K6T33v2aHqv1PqLm59eXgZuBscCFOUsHSZsBtZSvtKo1M73hRaV+Tf8gZeh8tumx2hYmAVaw/UjT8T9I9ycdp1kLQGEwcg3bjsDtrvwfT9K3SVkIV5J2J29seuwPtlctFlybJH2WlKL/HtLu9z7AT22fWDSwQciLKgeTVpRPs31LPr8xsLLtHxYMb0gknQC8HrgAeLFx3na337TMJGmVppKc6uUa448AN9m+NqdZbm67ivIPSfsAhwFPAY/afl8+vy5wrO3qeohIWsT2s6XjGA5JU0hpoec0LeY1HtvTdlenU+f3qmWAR0ifh6vYflnSMsBFticWDXAIcmnUtaRynBOB8cBRtqvZoZS0Aal8ZXHgq8BiwLds31A0sFFI0uHANqTNrOWA9fLG0FuAM3JmblVqf35Jmg9Y1PaTTecWId3XPVMustFJ0oakPlNLAsfb/mo+vw2wp+3dCobXNknfJd1TnUXKrtgV+LPtAzoeS+X32h0n6TJg61xTVbV84f+zpnTq5scWs/3vAmENWa6L3oq0Q3aZ7d8UDmnUknR6P6edU5CrIGlJ4GvAG2xvl8tA3l7jwlGvUGo+uRRwW+M9ON9Qzm/7r0WDa0Mu+zgNGGd7udw3ZH/b/1k4tFFHKW3iw6TFirNt/y2fXxdYyvZlJeMLdZJ0YMspk270r6vpvaoh34gtA1zeWFyVtArpPeyWosGNMv2U1DWeW9NtP10gpGHLz68TgbeSGs+OBZ6tNVu9F+Tn2bvy4RTb5xeJIxYq2iPph6RJAL+i707xt0vFNBz5wn95mtL3bE8pF9HQKHX9f8T2C/l4YWBp238pGtggSLqVvn0qGh86k4Hv5F4VocMkXUJqSvd522srjY+8xfZahUNri6Sn6fv86qOWCwHNPnbNwGO2HywRz3DksqIPktJ2183n7rBdzahPSbfT//OqUQ75tg6HFLK8yPoJUu+p5s/2rl8olnQRc36/qqLBtKSv9nN6Ainr83Db53Q4pCHT7JM9DPyrxqxiScfbPmig51kNz68BNoImkKZ/7Gu7uh5akm4m7dqfA0wk9c5b2fZhRQMbJEmHtJxqXMdPtX1/gZB6Rq21ZSXdn/8skP9UK/dF2BW4C5iRT5s0Oq825wAbNR3PyOc2KBNOWz7Yz7kJpM7B/wPs39lwhk7Soba/JelE+r8IaN1l6mZL2f6ppM8B5HTwGXP7pW5je1EASV8B/k7qbC5SI8eammke18+5CUojJHezPb3TAQ2H7QdbeiDU9tzarnQAI6GfhbzmheLP2368SGDD80tS6ccV1Pe8OjZ//QCpfLAxvWs3Zo1k7Hq50eRscnPN35CuT2oxjVmvkcab1jhJtwEfr2FDqEmjFO3YOf5UF8vNDmejNL77bOAdnY1oZNj+s6SxtmcAp0v6bemY2tDftdQKwOGSjrT9sw7HMyw5m+K/SRmsomA/xlioaJPzKFJJi6bDqmvBdgJW7ZEd+/lsv9Q4sP1SvoHperbv7ef0vcBNOduiJo0GmjcXjWJkPJt3khoNWjcAqkyrzN5ru/kC5nt5Z/9bpQJqh+1+pxFJmkha0Nu0sxENy4OSNgKc36cOZNZrpwq2Hygdw0hoLOQ1k/Ra4GOksbe7dDqmEfAap9HK1bF9DaSMBNvNr+mLcj+Uqtl+XJV1abW9Yn/n883MyaTxq1WwPS1/vaZxLr/e32S7tianfdh+IGd+1ui5/Fk4XdK3SD2DqmmK37g3bJWvIa8AqlqoIF0Xvr8bmuLHeNI2SVoz3zzeQZrWME1SsbEtw3QfdXX9n5N/SpqZsidpB9KuWO2qeo3avih/PaO/P6Xja9NngYuAlSRdQ2oq1PFGQiNohqTdJY2VNEbS7tS32zob2zcD40rH0aZPAp8C3gA8BKwDVNmfQtKGkm6S9IyklyTNkPRU6biGw/aTtr8DvLl0LEN0cW7iVrMllabhADPLO2sbQzwbSZsCVfX/Gkhujl3LpIw+JF0taXy+kbyNtINfZQl3g6RVaSpJr8yepOvdTwPPkhoB1zDeeo6cxvpWtTCZdc3kvsioaN8pwCG2JwNI2hw4lb5lB7V4jrR6eSV9+23UlJ7f8EngzNypVsCDpBq3rqc03q/Va0ljJKd2OJwRkWukPw+sTtPoSNu1zCnH9s2StiA1dxKpRKpmHwFOyH8MXJfPVU3S0syhpr1LrWp79+YTSlN+risUz3B8l9lri99SNKIRkHcma71GmgQcJulF6h2jfjBwtdKkNUhp1DWVQbb2noJU0vkE6bO9ekrjoqvaTGmymO2nJH0cON32EZKqyKgYoL/GBFKz01qfWzvaPgF4gTyqVNIk0vVKtSRtCTw51x/sPjdL+jldMLmv1g/hkhZpLFIA2L46jwSq0YX5T/Vy+cSG+YNTlXU+Pqnl2MDjwNXA9zoezcg4E/g5sC1pEemjwD+LRtQmSafa/gRptwVJryHVfr+naGBDlOuIdygdx1AN0PdkAmmReFLnIxqWE4HW5qD9natCzbXF/XTQh7RQ/GHgFx0OZ0T0V85SG9u/lrQysFo+dU9lZaqtvacMPF7bNDXot1EgpNfI9qSFyhrNlydGfQg4vHQwbWrtr9G4ZvxTcwl0ZT7K7IsSH+vnXFcaoLn0BOBhKtk0bTGetJm9VdM5A7FQUYH7JP0Xsxry7EFqrlmdClPxByRpQWBncpfzRgmo7a8UDGtQbL9r7j9VndfZPk3SpFwLek0un6jJPyWdaPsASYsDF5PmZFep5kkAWWvfk8bF2SG2Hy0QT9uUxpJuREprb774H08ax1ajqmuLgfe3HDeeVyfYvqRAPEMmaTXb9/QzIQeACsdIrgysSsrKW1sStn9UOKZB6a/3lNJI+B8UCGe4Whe+TGrMvIft2wvEMxK+AlxGmspwUy4z+lPhmAalub9Gg6TtbN9ZIp7hkLQbKbNzRUnNG6eLkt6Ha9HaXLqxMPlsiWCGa6CGrSXEeNI25aY7RwGbkNIppwBH2q4utSfvVhzN7On5Kw34S11K0q9JdZ/TaKq7t93fpICuJ+kC2zuWjmOoJN1ge0NJl5EaHT4M/MJ2VTXfko4jvTYmAsfZPrtwSEOWd7mvZfbXyLnFghomSevVdPMlaTNgc1KW0clNDz0NXGS7igvlZrnT/D9IU7AOBhYDThqgSXCYhySdYns/SZP7edg1ld5JOoL0WlkduBTYmnRT2d+UrCpIusV2lVlTrSS93vbfS8cRklqfW/nzY0XSvcgXmh56Gvi97VeKBDYCJO1n+5TScQyFpIWAfYE16Ht/2PGNrVioGMUkTQWOAL5D2lXam/ScOKJoYEMg6Q7ba5aOY6RIutX2uqXjGCpJ25Fuit9ESmkfDxxlu+tLjZqbspIWI48CfgdcAlDD36E/kqbbXqd0HCOp5ouzXpmakbOmTpjbuZrU+rzqJTmVem3gVttr514037fdmgFTjdo/15vV/hrJ2V9fA54Hfk16rh1k+ydz/MUu1UvPrV5R82tE0jnAPaRsl6+QxtnfbbvjZbZR+jFIAzSvmcn29gM91sUWtn2lJOWL5iMlXUtavKjNbyWtVXEaYqsqmjoNxPbF+dt/A/2OlexireMIbwdek8+bevu6XCxpG9uXlg5kBNXYTRtSucQxzL5bUc2Od5Oqa4sHUOvzaial8bcr0LfMq4qyiex5269KekXSeOBRoLpszxbVZkn2o/bXyFa2D5W0E2ny0i7AZKDKhQoqajTbH0kbkja13krKzhsLPFtZA+BWNb9G3mJ7F0k72D5D0k9JpVIdFwsVg9doXvMB4PXMejPbDfhLiYBGwAuSxgB/kvRp4G9UOmqKVIrzMUn3kzrUNrqc9zdRoytJWsf2dADbH83ntrb9q7KRtS/Xe54AvBN4FbgeONj2fXP8xS5ge09JY4FP2f6f0vGMoF6YBNCq39nlFWg0m92OepvN9kptcX+q6k3RStKPSaNVpzOrzMtATQsVN+feQKeSytWeAW4sG1L78rXVj2w/RXr/XRf4ou0rC4c2XKeWDmCY5s9ftwHOsv1Eo7dZLSTdDJwO/NR2da+NFr04Para7C/SNSLAvyStSepJs0KJQKL0o02SptjedG7naiBpA+BuYHHgq6Ta4m/ZvqFoYEOQ69xmU1N6taRbSM2p7srHuwCH2t6gbGTtk3QDaZrJWfnUrsABtt9RLqr2SLra9ual4wizy72CVqZvNsKUchG1R9I02+tL+n1jMVXSNbY3Kx3bYPVybXHtJN0NrO4eucCTtAIw3nZ1mYaN17ikrYADSRmrp9hev3BobZP0ZuAh2y9K2hx4G2kR5l9lI2ufpG+SMlyeB95Oug6+uLJrlLeQSrY/TGo2fTpweY2ve0k3257Y8pn4W9sblY6tXZK2ZfZsya5v7N9MaWzvuaTX+OnAOODLtk+e4y/Oi1gqfD4XlS8Atm3sDEtaEbjU9lvLRhYAJC1F3zeHvxYMpy35Q+ds0k39JqRGNttV2qj1d60f+I0Gm6Viapekr5F2h38GzOzcXOPFz1RbIQAAIABJREFUckPtN/gw8wN0EvBG0o7xhsD1NZVN9Eqz2V7SS6nHub74QNuPlI5lOCS9AVievuUrtb1f3ZZ7bHyH1Az03Fr7CUiaTtrtXoGUBn4hsKrtbUrGNVT58/Ap2zOUxo+Pr7FBaM6M3o40zv5V0mSZE2w/UTSwNkiaArwb+D5p9/4R4GO21y4aWJsknUwqFd6C9Hf5IHCj7X2LBlaxWKhok6T3AacAjRT2FYD9bRep3RkOSRNJ86NbLwSqKZdoyA0QjwOWJdWyLk9q/LJG0cDaJGk10pzivwE72H6ucEhDkncr/kW6yTdpxX9BUpYFNXyA5n4trVxj9hT0xg0+zGyytwFwg+118mvmKNsfLhzaoNXcbLZB0tP037epypKinEY9W+qx7cOLBtaGpl5aiwLrkEolXmw8XlMvLUn/TfrcuIum8pWa/g4Akn4ELAGsQtqdHANMqbHJXqM5oKTPAS/YPrHiRZe9+jtfWR8XJL2NlFWxDWnx6EzSRteeNTXPlrQ66XNwfmDb/PV7tv9cNLA2NWVQNb6OA86zvVXp2AZDfcemz8b2tzsVS0P0qGiT7V8rjfVcLZ+6x/aLc/qdLnYm8DlSs8BXC8cyXF8l3XhdYXtdSVuQ+od0PUm30veCf/H8darS3PjqLmhIF5gA++WvjeLPfUh/165vimb7XaVjGGGTmHWDv0XjBr9wTEPxgu0XJCFpQdv3SFq1dFCDlfufrJwbztbYbBYA24uWjmGk2f6zpLG2ZwCnK430rcmxc/+RauxI2q2v9fqqYW9gfeDPtp+TtAQpW7JGL+feNB9lVv39/HP4+W7WXFK7EPAfwC1U1MdF0jTShtBpwBeaXiu/k7RxucgGT9L8wDGkheG/kK4VlwZOzO/H69q+tWCI7Xo+f31O0rKkfk0rFoynXV33uR4LFW2S9IGWU2+W9G/gdtuPlohpGP5Z0w7eXLxs+3FJYySNsT0578jUoNq58K1y35MHba+Yjz8K7Ez6ADqyhkyKBkmH9Xfe9jc6HcsIqfoGv8lDucneBcBvJD1JKp2oQk4z3p40Fjp0j+ckLQBMVxpd+AiwSOGY2vU3YGnb1zWflLRpfqwm95FugqteqMiv9z+QrhVXm+svdLe9Sc1/v277/lz6XOWUDNsHNB9LWgz4caFwhmqXgRqU2269V+lWx5FKJZa3/TSA0pSfYyV9D3gfdd3oX5yvT44hLXyZVAJSBdtH5c2UA213xTVKlH60SdIlpEkGV5FW/jYHbiCl9X3FdjVvdJL+g5R1cCV900PPKxbUEEm6grQDczQpzfJRYINaGvHkN4ZbaqvHa5Ubgr47d9DelFT6cQApDfmttqtZlJH0+abDhUjpiHfa3rtQSMMi6XzSheZBwJbAk8D8tdYXA0jajNQE+Ne2Xyodz2BJ+jop7p/Tt//JLcWCGuVyc9BHSTfHB5P+f/63ptRjSRcDh7X20cllnkfYrqYLvaRzgbWZ/frkwGJBDYGkfYDPAG8gZa82sto2LxlX6Cvv7P++pn5zkpYGvgEsa3vrXD7xTtunFQ5t0CT9mZRh6JbzY4HHgK1dYYN/AEkLAgvZ/nfpWNolabLtrsj2jIWKNuUa0I/b/kc+XprUwObjpLrDNUvG1w5JPyGVsNzJrNIP296nXFRDI2kRUsrVGGB30kXmmbarGZMn6Szgs7Zr2/maqdE4LH9/Eilr58h8PL2mmslWkhYCLrD9vtKxDFeNN/iSJszp8cqydSb3c9q19QsJ3UXSHQNdg0i63fZanY5pqHI23mxsn9HpWIYj99R5O6kf0DqS1gC+ZLuK0lQASWfb/lD+uzTfNFQ3Br6hqZ8LpOvG1YFzbH9+4N/qLpJ+RZrIcHhu2DofcGtlr/M/2l6l3ce6jaQtbV/VT9Y9UN8GcDdtpkTpR/tWaCxSZI8Cq+Qd5JcH+qUutXZNb2gDySuvv7T9btKCS1UXMk2WAO6WdD193xhqSeEDGCtpPqfRhP/BrB4VUP/7zYJAdVMZBrjBvz1/HQfUcoM/jXRhKWA5UkaISD1d/kpF6aHdslMR5ngTBlTXXHqhOTy2cMeiGAG1LUjMwQu2n88ldwvYvrPCEpBJ+et2RaMYWc39XF4BHrD9UKlghmgJ22dL+iKA7VckzZjbL3WZuyTt1drEVNIewN2FYhqKzUiZ9v1lrZnUJL8mjWz05rGqJmXjdlTtNw4lXJvTK8/JxzsDU/KOfm2zpG+QtLrtu0oHMhy5BvQ5SYvVmGLV5JulAxgBZwHXSHqMlOFyLcwcvVrF/01joaWlyelYYBlSmmVtmm/wGxrHVTQ2BWjqe3IycKHtS/Px1qSxZl1P0h62fzJQZ+0SHbVDT92E3STpE7ZPbT4paV/S+0A1ctPyo0k73c3jlKt4v2rySK5Zvwi4TNITwD/m8jtdxXnMre0HchZxoxHljRX2ZgPA9jXNx5LGStrd9pmlYhqCZyW9jnydojRiuYrrrCafAs7LJVKNa5UNSAurO5UMrB22j8hfqywNbtVNmylR+tEmSSItTmxMutCfCpzbWl9VA0l3k3aI7yfVgNacxnc2aerHb+ibjVBbPesSpNF4ADfbfqxkPEORPyyXAS63/Ww+twowroYafM0awdacPfEK8PdaO9Dn96032f5r6ViGS9I02+u3nLvZ9sSBfqdbSNrf9v9JOqK/x23XOIUldIl8E3k+8BKzFiYmAgsAO9n+e6nY2iVpKnAEqens+0n9ddS4IahR7gu2GHBJjZ8lkj5EahJ4Nel68V3A52z/omRc7ciNGj9F6hlyIema8VOkCXjTbe9QMLy2SFqPNN56TeAOYEngg609amogaUtgDdLz6k7bVxYOaUgG2IT4NzDN9vROxzNU3dT/JBYqRrHcPGw2th/odCzD1Qv1rJJ2Jl2UXUt6s94IONj2+UUDG2VU6Vz4uenvBr9Gki4jvUZ+Qtp92QPY1PZ7iwYWqpZri/8bWIr0/ttYuB9fNLAhUBrP3ehVcaftq0rGMxSN96vm3hqSrnVlY6MlbW776pZzte3cA6kHFfCeRhaFpCVJI+GraQIu6ZekssHrSeWpryUt5E2q6UayIfelWJX0fvUH27WVoPcUST8lLQ5flE9tC9xE6gd4ju1vlYqtHd3U/yQWKtrUKxczksaQOhxX0/yzP5KW64VdYph5EbBVS6PWy2u6COgFkh4CBkzBrzU9Pzc3/aHtm0rHMhy558YRwKb51BTgqMqaaa5ImoazAk0lmLa3LxXTaJe7z7/fdk110XMlaT/bp5SOo12SriPt2P+CVPv9N+CbtqsaqZz/HrcAh5J6Ap1CuvbesWhgQ9DakDVfR95WU6+zloWvxmSJ5ZxHY9ZE0i6khthPS/oSsB7wtRoyV3tV3kjZ2fYz+Xgc6T1sJ1JWxeol4xssSTfZ3qB5465UQ/zoUdG+b9EDFzO2X5V0Ww/c6F9AenNG0rm2dy4cz3CMaWnU+k9SN+rQWWNJF5Sa2w9WZgvgk5L+QiqPqrLUKy9ITJrrD3a3C4DTSLsur87lZ0Nn/KP2z/UBfJJ0c1ybg4DXAAcCXyW9f/WbOdnl3kVapLiV9NlS1Rj7Fr/ON2Jn5eMPA5cWjGcoZmYc5P5m99e4SJH9l+1zJG0CvJfUIPR7wDvKhjWqLUcqvWt4GVg+N9Stqdyra/qfxEJF+3rpYmYZ4E5JN9K3r0NNu3rNN5O1NdlqdbmkS4Gf5uNdgcsKxjNaPWL7K3P/sepsXTqA4ZB0vO2D1Hes3EyVvW+9YPt/SgcR+rhZ0s9Ji0gzLyhd2Vi5flS14Ko0BnrRpsyvZ4C9c4ZhbY0CAcYDawMPka65lpakGvua2f5cLlFt9Gg7pcLS1LUlPZW/F7BwPq4xO7ox4WNb4Hu2fynpyILxhHT9fkMuMYLUX+esPHChpsEFnyH1cHlzzgpbEtilRCBR+tEmSScAr6cHLmYkbdbf+dZuyN2s0fiw9fsa5YaHuwCbkD40pwC/qPGCpma92qMCIO+8rGz79FxfPM72/aXjGgxJ69ue1iPvWx8BVgYup+/nSKTsFiLp9H5O2/Y+HQ9mBEl6oysauyjpFFI6+3kt53cHNrH9/8pENjSS/ggca/uUfLNyDPA225sUDi1UTmkC4d9IU6/WJ01auzHKhcuStD6zruOn2r65cEhD0i39T2Khok29djGTG2qubPsKSa8BxtaUBqc0M7qRxr4w8FzjISpZHZf0v8AXbD811x8O85ykCTX1OxisPGliIrCq7VUkLUtq7rRx4dAGLdcUn2F7j9KxDIeko4E9gXuZVfph2x2fUR5mPq8OtP2d0rGMhJo7z0u6a6A6bkl32l6j0zENh6QVWxeDJW1ZaYPTnujR1ivyNfv7gNtt/0nSMsBati8vHNqo1Cu9/wAk3QscY/vkpnMX2+74GO8o/WiTe2RGLoCkTwD7ARNIY0rfAJxM6oRcBdtjS8cwAv4CTJN0hO2fzu2Hw7zVi4sU2U7AuqTGbth+WNKiZUNqT64pXlLSArZfmvtvdK2dgJUq/zv0jPy82p40dakXTKT/zvOflNTtnefnVKpSXc8m2/dL2hV4s+2vS3oTdZawQI/0aOsF+ab4xuabYtuPAI+Ui2p066Hef5B6a2wh6R3A/vla5Q0lAomFikGSdKjtb0k6kf7row8sENZwfQp4O/A7gLwiu1TZkEaf/Lw6E/i2pH1JzZBebXq8urKi0JVesm1JjeZIi5QOaIj+Alwn6UL69tapaRrLbcDiwKOlAwkz/VbSd4Gf0/d5VWM5zuuA9Zo6zx9B6jy/KTCNdMPZrR6V9HbbNzaflLQBqcF0VfJzan7Sv/3XSc+tk4ENSsY1RL3Uo61qPXZT3Et6ofcfwHO2PyzpUOBaSR+in3vfToiFisFrvDlXWWs0gBdtv5RaI8ysR4paoAJs/03SJaQLmffTlA4OxEJFGAlnS/o/YPGcTbUPcGrhmIbi4fxnDFBVRkiTpYF7JN1E3x4VtV3M9JKN8tfmRroGaizHqbnz/OdI71U/JC2qQMoO2YvUYLo2G9leT9KtkDL2JC1QOqgh6tWGs7XqlZviXnJU6QBGiGDmRuo0UmP/CSUCiYWKwfsrgO0zWh+QVFVzpybXSDqM1PX4PcB/MitVNHSIpDVIWRQPA2/P6XshjCjbx+bX+VOkBklftv2bwmG1zfZRALlsxY1d48ocUTqA0JftLUrHMIKq7Txv+0ZJbydlfH4sn74DeIftGjOQXs5p+o1MttdR70ji8aQ+YFs1nYvNlHJ65aa4Z9i+Jk8oamRM3Vjp+9aXG9/YvlLSeyk0HjqaaQ6SpPuAXWxPazl/FKlmr7ppE/nDc19mfehcZvv7BUMalSTdDUyKBkghzJ2kNYEfM2t1/zFgL9t3lotqcCStZvue/P2Ctl9semxD2zeUi250k7QYaQFp03zqGuArtqvsJyBpIrPGSFbbeR5A0nq1leBIms/2K5L2IvWkmQj8APgQcJTtnxUNMPSE2hvi95pcInEMcDXpvfddwOds/6JkXO2SdKXt/5jbuY7EEgsVg5PHzZwD7G77+jxK8nvAKsCONU1skLQD8EbbJ+XjG0kzcg0cWtsLqnatNywhzAu90rFd0m+Bw21PzsebA9+wvdEcf7ELzGmccutx6CxJ55J27htZk3sCa9v+QLmohi5PMlmapszZWmvZa3xttLzW1yCNkBRwhe07igbXph7t0Va95ob4tt8saWXg5BI3kyGRdBvwnkYWhdIY+CtcychYSQsBrwEmA5szq7nxeOBXtt/a6Zii9GOQbE+TtCNwvqRPAZ/ID72vws7th9K31nMB0gzmccDppKZboUNikSJ0SK90bF+ksUgBYPvqihqDaoDv+zsOnfVm2zs3HR8lqatHeQ5E0gGk7JB/ADPIi5LA20rGNQw1vjZmxpyzvbo+42sOerFHWy+IhvjdZ0xLqcfj1DWtaH/gIGBZ8oS47CngpBIBxULFIEmaADxEqtG5ALgC+DQwTlJtIw0XsP1g0/HUHP8TFV3whxDa0ysd2++T9F+k8g+APYD7C8bTDg/wfX/HobOel7SJ7akAkjYGni8c01BNAla1/XjpQEZIjbX4S0o6ZKAHa5pSZPui/HW2Hm2hqGiI331+Leky4Kx8/GHgVwXjaYvtE4ATJB1g+8TS8UAsVLRjGrPeAJ4G3gHcyKydipUKxTUUr20+sP3ppsMlOxxLyCRtCdxg+7nSsYTekUs+oHc6tu9DunE5j/T+OwXYu2hEg/dGSf9DirvxPfm4yIzyMNMngR/lXhUAT1KoedgIeBCosrdGM0nbk3uGSJrRuGGuxFhSlmqN2SB9SLqIOdwAx5SJYqIhfpex/bl8zbUJ6bV/iu3zC4c1aJK2tH0V8Lema8eZSlwvRo+KUUjSmcDVtk9tOb8/sLnt3cpENrpJ+hGwISlV7Nr8Z6rtJ4sGFqom6fQ5PGzb+3QsmFFO0hxvfGPHsvMkTbJ9gqSNbV8naTxATX2nWkk6jTTZ5xL6LkpWs4sv6WhSWvuZ+dRuwM22v1guqsGrsa/GQCRtNqfHbV/TqVjCLC0N8UUaIfl9x41dUTU3OJV0lO0jBrhuLHK9GAsVo1CuYWvsqjZqkNYHFiQ1Bv1HqdgCSFoW+CDwWWBZ25H5FIatcSM2t3PdKnb1wrwgabrtdXrsxrLf8beN0b41kPR7YB3br+bjscCttqvosyHpVtvrlo4jhNA50eB05MVCxSiWSw3WyId35nSfUIikPUijjNYijVycClxr+/qigYWe0N+NWE03Z7GrF+YFSWcB7ySVPd7b/BBpB6mKG+NekxcqNm/0/8p9wq6u5f9D0oTKepcNSNLtzHmRuIr/k14xl/+PF0nvY0fbvq1zUQVIC9/kBqeNhUpJt9teq2xk7ZF0L3ADKbN7iu27SsUSO7WjWF6YiMWJ7nE86QPmZGCy7b+UDSf0AknvBDZi9uZu40l11LW4v9bxiqF72d5N0utJadNVZ+VIOt72QQNlH1WWdXQ0cKukyaRFo02BKso+gNoarM/NdqUDCH3M6f9jPmBN4IdAZPR0Xq80OF2d1IvxXcCxklYDbrO9U6cDiYWKQcqr+QPqsQ+lUIDtJfK89U2Br+eUsT/Y3rNwaKFuC5Caus0HLNp0/ilSiVEtLgDWA5B0bssoyRCGzPbfgSrm3M9FYxLOsUWjGAG2z5J0NbABaaHi8/n/KXSY7QdKxxBmGcT/x72SqsiU7EG90uB0BvBy/voqadT1o3P8jXkkSj8GSdL9pFUxAcuROoILWBz4q+0VC4YXekBu4rYxsBlpFXMJ0hSQWjvPhy4iaXnbD0haxPazpeNpV3PNd+3135JWAk4glRy8ClwPHGz7vqKBjUKSzrb9oX7SqaP0owBJq9m+Z6AbLdu39Hc+zDuSnqb/XeHGa2R8h0MKoSu1NDgFuMz29wuGNCSSngNuB74NXFFy1HUsVLRJ0snAhbYvzcdbA++2/ZmykYXa5ZrcqfnPFNsPFQ4p9JBcAnIaMM72cpLWBva3/Z+FQxuU5n4aNfXW6I+kG4CTmDVrfVfgANvvKBfV6CRpGduP5E7ts6lpN3kOiy7k4yeA423/svPRDY6kU2zvl0s+Wtn2lh0PKoQQ5kDSDsAbbZ+Uj28k9T0ycKjtX5SMr13577MJqd/GS8BvSfclV3Y8llioaI+kabbXbzl3s+2JpWIKvaXWHe/Q3ST9jlTqcWFTZsIdttcsG9ngSJoBPEvaxVsYeK7xEJXt6kn6XeuihKQbbG9YKqZQv7ktupCy9M60vVon42pX3pV8Zy0TiUIoITfEv8H2c3P94TBPSboO2NX2g/l4OrAlqez29FqnfuTeFFsDBwFL2V640zFEj4r2PSbpS8BPSCtlewDFUmJC72je8Qaq2/EO3c/2g40mT9mMUrG0y3ZNjT/71dTraLKkLwA/I32OfBi4pFhgo1gvpbXbfiR/HSgL5AFJu3cwpCGx/aqkY0mlUSGE/n0MOFnS46TpDNcCU20/WTSq0WmBxiJFNjX3LnxC0iKlghoqSecC6wB/Jj2v9gRuLBFLLFS0bzfgCOB80sXNlHwuhOE6HngvcCGA7dskbVo2pNBDHpS0EWBJCwAHAncXjmm0mcasXkcA+zc9ZuCrHY9olLO96Nx/qg6SptrepGnxRc1fbY+3Pa1okIN3uaSdgfMcqb8hzMb2XgCSliVlS54ELEvc25Xw2uYD259uOlyyw7EMmaQNgAeBbwK3kDbjdwaWAu4AXuh4TPH+PzSSxtl+pnQcoXc00sFbmgbeZrsXutGHwiQtQWrg+G7SjcvlwKSSTZJC6DaSlgIWahzHSNwy8mLLIsArpIvj6jJcQpiXJO1Bary+FvAYqb/ZtbavLxrYKCTpTOBq26e2nN8f2Nx2FRvakm4h9V18Im+U/gw4gJRd8VbbHZ8UF6tubco7kt8n0vPDyIsd7zDP2H4M6Pq079FC0pqkWeXNN8U/KhfR6CZpe+A40o7ko8DypPffNUrGNVSSNgFWtn16XqRc1Pb9peMarF7KdAlhHjkeuBc4GZhs+y9lwxnVDgYukPQRUiYCwPrAgsCOxaJq39hcsgKpJPUU2+cC5+a+Gx0XGRVtqr0hXeheseMd5gVJX57Dw7Yd5QYdJukIYHPSQsWlpGZVU0vsVoRE0m2k5mdX2F5X0hbAbrb3Kxxa2/LzayKwqu1Vcmr4ObY3LhxaWyS9FliZvot5U8pFFEJ3kbQGsClpQsPKwB9s71k2qtErNzhtLG7fafuqkvG0S9IdwDq2X5F0D7Bf4z231L1uZFQMQc0N6UL3ih3vMI/0N0FmEdKs79cRfRFK+CCwNnCr7b0lLU3K1AvlvGz7cUljJI2xPVnSf5cOaoh2AtYl7+zZflhSVRkKkj4OTALeCEwHNgSuJy0mhTDqSRoPLEfK/loBWAx4tWRMo11emKhqcaLFWcA1kh4Dnic10kTSW4B/lwgoFiraF+n5YUTFjneYl2wf1/g+36xMAvYm1R4eN9DvhXnq+TzZ4JV8sfkosFLpoEa5f0kaR2qQfaakR0n9EWr0km1LMqSR16UDGoJJwAak8Ytb5DF5RxWOKYRuMrXpz3dtP1Q4nlA521+XdCWwDHB5UyPjMaReFR0XCxXt+yQpPf8NwEOk9PzoTxGGI3a8wzyVx2IeQsrYOQNYL0aYFXWzpMWBU0mTQJ6h0Oiv0S7vFC0N7EDaQTqY9DpZnkIXZiPgbEn/Bywu6RPAPqTnWk1esP2CJCQtaPseSauWDiqEbmH7bZAWIm33dx0ZQtts39DPuT+WiAWiR0XbJG1s+7q5nQthKJp2vPcFzgaOs/1o2ahCzSQdA3wAOAU4KaYVlaVUN/jGxsx1SSsA423/vmRco5Wki4HDWv/9JU0EjrD9/jKRDY+k9wBbkfodXWb7N4VDaouk80mZXweRyj2eBOa3vU3RwELoEpLeCZwGjLMdzf1DT4qFijZJusX2enM7F0I7+tnxPiF2vMNIkPQq8CIpjb35DT/G/RUiaZrt9UvHEebcIEzS7bbX6nRMIyk3aX7cFV/sSdqMVH//K9svl44nhG4Qzf3DaBClH4OUVy43ApaUdEjTQ+OBsWWiCr2gZcd7rdjxDiPJ9pjSMYTZ3CBpA9s3lQ4kzJoo0Y+FOxbFCJC0IfBN4AlSyeCPgSWAMZL2sv3rkvG1Q9KPG9MLbF/TOAfERIMQsmjuH3pdXMAO3gLAONLizqJNf54irWiGMFSfAZYFvgQ8LOmp/OdpSU8Vji2EMPK2AK6XdK+k30u6XVKUfpRxU+7j0IekfUn9Q2ryXeAbpM7tVwEft/160vjCo0sGNgRrNB9IGgtEFlIIs/Rp7i/ps0Rz/9BjovSjTZKWt/1A6ThCCCHUSdLy/Z2Pz5bOy6NhzwdeYtbCxETS5sROtv9eKrZ2SZpue538/d2239r02K2N9PBuJumLwGGkbJbnGqdJ/z+n2P5iqdhC6Ca5rOsE4N2k18jlwCTbjxcNLIQRFAsVgyTpeNsHSbqIvnXeANjevkBYIYQQKpZHR+4IfMT2tqXjGa0kbQE0arvvtH1VyXiGorlfVmvvrNp6aUk6OhYlQghhdIuFikGStL7tabmp02waNZQhhBDCnEhaANgG+AjwPuBc4DzbFxUNLFRN0gzSuGsxe0bCQrbnLxXbUEh6A2lM7Mx+aranlIsohPIkfXkOD9t2jLQPPSMWKkIIIYQOyCMjdwPeC0wGfg6caHuFknGF0G0kfRPYFbiLWQ0CHdmrYbST9Jl+Ti9CGmv/OtvjOhxSCPNMLFS0SdLGwJHMWuVvjPhbqWRcIYQQulseFXst8DHb9+dz98XnRwh9SfoD8DbbL5aOJYRuJWlRYBJpkeJs4Djbj5aNKoSRE+NJ23cacDCp6VaMAQohhDBY65N2ia+QdB/wM2K8dQj9uQ+YH4iFihBaSJoAHALsDpwBrGf7ybJRhTDyIqOiTZJ+Z/sdpeMIIYRQr5ydtxuwMzAdON/2KWWjCqE7SDoXWBu4kqbFCtsHFgsqhC4g6RjgA8ApwEm2nykcUgjzTCxUtCnXTY4FzqPvh+ctxYIKIYRQJUljgPcAu9reu3Q8IXQDSf+PlPX7Kil79XkA22eUjCuE0nIJ4YvAK/SdQtgoRR9fJLAQ5oFYqGiTpMn9nLbtLTseTAghhBBCj5A0H/ANYB/gr6SbrzcBpwOH2X65YHghhBA6KBYqQgghhBBCcZK+AywKHGz76XxuPHAs8Jztg0rGF0IIoXNioWKQJB3ScsrAY8DURvf2EEIIIYQwNJL+BKzilotTSWOBe2yvXCayEEIInRZTPwZv0X7OrQAcLulI2z/rcDwhhBAqkju1D8j2E52KJYQu5dZFinxyhqTYWQshhFEkMiqGKV94XmF7vdKxhBBC6F6S7idl4wlYDngyf7848FfbKxYML4TiJF0AnGf7Ry3n9wA+ZHv7MpGFEELotMhpcgQcAAAFfElEQVSoGCbbT0hS6ThCCCF0t8ZChKSTgQttX5qPtwbeXTK2ELrEp4DzJO0DTCMt7G0ALAzsVDKwEEIInRUZFcMkaUvgSzH1I4QQwmBImmZ7/ZZzN9ueWCqmELpJvrZag5RxdKftKwuHFEIIocMio2KQJN1O33nFABOAh4G9Oh9RCCGESj0m6UvAT0ifK3sAj5cNKYTuYfsq4KrScYQQQignMioGSdLyLacMPG772RLxhBBCqFPubXQEsCnps2QK8JVophlCCCGEkMRCRQghhFCApHG2nykdRwghhBBCtxlTOoAQQghhNJG0kaS7gLvy8dqS/rdwWCGEEEIIXSMWKkIIIYTO+g7wXnJfCtu3kcpAQgghhBACsVARQgghdJztB1tOzSgSSAghhBBCF4qpHyGEEEJnPShpI8CSFgAOBO4uHFMIIYQQQteIZpohhBBCB0laAjgBeDcg4HLgwJj6EUIIIYSQxEJFCCGE0EGSNrZ93dzOhRBCCCGMVrFQEUIIIXSQpFtsrze3cyGEEEIIo1X0qAghhBA6QNI7gY2AJSUd0vTQeGBsmahCCCGEELpPLFSEEEIInbEAMI702bto0/mngA8WiSiEEEIIoQtF6UcIIYTQQZKWt/1A6ThCCCGEELpVLFSEEEIIHSDpeNsHSboImO3D1/b2BcIKIYQQQug6UfoRQgghdMaP89dji0YRQgghhNDlIqMihBBCCCGEEEIIXSMyKkIIIYQOkrQxcCSwPOlzWIBtr1QyrhBCCCGEbhEZFSGEEEIHSboHOBiYBsxonLf9eLGgQgghhBC6SGRUhBBCCJ31b9u/Kh1ECCGEEEK3ioyKEEIIoYMkfRMYC5wHvNg4b/uWYkGFEEIIIXSRWKgIIYQQOkjS5H5O2/aWHQ8mhBBCCKELxUJFCCGEEEIIIYQQukb0qAghhBA6QNIhLacMPAZMtX1/gZBCCCGEELrSmNIBhBBCCKPEoi1/xgMTgV9J2rVkYCGEEEII3SRKP0IIIYSCJE0ArrC9XulYQgghhBC6QWRUhBBCCAXZfgJQ6ThCCCGEELpFLFSEEEIIBUnaEniydBwhhBBCCN0immmGEEIIHSDpdlIDzWYTgIeBvTofUQghhBBCd4oeFSGEEEIHSFq+5ZSBx20/WyKeEEIIIYRuFQsVIYQQQgghhBBC6BrRoyKEEEIIIYQQQghdIxYqQgghhBBCCCGE0DVioSKEEEIIRUj6bZs/v7mki+dVPCGEEELoDrFQEUIIIYQibG9UOoYQQgghdJ9YqAghhBBCEZKeyV83l3S1pF9IukfSmZKUH3tfPjcV+EDT7y4i6QeSbpJ0q6Qd8vlDJP0gf7+WpDskvabAXy+EEEIIQxQLFSGEEELoBusCBwGrAysBG0taCDgVeD/wLuD1TT9/OHCV7Q2ALYBjJC0CHA+8RdJOwOnA/raf69xfI4QQQgjDFQsVIYQQQugGN9p+yParwHRgBWA14H7bf3Kap/6Tpp/fCviCpOnA1cBCwHL59z8G/Bi4xvZ1nfsrhBBCCGEkzFc6gBBCCCEE4MWm72cw6xrFA/y8gJ1t/6Gfx1YGngGWHbnwQgghhNApkVERQgghhG51D7CipDfn492aHrsMOKCpl8W6+etiwAnApsDrJH2wg/GGEEIIYQTEQkUIIYQQupLtF4D9gEtyM80Hmh7+KjA/8HtJd+RjgO8A/2v7j8C+wDclLdXBsEMIIYQwTEolnyGEEEIIIYQQQgjlRUZFCCGEEEIIIYQQukYsVIQQQgghhBBCCKFrxEJFCCGEEEIIIYQQukYsVIQQQgghhBBCCKFrxEJFCCGEEEIIIYQQukYsVIQQQgghhBBCCKFrxEJFCCGEEEIIIYQQusb/ByYwwbGsE/sfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show bar graph for betweenness\n",
    "title = 'Closeness centrality distribution for top 20 nodes'\n",
    "globalcloseness.plot.bar(x='index', y='Degrees', figsize=(18,7), title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Betweenness Centrality\n",
    "Shows the bridges to clusters. These places are highly likely to spread the infection to large areas of the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.116096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Florida-US</td>\n",
       "      <td>0.104615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>France</td>\n",
       "      <td>0.093584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0.076886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>California-US</td>\n",
       "      <td>0.058538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>0.054770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0.051121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>New York-US</td>\n",
       "      <td>0.050357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0.040907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Beijing-China</td>\n",
       "      <td>0.036739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Russia</td>\n",
       "      <td>0.033950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Illinois-US</td>\n",
       "      <td>0.024812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Texas-US</td>\n",
       "      <td>0.024619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>0.020531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.020043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Korea, South</td>\n",
       "      <td>0.019718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Hong Kong-China</td>\n",
       "      <td>0.018369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Ontario-Canada</td>\n",
       "      <td>0.018082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Shanghai-China</td>\n",
       "      <td>0.016187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Japan</td>\n",
       "      <td>0.014757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   index   Degrees\n",
       "0         United Kingdom  0.116096\n",
       "1             Florida-US  0.104615\n",
       "2                 France  0.093584\n",
       "3                Germany  0.076886\n",
       "4          California-US  0.058538\n",
       "5                 Turkey  0.054770\n",
       "6   United Arab Emirates  0.051121\n",
       "7            New York-US  0.050357\n",
       "8                  Spain  0.040907\n",
       "9          Beijing-China  0.036739\n",
       "10                Russia  0.033950\n",
       "11           Illinois-US  0.024812\n",
       "12              Texas-US  0.024619\n",
       "13          South Africa  0.020531\n",
       "14           Netherlands  0.020043\n",
       "15          Korea, South  0.019718\n",
       "16       Hong Kong-China  0.018369\n",
       "17        Ontario-Canada  0.018082\n",
       "18        Shanghai-China  0.016187\n",
       "19                 Japan  0.014757"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the betweenness centrality\n",
    "betweenness = nx.betweenness_centrality(GlobalGraphFlights, weight='weight')\n",
    "dfbetweenness = dictToDataframeGlobal(betweenness).head(20)\n",
    "dfbetweenness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df2aaad588>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8ZJ7+9KfnQx/6UH7rt34rz3zmM3PQQQfla1/7Wg4++OB84QtfyEtf+tIcd9xxOfDAA3PppZdmzZo1k49JwAAAAAAz0N1Jkve973357Gc/e03rhIsvvjhf/vKX89GPfjSPfvSjc4Mb3CC3uc1t8qAHPeha6z/mMY9Jklx66aX5+Mc/nkc/+tHXzPv+97+fJPnABz6Qz3/+89dM//a3v51LLrkkBx54YJ71rGfl8Y9/fB7xiEdkzz1/aNSDLSZgAAAAgO3snHPOyU477ZRb3/rW6e688pWvzMEHH3ytZU4+edODUd7kJjdJMnSluPnNb37NGA+LXX311fnEJz6RG93oRteafvTRR+ewww7LKaeckvvf//75wAc+kB//8R+fdEzGYAAAAIDt6IILLsjTnva0HHXUUamqHHzwwXn1q1+dK664Iknyn//5n/nOd76Tgw46KO985ztz9dVX5+tf/3o+8pGPLLu9m93sZtl7773z9re/PcnQMuIzn/lMkuQhD3lIXvWqV12z7EII8ZWvfCV3v/vd85znPCf77bffNWM2TKEFAwAAANcrK7mt5Lb2ve99L/e6172uuU3lE57whDzrWc9KkjzlKU/J+vXrc5/73CfdnbVr1+Zd73pXHvnIR+aDH/xgfuInfiJ3vvOdc7/73S+77777stt/85vfnN/4jd/Ii1/84lxxxRU54ogjcs973jOveMUr8pu/+Zu5xz3ukSuvvDIPeMAD8prXvCYvf/nL8+EPfzg77bRT9t133xx66KGTj7EW+nysFvvtt1+fccYZsy4DAACAHcQXvvCF3PWud511GVvl0ksvzW677ZYLL7wwBxxwQD72sY/lNre5zXbZ93KvW1Wd2d37Lbf8DtGCYd3Rm+6Xsi3MIuECAADg+u1hD3tYvvWtb+Xyyy/P8573vO0WLmyNHSJgAAAAgB3RxsZdWI0M8ggAAMAOb7UND7Dabc3rJWAAAABgh7ZmzZpceOGFQoYV6u5ceOGFWbNmzRatp4sEAAAAO7Q999wzGzZsyAUXXDDrUubGmjVrsueee27ROgIGAAAAdmi77LJL9t5771mXscPTRQIAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyXaedQH8wLqjT77O97H+2MOu830AAABw/aMFAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEy2ooChqg6pqi9V1dlVdfQy8x9QVZ+qqiur6lFL5h1ZVV8ef47cVoUDAAAAq8dmA4aq2inJcUkOTbJvksdW1b5LFvtakicmecuSdW+Z5AVJ7pfkgCQvqKpbTC8bAAAAWE1W0oLhgCRnd/c53X15khOTHL54ge5e392fTXL1knUPTvL+7r6ou7+Z5P1JDtkGdQMAAACryEoChtslOXfR8w3jtJVY0bpV9dSqOqOqzrjgggtWuGkAAABgtVhJwFDLTOsVbn9F63b38d29X3fvt3bt2hVuGgAAAFgtVhIwbEhy+0XP90xy3gq3P2VdAAAAYE6sJGA4Pck+VbV3Vd0wyRFJTlrh9k9N8pCqusU4uONDxmkAAADADmSzAUN3X5nkqAzBwBeSvK27z6qqY6rq4UlSVftX1YYkj07y2qo6a1z3oiR/mCGkOD3JMeM0AAAAYAey80oW6u5TkpyyZNrzFz0+PUP3h+XWfV2S102oEQAAAFjlVtJFAgAAAGCTBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEy286wLYMez7uiTt8t+1h972HbZDwAAAJunBQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJVhQwVNUhVfWlqjq7qo5eZv6uVfXWcf4nq2rdOH2XqnpDVX2uqr5QVb+3bcsHAAAAVoPNBgxVtVOS45IcmmTfJI+tqn2XLPbkJN/s7jsleVmSPx2nPzrJrt199yT3TfLrC+EDAAAAsONYSQuGA5Kc3d3ndPflSU5McviSZQ5P8obx8TuSPLiqKkknuUlV7ZzkRkkuT/LtbVI5AAAAsGqsJGC4XZJzFz3fME5bdpnuvjLJxUn2yBA2fCfJ/yT5WpKXdvdFS3dQVU+tqjOq6owLLrhgiw8CAAAAmK2VBAy1zLRe4TIHJLkqyW2T7J3k2VV1xx9asPv47t6vu/dbu3btCkoCAAAAVpOVBAwbktx+0fM9k5y3sWXG7hC7J7koyeOS/FN3X9Hd5yf5WJL9phYNAAAArC4rCRhOT7JPVe1dVTdMckSSk5Ysc1KSI8fHj0ryoe7uDN0ifrYGN0ly/yRf3DalAwAAAKvFZgOGcUyFo5KcmuQLSd7W3WdV1TFV9fBxsROS7FFVZyd5VpKFW1kel2S3JP+RIah4fXd/dhsfAwAAADBjO69koe4+JckpS6Y9f9HjyzLcknLpepcuNx0AAADYsaykiwQAAADAJgkYAAAAgMkEDAAAAMBkAgYAAABgshUN8gjXV+uOPvk638f6Yw+7zvcBAABwXdOCAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMNmKAoaqOqSqvlRVZ1fV0cvM37Wq3jrO/2RVrVs07x5V9YmqOquqPldVa7Zd+QAAAMBqsNmAoap2SnJckkOT7JvksVW175LFnpzkm919pyQvS/Kn47o7J3lTkqd1992SPDDJFdusegAAAGBVWEkLhgOSnN3d53T35UlOTHL4kmUOT/KG8fE7kjy4qirJQ5J8trs/kyTdfWF3X7VtSgcAAABWi5UEDLdLcu6i5xvGacsu091XJrk4yR5J7pykq+rUqvpUVf3u9JIBAACA1WbnFSxTy0zrFS6zc5KDkuyf5LtJPlhVZ3b3B6+1ctVTkzw1Sfbaa68VlAQAAACsJitpwbAhye0XPd8zyXkbW2Ycd2H3JBeN00/r7m9093eTnJLkPkt30N3Hd/d+3b3f2rVrt/woAAAAgJlaScBwepJ9qmrvqrphkiOSnLRkmZOSHDk+flSSD3V3Jzk1yT2q6sZj8PAzST6/bUoHAAAAVovNdpHo7iur6qgMYcFOSV7X3WdV1TFJzujuk5KckOSNVXV2hpYLR4zrfrOq/jxDSNFJTunuk6+jYwEAAABmZCVjMKS7T8nQvWHxtOcvenxZkkdvZN03ZbhVJTAj646+7nO99ccedp3vAwAAWL1W0kUCAAAAYJMEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYLKdZ10AwEqtO/rk63wf64897DrfBwAA7Ii0YAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJPtPOsCAK5v1h198nW+j/XHHnad7wMAABbTggEAAACYTMAAAAAATKaLBABbTXcPAAAWaMEAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyXZeyUJVdUiSv0iyU5K/7u5jl8zfNcnfJrlvkguTPKa71y+av1eSzyd5YXe/dNuUDgDbxrqjT77O97H+2MOu830AAMzSZlswVNVOSY5LcmiSfZM8tqr2XbLYk5N8s7vvlORlSf50yfyXJXnv9HIBAACA1WglXSQOSHJ2d5/T3ZcnOTHJ4UuWOTzJG8bH70jy4KqqJKmqX0hyTpKztk3JAAAAwGqzkoDhdknOXfR8wzht2WW6+8okFyfZo6pukuQ5SV60qR1U1VOr6oyqOuOCCy5Yae0AAADAKrGSgKGWmdYrXOZFSV7W3ZduagfdfXx379fd+61du3YFJQEAAACryUoGedyQ5PaLnu+Z5LyNLLOhqnZOsnuSi5LcL8mjquolSW6e5Oqquqy7XzW5cgAAAGDVWEnAcHqSfapq7yT/neSIJI9bssxJSY5M8okkj0ryoe7uJD+9sEBVvTDJpcIFAAAA2PFsNmDo7iur6qgkp2a4TeXruvusqjomyRndfVKSE5K8sarOztBy4YjrsmgAAABgdVlJC4Z09ylJTlky7fmLHl+W5NGb2cYLt6I+AAAAYA6sZJBHAAAAgE0SMAAAAACTCRgAAACAyQQMAAAAwGQrGuQRAFj91h198nW+j/XHHnad7wMAmE9aMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAm23nWBQAALLbu6JOv832sP/aw63wfAHB9owUDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMtvOsCwAA2BGtO/rk7bKf9ccetl32AwCbI2AAAGCTtkdYIigBmH+6SAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMlWFDBU1SFV9aWqOruqjl5m/q5V9dZx/ierat04/eeq6syq+tz4789u2/IBAACA1WCzAUNV7ZTkuCSHJtk3yWOrat8liz05yTe7+05JXpbkT8fp30jy89199yRHJnnjtiocAAAAWD12XsEyByQ5u7vPSZKqOjHJ4Uk+v2iZw5O8cHz8jiSvqqrq7n9ftMxZSdZU1WecyfoAACAASURBVK7d/f3JlQMAwBZYd/TJ1/k+1h972HW+D4DVaiUBw+2SnLvo+YYk99vYMt19ZVVdnGSPDC0YFjwyyb8vFy5U1VOTPDVJ9tprrxUXDwAA1zeCEmC1WknAUMtM6y1ZpqrulqHbxEOW20F3H5/k+CTZb7/9lm4bAADYwQhKYMezkkEeNyS5/aLneyY5b2PLVNXOSXZPctH4fM8k/5DkV7r7K1MLBgAAAFaflQQMpyfZp6r2rqobJjkiyUlLljkpwyCOSfKoJB/q7q6qmyc5OcnvdffHtlXRAAAAwOqy2YChu69MclSSU5N8Icnbuvusqjqmqh4+LnZCkj2q6uwkz0qycCvLo5LcKcnzqurT48+tt/lRAAAAADO1kjEY0t2nJDllybTnL3p8WZJHL7Pei5O8eGKNAAAAwCq3ooABAACA5e0oA1buKMfB7KxkDAYAAACATRIwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDJ3kQAAAGCH4W4Ys6MFAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYzyCMAAACsMvM4WKUWDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGAyAQMAAAAwmYABAAAAmEzAAAAAAEwmYAAAAAAmEzAAAAAAkwkYAAAAgMkEDAAAAMBkAgYAAABgMgEDAAAAMJmAAQAAAJhMwAAAAABMJmAAAAAAJhMwAAAAAJMJGAAAAIDJBAwAAADAZAIGAAAAYDIBAwAAADCZgAEAAACYTMAAAAAATCZgAAAAACYTMAAAAACTCRgAAACAyQQMAAAAwGQCBgAAAGCyFQUMVXVIVX2pqs6uqqOXmb9rVb11nP/Jqlq3aN7vjdO/VFUHb7vSAQAAgNViswFDVe2U5LgkhybZN8ljq2rfJYs9Ock3u/tOSV6W5E/HdfdNckSSuyU5JMlfjtsDAAAAdiAracFwQJKzu/uc7r48yYlJDl+yzOFJ3jA+fkeSB1dVjdNP7O7vd/d/JTl73B4AAACwA6nu3vQCVY9Kckh3P2V8/oQk9+vuoxYt8x/jMhvG519Jcr8kL0zyr939pnH6CUne293vWLKPpyZ56vj0Lkm+NP3QNutWSb6xHfZzXXMcq4vjWH12lGNxHKvLjnIcyY5zLI5jdXEcq8+OciyOY3XZUY4j2XGOZXscxx26e+1yM3Zewcq1zLSlqcTGllnJuunu45Mcv4JatpmqOqO799ue+7wuOI7VxXGsPjvKsTiO1WVHOY5kxzkWx7G6OI7VZ0c5Fsexuuwox5HsOMcy6+NYSReJDUluv+j5nknO29gyVbVzkt2TXLTCdQEAAIA5t5KA4fQk+1TV3lV1wwyDNp60ZJmTkhw5Pn5Ukg/10PfipCRHjHeZ2DvJPkn+bduUDgAAAKwWm+0i0d1XVtVRSU5NslOS13X3WVV1TJIzuvukJCckeWNVnZ2h5cIR47pnVdXbknw+yZVJfrO7r7qOjmVLbdcuGdchx7G6OI7VZ0c5Fsexuuwox5HsOMfiOFYXx7H67CjH4jhWlx3lOJId51hmehybHeQRAAAAYHNW0kUCAAAAYJMEDAAAAMBkAgYAALaZqrrprGsAYDauN2MwVNVOSQ5Lsi6LBrfs7j+fVU3AtldVn0zyuiR/193fnnU9W6uqbpLke919dVXdOcmPJ3lvd18x49JWpKrWJLliYWDfqrpTkkOTfHUcHBjYAVTVHyR5Z3d/cbzb2MlJDkhyWZLHdveHZlrgVqiqW2S489mahWnd/c+zq4jxe/yP5Nrf4b82u4pgdaiqA5O8MMkdMvx9VJLu7jvOqqbrUwuG9yR5YpI9ktx00c/cqKqfr6o7LHr+/Kr6TFWdNN4GdO5U1UFV9avj47XzeBxV9dKqutus69haVXWHqtp90fMHVdVfVNWzxi+L8+bIJHdM8umqelNVPXjWBW2lf06ypqpul+SDSX41yd/MtKItc2qSH0uSqvqxDLco3jfJs6rqj2ZZGNd83r60qk6pqg8t/My6ri1VVftU1Tuq6vNVdc7Cz6zrWqmq2r+qbrPo+a9U1bur6hVVdctZ1rYFHpfkS+PjX0lywwzftX42yZ/MqqitVVVPyfD5e2qSF43/vnCWNU1RVQeOgXWq6per6s8Xf5ecB1X1f5J8Pcn7MwRYJyf5x5kWtRWq6v5VdXpVXVpVl1fVVVU1lxdC5v2zd7GqukVVHVBVD1j4mXVNW+iEJH+e5KAk+yfZb/x3Zq5PAcOe3f2I7n5Bd79o4WfWRW2hP0pyQZJU1cOS/HKSJyU5KclrZljXVqmqFyR5TpLfGyftkuRNs6toq30xyfFV9cmqetrik/U58bYkC18+7pXk7Um+luSeSf5yhnVtle7+Ync/J8PVp3cm+duq+q+qel5V3XzG5W2J6u7vJnlEkld29y9mOEGfF7fs7v8cHx+Z5MTu/o0kByd5+OzK2npV9Yiq+nJVXVxV366qS+b1y2GSNyf5QpK9M5xErU9y+iwL2kqvT/LqDLfCflCSv03yxplWtGVem+TyJBm/1B6b4RguzvzcLu3y/kFz2EMytB67srvPyvD/+rz5rQxfzr/a3Q9Kcu+M373m1KuTfLeq7pnkd5N8NcN7bJ78VpK7dPfduvvu4889Zl3UVnhVkscm+XKSGyV5SpJXzrSirTfvn71JdphA8eLufm93n9/dFy78zLKg61PA8N6qesisi5ioxxOOZDjpOKG7z+zuv06ydoZ1ba1fzHCi8Z0k6e7zMmetSpKku/+6uw/McOVmXZLPVtVbqupBs61sxW40vvbJEFq9rrv/X4Yr5gfMrqytV1X7Zvii/idJ3p3huC5PMk9XaKuqfjLJ4zNcrUkWNQ2dA4v73/1shitP6e7vJ7l6JhVN95IkD+/u3bv7Zt190+6+2ayL2kp7dPcJGbqxnNbdT0py/1kXtRVu1N0fzBDIfbW7X5jh/TYvdurui8bHj0lyfHe/s7ufl+ROM6xrS3y/qu5aVQutFt63aN6NZ1TTFJd192VJUlW7dvcXk9xlxjVNceUYAB2e5C+6+y8yf9+1zs0Qus297j47w9/9Vd39+gwn5/No3j97F+wIgeKHq+rPquonq+o+Cz+zLGievqxO9a9J/qGqbpDkivygf8o8fTmsqtotyXeTPDjXvrq8ZvlVVrXLu7urqpNr+pzPpbFv4I+PP99I8pkMTcF/vbuPmGlxm1eLHv9sxhYlY9//2VQ0wTgGw/cyjMPw/O7+3jjrY2M/tXnxjAy/i3/o7rOq6o5JPjzjmrbEWVV1bJL/TnLnjCcdYwuf+XtjDb7e3V+YdRHbyMJYHv9TVYclOS/JnjOsZ2tdNv6//uWqOirD++3WM65pS+xUVTt395UZ/l9/6qJ58/Id7dkZWlLeKsMJ7DlJUlUPTfLZWRa2lTaMrd3eleT9VfXNDH8f8+qSqvq9DEH7A8bvK3PRsqSqnjU+PCfJR6rq5CTfX5g/h+OofXfsevrpqnpJkv/J2IJ0Ds37Z++Cy7r7sqq6JlCsqnkLFO83/rvfommdGQY+16dBHs9J8gtJPtdzetBV9aQkv5/k20nO7+5Dxun3TvLS7p6rvuZV9dsZmrH/XIYrzU9K8pbunqvmYlX15xlaYnwwQ6uSf1s070vdvao/qKrqL5L8aIb/6B6e5M7dfUVV/WiS93T3fpvcwCpTVXde1DR/7lXVTbr7O7OuY0uNgeEzM7y3TujuT43TD0yyT3f/zQzL2yrj38ptMpx4LP6S+/czK2orjd3s/iXJ7TM00b1ZkhfN2wCcVbV/hq4eN0/yh0l2T/KS7v7XmRa2QjUMkPjQDMH0XknuMwbvd0ryhrF1HDNSVT+T4T31T919+azr2RrjGB+PS3J6d/9LVe2V5IHdveq7SYxdaTemu/uY7VbMNjCOffH1DOOUPDPDe+svx1YNc2XeP3sXVNU/ZGix+4wMJ+TfTLJLdz90poXNuetTwHBqkkO7e16b5iZJahjw7dZJPrNwLOOJ4C7zOJpuVf1ckodkuKJ5ane/f8YlbbEx+DlxUfeVxfN27+5V3ayvhmYKj8lwIvi27v7vcfq9k9y6u0+dZX1bqqrWJnlxktt198PG7hIHzNsJ7dg94oQku3X3XmP/2V/v7qfPuLTrrap6/TKTe+xeAFulqu6f4fP3fQthYg13jtltIZhbzarq/y6Z1BkCk4/N6feS+yc5q7svGZ/fNMm+3f3J2VZ2/VVVj+7ut29uGkwxz4Hi2BLxbrn2nW9mFsBdnwKGv8kwsvx7M6fNq5bpT9NJvtHd586inqlquGPE/yzq63ijJD/S3etnWthWGIOfhdvDJHFLq1kZm1C+OclzuvueVbVLkk91991nXNoWGbt6PCrJSd1973Haf3T3T8y2spWpqn/PtcdhWDjp+HCSl41jMTAjYxD3a/nhWzfPRVhSVS/v7mdU1Xty7fdZkqS752Ig0frhO0V0km/NU0vLqvrDZSbfMkPrxD+Yt5PA8bPrPgu/g7EZ+BndPdM+zVuqqi7JMn8bC+api3BVfWrp67/ctNWufvh2gkmSnuHtBLfUxj5zF8zLZ+9iNee3QK2q12QY7+ZBSf46w3fHf+vuJ8+qpnnp37ct/Nf4c8PxZx79v2Wm3XLsz/XY7v709i5oorcn+alFz68ap8301ipbauxnfkSSz2c4hmT48J2LgGGZLyGLTwSf0zMeiXYr3Lq731JVv5MkY3ePqza30mrU3ecuGQdjno7jUctMu2WGO0q8Ismvb99ytl5V/W53v6SqXpnlT2aXXsGdB+/O0EXiA5mv99WChdHKXzrTKqY7Mz94Ty38se9WVZ9J8pR5CNzHASl/yDjo4/sz/L8+T2pxwDOORzR335e7+6ZJUlXHJPnfDH8zlWHg4LkY5LGqDs3Qheh2VfWKRbNuluHuBfPmhAxdI87MfH7uJj/4zH1Ehi6DC3d/e2yGuxHNlRpugfqCDF1XFlq5d5J5ukvJT3X3Parqs939oqr6f0lm2nVz7j4wt1aPt6Qcm7p1d18645K22Di66Q+pqv0yfGGft/u27ry4CVJ3Xz6GJfPmFzPcPmkur8gufAlZrKpukeSJGW5/+ujtXdNE3xmvCi5cfdo/ySWzLWmrnFtVP5Wkx7+L/5uhv+Nc6O6vLDP5K0lOH68QzpOF1/2MmVaxbd24h9u5zqXuPnP897SFaePn1u27e24GFuzuvZebXlWPyPD5e8j2rWjb6e4Lax5HCk7OGbt9vHp8/vQMgwzOq4O7+36Lnr96bCH3klkVtAXOy/C5+/AMJ+ULLslwoj5vLu7u9866iCkWPnOr6g+7e/F5x3uqai4urC2xcAvUebuYttjCYObfrarbJrkwwy2oZ+Z6EzBU1U9kSG9vOT7/RpJf6eE+zXOtu8+o4e4S8+aCqnr4wqBiVXV4hivn8+acDCMyz2XAsJzu/maSl1XVE2Zdy1b47STvSXLHqjotye2y/NX01e5pSf4iQ/0bMtyFYUcZf2GubpHc3e8Z/33DrGvZhv6xqh7a3afMupApquojGU4+dk7y6Qz/r5zW3c/a5IqrXHf/fVU9d9Z1TFFVD8h83lrwaRku2jw3Q1D9wVz77h7z5qqqenySEzMcz2MzJ1fPu/szST5TVW/u4U4r8+7DVfVnGa4uL+6uverHWlnG2qq6Y//grjF7J1k745q2xo5wC9R/rOHON3+W5FMZ/s7/apYFXZ/GYPh4hr6AHx6fPzDJH3f3T21yxTlQVT+S5JTuvu+sa9kSVfVjGfrK3zZDs71zM4Q+czWablW9M8k9M3wJWfwfxjw2m77GOHbBmd09T83EkiTjFf+7ZnhffT4ZWsjMtKgtVFUHdvfHNjdttaqq5d43t8hwq7TLu/s3t3NJk43jFjwnyb659kBKc3fv77Fr1E0yfGbN662bU1X/3t33rqqnZGi98IKxmejcfW4tNl40+Gh332vWtWzOMuOtJMPFnIuS/HJ3f377V8WCqlqXIaw+MMPv6WNJnjEP3W8WVNV/ZfnuaXMzdkGSVNVyt5ruOf0/5JAkx+cHrXvWZRiIet4GBj8hyV2SzPstUJMkVbVrkjWzHmD+etOCIclNFsKFJOnuj9RwG7W5sZH+v7fMMI7Bb23/iqYZm1Dff/wiVQsjNs+hk8afuTQ2xV3qFhnuLPGO7VzOZFX1V939a0k+Mz6/cYb+5j8308K23CuTLB3Aarlpq9VxS553hmZ7H8kPmh7PmzcneWuSwzJc5TwyyQUzrWgrLdc1ak7tPN5J6ZeS/MGsi9lSVbVcS4tbZGiV8artXM7WWtpCrJNcOOsvuFtqBx1rJWOQcPis65ho8e2y12Tourl0gNRVb2NdnedRd/9TVe2T/P/t3XeUpFW1/vHvM0MUGBRBFJCgJAEdwpCDoKKigiIqoICC8aowwL2g4u8KZlSUqCKoCIioSBAkSmZA0sAgabwqSFAECQJKkPD8/jinZqqL6p6u6pk+dar2Z61ZXe+p7rV2T1e99b7n7LM3q+ehmZVuFb47/6u2Rp+khUgZrpuRzl3TJH2/UUS/hEGaYLhD0v8yuzDULqSijzVp3f/buGDf1/YDBeIZkzzLtgO5inljq2bJtird6IO06W1bjhuvq8Ntn10gnrH6h6Qjbe+ZU8Z+A/ykcEyjptSechNS+mHzzcckYGKZqDpne/PSMcwDL7X9I0lT8z7Uy/I2nGpIWt32zDZdiYAqU3W/BJxPWu2/TtKrgD8WjqkTrRM9JhXk28X2zQXi6Vi7eitK7Zt/XCCcsejHWivVd4yBVM+jZegwSdOAL5SIp1OSdrH902EmFKtdLQdWIa3+LwRMloTtEwrH1JFGjb7KnUCqS3JkPt6ZdL9brIbaIE0w7AF8kbTvSaQK/7sXjahD7W5kJa1b4+RC9mvSvqfpVFy/IM/gfp0Xpk1Xkbpnu6r3wZzYPkDStyV9l7Tq8W3bvywdVwcWABYlnZ+bbz4eo85aErNIOsP2u0rHMQbP5K/3KfWc/huwXMF4urEvaT95u65EBqpK1c0tEE9pOr6DNHFdhXYXt5JebvvvJeKZiz5NZRMMts/K7erWsr1f6Xjmoto7xrS2aZ9A+myvKQurkTFdU8wjknQgsCXp2vccYBtgGulmt+epT1odZ6vZntx0fEnuRFTMwNRg6Fc19gFukHSL7bVKxzFWeRb9QOBQUjbA7qT31oFFAxuDGl9Xkpo/DESaULyGtK+ORjHRWkhawfZdpeOYmxr75UvH0S1J7yBdqL+StFIwCfhiba+tfiLpm8BXSFW0zyPVw9nb9k9H/MEeVuP5t1XN73VJF9e4J344kmbUUMtjJC21C54ltUM8xPYfykQUJN1MOt/eaHtyrgf3Q9utWbE9SdJ6tqdLen2755s7FPU6ST8BjrZ9dT7eEPig7WKFwfs+g2G4mamGymao2qmxBVTDVZJeW0sa6AgWtn2RJOUbwoMkXUGadKhVja+r1lSwm4EX5XFTX52MJ3K16TWpvKBgk2raB7Zj+zf54aNA9XtpcxvUFRmaNl3F6lOTN9veX9L2pG4r7wUuYXZv9hrVeP5tVXOm0o2SziRlxvy7MWi7aF/5Mai+Y0y/1C7oh+0qTZ60/bykZyVNAh4Aqsjchfatjiu2IbCbpLvz8fLA7XkSyCWKHvf9BANwSP76buDlzL7o2Jk0A1q7mvcObQZ8KFcHfprZVcxrq/79lKQJwB8lfRr4K/CywjGNVXW1F2zvmtNbP2X7iNLxzAWNgoLvoOKCgpLWtj0DwPYH89g2rrAXeN7ffziwMfA88Dtgn0abrppIOhF4Nam1YyNt2lSS3tpk/vz1bcDJth9u1POpWNH2Yt3Kn38n2H4MOEDSOsDnbF9UOLROLUGqQ9Q8mWvSFtsaTSX9PartGCNpcdKizRZ56DLgS7UVEqUPtqs0uT7XuTqWtNX5X8C1ZUPqnKRNgYOAFUj3xY33RzWTJcBbSwfQamC2SEi63PYWcxqrhaSXkIqrNK9sXl4uos5JWqHdeG1p4ZLWJxWHejHwZWBx4JuNVKUwviRdanvL0nGMlaTpttdrbrkn6TLbbdP5epWkG2hqVSfpvcD+ttcvG1nnJF1N6o5xch7aCdjT9oblouqOpNuBNVz5RYCkg0mr5U8CG5DOw7+p7W+i1Lb5XttPK7XRfh3pZv2fZSMbvca5StKbgb1IN4THuJIW2pK+Yfszkt6ba3uEHqHUDvwWoFGLbFdgsu12XbB6Vj9sV2knt0KdZLu6LEVJM4F9SJMksyZ92hQW7XmSXsbQ+8K7R/j2eRtL5dcWo5Yvpt7eWGmStBJwju3XlI2sc0r9vqeSiovNADYCfldr6nQvvSEGmaSNSPvKX0MqNDgR+HdNqxwAkr5CKqT0c4amt1b1wSfpatsbSTofOIJUUPBXtl9dOLSOSFoZ+CXpZnwz4MPAO2w/UjSwLki6pvXGtfF3KhVTtySdAuxl+77SsYxVnnB/zPZzuS3tpNqKJEqaQSpctyKpK8aZpMJdbysZVyck3ZT3Yh9K6upxak21GHI68brANbXXwGhV+6JUuxvzGm/W8/XJVTVvV2kmaVlmr/wDdb2uoP3nem1yDbJvA8uQtqqsANxue81SMQ3CFomGfYBLJTVSWVcEPl4unDGZCqwPXG17K0mrU+FWieHeEKQ959WQNIXUf731JFvbVo+jSDeBp5AudHcDVi4aUXcaK/zNF4hmdmplLb6S00L/m9kFBfcpG1LnbP9J0vuBM0jbh7a2/UThsLp1iaTPkiavDOwInC1pCQDbD5cMbjSa6hItBtwm6VqauvjUVpdI0m5Nj5ufqm2rx/O2n821JA6zfaSkG0sH1aGbJJ0DrAp8XtKijFADqwedBzwILCLpsabx6rYUNBtuUYq6OsY8KWkz29NgVlr7k4VjGjVJj5PeC6Ly7SoNkr5B+gy8jaHb7KqYYNDsziSX5HpXpzH0s7Cmls1fJr2vL7S9jqStSKUAihmYDAYASQsCq+fDmbarbI0o6Trb6+cVjw1zSmWNM7k3kT7ghrwhbH+scGgdkfQHYD9SUcHnG+MVbvW43vaUlpT8q2xvUjq2QZNrSexl+9DSsXQr3xw1f8C8HPgn8BRAjSuEuV4MzP69mu9oq9izOVzF7IbaCl5JOrLpcCHgjcANtqtq6SrpGuAw0mT1trbvVGWdlvJ5az3gT7kWxpLAK21XNVEi6de231k6jrklZ2Y0FqXWbixK2d6xcGijJmkyadJwcdJ592HgQ7aLtuIbZPna93UV30tdMsLTrikrvOn6/SZgnVx881rbG5SKaWAyGCS17tN6taRHgZttP1AipjG4NxdWOQP4raRHSOnTtXnG9kOSJkiaYPuSPCNam3+4P9rUPSFpAWCGUuu3+5jdu7kakg5oN277a+MdS7dyqvd2pNantarqBm8kuc7KPbZXyscfBHYgFQo+qIbMhSZ/BZa2fWXzoKQt8nNVsb1n83HO+jmxUDhjsTupmOtX8+TCSlTWCSOft/5Aur5afY4/0KNaJxfyavn7bX+qUEhj9ZTtpyQhaUHbMyWtVjqoTuSJhMlK3QrIhUSrIektwGK2f9Uy/n7SNeRvy0Q2JneQiuxWOcHgPulMkv0zZ4xdAZwk6QFSO9diBiaDQdLZpMrfF5NmP7cErial8n3Jdo0XJI3VqMWB82z/p3Q8nZB0Iak419eBJUnbJNavbcVc0htJqUgXMTS9qqqK00pFNx8gfWDsQ3pdfc/2n4oG1iFJn2k6XAh4O3Cr7d0LhdQVSV8l/Q1+wdBaEtWk7eUVzRtsTy4dy1goFap8U16V3YK0RWJPYG3gNTWtlkv6DXBAa02SvNXrQFfSw3w4kuYHfl9jfaXaSdqDtKVrWVJGX2PVfMuScXVD0trA+4H3AXcCp9k+cuSf6k2STidNYO1Nyhp9BJi/hvoekrYlvZ/vysdfIE3u3gVMtX3nSD/fK5QKBG9r+x8t4y8HTre9cZnIuqdUeHMyL7z23atYUB2QtC/wqO0ftYzvCUy0fViZyDqXaw89Rbq/3YW0pfakkosfgzTBcBbwEdv35+Olge8DHwEuryENsbHPdziVraIhaRHSHroJwAdIN1Mn1Va5VdJPSVtvbmX2Fgm7zr7GfUfSQsAZtnuujc9IhknfqyptD0DSycD/2K5udbyhUbwuP/4uacXpoHxc1fa0kdLuJd1s+7XjHdNYNNWUgPRZsgZwiu3PDP9TvUPSL22/L6exN1+QVde2Of8OG5CKTq8taU3g/9kuuhd4tCStSqpDtDOpTeUvSOeuth2valTbopSk3wMb2X5C0juA75D+PusA77X9lqIBjlLz1tNOnutlOZPvBWwf326810i6BVi39X2Qt9NfV8PfpKm2x5Dh/PUp4M/A512gVfDAbJEAVmxMLmQPAKvmFalnSgXVoenMLhKzPGkWWqS2XHcDK5ULrTN5ZfPXtt9Euimv4oQ0jMm1XZQ3G+ECF6iyWGWrBYGqOi9AX6XvLQncLul3DM3EqKm92ERJ89l+lrTHv7lOTG2fowuN8NzC4xbF3HNI0+Nngbts31sqmC5MzV/fUTSKueMp20/mVPwFbN9a2VaJmaQU420bmXuSqius2zDMotTN+euipDoGvc6eXRT43cCPbE8Hpkv6ZMG4OrVQ02fILDnjqsbzbjUTCSNwu0m2XNdO7X6g19hebLjn8n3WWsBJ+eu4qu3CaCyuyKmhjd7GOwCX51X0KvpMN+3/PRo407nNjaRtgDeVjK1Tea/mE5IWt/1o6XjG6GpJa9i+rXQgXeqLC9zGh3dLccGJwCuAauovSNrF9k9z+t4L2P7OeMc0RgeXDmAuOBm4vEHNxAAAIABJREFUTNKDpKyrK2BWC87azl/XSfqo7WObByV9mDSJXZXWopSSJkr6gO2TSsXUCec2obbvypmV6+enrq2wPtR9uT7UWcD5kh4G7p/Dz/SSHUgZDJdIOo+0FaqKG41hNC9KNTSODfR8UVpAeW/5E6TJ3e81PTfSZGmvOQ04VtKnbf8bZmXxHpGfq46kVUhbnNdgaPvTGl5XQMpmb1l8bmS4V8/2c6TOPkW2dg3SFgmRPjw2JZ1cpwGnusL/AEnTba/XMna97SmlYuqGpF+S2qr8lqErm1Xs32qQdDtphfxO0j606lJb+4GkG2yvK6k5W+FZ4O+uqMqxpI/b/oGkA9s9b7vGlrRLklqfAlxv+8GS8XRD0kakyaoLmi4QVwUWrawuxtLA6cB/mD2hMAVYANje9t9LxdaJXOztU6T9/meSPkc+ReroM6O1UF+vk/Q+4FvApaTPkM2B/VqLwtUi1yZaHDi7pvMvzLrxexcpFf8NpAzL021fUDSwLuRr31favrt0LN3IdT0OAB4DHmhsdZS0DnCI7TeWjG+0JM0HfIW0LbvRYWx54EfA/9quJZN6FknTgANJxai3JdX5kO221y69RqnF8V6kujGNz/D1gG8C3+2DDI2iBmaCoZ9IOp+0gvZT0iz0LsAWtexFa6h9/1ZDLo74Aq6vTeW7gW8ALyNd4FbVn1nSjbbXKR1HGErSDqQLkCtIr6lNgH1sn140sAGn1Ba4kTZ5q+2LS8bTKUm/Jm0T/B1pZfMlpEmSqbZnlIytG0rtxbZuZC1IWorUwrmaAqmStrR9actYNdkk7eRtBu8Fdqyt/k1Du0WpmkhalnRdcpPt5/PYK0iFKquaOJG0MLByPvyT7SdLxjMWjddVc+0eSVfY3rx0bKOVM8A/S/osNKmW2sG2zy0aWB8YmAmG2m+emuUPvAOBLfLQ5aSexjXsp0PS8rV9KAxH0gRSheOeLxI6J5L+RNp7envpWLoh6V5SAai2attaoNSmbk9gRZq2s9nerlRM3cg3Tm/20AK7F9R049TvJH3M9jGl4+hEy0XtROBBYHnbj5eNrDutBTbzZ8tNNdX3kXQlaSVwf9Ie/2NI15nvKhrYgMuFaX9i+7rSsYTZJB1j+2Nz/s7elN/vmwO/InXo+yvp5ryqFqhh3hikGgzfpOKbp2Z5ImHqHL+xd50BrAupzY3tHQrH0zXbz0u6qU8mTe6v/P0xkXRRW/Oe2WZnkNInz2J2d5IaTWjZ4/gPUrX/0Ds+QboZrMmslOJc0+fOWicXsvNyduLJ+XhH4JyC8XRjc9Lkwo2k83G1LcD7zFbAJyT9hbQdNbZx9oaqtjW3sTfwItI2gy+TXmdtM5Nr0dhqWzqOfjBIEwy13zwh6TDbe2toW65ZKlrZbL4BrKYYzAheAdwq6VqG1pKo5e/RcL2kX5BubJt7GtdSgOg+218qHcRc9JTtI0oHMRdcIOkc4Gf5eCfg/ILxhBeqcVJusqTH8mMBC+fjKrMTbe+XtxM16kQdU+E2oknAZOBe0ufi0pJUY62rPrNN6QBCW7UVcQVmtf5erCkj5l/A7jk7sbaix61q/CzsSYM0wVD7zRNAYyXgkBG/q/d5mMe1qq7o3jAmkSo1v7lpzNRT4bjfPhgOz4UeL2DoOauagoLZ/5D2MG9G+hsdT0qpDL1j29IBdMr2xNIxzG22TwVOLR3HGFxLKrx3TC6U+C1S7ZXNyoY12HKHks2AVWwfl+t7LFo6rkHXKFhZoSOA83jhteGbSO/1/xr3iOaes0sH0C8GqQbDcW2GbXuPcQ9mDPJe0+Nt71I6lm5Jeo7ZaXoLk25qodKVJ5hV6HEV2xdKehEwsaZ03fy62sv2oaVj6ZakJWqpQzIakr4O7Ar8mdlbJFxLoTFJ3wM+a/uxOX5zGHfDtEF9FJheY5HE2vVDnShJK9m+s2XsDRUWEK3+b9EsT1RPAVazvaqkZYBTbG9aOLSu5e5dkKr9H1U0mA4Mk4H8KHA98APbT41/VJ2RdJvtNYZ57lbba453TKH3DEwGg+3dS8cwN+S9pktJWsD2f0rH041+W3mS9FHgY8ASpHaVywJHkyqbVyG/rrYjVfuvUj9NLmTbA6+q9X0O/AWYLulA2z+b0zeHcTcl/zsrH78duI60V/sU298sFtlgqr5OlO07Je0EvNr2VyW9kjpTpqv/W7TYHliH3IrP9t8kLVY2pLGx/Zrc/njD0rF06A5gKYbWWrkfWBU4lrSo0OtGyhatpr6SpGm2N5P0OEMnfaqeUOwVfT/BIGl/29+UdCTt6xbsVSCssfoLcKWkMxm657+qKvl95FPABsA1ALb/KOllZUPqylWSjgJ+wdDXVW0p+f3iJuDFVLpPM593TwK+I+nDwPdpKlZZ2fa0fvRSYF3b/4JZq5y/InUnmk66yQrjpx/qRB0FzE96DX2V9DlyNLB+ybi6UP3fosV/bFuSAfL2lerZfpD6UtrXsb1F0/FZki63vYWkW4tF1ZkHJG1g+9rmQUnrk4o4V8H2Zvlr1ZNtvarvJxiAxofE9UWjmLv+lv9NAOKNUd7Ttv8jpUldSfNRZ22JTfLX5kKJBqpIye9DSwMzJV3H0BoM1RQPtf1XSWeTbja2pWmrB/XU9uhXywPN2THPACvYflLS08P8TJh3+qFO1Ca215V0I6SsMkkLlA5qtPLWCOiPv0WzX0r6AfDinHG5B2m1vBqSNgKOBF4DLEDqUvLvCleZl2ruOiZpeWDJ/Fwt2Yr7kV5TPyFNRkPKhtuNVMS5SnlhcKHGcR90hitqECYY7gawfXzrE5KqLERi+4sAOcXNjRWoUMxlkg4gVTHfGvgks9OOq2F7q9IxhCEOLB3AWEhak5S18DdgA9v3FQ4pDPUz4GpJv87H2wIn59XN28qFNbBqL7IL8IykCeQJdkkvpa4Wu83FTmv/W8xi+5B8bfIYsBrwBdu/LRxWp44i3byewuyb2ZWLRtSd/wamSfozKRV/JeCT+bz7gvuUXmT7WkkbkLJ3P5SHbwE2tF1dxmXeHvxtYBlSxugKpMXpqCUxBn1f5FHSHcB7bU9vGf8iaY9ddf1OJa1F6iixRB56ENjNdi3pVX0lX1B9mNkXI+fb/mHBkLoiaXHSTW0jfe8yUh/zGvfQVkvS6rZn5scL2n666bmNbF9dLrrRy0W4ptq+oHQsoT1JU5jdFnGa7X7K9AvjRNJ8tp+VtBtpv/8U4MfA+4Av2v550QA7JGlT21fOaSyMH0nX254i6fe2X5fHrrK9yZx+ttdIWhBYnXTenVlDYcc5kbRurdtpJd1EytS90PY6krYCdrb9scKhVW0QJhjWI814fsD275Ty2L9PKqjyrhornEu6Cvi87Uvy8ZbA12o80dZM0juB5Wx/Nx9fSyreY2B/21W14pN0KmkWujGLvisw2fa7h/+pMLdJuqEx8dn8uN1xL2udHAm9J3ePWZqmbMZICx1f/VAnquWctSapXZ1IF+y3FA2uC+3OszWde1v1Q1cMSZeTXlc/BP4O3Ad8yPbkooF1QdImwIoMPe+eUCyguaDy90dj8uomUo2M5yVda3uD0rHVrO+3SNieLuldwOmSPgV8ND/11oqrsy/SmFwAsH1pvxTtqcz+DN1vtgCwHqm/9HGkgmk1ebXtHZqOvygp2tWNPw3zuN1xz4rJhd4maU9SxtL9wHPkmw7gdSXjGkD9UCdq1nkpZ1JWmU0paWNSLaKlWtq4TiLt+a9VP3TF2JVUd+zTwD7AK4EdRvyJHiTpRFK3sRmk8y6k827VEwxUdG3Sxj8lLQpcDpwk6QHg2cIxVa/vJxgkLQHcC3yQVLDnQtIJalFJtba2u0PS/5K2SQDsAtw5wveHeWMB2/c0HU/Lr6eHK53weVLSZranQUoJBZ4sHNMg8jCP2x2H0K2pwGq2HyodyCCzfVb+WsX+62G03pAPUVGHqwVICwTzMbSA9mPAe4pENHdU3xXD9l2SFgZe0ahDVqkpwBruv/Txmv8m7wSeIk1cfQBYnKHFzkMX+n6CgVThtPFGfpzUM/daZq/WvKpQXGOxB+nNfBrp97gc2L1oRIPpJc0Htj/ddLjUOMcyN3wCOCHXYgB4hDQxF8bXcpKOIL23G4/Jx8uWC6s7kt4AXG37idKxhCHuAaK+SmGSzmKEicNKusZMJN2Y17yKie3LSEWbf2L7rtLxjFU/dcWQtC1wCGkSaCVJa5NqRNXw/mh2C/By0haP6uUCiVvkx881JkxrYvvfTYc1T/T2lL6vwRDCvCLpJOBS28e2jH8c2NL2zmUi64ykqbYPbxSxkjQJoMb6JP1A0oiTOrWtdEo6AdgIeAi4Iv+bZvuRooENOEk/IlWUP5uhNx21rDb3BUmvH+n5fNPb02ref92OpEtoXw+jqpbNko4b4Wnb3mPcghkjSdNJhfgutb1OHptV8LEW+bW1Nmmhs8r20w2Svg5sAJyUh3YGrrf9uXJRda4fapT0ophgqEifrHT0jdwzt7Ei0Kieux6wIKmA6P2lYuuEpBm21+63i8TQWyQtQ0oz/h9gGduDkEHXsyS1bYNaefpxKEDSjY2bvn6Qi4M3LETa6/+s7f0LhTQm/dAVQ9I1tjdsfq1VOsHQdkKxhonEVpJ+D6xt+/l8PBG4scK/yZ+ov0ZJz4kLvLocUjqAMFvu97tJTgFv9Ms92/bFBcPqxu2S/kLaR/v7pvHGLG5VHxaht0jaBdgceC2ppe5RpCyGUFBMJPQGSTcz8sJBDeffN5YOYG5qbWsOXCmpuhvAJkcCrYsH7cZ62S2S3g9MlLQKsBdwVeGYOmb7MklLA+vnoWvztWStXgw0atktPtI39rDqa5T0oshgqIik5aOFWJgXJL0cOB94QRZMP+xFDeVIehD4M3A0cIntv5SNaLBJOsz23sNlxEUm3PiStMJIz8f5d/zl4uANE0iZiUfYXq1QSF1p6oqxN3Bo01OTgO1ravEo6UXA54E356Hzga/YfqpcVJ2T9D7gW8ClpEWczYH9amtrDiBpZ+Bg4BLS77IF8DnbPy8a2Cg11Sh5PakuRrU1SnpR308wtHxQvEBNXSRaek2f2tJSMIQQepKkNUkXH5sBqwB/sL1r2agGk6T1cvvmvknVDWFuknQnafJNpHZ1d5IKCk4rGliH8nt8S1IB56ObnnocOMv2H0vE1amcen+w7f1KxzJWkm4Ctm5kLUhaCriwpsmeZpJeQcrGEHCN7b8XDmnU+qlGSS8ahC0SjS4SApYnVcYXKa3nbmClcqF1rLlCc43dL0IPkvRL2+9rk6obWyQKkvQq4HBgY+B54HfAPrbvKBpYh3LR0OWBFYAVSWmUz5eMaZA10r9jIqE3SHqc9lskotBYIbZrui4cVmtXDEmLtFTMr4Lt51rqYtRsQsuWiIdIWTLVkLS67ZmSGlts7s1fl5G0jO0bhvvZXmI7uu/NQ30/wdD4oJB0NHCm7XPy8TbAm0rG1gUP8ziEsZiav76jaBSh1c+A7wLb5+OdgJNJrXZrMq3p31G2753D94d5aIQJRfLxw8Bhtn89/tENHtuLlY4hDCVpfuC/yO33SOnsP7D9TLGgxmYZSeeSWokuL2ky8HHbnywcVydulHQmcAowa5KkwjT28ySdT/osB9gROLdgPN3YF/gY8O02z5nU7aMaTa3Amz1K6ogRn4Nd6vstEg2Sptter2XsettTSsXUKUnPkU6sAhYGGn3lY6UjhD7TqJrdMna17Y1KxTQWta6e9RtJr7B93wh7/5cETrK9+njGFUKvkPRDYH6g0RJ4V+A52x8pF1X3JF1D6uBzZlMHhltsr1U2stEbJp29yjT2vPd/M9K1++XAGa7sZkzSBGDjmjqRDEfSMcDqpMkrSF1jbgVeCdxhe+9SsdVskCYYzidVLv8paYZtF2AL228pGlgIhUWKbm9pqhuzP/BP4Oekv8+OwIK2v1wqtm7kQmM/Aha1Xevq2UBp1GkoHUcIJUi6qXVPfLuxWgzT4rHa36dGkj5o+/g24/MDJ9jeuUBYYyLpd7Y3Lh3HWEm6GHiz7Wfz8XzABcDWwM221ygZX636fotEk52BA4HTSRfrl+exEAZapOj2nOa6MQAfb3rOQFUTDMBhwFuAMwFs3yRpi5F/JMwrkqbZ3qxpYlHNX21PismFMOCek/Rq23+GWfVwnisc01jcI2kTwJIWILV4rKotn6TlSK01NyWdr6YBUyvacjdV0oK2j2kMSFqE1Lmg1u5wF0jaATittgyMFssCi5C2RZAfL5Nrfzw9/I+FkQzMBEPuFjFV0qK2/1U6nhB6laSXAQs1jqM16vjqlwJjzWzfIzXXqK36Yr1qtjfLX2NiMYT29gMukXQHaeJtBaDmgnCfIBUMXpZUkO8C4FNFI+rccaS6RO/Nx7vksa2LRdSZN5HqLyxk+4jcPeIc4CLbny0cW7f2Jd2MPyvpKerNev0mMEPSpcxut/m1PAF0YcnAajZIWyQ2AX5IpOmG0Jak7UhFe5YBHiBdVN1ue82igQ0wSWsBazB0wueEchF1TtKvgO8ARwEbkVbPptjeqWhgAUmbAavYPk7SksBitu8sHVcIpUlaEFiNdMMx03asZBYkaYbttec01styR6VzSdu13wl833a7AoNhnOV2mxuQ3u/X2v5b4ZCqNzAZDMChRJpuCCP5MukG8ELb60jaithGVIykA0k9zNcgrXRsQ0oLrWqCgf5YPes7+fU1hXQTdRywAKlG0aYl4wqhFEnrA/fY/rvtpyWtTSr4dpekg3ImbDUkfWGEp11ZPZ8HJe3C7O4LO5NaPFYhF3YEOIY04X4RcG9jvMJuGABIegmwCkMXQS4vF1HXJgD/IN0Xryxp5Up/j54xSBMMkaYbwsiesf2QpAmSJti+RNI3Sgc1wN4DTAZutL27pKVJWVhVsf0g8IHScYQX2B5YB7gBwPbfJMW2iTDIfkBuX54XoA4G9gTWJt0YvqdcaF1p17VnEeDDwEupq57PHqQsuENJNRiuoq5tK9s2PT6zZcxAdRMMkj5CanO+HDCDtED1O+prU/kNUhHtW4Hn83CjVl/o0iBNMFRf5CaEeeyfkhYlnVRPkvQA8GzhmAbZk7afl/RsTq18AHhV6aBGq89Wz/rRf2xbkmFWwbEQBtnEpiyFHYFjbJ8KnCppRsG4umL7243HefJwKumm/Oek7ZA9T9Jytu/NtaC2a3luWyopkGi7psmQ0ZoKrA9cbXsrSasDXywcUzfeBawW26DmrgmlAxhHnyCl5TbSdNcGov5CGHiSVpa0KWlP4BPAPsB5pPTDPUvGNuCul/Ri4FhSZ4kbgGvLhtSRf7f5B2n17DOlggqz/FLSD4AXS/ooqZjVsYVjCqGkiblFHcAbgYubnqtyQU7SEpK+Avye9Dusa/szth8oHNpoXSRpxdZBSbuTOhSFcp6y/RSkmiW2Z5K23NXmDmD+0kH0mypPmF1azfaQNN18U3VloXhC6BWHAQfYbtwAPg8cL2kKcBBDU/vCOFDay/V12/8EjpZ0HjDJ9u8LhzZq/bB61s9sHyJpa+Ax0kXhF2z/tnBYIZR0MnCZpAeBJ0nF+JC0MrNb2FVD0reAd5O2d7y20g5q+wC/lfQ2238EkPQ54P3A64tGFu7NiyBnkP5GjwA1Fkd8gtRF4iJgVhaD7b3KhVS/QeoicYPtdec0FsKgkXSL7bWGee5m268d75gCSJpue73ScYyFpCVIraw+ABwPHG77kbJRhVa5g8RDlfcyD2HMJG0EvAK4oDHpLmlVUgeyG4oG1yFJz5NumJ4l7Smf9RQVtROU9EZSfYx3AR8hpeW/Iz5Leoek1wOLA+fafqZ0PJ2Q9MF247aPH+9Y+knfZzBI2hjYBFhK0r5NT00CJpaJKoSestAIzy08blGEVldLWt/2daUD6UafrJ71nXwDdTDwMKnI24nAksAESbvZPq9kfCGUZPvqNmP/VyKWsbLdF9ugbV8k6UPApaTijm9spObXpqmbRLNHgZsr2rYCgKQTbe8KYPuyxhiwa9HAOhQTCfNG32cw5Fm1LUk1GI5ueupx4KxGylUIg0rSycDFto9tGf8w8GbbO5aJbLBJug1YFbiLVL+gser0uqKBjVK/rJ71G0nXAweQVpuOAbaxfXUu0HWy7XWKBhhCCJmkx0mfHwIWBJ4hdYCr8nNE0tnAxsAleWhL4GrSZ/2XbJ9YKLSOtWaBS5pImihZo2BYHZO0CvB1Ukvw5nab1RTV7kV9P8HQIGkF23eVjiOEXpPbH54O/IdUTBBgCrAAsL3tv5eKbZBJWqHdeJzHwlhImmF77fz4dtuvaXruxphgCCGEeUPSWcBHbN+fj5cGvk/a+nH5cNtVe0mugXEAKcP1icYw6RryGNufKxVbNyRNAw4ktUDdllQrSrYPLBpY5fp+gkHSYbb3zm/qF/yytrdr82MhDBxJWwGND7dbbV880veH8ZNbCL4LeL/tt5eOJ9SredWpzQpU1CUKIYR5pLWuVS7ofLPttWqb4JX09domE9pp1Ltq/ttIusL25qVjq1nf12Ag7S8FOKRoFCH0ONuXMDttLxQmaQHgbaRq2W8FTmXoNq8QujFZ0mOkFaeF82Py8Uj1WEIIIYzNFZJ+A5ySj3cALs+LCP8sF1bnbH9O0rLACjTdT9q+vFxUXXlK0gTgj5I+DfwVeFnhmKrX9xkMIYRQk9w6cGfgLaQJn18AR9pesWRcIYQQQuhezljYAdiUNKk7DTi1xg4+kg4GdgJuI9XFgFQXo6rMcEnrA7cDLyYVPl4c+Ga7gq9h9AZmgkHSpsBBzJ5paxSIiSIeIYSekYsjXgF8yPadeeyOOFeFEEIIoRdI+gPwOttPl44l9J5B2CLR8CNgH1IRu+fm8L0hhFDKeqRVgQsl3QH8nGipG0IIIVQtt6n8BikFX1TaDSO7A5if1C2qWpJWBfbjhVs93lAsqD4wSBkM19jesHQcIYQwWjnzamdSSuUM4HTbx5SNKoQQQgidkvQnYFvbt5eOZawknQpMBi6iaZLB9l7FguqCpJtI9a2GLEDbnj7sD4U5GqQJhoNJq4CnMfSNcEOxoEIIYRRyAaKtgZ1s7146nhBCCCF0RtKVtjctHcfcIOm/SCv+z5NuzJ8EsH18ybg61egiUTqOfjNIEwztquM7UmBCCCGEEEII85Kkw4GXA2cwdLHztGJBdUjSfMDXgD2Au0nbPF4JHAccYPuZguGNmqQl8sO9gAeA0xn6N3m4RFz9YmAmGEIIIYQQQgihBEnHtRm27T3GPZguSToUWAzYx/bjeWwScAjwhO29S8Y3WpLuBEyaICE/niUKa49N308wSNq3ZcjAg8C0RoX2EEIIIYQQQgjDk/RHYNXW1pqSJgIzba9SJrLOSNoAuMf2ffn4g6R6V38BDooMhrGZUDqAcbBYy79JwBTgXEk7lQwshBBaSVpipH+l4wshhBBC5yQtJ+l0SQ9Iul/SqZKWKx1Xh9w6uZAHn6MlC6DHHU3eEiFpC+DrwPHAo0AU0x6jvs9gGE6+UL/Q9rqlYwkhhIaWtL3lgUfy4xcDd9teqWB4IYQQQuiCpN8CPwNOzEO7AB+wvXW5qDoj6QzgNNsntIzvArzP9nZlIuuMpJtsT86Pvwv8w/ZB+XiG7bVLxle7+eb8Lf3J9sOSNOfvDCGE8dOYQJB0NHCm7XPy8TbAm0rGFkIIIYSuLWW7uQ7DTyRVUbOgyaeA0yTtQWrtaGB9YGFg+5KBdWiipPlsPwu8EfhY03MDe388twzsf6CkN5BWBkMIoRetb/sTjQPb50r6csmAQgghhNC1B/NK/8n5eGfgoYLxdMz2X4EN833UmqQMy3NtX1Q2so6dDFwm6UFSi80rACStTNomEcag77dISLqZF+4JWgL4G7Cb7ZnjH1UIIYxM0vmkD7yfks5huwBb2H5L0cBCCCGE0DFJywNHARuTPtevAqbavqtoYANK0kbAK4ALbP87j60KLGr7hqLBVW4QJhhWaBky8FDjhRRCCL0o14k5ENiCdN66HPhSVDYOIYQQQgi9qu8nGEIIoWaSFrX9r9JxhBBCCKFzko5khA4Ltvcax3BCmOcGoU1lCCFUR9Imkm4DbsvHkyV9r3BYIYQQQujM9aSCiNOB7ZoeN/6F0FcigyGEEHqQpGuA95A6SayTx26xvVbZyEIIIYTQDUk3Nj7TQ+hXkcEQQgg9yvY9LUPPFQkkhBBCCHNDrOyGvjewbSpDCKHH3SNpE8CSFgD2Am4vHFMIIYQQQgjDii0SIYTQgyQtCRwOvInUZ/oCYK/oIhFCCCHUQ9LjzM5ceBHwROMpwLYnFQkshHkkJhhCCKEHSdrU9pVzGgshhBBCCKFXxARDCCH0IEk32F53TmMhhBBCCCH0iqjBEEIIPUTSxsAmwFKS9m16ahIwsUxUIYQQQgghzFlMMIQQQm9ZAFiUdH5erGn8MVLbyhBCCCGEEHpSbJEIIYQeJGkF23eVjiOEEEIIIYTRigmGEELoIZIOs723pLNo0y/b9nYFwgohhBBCCGGOYotECCH0lhPz10OKRhFCCCGEEEKHIoMhhBBCCCGEEEIIYxYZDCGE0IMkbQocBKxAOlcLsO1XlYwrhBBCCCGE4UQGQwgh9CBJM4F9gOnAc41x2w8VCyqEEEIIIYQRRAZDCCH0pkdtn1s6iBBCCCGEEEYrMhhCCKEHSToYmAicBjzdGLd9Q7GgQgghhBBCGEFMMIQQQg+SdEmbYdt+w7gHE0IIIYQQwijEBEMIIYQQQgghhBDGLGowhBBCD5G0b8uQgQeBabbvLBBSCCGEEEIIozKhdAAhhBCGWKzl3yRgCnCupJ1KBhZCCCGEEMJIYotECCFUQNISwIW21y0dSwghhBBCCO1EBkMIIVTA9sOASscRQgghhBDCcGKCIYQQKiDpDcAjpeMIIYQQQghhOFHkMYQQeoikm0mFHZstAfwN2G38IwqDWvyDAAACPElEQVQhhBBCCGF0ogZDCCH0EEkrtAwZeMj2v0vEE0IIIYQQwmjFBEMIIYQQQgghhBDGLGowhBBCCCGEEEIIYcxigiGEEEIIIYQQQghjFhMMIYQQQhg1SVd1+P1bSvrNvIonhBBCCL0jJhhCCCGEMGq2NykdQwghhBB6U0wwhBBCCGHUJP0rf91S0qWSfiVppqSTJCk/99Y8Ng14d9PPLiLpx5Kuk3SjpHfm8X0l/Tg/fq2kWyS9qMCvF0IIIYQxiAmGEEIIIXRrHWBvYA3gVcCmkhYCjgW2BTYHXt70/Z8HLra9PrAV8C1JiwCHAStL2h44Dvi47SfG79cIIYQQwtwQEwwhhBBC6Na1tu+1/TwwA1gRWB240/YfnXph/7Tp+98MfFbSDOBSYCFg+fzzHwJOBC6zfeX4/QohhBBCmFvmKx1ACCGEEKr1dNPj55h9XeFhvl/ADrb/0Oa5VYB/AcvMvfBCCCGEMJ4igyGEEEIIc9NMYCVJr87HOzc9dz6wZ1OthnXy18WBw4EtgJdKes84xhtCCCGEuSQmGEIIIYQw19h+CvgYcHYu8nhX09NfBuYHfi/plnwMcCjwPdv/B3wYOFjSy8Yx7BBCCCHMBUrbI0MIIYQQQgghhBC6FxkMIYQQQgghhBBCGLOYYAghhBBCCCGEEMKYxQRDCCGEEEIIIYQQxiwmGEIIIYQQQgghhDBmMcEQQgghhBBCCCGEMYsJhhBCCCGEEEIIIYxZTDCEEEIIIYQQQghhzP4/TcVDvQAkaT0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show bar graph for betweenness\n",
    "title = 'Betweenness distribution for top 20 nodes'\n",
    "dfbetweenness.plot.bar(x='index', y='Degrees', figsize=(18,7), title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above table and bar graph shows the most bridging airports that connect the world. These are key nodes that bridge between clusters the most."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Australian Border Closure Timeline\n",
    "### Australia border from China closed 1 Feb 2020\n",
    "https://www.pm.gov.au/media/updated-travel-advice-protect-australians-novel-coronavirus\n",
    "https://www.pm.gov.au/media/extension-travel-ban-protect-australians-coronavirus\n",
    "https://www.bbc.com/news/world-51338899\n",
    "\n",
    "### Australia border closure from Iran 1 March 2020\n",
    "https://www.pm.gov.au/media/update-novel-coronavirus-covid-19-australia\n",
    "### Australia border closure from South Korea 5 March 2020\n",
    "https://www.pm.gov.au/media/update-novel-coronavirus-covid-19-australia-0\n",
    "### Australia social distancing measures 1 18 March 2020\n",
    "https://www.pm.gov.au/media/update-coronavirus-measures\n",
    "### Australia border closure from all 20 March 2020\n",
    "https://www.pm.gov.au/media/border-restrictions\n",
    "### Australia social distancing measures 2 23 March 2020\n",
    "https://www.pm.gov.au/media/update-coronavirus-measures-220320\n",
    "### Australia social distancing measures 3 28 March 2020\n",
    "Mandatory 14 day self-isolation\n",
    "https://www.pm.gov.au/media/update-coronavirus-measures-270320\n",
    "\n",
    "### WA border closure 5 April 2020 Sunday \n",
    "https://www.wa.gov.au/organisation/department-of-the-premier-and-cabinet/covid-19-coronavirus-wa-border-closure\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "Resources that were useful.\n",
    "\n",
    "ipyleaflet:\n",
    "* https://ipyleaflet.readthedocs.io/en/latest/\n",
    "* https://github.com/jupyter-widgets/ipyleaflet\n",
    "\n",
    "Layers Control:\n",
    "* Documentation: https://ipyleaflet.readthedocs.io/en/latest/api_reference/layers_control.html\n",
    "* Layers Control naming: https://github.com/jupyter-widgets/ipyleaflet/issues/293"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}