{ "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": "stderr", "output_type": "stream", "text": [ "/home/aagesenh/.conda/envs/python-gis/lib/python3.8/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.9.1dev-CAPI-1.14.1) is incompatible with the GEOS version PyGEOS was compiled with (3.9.1-CAPI-1.14.2). Conversions between both will be slow.\n", " warnings.warn(\n" ] }, { "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", "print('-----------')\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(f\"Number of intersections: {len(intersections)}\")\n", "print(f\"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. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "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": 6, "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": 7, "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": 8, "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": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.22 ms ± 328 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": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.81 ms ± 365 µs per loop (mean ± std. dev. of 7 runs, 100 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": 11, "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": 11, "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": 12, "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": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "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 (ipykernel)", "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }