v.ppa: Point Pattern Analysis

vector/Makefile

@@ -40,6 +40,7 @@ SUBDIRS = \
 	v.lidar.growing \
 	v.lrs \
 	v.proj \
+	v.ppa \
 	v.mkgrid \
 	v.neighbors \
 	v.net \

vector/v.ppa/Makefile

@@ -0,0 +1,14 @@
+
+MODULE_TOPDIR = ../..
+
+PGM=v.ppa
+
+LIBES = $(VECTORLIB) $(DBMILIB) $(BTREE2LIB) $(GISLIB) $(PARSONLIB)
+EXTRA_LIBS = $(OPENMP_LIBPATH) $(OPENMP_LIB)
+DEPENDENCIES = $(VECTORDEP) $(DBMIDEP) $(BTREE2DEP) $(GISDEP)
+EXTRA_INC = $(VECT_INC) $(OPENMP_INCPATH)
+EXTRA_CFLAGS = $(VECT_CFLAGS) $(OPENMP_CFLAGS)
+
+include $(MODULE_TOPDIR)/include/Make/Module.make
+
+default: cmd

vector/v.ppa/testsuite/README.md

@@ -0,0 +1,12 @@
+# Test Data
+
+Test data was downloaded from the City of Raleigh, NC open data portal.
+The data is a subset of the Daily Police Incidents dataset filtered from 2022 - 2024.
+
+[Daily_Police_Incidents](https://services.arcgis.com/v400IkDOw1ad7Yad/arcgis/rest/services/Daily_Police_Incidents/FeatureServer/0/query?where=1%3D1&outFields=*&outSR=3358&f=json)
+
+```bash
+v.import input=https://services.arcgis.com/v400IkDOw1ad7Yad/arcgis/rest/ \
+services/Daily_Police_Incidents/FeatureServer/0/query?where=1%3D1 \
+&outFields=*&outSR=3358&f=json layer=ESRIJSON output=raleigh_crime_2022_2024
+```

vector/v.ppa/testsuite/outputs/f_crime.csv

@@ -0,0 +1,102 @@
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vector/v.ppa/testsuite/outputs/f_crime.json

@@ -0,0 +1 @@
+{}

vector/v.ppa/testsuite/outputs/g_crime.csv

@@ -0,0 +1,101 @@
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vector/v.ppa/testsuite/outputs/g_crime.json

@@ -0,0 +1 @@
+{}

vector/v.ppa/testsuite/outputs/k_crime.csv

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+        452486831.80370015,
+        452486831.80370015
+    ]
+}

vector/v.ppa/testsuite/outputs/l_crime.csv

@@ -0,0 +1,101 @@
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vector/v.ppa/testsuite/outputs/l_crime.json

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vector/v.ppa/testsuite/test_v_ppa.py

@@ -0,0 +1,188 @@
+"""
+Name:       v.ppa test
+Purpose:    Tests v.ppa functions.
+Author:     Corey T. White
+Copyright:  (C) 2024 by Corey White and the GRASS Development Team
+Licence:    This program is free software under the GNU General Public
+            License (>=v2). Read the file COPYING that comes with GRASS
+            for details.
+"""
+
+from grass.gunittest.case import TestCase
+
+
+class TestPointPatternAnalysis(TestCase):
+    input = "crime"
+
+    @classmethod
+    def setUpClass(cls):
+        cls.use_temp_region()
+        cls.runModule(
+            "v.import",
+            input="data/raleigh_crime_2022_2024.fgb",
+            output=cls.input,
+            overwrite=True,
+        )
+        cls.runModule("g.region", vector=cls.input, res=30)
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.runModule("g.remove", type="vector", flags="f", name=cls.input)
+        cls.del_temp_region()
+
+    def test_g_function_csv(self):
+        """Testing g function csv output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/g_{self.input}.csv",
+            method="g",
+            format="plain",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_g_function_json(self):
+        """Testing g function json output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/g_{self.input}.json",
+            method="g",
+            format="json",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_f_function_plain(self):
+        """Testing f function csv output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/f_{self.input}.csv",
+            method="f",
+            format="plain",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_f_function_json(self):
+        """Testing f function json output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/f_{self.input}.json",
+            method="f",
+            format="json",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_f_function_num_distances(self):
+        """Testing f function num_distance parameter"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/g_{self.input}.json",
+            method="f",
+            format="json",
+            num_distances=400,
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_f_function_random_points(self):
+        """Testing f function random points parameter"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/g_{self.input}.json",
+            method="f",
+            format="json",
+            random_points=2000,
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_k_function_plain(self):
+        """Testing k function csv output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/k_{self.input}.csv",
+            method="k",
+            format="plain",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_k_function_json(self):
+        """Testing k function json output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            # output=f"outputs/k_{self.input}.json",
+            method="k",
+            format="json",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_k_function_save_json_file(self):
+        """Testing k function json output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/k_{self.input}.json",
+            method="k",
+            format="json",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_l_function_plain(self):
+        """Testing l function csv output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/l_{self.input}.csv",
+            method="l",
+            random_points=100,
+            format="plain",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_l_function_json(self):
+        """Testing l function json output"""
+        self.assertModule(
+            "v.ppa",
+            input=self.input,
+            output=f"outputs/l_{self.input}.json",
+            method="l",
+            random_points=100,
+            format="json",
+            seed=1,
+            overwrite=True,
+        )
+
+    def test_simulation(self):
+        pass
+
+    def test_monte_carlo_envelope(self):
+        pass
+
+    def test_generate_random_points(self):
+        pass
+
+    def test_euclidean_distance(self):
+        pass
+
+    def test_max_distance(self):
+        pass
+
+
+if __name__ == "__main__":
+    from grass.gunittest.main import test
+
+    test()

vector/v.ppa/v.ppa.html

@@ -0,0 +1,47 @@
+<h2>DESCRIPTION</h2>
+
+<em>v.ppa</em> allows users to perform point pattern analysis with the G, F, K, L, and bivariate Ripley's K functions.
+
+<h2>Notes</h2>
+
+<h2>EXAMPLES</h2>
+
+<h3>G Function</h3>
+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.
+<div class="code"><pre>
+   v.ppa input=crash output=crash_g.csv method=g
+</pre></div>
+
+<br>
+<h3>F Function</h3>
+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.
+<div class="code"><pre>
+   v.ppa input=crash output=crash_f.csv method=f
+</pre></div>
+
+<br>
+<h3>K and L Functions</h3>
+Determine if the points are clustered, dispersed, or randomly distributed across spatial scales in the compuational region.
+<div class="code"><pre>
+   v.ppa input=crash output=crash_k.csv method=k
+</pre></div>
+
+The <em>L-Function</em> is a transformation of the K-Function that allows for easier interpretation of the results.
+
+<ul>
+   <li>If L(r) is less than the expected value, the points are dispersed.</li>
+   <li>If L(r) is greater than the expected value, the points are clustered.</li>
+   <li>If L(r) is equal to the expected value, the points are randomly distributed (Poisson Process).</li>
+</ul>
+
+<div class="code"><pre>
+   v.ppa input=crash output=crash_l.csv method=l
+</pre></div>
+
+
+<h2>AUTHORS</h2>
+Corey T. White, OpenPlains Inc.
+<br>

vector/v.ppa/v.ppa.md

@@ -0,0 +1,55 @@
+## 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
+patial 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.