{ "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, 6680000)" ] }, "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": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of groups: 908 \n", "\n", "Group 0 contains 70 geometries, bounding box: [270305.195, 6632767.892, 279497.993, 6638674.424]\n", "Group 2 contains 70 geometries, bounding box: [272155.57, 6638680.047, 285753.31, 6638976.355]\n", "Group 3 contains 70 geometries, bounding box: [271904.727, 6638983.54, 286463.059, 6639181.123]\n", "Group 4 contains 70 geometries, bounding box: [271790.007, 6639184.018, 284495.801, 6639351.784]\n", "Group 5 contains 70 geometries, bounding box: [271952.787, 6639354.724, 284589.912, 6639564.576]\n", "Group 6 contains 70 geometries, bounding box: [271784.854, 6639571.546, 285241.406, 6639760.222]\n", "Group 7 contains 70 geometries, bounding box: [271813.417, 6639762.182, 285147.276, 6639945.834]\n", "Group 8 contains 70 geometries, bounding box: [271646.281, 6639949.717, 283451.674, 6640149.228]\n", "Group 9 contains 70 geometries, bounding box: [272335.073, 6640150.564, 282567.285, 6640318.718]\n", "Group 10 contains 70 geometries, bounding box: [272318.947, 6640321.762, 286352.142, 6641062.815]\n" ] } ], "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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\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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" ] }, "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": [ "14.1 ms ± 469 µs per loop (mean ± std. dev. of 7 runs, 100 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": [ "247 ms ± 4.18 ms per loop (mean ± std. dev. of 7 runs, 1 loop 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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" ], "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
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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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\n", "text/plain": [ "
" ] }, "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }