{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "faa65b59",
   "metadata": {},
   "source": [
    "# Introduction to spatial data analysis with geopandas\n",
    "\n",
    "Now as we have learned how to create and represent geographic data in Python using `shapely` objects, we will continue and use [geopandas](https://geopandas.org/) [^geopandas] as our main tool for spatial data analysis. Geopandas extends the capacities of pandas (which we covered in the Part I of the book) with geospatial operations. The main data structures in geopandas are `GeoSeries` and `GeoDataFrame` which extend the capabilities of `Series` and `DataFrames` from pandas. This means that we can use many familiar methods from pandas also when working with geopandas and spatial features. A `GeoDataFrame` is basically a `pandas.DataFrame` that contains one column for geometries. The geometry column is a `GeoSeries` which contains the geometries  as `shapely` objects (points, lines, polygons, multipolygons etc.). \n",
    "\n",
    "## Getting started with geopandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b88e99",
   "metadata": {},
   "source": [
    "![_**Figure 6.10**. Geometry column in a GeoDataFrame._](../img/geodataframe.png)\n",
    "\n",
    "_**Figure 6.10**. Geometry column in a GeoDataFrame._\n",
    "\n",
    "Similar to importing import pandas as `pd`, we will import geopandas as `gpd`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1f9eb39-cf4b-4eae-85b7-200e6b80dcb9",
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"USE_PYGEOS\"] = \"0\"\n",
    "import geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dc0c3d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b4ee3e0",
   "metadata": {},
   "source": [
    "## Reading a file\n",
    "\n",
    "In `geopandas`, we can use a generic function `.from_file()` for reading in various data formats. The data-folder contains some census data from Austin, Texas downloaded from the [U.S Census bureau](https://www.census.gov/programs-surveys/acs/data.html) [^us_census]. Let's first define the path to the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "606cfa1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\\Austin\\austin_pop_2019.gpkg\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "data_folder = Path(\"data/Austin\")\n",
    "fp = data_folder / \"austin_pop_2019.gpkg\"\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97366979-b3f7-4345-a589-a00a0e895730",
   "metadata": {},
   "source": [
    "Now we can pass this filepath to `geopandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db42e539",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = gpd.read_file(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c82314a",
   "metadata": {},
   "source": [
    "Let's check the data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dbb84a0d",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2028763e",
   "metadata": {},
   "source": [
    "Here we see that our `data` -variable is a `GeoDataFrame` which extends the functionalities of\n",
    "`DataFrame` to handle spatial data. We can apply many familiar `pandas` methods to explore the contents of our `GeoDataFrame`. Let's have a closer look at the first rows of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64f2c58f",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop2019</th>\n",
       "      <th>tract</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6070.0</td>\n",
       "      <td>002422</td>\n",
       "      <td>POLYGON ((615643.487 3338728.496, 615645.477 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2203.0</td>\n",
       "      <td>001751</td>\n",
       "      <td>POLYGON ((618576.586 3359381.053, 618614.330 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7419.0</td>\n",
       "      <td>002411</td>\n",
       "      <td>POLYGON ((619200.163 3341784.654, 619270.849 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4229.0</td>\n",
       "      <td>000401</td>\n",
       "      <td>POLYGON ((621623.757 3350508.165, 621656.294 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4589.0</td>\n",
       "      <td>002313</td>\n",
       "      <td>POLYGON ((621630.247 3345130.744, 621717.926 3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop2019   tract                                           geometry\n",
       "0   6070.0  002422  POLYGON ((615643.487 3338728.496, 615645.477 3...\n",
       "1   2203.0  001751  POLYGON ((618576.586 3359381.053, 618614.330 3...\n",
       "2   7419.0  002411  POLYGON ((619200.163 3341784.654, 619270.849 3...\n",
       "3   4229.0  000401  POLYGON ((621623.757 3350508.165, 621656.294 3...\n",
       "4   4589.0  002313  POLYGON ((621630.247 3345130.744, 621717.926 3..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f42d8a0a",
   "metadata": {},
   "source": [
    "#### Question 6.2\n",
    "\n",
    "Figure out the following information from our input data using your `pandas` skills:\n",
    "    \n",
    "- Number of rows?\n",
    "- Number of census tracts (based on column `tract`)?\n",
    "- Total population (based on column `pop2019`)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3101fec",
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# You can use this cell to enter your solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb8e9b76",
   "metadata": {
    "tags": [
     "remove_book_cell",
     "hide_cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows 130\n",
      "Number of census tract 130\n",
      "Total population 611935.0\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "print(\"Number of rows\", len(data))\n",
    "print(\"Number of census tract\", data[\"tract\"].nunique())\n",
    "print(\"Total population\", data[\"pop2019\"].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe464d8",
   "metadata": {},
   "source": [
    "It is always a good idea to explore your data also on a map. Creating a simple map from a `GeoDataFrame` is really easy. You can use the ``.plot()`` -function from geopandas that creates a map based on the geometries of the data. `geopandas` actually uses `matplotlib` for plotting which we introduced in Part 1 of this book. Let's try it out, and do a quick visualization of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cfb617c9",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40dd1f34",
   "metadata": {},
   "source": [
    "_**Figure 6.11**. Census tract polygons for Austin, Texas, USA._\n",
    "\n",
    "Voilá! Now we have a quick overview of the geometries in this data. The x and y axes in the plot are based on the coordiante values of the geometries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861f9bff",
   "metadata": {},
   "source": [
    "## Geometries in geopandas\n",
    "\n",
    "A `GeoDataFrame` has one column for storing geometries. By default, `geopandas` looks for the geometries from a column called `geometry`. It is also possible to define other columns as the geometry column. Th geometry column is a `GeoSeries` that contains shapely's geometric objects.  Let's have a look at the geometry column of our sample data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eeaf22f4",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"geometry\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7b7cce",
   "metadata": {},
   "source": [
    "As we can see here,  the `geometry` column contains polygon geometries. Since these polygons are  `shapely` objects, it is possible to use `shapely` methods for handling them also in `geopandas`. Many of the methods can be applied all at once to the whole `GeoDataFrame`. \n",
    "\n",
    "Let's proceed to calculating area of the census tract polygons. At this point, it is good to note that the census data are in a metric coordinate reference system, so the area values will be given in square meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "582806b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      4.029772e+06\n",
       "1      1.532030e+06\n",
       "2      3.960344e+06\n",
       "3      2.181762e+06\n",
       "4      2.431208e+06\n",
       "           ...     \n",
       "125    2.321182e+06\n",
       "126    4.388407e+06\n",
       "127    1.702764e+06\n",
       "128    3.540893e+06\n",
       "129    2.054702e+06\n",
       "Length: 130, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"geometry\"].area"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05b2057b-67c8-4443-baa2-dba527805635",
   "metadata": {},
   "source": [
    "The same result can be achieved by using the syntax `data.area`. Let's convert the area values from square meters to square kilometers and store them into a new column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "997fc7d0-fda3-4dec-884f-6e631a78d5c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get area and convert from m2 to km2\n",
    "data[\"area_km2\"] = data.area / 1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0490560a",
   "metadata": {},
   "source": [
    "Check the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c7ced87-0bf1-4713-bec3-53c8c8cd6065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.029772\n",
       "1    1.532030\n",
       "2    3.960344\n",
       "3    2.181762\n",
       "4    2.431208\n",
       "Name: area_km2, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"area_km2\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f7fd975-d531-4edb-8fb1-b24ff09792a0",
   "metadata": {},
   "source": [
    "#### Question 6.3\n",
    "\n",
    "Using your `pandas` skills, create a new column `pop_density_km2` and populate it with population density values (population / km2) calculated based on columns `pop2019` and `area_km2`. Print out answers to the following questions:\n",
    "\n",
    "- What was the average population density in 2019?\n",
    "- What was the maximum population density per census tract?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "099893e9-6fbc-4bf7-98ec-c1f3fe986f57",
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Use this cell to enter your solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea520200-adf0-403d-a6cf-eff52c8a7523",
   "metadata": {
    "tags": [
     "remove_book_cell",
     "hide_cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average: 2397 pop/km2\n",
      "Maximum: 11324 pop/km2\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# Calculate population density\n",
    "data[\"pop_density_km2\"] = data[\"pop2019\"] / data[\"area_km2\"]\n",
    "\n",
    "# Print out average and maximum values\n",
    "print(\"Average:\", round(data[\"pop_density_km2\"].mean()), \"pop/km2\")\n",
    "\n",
    "print(\"Maximum:\", round(data[\"pop_density_km2\"].max()), \"pop/km2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c064a1",
   "metadata": {},
   "source": [
    "## Writing data into a file\n",
    "\n",
    "It is possible to export spatial data into various data formats using the `.to_file()` method in `geopandas`. Let's practice writing data into the geopackage file format. Before proceeding, let's check how the data looks like at this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5803bf11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop2019</th>\n",
       "      <th>tract</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>pop_density_km2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6070.0</td>\n",
       "      <td>002422</td>\n",
       "      <td>POLYGON ((615643.487 3338728.496, 615645.477 3...</td>\n",
       "      <td>4.029772</td>\n",
       "      <td>1506.288769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2203.0</td>\n",
       "      <td>001751</td>\n",
       "      <td>POLYGON ((618576.586 3359381.053, 618614.330 3...</td>\n",
       "      <td>1.532030</td>\n",
       "      <td>1437.961408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7419.0</td>\n",
       "      <td>002411</td>\n",
       "      <td>POLYGON ((619200.163 3341784.654, 619270.849 3...</td>\n",
       "      <td>3.960344</td>\n",
       "      <td>1873.322183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4229.0</td>\n",
       "      <td>000401</td>\n",
       "      <td>POLYGON ((621623.757 3350508.165, 621656.294 3...</td>\n",
       "      <td>2.181762</td>\n",
       "      <td>1938.341868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4589.0</td>\n",
       "      <td>002313</td>\n",
       "      <td>POLYGON ((621630.247 3345130.744, 621717.926 3...</td>\n",
       "      <td>2.431208</td>\n",
       "      <td>1887.538655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop2019   tract                                           geometry  \\\n",
       "0   6070.0  002422  POLYGON ((615643.487 3338728.496, 615645.477 3...   \n",
       "1   2203.0  001751  POLYGON ((618576.586 3359381.053, 618614.330 3...   \n",
       "2   7419.0  002411  POLYGON ((619200.163 3341784.654, 619270.849 3...   \n",
       "3   4229.0  000401  POLYGON ((621623.757 3350508.165, 621656.294 3...   \n",
       "4   4589.0  002313  POLYGON ((621630.247 3345130.744, 621717.926 3...   \n",
       "\n",
       "   area_km2  pop_density_km2  \n",
       "0  4.029772      1506.288769  \n",
       "1  1.532030      1437.961408  \n",
       "2  3.960344      1873.322183  \n",
       "3  2.181762      1938.341868  \n",
       "4  2.431208      1887.538655  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0d867d-5031-4d0a-b230-ab39e553505c",
   "metadata": {},
   "source": [
    "Write the data into a file using the `.to_file()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "49d317f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a output path for the data\n",
    "output_fp = data_folder / \"austin_pop_density_2019.gpkg\"\n",
    "\n",
    "# Write the file\n",
    "data.to_file(output_fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32bd2bc1",
   "metadata": {},
   "source": [
    "#### Question 6.4\n",
    "\n",
    "Read the output file using `geopandas` and check that the data looks ok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad7325ea",
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Use this cell to enter your solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c947fbba",
   "metadata": {
    "tags": [
     "remove_book_cell",
     "hide_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop2019</th>\n",
       "      <th>tract</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>pop_density_km2</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6070.0</td>\n",
       "      <td>002422</td>\n",
       "      <td>4.029772</td>\n",
       "      <td>1506.288769</td>\n",
       "      <td>POLYGON ((615643.487 3338728.496, 615645.477 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2203.0</td>\n",
       "      <td>001751</td>\n",
       "      <td>1.532030</td>\n",
       "      <td>1437.961408</td>\n",
       "      <td>POLYGON ((618576.586 3359381.053, 618614.330 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7419.0</td>\n",
       "      <td>002411</td>\n",
       "      <td>3.960344</td>\n",
       "      <td>1873.322183</td>\n",
       "      <td>POLYGON ((619200.163 3341784.654, 619270.849 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4229.0</td>\n",
       "      <td>000401</td>\n",
       "      <td>2.181762</td>\n",
       "      <td>1938.341868</td>\n",
       "      <td>POLYGON ((621623.757 3350508.165, 621656.294 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4589.0</td>\n",
       "      <td>002313</td>\n",
       "      <td>2.431208</td>\n",
       "      <td>1887.538655</td>\n",
       "      <td>POLYGON ((621630.247 3345130.744, 621717.926 3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "temp = gpd.read_file(output_fp)\n",
    "\n",
    "# Check first rows\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "64223fd7",
   "metadata": {
    "tags": [
     "remove_book_cell",
     "hide_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# You can also plot the data for a visual check\n",
    "temp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e930a43f",
   "metadata": {},
   "source": [
    "## Footnotes\n",
    "\n",
    "[^geopandas]: <https://geopandas.org/>\n",
    "[^NLS_topodata]: <https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/topographic-database>\n",
    "[^NLS_lisence]: <https://www.maanmittauslaitos.fi/en/opendata-licence-cc40>\n",
    "[^OGC_sfa]: <https://www.ogc.org/standards/sfa>\n",
    "[^paituli]: <https://avaa.tdata.fi/web/paituli/latauspalvelu>\n",
    "[^topodata_fair]: <https://etsin.fairdata.fi/dataset/5023ecc7-914a-4494-9e32-d0a39d3b56ae>\n",
    "[^us_census]: <https://www.census.gov/programs-surveys/acs/data.html>"
   ]
  }
 ],
 "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
}