{ "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", 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" ] }, "metadata": {}, "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": {}, "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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8pmQfzP8nLP43xLSFS9+FbkPdThX2ysor2JZTeMDnf9iWA0B8kA+DBN/KfSmQaIzpBKQDo4DRNbb5GLgUeM0YE4tzmmajP4OKeMbGL2HGeMjZDEnXwtkPOqdjxHX3J6/m7SVba92uQ4tGAUhzZGotd2ttmTFmHDAX53z6FGvtamPMQ0CKtTa58rlzjDFrgHLgNmvtrroMLhJyCnPh03th2RvQsjNc/QkknOJ2Kqlm+dZcjo9vxphTOh1wm9ZNGhAXxIt0VPFp0Ky1dhYwq8bn7qv2sQUmVP4RkZp+/gRmToCCHc549cF3Qf3gP28bTsorLOuz93L1SQn8oU/oj1LSHREidWnvDph9O6z+H7TpCZdOg/i+bqeS/diyq4CSsgoS47wxu6bKXaQuWAsr34U5d0JJgTMl78k3Q2Rw37IeztZm5QPQrW0Tl5P4h8pdxN9ytzlzra//DDoMhOEvQOtubqeSWqzN2gtAFx25i8hvVFRAyivw2QPOkft5T0D/6zV1QIhYm5VPx5aNaBTljVr0xl6IuG3nOmfqgK3fwjFnwrBnocXRbqeSQ7A2K5+ubbxx1A4qd5EjU14K3zwPCx53Rr/84T/Q+1JNHRBiSsoq2JhdwNnd27gdxW9U7iKHa/sKmP5XyPwReoyA856EJt4ph3CyeVcBZRWWrm28cTEVVO4ih660EL58HBY978wDM/JN6DHc7VRyBKpGyqjcRcLVlm+cc+u71kOfy+Gcf0DDFm6nkiO0NjOfCAOdWzd2O4rfqNxFfFGc74yCWfoyNO/orGN6zBlupxI/WZu1l4TYxkTXj3Q7it+o3EVqs+5TmHEz7EmHQf/PuSEpyjtHeFI1UsY7p2RA5S5yYAW7YO5dzp2msd1gzKfQob/bqcTPikrL2byrgGG9j3I7il+p3EVqstaZC2bWbVCUC6ffAaf+Deo1cDuZ1IEN2XupsHhqjDuo3EV+a892+ORvkPoJHNUHhk+Htj3dTiV1aF3ltAPddFpGxIOsdeZZn3cvlBc7o2AG3gCR+hHxutSsfOpHGhJivXUdRd+5Irs3QvJ42PwVJJwKFz4HrY5xO5UEyLqsfDrFNqZ+pLfmAFK5S/iqKIfF/4Ev/uFMxXvhc9DnSk30FWZSs/Lp3b652zH8TuUu4SlrDSSPg/Tvoet5MOxpaOqt0RJSu30lZWzbXcif+3VwO4rfqdwlvJQVw1dPw1dPQXQzuHgKHPdHTfQVpqoupnptjDuo3CWcpKXA9HGQ/RMcPxKGPgaNW7mdSlz065wy3hoGCSp3CQclBfDFP2Hxv51TL6Pfh67nuJ1KgsDarHyi6kVwdCtvjZQBlbt43cYFzkiY3C2QNAbOfgCim7ocSoLF2qy9dGkdQ2SE907LqdzFmwpzYd49sPxNaHkMXD0LEk52O5UEmbVZ+Qzq7M1Tcyp38Z6fZjp3mRZkw8k3w+A7nVWSRKrZU1TK9rwiEj14vh1U7uIle3c488Gs+RjaHg+j34WjTnA7lQSpdZUXU7027UAVlbuEPmthxTsw504o3Qdn3gsn3+TcmCRyAGs9PAwSVO4S6nK3OnOtb/gcOgyE4ROhdVe3U0kISM3Mp1FUJPHNvXnKTuUuoamiAlJecVZHstZZnLr/dZo6QHy2bkc+iXExRHhwpAyo3CUUZa911jHdthiOOQsufNZZ+k7kEKRm7mVwt9Zux6gzKncJHeWlsOg5+PJxqN8I/vAi9B6lqQPkkOUUlLBzb7FnL6YC+PQ7rDFmqDEm1Riz3hhz50G2u9gYY40xSf6LKAJk/AAvnQFfPAzdzodxS+GES1Xscliqph3w6jBI8OHI3RgTCUwChgBpwFJjTLK1dk2N7ZoA44EldRFUwlRpoXOkvuh5aBwLl7wF3S90O5WEuKpy79bWu0fuvpyWGQCst9ZuBDDGvAOMANbU2O5h4AngVr8mlPC15Rvn3Pqu9dDnCjjnYWjYwu1U4gFrs/bSpEE92jaNdjtKnfHltEw8sK3a47TKz/3CGNMH6GCtnenHbBKuivbAzAnw6nnOefYrPoYRE1Xs4jepWfl0bdsE4+HTer4cue9v7+0vTxoTATwDXF3rCxkzFhgL0LGjRjfIfqydBzNvgT3pMOivcObdEOW9GfvEPdZa1mXlM7RnW7ej1Clfyj0NqL5MSXsgo9rjJkBPYEHl/4JtgWRjzHBrbUr1F7LWTgYmAyQlJVlEqhTsgrl3wcp3ofWxMOZT6NDf7VTiQdl7i8nZV+rZO1Or+FLuS4FEY0wnIB0YBYyuetJamwfEVj02xiwAbq1Z7CL7ZS2s/ghm3Q5FuXD6nXDqBKjXwO1k4lFeXn2pulrL3VpbZowZB8wFIoEp1trVxpiHgBRrbXJdhxSP2pPhzN6YOguO6gsjkqHNcW6nEo9LzaxafSnMyx3AWjsLmFXjc/cdYNvBRx5LPM1aWPY6zLvXuWB6zj9h0A0QEel2MgkD63bk06JRfWJjotyOUqd0h6oE1q4NMOMm2PwVJJwKw5+Hlp3dTiVhJDUzn65tvD1SBlTuEijlZbDkP85appH14cLnoe+VusNUAsoZKbOXP/SJr33jEKdyl7qXtRqmj4OMZc7UARc85SxULRJg2/OKyC8uo6uH70ytonKXulNWDF895fyJbg4XvwrHXaSjdXFN1bQDXeO8O6dMFZW71I1tSyF5HGT/DL1GwdBHoVFLt1NJmPul3D0+UgZU7uJvJQXwxT9g8X+gaTxc9gEkDnE7lQjgzCnTukkDWjT29kgZULmLP22YDzPGO0vf9b8Ozrofopu6nUrkF2uz8unq4Wl+q1O5y5ErzIF598Dyt6BVF7hmNhx9ktupRH6josIZKTNqQIfaN/YAlbscmZ9mOHeZFuyEUybA6XdAfe9OoyqhKy2nkMLS8rA43w4qdzlc+Vkw+zZYMx3aHg+j34OjTnA7lcgBhdPFVFC5y6GyFlZMgzl3OasknXUfnDTeuTFJJIilhsHSetWp3MV3OVtg5s2w4QvoMAiGvwCtu7qdSsQn67LyOapZNE2jw+NAROUutauogKUvwWcPOjcgnf8vSBoDET6try4SFFKz9pIYJqdkQOUutclOddYx3bYEjjkLLnwWmmsVLQktZeUVbMjey6mJsbVv7BEqd9m/8lJY9Cx8+YSzzN1F/4Vel2jqAAlJW3bvo6SsgsQwmHagispdfi9juTPRV9YqZy6Y856AmDi3U4kctnWVF1O7hcGEYVVU7vKr0kJY8Ch8MxEat4ZL3obuw9xOJXLEUjOdpfW66Mhdws7mryF5POze4MyzPuRhaNjc7VQifrF2Rz4dWzaiUVT4VF747KnsX9Ee+Ox+SJkCLRLgyunQebDLoUT8a10YzSlTReUeztbOhZm3QP52OHEcnPF35+KpiIeUlFWwMbuAs7q3cTtKQKncw1HBTphzJ/z4PrTuDiPfgPZJbqcSqRObdxVQVmHpFkZj3EHlHl6shVUfwuzbndMxg+9yJvuq5/25rSV8pWaG17QDVVTu4SIvHT6ZAGvnQHw/GD4R2vRwO5VInVuXlU+EgWNaq9zFSyoqYNnr8Ol9zo1J5z4CA/8CEZFuJ5MwsH5HPtn5Ja5mWLo5h4RWjYmuH17f8yp3L9u1AWbcBJu/gk6nwYXPQ8tObqeSMFBcVs4Tc1J55etNbkcBYHjvo9yOEHAqdy8qL4PFk2D+IxDZwJm9sc8VmjpAAmLTzgJunLaMVel7uGLQ0Zx/fDu3I3FcfPgt96hy95rMVZA8zplCoNsFcMFT0NT9Hy4JD/9bnsY9/1tFvcgI/ntFP849rq3bkcKWyt0ryoph4b/g66ehYQv482vQ4w86WpeAKCgu497pq/hoWToDElry7KgTOKp5Q7djhTWVuxds+86Z6GtnKvS+1Llo2qil26kkTKxKz+PGacvZsquA8WclMv7MLtSL1Fz/blO5h7LivfDFP2DJi9CsPVz2ISSe7XYqCRPWWl5dtJnHZv9My8ZRTL1+EIM6t3I7llRSuYeqDV84I2Fyt8KAsc5apg3C6w48cc/ughJue38Fn/+8g7O7x/Hkxb1p0Vg3wwUTn8rdGDMUeA6IBF621j5W4/kJwHVAGZANXGut3eLnrAJQmANz74Ef3oJWiXDNHDj6RLdTSRj5dsMubn53OTkFpTxwYQ+uOikBo2s7QafWcjfGRAKTgCFAGrDUGJNsrV1TbbPlQJK1dp8x5gbgCeCSuggc1tYkw6xbnblhTpkAp98B9aPdTiVhoqy8guc+X8fE+evpFNuYKVf357ijmrkdSw7AlyP3AcB6a+1GAGPMO8AI4Jdyt9bOr7b9YuByf4YMe/lZTqn/lAxte8Fl70O73m6nkjCSnlvITdOWk7Ilh4v7tefB4cfRuIHO6gYzX7468cC2ao/TgIEH2X4MMHt/TxhjxgJjATp21CLLtbIWfpgKc++C0iI4+wFnat7I+m4nkzAyZ9V2bv9gJRUWnht1AiNOiHc7kvjAl3Lf38k0u98NjbkcSAJO39/z1trJwGSApKSk/b6GVMrZ4lww3TgfOp4Ew5+H2ES3U0kYKSot5x+frOGtxVvp1b4ZL1zah6Nbab7/UOFLuacBHao9bg9k1NzIGHM2cDdwurW22D/xwlBFOXz3Enz+kHMD0gVPQb9rIULjhiVw1mXlc+O05fycmc/Y0zpz6zndiKqn78FQ4ku5LwUSjTGdgHRgFDC6+gbGmD7Af4Gh1todfk8ZLnb8DMk3Qtp30GUIDHsGmneo/e+J+Im1lneXbuOBGatpHFWP167pz+BucW7HksNQa7lba8uMMeOAuThDIadYa1cbYx4CUqy1ycCTQAzwfuWQqK3W2uF1mNtbykpg0XOw8AmIioGLJkOvkZo6QAJqT1Epd330I5+s3M4pXWJ5emRv4ppqNFao8ulyt7V2FjCrxufuq/axbos8XOnLnKP1rFXQ808w9HGIae12Kgkzy7bmMH7acrbnFXH70G785bRjiIjQwUUo01gmt5TsgwWPwrcTIaYNjJoGx57vdqqg9cH3aUxdsmX/V/LliFgLP6bn0a5ZNO//5UT6dmzhdiTxA5W7GzZ9BTPGw+6N0O9qGPIQROtmkAN5P2Ubt32wkm5tmhDXtIHbcTxp9ICO3HpuN5o11DBbr1C5B1JRHnx6P3z/KrToBFfNcFZIkgOasSKDOz5cyamJsbx0ZVLYLZUmcrhU7oGSOhtmToC9mc6NSGfcDVGN3E4V1OatzuSWd38gKaElk69QsYscCpV7XSvYCbPvgFUfQNxxMOotiO/ndqqg9+XabMZNXU7P+GZMubo/DaNU7CKHQuVeV6yFH993ir043zlSP/lmqKdpUWvz7YZdjH0jhS5xMbx+zQBiNIeJyCHTT01dyEtzTsGsmwvt+zsLVMd1dztVSFi2NYcxry+lY8tGvDlmAM0a6QKfyOFQuftTRYVzsfTT+8GWw9DHnIU0InRKwRer0vO4asp3xDVpwNvXDaRVjEbGiBwulbu/7Nrg3Iy0ZRF0HgwXPgctElwOFTrWZuVzxStLaBpdn7evH6Q7I0WOkMr9SJWXOTciLXgU6jWAEZPghMs0dcAh2Ji9l9EvLaF+ZARTrx9IfPOGbkcSCXkq9yOR+SNM/ytsXwHHDnNmcGzS1u1UIWXb7n1c9vISrLVMHTtIU8qK+InK/XCUFsHCJ2HRs9CwJYx8A3qMcDtVyMnMK+Kyl5dQUFzGO2NPpEucFvgW8ReV+6Hautg5t75zLfQeDef+Exq1dDtVyMnOL2b0y4vZXVDCW9cNpMdRTd2OJOIpKndfFe91FtD4bjI06wCXfwhdNBnm4cjdV8IVryxhe24Rb4wZwAkdmrsdScRzVO6+WP8ZzLgF8rbBwP+DM++FBjFupwpJe4pKuXLKd2zcWcCUq/rTP0G/9YjUBZX7wezbDXPvhhVTIbYrXDsXOh5sbXA5mH0lZVz76lLWZOxh8pX9OCUx1u1IIp6lcj+Q1R/DrFuhMAdOvRVOuw3qa+z14SoqLee611NYtjWHiaP7cuaxbdyOJOJpKvea8jPhk7/BzzOhXW+4/CNo18vtVCGtpKyCG976nm837uLpkb05//h2bkcS8TyVexVrYflbMO9uKCt2FtAY9FeI1D/RkSgrr2D8tOVVLQHDAAAH1UlEQVTMT83mkYuO56I+7d2OJBIW1FwAOZthxk2wcQEcfbIz0VerY9xOFfLKKyy3vr+COaszuW9YD0YP7Oh2JJGwEd7lXlHuDG38/CEwkTDsGeh7NUREuJ0s5Flruft/P/LxDxncdm43rj2lk9uRRMJK+Jb7jp8heRykLYXEc51ibxbvdipPsNby4Iw1vLN0Gzee2YW/ntHF7UgiYSf8yr2sBL5+xpk+oEET+OPLcPzFmujLT6y1PD4nlde+2cx1p3RiwpCubkcSCUvhVe7p38P0G2HHauh5MZz3ODTWWGt/euGL9bz45QYuG9iRuy/ojtF/miKuCI9yL9kHCx6BbydBTFu49B3odp7bqTznpYUbefrTtfyxbzwPj+ipYhdxkffLfdNCSB4POZug3zUw5EGIbuZ2Ks9589vN/HPWT1zQqx1P/KkXEREqdhE3ebfci/Lg0/vg+9egZWe4aiZ0OtXtVJ70fso27p2+mrO7t+HZS06gXqRGG4m4zZvl/vMs+GQC7M2Ck8bD4LsgqpHbqTxpxooM7vhwJacmxjJxdB/qq9hFgoK3yn1vNsy+HVZ/BHHHwaipEN/X7VSeNW91Jre8+wNJCS2ZfEUS0fW1ELhIsPBGuVsLK9+DOXdASQGccQ+cfBPUi3I7mWd9uTabcVOX0zO+GVOu7k/DKBW7SDDx6XdoY8xQY0yqMWa9MebO/TzfwBjzbuXzS4wxCf4OekB5aTB1JPxvLLRKhP/7Ck6/TcVeh77dsIuxb6TQJS6G168ZQEwDbxwjiHhJrT+VxphIYBIwBEgDlhpjkq21a6ptNgbIsdZ2McaMAh4HLqmLwL+oqICUV+CzB8BWwNDHYMBYiNARZF1atjWHMa8vpWPLRrw5ZgDNGtV3O5KI7Icvh1wDgPXW2o0Axph3gBFA9XIfATxQ+fEHwERjjLHWWj9m/dXOdc7wxq3fQOcz4MJnoUVCnbyV/GpVeh5XTfmOuCYNePu6gbSKaeB2JBE5AF/KPR7YVu1xGlBzOaJftrHWlhlj8oBWwE5/hKxu6UfP0XvlwxQTxYvRNzEv+0x4dQuwxd9vJTVk5BbSvFEUb18/iLimWrhEJJj5Uu77uxul5hG5L9tgjBkLjAXo2PHwpn+1rY5hVcyJvNd6PHvqtSTxsF5FDkev9s0Zf1YX4ps3dDuKiNTCl3JPAzpUe9weyDjANmnGmHpAM2B3zRey1k4GJgMkJSUd1imbAacPg9OHoQGOIiIH5stomaVAojGmkzEmChgFJNfYJhm4qvLji4Ev6ux8u4iI1KrWI/fKc+jjgLlAJDDFWrvaGPMQkGKtTQZeAd40xqzHOWIfVZehRUTk4HwaoGytnQXMqvG5+6p9XAT82b/RRETkcGkiEBERD1K5i4h4kMpdRMSDVO4iIh6kchcR8SDj1nB0Y0w2hz9nQCx1MLVBkNM+hwftc3g4kn0+2lrburaNXCv3I2GMSbHWJrmdI5C0z+FB+xweArHPOi0jIuJBKncREQ8K1XKf7HYAF2ifw4P2OTzU+T6H5Dl3ERE5uFA9chcRkYMI6nIP6oW564gP+zzBGLPGGLPSGPO5MeZoN3L6U237XG27i40x1hgT8iMrfNlnY8zIyq/1amPM1EBn9Dcfvrc7GmPmG2OWV35/n+9GTn8xxkwxxuwwxqw6wPPGGPN85b/HSmOMf5epsNYG5R+c6YU3AJ2BKGAF0KPGNv8PeLHy41HAu27nDsA+nwE0qvz4hnDY58rtmgALgcVAktu5A/B1TgSWAy0qH8e5nTsA+zwZuKHy4x7AZrdzH+E+nwb0BVYd4Pnzgdk4K9kNApb48/2D+cj9l4W5rbUlQNXC3NWNAF6v/PgD4CxjzP6W/AsVte6ztXa+tXZf5cPFOCtjhTJfvs4ADwNPAEWBDFdHfNnn64FJ1tocAGvtjgBn9Ddf9tkCTSs/bsbvV3wLKdbahexnRbpqRgBvWMdioLkxpp2/3j+Yy31/C3PHH2gba20ZULUwd6jyZZ+rG4PzP38oq3WfjTF9gA7W2pmBDFaHfPk6dwW6GmMWGWMWG2OGBixd3fBlnx8ALjfGpOGsH3FjYKK55lB/3g+JT4t1uMRvC3OHEJ/3xxhzOZAEnF6niereQffZGBMBPANcHahAAeDL17kezqmZwTi/nX1ljOlprc2t42x1xZd9vhR4zVr7lDHmRJzV3XpaayvqPp4r6rS/gvnI/VAW5uZgC3OHEF/2GWPM2cDdwHBrbXGAstWV2va5CdATWGCM2YxzbjI5xC+q+vq9Pd1aW2qt3QSk4pR9qPJln8cA7wFYa78FonHmYPEqn37eD1cwl3s4Lsxd6z5XnqL4L06xh/p5WKhln621edbaWGttgrU2Aec6w3BrbYo7cf3Cl+/tj3EunmOMicU5TbMxoCn9y5d93gqcBWCM6Y5T7tkBTRlYycCVlaNmBgF51trtfnt1t68o13K1+XxgLc5V9rsrP/cQzg83OF/894H1wHdAZ7czB2CfPwOygB8q/yS7nbmu97nGtgsI8dEyPn6dDfA0sAb4ERjlduYA7HMPYBHOSJofgHPcznyE+zsN2A6U4hyljwH+Avyl2td4UuW/x4/+/r7WHaoiIh4UzKdlRETkMKncRUQ8SOUuIuJBKncREQ9SuYuIeJDKXUTEg1TuIiIepHIXEfGg/w8Wr59q8KJ0WgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "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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\n", 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" ] }, "metadata": {}, "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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m4egONKlVqegvslRuJmx8D7Z/Cd41YfBiaHSX9Y4v7ErCXQgncfjcZR6eHYFSisVjOtCghhUf7T+5GcLHQ8op42Zpj7ehrJX/RyDsSsJdCCdwMCGVod9E4OnhxsLRHahX3ds6B85OhTWvw675ULUuDF8OdbpY59jCoSTchSjh9sVf4pFvd1DB052FozsQ7FvBOgc+vBJWPAfpidBpPHR9BTzLW+fYwuEk3IUowXadTmH4tzvwKV+GRaM7EFjVCuGbfh5WTYKDv4JfMxi0EPxbF/+4okSRcBeihIo8dZGRcyOp5u3JwtEd8K9crngH1Br2/QS/vwi5GdDtNej8NHh4WqdgUaJIuAtRAv22+wwv/rIP/8rlWDi6AzV9irnSYmo8LH8Wjq2BgHbQ/z/g19g6xYoSScJdiBLElG/mw1WH+WbLSTrUrcqMIa2p5u118wc0myHqW1j3Fmgz9J4C7UaDm7vVahYlk4S7ECVESkYu4xftZktMMiM6BfPq3U0o416M9VuSj0H4BDj9F9TtBv0+hyrBVqtXlGwS7kKUAIfPXWb0gigSU3OKv5F1vgn++gI2fWhsnDHgS2g1RJYOKGUk3IVwsFX7z/L8kr14e3mw+PEOxVsA7Ow+CB8HZ/dCk/7Q52OoaMPdmESJJeEuhIOYzZpP1x5l+sYYQoMq8/XQNvhVuskbp3nZsHkqbPkcyleDgQug6QDrFiycioS7EA5wOTuPZxfvYf3hJB4KC2TyPc3w8rjJm5yx24ylAy4cg1ZDoec7UN4G67oLpyLhLoSdHT+fzugFUZy+kMk7A5oxtENt1M2Mh+ekwbq3IXI2VA4y9jGtf6f1CxZOScJdCDvacDiRpxftwdPDje8fa0+Huje5Jd6xdbD8GWP+evsn4Y7XwMtK680IlyDhLoQdaK35ctNxPl5zhKa1KjFrWNjNPXGaeRFWvwJ7F4FvIxi1BgLbWb9g4fQk3IWwsYwcEy/8vJeV+8/Rv+UtTLk/hHKeNzi+rjVE/wYrX4CsFLhtEtw2ETyK8YCTcGkS7kLYUNzFTEYviOJoYhqv9GnM6C51b3x8/fJZWDkRDi+HWq3gkd+gZnPbFCxchoS7EDayNSaZsQt3YTZr5o5sx+0Nq9/YAbSG3d/B6tcgPwd6TIYOY8Fdfm1F0Sx6tlkp1VspdUQpFaOUeukabQYqpaKVUgeVUgutW6YQzkNrzbdbTjJszg78KnoRPu7WGw/2iydhwQBjimPNFvDkX8YKjhLswkJF/qQopdyBGUAPIB6IVEqFa62jC7RpALwMdNZapyil/GxVsBAlWXZePq/8335+3XWGXs1q8MnAVnh73UAgm/MhYiasfwfcPODuT41t79yKscaMKJUs+alrB8RorU8AKKUWAwOA6AJtRgMztNYpAFrrJGsXKkRJdzY1i8e/28m++FSe69GQcd3q4+Z2A+PridHGlfqZKGjY2wh2H3/bFSxcmiXh7g/EFXgdD7Qv1KYhgFJqK+AOvKW1/r3wgZRSY4AxAEFBQTdTrxAlUtSpizzx/S6y8/KZPSyMHk1vYD0XUy5s+RQ2fwxlK8H930Lz+2WhL1EsloT71X7C9FWO0wDoCgQAfyqlmmutL/3ji7SeBcwCCAsLK3wMIZzSDxGxvBV+kIAq5Vk8pj31/Spa/sXxO42FvpKiocWD0PtDqOBru2JFqWFJuMcDBdcfDQASrtJmu9Y6DziplDqCEfaRVqlSiBIo12TmzfCDLNpxmq6NqjNtUCg+5cpY+MWZsPE92P4leNeEwT9Co962LViUKpaEeyTQQClVBzgDDAKGFGrzGzAYmKeU8sUYpjlhzUKFKEmS0rJ56vtdRMWm8GTXekzs2Qh3S8fXT/wByyZAyikIexS6v20MxwhhRUWGu9bapJQaB6zGGE+fo7U+qJSaDERprcOvvNdTKRUN5AMvaK0v2LJwIRxlb9wlHv9uJ6lZeUwfEkrfkFss+8KsS7D2ddi1AKrWhRErIPhW2xYrSi2ltWOGvsPCwnRUVJRD+hbiZv2yM56X/28/1b29mD0sjKa3WHjFfXgFLH8OMpKg03jo+jKUuYm1ZUSpp5TaqbUOK6qdPBEhhAVM+WbeX3mYOVtP0rFuNWY83JqqFTyL/sL0JFg1CQ7+H9RoDoMXgX9r2xcsSj0JdyGKcCkzl7ELd7E15gIjOwfzSh8LNq7WGvb9CL+/BLkZxpK8nZ8BdwtvuApRTBLuQlxHfEomI+ZGcvpCJh89EMKDlmxcfSnOWGs9Zh0Etof+/4HqjWxfrBAFSLgLcQ0HzqQycl4kOXn5LBjVruiNNcxmiPoW1r1lXLnfNRXajpalA4RDSLgLcRWbj57nye934lOuDD882YmGNYp4MCn5mLF0wOltUO8O6Ps5VKltn2KFuAoJdyEK+XlnPC/9so/6ft7Mf7QdNSqVvXbj/Dz46wvYNMWY/XLPV9BysCwdIBxOwl2IK7TWTN8Qwydrj3JrfV++GtqaimWvcwP07F5YOhbO7YemA+Cuj6DiDawpI4QNSbgLgTHV8fWlB1i0I477Qv358P4QPD2uMVaelwV/TIGtXxjrwAz8Dpr2t2/BQhRBwl2Uepm5JsYt3M2Gw0mM7WYsJXDNrfBi/zLG1i/EQOhQ6PkulKti34KFsICEuyjVzqflMGp+JAfOpPLuPc0Z2uEaN0Fz0oxZMJHfQOUgYx/Tet3sWqsQN0LCXZRaJ86nM2JuJElp2cx6JIzu11qD/dhaWPYMXD4DHZ4yHkjyrGDfYoW4QRLuolTaGZvCY/MjUUqxeExHWgVW/nejjAuw+mXjSVPfRjBqLQS2tX+xQtwECXdR6qw5eI7xi3ZT06cs80e2I9i30FW41sZaMCtfgOxLcPuL0OV58PByTMFC3AQJd1GqLNh2irfCD9IioDJzhodRzbtQYF8+CyuehyMr4JZQ6L8UajZ3SK1CFIeEuygVzGbN1NVHmPnHcbo38eM/g1tTztP9fw20NtZZX/M65OcYs2DaPwnu8isinJP85AqXl2PKZ9LP+1i6J4GH2wfxdv9meBRc1fHiCQifAKf+hOAu0G8aVKvnuIKFsAIJd+HSUrPyeOK7nWw7cYFJvRvx5O31/jeH3ZwP27+CDe8aS/H2mwahw2ShL+ESJNyFyzqbmsWIOZEcP5/OpwNbcl/rgP+9mRgN4ePgzE5oeBf0/RQqWbhdnhBOQMJduKTD5y4zYk4k6Tkm5o1sx60NfI03TDnw56fw5ydQ1gcemAPN7pOFvoTLkXAXLuevmGQe/24n5b3c+enxjv/b5zQ+CpaOg/OHoMVA6P0hVChijXYhnJSEu3ApS/ecYeKSvQRXq8C8R9vhX7mcsc3dhvdg+5fG0MuQJdCwp6NLFcKmJNyFS9Ba8/XmE3y46jDt61Rl1iNh+JQvAyc2GTNhLsVC2Cjo/haUreTgaoWwPQl34fTyzZq3lx1kwbZY+rW8hY8fDMErLw2Wvga7v4Oq9WDESgju7OhShbAbCXfh1LJy85mweDdroxMZc1tdXurdGLcjK4ynTDPOQ+dnoOtLxi5JQpQiEu7CaV3MyGXU/Ej2xF3izX5NGdmyAvw8AqJ/g5otYMiPcEsrR5cphENIuAundPpCJsPn7iDhUhZfDQmld/4fMP0lyMuEO16Hzk8bDyYJUUpJuAunszfuEqPmR2Iya5YM8idk9zg4vh4C20P/6VC9oaNLFMLhJNyFU9lwOJGxP+zGt4IHv7Y9QvXwkcaiX3d9BG0fk6UDhLhCwl04jUU7TvPabwfo4XeJL8rPxfPPHVDvTuj3ubH1nRDibxLuosTLyzfz0eojzNl8lA9rbOSBtB9Q2eXhnpnQcpAsHSDEVVgU7kqp3sA0wB34Rmv94TXaPQAsAdpqraOsVqUotRIuZTF+0W6yT+9ic5V53JJ6DJreA30+Am8/R5cnRIlVZLgrpdyBGUAPIB6IVEqFa62jC7WrCEwAImxRqCh9NhxO5OUfd/CYeQmjyi7Hzd0XHvoemvRzdGlClHiWXLm3A2K01icAlFKLgQFAdKF27wBTgYlWrVCUOnn5Zj5ec4Rdm1fyS7lvCVBnoNUj0PMdKFfF0eUJ4RQsCXd/IK7A63igfcEGSqlQIFBrvVwpJeEublrCpSwm/bCFXmdn8rLXOsyVakO/36BeN0eXJoRTsSTcr3a3Sv/9plJuwGfAiCIPpNQYYAxAUJDMbhD/tOFwIr/+OJePzLOo6XEROozF7Y5XwbOCo0sTwulYEu7xQGCB1wFAQoHXFYHmwKYr25fVBMKVUv0L31TVWs8CZgGEhYVphMAYhpmxIoKgyHeZ7r6F3GoNUff+CIFtHV2aEE7LknCPBBoopeoAZ4BBwJD/vqm1TgV8//taKbUJmCizZYQlElIyWTRvGsMvfUkV9wxMXSbheftE8PBydGlCOLUiw11rbVJKjQNWY0yFnKO1PqiUmgxEaa3DbV2kcE1bdu0lL/w5nieKlKotcB/0NdRo5uiyhHAJFs1z11qvBFYW+twb12jbtfhlCVeWZ8pn7Q9TufXENDyVmQud36TanU+Dm7ujSxPCZcgTqsKuEk9Fk/TDE/TJ28vxiq0JGDaban71HV2WEC5Hwl3YR76JY8umErDnM8prd/a0nkyr/hNk6QAhbETCXdhcXsJ+zv8whgYZ0Wwr0x7/h7+kVbBcrQthSxLuwnZMOaSt/ZByEdPw1BVYFPw29z48jrKe8mMnhK3Jb5mwjbhI0pc8QcXLMSzVXfC8ewqD28lMGCHsRcJdWFduBvnr38EtYiapuipTKr7FoyPGUMdXnjIVwp4k3IX1HN+Iael4PC7HscDUg9jQibzavy1ly8gURyHsTcJdFF9WCqx5DXZ/Tzy1eN38Fg8++BDDWt7i6MqEKLUk3EXxHFqGXvE8Oj2Zr0z9We07gmlDO8gwjBAOJuEubk5aIqx6AaKXctKjLuNzJhDa/nZ+urupDMMIUQJIuIsbozXsXQS/v0x+biYz1BDm5PXlnUGh9JNhGCFKDAl3YbmUWFj+DBzfQJx3CCNSH8GrZhP+7+HWMgwjRAkj4S6KZjZD5GxY9zZm4Bvvp/gguRMPdwjmNRmGEaJEknAX13f+CISPh7gILtTswtDEwcTl+vLF4BYyDCNECSbhLq4uPw+2fg5/TEWXqcCSwNeYdKwJTWv5sEyGYYQo8STcxb8l7Ial4yDxAElBfRh57gEOxZRldJc6PN+zkQzDCOEEJNzF/+RlwaYP4K/pmMv7siDwPd46Wof6ft78MjSE0KAqjq5QCGEhCXdhOLUFwifAxeOcqfMgj8T1I/Z4GcZ2q8v4OxrI1boQTkbCvbTLvgzr3oSoOeT7BDMz8BM+OlSLxjUr8tuIlrQI8HF0hUKImyDhXpodXQ3Ln4W0s5xsMIJHTnTn3Hl3nulen6e61sfTw83RFQohbpKEe2mUkQy/vwT7l2Cq1pjPAl5hxv4qNPevxOzHWtKkViVHVyiEKCYJ99JEazjwC6yahM6+zJHGY3nkSGdSz7nxQq8GjLmtLmXc5WpdCFcg4V5apJ6BFc/B0d/JqxnKu1XHMn9PeVoFVmbhAyE0qFHR0RUKIaxIwt3Vmc2waz6sfQOdn8e+ppMYHt2aLBO80qcho26ti7ubcnSVQggrk3B3ZReOw7Kn4dSf5ATeyuv5Y/hplwdtg32Ycn8Idat7O7pCIYSNSLi7onwTbJ8BG99Hu3uyo8XbPLavMSYzvNmvEcM7BuMmV+tCuDQJd1dz7gCEj4OE3WTW7cWkrBEsj9R0rFuZKfeHEFStvKMrFELYgYS7qzDlwOaPYcun6HJV2NzyI57cHYhC8e49TRjSLkiu1oUoRSTcXUHcDmOhr+QjpDd6kKcvPcj6CBNdGlTlg/taEFBFrtaFKG0k3J1ZTjpseBciZqJ9/FndagbP7KxGGXeYen8ID4YFoJRcrQtRGkm4O6vjG4yZMJdOc6nFSMae68vW7Tnc0diX9+9tQU2fso6uUAjhQBY9jqiU6q2UOqKUilFKvXSV959TSkUrpfYppdYrpWpbv1QBQFYK/DYWvrsX7e7Fb6Hf0m53Lw4km/nsoZZ8OzxMgl0IUfSVu1LKHZgB9ADigUilVLjWOrpAs91AmNY6Uyn1JDAVeMgWBZdml3JvAAANeklEQVRq0eGwciJkJHMhdByPn76TqG1Z9GpWnXfuaY5fRQl1IYTBkmGZdkCM1voEgFJqMTAA+DvctdYbC7TfDgy1ZpGlXlqiEeqHwtE1Q/ix4Se8HuFOxbL5TB8Syt0tasnYuhDiHywJd38grsDreKD9ddqPAlZd7Q2l1BhgDEBQUJCFJZZiWsOehbD6ZcjLJq/bmzwb15nlfyXTN6Qmb/dvRjVvL0dXKYQogSwJ96tdEuqrNlRqKBAG3H6197XWs4BZAGFhYVc9hrgiJda4YXpiIwR1Ir3XJ4xankrEyWReu7sJj3Wp6+gKhRAlmCXhHg8EFngdACQUbqSU6g68Ctyutc6xTnmlkDkfdsyG9ZNBKbj7E5IaDmH4vJ3EJKUxbVArBrTyd3SVQogSzpJwjwQaKKXqAGeAQcCQgg2UUqHA10BvrXWS1assLZIOQ/h4iN8B9XtA3884kVeFYTO3czEjl2+Ht+W2htUdXaUQwgkUGe5aa5NSahywGnAH5mitDyqlJgNRWutw4CPAG1hy5cbeaa11fxvW7VpMubB1GmyeCp7ecO8sCBnInvhUHp23DQUsHtOBkIDKjq5UCOEkLHqISWu9ElhZ6HNvFPi4u5XrKj3O7DKu1hMPQPP7ofcU8K7OH0fP8+T3O6nm7cmCR9tTx7eCoysVQjgReULVUXIzYdMHsG06eNeAQYugcR8A/m93PC8s2UfDGhWZ92hbmb8uhLhhEu6OcPJPWDYBLp6ANiOgx2Qo6wPArM3HeX/lYTrWrcasYW2oWLaMY2sVQjglCXd7yk6FtW/CzrlQpQ4MXwZ1bgPAbNa8v/IQ32w5yd0htfh0YEu8PNwdXLAQwllJuNvLkVWw/DlIPwcdx0G3V8HTWIo312Rm0s97+W1PAsM71ubNfs1k7XUhRLFIuNtaRjKsehEO/Ax+zWDQ9+Df5n9v55h44vud/HksmRd6NeKprvVkKQEhRLFJuNuK1rB/iRHsOWnGlXrnZ8DD8+8myek5PDovkoMJl5l6fwgD2wZe54BCCGE5CXdbSI03hmCOrYaAttD/P+DX5B9N4i5m8si3EZxNzebroW3o3rSGg4oVQrgiCXdrMpuNm6Vr3wSdD70/hHZjwO2fN0YPJqQyYm4kuSYzC0e3p03tqg4qWAjhqiTcreXCceNhpNitULcr9JsGVYL/1eyv48k8vmAn3mU9WPhERxrUqGjvSoUQpYCEe3Hlm4wHkTZ9AB5eMGAGtHrYWPSrkBX7zvLsj3uoXa08C0a1o5ZPOQcULIQoDSTci+Pcflg6Fs7uhcZ94e5PoGLNqzZdsO0Ub4YfpE1QFb4ZHkbl8p5XbSeEENYg4X4z8rJh80ew9XMoVxUGLoCmA67aVGvNJ2uOMn1jDN2b1GD6kFDKlpGHk4QQtiXhfqNObzfG1pOPQssh0Os9KH/1G6KmfDOv/t8BfoyKY1DbQN69pzke7hbtSS6EEMUi4W6pnHRjA40ds8AnEIb+AvWvvRhmVm4+4xftZt2hRMbfUZ/nejSUh5OEEHYj4W6JmHWw7FlIjYP2j8Mdr4OX9zWbX8rMZdT8KHadTmHygGYM6xhsv1qFEAIJ9+vLvAirX4W9C8G3ITy6GoKutzc4JFzKYvicHcReyGTGkNb0aVHLTsUKIcT/SLhfy8HfYOVEyEqBLhPhthegzPXXVT+WmMawOTtIzzYx79G2dKrna6dihRDinyTcC0s7Byueh8PLoVZLGPor1Aq57pfkmzXL9yXwxtKDeHq4sfjxDjS7xcdOBQshxL9JuP+X1rD7e1jzKphyjA00OowF92v/FeWazPy2+wxfborh1IVMGtesyOxhYQRWLW/HwoUQ4t8k3AFSTsGyp+HEJqjd2Vjoq1q9azbPzsvnp6g4Zm46TkJqNs39KzFzaGt6Nq0p67ALIUqE0h3u5nxjauP6yaDcoe9n0HoEuF19LnpGjokfImKZtfkkyek5tKldhffua0HXhtVlmqMQokQpveGedBjCx0F8JDToZQS7j/9Vm6Zm5TH/r1PM2XqSS5l53Frfl7HdQulQt6qEuhCiRCp94W7KhS2fGcsHeFWE+76BFg9cdaGvC+k5fLvlJAu2xZKeY6J7Ez/GdqtPaFAVBxQuhBCWK13hfmYnLB0PSQeh+QNw1xSo8O/piudSs5m1+QQLd8SSYzLTp0UtxnatT9NbKjmgaCGEuHGlI9xzM2HT+7BtBnjXhMGLodFd/2oWdzGTr/44zs9R8eRrzT2t/Hmyaz3q+137aVQhhCiJXD/cT26G8AmQchLajIQeb0PZf85Bj0lK58tNMSzdk4C7UjwYFsATt9eTKY1CCKfluuGenQpr34Cd86BqXRi+HOp0+UeTgwmpfLnxOCsPnKWshzsjOgUzuktdavpc/0lUIYQo6Vwz3A+vhBXPQXoidJoAXV8GT+Mq/FxqNusPJ/L7gXP8eSyZil4ePNW1Ho92rkM1by8HFy6EENbhWuGefh5WTYKDv4JfMxi0EH1LKAcTLrP+0DHWHUpk/5lUAIKqluf5Hg0Z1ikYn3JlHFy4EEJYl2uEu9aw7yf4/UXIzcB0+ytsrfEw6yIvsf7QBhJSs1EKQgMrM6l3I3o0qUF9P2+Zoy6EcFkWhbtSqjcwDXAHvtFaf1jofS9gAdAGuAA8pLU+Zd1SryE1HpY/C8fWcKFKK6ZXn8BPGyuQkbuXcmXcua2hL8/0aMgdjf3wlWEXIUQpUWS4K6XcgRlADyAeiFRKhWutows0GwWkaK3rK6UGAVOAh2xR8N/MZsyR32Je+wam/Hymmh5h3tleVM8oxz2hNejepAYd61WT/UqFEKWSJVfu7YAYrfUJAKXUYmAAUDDcBwBvXfn4Z2C6UkpprbUVa/1b+plo0pY8Ra1Lu9mS34LpFcbSoVMbljapQXP/SjLcIoQo9SwJd38grsDreKDwdkR/t9Fam5RSqUA1INkaRRa049dptNz7DuUpwwyf56jXYzSLmtXCXVZjFEKIv1kS7ldLzcJX5Ja0QSk1BhgDEBQUZEHX/1a2RiOiK3XCq9/HjG3Y8KaOIYQQrs6ScI8HAgu8DgASrtEmXinlAfgAFwsfSGs9C5gFEBYWdlNDNiGde0Pn3jfzpUIIUWpcfeHyf4oEGiil6iilPIFBQHihNuHA8CsfPwBssNV4uxBCiKIVeeV+ZQx9HLAaYyrkHK31QaXUZCBKax0OfAt8p5SKwbhiH2TLooUQQlyfRfPctdYrgZWFPvdGgY+zgQetW5oQQoibZcmwjBBCCCcj4S6EEC5Iwl0IIVyQhLsQQrggCXchhHBBylHT0ZVS54HYm/xyX2ywtEEJJ+dcOsg5lw7FOefaWuvqRTVyWLgXh1IqSmsd5ug67EnOuXSQcy4d7HHOMiwjhBAuSMJdCCFckLOG+yxHF+AAcs6lg5xz6WDzc3bKMXchhBDX56xX7kIIIa6jRIe7Uqq3UuqIUipGKfXSVd73Ukr9eOX9CKVUsP2rtC4Lzvk5pVS0UmqfUmq9Uqq2I+q0pqLOuUC7B5RSWinl9DMrLDlnpdTAK9/rg0qphfau0dos+NkOUkptVErtvvLz3ccRdVqLUmqOUipJKXXgGu8rpdQXV/4+9imlWlu1AK11ifyDsbzwcaAu4AnsBZoWavMUMPPKx4OAHx1dtx3OuRtQ/srHT5aGc77SriKwGdgOhDm6bjt8nxsAu4EqV177ObpuO5zzLODJKx83BU45uu5invNtQGvgwDXe7wOswtjJrgMQYc3+S/KV+98bc2utc4H/bsxd0ABg/pWPfwbuVM69O3aR56y13qi1zrzycjvGzljOzJLvM8A7wFQg257F2Ygl5zwamKG1TgHQWifZuUZrs+ScNVDpysc+/HvHN6eitd7MVXakK2AAsEAbtgOVlVK1rNV/SQ73q23M7X+tNlprE/DfjbmdlSXnXNAojH/5nVmR56yUCgUCtdbL7VmYDVnyfW4INFRKbVVKbVdKOfvekpac81vAUKVUPMb+EePtU5rD3Ojv+w2xaLMOB7HaxtxOxOLzUUoNBcKA221ake1d95yVUm7AZ8AIexVkB5Z8nz0whma6Yvzv7E+lVHOt9SUb12YrlpzzYGCe1voTpVRHjN3dmmutzbYvzyFsml8l+cr9Rjbm5nobczsRS84ZpVR34FWgv9Y6x0612UpR51wRaA5sUkqdwhibDHfym6qW/mwv1Vrnaa1PAkcwwt5ZWXLOo4CfALTW24CyGGuwuCqLft9vVkkO99K4MXeR53xliOJrjGB39nFYKOKctdapWmtfrXWw1joY4z5Df611lGPKtQpLfrZ/w7h5jlLKF2OY5oRdq7QuS875NHAngFKqCUa4n7drlfYVDgy7MmumA5CqtT5rtaM7+o5yEXeb+wBHMe6yv3rlc5MxfrnB+OYvAWKAHUBdR9dsh3NeByQCe678CXd0zbY+50JtN+Hks2Us/D4r4FMgGtgPDHJ0zXY456bAVoyZNHuAno6uuZjnuwg4C+RhXKWPAp4AnijwPZ5x5e9jv7V/ruUJVSGEcEEleVhGCCHETZJwF0IIFyThLoQQLkjCXQghXJCEuxBCuCAJdyGEcEES7kII4YIk3IUQwgX9P57TCrSyDJrdAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "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": {}, "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": {}, "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)}$$\n", "\n", "If $J(d)<1$, the underlying point process is a cluster point process; if $J(d)=1$, the underlying point process is a random point process; otherwise, it is a regular point process." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jp1 = J(pp, intervals=20)\n", "jp1.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above figure, we can observe that $J$ function is obviously above the $J(d)=1$ horizontal line. It is approaching infinity with nearest neighbor distance increasing. We might tend to conclude that the underlying point process is a regular one." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interevent Distance Functions\n", "\n", "Nearest neighbor distance functions consider only the nearest neighbor distances, \"event-event\", \"point-event\" or the combination. Thus, distances to higer order neighbors are ignored, which might reveal important information regarding the point process. Interevent distance functions, including $K$ and $L$ functions, are proposed to consider distances between all pairs of event points. Similar to $G$, $F$ and $J$ functions, $K$ and $L$ functions are also cumulative distribution function.\n", "\n", "#### $K$ function - \"interevent\"\n", "\n", "Given distance $d$, $K(d)$ is defined as:\n", "$$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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at1TMLNfM5pjZU/H9QDN71cwWm9nDZpYf2wvi+5I4fUDKMq6O7YvM7NSU9mGxrcTMrkr3ZxGRVqRiKzz8NVj1FpwzCXofkXRFWaE5dn99D1iY8v6XwM3uPghYA4yL7eOANe6+H3Bz7IeZHQyMBg4BhgG3x6DKBW4DhgMHA1+NfUVEdk9VFfzlW/De32HkbbDfkKQryhppDRUz6wt8Gbg7vjfgZODR2GUiMCq+HhnfE6cPif1HApPdfau7vweUAMfER4m7v+vuZcDk2FdEZPc8/18w71EYci0cPjrparJKurdUfgv8EKiK7/cAPnH3ivi+FOgTX/cBlgDE6Wtj/0/bd5intvadmNl4Mys2s+JVq1bt7mcSkZbs5dvh/34Hx4yHL3w/6WqyTtpCxcxGACvdfXZqcw1dvZ5pu9q+c6P7Xe5e5O5FPXq07mGpRaQO8x6D6T+Gg06HYTfp4sZGSOfZX8cBZ5jZaUBboBNhy6WLmeXFrZG+wNLYvxToB5SaWR7QGVid0l4tdZ7a2kVEds17L8HjX4f+g+Erf4Sc3KQrykpp21Jx96vdva+7DyAcaH/B3c8H/gqcFbuNAZ6Ir6fG98TpL7i7x/bR8eywgcAg4DVgFjAonk2WH3/G1HR9HhFpwUqLYfJ50HUgjP4TtGmXdEVZK4nrVH4ETDazG4E5wD2x/R7gfjMrIWyhjAZw9/lmNgVYAFQAl7p7JYCZfRuYDuQCE9x9frN+EhHJfqXFcP+ZULgHXPA4FHZLuqKsZmFjoPUoKiry4uLipMsQkUxQOhvuHxWCZOzT0Llv0hVlJDOb7e5FDemrYVpEpHX6cHbcQlGgNCWFioi0Ph/OhklnQrsu4d7yCpQmo1ARkdblw9e3BcrYp8PIw9JkFCoi0nosnROOobTrDGOfUqCkgUJFRFqHpXNg0kho2zluofRPuqIWSaEiIi3f0n/BpFFQ0DkcQ1GgpI1CRURatqX/ClsoBZ3CLq+ueyddUYumUBGRlmvZGzFQOipQmolCRURapmVvKlASoFARkZZn2Zsw6Qxo0x7GPKnbADcjhYqItCzL524LlLFPQbeBSVfUqihURKTlWD4XJlYHypMKlAQoVESkZfg0UNrFQNkn6YpaJYWKiGS/5fNSAuUpBUqCFCoikt1WzA/HUPLahoPyCpREKVREJHutmA8TT4fcgrCFsse+SVfU6ilURCQ7rVgQAyVfgZJBFCoikn22C5SnFSgZRKEiItll5cIQKDl5YXBIBUpGUaiISPZY+da2QBn7NHTfL+mKZAcKFRHJDivfgokjwHLDMRQFSkZSqIhI5lu1KGyhWE4MlEFJVyS1UKiISGZbtQjuGwFm4RiKAiWjKVREJHOtejsECoRA6bF/svVIvfIa0snM+gKjgeOB3sBmYB7wNDDN3avSVqGItE6r3g7HUCDs8lKgZIV6t1TM7F5gAlAG/BL4KvAt4HlgGPAPMzuhhvnamtlrZvaGmc03s+ti+0Aze9XMFpvZw2aWH9sL4vuSOH1AyrKuju2LzOzUlPZhsa3EzK7anRUhIhlkyWsw4VTwqjD0So8Dkq5IGqghWyq/dvd5NbTPAx6LodC/hulbgZPdfYOZtSGEzzTgcuBmd59sZncC44A74vMad9/PzEYTAuxcMzuYsJV0CGEr6Xkzq/6T5TbgFKAUmGVmU919QQM/u4hkooVPwZ/HQafe8LU/ayyvLFPvlkotgZI6vczdS2pod3ffEN+2iQ8HTgYeje0TgVHx9cj4njh9iJlZbJ/s7lvd/T2gBDgmPkrc/V13LwMmx74ikq1e+yNMuQB6HgrjZihQslC9WypmNpcQBjVy98/UMW8uMBvYj7BV8Q7wibtXxC6lQJ/4ug+wJC6zwszWAnvE9ldSFps6z5Id2o+tpY7xwHiA/v1r2qgSkURVVcHM6+Cfv4X9h8NZEyC/MOmqpBEasvsrHinj0vh8f3w+H9hU14zuXgkcYWZdgMeBg2rqFp+tlmm1tde0lVVj+Ln7XcBdAEVFRbUGpIgkoKIMnrgU5k6Boy+C034FuQ06h0gyUL3/cu7+AYCZHefux6VMusrM/glc34BlfGJmLwKDgS5mlhe3VvoCS2O3UqAfUGpmeUBnYHVKe7XUeWprF5FssGUtPHwBvPc3OPmncPwV4XoUyVq7cp1KezP7QvUbM/s80L62zmbWI26hYGbtgC8BC4G/AmfFbmOAJ+LrqfE9cfoL7u6xfXQ8O2wgMAh4DZgFDIpnk+UTDuZP3YXPIyJJWrcU7j0NPvgnjLoTTviBAqUF2JVtzHHABDPrTNjNtBa4uI7+vYCJ8bhKDjDF3Z8yswXAZDO7EZgD3BP73wPcb2YlhC2U0QDuPt/MpgALgArg0rhbDTP7NjAdyAUmuPv8Xfg8IpKUlQvhgbNgyydw3hTYb0jSFUkTsbAxsAszmHWK861NT0npVVRU5MXFxUmXIdJ6vf8PmHxeuP3v+Y9Cr1rP9ZEMYWaz3b2oIX0bcvHj18zs037uvi41UMxs39TdYiIitZr3GNx/JnToCZc8r0BpgRqy+2sPYI6ZzSacHrwKaEs4TfiLwEeArmYXkbq9fBtM/zH0/xyM/hMUdku6IkmDhpz9dYuZ/Z5w0eJxwGcIY38tBC5w93+nt0QRyWpVVfDcNfDK7XDQGfCVP0KbtklXJWnSkIsf+7p7KTAjPlKnnQ4oVESkZuVb4PGvw4K/wLHfgFP/G3Jyk65K0qghpxTPTB3csZqZXQz8tqkLEpEWYvMaeOArIVCG3gjDblKgtAINOabyfWCGmZ3m7oshjBoMnEc4piIisr1PlsAD/wFr3oP/uAcOO6v+eaRFaMgxlWfMbCswzcxGAZcAnwVOcPc16S5QRLLM8rnhGpTyzfC1x2Dg8UlXJM2oQVfUu/tMYCzwIrAPMESBIiI7eeevMGF42M118bMKlFaoIQfq17NtYMcCYAiwMg5L7+7eKb0likhWeGNyGBiy+wFw/iPQuU/980iL05DdXx2boxARyVLu8I/fwMzrYcDxMPpBaNs56aokIRpfWkQar6oSnrkSiu+Bw86GkbdBXkHSVUmCFCoi0jhlm8Jtfxc9A8ddBkOuhZxdGfhcWiKFiojsuo0fw0PnQmlxuKnWMf+ZdEWSIRQqIrJrVr8bThle9yGcez8cdHrSFUkGUaiISMN9OBv+dC5UVcCFU6H/sUlXJBlGO0BFpGHmPw73jYA27WDcDAWK1EhbKiJSt4oymPFTePVO6PtZOPdB6Ngz6aokQylURKR2a0vhkbFQOguO/Saccj3k5SddlWQwhYqI1OydF+DPl0DFVjj7PjjkzKQrkiygUBGR7VVVwd//F178BfQ4MJzh1X1Q0lVJllCoiMg2Gz+Gx/4T3pkJnxkNI34D+e2TrkqyiEJFRILSYpgyBjauhBG/haPHglnSVUmWUaiItHbu8NpdMP0a6NQLxj0HvY9MuirJUgoVkdZs63p48nsw78+w/zA4805o1zXpqiSLKVREWquVC2HKhfBxSRgM8rjLNCCk7La0/Q8ys35m9lczW2hm883se7G9m5nNMLPF8blrbDczu9XMSszsTTM7KmVZY2L/xWY2JqX9aDObG+e5Nd44TETq8+Yj8MeTYfMauPAJOP5yBYo0iXT+L6oArnD3g4DBwKVmdjBwFTDT3QcBM+N7gOHAoPgYD9wBIYSAa4FjgWOAa6uDKPYZnzLfsDR+HpHsV7EVnrocHrsEeh0BX38JBp6QdFXSgqQtVNx9mbu/Hl+vBxYCfYCRwMTYbSIwKr4eCUzy4BWgi5n1Ak4FZrj7andfA8wAhsVpndz9ZXd3YFLKskRkR2s+gAmnhhtqff67MObJcGBepAk1yzEVMxsAHAm8CvR092UQgsfM9ozd+gBLUmYrjW11tZfW0F7Tzx9P2KKhf//+u/dhRLLR29PhsfHgVWHsroNGJF2RtFBp34lqZh2APwOXufu6urrW0OaNaN+50f0udy9y96IePXrUV7JIy1FVCTNvgD+dA537wdf/pkCRtEprqJhZG0KgPOjuj8XmFXHXFfF5ZWwvBfqlzN4XWFpPe98a2kUEYMMquH8UvPQrOPICuGQGdNsn6aqkhUvn2V8G3AMsdPffpEyaClSfwTUGeCKl/cJ4FthgYG3cTTYdGGpmXeMB+qHA9DhtvZkNjj/rwpRlibRuH7wMfzgelrwGI2+Dkb8P90ERSbN0HlM5DrgAmGtm/4ptPwZuAqaY2Tjg38DZcdozwGlACbAJuAjA3Veb2Q3ArNjvendfHV9/E7gPaAdMiw+R1ssdXr4NZvwXdN0bzn8E9jos6aqkFbFw4lTrUVRU5MXFxUmXIdL0tqyFJy6FhU/CgSNg1O3QtnPSVUkLYGaz3b2oIX11Rb1IS7B8brg6fs0HMPRG+Ny3NRikJEKhIpLt5jwIT18ObbvA2Kdh788lXZG0YgoVkWy1ZR1MvxrmPAADjoezJkCHPeufTySNFCoi2ejt6fDkZbBhORx/BZx0DeTkJl2ViEJFJKts/AievQrmPgI9Dgq3+u3boOOnIs1CoSKSDdzDPU+m/TDs9jrxavjC5ZCXn3RlIttRqIhkunVLw8jCb0+DPkfDGb+HngcnXZVIjRQqIpnKHV6fCM/9FCrLYejPYfA3dexEMppCRSQTffxOuM3v+y+FM7vOuFXjdklWUKiIZJKqSnjldnjh55DbBk6/BY4aowsZJWsoVEQyxYoFYZiVpa/D/sNhxG+gU++kqxLZJQoVkaRVlMFLvw6Ptp3DRYyHfEVbJ5KVFCoiSSqdHbZOVi2Ew86BYTdB+z2Srkqk0RQqIkko2wR//Xk4ftKxF5w3BfY/NemqRHabQkWkub33d5j6HVjzPhRdDF+6Dtp2SroqkSahUBFpLps/CTfPen1iOD147NMw4AtJVyXSpBQqIs3hrWfC8PQbVsBx3wvDrOj2vtICKVRE0mnDqjBe1/zHYM9DYPSfoM9RSVclkjYKFZF0cA8jCU/7EZRtgJN+ErZQNACktHAKFZGmtrY0DAC5eDr0/WwYAHLPA5OuSqRZKFREmkrFVph1N/z1F+CV4ZqTY8ZrAEhpVRQqIrurqhLemAwv/gLWLoF9h4QhVroOSLoykWanUBFpLHd462l44QZY9Rb0PhLO+B3se1LSlYkkRqEi0hjvvQTP/ww+LIY9BsE5k+CgMzRel7R6ChWRXbHsDXj+OnhnJnTsHbZMDj8PcvWrJAKQk64Fm9kEM1tpZvNS2rqZ2QwzWxyfu8Z2M7NbzazEzN40s6NS5hkT+y82szEp7Ueb2dw4z61m+hNR0ujjd+DRi+EPJ4Sh6YfeCN99HY66UIEikiJtoQLcBwzboe0qYKa7DwJmxvcAw4FB8TEeuANCCAHXAscCxwDXVgdR7DM+Zb4df5bI7lu3DJ76Ptx2DCyaBidcCd97Az7/HV0RL1KDtP2J5e5/N7MBOzSPBE6MrycCLwI/iu2T3N2BV8ysi5n1in1nuPtqADObAQwzsxeBTu7+cmyfBIwCpqXr80grs3kN/PMWeOVOqCqHoy8KgdKxZ9KViWS05t5u7+nuywDcfZmZ7Rnb+wBLUvqVxra62ktraK+RmY0nbNXQv3//3fwI0qKVbYLX/gD/uBm2rIPDzoaTfgzdBiZdmUhWyJSdwTUdD/FGtNfI3e8C7gIoKiqqtZ+0YpXlMOd+ePGXsGE5DDoVhvwU9jos6cpEskpzh8oKM+sVt1J6AStjeynQL6VfX2BpbD9xh/YXY3vfGvqL7JqqKljwOLxwI6x+F/oNhrPvhb0/n3RlIlkpnQfqazIVqD6DawzwREr7hfEssMHA2ribbDow1My6xgP0Q4Hpcdp6Mxscz/q6MGVZIvVzh5Ln4a4vhrO68trCVx+Gi59VoIjshrRtqZjZQ4StjO5mVko4i+smYIqZjQP+DZwduz8DnAaUAJuAiwDcfbWZ3QDMiv2urz5oD3yTcIZZO8IBeh2kl4ZZMgtmXgfvvwRd+sOZd8FhZ2mMLpEmYOGEq9ajqKjIi4uLky5DkrByYdjN9dZT0L4HnPBDOHqshqMXqYeZzXb3oob0zZQD9SLp88m/4cWb4I2HoE37cG+Twd+Egg5JVybS4ihUpGVyh9JZYSj6+Y9QeumiAAANP0lEQVQDBoO/BV+4HNrvkXR1Ii2WQkValrKN4Y6Ls+6G5XMhvyMcNSbcdbFLv/rnF5HdolCRlmHVIph1T9jFtXUd9DwURtwcLl4s6Jh0dSKthkJFsldlebifyay7w5lcuflw8Ej47CXQ71gNQy+SAIWKZJ+1H8LrE2H2xHD1e+f+MORaOPIC6NAj6epEWjWFimQHd3j3RSi+B956BrwK9vsSfPYWGHSKrjERyRAKFclsm9fAvx4KYfJxCbTrBp//dhg1WIM8imQchYpkpqVzwoH3uY9CxWboewyc+Qc4eBS0aZt0dSJSC4WKZI7yzeGakll3w4ezoU0hfOYc+Ow46HV40tWJSAMoVCR5q9+F4gkw54Gwu6v7/jDsl3D4aGjXJenqRGQXKFQkGVWV8Pb0sFXyzkywXDhoRDgdeMDxOh1YJEspVKT5uMPyN8O93uc8AGuXQMdecOLV4ar3Tr2SrlBEdpNCRdKrbCO8+zdYPB3efg7Wx3upDTwBTv1vOGA45LZJtkYRaTIKFWl6az6Axc+F3Vvv/R0qt0J+B9j3JNj/GtjvFOjYM+kqRSQNFCqy+yorwojAbz8bgmTVwtDebZ9w5tagoeFuinkFydYpImmnUJHG2bQaSmaG3VqLZ8CWTyAnD/p/Do78Oew/DLrvl3SVItLMFCrSMO6w6q24NfIcLHklDJVS2B0OOA32Hwr7ngxtOyddqYgkSKEitSvfAu//IwTJ4unhDooAe30Gjr8ibI30PlLjbonIpxQqsr11S7cdZH/3RSjfFK5s3+fEcNfEQUOhc5+EixSRTKVQac3cYcNKWDkfPng5bJEsfzNM69wfjjg/bI0M+ILG2xKRBlGotBZlm8JZWSvmw4oFIUhWLIBNH4XplgP9BsOXfhaCpMeBuqq9hXN3tlZUhUd5ZXxdyZby8Ly1vIot8XlrRRVbyispq6yissqprHLcodKdKneqqpwqJ7Z7bCe2O5VVhH6+7b17WE6Vk9Iel1tV3RcgtHmsObSwUxuftsX+8TUpfamhzb26pWXr0q4N9150TNp/jkKlpamqhDXvw4p5KeExH1a/B9W/Om0KYc+DwoWHPQ8Jj70Og3Zdk6y8VXJ3yis9fInHL+7wJZ/6hb7DtNQQKK9kS8r71D5baguKlD7pYAY5ZuSaYQa5OUaOGTkGOTnV7UZuDrHdyMmB3E9fx76xn8VlmoFhn/6tY/GHfTr905+f2rZtghH+djJyti1jh2W2ZO0LmufrXqGSzTZ+tHN4rHwrDBUPgMEe+4b7tX/m3BAeex4MXQdCTk6ipWeLqipnY1kFm8oq2bC1gk1b43NZRXyuZOPWCjZurWRjWQUbt1awuWzHL/i6A6NqN/9Mbtsmh4K83E+fC/JyaNum+jmHLu3aUFBDn4LYZ/v+26a1TelT3Z6fl0NeTnUobPvyz0l5b63hG1pqpVDJBuWbw+m8KxbE4Ii7rjau3NanfY8QGEUXbQuPHgdCfmFydadZVZVTVhn+4i6vrKKsIjzKY1tZSlsIgcptYZASDqmBsO11CIvN5ZUNrqcgL4cOBXm0bRO+vD/9gs7LpUP7vPBl3iaHtvF5uy/xGqYVpEzbKTBin/zcHH2JS0bJ+lAxs2HALUAucLe735RwSQ1TVQVb14XHlpTnLWvD640fxWMgC2D1O+GaEIC8tiEsBg2FngeH8Oh5CHTYM7GPUr1vvvqv9s3l4XlTWWV8hC/pTWXb2raUV24LgsptobA1JRiqQ6G80j+dljpP5W78iZ9j0D4/j/YFeRQW5NKhII/C/Fx6d2lLYWxvn58bngvicy3thfmhLS9XW38iWR0qZpYL3AacApQCs8xsqrsvSOsPrqqCsvXbh8HWGAjVobDTtHXbTytbX9+ng64DQmAc+pUYHofiXQdQQc6nBzIrqsJB0soNW8MB1Hiwc7vHDm1V7lRUxoOpVVBRFb64a/ryr27bXL7ztOpdPRvLKnZpF05+Xg5t466U/Nzw3CY+V79uX5BHl9ydpxfk5dAm1+K8ubTJM/Jzq9u3X0Z+Xg4F8TkExbZwaNtGf+GLpENWhwpwDFDi7u8CmNlkYCTQ5KFScsNRdKxaS3vfRCGbyannfJFy8thAIRspZIMVsoFCNtCJDbYX6ylkQ051W3isp5B1tA+vvR3raM+WNXlUfuxUzqsOkEW4L2rqj1ar8GWcS/v88Fd8eOTRq3Mb2sW/zgurpxWEfu2q+xdsP1/7gjCtsI3+ohdpybI9VPoAS1LelwLH7tjJzMYD4wH69+/fqB/0Sft9+MiNzbkd2JLTni3xeXNuB7bktmdrdVtuB7bkdKA8Jx+znB3OStl2psm2P5KNXIOuQLfqs1Vi39x4pkxuzrZHjhl5OeEMmbzUttxtB0vrmm+ntjhffm7OdsFQmJ9LG335i8guyvZQqWn/xU6bEO5+F3AXQFFRUaN2xBdd/mhjZhMRaVWy/U/RUqBfyvu+wNKEahERafWyPVRmAYPMbKCZ5QOjgakJ1yQi0mpl9e4vd68ws28D0wmnFE9w9/kJlyUi0mpldagAuPszwDNJ1yEiItm/+0tERDKIQkVERJqMQkVERJqMQkVERJqMpd7gpjUws1XAB42cvTvwUROW0xyyreZsqxdUc3PItnqhZdW8t7v3aMgCWl2o7A4zK3b3oqTr2BXZVnO21QuquTlkW73QemvW7i8REWkyChUREWkyCpVdc1fSBTRCttWcbfWCam4O2VYvtNKadUxFRESajLZURESkyShURESkyShUGsDMhpnZIjMrMbOrkq6nJmbWz8z+amYLzWy+mX0vtnczsxlmtjg+d0261lRmlmtmc8zsqfh+oJm9Gut9ON7SIGOYWRcze9TM3orr+nNZsI6/H/9PzDOzh8ysbaatZzObYGYrzWxeSluN69WCW+Pv45tmdlQG1fy/8f/Gm2b2uJl1SZl2dax5kZmdmik1p0z7gZm5mXWP7xu1nhUq9TCzXOA2YDhwMPBVMzs42apqVAFc4e4HAYOBS2OdVwEz3X0QMDO+zyTfAxamvP8lcHOsdw0wLpGqancL8Ky7HwgcTqg9Y9exmfUBvgsUufuhhFtEjCbz1vN9wLAd2mpbr8OBQfExHrijmWrc0X3sXPMM4FB3/wzwNnA1QPxdHA0cEue5PX63NLf72LlmzKwfcArw75TmRq1nhUr9jgFK3P1ddy8DJgMjE65pJ+6+zN1fj6/XE77s+hBqnRi7TQRGJVPhzsysL/Bl4O743oCTgep7N2davZ2AE4B7ANy9zN0/IYPXcZQHtDOzPKAQWEaGrWd3/zuweofm2tbrSGCSB68AXcysV/NUuk1NNbv7c+5eEd++QrgbLYSaJ7v7Vnd/DyghfLc0q1rWM8DNwA/Z/nbsjVrPCpX69QGWpLwvjW0Zy8wGAEcCrwI93X0ZhOAB9kyusp38lvAfuSq+3wP4JOWXMtPW9T7AKuDeuMvubjNrTwavY3f/EPgV4S/QZcBaYDaZvZ6r1bZes+V38mJgWnydsTWb2RnAh+7+xg6TGlWzQqV+VkNbxp6HbWYdgD8Dl7n7uqTrqY2ZjQBWuvvs1OYaumbSus4DjgLucPcjgY1k0K6umsTjECOBgUBvoD1ht8aOMmk91yfT/59gZtcQdkk/WN1UQ7fEazazQuAa4L9qmlxDW701K1TqVwr0S3nfF1iaUC11MrM2hEB50N0fi80rqjdZ4/PKpOrbwXHAGWb2PmGX4smELZcucTcNZN66LgVK3f3V+P5RQshk6joG+BLwnruvcvdy4DHg82T2eq5W23rN6N9JMxsDjADO920XAmZqzfsS/uB4I/4u9gVeN7O9aGTNCpX6zQIGxbNl8gkH26YmXNNO4vGIe4CF7v6blElTgTHx9RjgieaurSbufrW793X3AYR1+oK7nw/8FTgrdsuYegHcfTmwxMwOiE1DgAVk6DqO/g0MNrPC+H+kuuaMXc8paluvU4EL49lJg4G11bvJkmZmw4AfAWe4+6aUSVOB0WZWYGYDCQe/X0uixlTuPtfd93T3AfF3sRQ4Kv5fb9x6dnc96nkApxHO5HgHuCbpemqp8QuETdM3gX/Fx2mE4xQzgcXxuVvStdZQ+4nAU/H1PoRfthLgEaAg6fp2qPUIoDiu578AXTN9HQPXAW8B84D7gYJMW8/AQ4RjPuXxi21cbeuVsFvmtvj7OJdwZlum1FxCOA5R/Tt4Z0r/a2LNi4DhmVLzDtPfB7rvznrWMC0iItJktPtLRESajEJFRESajEJFRESajEJFRESajEJFRESajEJFJIOY2c/M7AdJ1yHSWAoVERFpMgoVkYSZ2TXxHhvPAwfUO4NIBsurv4uIpIuZHU0YpuZIwu/j64RRhEWykkJFJFnHA497HCfKzDJuXDmRXaHdXyLJ01hJ0mIoVESS9XfgTDNrZ2YdgdOTLkhkd2j3l0iC3P11M3uYMKLtB8BLCZcksls0SrGIiDQZ7f4SEZEmo1AREZEmo1AREZEmo1AREZEmo1AREZEmo1AREZEmo1AREZEm8//pSZl6jgV1KgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "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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\n", "text/plain": [ "
" ] }, "metadata": {}, "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": {}, "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": {}, "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": {}, "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": {}, "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": {}, "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": {}, "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. , 0.14, 0.32, 0.54, 0.69, 0.83, 0.87, 0.91, 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, 0.36, 0.57, 0.74, 0.86, 0.93, 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": {}, "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", "text/plain": [ "
" ] }, "metadata": {}, "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": {}, "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": {}, "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": "3.6.5" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }