{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Distance Based Statistical Method for Planar Point Patterns\n", "\n", "**Authors: Serge Rey and Wei Kang **\n", "\n", "## Introduction\n", "\n", "Distance based methods for point patterns are of three types:\n", "\n", "* [Mean Nearest Neighbor Distance Statistics](#Mean-Nearest-Neighbor-Distance-Statistics)\n", "* [Nearest Neighbor Distance Functions](#Nearest-Neighbor-Distance-Functions)\n", "* [Interevent Distance Functions](#Interevent-Distance-Functions)\n", "\n", "In addition, we are going to introduce a computational technique [Simulation Envelopes](#Simulation-Envelopes) to aid in making inferences about the data generating process. An [example](#CSR-Example) is used to demonstrate how to use and interprete simulation envelopes." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import scipy.spatial\n", "import libpysal as ps\n", "import numpy as np\n", "from pointpats import PointPattern, PoissonPointProcess, as_window, G, F, J, K, L, Genv, Fenv, Jenv, Kenv, Lenv\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean Nearest Neighbor Distance Statistics\n", "\n", "The nearest neighbor(s) for a point $u$ is the point(s) $N(u)$ which meet the condition\n", "$$d_{u,N(u)} \\leq d_{u,j} \\forall j \\in S - u$$\n", "\n", "The distance between the nearest neighbor(s) $N(u)$ and the point $u$ is nearest neighbor distance for $u$. After searching for nearest neighbor(s) for all the points and calculating the corresponding distances, we are able to calculate mean nearest neighbor distance by averaging these distances.\n", "\n", "It was demonstrated by Clark and Evans(1954) that mean nearest neighbor distance statistics distribution is a normal distribution under null hypothesis (underlying spatial process is CSR). We can utilize the test statistics to determine whether the point pattern is the outcome of CSR. If not, is it the outcome of cluster or regular\n", "spatial process?\n", "\n", "Mean nearest neighbor distance statistic\n", "\n", "$$\\bar{d}_{min}=\\frac{1}{n} \\sum_{i=1}^n d_{min}(s_i)$$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "points = [[66.22, 32.54], [22.52, 22.39], [31.01, 81.21],\n", " [9.47, 31.02], [30.78, 60.10], [75.21, 58.93],\n", " [79.26, 7.68], [8.23, 39.93], [98.73, 77.17],\n", " [89.78, 42.53], [65.19, 92.08], [54.46, 8.48]]\n", "pp = PointPattern(points)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Pattern\n", "12 points\n", "Bounding rectangle [(8.23,7.68), (98.73,92.08)]\n", "Area of window: 7638.200000000002\n", "Intensity estimate for window: 0.0015710507711240865\n", " x y\n", "0 66.22 32.54\n", "1 22.52 22.39\n", "2 31.01 81.21\n", "3 9.47 31.02\n", "4 30.78 60.10\n" ] } ], "source": [ "pp.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We may call the method **knn** in PointPattern class to find $k$ nearest neighbors for each point in the point pattern *pp*." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[ 9],\n", " [ 3],\n", " [ 4],\n", " [ 7],\n", " [ 2],\n", " [ 9],\n", " [11],\n", " [ 3],\n", " [ 5],\n", " [ 5],\n", " [ 5],\n", " [ 6]]), array([[25.59050019],\n", " [15.64542745],\n", " [21.11125292],\n", " [ 8.99587128],\n", " [21.11125292],\n", " [21.93729473],\n", " [24.81289987],\n", " [ 8.99587128],\n", " [29.76387072],\n", " [21.93729473],\n", " [34.63124168],\n", " [24.81289987]]))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# one nearest neighbor (default)\n", "pp.knn()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first array is the ids of the most nearest neighbor for each point, the second array is the distance between each point and its most nearest neighbor." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[ 9, 11],\n", " [ 3, 7],\n", " [ 4, 10],\n", " [ 7, 1],\n", " [ 2, 7],\n", " [ 9, 0],\n", " [11, 0],\n", " [ 3, 1],\n", " [ 5, 9],\n", " [ 5, 0],\n", " [ 5, 2],\n", " [ 6, 0]]), array([[25.59050019, 26.78023898],\n", " [15.64542745, 22.62422816],\n", " [21.11125292, 35.86682729],\n", " [ 8.99587128, 15.64542745],\n", " [21.11125292, 30.2544443 ],\n", " [21.93729473, 27.87924317],\n", " [24.81289987, 28.07242775],\n", " [ 8.99587128, 22.62422816],\n", " [29.76387072, 35.77753625],\n", " [21.93729473, 25.59050019],\n", " [34.63124168, 35.86682729],\n", " [24.81289987, 26.78023898]]))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# two nearest neighbors\n", "pp.knn(2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "34.63124167568931" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp.max_nnd # Maximum nearest neighbor distance" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.995871275201752" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp.min_nnd # Minimum nearest neighbor distance" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21.612139802089246" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp.mean_nnd # mean nearest neighbor distance" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[25.59050019],\n", " [15.64542745],\n", " [21.11125292],\n", " [ 8.99587128],\n", " [21.11125292],\n", " [21.93729473],\n", " [24.81289987],\n", " [ 8.99587128],\n", " [29.76387072],\n", " [21.93729473],\n", " [34.63124168],\n", " [24.81289987]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp.nnd # Nearest neighbor distances" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21.612139802089246" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp.nnd.sum()/pp.n # same as pp.mean_nnd" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pp.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nearest Neighbor Distance Functions\n", "\n", "Nearest neighbour distance distribution functions (including the nearest “event-to-event” and “point-event” distance distribution functions) of a point process are cumulative distribution functions of several kinds -- $G, F, J$. By comparing the distance function of the observed point pattern with that of the point pattern from a CSR process, we are able to infer whether the underlying spatial process of the observed point pattern is CSR or not for a given confidence level." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $G$ function - event-to-event\n", "\n", "The $G$ function is defined as follows: for a given distance $d$, $G(d)$ is the proportion of nearest neighbor distances that are less than $d$.\n", "$$G(d) = \\sum_{i=1}^n \\frac{ \\phi_i^d}{n}$$\n", "\n", "$$ \n", "\\phi_i^d =\n", " \\begin{cases}\n", " 1 & \\quad \\text{if } d_{min}(s_i)" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gp1 = G(pp, intervals=20)\n", "gp1.plot()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A slightly different visualization of the empirical function is the quantile-quantile plot:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gp1.plot(qq=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "in the q-q plot the csr function is now a diagonal line which serves to make accessment of departures from csr visually easier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is obvious that the above $G$ increases very slowly at small distances and the line is below the expected value for a CSR process (green line). We might think that the underlying spatial process is regular point process. However, this visual inspection is not enough for a final conclusion. In [Simulation Envelopes](#Simulation-Envelopes), we are going to demonstrate how to simulate data under CSR many times and construct the $95\\%$ simulation envelope for $G$." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 1.73156208, 3.46312417, 5.19468625, 6.92624834,\n", " 8.65781042, 10.3893725 , 12.12093459, 13.85249667, 15.58405875,\n", " 17.31562084, 19.04718292, 20.77874501, 22.51030709, 24.24186917,\n", " 25.97343126, 27.70499334, 29.43655542, 31.16811751, 32.89967959,\n", " 34.63124168, 36.36280376])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gp1.d # distance domain sequence (corresponding to the x-axis)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0.16666667, 0.16666667, 0.16666667, 0.16666667,\n", " 0.25 , 0.25 , 0.25 , 0.58333333, 0.58333333,\n", " 0.83333333, 0.83333333, 0.83333333, 0.91666667, 0.91666667,\n", " 1. , 1. ])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gp1.G #cumulative nearest neighbor distance distribution over d (corresponding to the y-axis))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $F$ function - \"point-event\" \n", "\n", "When the number of events in a point pattern is small, $G$ function is rough (see the $G$ function plot for the 12 size point pattern above). One way to get around this is to turn to $F$ funtion where a given number of randomly distributed points are generated in the domain and the nearest event neighbor distance is calculated for each point. The cumulative distribution of all nearest event neighbor distances is called $F$ function." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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mrHTWbN3LqBF9wz9+0JY0eOMy2LYmuC6g95XhfT+RI6RCIHEjOy+f579cQZ/W9TixfZj75ldMgXFXBxeHXTURWp0Q3vcTOQpRMMWSSPkYP2cdG3bs51dntA/ftQLuMGMkjLoYajWD679QEZCoF9ZCYGZnm9lSM0szs3uLWd/SzL4ws7lmNt/MzglnHolfufkFPDc1jZ4t6tA/XAPJ5efCh3fA5N9ChwEw4hONEyQxIWyFwMwSgGeBgUAXYKiZdSmy2e+Bt9y9FzAEeC5ceSS+vTt3HRnb9nFbuFoDe7Pg9Qtg9svQ/zcwZDRUrln27yMSBuE8RtAHSHP3lQBmNhYYDCwqtI0DtUL3awPrw5hH4lRefgHPTV1B12a1OK1Tw7J/g81LYcxlsHMdXPBv6DGk7N9DJIzCWQiaAemFHmcAfYts8xDwiZn9CqgOnFncC5nZDcANAC1btizzoFKxfTB/A6u27OH5y48r+9bA8k/h7WshsQpcPQlaHF+2ry9SDsJ5jKC4vzgv8ngo8B93bw6cA7xuZj/J5O4j3T3V3VNTUlLCEFUqqoIC55kv0ujUqCYDupThWD7u8M2zMObS4DjA9VNUBCRmhbNFkAG0KPS4OT/t+hkBnA3g7t+YWRWgAZAZxlwSRyYv2Eha5m6eHtqr7AaUy8sODgrPHQWdz4MLnofk6mXz2iIREM4WwSygg5m1MbNkgoPBE4tssxY4A8DMOgNVgM1hzCRxpKDAeXrKctqlVC+72cb2bIHXBgdF4JR7gkHjVAQkxoWtReDueWZ2K/AxkAC87O4LzexhYLa7TwTuBF4ws98QdBtd7e5Fu49EjshnizexZOMu/nFZDxLKojWQuRhGXwp7MuHil6HrRUf/miJRIKxXFrv7JGBSkWUPFrq/CDgxnBkkPrk7T09Jo1X9agzq3vToX3DTInj1XKiUBNdMhma9j/41RaKEriyWCmnq0s18v24Hvzy1PYkJR/nfPHMxvDooVAQmqQhIhaNCIBWOu/PUlOU0q1OVC3of5dy/mUtCRSARrv4A6rcrm5AiUUSFQCqc/6ZtZe7a7dx8ajuSjqY1sHlZUASsElz1PjToUHYhRaKICoFUOE9NWU7jWlW4JLX5kb/IluXBMQEIikBKx7IJJxKFVAikQpm+ciszV2Vx4yltqZyYcGQvsiUN/nMueEGoCHQq25AiUUaFQCqUp6csp0GNygztc4RDkWxdEbQECvLgyonQ8JiyDSgShVQIpMKYs2Yb/03byo0nt6VK0hG0BrauCFoC+TnBZDKNig6WK1IxqRBIhfH0lOXUq57M8H5H0BrIWhkcGM7bH7QEGh1b9gFFopQKgVQI8zO2M3XpZkb0b0O15MO8TjJrFfxnEOTuDVoCjbuGJ6RIlFIhkArh6Slp1K6axJU/O8wZwbatCVoCObuDlkDjbuEJKBLFVAgk5i1av5NPF23imhNbU7NKUumfuH1tcEwgexdc+R406R6+kCJRLKxjDYmUh2e+WE6Nyolcc0Kb0j9pe3qoCOwIikDTnuELKBLl1CKQmLZ80y4mL9jIVSe0ona1UrYGdmQEp4ju2w5XvAtNe4U3pEiUU4tAYtozX6RRNSmBEf3blu4Je7YExwT2ZgVFQAPIiahFILFr1ZY9vD9vPVf0a0W96smHfkLufhg7DHauh8vHQ/Pjwh9SJAaoRSAx69kv0khOrMR1J5WiNeAOE2+F9BlwyX+gRZ+w5xOJFWoRSExKz9rLhLnrGNqnJSk1Kx/6CV/+Fb4fB6c/AMdeEP6AIjFEhUBi0ovTVpJgxo0nl2J+gPnjYOqj0GMYnHRn+MOJxBgVAok5e3PyeOfbdZzTrTGNa1c5+MZrZ8B7t0Cr/jDoSbAymLtYpIJRIZCY8/689ezKzmN4v0NcRZy1Kjg4XLsFXPY6JJbigLJIHFIhkJgzavpaOjaqQWqruiVvtG87jLksGE562FtQrV75BRSJMSoEElPmZ2zn+3U7uLxfK6ykbp78XBh3VTCi6JDR0KB9+YYUiTE6fVRiyujpa6malMD5vUqYlN4dJv0WVk6Fwc9B6/7lmk8kFqlFIDFjx75cJs5bz+CeTalV0uBy3zwLc16B/ndAr+HlG1AkRqkQSMyY8G0G+3LzGd63hIPESybBJ7+HLoOD6wVEpFRUCCQmuDujZ6ylR/PadGte+6cbbJgH40cEA8id/zxU0n9tkdLSX4vEhFmrt7E8c3fxrYGd62HMEKhWH4aOheRq5R9QJIbpYLHEhNEz1lCzSiLn9mjy4xU5e4LTRLN3wrUfQ81GkQkoEsPUIpCot3V3NpO/38hFvZv/eD7ignwYfz1sWgAXv6K5hkWOkFoEEvXGzckgJ7+A4X1b/njFZ3+ApR/CwCeg44DIhBOpAMLaIjCzs81sqZmlmdm9JWxzqZktMrOFZjYmnHkk9hQUOGNmrKVPm3p0aFTzhxXfvg7/72nocwP0vSFyAUUqgLC1CMwsAXgW+DmQAcwys4nuvqjQNh2A3wEnuvs2M2sYrjwSm75O28LarL3cOaDjDws3zIcP74S2p8JZj0YqmkiFEc4WQR8gzd1XunsOMBYYXGSb64Fn3X0bgLtnhjGPxKBR09dQv3oyZ3dtHCzYvyMYPqJafbjoJUhQ76bI0QpnIWgGpBd6nBFaVlhHoKOZ/dfMppvZ2cW9kJndYGazzWz25s2bwxRXos2GHfv4fEkml6S2oHJiQjB8xHu3wrY1cPHLUL1BpCOKVAjhLATFjQjmRR4nAh2AU4GhwItmVucnT3If6e6p7p6akpJS5kElOr05K50Cd4b1CR0knvFvWDwRznwIWv0sktFEKpRwFoIMoEWhx82B9cVs856757r7KmApQWGQOJeXX8DYmemc3CGFlvWrQcbsYPiITr+AE34V6XgiFUo4C8EsoIOZtTGzZGAIMLHINu8CpwGYWQOCrqKVYcwkMeLzJZls3Lk/OGV0bxa8dRXUagLnP6tZxkTKWNiOtLl7npndCnwMJAAvu/tCM3sYmO3uE0PrBpjZIiAf+K27bw1XJokdo2espXGtKpzeqQGMHQJ7MmHEJ1D1IJPRiMgRCespF+4+CZhUZNmDhe47cEfoRwSAtVv38tWyzdx+ZgcSv3kS0j6FX/xfMKCciJQ5DTEhUWfMzLUkVDKubJwOU/4MXS+C1BGRjiVSYakQSFTJzsvnrdnpXNghkXqTb4J67WDQkzouIBJGuhpHospHCzayfc9+7t/3NGTvgivfg8o1D/1EETliKgQSVUbPWMtDNd+jTuYMOP9f0KhLpCOJVHjqGpKosXzTLqqtmcKVueOg1xXQc1ikI4nEBRUCiRofTJvJP5KeIy+lC5zzRKTjiMQNFQKJCnv37eX0BfdQNaGAxCGjIKlqpCOJxI3DLgRmVj00xLRImcl46256sJyMk5+A+u0iHUckrhyyEJhZJTMbZmYfmlkmsATYEJpI5onQnAIiR27Re3Rc9ToTkgfR7pThkU4jEndK0yL4AmhHMIFMY3dv4e4NgZOA6cBjZnZ5GDNKRbZ1BfkTfsl3Be3YffIfMF0vIFLuSnP66Jnunlt0obtnAeOB8WaWVObJpOLL3Q/jriK7AO703zAhtXWkE4nEpdIUgpoH+5bm7lnFFQqRQ/r8Ydj4PXfm383xPXtQq4q+T4hEQmkKwRyCCWUMaAlsC92vA6wF2oQtnVRcaZ/D9GdZ0nIIk5f15P2+rSKdSCRuHfIYgbu3cfe2BENGD3L3Bu5eHzgXeCfcAaUC2rMF3r0ZT+nMXdsvonvz2nRrXjvSqUTi1uGcPnp8aFhpANx9MnBK2UeSCu3AvMP7tjOm+YMsyMzlhpPbRjqVSFw7nEKwxcx+b2atzayVmd0PaBIZOTyzX4Jlk1nd+24emO5c2KsZ53ZvGulUInHtcArBUCAFmBD6SQktEymdzCXw8f3ktj6NYfN60LJeNR4+v2ukU4nEvVKPPho6XfTXYcwiFVleNoy/Dk+uwT35t7B5Ty4TbulDjcoaAFck0kpzZfFIM+tWwrrqZnatmelyUDm4zx+GTd/zeccHeWd5Lr8b2JmuzXSAWCQalObr2HPAA6FisADYDFQBOgC1gJeB0WFLKLEv7XP45hmyulzJLbMacsYxDbjmxNaRTiUiIYcsBO7+HXCpmdUAUoEmwD5gsbsvDXM+iXWhU0ULGnRi2JpzqVs9gScu6aGhJESiyCELgZm1dPe17r4bmBr+SFJhuMPEX8G+bfy94aMsW5fHmOtTqVc9OdLJRKSQ0pw19O6BO2Y2PoxZpKKZ/TIsncT8zrfzzKIq3Hp6B/q1rR/pVCJSRGmOERRuw+vKHymdzUvh4/vZ2+IUhs3vRZ/Wdbnt9PaRTiUixShNi8BLuC9SvLxseHsEnlyNG3ePIDExkX8O6UligibEE4lGpWkR9DCznQQtg6qh+4Qeu7vXCls6iU2hU0XfbPc40xYmMvKK7jSto6knRaJVac4a0rSUUnorpsA3z5DRbhj3LmzO1Se0ZsCxjSOdSkQOQm11KTt7tsKEm8mr15FLVp5Dlya1uHfgMZFOJSKHoEIgZSN0qqjvy+K+Sr9mR34STw/rRZUkNShFop0KgZSNOa/A0g+Z1vIW3sqoy8ODu9IupUakU4lIKagQyNHbvAw+uo/tTU7imiWpXNCrGRf1bhbpVCJSSmEtBGZ2tpktNbM0M7v3INtdbGZuZqnhzCNhkJcD71xHQVJVLt96FS3q1eBP53fVEBIiMSRshcDMEoBngYFAF2ComXUpZruawG3AjHBlkTD64hHYMI/nat7O0j3VeXpobw0tLRJjwtki6AOkuftKd88BxgKDi9nuT8DjwP4wZpFwWDUN/vsky5tfxN/WtufegZ0197BIDApnIWgGpBd6nBFa9j9m1gto4e4fHOyFzOwGM5ttZrM3b95c9knl8O3bBhNuIq9uG4ZnDOaEdvW5VkNLi8SkcBaC4jqJ/zdEhZlVAv4B3HmoF3L3ke6e6u6pKSkpZRhRjog7fHgn7NrA07XvZntuso4LiMSwcBaCDKBFocfNgfWFHtcEugJTzWw10A+YqAPGMWD+W7BgPGu738aTS2px4yltdaqoSAwL51G9WUAHM2sDrAOGAMMOrHT3HUCDA4/NbCpwl7vPDmMmOVrb1sCkuyho3pcRK06iRT3jl6dpVFGRWBa2FoG75wG3Ah8Di4G33H2hmT1sZueF630ljAryYcKN4M6opvezfMt+Hj6vq64eFolxYT3Pz90nAZOKLHuwhG1PDWcWKQNf/x3WfsPWnz/FXz7ay1nHNuK0YxpGOpWIHCVdWSyls24OTH0Mjr2Qe5Z3wTAeHHRspFOJSBlQIZBDy9kD46+HGo35osPv+GxJJref2YFmmmNApELQJaByaB/fB1kr2T/8XX4/PoOOjWpwbf82kU4lImVELQI5uCUfwpz/wIm38eSKxqzbvo8/n9+NJE07KVJh6K9ZSrZrE0z8FTTuzvJjf80LX63kot7N6dOmXqSTiUgZUiGQ4rnDe7dAzh78whd44INlVK+cyO/O0YxjIhWNCoEUb+ZISPsMBvyZd9fVYPrKLO4+uxMNalSOdDIRKWMqBPJTmYvhkwegwwB2dL2KRz5cTI8WdRh6fMtIJxORMFAhkB/Ly4bx10HlmjD4Wf72yTKy9uTwyPldqVRJg8qJVEQ6fVR+7POHYdMCGPom87cnM2rGGq76WWu6NtM8AyIVlVoE8oOVU+GbZyD1WvI7nMXv311AgxqVuWNAx0gnE5EwUiGQwJ6tMOFmqN8BBjzCmBlrmJ+xg9//ojO1qiRFOp2IhJG6hgQKCuDdm2DvFhj6BpuzE3j846Wc2L4+5/VoGul0IhJmahFI0B20/BMY8Ag07clfJi1mf24+Dw/WrGMi8UCFIN6lz4LP/widB0Gf6/lmxVYmzF3HjSe306xjInFChSCe7c2Ct6+BWs3gvGfIyXceeG8BzetW1axjInFExwjilTu890vYtRFGfAxV6/Di1DTSMnfz0lWpVE3WrGMi8UKFIF7NeB6WToKz/gLNjmPBuh08+dlyBnRpxBmdG0U6nYiUI3UNxaN1c4IhJDqdA/1uYfOubG54bTb1qyfzyAXdIp1ORMqZWgTxZt92GHcN1BxInscAAA6WSURBVGgEg58lJ9+5edQcsvbm8PZNJ5BSU4PKicQbFYJ44g7v3wY7MuCayXjVuvxhwvfMXrONp4b20jASInFKXUPxZNaLsOg9OONBaNmXUdPX8MbMdG4+tZ0uHBOJYyoE8WLDvGDu4fY/hxNu45sVW/nj+4s4/ZiG3DWgU6TTiUgEqRDEg/07YdzVUK0+XPBv0rfv55djvqVV/Wr8c0hPEjS8tEhcUyGo6Nzhg9th22q46CX2JtXm+tdmk5tfwAtXpmpAORFRIajwvn0VFoyH0+7DW53AXePmsWzTLp4Z1pu2GkJCRFAhqNg2LoDJ90Db06D/nTw9JY1J32/k3oHHcErHlEinE5EooUJQUWXvDo4LVKkNF47kk8WZ/P3TZVzQqxnXn9Q20ulEJIqoEFRUk+6CrWlw4Qss3V2V37z5HT2a1+bRC7tpaGkR+REVgopo7miY9waccg/bG/+M61+bTbXKifz7ilSqJGkwORH5sbAWAjM728yWmlmamd1bzPo7zGyRmc03s8/NrFU488SFzMVBa6D1SeT1v4tbx8xl4479PH/5cTSuXSXS6UQkCoWtEJhZAvAsMBDoAgw1sy5FNpsLpLp7d+Bt4PFw5YkLe7bAG0MguTpc9CKPfLSMr9O28OcLunJcq7qRTiciUSqcLYI+QJq7r3T3HGAsMLjwBu7+hbvvDT2cDjQPY56KLXc/jB0ezC8w5A3eWprLK/9dzTUntubS1BaRTiciUSychaAZkF7ocUZoWUlGAJOLW2FmN5jZbDObvXnz5jKMWEG4w8RbIX06nP8v5hS05/cTFnBi+/rcf07nSKcTkSgXzkJQ3KkpXuyGZpcDqcATxa1395HunuruqSkpOv/9J778K3w/Dk5/gA0tBnLTqDk0rl2FZ4b2JjFB5wOIyMGFcxjqDKBwn0RzYH3RjczsTOB+4BR3zw5jnopp/jiY+ij0GMa6brcwfOR09mbnMfq6vtStnhzpdCISA8L5dXEW0MHM2phZMjAEmFh4AzPrBfwbOM/dM8OYpWJaOx3euwVa9WfVCX/h0n9PZ+ueHF4b0ZeOjWpGOp2IxIiwtQjcPc/MbgU+BhKAl919oZk9DMx294kEXUE1gHGhi5zWuvt54cpUoWStgrHDoHYL0k7/F0NfnEN+gfPG9f00wYyIHJawzlDm7pOASUWWPVjo/pnhfP8Ka992GHMpeAHLznyZy15dQlJCJd68oR8d1BIQkcOkqSpjTX4uvHUlZK1i6VmvcfGbm6hVNYnR1/WldYPqkU4nIjFIhSCWuMOHd8CqL1n+s8e54MNKNKyZzOjr+9GsTtVIpxORGKVCEEv+39Pw7Wus7nIzv5jWktb1qzJqRF8a1tLQESJy5FQIYsXiD+DTB1nffCAD5vWnU+OavHZtH50iKiJHTYUgFqyfC+9cz9Y63ThjxWV0a1mPV645XtNMikiZUCGIdjvWwZgh7E6ow1kbb6Z3+ya8cGUq1ZL1TyciZUPjD0Sz7N0w5jJy9u3iwh2/pvsxHXjpquNVBESkTKkQRKuCfHz8tRRsWsR1+26lQ9c+PH/5cZpYRkTKnL5aRiN3/KPfYcs+5oHca0jp+Qv+elE3DSAnImGhQhBtCvLxSXdjs1/kxbyB2PHX8cR5x1KpkuYZFpHwUCGIJnnZMOFGbOEEns8bRNbP7uPhczprsnkRCSsVgmiRvRveHA4rp/JI7jD2pN7CIyoCIlIOVAiiwZ6tMPpiCjbM4+7cG9nb5TKeHtxVRUBEyoUKQaRtT4fXLyB/+1puzrmdPW0G8PJlPUnQMQERKScqBJG0eSm8fgF5+3ZyVc697GzchzeuSKVyok4RFZHyo0IQKRmzYfTF5FkSQ3IfIKt2J8Zdczw1KuufRETKlz51IiHtc3jzCvKqNuDivXezMbkJb4/oQ/0alSOdTETikApBefv+bZhwE3n1O3Lp7rtYlV+Dcdf3oXndapFOJiJxSoWgPM18ASb9lrwW/Ri663YW74ZR1x2vieZFJKJUCMqDO0x9DL58jPwOA7lm903MzdzLi1elclyrupFOJyJxToUg3AryYfLdMOtFCnoM59ZdVzFt1RaeHNKTUzs1jHQ6EREVgrDKy4YJN8HCd/ATfs3vdlzE5EUZ/GFQFwb3bBbpdCIigApB+Cz/DD66F7Yuh5//iSd2DeDNOSv41entuebENpFOJyLyPyoEZW3rCvj4flg2Geq1hWHjeHFTe56buphhfVtyx887RjqhiMiPqBCUlezdMO1v8M2zkJAMZ/4R+t3M+Hmb+fOH8zinW2P+pPGDRCQKqRAcLXeY/xZ8+iDs3gg9hsKZD5HpdRgzdQ1PT0njxPb1+YfGDxKRKKVCcDTWfQuT74GMmdC0N37Z63yT3ZZRE9fwycJvyStwzuzckH8O6aXxg0QkaqkQHIndmfD5H2HuaKjegL0Dn2JM9omMeTODlVtmUKdaEtec2JphfVvRpkH1SKcVETkoFYLDkZcDM0fCl3/Fc/eyuet1PJV3AePe30l23lJ6t6zD3y/twTndmmiSeRGJGSoEpVXodND1KSfyUM4VfDK7FtWTd3Hxcc0Z3rcVXZrWinRKEZHDpkJQkl0bYf1cWP8drPkvrJ7G1srNedDv4cP07hzTuBZ/Or8V5/dsSs0qSZFOKyJyxMJaCMzsbOBJIAF40d0fK7K+MvAacBywFbjM3VeHM1Oxdm2E9d/h6+eSkz4H2zCP5H2ZABRQifSE5ozJHcqo3HMY0L0l4/u1pHfLujoVVEQqhLAVAjNLAJ4Ffg5kALPMbKK7Lyq02Qhgm7u3N7MhwF+By8KVCSBn+way0mayb/VsEjfNo/b2hdTK3QKAu7HWmzLfO7Kg4CwWehu21TqGhg3qc3KHFKaltqBe9eRwxhMRKXfhbBH0AdLcfSWAmY0FBgOFC8Fg4KHQ/beBZ8zM3N3LOsys8f+g9fdPkUIWjYECN1Z4U77lGNZX68SOut2gcTeaNmxAy3rVuLJ+dZrVqUpyYqWyjiIiElXCWQiaAemFHmcAfUvaxt3zzGwHUB/YUngjM7sBuAGgZcuWRxQmqU4T1tTqzcJ6XaFpb2q07kWLxg05r2ZldfGISFwLZyEo7tO16Df90myDu48ERgKkpqYeUWuh5xlD4IwhR/JUEZEKLZz9HhlAi0KPmwPrS9rGzBKB2kBWGDOJiEgR4SwEs4AOZtbGzJKBIcDEIttMBK4K3b8YmBKO4wMiIlKysHUNhfr8bwU+Jjh99GV3X2hmDwOz3X0i8BLwupmlEbQE1HcjIlLOwnodgbtPAiYVWfZgofv7gUvCmUFERA5O50aKiMQ5FQIRkTinQiAiEudUCERE4pzF2tmaZrYZWHOET29AkauWo5Ryli3lLFuxkDMWMkL55mzl7inFrYi5QnA0zGy2u6dGOsehKGfZUs6yFQs5YyEjRE9OdQ2JiMQ5FQIRkTgXb4VgZKQDlJJyli3lLFuxkDMWMkKU5IyrYwQiIvJT8dYiEBGRIlQIRETiXNwUAjM728yWmlmamd0b6TwlMbPVZva9mX1nZrMjnecAM3vZzDLNbEGhZfXM7FMzWx66rRvJjKFMxeV8yMzWhfbpd2Z2ToQztjCzL8xssZktNLNfh5ZH1f48SM5o259VzGymmc0L5fxjaHkbM5sR2p9vhobDj8ac/zGzVYX2Z89yzxYPxwjMLAFYBvycYDKcWcBQd1900CdGgJmtBlLdPaouhjGzk4HdwGvu3jW07HEgy90fCxXXuu5+TxTmfAjY7e5/i2S2A8ysCdDE3b81s5rAHOB84GqiaH8eJOelRNf+NKC6u+82syTga+DXwB3AO+4+1syeB+a5+7+iMOdNwAfu/nakssVLi6APkObuK909BxgLDI5wppji7l/x09njBgOvhu6/SvAhEVEl5Iwq7r7B3b8N3d8FLCaYvzuq9udBckYVD+wOPUwK/ThwOnDgwzUa9mdJOSMuXgpBMyC90OMMovA/dIgDn5jZHDO7IdJhDqGRu2+A4EMDaBjhPAdzq5nND3UdRbwL6wAzaw30AmYQxfuzSE6Isv1pZglm9h2QCXwKrAC2u3teaJOo+JsvmtPdD+zPR0L78x9mVrm8c8VLIbBilkVFJS7Gie7eGxgI/DLU1SFH519AO6AnsAH4v8jGCZhZDWA8cLu774x0npIUkzPq9qe757t7T4K50fsAnYvbrHxTFROgSE4z6wr8DjgGOB6oB5R7d2C8FIIMoEWhx82B9RHKclDuvj50mwlMIPhPHa02hfqRD/QnZ0Y4T7HcfVPoD7AAeIEo2KehPuLxwGh3fye0OOr2Z3E5o3F/HuDu24GpQD+gjpkdmIUxqv7mC+U8O9QF5+6eDbxCBPZnvBSCWUCH0FkEyQRzI0+McKafMLPqoYNymFl1YACw4ODPiqiJwFWh+1cB70UwS4kOfLiGXECE92nooOFLwGJ3/3uhVVG1P0vKGYX7M8XM6oTuVwXOJDie8QVwcWizaNifxeVcUqj4G8FxjHLfn3Fx1hBA6BS3fwIJwMvu/kiEI/2EmbUlaAVAMJ/0mGjJaWZvAKcSDJu7CfgD8C7wFtASWAtc4u4RPVBbQs5TCboxHFgN3HigLz4SzKw/MA34HigILb6PoP89avbnQXIOJbr2Z3eCg8EJBF9u33L3h0N/T2MJulvmApeHvnVHW84pQApBF/Z3wE2FDiqXT7Z4KQQiIlK8eOkaEhGREqgQiIjEORUCEZE4p0IgIhLnVAhEROKcCoFIGQuNznlXpHOIlJYKgYhInFMhECkDZna/BfNdfAZ0inQekcOReOhNRORgzOw4gmFLehH8TX1LMHa/SExQIRA5eicBE9x9L4CZRd04ViIHo64hkbKhsVokZqkQiBy9r4ALzKxqaPTYQZEOJHI41DUkcpRCc/q+STBy5BqCETtFYoZGHxURiXPqGhIRiXMqBCIicU6FQEQkzqkQiIjEORUCEZE4p0IgIhLnVAhEROLc/wcgFXmFn23GmwAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fp1 = F(pp, intervals=20) # The default is to randomly generate 100 points.\n", "fp1.plot()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fp1.plot(qq=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can increase the number of intervals to make $F$ more smooth." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fp1 = F(pp, intervals=50)\n", "fp1.plot()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fp1.plot(qq=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$F$ function is more smooth than $G$ function. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $J$ function - a combination of \"event-event\" and \"point-event\"\n", "\n", "$J$ function is defined as follows:\n", "\n", "$$J(d) = \\frac{1-G(d)}{1-F(d)}$$\n", "\n", "If $J(d)<1$, the underlying point process is a cluster point process; if $J(d)=1$, the underlying point process is a random point process; otherwise, it is a regular point process." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "jp1 = J(pp, intervals=20)\n", "jp1.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above figure, we can observe that $J$ function is obviously above the $J(d)=1$ horizontal line. It is approaching infinity with nearest neighbor distance increasing. We might tend to conclude that the underlying point process is a regular one." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interevent Distance Functions\n", "\n", "Nearest neighbor distance functions consider only the nearest neighbor distances, \"event-event\", \"point-event\" or the combination. Thus, distances to higer order neighbors are ignored, which might reveal important information regarding the point process. Interevent distance functions, including $K$ and $L$ functions, are proposed to consider distances between all pairs of event points. Similar to $G$, $F$ and $J$ functions, $K$ and $L$ functions are also cumulative distribution function.\n", "\n", "#### $K$ function - \"interevent\"\n", "\n", "Given distance $d$, $K(d)$ is defined as:\n", "$$K(d) = \\frac{\\sum_{i=1}^n \\sum_{j=1}^n \\psi_{ij}(d)}{n \\hat{\\lambda}}$$\n", "\n", "where\n", "$$ \n", "\\psi_{ij}(d) =\n", " \\begin{cases}\n", " 1 & \\quad \\text{if } d_{ij}\\pi d^2$ indicates that the underlying point process is a cluster point process. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "kp1 = K(pp)\n", "kp1.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $L$ function - \"interevent\"\n", "\n", "$L$ function is a scaled version of $K$ function, defined as:\n", "$$L(d) = \\sqrt{\\frac{K(d)}{\\pi}}-d$$" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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ce1CnQESYPqIXWfkl3P/+BpJiwklNbGV1LKWUj/CqYSXlWsGBAbw8diBtW4Zx0/w09hzSDialVP1ocWjiWoaHMGeCjfJj1Uyel0aJdjCdkhnfZLJ4zR6rYyjlMVoc/EDXuCheHDOArQcKueOdtVRrB1ODZOYW8cwXW7lz0Tq+2ZJrdRylPEKLg584u1scD17Sky835fDc0gyr4/iU11fuJCQogJT4KG5ZuIZtOUVWR1LK7bQ4+JEbhyQxalAHXvomkw/X7rU6jk8oKKngvbQ9XDmgPa//aRBhwYFMnGunoKTC6mhKuZUWBz8iIvz18t4M7tSKu/+7nrW7D1sdyest+Cmb8spqbhzaiXYtmzFrfCoHCsuY+mYaFZXVVsdTym20OPiZkKAAXr0+lbioUCbPs7P/yFGrI3mt8soq5v6QzVkpsaTERwEwoGM0T1/Vl5+yCnh0yUaM0eM3qmnS4uCHWkWEMGfCIErLK5k8z87RiiqrI3mlj9ftJ6+onIlDO/3m/pED2vPns7uw8OddzP1+pzXhlHIzLQ5+qlubKF4YPYCN+wq569112sF0HGMMc1ZkkRwXyVnJteebuuuCbpzfM56/fryJ5Rl6VWDV9Ghx8GPn9YjnvuHd+WTDfl74epvVcbzKjzsK2LS/kIlDOyEitR4PCBCev64/KfFRTFuwmszcYgtSKuU+Whz83JSzOnPVwASeX7aNT9bvtzqO15izIotWESGMHND+hMtEhAYxe4KNkMAAJs1dxeFS7WBSTYcWBz8nIjx1ZW9SE6O58921bNhzxOpIlsvKL+GrLTlcf1pHwoIDf3fZhOhwXhuXyr7DZUxbsJpjVdrBpJoGLQ6K0KBAXhuXSuuIUCbNW0VOYZnVkSz1xsosggMCuP6MxHotb0tqxZNX9GZl5kEe/3iTm9Mp5RlaHBQAMZGhzBpvo6iskinz7JQd888OpiOlx3jXvocR/dsRFxVW7+ddY+vAlLM6M++HbOb/mO3GhEp5hhYH9aue7Zrzr+v6s27PEe7573q/7OFf8PMujh6r4sYhnU6+8HHuHd6dc7vHMX3JRr7PzHdDOqU8R4uD+o0Le7Xh7gu7sWTdPmZ8k2l1HI86VlXN3O93cmaX1vRs17zBzw8MEP49qj9dYiOY+tZqsvJL3JBSKc/Q4qBq+fPZXRjZvx3PfpnB5+n+M0Prpxv2c6CwrNZJbw0RFRbM7PGDCBCYOHcVR44ec2FCpTxHi4OqRUT4+1V96dehJXe8s5aN+5p+B9MvJ711jongnG5xjVpXx9bhvHJ9KrsOlnLLwjVUageT8kFaHFSdwoIDmTUulZbhwUyeayevqNzqSG5lzz7E+j1HuGFoJwICap/01lCnd27NEyN7szwjjyc/3eyChEp5lhYHdUJxzcOYNd5GQWkFN81v2h1Mc77LokWzYK4aeOKT3hpq1OCO3DikE2+s3MnCn3e5bL1KeYIWB/W7erdvwXPX9mf1rsM8sHhDk+xg2l1QypebDjD2tI6EhwS5dN0PXNyds1JiefiDdH7ccdCl61bKnbQ4qJO6uE9b7hiWwvur9/La8h1Wx3G5N1buJECE8WckuXzdQYEBvDRmAImtw5n6Zhq7Dpa6fBtKuYMWB1Uvt57XlUv7tuUfn29h6aYcq+O4TGHZMd5ZtYtL+7alTYv6n/TWEM3Dgpk9YRDVxtHBVFSmHUzK+3llcRCRW0Rkq4hsFJGnrc6jHB1Mz1zdjz7tW3D722vYcqDQ6kgusWjVbkoqqpg4tLNbt9MpJoJXxg5kR34Jty5cQ5VeIl15uXoVBxFJEJG7RORDEVklIstF5GURuUREXFpgROQc4HKgrzGmF/CsK9evTl2zkEBmjrMRERrEpLl2Dhb7dgdTZVU1b6zcyeBOreiT0MLt2zuzawzTR/Tim615/OPzLW7fnlKNcdIPdhF5A3gdqAD+AYwG/gwsA4YDK0TkLBdmmgr83RhTDmCMyXXhulUjtWnh6GDKKyrnZh+fR/mLjTnsPXy0USe9NdS40xMZf0YiM5fv4F37bo9tV6mGqs+3/n8aYy4wxrxgjPneGJNpjEk3xrxvjLkFOBvY58JMKcAfROQnEfmfiAyqayERmSIidhGx5+XpTFye1K9DS565ph+rdh7ioQ98t4NpzooddGwVzrAe8R7d7iOX9mRo1xgeWLwB+84Cj25bqfo6aXEwxqSf5PEKY0yDLsIjIstEJL2O2+VAEBANnA7cDSySOqbiMsbMNMbYjDG22NjYhmxeucCIfu245dyuLLLvYc6KLKvjNNiaXYdYveswNwxJItAFJ701RFBgADPGDCQhOpyb5qexu0A7mJT3OWlTt4hsAE741dAY07ehGzXGDPud7U0F3jeOr6M/i0g1EAPo7oGXuWNYCttyinnq0810iYts9GUnPGnOiiyiwoK4xtbBku23CA9m9gQbI2esZPI8O+9NPZOIUNeeY6FUY9RnWOlS4DLgc+dtrPP2KfBfN2T6ADgXQERSgBBAr3/shQIChOeu60f3Ns25dcEatuUUWR2pXvYePspn6QcYPbgjkRZ+IHeJjWTGmIFsyy3m9nfWUq0dTMqL1GdYKdsYkw0MMcbcY4zZ4LzdB1zohkyvA51FJB14G5hgfHVQ2w+EhzjmUQ4NDmTiXDuHSrx/HuW53+8EYMKZSZbmABxnT1/Sg6Wbcnjmy61Wx1HqVw1pQ40QkaG//CIiZwIRrg7kPIZxvTGmtzFmoDHma1dvQ7lWu5bNmDk+lQOFZUx9y7s7mIrLK1n48y4u6t2G9i2bWR0HcBSp0YM78sq321m8Zo/VcZQCGlYcJgIzRGSniGQBLwM3uieW8jUDO0bzj6v68OOOAh5dstFrO5jete+mqKzSo+2rJyMi/PXyXpzeuRX3vreB1bsOWR1JqfoXB2NMmjGmH9AX6G+M6W+MWe2+aMrXXDEggalnd2Hhz7t+HbrxJlXVhjdW7mRgx5YM6BhtdZzfCA4M4JWxqbRpHsaUeWnsO3zU6kjKz9XnJLjra54FbYwpNMYcqfF4l5rDTcq/3X1BN4b1iOevH29ieYZ3NZgt25zDroJSt18q41RFR4QwZ4KN8mNVTJprp7Si0upIyo/VZ8+hNbBGRF4XkWkicq2IjBeRv4rI/4CngaZzJTbVKAEBwvOj+pMSH8W0BavZnldsdaRfzVmRRfuWzbiwl2dPemuI5PgoXhgzgC0HCrlz0TrtYFKWqU+30r+BgcBCIBY4z/n7XmCcMeYqY8w2t6ZUPiUyNIhZ422EBAYwaa6dI6XWX4V0w54j/JxVwA1DkggK9MrrTf7qnG5xPHBxDz5LP8DzyzKsjqP8VL3+lRhjqowxS40x040xNxljbjfGvAZc6eZ8ykd1aBXOq+NS2XOolGkLVnPM4nmU56zYQURIINcOsuakt4aaOLQT19oSeOHrTJasc+XVaZSqn8Z+hfo/l6RQTdKgpFY8dUUfVmTm88THmyzLceBIGR+v38+1gzrQPCzYshwNISI8PrI3g5KiufvddazbfdjqSMrPNLY4ePaiNMrnXGPrwOQ/dGLuD9m8+WO2JRnm/rCTamO44UzvaV+tj9CgQF69PpWYyFAmz7Nz4EiZ1ZGUH2lscdCjZeqk7ruoB+d0i+XRJRv5PtOzV0IprahkwU+7uKBnGzq2Dvfotl2hdWQoc/5ko6S8kinz7RytqLI6kvIT9WllLRKRwjpuRUA7D2RUPi4wQHhh9AA6x0Qw9a3V7Mwv8di231u9lyNHjzHxD76111BT9zbN+feoAWzYe4S7/7vOa08wVE1LfbqVoowxzeu4RRlj9DKSql6iwoKZM2EQAeKYR/nIUfd3MFVXG95YkUW/hBbYEr3rpLeGGtYznnuHd+fj9ft58esGXSFfqVPi3T19qknp2Dqcl8emkn2wlFsWrqHSzR1M32zNZUd+CTcO7UQdU4L4nJvO6syVA9vz3NIMPtuw3+o4qonT4qA86owurXl8ZG+WZ+Tx1KfunUd5zoos2rYI4+I+bd26HU8REZ66og8DO7bkjkVrSd975ORPUuoUaXFQHjd6cEduGJLE6yuzePvnXW7ZxqZ9hXy//SDjz0gi2MtPemuIsOBAXhtno1V4CJPn2ckt1A4m5R5N51+N8ikPXtzDMZfBh+n8tOOgy9c/Z0UWzYIDGTO4o8vXbbXYqFBmTbBxuPQYU+anUXZMO5iU62lxUJYICgzgxdED6NAqnJvfTGPXQdfNo5xbVMZH6/ZxjS2BFuG+cdJbQ/Vq14J/XdePtbsPc99767WDSbmcFgdlmRbNHB1M1QYmzVtFUZlrOpje/CGbY9XV3DDEd9tX62N477bcdUEKH6zdx8vfbrc6jmpitDgoS3WKieCVsQPZnlfCbW+vpaqRVyEtO1bFmz/t4rzu8XSKcflEhV5n2jldGdGvHc98sZUvNx6wOo5qQrQ4KMud2TWG6SN68fWWXJ7+vHEdTIvX7KWgpMKrZnpzJxHh6av70i+hBbe/s5ZN+wqtjqSaCC0OyiuMOz2Rcacn8tryHbxr331K6zDG8PqKLHq2bc7pnVu5OKH3CgsOZNZ4G83Dgpk8z05+cbnVkVQToMVBeY1HLuvJkK6teXBxOvadBQ1+/vJt+WzLLWZiEznprSHimocxa7yNgyXl3Dw/jfJK7WBSjaPFQXmN4MAAZowZSLuWYdw0P409hxrWwTT7ux3ERYVyWT//vORXn4QW/POa/tizD/HA++nawaQaxeuKg4j0F5EfRWStiNhFZLDVmZTntAwPYfaEQVRUVTNprp2S8vrNo5yRU8R32/IZf0YiIUFe97b2mEv6tuX2Ycm8t3oPs77bYXUc5cO88V/R08Bjxpj+wCPO35Uf6RoXyYwxA8nIKeKOd9bWax7l11dkERoUwJjTEj2Q0Lvdem4yl/Rpy98+28JXm3V6d3VqvLE4GKC58+cWgM6R6IfOSonl4Ut78uWmHJ79cuvvLnuwuJz31+zlyoEJtIoI8VBC7xUQIDx7TT96tWvOrQvXsPVAkdWRlA/yxuJwO/CMiOwGngXutziPssifzkxi9OCOvPztdj5Ys/eEy7354y4qKquZODTJc+G8XLMQRwdTRGgQk+atoqCkwupIysdYUhxEZJmIpNdxuxyYCtxhjOkA3AHMOcE6pjiPSdjz8vI8GV95iIjw2IhenNapFfe8t541uw7VWqa8sor5P2ZzdrdYusZFWZDSe7Vt0YyZ423kFJZz85tpVFS69xLpqmmxpDgYY4YZY3rXcfsQmAC871z0XaDOA9LGmJnGGJsxxhYbG+up6MrDQoICePX6VNo0D2PyvDT2HT76m8eXrN1HfnG535z01lD9O7Tkmav78nNWAY98qB1Mqv68cVhpH/BH58/nAtsszKK8QHRECLMn2Cg7VsXkeXZKKxwdTMYY5qzIolt8FEO7xlic0ntd3r89fzmnK2+v2s3rK3daHUf5CG8sDpOBf4rIOuApYIrFeZQXSImP4sXRA9i0v5A7F62jutrw/faDbDlQ5JcnvTXU/52fwoW94nnyk018uzXXkgxV1YbN+wtZ8NMunajIB0hT2M202WzGbrdbHUN5wKzlO3jy083cel4y6XuPsH7PYVbcey5hwYFWR/N6JeWVXP3qD+wpKGXxtDPdfoymtKKStbsOY88+hD37EGuyD1HkPG+lRbNgPpw2hCQ/uDiiNxORNGOMrc7HtDgoX2KM4Z7/rufdtD0A3HZeMnecn2JxKt+x9/BRLn9pBRGhQXzw5yFEu7D198CRMuzZBdh3HiIt+xCb9hdSVW0QgZS4KFKTorElRpMQHc6U+XZaR4SweNoQmoc1zTk3fIEWB9WklFdWcf3sn9iw9wjf3XMusVGhVkfyKWnZhxg980dsSdHMvXHwKU2jWlVt2HqgiLTsAseewc5D7HU2C4QFB9C/Q0tsia1ITYpmYMdoWjT7bQH4YftBxs35iSFdY5gzwUZQE5rK1ZdocVBNTtmxKvKLy0mIDrc6ik96L20Pd767jutP78gTI/ucdPmS8krW7j6Mfech7NkFrN11+NchorioUGxJ0aQmtsKWGE3Pds3rVXAW/LSLBxZv4MYhnXjksp6N/ptUw/1ecQjydBilXCEsOFALQyNclZpARpvi0AYAAA9dSURBVG4Rr/1vBynxUYw/I+k3j+8/cvTX4SF7dgGb9xf9OkTULT6KEf3bYUuKxpbYioToZqfUEDDmtI5k5BTx+sosUuIjGdUE5/v2ZVoclPJT91zYne25xTz20SbCQ4I4WlFZ5xDRgA7R/PnsLqQmRjOgjiGixnjokh5szyvm4Q/T6RQTwWmdW7ts3apxdFhJKT9WXF7JVS9/z9Ycx/WXTnWIqDGOHD3GFTNWcqi0giV/GUqHVrpH6Cl6zEEpdUK5hWX8lFVA/w4tT3mIqLF25BUzcsZK2rQI472pZxKlHUwe8XvFQVsElPJzcc3DuKxfOzq0CrfsZMLOsZG8PDaV7Xkl3P72WqrqcZl25V5aHJRSXmFocgzTL+vJV1tyefrzLVbH8Xt6QFop5TXGnZHE1pwiXlu+g+T4KK5OTbA6kt/SPQellFd59LJenNmlNQ+8v4G07AKr4/gtLQ5KKa8SHBjAy2MH0q5lGDfNT2PPoVKrI/klLQ5KKa/TMjyE2RMGUV5ZzaS5dkqcZ2Mrz9HioJTySl3jInlx9AAycoq44521VGsHk0dpcVBKea2zu8Xx0CU9+XJTDv9cutXqOH5Fu5WUUl7thiFJbMstYsY320mOi2LkgPZWR/ILuueglPJqIsJjI3pzWqdW3PPeetbsOmR1JL+gxUEp5fVCggJ45fpU4puHMmV+GvucFwZU7qPFQSnlE1pFhDBnwiCOVlQxeZ6d0grtYHInLQ5KKZ+REh/FC6P7s2l/IXe9u87vO5hWZuaz/4h79qK0OCilfMq53eO5/6LufLrhAP/+apvVcSxTUFLBtAWrue+9DW5Zv3YrKaV8zuQ/dGZbTjH//mobyfGRXNq3ndWRPO7pz7dQVFbJAxf3cMv6dc9BKeVzRIQnruiNLTGaOxetY/2ew1ZH8qi07EO8vWo3E4d2olubKLdsw5LiICLXiMhGEakWEdtxj90vIpkislVELrQin1LK+4UGBfLquFRiIkOZPM9OTmGZ1ZE8orKqmoc+SKdN8zBuOy/Zbduxas8hHbgSWF7zThHpCYwCegHDgZdFJNDz8ZRSviAmMpTZE2wUlVUyZZ6dsmNVVkdyu3k/ZLN5fyGPXtaTiFD3HRmwpDgYYzYbY+o6F/5y4G1jTLkxJgvIBAZ7Np1Sypf0aNuc56/rz/q9R7j7v+tpClMfn0huYRnPLc3gjymxDO/dxq3b8rZjDu2B3TV+3+O8rxYRmSIidhGx5+XleSScUso7XdCrDXdf2I2P1u3jpa8zrY7jNk98spmKqmoeG9HL7VO6um2fRESWAXWVtgeNMR+e6Gl13Ffn1wBjzExgJoDNZmu6XxWUUvUy9Y9d2JZTzD+XZpAcH8nw3m2tjuRSKzPzWbJuH7edl0xSTITbt+e24mCMGXYKT9sDdKjxewKwzzWJlFJNmYjwtyv7kJVfwh3vrCMhOpze7VtYHcslyiurePjDdBJbhzP17C4e2aa3DSstAUaJSKiIdAKSgZ8tzqSU8hFhwYHMHJ9Ky/Bgpsyzk1vUNDqYZn+XxY68EqaP6EVYsGd6dKxqZb1CRPYAZwCfiMgXAMaYjcAiYBPwOTDNGNP02w+UUi4TFxXGrPE2DpUe46b5aT7fwbS7oJQXv97GRb3bcE63OI9t16pupcXGmARjTKgxJt4Yc2GNx540xnQxxnQzxnxmRT6llG/r3b4Fz13bjzW7DvPA+xt8uoPpsY82ESDCw5f29Oh2vW1YSSmlXOKiPm35v/NTeH/NXl793w6r45ySpZtyWLY5h9vOS6Zdy2Ye3bZeW0kp1WTdcm5XtuUW8/QXW+gaF8n5PeOtjlRvRyuqmL5kI8lxkdw4tJPHt697DkqpJktEeObqvvRp34Lb3l7D5v2FVkeqt5e+2cbew0d5YmRvggM9/1GtxUEp1aSFBQcya7yNqLAgJs21k19cbnWkk8rMLWbm8h1cObA9p3VubUkGLQ5KqSYvvnkYM8fZyC8uZ+qbaZRXem8HkzGGRz5Mp1lwIPdf5J7LcdeHFgellF/o16Elz17Tj1U7D/HQ4nSv7WD6aP1+vt9+kLuHdyc2KtSyHHpAWinlNy7r145tucW88NU2urWJYtIfOlsd6TcKy47x+Meb6JvQgjGDO1qaRYuDUsqv3H5eMttyinjq0810iY3knO6eO7HsZP61NIP84nJmj7cRGODeC+udjA4rKaX8SkCA8M9r+9GjbXNuWbiGjJwiqyMBsHHfEeZ+v5Oxp3WkX4eWVsfR4qCU8j/hIUHMGm8jLDiQSXPtFJRUWJqnutrw0AfpRIeHcPcF3S3N8gstDkopv9SuZTNmjU/lQGEZU99Mo6Ky2rIs76btdlzq4+IetAgPtixHTVoclFJ+a0DHaJ6+qi8/ZRXw6BJrOpgKSir422dbGJzUiisH1jm3mSX0gLRSyq+NHNCejJwiXv52OynxUdwwxLOXqnj68y0UlVXy+Mjebp/drSF0z0Ep5ffuuqAb5/eM5/GPN7E8w3PTDqdlH+LtVbuZOLQT3dpEeWy79aHFQSnl9wIChOev609KfBTTFqwmM7fY7dusrKrmoQ/SadM8jNvOS3b79hpKi4NSSgERoUHMnmAjJDCASXNXcbjUvR1M837IZvP+Qh69rCcRod43wq/FQSmlnBKiw3ltXCr7DpcxbcFqjlW5p4Mpt7CM55Zm8MeUWIb3buOWbTSWFgellKrBltSKJ6/ozcrMg/z1o01u2cYTn2ymoqqax0b08qqD0DV5376MUkpZ7BpbB7Y5L5udEh/JuDOSXLbulZn5LFm3j9uHJZMUE+Gy9bqa7jkopVQd7h3enfO6xzH9o02szMx3yTrLK6t4+MN0EluHc/Mfu7hkne6ixUEppeoQGCA8P6o/XWIj+PNbq8nKL2n0Omd/l8WOvBKmj+hFWHCgC1K6jxYHpZQ6gaiwYGaPH0SAwMS5qzhy9Ngpr2t3QSkvfr2Ni3q34Zxu3nMl2BPR4qCUUr+jY+twXr0+lV0HS/nLgtVUnmIH02MfbSJAhIcv7enihO5hSXEQkWtEZKOIVIuIrcb954tImohscP73XCvyKaVUTad1bs0TI3vz3bZ8nvx0c4Ofv3RTDss253D7sGTatWzmhoSuZ1W3UjpwJfDacffnA5cZY/aJSG/gC8B7rkSllPJbowZ3JCOnmNdXZpESH8Xoes7UdrSiiulLNpISH+nx6zY1hiXFwRizGajV32uMWVPj141AmIiEGmPKPRhPKaXq9MDF3cnMK+bhD9LpFBPB6Z1bn/Q5L32zjb2Hj/LOlNMJDvSdkXxvTnoVsOZEhUFEpoiIXUTseXmeu1CWUsp/BQUG8NKYASS2Dmfqm2nsOlj6u8tnOs+VuHJge06rRyHxJm4rDiKyTETS67hdXo/n9gL+Adx0omWMMTONMTZjjC02NtaV0ZVS6oSahwUzZ8Igqo2jg6morO4OJmMMjy5Jp1lwIPdf1MPDKRvPbcXBGDPMGNO7jtuHv/c8EUkAFgPjjTHb3ZVPKaVOVVJMBK+MHUhWfgm3LlxDVXXtSYI+Wr+flZkHuXt4d2KjQi1I2TheNawkIi2BT4D7jTErrc6jlFIncmbXGKaP6MU3W/P4+2e/7WAqLDvG4x9vom9CC8bU88C1t7GqlfUKEdkDnAF8IiJfOB/6C9AVeFhE1jpv3n+2iFLKL11/eiLjz0hk1ndZLLLv/vX+fy3NIL+4nCdG9iYwwDsvrHcyVnUrLcYxdHT8/U8AT3g+kVJKnZpHLu3JjrwSHly8gU4xEYSHBDL3+52MPa0jfRNaWh3vlOlVWZVSqhGCAgOYMWYgI19eyU3z02jbIozo8BDuvqC71dEaxauOOSillC9qER7M7Ak2Kquq2bivkAcu7kGL8GCrYzWK7jkopZQLdImN5I0bBrEy8yBXDvT9CztocVBKKRdJTWxFamIrq2O4hA4rKaWUqkWLg1JKqVq0OCillKpFi4NSSqlatDgopZSqRYuDUkqpWrQ4KKWUqkWLg1JKqVrEmNrXIfc1IpIHZDdiFTE45q/2Z/oaOOjroK/BL/zhdUg0xtQ5W1qTKA6NJSJ2Y4zN6hxW0tfAQV8HfQ1+4e+vgw4rKaWUqkWLg1JKqVq0ODjMtDqAF9DXwEFfB30NfuHXr4Mec1BKKVWL7jkopZSqRYuDUkqpWvy6OIjIcBHZKiKZInKf1XmsIiI7RWSDiKwVEbvVeTxBRF4XkVwRSa9xXysRWSoi25z/jbYyoyec4HWYLiJ7ne+HtSJysZUZ3U1EOojINyKyWUQ2ishtzvv97v1Qk98WBxEJBGYAFwE9gdEi0tPaVJY6xxjT34/6uv8DDD/uvvuAr4wxycBXzt+buv9Q+3UA+Jfz/dDfGPOphzN5WiVwpzGmB3A6MM35WeCP74df+W1xAAYDmcaYHcaYCuBt4HKLMykPMcYsBwqOu/tyYK7z57nASI+GssAJXge/YozZb4xZ7fy5CNgMtMcP3w81+XNxaA/srvH7Hud9/sgAX4pImohMsTqMheKNMfvB8YEBxFmcx0p/EZH1zmEnvxlOEZEkYADwE37+fvDn4iB13Oevfb1DjDEDcQyxTRORs6wOpCz1CtAF6A/sB/5pbRzPEJFI4D3gdmNModV5rObPxWEP0KHG7wnAPouyWMoYs8/531xgMY4hN3+UIyJtAZz/zbU4jyWMMTnGmCpjTDUwCz94P4hIMI7C8JYx5n3n3X79fvDn4rAKSBaRTiISAowCllicyeNEJEJEon75GbgASP/9ZzVZS4AJzp8nAB9amMUyv3wgOl1BE38/iIgAc4DNxpjnajzk1+8Hvz5D2tmi9zwQCLxujHnS4kgeJyKdcewtAAQBC/zhdRCRhcDZOC7LnAM8CnwALAI6AruAa4wxTfpg7Qleh7NxDCkZYCdw0y9j702RiAwFvgM2ANXOux/AcdzBr94PNfl1cVBKKVU3fx5WUkopdQJaHJRSStWixUEppVQtWhyUUkrVosVBKaVULVoclPIA55VO77I6h1L1pcVBKaVULVoclHITEXnQOV/IMqCb1XmUaoggqwMo1RSJSCqOS7IMwPHvbDWQZmkopRpAi4NS7vEHYLExphRARPzuul3Kt+mwklLuo9emUT5Li4NS7rEcuEJEmjmvenuZ1YGUaggdVlLKDYwxq0XkHWAtkI3jqp9K+Qy9KqtSSqladFhJKaVULVoclFJK1aLFQSmlVC1aHJRSStWixUEppVQtWhyUUkrVosVBKaVULf8PNkXqFFF14xQAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lp1 = L(pp)\n", "lp1.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation Envelopes\n", "\n", "A [Simulation envelope](http://www.esajournals.org/doi/pdf/10.1890/13-2042.1) is a computer intensive technique for inferring whether an observed pattern significantly deviates from what would be expected under a specific process. Here, we always use CSR as the benchmark. In order to construct a simulation envelope for a given function, we need to simulate CSR a lot of times, say $1000$ times. Then, we can calculate the function for each simulated point pattern. For every distance $d$, we sort the function values of the $1000$ simulated point patterns. Given a confidence level, say $95\\%$, we can acquire the $25$th and $975$th value for every distance $d$. Thus, a simulation envelope is constructed. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simulation Envelope for G function\n", "\n", "**Genv** class in pysal." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "realizations = PoissonPointProcess(pp.window, pp.n, 100, asPP=True) # simulate CSR 100 times\n", "genv = Genv(pp, intervals=20, realizations=realizations) # call Genv to generate simulation envelope\n", "genv" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. ],\n", " [ 1.73156208, 0. ],\n", " [ 3.46312417, 0. ],\n", " [ 5.19468625, 0. ],\n", " [ 6.92624834, 0. ],\n", " [ 8.65781042, 0. ],\n", " [10.3893725 , 0.16666667],\n", " [12.12093459, 0.16666667],\n", " [13.85249667, 0.16666667],\n", " [15.58405875, 0.16666667],\n", " [17.31562084, 0.25 ],\n", " [19.04718292, 0.25 ],\n", " [20.77874501, 0.25 ],\n", " [22.51030709, 0.58333333],\n", " [24.24186917, 0.58333333],\n", " [25.97343126, 0.83333333],\n", " [27.70499334, 0.83333333],\n", " [29.43655542, 0.83333333],\n", " [31.16811751, 0.91666667],\n", " [32.89967959, 0.91666667],\n", " [34.63124168, 1. ],\n", " [36.36280376, 1. ]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genv.observed " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "genv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above figure, **LB** and **UB** comprise the simulation envelope. **CSR** is the mean function calculated from the simulated data. **G** is the function estimated from the observed point pattern. It is well below the simulation envelope. We can infer that the underlying point process is a regular one." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simulation Envelope for F function\n", "\n", "**Fenv** class in pysal." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fenv = Fenv(pp, intervals=20, realizations=realizations)\n", "fenv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simulation Envelope for J function\n", "\n", "**Jenv** class in pysal." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "jenv = Jenv(pp, intervals=20, realizations=realizations)\n", "jenv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simulation Envelope for K function\n", "\n", "**Kenv** class in pysal." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "kenv = Kenv(pp, intervals=20, realizations=realizations)\n", "kenv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simulation Envelope for L function\n", "\n", "**Lenv** class in pysal." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lenv = Lenv(pp, intervals=20, realizations=realizations)\n", "lenv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CSR Example\n", "In this example, we are going to generate a point pattern as the \"observed\" point pattern. The data generating process is CSR. Then, we will simulate CSR in the same domain for 100 times and construct a simulation envelope for each function." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "from libpysal.cg import shapely_ext\n", "from pointpats import Window\n", "import libpysal as ps\n", "va = ps.io.open(ps.examples.get_path(\"vautm17n.shp\"))\n", "polys = [shp for shp in va]\n", "state = shapely_ext.cascaded_union(polys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate the point pattern **pp** (size 100) from CSR as the \"observed\" point pattern." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([list([1]), list([1, 2])], dtype=object)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = [[1],[1,2]]\n", "np.asarray(a)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = 100\n", "samples = 1\n", "pp = PoissonPointProcess(Window(state.parts), n, samples, asPP=True)\n", "pp.realizations[0]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "100" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp.n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulate CSR in the same domian for 100 times which would be used for constructing simulation envelope under the null hypothesis of CSR." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csrs = PoissonPointProcess(pp.window, 100, 100, asPP=True)\n", "csrs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct the simulation envelope for $G$ function." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "genv = Genv(pp.realizations[0], realizations=csrs)\n", "genv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the \"observed\" $G$ is well contained by the simulation envelope, we infer that the underlying point process is a random process." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0. , 0.1 , 0.32, 0.52, 0.68, 0.81, 0.87, 0.92, 0.95, 0.96,\n", " 0.97])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genv.low # lower bound of the simulation envelope for G" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0.14, 0.35, 0.54, 0.72, 0.85, 0.92, 0.97, 0.99, 1. , 1. ,\n", " 1. ])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genv.high # higher bound of the simulation envelope for G" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct the simulation envelope for $F$ function." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fenv = Fenv(pp.realizations[0], realizations=csrs)\n", "fenv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct the simulation envelope for $J$ function." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "jenv = Jenv(pp.realizations[0], realizations=csrs)\n", "jenv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct the simulation envelope for $K$ function." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "kenv = Kenv(pp.realizations[0], realizations=csrs)\n", "kenv.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct the simulation envelope for $L$ function." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lenv = Lenv(pp.realizations[0], realizations=csrs)\n", "lenv.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.4" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }