cherryGinkgoPhenology

This repository accompanies the manuscript "Cherry blossom and ginkgo leaf coloration phenology dataset of China from 2009 to 2019 extracted from big data" by Shenghong Wang, Haolong Liu, Xinyue Qin, Junhu Dai and Jun Liu *. All necessary Python scripts and data to reproduce included results are provided.

Abstract

Ground-based phenological observation data are the most accurate phenological monitoring data currently available. Making effective use of available information on social media to retrieve phenological data is of considerable value in alleviating the lack of phenological data in regions with missing observation sites. In this study, a logistic curve fitting method was developed to extract phenological data on specific species from social media data. After verifying the relationship between the site observation data and the temperature, timing data for two typical phenological phenomena in China, namely cherry blossom flowering in spring and ginkgo leaf coloration in autumn were reconstructed and published. The data availability is from 2010 to 2019 in 176 cities and 2009 to 2018 in 155 cities. This dataset is an effective supplement for existing phenological data, and this method also provides a reference for obtaining phenological data for specific species.

Disclaimer

The code in this repository represents one version of the code developed for the project and may yet undergo changes and revisions.

Authors

The code was developed through collaboration between Shenghong Wang and Jun Liu.

Contacts

Shenghong Wang - wangshenghong@stu.scu.edu.cn
Jun Liu - liujun_igsnrr@126.com

Usage

• The "logisticRegression" folder contains algorithms for keywords filter and plant viewing timing reconstruction.
• The "dataValidation" folder contains Python scripts for data validation using temperature data.

Data access

• Weibo data: Everyone can only obtain Weibo data by applying to Sina Weibo for commercial API (application programming interface) according to data usage agreement. We are prohibited to provide any detail of the raw data (including URLs) to open access according to the term "4.2 Party B's rights and obligations" of contract. To repeat this work, please visit http://openapi.sc.weibo.com or contact businessapi@staff.weibo.com for data support. The search settings are detailed in "Table 1 Search settings and results".
• Observation data: The phenological data is available at http://www.cpon.ac.cn.
• Meteorological data: The meteorological data can be obtained from http://data.cma.cn/.

Requirements

• Database:
  • Mysql: 5.7.33
  • Redis: 6.2.5
  • Mongo: 5.0.2
• Python: 3.6.3
• Other attached packages:
  • numpy: 1.13.1
  • pandas: 0.20.3
  • pymongo: 3.4.0
  • PyMySQL: 0.7.9
  • redis: 4.3.1
  • scikit_learn: 1.1.2
  • scipy: 0.19.1