{ "cells": [ { "cell_type": "markdown", "id": "70db89f0", "metadata": {}, "source": [ "# Geometric data manipulations" ] }, { "cell_type": "markdown", "id": "5e8c8dab", "metadata": {}, "source": [ "Here we demonstrate some of the most common geometry manipulation functions available in `geopandas`. We will continue exploring the census tract data from Austin, Texas. It is often useful to do geometric manipulations on administrative borders for further analysis and visualization purposes. We will learn how to generate centroids, different outlines and buffer zones for the polygons. " ] }, { "cell_type": "code", "execution_count": 1, "id": "2573c0ff-abee-4733-956d-25ca18773021", "metadata": { "tags": [ "remove_cell" ] }, "outputs": [], "source": [ "import os\n", "\n", "os.environ[\"USE_PYGEOS\"] = \"0\"" ] }, { "cell_type": "code", "execution_count": 2, "id": "3035ea5e", "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "from pathlib import Path" ] }, { "cell_type": "code", "execution_count": 3, "id": "7acca403-0eee-41cc-ba9c-9866ea5f1b8c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pop2019tractarea_km2pop_density_km2geometry
06070.00024224.0297721506.288769POLYGON ((615643.487 3338728.496, 615645.477 3...
12203.00017511.5320301437.961408POLYGON ((618576.586 3359381.053, 618614.330 3...
27419.00024113.9603441873.322183POLYGON ((619200.163 3341784.654, 619270.849 3...
34229.00004012.1817621938.341868POLYGON ((621623.757 3350508.165, 621656.294 3...
44589.00023132.4312081887.538655POLYGON ((621630.247 3345130.744, 621717.926 3...
\n", "
" ], "text/plain": [ " pop2019 tract area_km2 pop_density_km2 \\\n", "0 6070.0 002422 4.029772 1506.288769 \n", "1 2203.0 001751 1.532030 1437.961408 \n", "2 7419.0 002411 3.960344 1873.322183 \n", "3 4229.0 000401 2.181762 1938.341868 \n", "4 4589.0 002313 2.431208 1887.538655 \n", "\n", " geometry \n", "0 POLYGON ((615643.487 3338728.496, 615645.477 3... \n", "1 POLYGON ((618576.586 3359381.053, 618614.330 3... \n", "2 POLYGON ((619200.163 3341784.654, 619270.849 3... \n", "3 POLYGON ((621623.757 3350508.165, 621656.294 3... \n", "4 POLYGON ((621630.247 3345130.744, 621717.926 3... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define path do the data\n", "data_folder = Path(\"data/Austin\")\n", "fp = data_folder / \"austin_pop_density_2019.gpkg\"\n", "\n", "# Read in the data and check the contents\n", "data = gpd.read_file(fp)\n", "data.head()" ] }, { "cell_type": "markdown", "id": "6f986ea3", "metadata": {}, "source": [ "For the purposes of geometric manipulations, we are mainly interested in the geometry column which contains the polygon geometries. Remember, that the data type of the geometry-column is `GeoSeries`. Individual geometries are eventually `shapely` objects and we can use all of `shapely`'s tools for geometry manipulation directly via `geopandas`." ] }, { "cell_type": "code", "execution_count": 4, "id": "3c5b8fd0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 POLYGON ((615643.487 3338728.496, 615645.477 3...\n", "1 POLYGON ((618576.586 3359381.053, 618614.330 3...\n", "2 POLYGON ((619200.163 3341784.654, 619270.849 3...\n", "3 POLYGON ((621623.757 3350508.165, 621656.294 3...\n", "4 POLYGON ((621630.247 3345130.744, 621717.926 3...\n", "Name: geometry, dtype: geometry" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check contents of the geometry column\n", "data[\"geometry\"].head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "bf17c2a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "geopandas.geoseries.GeoSeries" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check data type of the geometry column\n", "type(data[\"geometry\"])" ] }, { "cell_type": "code", "execution_count": 6, "id": "2640dc73", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "shapely.geometry.polygon.Polygon" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check data type of a value in the geometry column\n", "type(data[\"geometry\"].values[0])" ] }, { "cell_type": "markdown", "id": "e524d8e9", "metadata": {}, "source": [ "Let's first plot the original geometries. We can use the in-built plotting function in `geopandas` to plot the geometries, and `matplotlib.pyplot` to turn off axis lines and labels." ] }, { "cell_type": "code", "execution_count": 7, "id": "c8e4e801", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.plot(facecolor=\"none\", linewidth=0.2)\n", "\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "bb86c9ad-0465-4516-830f-84d52688fefc", "metadata": {}, "source": [ "_**Figure 6.13**. Basic plot of the census tracts._" ] }, { "cell_type": "markdown", "id": "11e9d77c", "metadata": { "tags": [] }, "source": [ "## Centroid\n", "\n", "Extracting the centroid of geometric features is useful in many cases. Geometric centroid can, for example, be used for locating text labels in visualizations. We can extract the center point of each polygon via the `centroid`-attribute of the geometry-column. The data should be in a projected coordinate reference system when calculating the centroids. If trying to calculate centroids based on latitude and longitude information, `geopandas` will warn us that the results are likely incorrect. Our sample data are in WGS 84 / UTM zone 14N (EPSG:32614), which is a projected , and we can proceed to calculating the centroids." ] }, { "cell_type": "code", "execution_count": 8, "id": "2c71636e-13f3-4562-8e80-abfa3e6a6798", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'WGS 84 / UTM zone 14N'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.crs.name" ] }, { "cell_type": "code", "execution_count": 9, "id": "513407ce", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 POINT (616990.190 3339736.002)\n", "1 POINT (619378.303 3359650.002)\n", "2 POINT (620418.753 3342194.171)\n", "3 POINT (622613.506 3351414.386)\n", "4 POINT (622605.359 3343869.554)\n", "dtype: geometry" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"geometry\"].centroid.head()" ] }, { "cell_type": "markdown", "id": "d0446d74", "metadata": {}, "source": [ "We can also apply the method directly to the `GeoDataFrame` to achieve the same result using the syntax `data.centroid`. At the same time, we can also plot the centroids for a visual check." ] }, { "cell_type": "code", "execution_count": 10, "id": "20270dc2", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.centroid.plot(markersize=1)\n", "\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d41ab0cb-7c22-4358-b098-85a167bec1c9", "metadata": {}, "source": [ "_**Figure 6.14**. Basic plot of census tract centroids._" ] }, { "cell_type": "markdown", "id": "c055ea6a", "metadata": {}, "source": [ "## Unary union\n", "\n", "We can generate a joint outline for the administrative areas through creating a geometric union among all geometries. This could be useful, for example, for visualizing the outlines of a study area. The `unary_union` returns a single geometry object, which is automatically visualized when running the code in a Jupyter Notebook." ] }, { "cell_type": "code", "execution_count": 11, "id": "201e8215", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.unary_union" ] }, { "cell_type": "markdown", "id": "99991d71-d302-4a4f-8d39-480f9b723413", "metadata": {}, "source": [ "_**Figure 6.15**. Union of all of census tract polygon geometries._" ] }, { "cell_type": "markdown", "id": "d41ed12b", "metadata": {}, "source": [ "## Simplifying geometries\n", "\n", "Geometry simplification is a useful process especially when visualizing data that has very detailed geometry. With our sample data, we can generate simplified version of the outline extent. The tolerance parameter controls the level of simplification." ] }, { "cell_type": "code", "execution_count": 12, "id": "043d2c81", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.unary_union.simplify(tolerance=1000)" ] }, { "cell_type": "markdown", "id": "33263160-b939-4ba2-978f-51dba89963d6", "metadata": {}, "source": [ "_**Figure 6.16**. Simplified union of the census tract polygons._" ] }, { "cell_type": "markdown", "id": "1684a61e", "metadata": {}, "source": [ "## Bounding polygon\n", "\n", "Bounding polygons are useful in many cases for describing the approximate extent of geographic data. A minimum bounding rectangle, also called a bounding box or an envelope is the smallest rectangular polygon surrounding a geometric object. In a `GeoDataFrame`, the `envelope` attribute returns the bounding rectangle for each geometry." ] }, { "cell_type": "code", "execution_count": 13, "id": "97e9b2da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 POLYGON ((615643.487 3337909.895, 618358.033 3...\n", "1 POLYGON ((618529.497 3358797.000, 620192.632 3...\n", "2 POLYGON ((619198.456 3340875.421, 621733.880 3...\n", "3 POLYGON ((621599.087 3350329.320, 623714.366 3...\n", "4 POLYGON ((621630.247 3343015.679, 624133.189 3...\n", "dtype: geometry" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.envelope.head()" ] }, { "cell_type": "markdown", "id": "ca420340", "metadata": {}, "source": [ "In order to get the bounding rectangle for the whole layer, we first create an union of all geometries using `unary_union`, and then create the bounding rectangle for that polygon." ] }, { "cell_type": "code", "execution_count": 14, "id": "b4f4fa02", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.unary_union.envelope" ] }, { "cell_type": "markdown", "id": "503304c5", "metadata": {}, "source": [ "_**Figure 6.17**. Minimum bounding box for the census tracts._\n", "\n", "Corner coordinates of the bounding box for a `GeoDataFrame` can be fetched via the `total_bounds` attribute. " ] }, { "cell_type": "code", "execution_count": 15, "id": "71b34782", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 608125.39434001, 3337909.89495403, 629828.38846075,\n", " 3370513.6825782 ])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.total_bounds" ] }, { "cell_type": "markdown", "id": "ba66e485-9bf8-44a8-a9f2-1d153ea0508a", "metadata": {}, "source": [ "The `bounds` attribute returns the bounding coordinates of each feature." ] }, { "cell_type": "code", "execution_count": 16, "id": "4716ca4d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " minx miny maxx maxy\n", "0 615643.487492 3.337910e+06 618358.032738 3.341257e+06\n", "1 618529.497100 3.358797e+06 620192.631882 3.360614e+06\n", "2 619198.455980 3.340875e+06 621733.879615 3.343443e+06\n", "3 621599.086586 3.350329e+06 623714.365506 3.352436e+06\n", "4 621630.247042 3.343016e+06 624133.188692 3.345131e+06" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.bounds.head()" ] }, { "cell_type": "markdown", "id": "77a0fb3f", "metadata": {}, "source": [ "### Convex hull\n", "\n", "A bit more detailed delineation of the data extent can be extracted using a convex hull which represents the smalles possible polygon that contains all points in an object. If we apply the convex hull method on the whole `GeoDataFrame`, we will get a GeoSeries containing a convex hull for each polygon separately." ] }, { "cell_type": "code", "execution_count": 17, "id": "bb36174e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 POLYGON ((616870.883 3337909.895, 616852.964 3...\n", "1 POLYGON ((619496.705 3358797.000, 618962.703 3...\n", "2 POLYGON ((619848.500 3340875.421, 619811.394 3...\n", "3 POLYGON ((622145.426 3350329.320, 622132.429 3...\n", "4 POLYGON ((623931.770 3343015.679, 622426.307 3...\n", "dtype: geometry" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.convex_hull.head()" ] }, { "cell_type": "markdown", "id": "43b74573", "metadata": {}, "source": [ "In order to create a covex hull for the whole extent, we need to first create an union of all polygons. " ] }, { "cell_type": "code", "execution_count": 18, "id": "a5787843", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.unary_union.convex_hull" ] }, { "cell_type": "markdown", "id": "87d2ea8a-e1a3-4cd0-a84e-8bf32367d1db", "metadata": {}, "source": [ "_**Figure 6.18**. Smallest convex polygon for the census tracts._" ] }, { "cell_type": "markdown", "id": "2be02342", "metadata": {}, "source": [ "## Buffer\n", "\n", "Buffering is a common spatial operation that has a multitude of use cases in spatial analyses. For example, in transport network analyses, it is good to fetch the transport network also from outside the study area in order to capture routes that go beyond the study area border. The distance parameter in the `buffer` function defines the radius or the buffer (according to the coordinate reference system of the data). Applying the buffer function on the entire data frame will produce separate buffers for each census tract." ] }, { "cell_type": "code", "execution_count": 19, "id": "7a0d6e13-c50b-454c-821f-49054ad5e5de", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1000 m buffer for each polygon\n", "data.buffer(1000).plot(edgecolor=\"white\")\n", "\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f8b812d7-ca56-42fc-94b2-14cd15a10639", "metadata": {}, "source": [ "_**Figure 6.19**. 1km buffer for each census tract._" ] }, { "cell_type": "markdown", "id": "6a274f4d-c74b-46fe-a6b7-b50af55cc61a", "metadata": {}, "source": [ "If we want one buffer for the whole area, we first need to combine the geometries into one object before the buffer analysis. " ] }, { "cell_type": "code", "execution_count": 20, "id": "4c309b0e-d43b-43bd-9807-6b39f57d8552", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1000 m buffer for each polygon\n", "data.unary_union.buffer(1000)" ] }, { "cell_type": "markdown", "id": "e23b5d9e-0cad-424c-b8ec-fe6edb1ab44e", "metadata": {}, "source": [ "_**Figure 6.20**. 1km buffer for each census tract._" ] }, { "cell_type": "markdown", "id": "9c497d6b", "metadata": { "tags": [] }, "source": [ "## Dissolving and merging geometries\n", "\n", "Spatial data aggregation refers to combining geometries into coarser spatial units based on some attributes. The process may also include the calculation of summary statistics. \n", "\n", "In `pandas`, we learned how to group and aggregate data using the `groupby`method. In `geopandas`, there is a function called `dissolve()` that groups the data based on an anttribute column and unions the geometries for each group in that attribute. At the same time, we can also get summary statistics of the attributes. Read more about the details of the dissolve-function and related aggregation options in the `geopandas` [online documentation](https://geopandas.org/en/stable/docs/user_guide/aggregation_with_dissolve.html) [^gpd_dissolve]." ] }, { "cell_type": "markdown", "id": "c15afe13-00c7-45dc-9e16-e558fbfec308", "metadata": {}, "source": [ "To exceplify how dissolve works with our sample data, let's create create a new column to indicate census tracts with above average population density. We can do this by adding a new empty column `dense` and adding values that indicate above and below average population densities per census tract." ] }, { "cell_type": "code", "execution_count": 21, "id": "3162e4db-debd-48c1-9692-1f217e9bc3bd", "metadata": {}, "outputs": [], "source": [ "# Create a new column and add a constant value\n", "data[\"dense\"] = 0" ] }, { "cell_type": "code", "execution_count": 22, "id": "9a37725a-2323-4cf4-aa3d-b13ee6df8860", "metadata": {}, "outputs": [], "source": [ "# Filter rows with above average pop density and update the column dense\n", "data.loc[data[\"pop_density_km2\"] > data[\"pop_density_km2\"].mean(), \"dense\"] = 1" ] }, { "cell_type": "code", "execution_count": 23, "id": "f118efac-088c-4e9f-a11f-fbdbe533f619", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 86\n", "1 44\n", "Name: dense, dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check number of rows per category\n", "data.dense.value_counts()" ] }, { "cell_type": "markdown", "id": "08f33ba0-a40c-4ff9-a89e-431b0aadbfae", "metadata": {}, "source": [ "Now we have a new column with value 1 indicating above average population density which we can use for dissolving the data into two groups using the `.dissolve()` funcition. At the same time, we can sum up the population and area columns valuens using the `aggfunc` parameter. The aggregation requires that we do a selection of the numerical columns we want to include in the output." ] }, { "cell_type": "code", "execution_count": 24, "id": "6599efec", "metadata": {}, "outputs": [], "source": [ "# Conduct the aggregation\n", "dissolved = data[[\"pop2019\", \"area_km2\", \"dense\", \"geometry\"]].dissolve(\n", " by=\"dense\", aggfunc=\"sum\"\n", ")" ] }, { "cell_type": "code", "execution_count": 25, "id": "f8589bea-8f01-43ee-81a7-c5ae977cda90", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
geometrypop2019area_km2
dense
0MULTIPOLYGON (((614108.230 3339640.551, 614288...368992.0231.131494
1MULTIPOLYGON (((612263.531 3338931.800, 612265...242943.071.234570
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" ], "text/plain": [ " geometry pop2019 area_km2\n", "dense \n", "0 MULTIPOLYGON (((614108.230 3339640.551, 614288... 368992.0 231.131494\n", "1 MULTIPOLYGON (((612263.531 3338931.800, 612265... 242943.0 71.234570" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the result\n", "dissolved" ] }, { "cell_type": "markdown", "id": "2c4cfd0a", "metadata": {}, "source": [ "The dissolved data should have as many rows of data as there were unique values in the column - one row for each unique value. Our data have been compressed into two geometric objects and the column used for dissolving the data can now be found in the index. Attribute columns represent the sum of the values per group. We can reset the index and insert the categorical information into a new column after which we can do a quick visualization of the result." ] }, { "cell_type": "code", "execution_count": 26, "id": "dba0696b-d2e1-4d0e-9198-eef3a08a9ef0", "metadata": {}, "outputs": [], "source": [ "dissolved = dissolved.reset_index()" ] }, { "cell_type": "code", "execution_count": 27, "id": "f47f66dc-ce30-4133-b2a3-84bd4443ce9f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dissolved.plot(column=\"dense\")\n", "\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f38f2807-db59-4b53-a8b1-45d292939767", "metadata": {}, "source": [ "_**Figure 6.21**. Dissolved census tract geometries._" ] }, { "cell_type": "markdown", "id": "8ae726de-aa36-42ff-bcde-dfbc3235d59d", "metadata": {}, "source": [ "## Footnotes\n", "\n", "[^gpd_dissolve]: " ] } ], "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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 }