Added markdown to fix build issue

vector/v.ppa/testsuite/test_v_ppa.py

@@ -30,9 +30,6 @@ def tearDownClass(cls):
         cls.runModule("g.remove", type="vector", flags="f", name=cls.input)
         cls.del_temp_region()
 
-    def tearDown(cls):
-        pass
-
     def test_g_function_csv(self):
         """Testing g function csv output"""
         self.assertModule(

vector/v.ppa/v.ppa.md

@@ -0,0 +1,45 @@
+## Description
+
+*v.ppa* allows users to perform point pattern analysis with the G, F, K, L, and bivariate Ripley's K functions.
+
+## Notes
+
+## Examples
+
+### G Function
+
+Determine the distribution of distances from each point to its nearest neighbor to learn if the points are clustered, dispersed, or randomly distributed across the computational region.
+
+```bash
+v.ppa input=crash output=crash_g.csv method=g
+```
+
+### F Function
+
+Determine the distribution of the distances from random points in the computational region to the points in the input vector map. This function is to determine if the points are clustered, dispersed, or randomly distributed across the computational region. The output file will contain the distances from the random points to the nearest crash location.
+
+```bash
+v.ppa input=crash output=crash_f.csv method=f
+```
+
+### K and L Functions
+
+Determine if the points are clustered, dispersed, or randomly distributed across spatial scales in the computational region.
+
+```bash
+v.ppa input=crash output=crash_k.csv method=k
+```
+
+The *L-Function* is a transformation of the K-Function that allows for easier interpretation of the results.
+
+- If L(r) is less than the expected value, the points are dispersed.
+- If L(r) is greater than the expected value, the points are clustered.
+- If L(r) is equal to the expected value, the points are randomly distributed (Poisson Process).
+
+```bash
+v.ppa input=crash output=crash_l.csv method=l
+```
+
+## Authors
+
+Corey T. White, OpenPlains Inc.