{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tbeucler/2023_MLEES_JB/blob/main/ML_EES/IP/W3_S2_Tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e1cdc987",
      "metadata": {
        "id": "e1cdc987"
      },
      "source": [
        "# Geospatial Data with Geopandas"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![geopandas_logo_green.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "gbmLcVZY4V6O"
      },
      "id": "gbmLcVZY4V6O"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installing GeoPandas\n",
        "Please run the following code blocks in this section to:\n",
        "* Install the GeoPandas's dependencies and GeoPandas\n",
        "* Download and unzip the data used in this notebook\n",
        "* Import GeoPandas and other required modules for the notebook"
      ],
      "metadata": {
        "id": "FGK9JHUkutNy"
      },
      "id": "FGK9JHUkutNy"
    },
    {
      "cell_type": "code",
      "source": [
        "#Install the GeoPandas's dependencies\n",
        "!pip install --upgrade pyshp\n",
        "\n",
        "!pip install --upgrade shapely\n",
        "\n",
        "!pip install --upgrade descartes\n",
        "\n",
        "!pip install --upgrade rtree"
      ],
      "metadata": {
        "id": "wcyinTQVuoOm"
      },
      "id": "wcyinTQVuoOm",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Install the GeoPandas\n",
        "\n",
        "!pip install --upgrade geopandas"
      ],
      "metadata": {
        "id": "tIRleXwFvDC2"
      },
      "id": "tIRleXwFvDC2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Download the data used in this notebook\n",
        "!gdown 1b1lngOIvuNnZxepbT8RyV3KX1itRky5z"
      ],
      "metadata": {
        "id": "bdKuowGVyGeO"
      },
      "id": "bdKuowGVyGeO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Unzip the data used in this notebook\n",
        "!unzip '/content/data.zip'"
      ],
      "metadata": {
        "id": "5rkcEABq13_8"
      },
      "id": "5rkcEABq13_8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77589768",
      "metadata": {
        "id": "77589768"
      },
      "outputs": [],
      "source": [
        "#Import GeoPandas and other required modules for the notebook\n",
        "import geopandas as gpd\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f4a3ef56",
      "metadata": {
        "id": "f4a3ef56"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "References:    \n",
        "1. Geopandas official website: Introduction to GeoPandas\n",
        "https://geopandas.org/en/stable/getting_started/introduction.html   \n",
        "2. Automating GIS process\n",
        "https://autogis-site.readthedocs.io/en/latest/notebooks/L2/01-geopandas-basics.html    \n",
        "3. Use Data for Earth and Environmental Science in Open Source Python\n",
        "https://www.earthdatascience.org/courses/use-data-open-source-python/\n",
        "4. The Shapely User Manual\n",
        "https://shapely.readthedocs.io/en/stable/manual.html\n",
        "5. Geospatial Analysis with Python and R\n",
        "https://kodu.ut.ee/~kmoch/geopython2020/index.html\n",
        "6. Introduction to Geospatial Data in Python\n",
        "https://www.datacamp.com/tutorial/geospatial-data-python"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Learning goals**\n",
        "\n",
        "1. Understand the data structure of GeoPandas\n",
        "2. Read and write geospatial data with GeoPandas\n",
        "3. Access geospatial data attributes and methods using GeoPandas\n",
        "4. Perform spatial operations with GeoPandas"
      ],
      "metadata": {
        "id": "_s3ytYuzvg4N"
      },
      "id": "_s3ytYuzvg4N"
    },
    {
      "cell_type": "markdown",
      "id": "ecf116e7",
      "metadata": {
        "id": "ecf116e7"
      },
      "source": [
        "**GeoPandas** extends the data science library pandas for geospatial data by combining:\n",
        "* **Pandas**: data analysis\n",
        "* **Shapely**: handle geometries (Deterministic spatial analysis - set theoretic manipulations of planar features)\n",
        "* **Fiona**: read and write files\n",
        "* **pyproj**: manage coordinate reference systems\n",
        "* **matplotlib**: plotting.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ef9eb787",
      "metadata": {
        "id": "ef9eb787"
      },
      "source": [
        "## Geopandas Data Structure\n",
        "* The main difference between GeoDataFrame and DataFrame is that GeoDataFrame contains at least one column **'geometry'**, which contains shapely objects, e.g. points, lines, polygons, multipolygons, etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a6229cfa",
      "metadata": {
        "id": "a6229cfa"
      },
      "outputs": [],
      "source": [
        "### Create a GeoPandas GeoDataFrame from a Pandas DataFrame with coordiantes\n",
        "### Tutorial: https://geopandas.org/en/stable/gallery/create_geopandas_from_pandas.html\n",
        "\n",
        "# Create Pandas DataFrame from csv used in the Pandas exercise\n",
        "path = r'/content/data/usgs_earthquakes_2014.csv'\n",
        "df_earthquakes = pd.read_csv(path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a5158be7",
      "metadata": {
        "id": "a5158be7"
      },
      "outputs": [],
      "source": [
        "#Have a look at the Pandas DataFrame\n",
        "df_earthquakes.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "953af241",
      "metadata": {
        "id": "953af241"
      },
      "outputs": [],
      "source": [
        "# Create GeoPandas GeoDataFrame from the Pandas DataFrame\n",
        "gdf_earthquakes = gpd.GeoDataFrame(df_earthquakes,\n",
        "                                   geometry=gpd.points_from_xy(df_earthquakes.longitude,\n",
        "                                                               df_earthquakes.latitude))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "293ce6f8",
      "metadata": {
        "id": "293ce6f8"
      },
      "outputs": [],
      "source": [
        "# Have a look at the GeoPandas GeoDataFrame and notice the 'geometry' column\n",
        "gdf_earthquakes.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The core data structure is **geopandas.GeoDataFrame**, a subclass of pandas.DataFrame."
      ],
      "metadata": {
        "id": "SlUqDTTbrTrH"
      },
      "id": "SlUqDTTbrTrH"
    },
    {
      "cell_type": "code",
      "source": [
        "# See the type of the geodataframe\n",
        "type(gdf_earthquakes)"
      ],
      "metadata": {
        "id": "ZaMk0-LyrWia"
      },
      "id": "ZaMk0-LyrWia",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **geopandas.GeoSeries** is a subclass of pandas.Series. GeoSeries can contain any geometry type and has a `GeoSeries.crs` attribute (Coordinate Reference System) for projection."
      ],
      "metadata": {
        "id": "iS_EUsz6rjnQ"
      },
      "id": "iS_EUsz6rjnQ"
    },
    {
      "cell_type": "code",
      "source": [
        "# See the type of a column in the geodataframe\n",
        "type(gdf_earthquakes['time'])"
      ],
      "metadata": {
        "id": "v_i0iQQ7riMw"
      },
      "id": "v_i0iQQ7riMw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **GeoDataFrame combines** pandas.Series with traditional data (numerical, boolean, text, etc.) and geopandas.GeoSeries, thus can work with geospatial data."
      ],
      "metadata": {
        "id": "totlRGQHqiV1"
      },
      "id": "totlRGQHqiV1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5153c48",
      "metadata": {
        "id": "e5153c48"
      },
      "outputs": [],
      "source": [
        "# Take a quick look at the geospatial data contained in the GeoDataFrame on a map\n",
        "ax = gdf_earthquakes.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6a9e94f8",
      "metadata": {
        "id": "6a9e94f8"
      },
      "source": [
        "## Read and write files"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reading files\n",
        "* Assuming you have a file containing both data and geometry (e.g. GeoPackage, GeoJSON, Shapefile), you can read it using `geopandas.read_file()`, which automatically detects the filetype and creates a GeoDataFrame.\n",
        "\n"
      ],
      "metadata": {
        "id": "F_7OvY6_Ph6p"
      },
      "id": "F_7OvY6_Ph6p"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5afc2588",
      "metadata": {
        "id": "5afc2588"
      },
      "outputs": [],
      "source": [
        "# Set filepath\n",
        "path = r\"/content/data/damselfish-data/DAMSELFISH_distributions.shp\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8b3d29a5",
      "metadata": {
        "id": "8b3d29a5"
      },
      "outputs": [],
      "source": [
        "# Read data into a GeoDataFrame\n",
        "gdf_DAMSELFISH = gpd.read_file(path)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GeoPandas for handing vector format spatial data\n",
        "\n",
        "* Three fundamental types of **geometric objects** (the spatial data model implemented by shapely & defined by *interior, boundary, and exterior*):\n",
        "    * **Point**: A single point defined by a pair of x, y coordinates, e.g. locations of trees\n",
        "        * *Point class*. *Interior* - exactly one point. *Boundary* - exactly no points. *Exterior* - all other points\n",
        "    * **Line**: At least two connected points, e.g. roads, streams\n",
        "        * *LineString and LinearRing classes*. *Interior* - the infinitely many points along its length. *Boundary* - Two end points. *Exterior* - all other points.\n",
        "    * **Polygon**: At least three points connected and closed by lines, e.g. boundaries of lakes, countries.\n",
        "        * *Polygon class*. *Interior* - the infinitely many points within. *Boundary* - one or more lines. *Exterior* - all other points.  "
      ],
      "metadata": {
        "id": "gwpCspSesUj2"
      },
      "id": "gwpCspSesUj2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c36d351e",
      "metadata": {
        "id": "c36d351e"
      },
      "outputs": [],
      "source": [
        "# Check type of the data\n",
        "type(gdf_DAMSELFISH)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b9d825ae",
      "metadata": {
        "id": "b9d825ae"
      },
      "outputs": [],
      "source": [
        "# Take a quick look at the data\n",
        "gdf_DAMSELFISH.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e6f42f69",
      "metadata": {
        "id": "e6f42f69"
      },
      "outputs": [],
      "source": [
        "# Look at the geometries in the GeoDataFrame\n",
        "gdf_DAMSELFISH['geometry'].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See the type of geometric objects in the data\n",
        "type(gdf_DAMSELFISH.iloc[0]['geometry'])"
      ],
      "metadata": {
        "id": "EE9o2RIVsjhu"
      },
      "id": "EE9o2RIVsjhu",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# See the geometric objects in the data\n",
        "gdf_DAMSELFISH.iloc[0]['geometry']"
      ],
      "metadata": {
        "id": "ilV2ljdnsyGU"
      },
      "id": "ilV2ljdnsyGU",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c6385cc",
      "metadata": {
        "id": "2c6385cc"
      },
      "outputs": [],
      "source": [
        "# Take a quick look at the data on a map\n",
        "ax = gdf_DAMSELFISH.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Writing files\n",
        "* To write a GeoDataFrame back to file use GeoDataFrame.to_file(). The default file format is Shapefile, but you can specify your own with the driver keyword."
      ],
      "metadata": {
        "id": "H1TC8Y3usa_H"
      },
      "id": "H1TC8Y3usa_H"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "901280c2",
      "metadata": {
        "id": "901280c2"
      },
      "outputs": [],
      "source": [
        "# Create a output path for the data\n",
        "out_file_path = r\"/content/data/damselfish-data/DAMSELFISH_distributions_SELECTION.shp\"\n",
        "\n",
        "# Select first 50 rows, this a the numpy/pandas syntax to ``slice``\n",
        "# parts out a dataframe or array, from position 0 until (excluding) 50\n",
        "selection = gdf_DAMSELFISH[0:50]\n",
        "\n",
        "# Write those rows into a new Shapefile (the default output file format is Shapefile)\n",
        "selection.to_file(out_file_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b3440cb5",
      "metadata": {
        "id": "b3440cb5"
      },
      "source": [
        "## Attributes and Methods\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Area and distance\n",
        "\n",
        "* To measure the area of each polygon (or MultiPolygon in this specific case), access the [`GeoDataFrame.area`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.area.html) attribute, which returns a pandas.Series. Note that `GeoDataFrame.area` is just `GeoSeries.area` applied to the active geometry column.\n",
        "* The Euclidian distance between points can be accessed using `GeoDataFrame.distance`, which calls [`GeoDataFrame.distance`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.distance.html) to measure the distance from the point of interest\n"
      ],
      "metadata": {
        "id": "JvxLOhLWPnTQ"
      },
      "id": "JvxLOhLWPnTQ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e1a7ee78",
      "metadata": {
        "id": "e1a7ee78"
      },
      "outputs": [],
      "source": [
        "# Calculate and store the areas individual polygons\n",
        "gdf_DAMSELFISH['area'] = gdf_DAMSELFISH.area\n",
        "gdf_DAMSELFISH['area'].head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Boundary and centroid\n",
        "\n",
        "* To get the boundary of each polygon (LineString), access the [`GeoDataFrame.boundary`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.boundary.html)\n",
        "* The Centroid of a given geometry (line, polygon, etc.) can be accessed via the [`.centroid`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.centroid.html) attribute\n"
      ],
      "metadata": {
        "id": "8dunLtmptbp0"
      },
      "id": "8dunLtmptbp0"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "92d50011",
      "metadata": {
        "id": "92d50011"
      },
      "outputs": [],
      "source": [
        "# Get the boundary of each polygon\n",
        "gdf_DAMSELFISH['boundary'] = gdf_DAMSELFISH.boundary\n",
        "gdf_DAMSELFISH['boundary'].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8d6526e8",
      "metadata": {
        "id": "8d6526e8"
      },
      "outputs": [],
      "source": [
        "# Get the centroid of each polygon\n",
        "gdf_DAMSELFISH['centroid'] = gdf_DAMSELFISH.centroid\n",
        "gdf_DAMSELFISH['centroid'].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "450b9bfb",
      "metadata": {
        "id": "450b9bfb"
      },
      "outputs": [],
      "source": [
        "# Measure distance between each centroid and the first centroid\n",
        "first_point = gdf_DAMSELFISH['centroid'].iloc[0]\n",
        "gdf_DAMSELFISH['distance'] = gdf_DAMSELFISH['centroid'].distance(first_point)\n",
        "gdf_DAMSELFISH['distance'].head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Projection\n",
        "\n",
        "* [Coordinate reference systems (CRS)](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.crs.html) are important because the geometric shapes in a GeoDataFrame are simply a collection of coordinates in an arbitrary space. A CRS tells Python how those coordinates related to places on the Earth. A map projection (or a projected coordinate system) is a systematic transformation of the latitudes and longitudes into a plain surface where units are quite commonly represented as meters (instead of decimal degrees).\n",
        "\n",
        "* As map projections of gis-layers are fairly often defined differently (i.e. they do not match), it is a common procedure to redefine the map projections to be identical in both layers. It is important that the layers have the same projection as it makes it possible to analyze the spatial relationships between layers, such as in conducting the Point in Polygon spatial query.\n",
        "* The EPSG number (“European Petroleum Survey Group”) is a code that tells about the coordinate system of the dataset.\n"
      ],
      "metadata": {
        "id": "3JdAFREztjTF"
      },
      "id": "3JdAFREztjTF"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "46181537",
      "metadata": {
        "id": "46181537"
      },
      "outputs": [],
      "source": [
        "# Reading the data from the Europe_borders.shp file\n",
        "path =  r\"/content/data/Europe_borders/Europe_borders.shp\"\n",
        "gdf_Europe_borders = gpd.read_file(path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "022d2007",
      "metadata": {
        "id": "022d2007"
      },
      "outputs": [],
      "source": [
        "# See the current coordinate reference system from .crs attribute\n",
        "gdf_Europe_borders.crs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c0d84473",
      "metadata": {
        "id": "c0d84473"
      },
      "outputs": [],
      "source": [
        "# Convert (aka reproject) into Lambert Azimuthal Equal Area projection (EPSG: 3035)\n",
        "gdf_Europe_borders_proj = gdf_Europe_borders.to_crs(epsg=3035)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dfbb8c1f",
      "metadata": {
        "id": "dfbb8c1f"
      },
      "outputs": [],
      "source": [
        "# See the current coordinate reference system from .crs attribute\n",
        "gdf_Europe_borders_proj.crs"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot\n",
        "\n",
        "* GeoPandas can plot maps, so we can check how the geometries appear in space. To plot the active geometry, call [GeoDataFrame.plot()](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.plot.html)."
      ],
      "metadata": {
        "id": "meXlrhFjttoN"
      },
      "id": "meXlrhFjttoN"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d11d7a7e",
      "metadata": {
        "id": "d11d7a7e"
      },
      "outputs": [],
      "source": [
        "# Plot GeoDataFrames\n",
        "# Understand the difference between the projections\n",
        "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n",
        "# Plot the WGS84\n",
        "gdf_Europe_borders.plot(facecolor='gray', ax=axs[0]);\n",
        "\n",
        "# Add title\n",
        "axs[0].set_title(\"WGS84 CRS\", y=1.05);\n",
        "\n",
        "# Plot the one with ETRS-LAEA projection\n",
        "gdf_Europe_borders_proj.plot(facecolor='blue', ax=axs[1]);\n",
        "\n",
        "# Add title\n",
        "axs[1].set_title(\"ETRS Lambert Azimuthal Equal Area projection\", y=1.05);\n",
        "\n",
        "# Remove empty white space around the plot\n",
        "fig.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59378219",
      "metadata": {
        "id": "59378219"
      },
      "source": [
        "## Spatial Relationships and Operations"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Geometric Manipulations\n",
        "GeoPandas makes available all the tools for geometric manipulations in the shapely library: buffer, boundary, convex_hull, envelope, unary_union\n"
      ],
      "metadata": {
        "id": "xhQpmrBxPuNZ"
      },
      "id": "xhQpmrBxPuNZ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "55e353ac",
      "metadata": {
        "id": "55e353ac"
      },
      "outputs": [],
      "source": [
        "# Tutorial: https://geopandas.org/en/stable/docs/user_guide/geometric_manipulations.html\n",
        "\n",
        "# Generate a GeoSeries containing 2000 random points\n",
        "import numpy as np\n",
        "from shapely.geometry import Point\n",
        "xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000\n",
        "xc = (xmax - xmin) * np.random.random(2000) + xmin\n",
        "yc = (ymax - ymin) * np.random.random(2000) + ymin\n",
        "pts = gpd.GeoSeries([Point(x, y) for x, y in zip(xc, yc)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7ba82ce3",
      "metadata": {
        "id": "7ba82ce3"
      },
      "outputs": [],
      "source": [
        "# Draw a circle with fixed radius around each point\n",
        "circles = pts.buffer(2000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "82ef3ea9",
      "metadata": {
        "id": "82ef3ea9"
      },
      "outputs": [],
      "source": [
        "# Collapse these circles into a single MultiPolygon geometry with unary_union\n",
        "# https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.unary_union.html\n",
        "mp = circles.unary_union"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a70507ea",
      "metadata": {
        "id": "a70507ea"
      },
      "outputs": [],
      "source": [
        "# See unified multipolygon\n",
        "mp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c0dcc61f",
      "metadata": {
        "id": "c0dcc61f"
      },
      "outputs": [],
      "source": [
        "# Check the type of the unified multipolygon\n",
        "type(mp)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set-Operations with Overlay\n",
        "\n",
        "When working with multiple spatial datasets – especially multiple polygon or line datasets – users often wish to create new shapes based on places where those datasets overlap (or don’t overlap). These manipulations are often referred using the language of sets – intersections, unions, and differences. These types of operations are made available in the geopandas library through the [overlay()](https://geopandas.org/en/stable/docs/user_guide/set_operations.html?highlight=overlay) method."
      ],
      "metadata": {
        "id": "7OkpKQ_nuBNi"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "708c8b59",
      "metadata": {
        "id": "708c8b59"
      },
      "outputs": [],
      "source": [
        "# Tutorial: https://geopandas.org/en/stable/docs/user_guide/set_operations.html\n",
        "# Create some example data\n",
        "from shapely.geometry import Polygon\n",
        "\n",
        "polys1 = gpd.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]),\n",
        "                              Polygon([(2,2), (4,2), (4,4), (2,4)])])\n",
        "\n",
        "\n",
        "polys2 = gpd.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]),\n",
        "                              Polygon([(3,3), (5,3), (5,5), (3,5)])])\n",
        "\n",
        "\n",
        "df1 = gpd.GeoDataFrame({'geometry': polys1, 'df1':[1,2]})\n",
        "\n",
        "df2 = gpd.GeoDataFrame({'geometry': polys2, 'df2':[1,2]})\n",
        "\n",
        "ax = df1.plot(color='red');\n",
        "\n",
        "df2.plot(ax=ax, color='green', alpha=0.5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9503ce15",
      "metadata": {
        "id": "9503ce15"
      },
      "outputs": [],
      "source": [
        "# The overlay() method with how='intersection', it returns only those geometries that are contained by both GeoDataFrames:\n",
        "res_intersection = df1.overlay(df2, how='intersection')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c57bc475",
      "metadata": {
        "id": "c57bc475"
      },
      "outputs": [],
      "source": [
        "# Plot and check the result of the overlay\n",
        "ax = res_intersection.plot(cmap='tab10')\n",
        "\n",
        "df1.plot(ax=ax, facecolor='none', edgecolor='k');\n",
        "\n",
        "df2.plot(ax=ax, facecolor='none', edgecolor='k')"
      ]
    }
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