{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spatial index - How to boost spatial queries?\n", "\n", "While using the technique from previous examples produces correct results, it is in fact quite slow from performance point of view. Especially when having large datasets (quite typical nowadays), the point in polygon queries can become frustratingly slow, which can be a nerve-racking experience for a busy geo-data scientist. \n", "\n", "Luckily there is an easy and widely used solution called **spatial index** that can significantly boost the performance of your spatial queries. Various alternative techniques has been developed to boost spatial queries, but one of the most popular one and widely used is a spatial index based on [R-tree](https://en.wikipedia.org/wiki/R-tree) data structure. \n", "\n", "The core idea behind the **R-tree** is to form a tree-like data structure where nearby objects are grouped together, and their geographical extent (minimum bounding box) is inserted into the data structure (i.e. R-tree). This bounding box then represents the whole group of geometries as one level (typically called as \"page\" or \"node\") in the data structure. This process is repeated several times, which produces a tree-like structure where different levels are connected to each other. This structure makes the query times for finding a single object from the data much faster, as the algorithm does not need to travel through all geometries in the data. In the example below, we can see how the geometries have been grouped into several sub-groups (lower part of the picture) and inserted into a tree structure (upper part) where there exists two groups on the highest level (`R1` and `R2`), which are again grouped into five lower level groups (`R3-R7`):\n", "\n", "![Rtree](img/Rtree-IBM.png)\n", "Simple example of an R-tree for 2D rectanges (source: [IBM](https://www.ibm.com/support/knowledgecenter/en/SSGU8G_11.50.0/com.ibm.rtree.doc/sii-overview-27706.htm))\n", "\n", "In the next tutorial we will learn how to significantly improve the query times for finding points that are within a given polygon. We will use data that represents all road intersections in the Uusimaa Region of Finland, and count the number of intersections on a postal code level. *Why would you do such a thing?*, well, one could for example try to understand the vitality of city blocks following [Jane Jacobs'](https://en.wikipedia.org/wiki/Jane_Jacobs) ideas. \n", "\n", "### Motivation\n", "\n", "As a motivation for counting intersections, we can use an example/theory from [Jane Jacobs'](https://en.wikipedia.org/wiki/Jane_Jacobs) classic book [\"The Death and Life of Great American Cities\"](https://en.wikipedia.org/wiki/The_Death_and_Life_of_Great_American_Cities) (1961), where she defines four requirements\n", "that makes a vital/vibrant city:\n", "\n", " 1. \"The district, and indeed as many of its internal parts as possible, must serve more than one primary function; preferably more than two. \n", "These must insure the presence of people who go outdoors on different schedules and are in the place for different purposes, \n", "but who are able to use many facilities in common.\" *(--> One could use e.g. OSM data to understand the diversity of services etc.)*\n", "\n", "2. \"Most blocks must be short; that is, streets and **opportunities to turn corners** must be frequent.\" --> intersections!\n", "\n", "\n", "3. \"The district must mingle buildings that vary in age and condition, including a good proportion of old ones so that they vary in the economic yield they must produce. This mingling must be fairly close-grained.\" (--> one could use e.g. existing building datasets that are available for many cities in Finland)\n", "\n", "4. \"There must be a sufficiently dence concentration of people, for whatever purposes they may be there. This includes dence concentration in the case of people who are there because of residence.\" \n", "\n", "The following tutorial only covers one aspect of these four (2.), but it certainly would be possible to measure all 4 aspects if combining more datasets together.\n", "\n", "\n", "## Spatial index with Geopandas \n", "\n", "In this tutorial, we will first go through a step by step example showing how spatial index works, and in the end we put things together and produce a practical function for doing fast spatial queries. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's start by reading data representing road intersections (parsed from [Digiroad road network data](https://vayla.fi/web/en/open-data/digiroad/data#.Xca1TzP7Q2w)) and postal code areas (obtained from [Statistics Finland](https://www.tilastokeskus.fi/tup/karttaaineistot/postinumeroalueet.html)). In this time, we will read the data from Geopackage files:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " x y geometry\n", "0 330888.502 6675829.949 POINT (330888.502 6675829.949)\n", "1 348059.990 6670041.685 POINT (348059.990 6670041.685)\n", "2 348022.592 6670202.858 POINT (348022.592 6670202.858)\n", "3 297208.220 6669048.357 POINT (297208.220 6669048.357)\n", "4 330835.341 6675586.834 POINT (330835.341 6675586.834) \n", "-------\n", " posti_alue he_vakiy geometry\n", "0 00100 18284.0 MULTIPOLYGON (((385653.893 6671591.048, 385573...\n", "1 00120 7108.0 MULTIPOLYGON (((385316.092 6671076.984, 385279...\n", "2 00130 1508.0 MULTIPOLYGON (((386212.111 6671061.262, 386176...\n", "3 00140 7865.0 MULTIPOLYGON (((386577.050 6670280.544, 386552...\n", "4 00150 9496.0 MULTIPOLYGON (((384846.102 6669565.816, 384823...\n" ] } ], "source": [ "import geopandas as gpd\n", "\n", "# Filepaths\n", "intersections_fp = \"data/uusimaa_intersections.gpkg\"\n", "postcode_areas_fp = \"data/uusimaa_postal_code_areas.gpkg\"\n", "\n", "intersections = gpd.read_file(intersections_fp)\n", "postcode_areas = gpd.read_file(postcode_areas_fp)\n", "\n", "# Let's check first rows\n", "print(intersections.head(), '\\n-------')\n", "print(postcode_areas.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's see how many intersections and postal code areas we have:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of intersections: 63518\n", "Number of postal code areas: 370\n" ] } ], "source": [ "print(\"Number of intersections:\", len(intersections))\n", "print(\"Number of postal code areas:\", len(postcode_areas))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, as we can see there are 63.5 thousand intersections in the region and 370 postal code areas. These are not yet huge datasets, but big enough so that we can see the benefits in using a spatial index. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's still explore quickly how our datasets look on a map before doing the point in polygon queries." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6667500.0, 6680000.0)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "ax = postcode_areas.plot(color='red', edgecolor='black', alpha=0.5)\n", "ax = intersections.plot(ax=ax, color='yellow', markersize=1, alpha=0.5)\n", "\n", "# Zoom to closer (comment out the following to see the full extent of the data)\n", "ax.set_xlim([380000, 395000])\n", "ax.set_ylim([6667500, 6680000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the map, we have a large number of points (intersections) that are scattered around the city. \n", "\n", "Next, we want to calculate how many of those points are inside each postal code area visible on the map. For doing this, we are going to take advantage of spatial index.\n", "\n", "- Building a spatial index for GeoDataFrame is easy in Geopandas. We can extract that by calling an attribute `.sindex`. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's build spatial index for intersection points\n", "intersection_sindex = intersections.sindex\n", "\n", "# Let's see what it is\n", "intersection_sindex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, as we can see the variable contains a `SpatialIndex` object. Fundamentally, this object contains now the geometries in an R-tree data structure as introduced in the beginning of this page. \n", "\n", "From this spatial index, we can e.g. see, how the geometries have been grouped in the spatial index. \n", "\n", "- Let's see how many groups we have, and extract some basic information from them. We can extract this information using `.leaves()` function." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'PyGEOSSTRTreeIndex' object has no attribute 'leaves'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# How many groups do we have?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Number of groups:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mintersection_sindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mleaves\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# Print some basic info for few of them\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mn_iterations\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'PyGEOSSTRTreeIndex' object has no attribute 'leaves'" ] } ], "source": [ "# How many groups do we have?\n", "print(\"Number of groups:\", len(intersection_sindex.leaves()), '\\n')\n", "\n", "# Print some basic info for few of them\n", "n_iterations = 10\n", "for i, group in enumerate(intersection_sindex.leaves()):\n", " group_idx, indices, bbox = group\n", " print(\"Group\", group_idx, \"contains \", len(indices), \"geometries, bounding box:\", bbox)\n", " i+=1\n", " if i == n_iterations:\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We seem to have 908 groups formed in the R-tree, and as we can see, each group seem to consist of 70 geometries. Okay, now as we understand a bit what the `R-tree` index is like. Let's take that into action.\n", "\n", "For conducting fast spatial queries, we can utilize the spatial index of the intersections, and compare the geometry of a given postal code area to the **bounding boxes** of points inside the R-tree spatial index. Let's start with a single postal code area, to keep things simple." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Select a postal code area representing the city center of Helsinki\n", "city_center_zip_area = postcode_areas.loc[postcode_areas['posti_alue']=='00100']\n", "city_center_zip_area.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Okay, now we can make a spatial query in which we want to select all the points, that are inside this Polygon. We conduct the point in polygon query in two steps: \n", " \n", " - **first**, we compare the bounds of the Polygon into the spatial index of the Points. This gives us point **candidates** that are likely to be within the Polygon (at this stage based on the MBR of the points that is stored inside the R-tree).\n", " - **secondly**, we go through the candidate points and make a normal spatial intersection query that gives us the accurate results:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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F5s3Oy2j79k7VRWFVSPGwYRnfexAktYvlbQgYb73lVEvjxuW6JYHCz7RxJLALeFBEJgErgDtV9WBEmTFATxF5EegPLFDVh6LquZ6oPNIR3MYx9dMS4FPAe0BfYJ6qfuCjnflHayssW+ZWC7mICrpzJyxa5GLez5iR2CqiudkJs3ffdQJh+/aE+5DNvQdBGpCTcasN0sqnIPnTn+C009zK2wD87ZDugdP//0JVzwUOAt+OUWYKcCVQCnxPRMaET3qqqFnAo9GVi8h3cbmiH/YOXQC0AKcAZwD/KiIjY1w3R0SWi8jyXVE67Lxg71741a+ccEhwUA1t20bZ4sWE0rHSaG2Fv/0N/vd/nXG4Iz76yHkT/eUvbmfyT34Cv/mNE2x1dYS2bEm4TeFBMhuDXTbv1RGpfG7hlU/FsmUZaJnBgQNu93SKBDKiQJL4mSrWAXWqWuP9v4T2wqEOZ4Q+CBwUkZeAScAG7/wVwEpVbaNMF5GbgauAmXrM+HEjUKmqHwE7ReQVYCqwKfJaVV0ILARnc/DRj+CweTM8+mhSGcUgQyqSXbvggQfcKuKSS9yxurpjaqK6urhupkFS2wSVVJ5RkFY+BcuqVU69NGZM52U7IBARBdJEp8JBVd8XkW0iMlZV1+NsB2ujij0B3OvZCXoBxcD8iPM3EKVSEpEy4FvAdFWNHCW3ApeKyGKcWmkazgsq/1GF1193M5QU1EgZGyhU4ZVX3I+kqSlurKKstamASOUZ2Q7vLPGnP8FXvpJ0atGcRRTIAH69lSbj3Et74WbwtwKfA1DV+70y3/SOtwKLVPUe73hfYBswUlX3RtT5DnAc0OAdqlbVuSJyPPAgMB4Q4EFVvTte+/LCW6m11QmF117LdUsMw4jHeefBrFm5bkVWsDShuaa5GZYscW6dhmEEny9+EUaNynUr2hEzB3oKWMjuXHLwIPz61yYYckhaDfhG1+DJJ91enYARjspaU1NDRUVFRu9lwiGT7NnjPHvq63PdkrwgU4O4X08fEyLGUfbuheefz3Ur2lFeXk5xcTHFxcUZt2vkaXCePOD992Hx4o7DXxjtyJTHk8UnMpLi9dfhnHMgQPtKSkpKqK6uzsq9bOWQCTZvhgcfNMGQAKFt22hsaqJ42LC0ezz53eNQPn16hzu1bVXRRdmyJdctyBm2ckg3a9bA0qWBDj0dRCqWLTsaXylXG9XiuYvaqqKLsmdPrluQM0w4pJPXXoM//zlvQlAHiaDvkwh6+4wM4WVd64qYK2s6UIW//hVeeil3bTACi8VFymMGDYKvfS3XrcgYlgkuk7S2ul2VK1fmuiVGQDGVVB7T2Oh+4926nnm26/U4nTQ3wyOPmGAw4hLP0B3GDN4BpbUV9hV2xoCOMOGQLPv3u6iq69fnuiVGwPHjLZVPUVe7nCDrokZpEw7JsGOHy4OwfXuuW2IUCH5WF5HkcoDOJ0GWFrqocDCbQ6K8844Ltx3ArfVG/pJo1NVc2jG6nOeWCQejU1asgKefzk3WNsOIIJcDdJcLH95FhYOplfygCs89B089ZYKhixB0vXquM9ul8/kE/VmbcIiDiBSJyBIRWScitSLSLk6siMwQkTdFZI2ILPOOjfWOhV/7ROTr3rm7vfpWi8jjIlIUUdc5IhLy6npLRJLLvJEOPvrIqZFeeSVnTTCyTz7q1bM5yKbz+QT+WZtwiMsCXOrOcbj0n7WRJ72B/T5glqpOAK4DUNX1qjpZVSfjckx/CDzuXfYcMFFVz8GlE73Lq6sHsBiY69U1A/goyf6lRlOTy5O8NjrxnZGPJDJ4JmogDgKJDrKpCJN0Pp/AP+sPP+ySNsZObQ4iMgC4GLgFQFWbgeaoYjcCS1V1q1dmZ4yqZgIbVXWLV+bZiHPVwLXe+8uB1aq6yivXQC7Yv99FVd2xo/OyRl6QiBE3W3r1dO6eTtQOkYpRO53PJy9sGI2NMHRorluRVfwYpEcCu4AHRWQSsAK4U1UPRpQZA/QUkReB/sACVX0oqp7ricojHcFtwCMRdamIVAFDgN+r6k/9dCZt7Nvn9jB88EFWb2tkliB62aTT6yjRQTaIzyOw7NnT5YSDH7VSD+A84Beqei5wEPh2jDJTgCuBUuB7IjImfFJEegGzgEejKxeR7wJHgIcj6voE8Hnv76dFZGaM6+aIyHIRWb5r1y4f3fDJ/v0uc5sJhpwRqe5Ipx4910bcWKRDpeLnGcUqE8TnEVhybHcIhUKUlZURCoWydk8/wqEOqFPVGu//JThhEV2mUlUPqupu4CWcbSLMFcBKVW2joxGRm4GrgM/rsQiAdcAyVd2tqh8Cz8S4H6q6UFWnqurUIUOG+OiGDw4fdjaGhtxosoJOtgyekbrzwBsrUyQdA7SfZ1TozzHj5Fg4VFRUUFVVRUVFRdYERadqJVV9X0S2ichYVV2Psx1EW2ifAO71jMm9gGJgfsT5G4hSKYlIGfAtYLonBMJUAf8mIn1xto3pUXVlhtZWeOwx2BnLXGJAelUg8XTt5dOn09jURGNTE7ede+7RY0Zs/KiHTIWUIjkWDuGUoOXl5UcFBUBlZWXG7ul3E9xXgYc99dAm4FYRmQugqveraq2IVAKrgVZgkaq+DeAN8pcBX46q817gOOA5EQGoVtW5qrpHRP4beB1Q4BlVfTqlXvrhb3+DDRsyfpt8Jp0DTDxBUzJ8OEW9e1O1cSNFvXsH31iZBlIxTPuxNcQrE7737LPOYmltrYUWj0WOhUNJSclRQRApKDKJL+Ggqm8C0TG/748qczdwd4xrPwQGxzg+Os79FuPcWbPDBx9YLgYfpNOrpDNBk+8z3UQH+1yGwwjfe/n27TQcOpSTNgSexka3GdZNZLNGKBSioqKC8vJySkrabS/LKBY+A+D55+HIkVy3okslhelM0OSFe2McEh3scykMw/eMXDlkk9C2bcyrqmL/4cP0P+445peWBu/7f+SIc1YZMCCrt42lQgqaWqlwaWoKTNhtSwpTOCQ62OdSGEbee86UKVm/fzh/eOT/gfz+79mTdeEQVh3Nnj2bsrIyysvLg6VWKmj+/ndoacl1K4D8V6UYx/Cj4+8KK0Q/hB0QwiuHwH7/GxvhtNOyesuwraGsrKzNaiGTK4YwJhwClOUp31Up+UK8wTkbA7etENtSMnw41V/6Uq6b0TmZMEq3trrwHPv3u7Ho5JNh4MB2xbK1WojEhIPR5Yg3OGdj4Pa7QrQVRsBIRDiowqFDbtCP9zpwoG2k5+uuiykcIr2VsoUJh759c90CI8tE7qMIbdvWZuDNhmrP7woxFUGVrGAxgRSHPXvcoN/cHHug37ev7f/JqKs/yk2M0ViYcBgxItctMNJAIoNa5D6KaONnkFR7qQiqZAWLqbziUFcHP/6xEw6ZIgBek2FMOAwaBP37O0lvBJbOBv98ch31SyqCKtn+5cNzyRmtrZkVDBColYMcC2mUv0ydOlWXL1+efAUvvAAvv5y+BgWQfFUXhNvd2NRETX09paNGxRww87V/kRRCH4wUufRSuPjirN1ORFaoavQGZ8DShDqmTMn6zsdsE8TAa34C+UWuCOJFLy2ECKNB/IyMLBMgtZIJB4CiIpgwIdetyChBzLblZzAMt3t+aWneD/6dEcTPyMgORydKq1Z1XjZLUVlNOIS55BLoVpiPI6jqCj+DYSGsCPzSlfoaTTbzX+fynh1xdKK0uPOQcpHhuzOJGaTDDB7s1Euvv57rlqSdoHqgBMkzKFmCKnjzjVx8R4P0uzjqCHDddZ2XtfAZOeCSS2DNGrdjsYAwD5T0E20oh+wMMIUqjHLxHQ3S7+LoRGl0h8Gqj5XN0oY4X3oUESkSkSUisk5EakWkXexYEZkhIm+KyBoRWeYdG+sdC7/2icjXvXN3e/WtFpHHRaQoqr4RInJARL6Rejd90rcvlJVl7XbZoiurK1KlI9WDX0N5ovV2RqEarXPxHQ3k7yJArqx+lewLcGlAx+HSf9ZGnvQG9vuAWao6AbgOQFXXq+pkVZ2MyzH9IfC4d9lzwERVPQfYANwVdc/5wJ8T7VDKnH02nHlm1m9r5I54A3VHg3GqhvJkB/lcGa2DpJ8vNNo82wAJh07VSiIyALgYuAVAVZtx6TsjuRFYqqpbvTKxcm3OBDaq6havzLMR56qBayPueQ0u49xBn/1IHyIwaxb84hcFp17qCiSjdomne+5I9ZCqvSRZlUYu7DShbdu4+ne/s0RAGaLN9+/CC3PcmmP4WTmMBHYBD4rIGyKySET6RZUZA5wgIi+KyAoRuSlGPdcTlUc6gtvwVgle3d8CMmuKj0f//k5AdHHycbaYzIw83mw8U6qHdNSbrc+nYtkyGg4dYnCfPoHQzxcabb5/AVo5+BEOPYDzgF+o6rm42fy3Y5SZAlwJlALfE5Ex4ZNe7ulZwKPRlYvId4EjwMPeoQpgvqoeiNcoEZkjIstFZPmuXbt8dCNBxo2D4uL015tH5KN+Oxm1S7oEQLaFabY+n/AzfeqGG4Klny8Q2nz/Mh2eIwH8eCvVAXWqWuP9v4T2wqEO2K2qB4GDIvISzjaxwTt/BbBSVXdEXiQiNwNXATP1WByPYuBaEfkpUAS0ikiTqt4bea2qLgQWgguf4aMfiXP55bB9O+TRzDldhLZto7GpieJhw/JqtphL99hsu0Zmy9umEFyO84bDh3PdgqN0unJQ1feBbSIy1js0E1gbVewJ4CIR6SEifXEDfKTR+gaiVEoiUoZTH81S1aPKfVW9SFVPV9XTgXuAH0ULhqzRvbuLr3788Tm5fS4Jp24s6t3bZos+KZ8+neJhw46GAs80mfS2yZVKMR9VmWkln4SDx1eBh0VkNTAZ+JGIzBWRuQCqWgtUAquB14BFqvo2gCcsLgOWRtV5L9AfeM5zc70/1c5khAED4IYboGfPXLckq1goh8QJhwKvqa/PK1VcLHKlUsxHVWZa+eijtsl/coivTXCq+iYQHbnv/qgydwN3x7j2Q2BwjOOd7vZQ1R/4aV/GGTYMPv1p+MMfct2SrGGqhOQI0saqVMhVPwrl+aVEczP07p3rVljI7oR4/XV4+unM38cwkqBQd0/nmvBznX3WWSytrc18dr1582KmCs0EFrI7XZx/Pnzyk7luRZcmrJNeuGJFQeqmU9G5d3mVTBLEet7Rx8LP9TsvvJDU8034cwmIx5LFVkqUT3zC6QT/8pdct6Tg8DPDCv/Qlm/fHndTVr7OolPxeDKVTOLEet7Rx8LPM3LlkAgJfy4BMUqbcEiGiy+GPn3gmWdcwnEjLfgZGP3+UIMUcTMRUhngzU6UOLGed/SxkuHDKZ8+PenJRsKfS0CEg9kcUmHtWvjjHwOzDMw1qc7W0znbT7aufF1xJEJX6GO6KVu8mKqNGztMU5tWPvc5OOuszN7DI57NwVYOqTB+PJx0kvNi2hkrnFTXYl5VFTX19TQ2NVH9pS8lfH06Z77J1hW54khlthhk8nVVlUuyqrILyMrBhEOqnHgifOlL8NJLEApBS0uuW2SkQOQgUGiDaKTXDZhtIhGyqrILiHAwb6V00KuX82K64w63HBTJdYtywvzS0qNhrPOVyF3H6dwI6NcLKZM7hMPCbmltbfDyGBjHCIia2oRDOhk0yOkL77wTpk+HoqJctyirBDJ5Sgqksz9+3Rkz6Y6aiV3vkcKsy4e+SBc+Vg6hUIiysjJCoVDGmmFqpUxQVORSjs6YAR98AJs2wdatsGMH7N4dmO3xRvbwq7NOl2472uicKSN0pOoNKCg1XM7wIRwqKiqoqqoCyFjKUBMOmUQEBg92r/PPd8eOHHECY88e99q3Dw4cOPY6eNC9jILCr846XbrtaHtJpuwn8VxBjSTxoVYqLy9v8zcTmCtrEGlpcVnowgJj/34nRPbtg717oaHB/S2Az85IH5GrAyArKwcjA4wbB9dfn5VbmStrvtG9u8tG179/x2WOHHFC4r333Ku+3uWeMJVVlyV6dRC5QsiGt026BFCXF2QB8VYy4ZCv9OgBQ4e61+TJ7lhzs0tMtHkzbNhQ8HsvuvwgEkWuw2fEU10l8lkVmgtxwgTEW8mXcBCRImARMBFQ4DZVDUWVmYFLztMTlxVuupcg6JGIYiOB76vqPSJyN3A10AxsBG5V1UYRuQz4T6CXd+6bqmqBjPzQqxeMGuVen/yks2msWwdvveVWFQVGlx9Eokg1zEOqRIY2KVu8uI16q7GpiZr6eqDzzyrXQi7nBGTl4MvmICK/Bl5W1UVePui+qtoYcb4IeBUoU9WtInKSqu6MqqM7UA8Uq+oWEbkc+IuqHhGRnwCo6rdE5Fxgh6puF5GJQJWqDovXvoKzOWSC3bvhjTfc68MPOy+fBwQh3EbQyGqYBx9tAOfBVDxsGEW9ewfi+Qb+s+7fH/71X7Nyq5RsDiIyALgYuAVAVZtxM/pIbgSWqupWr0wsfcZMYKOqbvHKPBtxrhq41jv+RsTxNUBvETlOVYMhTvOVE0+Eyy5z7rVr1jj10/797hU2fAfAwJ3IDzedevSgr0L8PpcgzLo78mBKNMbVPM9Vc35pacbcb4P4WQdl5eBHrTQS2AU8KCKTgBXAnaoa6W85BugpIi/iUn8uUNWHouq5nqg80hHcRlv1U5jPAG/EEgwiMgeYAzBixAgf3TAAl+508uRjdoowra1uRbF/v/OEeuYZ5x2VZXL1ww3CoBoPv88lCJFZo9uQbIyrsBqqYtmyjLvfBoojRzotEgqFqKiooLy8nJKSkow0w88O6R7AecAvVPVc4CDw7RhlpgBXAqXA90RkTPikp4qaBTwaXbmIfBc4AjwcdXwC8BPgy7EapaoLVXWqqk4dMmSIj24YcenWDY4/Hj72MedKd/PN7v8s43cXb7p34wZ9d3dXy+ldPn06xcOGUTxsWNr7nMxnnY3d30fvsXVrp2XDm+AqKioy1h4/K4c6oE5Va7z/l9BeONThjNAHgYMi8hIwCdjgnb8CWKmqOyIvEpGbgauAmRph/BCRU4HHgZtUdSNG9hk82AmIX/0qq5vy/M58wzPpxqamwOiyM0n4uYQHkK7Q32Qi+8YjFVtDNla0be6hGjdGWzY2wXW6clDV94FtnucRONvB2qhiTwAXiUgPEekLFAO1EedvIEqlJCJlwLeAWar6YcTxIuBp4C5VfSWx7hhpZcgQuOkml9goYIRn0kBBpMb0OzPtaqlA0zljT+XZZWPl1uYenexXKikpobKyMmMqJfAfeO+rwMMishqYDPxIROaKyFwAVa0FKoHVwGvAIlV9G8ATFpcBS6PqvBdnn3hORN4Ukfu94/8MjMappt70Xicl3UMjNYYOhRtvdPsqAkR4Jh2OBJvv6ha/A1cmB6kgBs6bV1VF1caNR43TqTD7rLMY3KfP0ZDliZANtWObewRgM6uvX7yqvglEuzvdH1XmbuDuGNd+CAyOcXx0B/f6IfBDP+0yssTw4XDNNbBkSa5b0o5UDbBBcWv0ayTNpME58F48KbK0tpaGQ4dYWlvLnClTct2c+ARAOFjIbsMfEyfCzJm5bkUb0jHT9Ttj93OvVNoTOejnavYeRKN3OnOEBLF/HRIA4RAsXYERbD7xCRdR9o03Oi+bBdJhlPY7Y/czq07HzDuXs/cguMFGE4TUsTnBhIORV4jAVVdBY6OL35RjwgN6Y1NT0gOq3wHDjxBJh/984H3w00xQ1HqBIwDCwUJ2G4nT1AQPPAC7duW6JYANMIkQtGcVDrUxuE8fnrrhhkC0KRDMmwcDB2b8NvHCZ5jNwUic3r2dB1O/frluCRDcDWxB9P4JkitsaNs2GpuaGHjccTQcOhSINgWGAKwcTDgYyXHCCXDDDYFzcQ0SQRqIwwTJKBsOkTHuxBMD06Z4JCPsw9csXLHC17VH71FTE7+c5ZA2As2pp8Ls2fCHP+S6JYEkiPaDIBllI59PNlZ9qarUknEWCF+zfPt2Gg4d6vTao/f46U+pLCvruJzlkDYCz/jxLnfE88/nuiWBo6uFvEiUbAuqVD3BkhH2kTkultbWdnrt0Xt0ErK7vLycxsZGGhsbCYVCGdkpbQZpI3VU4amnYOXKrN42Xs7kIJHNHAsLV6zgOy+8wI9mzgz+Rq8sEzRjfFzmzoWTT45bpKysjKqqKkpLS5NePVgOaSOziMCVVzoX102bsnbbyJkgENjdvdlUL/3bc8+x9/BhvvL005x90kk5HQSDNhgHSaXWKT4M0pkOvmfCwUgP3bvDZz+bVRfXjpLKBI1sDkrD+vdn7+HDtKimPQ9CohR6OI6M4kM4hIPvZQoTDkb66N0b/vEfXYjvjz5q+2pu7vjYoUMuO12C7nupJpUJ2sw2HSyaNetokLpcC8ogGuTzhgC4sppwMNJLr17ulSgXXACPPeZUU1miEGe2mciDkCx5pcYJGgEQDr72OYhIkYgsEZF1IlIrIu1M4yIywwuvvUZElnnHxkaE3X5TRPaJyNe9c3d79a0Wkce9PA7huu4SkXdEZL2IpB5xywg+w4c7I9z48Vm7ZZB8/v0StI11QWtPwRAA4eB35bAAqFTVa72Un30jT3oD+31AmapuDedfUNX1uPwPiEh3oB6X4Q3gOVxCnyMi8hPgLuBbIjIel296AnAK8LyIjFHVluS7aeQFvXvDddfBihVQWekrl24q5OPMNmgZ8Apx9RUIAiAcOl05iMgA4GLgAQBVbVbVxqhiNwJLVXWrV2ZnjKpmAhtVdYtX5llVDf/6q4FTvfefAn6vqodVdTPwDnBBQr0y8hcRmDrV2S4sN3g7wglrdhw8mLYkOKmQj6uvvMCHcMj0Lmk/K4eRwC7gQRGZBKwA7vTyRYcZA/QUkRdx2d0WqOpDUfVcT1Sq0AhuAx7x3g/DCYswdd4xoysxdKgTEJWVWd8/EWTCCWs+agnGQjofV19B5qiTxHvvUTJpkgtTM2hQ279FRdC9O/PmzaOmpobGxkaqq6s7rTtR/AiHHsB5wFdVtUZEFgDfBr4XVWYKbnXQBwiJSLWqbgDwVFGzcKqjNojId4EjwMPhQzHa0G6nnojMAeYAjBgxwkc3jLyjVy+YNQvOOAP+9Cc4fDhjt8oXz6VEd9wax0jHZ5xoHYmWb6Om+9jHYGcMJYyIi9h64EDC7U8EP8KhDqhT1XAkqCU44RBdZre3mjgoIi8Bk4AN3vkrgJWquiPyIhG5GbgKmKnHtmrXAZFP8VRge3SjVHUhsBDcDmkf/TDylbPPhmHDXJrS7e2+CikR/vE2NjVRU18P5E537mcgiZyp2w7oxMhFMqZEy/ty/1WFxkbmf/7z7vuSq01wqvq+iGwTkbGegXkmsDaq2BPAvSLSA+gFFAPzI87fQJRKSUTKgG8B070802GeBH4rIv+NM0ifCbyWWLeMgmPQILj9dhfDKY061vCPt3jYsJzrziODtFlug/STi2RMiZZPRE1XMmxYRjfB+YqtJCKTgUW4gX8TcCvwOQBVvd8r803veCuwSFXv8Y73BbYBI1V1b0Sd7wDHAQ3eoWpVneud+y7ODnEE+Lqq/jle+yy2Uhdjwwb44x/hww87LdoZQVInLVyxgq88/TQtqhQPG5bz/QpBejZGDIYMgTvuSKmKlGMrqeqbQHQF90eVuRu4O8a1HwKDYxwfHed+/wH8h5+2GV2QMWPcnoilS+Hdd1OqqrOZWjYHyKW1tbQEKBBmkN1UTXARCJuDYQSPAQPgppvg5ZfhxRedHjYDZHOALJ8+ncamJgDml+Z+72e4PY1NTYS2bQvUIBxkwZU1Dh1ye4EylHDLMsEZ+Uu3bjB9OtxyixMWGSCbfvzh0BfVX/pSUgNxuncrlwwfTlHv3tTU1wcmm124j7PPOoviYcOOCq4uSwZXDyYcjPzntNOcmmns2KOH0jVQBjU/dSwykZY0aJvcwn1cWlsbOMGVE/bvz1jVplYyCoO+feH666GmBp57rkt6/mQiCmrQNrnlS5j2rJHBlYNlgjPymlAoREVFBbNnz2bp0qWUl5dTcvrphB5/nKu/8x0a9u6ltLiYygULnBqqWze3iSj8/sgR2LoVNm7M6CzMDKhGRviHf3ARjZPEMsEZBUs40fry5ctpaHBe0ZWVlZR85Ss8de65VFRUuE1CxcUdV3Leec6gvXu3ExKbNjkvqObmtLVzXlUVNfX1VNfV8efPf75LCQgTjBkkgysHEw5GXjN79myWL1/O7bffzqpVq9rsFk0oU5aI8xsfMgSmTYOWFqirOyYs6uvT4hG19/DhnGdoiyQbA7d5FmUQEw6GEZulS5fS0NDAqlWr0rtbtHt3Z+g+7TS49FLnNvjuu8eExQcfJFTd/NLSwGRoiyQbA7dlhMsgZpA2jNhkOsn6Ufr0gbPOci+APXuckNi4ETZvdsIjDkHK0BZJNgZuv0ZtUz8lgbmyGkZswqqjkpJ2yQl9kXRM/BNOgClT4LOfhW9+04UXnzkTTj/drTqyRKouu0Fy1Y3rivuJT8Dxx2e/USnS5vMRcRvWevVyk43jj3fRVQcNghNPhKFDCR06RNmSJYSOHIFRo+DMM2HcOJgwAc45B849133vLriA0MCBlP3ylznN52AYBUvYoA0kr5bq1s1FjR02DC66yBmyt2xxK4u3387Y0j+0bRtX/+53NHirlo5m5/kyI+9wFfPJTzrhUFwMjz7qvMuSIDR0KBWPPEL53LmUjB0LBw+6l6oT6N26ub/Rr4jjobfeouK++yj/2tcomTq1w3LhV8U11zi13ejRVD7wQKdtrCgro2rNGjj1VCp/+MP4ZX/2M6peew0qKjISgM+Eg9Gl6UgtFXaRLS8vT3xV0quXm/GdeabLardoUcdqp+7dnfE7CeZVVdFw6BD9evaMG+IiU3aFdAudmOqn4mK48EL3vn9/uPlm+PWvOxUQ7dp2/vlU/M//ULVsGfTunfRgWvGNb1D1t79Bv35UXnddp+XLKyqgWzffas9E1KSZVqmacDC6NB15NKVlRQEweDB87nPwm98cEwInn3zMfrF3Lzz8cPw6OqG7yNGdwrEG/0zZFTJuzJ44EcrKnDomTPfu8KlPwf33w0cftbskZn6O738frriC8kGDgNQG00QH5IQ85hIsn2jdiWKb4AwjBp2tHBJeWaxZ4wTBWWc5e0UYVfjVr5waKtE2egNhZFa4bKqNMqquOussuPbaju031dUuhWwUZYsXH83PUdS7t2vb5Zc7AW20I94mOL/5HIpw+Rwm4lJ23qaqoagyM4B7gJ64rHDTRWQsx3JDg8tH/X1VvUdErgN+AJwFXKCqy716enr3Og+3snlIVX8cr30mHIxsU1ZWRlVVFaWlpanP3rZvh8ceg4aGzst2BcaNg+uui2/Yb211QjVKvRRTYHXrBv/yL3lp0M406dghvQCoVNVrvXzQfaNuUATcB5Sp6lYROQnAyxw32SvTHagHHvcuexuYDfxv1L2uA45T1bO9REFrReR3qvquz7YaRsZJq773lFPgn/8Z3n/frTBefdUNfl2RMWM6FwzgBvxPfQp+8QsXAsUjpt2itRVWrTpmuzB80akrq4gMAC4GHgBQ1WZVbYwqdiOwVFW3emViZMVmJrBRVbd4ZWo94RGNAv28lKN9gGZgn7/uGF2JsBvqwoULk3NHTYHOXGhDoRDTpk1jwoQJTJs2rfO2icDHPubcYbsqZ57pXIP9ugIPHuxCtvthxYqM5fwoVPzscxgJ7AIeFJE3RGSRiPSLKjMGOEFEXhSRFSJyU4x6ricqj3QHLAEOAu8BW4H/UtXEtqMaWSVXg3TYaPyd73yHqqoqKioqsnJfP1RUVFBTU8PatWupqanx37aWlq67ajjvvMQT13z84zB0aOflPvgg5ayByZDoPppEyie9R8cnfoRDD5z+/xeqei5u4P52jDJTgCuBUuB7IjImfNJTRc0CHvVxvwuAFuAU4AzgX0VkZHQhEZkjIstFZPmuXbt8VGtkilwN0uXl5ZSWlvKjH/2I0tLSzO+SToDy8nKKi4sZP348xcXF/tvWrRv80z/B7bfDF77g4jzlESltynviiQ7DknQ4EHbvDrNmtfVo6ojVqxNuUvR9Ex2Qw78Nv7+JRMonWnei+BHTdUCdqtZ4/y+hvXCowxmhDwIHReQlYBKwwTt/BbBSVXf4uN+NOPvGR8BOEXkFl796U2QhVV0ILARnkPZRr5EhwgNfZNjsbBDpyjdnzpys3NMvJSUlVFdXJ35ht25tZ8KjRkFTE7z5ZtralklScm9taoJHHnGCsVevY8cPH6bi3/7N7S8ghmvxsGFuP0Rnz3vtWhfiumdP302KdmlO1MU5UdtUXu1zUNX3RWSbiIz1bAQzgbVRxZ4A7vXsBL2AYmB+xPkb8KdSAqdKulREFuMM39NwXlBGQAnyIJ33iMDVV8O+fW7HNcBxx7md2EOHwsqVsH59YFRRcfdUDB3qVgYx9iccZccOeOopuOYa19/Vq2HdOsrHjAHVjgfCSy+F2lrnLtwRhw+7ZzVxov/+RA3Ats8hupDIZJx7aS/cDP5W4HMAqnq/V+ab3vFWYJGq3uMd7wtsA0aq6t6IOj8N/A8wBGgE3lTVUhE5HngQGA8I8KCq3h2vfebKahQ8TU1uZ/CIEc4I2zfCYXD/fnjjDScoGhsTqjZroTVOPx1uuMHN3p94ovM2vfwy5Rdd1LZN/frBHXe07Xskf/975xsKx4whdMYZye9+LzBS3ucQdEw4GF0CVULV1R0PbK2tbra9fDls2OBrNRHeNFY6alTmci2MHevcU3v0cB5Df/yjcy1Npk2TJsGnP93xvR57DN56K+apo7unBw2i5vXX07NHJc+JJxwsKqth5Amh6mquvvpqqqqquPrqq9sbRbt1g9GjXS7tefOcqqWoKG6d5dOnUzpqVOZCdk+e7HYnh72QRODKK10U0mTatGoVvPNOx/crK3MrjBiE7SH7d+9m4MCBbNu2LSdu0PmCrRwMI0+YNm0aNTU1dO/enZaWFn8z3yRWE2ljxgynAovlSbRjB/y//9dmA5tvBg506qVIo3Uk69bB73/f7vDRlUNrKzWbNwMwePBgGhoauuwqwlYOhlFAjB071r/rbrduMHIknHFGQl46KRHevTxjRscupkOHEho2LDm317174YUXOj4/bpxTP0VRMny4W400NzN+7FiKi4sD6QYdFEw4GEaeMH/+fEpLS1m0aJHvBEehxx+nbOJEQgsXOm+dTNOrF9x4o0tKEw9VKhYu7Di5T2e89hrEEypXXBFzZVGxbBk19fUMHzCA6upq5syZk1KyqELGhEMBkekdk4VKvjy3hLLeHTgAjz9OxTe/SVVtbXIDcCL07OlyV8yd6+we8Whuhscfp3z8+OTtHarw5JMdq6V693YriCjKZ86kdNIkyr8dvVXLiMbyORQQactB0MUoqOfW2gqvvw5/+QscPpxULoeE3Fv794cLLnCpKztyMSUixPmdd1KyeTPs3Ok7t3SH7NoFL78Ml1wS+/zEiW6fhIhTrZ1zDiXjxlF53HHt22Vure0w4VBAZHrHZKHS2XPLmwFk507nyrnjWCCCZAZgX7ucw0HvJkzwFSjvqADetInKG29MqD1xefllGD8+dnylUaOcemnChA7DdRfUxCDNmHAIMLEGpXgDVaZ3TBYqnT23vBlA+vVLS77qTlcbEye6XdsRM/BO6wwL4AsvTDotakxaW5166fbbnSE8ku7dXVgNP+2yCVU7zOYQYGIF1sp0sC2jPeEAf4EfQPr1c4N2ioRXG+1USt27u9hEn/lMQoIBIuwl3/iGc0VNJ/X1UFPTebl47UpgRZjJSKtBwoRDgIk1KOXNQFVAJDOAZIt2A89ZZ8E556T/RgMHwq23OvuCnwioHdGnj0v/GT3LT5HQww9TVlqalQE4k5FWg4QJhwDQ0cwi1qAU5IHKOEa2clzEHHiuuAIGDEjfTUaPhi9/GU49NT31DR/udm+ni1GjqFi3jqpnn83KAJzoBC1fJ3RmcwgA4R/48uXLeeqpp2zgLwDmzZtHTU0Nzz//PC2ejj0T9oqYOvM+fVyOg8WLU6tcxG1ku/ji1FYLsbjwQti8GTzDd1L07g2lpTB5MuWjR0OPHoEcgPPVFmgrhwBQXl5+dBt/vi09jfi0tLQwePDgjA1aHa4kR492+w6SpV8/+OIXOw5/kSoiLoBeB15EnTJunAuhce65IJL0ijoZe0C+qokSxVYOAaCkpISnnnrqqBeSkf/Mnz+fefPmHX2fk9Xg5Ze7mfmePYldd/bZcQPYpY3jj4fZs+E3v/Gf37lfP2cUHz8+LUIrGU+0ruLh5GvlICJFIrJERNaJSK2ItPumi8gMEXlTRNaIyDLv2FjvWPi1T0S+7p27zivbKiJTo+o6R0RC3vm3RKR3GvoaaMyWkJ/EsxdVV1dTXV2du8+0Vy+XNMfvIDpggAt98ZnPZF4whBk50iUu8sOkSW61MGFC2lYzs2fPZvDgwcyePdv3NYn+VhNNNep3NZNpLyi/K4cFuNSd13r5oNtshRSRIuA+oExVt4rISQBe5rjJXpnuQD3wuHfZ28Bs4H+j6uoBLAa+qKqrRGQwECd1lGHkjsDvgTjtNCgpgVdfjV9u6lT45CedHr8DMrYZcMYMePdd2Lo19vmBA+Gqq+DMM9N3T4+lS5fS0NDA0qVLM5bFMNFUo36/U5n+7nUqHERkAHAxcAuAqjYDzVHFbgSWqupWr8zOGFXNBDaq6havTK1Xf3S5y4HVqrrKK9fgsy+GkXWCqGJoN4hfeqnLgbAzxs/y5JPd3ohhwzqtN2ODUbdubrVy//1w6FDbc+ef74RWgvsqOiP8jMIrhkx+fommGvX7ncr0d6/TfA5eitCFuLzRk4AVwJ2qejCizD1AT2AC0B9YoKoPRdXzS2Clqt4bdfxF4Buqutz7/+vAFOAkXArR36vqT+O10fI5GNkgX8JolJWVUVVV1TZHwfbtsGjRsXwOIvDxj7u4RD38KRAy3v/16+F3Xqr5QYNc2O/TTkv/fejgGXVB4uVz8POt6AGcB3xVVWtEZAHwbeB7UWWm4FYHfYCQiFSr6gavAb2AWcBdPu/3CeB84EPgBa8DbQK4i8gcYA7AiBEjfFRrGKkReBWSR8wZ5SmnwMyZLoVmr15OKJxxRkL1Ztwlc+xYJ7DCLrQZzD8RxBVf0PAjHOqAOlUN709fghMO0WV2e6uJgyLyEm6VscE7fwVu1bCDzqkDlqnqbgAReQYnnNoIB1VdiFvRMHXq1PxPZ2cEnnwZUDocxC+80L2CzOWXZ+U2+br3IJt06q2kqu8D20RkrHdoJk7FFMkTwEUi0kNE+gLFQG3E+RuA3/lsUxVwjoj09YzT02PczzCyjnmUGV0Jv95KXwUe9tRDm4BbRWQugKrer6q1IlIJrAZagUWq+jaAJywuA74cWaGIfBr4H5xd4WkReVNVS1V1j4j8N/A6oMAzqvp0yj01DMMwfNOpQTofMIO0YRhG4sQzSFv4DMMwDKMdJhwMwzCMdphwMAzDMNphwsEwDMNohwkHwzAMox0F4a0kIruALbluR4Y4Edid60ZkGOtjYWB9zD9OU9UhsU4UhHAoZERkeUeuZoWC9bEwsD4WFqZWMgzDMNphwsEwDMNohwmH4LMw1w3IAtbHwsD6WECYzcEwDMNoh60cDMMwjHaYcMgAItJbRF4TkVUiskZEKrzjk0WkWkTeFJHlInJB1HUjROSAiHwj4tgUEXlLRN4RkZ+Jl1dVRI4TkUe84zUicnrENTeLyN+918150s8XRWS9d82b4Tzkue5non0UkdNF5FBEP+6PqCuQn2Wa+1gQn6N37hwRCXnl3xKR3t7xQH6OaUdV7ZXmFyDA8d77nkANMA14FrjCO/4PwItR1z0GPIpLmxo+9hpQ4tX554jrvwLc772/HnjEez8IF1Z9EHCC9/6EPOjni8DUGPfIaT8T7SNwOvB2B3UF8rNMcx8L5XPsgUtBMMn7fzDQPcifY7pftnLIAOo44P3b03up9xrgHR8IbA9fIyLX4L40ayKOfQwYoKohdd+yh4BrvNOfAn7tvV8CzPRmMKXAc6r6garuAZ4DytLdR0hfPzshp/1Mpo+xCPJnma4+dkK+9fFyYLWqrvKub1DVliB/junGb7IfI0FEpDuwAhgN/Fxd/u2vA1Ui8l84ld7HvbL9gG/hkiJ9I6KaYbi0qWHqvGPhc9sAVPWIiOzFzW6OHo9xTdpJUz/DPCgiLbiVxQ+9H1/O+5lIHz3OEJE3gH3A/1HVlwn4Z5mmPoYphM9xDKAiUoVLSPZ7Vf0pAf8c04mtHDKEqrao6mTgVOACEZkI/BMwT1WHA/OAB7ziFcD8iJlNGIlVdSfn4l2TdtLUT4DPq+rZwEXe64ve8Zz3M8E+vgeMUNVzgX8BfisiAzppb6H0EQrnc+wBfAL4vPf30yIys5P25ryP6cSEQ4ZR1UacHrYMuBlY6p16FAgbv4qBn4rIu8DXge+IyD/jZhinRlR3KseWvXXAcABxubYHAh9EHo9xTcZIsZ+oar33dz/w24hrAtNPP31U1cOq2uC9XwFsxM1C8+KzTLGPBfM5eu1apqq7VfVD4BngPPLkc0wLmTZqdMUXbhla5L3vA7wMXAXUAjO84zOBFTGu/QFtDbWv4wxnYePXP3jH76Ct8esP3vtBwGac4esE7/2gIPcTN0s70XvfE6evnRuEfibaR6982HA5EqgPtyuon2W6+lhgn+MJwEqgr9ev54Erg/w5pv2Z5boBhfgCzgHewHk7vA183zv+CZzOcxXOW2JKjGt/QFvhMNWrYyNwL8c2LvbGzXTewXlPjIy45jbv+DvArUHvJ9DPK78aZ6heEDH45LSfifYR+IzXh1Xe4HJ10D/LdPWxkD5H79wXvH68Dfw06J9jul+2Q9owDMNoh9kcDMMwjHaYcDAMwzDaYcLBMAzDaIcJB8MwDKMdJhwMwzCMdphwMAzDMNphwsEwDMNohwkHwzAMox3/HyfDIkZGmiBfAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the bounding box coordinates of the Polygon as a list\n", "bounds = list(city_center_zip_area.bounds.values[0])\n", "\n", "# Get the indices of the Points that are likely to be inside the bounding box of the given Polygon\n", "point_candidate_idx = list(intersection_sindex.intersection(bounds))\n", "point_candidates = intersections.loc[point_candidate_idx]\n", "\n", "# Let's see what we have now\n", "ax = city_center_zip_area.plot(color='red', alpha=0.5)\n", "ax = point_candidates.plot(ax=ax, color='black', markersize=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aha, as we can see, now we have successfully selected such points from the dataset that intersect with the **bounding box** of the Polygon. I.e. we conducted the first step of the process. \n", "\n", "Next, let's do the final selection using a \"normal\" intersect query, which is however, much faster because there is no need to go through all 63.5 thousand points in the full dataset:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make the precise Point in Polygon query\n", "final_selection = point_candidates.loc[point_candidates.intersects(city_center_zip_area['geometry'].values[0])]\n", "\n", "# Let's see what we have now\n", "ax = city_center_zip_area.plot(color='red', alpha=0.5)\n", "ax = final_selection.plot(ax=ax, color='black', markersize=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Putting pieces together - Performance comparisons\n", "\n", "Following functions both conduct the spatial query that we saw previously, the first one **without** utilizing spatial index and the second one **with** spatial index. We can use them and compare the performance, so that we can get an idea how much the spatial index affects the performance time-wise." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def intersect_using_spatial_index(source_gdf, intersecting_gdf):\n", " \"\"\"\n", " Conduct spatial intersection using spatial index for candidates GeoDataFrame to make queries faster.\n", " Note, with this function, you can have multiple Polygons in the 'intersecting_gdf' and it will return all the points \n", " intersect with ANY of those geometries.\n", " \"\"\"\n", " source_sindex = source_gdf.sindex\n", " possible_matches_index = []\n", " \n", " # 'itertuples()' function is a faster version of 'iterrows()'\n", " for other in intersecting_gdf.itertuples():\n", " bounds = other.geometry.bounds\n", " c = list(source_sindex.intersection(bounds))\n", " possible_matches_index += c\n", " \n", " # Get unique candidates\n", " unique_candidate_matches = list(set(possible_matches_index))\n", " possible_matches = source_gdf.iloc[unique_candidate_matches]\n", "\n", " # Conduct the actual intersect\n", " result = possible_matches.loc[possible_matches.intersects(intersecting_gdf.unary_union)]\n", " return result\n", "\n", "def normal_intersect(source_gdf, intersecting_gdf):\n", " \"\"\"\n", " Conduct spatial intersection without spatial index.\n", " Note, with this function, you can have multiple Polygons in the 'intersecting_gdf' and it will return all the points \n", " intersect with ANY of those geometries.\n", " \"\"\"\n", " \n", " matches = []\n", " \n", " # 'itertuples()' function is a faster version of 'iterrows()'\n", " for other in intersecting_gdf.itertuples():\n", " c = list(source_gdf.loc[source_gdf.intersects(other.geometry)].index)\n", " matches += c\n", " \n", " # Get all points that are intersecting with the Polygons\n", " unique_matches = list(set(matches))\n", " result = source_gdf.loc[source_gdf.index.isin(unique_matches)]\n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's compare their performance and time it. Here we utilize a special IPython magic function called `%timeit` that allows to test how long it takes to run a specific function (it actually runs the function multiple times to get a more representative timing). " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "23 ms ± 5.65 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "# Test the spatial query with spatial index\n", "%timeit intersect_using_spatial_index(source_gdf=intersections, intersecting_gdf=city_center_zip_area)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25.7 ms ± 3.61 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "# Test the spatial query without spatial index\n", "%timeit normal_intersect(source_gdf=intersections, intersecting_gdf=city_center_zip_area)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, as these tests demonstrate, using the spatial index gives a significant boost in the performance, by being around 17x faster. \n", "\n", "Making the spatial query only with a single Polygon (as in the example) might not make a big difference, but having hundreds or thousands of Polygons, and wanting to find all points that are inside those ones, start to make a drastic difference." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Counting the intersections\n", "\n", "The ultimate goal of this tutorial was to count the intersections per postal code. We can do that easily and fast with Geopandas, by conducting a `spatial join` between the two datasets. Spatial join in Geopandas is highly performant, and in fact, it utilizes spatial index to make the queries fast. The following parts might include a bit advanced tricks that we have not covered, but for the sake of completeness, the following steps count the intersections per postal code area. Finally, we plot a density of the intersections as a number of intersections per square kilometer (per postal code area). " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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posti_alue0
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" ], "text/plain": [ " posti_alue 0\n", "0 00100 203\n", "1 00120 35\n", "2 00130 50\n", "3 00140 44\n", "4 00150 68" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Count intersections by postal code area\n", "intersection_cnt = gpd.sjoin(postcode_areas, intersections).groupby('posti_alue').size().reset_index()\n", "intersection_cnt.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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posti_aluehe_vakiygeometryintersection_cnt
00010018284.0MULTIPOLYGON (((385653.893 6671591.048, 385573...203
1001207108.0MULTIPOLYGON (((385316.092 6671076.984, 385279...35
2001301508.0MULTIPOLYGON (((386212.111 6671061.262, 386176...50
3001407865.0MULTIPOLYGON (((386577.050 6670280.544, 386552...44
4001509496.0MULTIPOLYGON (((384846.102 6669565.816, 384823...68
...............
33825470279.0MULTIPOLYGON (((305067.212 6673543.615, 304620...16
339255003391.0MULTIPOLYGON (((277988.374 6665076.564, 277959...86
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343 rows × 4 columns

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" ], "text/plain": [ " posti_alue he_vakiy geometry \\\n", "0 00100 18284.0 MULTIPOLYGON (((385653.893 6671591.048, 385573... \n", "1 00120 7108.0 MULTIPOLYGON (((385316.092 6671076.984, 385279... \n", "2 00130 1508.0 MULTIPOLYGON (((386212.111 6671061.262, 386176... \n", "3 00140 7865.0 MULTIPOLYGON (((386577.050 6670280.544, 386552... \n", "4 00150 9496.0 MULTIPOLYGON (((384846.102 6669565.816, 384823... \n", ".. ... ... ... \n", "338 25470 279.0 MULTIPOLYGON (((305067.212 6673543.615, 304620... \n", "339 25500 3391.0 MULTIPOLYGON (((277988.374 6665076.564, 277959... \n", "340 25560 208.0 MULTIPOLYGON (((294139.092 6671931.426, 291442... \n", "341 31350 235.0 MULTIPOLYGON (((329443.822 6726297.286, 329125... \n", "342 31470 698.0 MULTIPOLYGON (((315714.104 6707215.302, 315265... \n", "\n", " intersection_cnt \n", "0 203 \n", "1 35 \n", "2 50 \n", "3 44 \n", "4 68 \n", ".. ... \n", "338 16 \n", "339 86 \n", "340 48 \n", "341 2 \n", "342 2 \n", "\n", "[343 rows x 4 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Merge with postcode data and plot\n", "intersection_cnt = intersection_cnt.rename(columns={0: 'intersection_cnt'})\n", "postcode_areas = postcode_areas.merge(intersection_cnt, on='posti_alue')\n", "postcode_areas" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot intersection density (number of intersections per square kilometer inside a Postal code)\n", "m2_to_km2_converter = 1000000\n", "postcode_areas['intersection_density'] = postcode_areas['intersection_cnt'] / (postcode_areas.area / m2_to_km2_converter)\n", "postcode_areas.plot('intersection_density', cmap='RdYlBu_r', legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the map, we can see that the intersection density is clearly highest in the city center areas of Helsinki (red colored areas). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Note\n", "\n", "As we have learned from this tutorial, spatial index can make the spatial queries significantly faster. There is however, a specific situation in which spatial index does not provide any improvements for the performance: if your polygon and points have more or less similar spatial extent (bounding box), the spatial index does not help to make the queries faster due to its design in working on a level of bounding boxes. This happens e.g. in following case:\n", "\n", "![los-angeles-boundary-intersections.png](img/los-angeles-boundary-intersections.png)\n", "*Example of a situation where spatial index does not provide boost in performance* (Source: [G. Boeing, 2016](https://geoffboeing.com/2016/10/r-tree-spatial-index-python/))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, in the map, there is a complex Polygon that share more or less identical extent as the point layer, which is problematic from performance point of view.\n", "\n", "There is, however, a nice strategy to deal with this kind of situation, by sub-dividing the Polygon into smaller subsets (having also smaller bounding boxes) that will enable the spatial index to boost the queries:\n", "\n", "![los-angeles-boundary-quadrats-intersections](img/los-angeles-boundary-quadrats-intersections.png).\n", "\n", "You can read more about this strategy from an excellent post from [G. Boeing](https://geoffboeing.com/2016/10/r-tree-spatial-index-python/)." ] } ], "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 }