{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ef7115f",
   "metadata": {},
   "source": [
    "# Preparing GeoDataFrames from geographic data\n",
    "\n",
    "Reading data into Python is usually the first step of an analysis workflow. There are various different GIS data formats available such as [Shapefile](https://en.wikipedia.org/wiki/Shapefile) [^shp], [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) [^GeoJson], [KML](https://en.wikipedia.org/wiki/Keyhole_Markup_Language) [^KML], and [GeoPackage](https://en.wikipedia.org/wiki/GeoPackage) [^GPKG]. Geopandas is capable of reading data from all of these formats (plus many more). \n",
    "\n",
    "This tutorial will show some typical examples how to read (and write) data from different sources. The main point in this section is to demonstrate the basic syntax for reading and writing data using short code snippets. You can find the example data sets in the data-folder. However, most of the example databases do not exists, but you can use and modify the example syntax according to your own setup."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9481338d",
   "metadata": {},
   "source": [
    "## Reading vector data\n",
    "\n",
    "In `geopandas`, we can use a generic function `.from_file()` for reading in various vector data formats. When reading files with `geopandas`, the data are passed on to the `fiona` library under the hood for reading the data. This means that you can read and write all data formats supported by `fiona` with `geopandas`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "386d2ddd-6f51-4ac6-bd03-4916f0bf51f7",
   "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": "b3d20a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import fiona"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6d38a6",
   "metadata": {},
   "source": [
    "Let's check which drivers are supported by `fiona`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "02103a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DXF': 'rw',\n",
       " 'CSV': 'raw',\n",
       " 'OpenFileGDB': 'raw',\n",
       " 'ESRIJSON': 'r',\n",
       " 'ESRI Shapefile': 'raw',\n",
       " 'FlatGeobuf': 'raw',\n",
       " 'GeoJSON': 'raw',\n",
       " 'GeoJSONSeq': 'raw',\n",
       " 'GPKG': 'raw',\n",
       " 'GML': 'rw',\n",
       " 'OGR_GMT': 'rw',\n",
       " 'GPX': 'rw',\n",
       " 'Idrisi': 'r',\n",
       " 'MapInfo File': 'raw',\n",
       " 'DGN': 'raw',\n",
       " 'PCIDSK': 'raw',\n",
       " 'OGR_PDS': 'r',\n",
       " 'S57': 'r',\n",
       " 'SQLite': 'raw',\n",
       " 'TopoJSON': 'r'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiona.supported_drivers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a237aa1-cca8-4e0c-ac6e-86c28c45c846",
   "metadata": {},
   "source": [
    "In the list of supported drivers, `r` is for file formats `fiona` can read, and `w` is for file formats it can write. Letter `a` marks formats for which `fiona` can append new data to existing files."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223208cc",
   "metadata": {},
   "source": [
    "Let's read in some sample data to see the basic syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ad209e1",
   "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",
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       "    }\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>fid</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>1.0</td>\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>2.0</td>\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>3.0</td>\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>4.0</td>\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>5.0</td>\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": [
       "   fid  pop2019   tract                                           geometry\n",
       "0  1.0   6070.0  002422  POLYGON ((615643.487 3338728.496, 615645.477 3...\n",
       "1  2.0   2203.0  001751  POLYGON ((618576.586 3359381.053, 618614.330 3...\n",
       "2  3.0   7419.0  002411  POLYGON ((619200.163 3341784.654, 619270.849 3...\n",
       "3  4.0   4229.0  000401  POLYGON ((621623.757 3350508.165, 621656.294 3...\n",
       "4  5.0   4589.0  002313  POLYGON ((621630.247 3345130.744, 621717.926 3..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Esri Shapefile\n",
    "fp = \"data/Austin/austin_pop_2019.shp\"\n",
    "data = gpd.read_file(fp)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1bb73dd-e272-4c59-b589-4cf7242f7b3e",
   "metadata": {},
   "source": [
    "The same syntax works for other commong vector data formats. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af1ac72f-c900-4fca-9cd8-7f78e813b700",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read file from Geopackage\n",
    "fp = \"data/Austin/austin_pop_2019.gpkg\"\n",
    "data = gpd.read_file(fp)\n",
    "\n",
    "# Read file from GeoJSON\n",
    "fp = \"data/Austin/austin_pop_2019.geojson\"\n",
    "data = gpd.read_file(fp)\n",
    "\n",
    "# Read file from MapInfo Tab\n",
    "fp = \"data/Austin/austin_pop_2019.tab\"\n",
    "data = gpd.read_file(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf98fe7-1ce6-4e45-86fe-a23dbed0cb1e",
   "metadata": {},
   "source": [
    "Some file formats such as GeoPackage and File Geodatabase files may contain multiple layers with different names wihich can be speficied using the `layer` -parameter. Our example geopackage file has only one layer with the same name as the file, so we don't actually need to specify it to read in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4daa219-0b52-4667-bfc7-33b6a34e4996",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read spesific layer from Geopackage\n",
    "fp = \"data/Austin/austin_pop_2019.gpkg\"\n",
    "data = gpd.read_file(fp, layer=\"austin_pop_2019\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b90aa4fb-5f0c-4bf8-a995-2d769d1510f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read file from File Geodatabase\n",
    "# fp = \"data/Finland/finland.gdb\"\n",
    "# data = gpd.read_file(fp, driver=\"OpenFileGDB\", layer=\"municipalities\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec9d506",
   "metadata": {},
   "source": [
    "(write intro about enabling additional drivers and reading in the KML file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf159391",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable KML driver\n",
    "gpd.io.file.fiona.drvsupport.supported_drivers[\"KML\"] = \"rw\"\n",
    "\n",
    "# Read file from KML\n",
    "fp = \"data/Austin/austin_pop_2019.kml\"\n",
    "# data = gpd.read_file(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcccac68-ba0e-4c2f-83fb-159f477146e8",
   "metadata": {},
   "source": [
    "## Writing vector data\n",
    "\n",
    "We can save spatial data to various vector data formats using the `.to_file()` function in `geopandas` which also relies on `fiona`. It is possible to specify the output file format using the `driver` parameter, however, for most file formats it is not needed as the tool is able to infer the driver from the file extension. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a03816fd-1c7c-44de-842a-4025527ecf01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to Shapefile (just make a copy)\n",
    "outfp = \"data/temp/austin_pop_2019.shp\"\n",
    "data.to_file(outfp)\n",
    "\n",
    "# Write to Geopackage (just make a copy)\n",
    "outfp = \"data/Temp/austin_pop_2019.gpkg\"\n",
    "data.to_file(outfp, driver=\"GPKG\")\n",
    "\n",
    "# Write to GeoJSON (just make a copy)\n",
    "outfp = \"data/Temp/austin_pop_2019.geojson\"\n",
    "data.to_file(outfp, driver=\"GeoJSON\")\n",
    "\n",
    "# Write to MapInfo Tab (just make a copy)\n",
    "outfp = \"data/Temp/austin_pop_2019.tab\"\n",
    "data.to_file(outfp)\n",
    "\n",
    "# Write to same FileGDB (just add a new layer) - requires additional package installations(?)\n",
    "# outfp = \"data/finland.gdb\"\n",
    "# data.to_file(outfp, driver=\"FileGDB\", layer=\"municipalities_copy\")\n",
    "\n",
    "# Write to KML (just make a copy)\n",
    "outfp = \"data/Temp/austin_pop_2019.kml\"\n",
    "# data.to_file(outfp, driver=\"KML\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c230915",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0fb88cd4",
   "metadata": {},
   "source": [
    "## Creating a GeoDataFrame from scratch\n",
    "\n",
    "It is possible to create spatial data from scratch by using `shapely`'s geometric objects and `geopandas`. This is useful as it makes it easy to convert, for example, a text file that contains coordinates into spatial data layers. Let's first try creating a simple `GeoDataFrame` based on coordinate information that represents the outlines of the [Senate square in Helsinki, Finland](https://fi.wikipedia.org/wiki/Senaatintori). Here are the coordinates based on which we can create a `Polygon` object using `shapely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aec1fbab-b4e0-4641-816a-cdc37ab56432",
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import Polygon\n",
    "\n",
    "# Coordinates of the Helsinki Senate square in decimal degrees\n",
    "coordinates = [\n",
    "    (24.950899, 60.169158),\n",
    "    (24.953492, 60.169158),\n",
    "    (24.953510, 60.170104),\n",
    "    (24.950958, 60.169990),\n",
    "]\n",
    "\n",
    "# Create a Shapely polygon from the coordinate-tuple list\n",
    "poly = Polygon(coordinates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "727717a5-e176-4d55-bd30-a444bfc6758c",
   "metadata": {},
   "source": [
    "Now we can use this polygon and `geopandas` to create a `GeoDataFrame` from scratch. The data can be passed in as a list-like object. In our case we will only have one row and one column of data. We can pass the polygon inside a list, and name the column as `geometry` so that `geopandas` will use the contents of that column the geometry column. Additionally, we could define the coordinate reference system for the data, but we will skip this step for now. For details of the syntax, see documentation for the `DataFrame` constructor and `GeoDataFrame` constructor online."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d8178e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "newdata = gpd.GeoDataFrame(data=[poly], columns=[\"geometry\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a035fc6f-1149-436c-9f27-537b20ff95fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((24.95090 60.16916, 24.95349 60.16916...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  POLYGON ((24.95090 60.16916, 24.95349 60.16916..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98400110-6519-4422-83fa-39d54d6740aa",
   "metadata": {},
   "source": [
    "We can also add additional attribute information to a new column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bff22229",
   "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>geometry</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((24.95090 60.16916, 24.95349 60.16916...</td>\n",
       "      <td>Senate Square</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry           name\n",
       "0  POLYGON ((24.95090 60.16916, 24.95349 60.16916...  Senate Square"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column and insert data\n",
    "newdata.at[0, \"name\"] = \"Senate Square\"\n",
    "\n",
    "# Check the contents\n",
    "newdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237b04ca",
   "metadata": {},
   "source": [
    "There it is! Now we have two columns in our data; one representing the geometry and another with additional attribute information. From here, you could proceed into adding additional rows of data, or printing out the data to a file. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f988ec7-f19f-48e0-b184-bfed31ed19dc",
   "metadata": {},
   "source": [
    "## Creating a GeoDataFrame from a text file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62336285-f0ec-4779-b4bd-57bffc21bd0f",
   "metadata": {},
   "source": [
    "A common case is to have coordinates in a delimited textfile that needs to be converted into spatial data. We can make use of `pandas`, `geopandas` and `shapely` for doing this. \n",
    "\n",
    "The example data contains point coordinates of airports derived from [openflights.org](https://openflights.org/data.html) [^openflights]. Let's read in a couple of useful columns from the data for further processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3196735-f0f6-4f08-a1e1-9fd86406a13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "afde321f-1755-4287-a679-b35c2844987f",
   "metadata": {},
   "outputs": [],
   "source": [
    "airports = pd.read_csv(\n",
    "    \"data/Airports/airports.txt\",\n",
    "    usecols=[\"Airport ID\", \"Name\", \"City\", \"Country\", \"Latitude\", \"Longitude\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0a8f3c4-0a2d-45e4-8f87-dfa0b42d3477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Airport ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Goroka Airport</td>\n",
       "      <td>Goroka</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-6.081690</td>\n",
       "      <td>145.391998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Madang Airport</td>\n",
       "      <td>Madang</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-5.207080</td>\n",
       "      <td>145.789001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mount Hagen Kagamuga Airport</td>\n",
       "      <td>Mount Hagen</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-5.826790</td>\n",
       "      <td>144.296005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Nadzab Airport</td>\n",
       "      <td>Nadzab</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-6.569803</td>\n",
       "      <td>146.725977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Port Moresby Jacksons International Airport</td>\n",
       "      <td>Port Moresby</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-9.443380</td>\n",
       "      <td>147.220001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airport ID                                         Name          City  \\\n",
       "0           1                               Goroka Airport        Goroka   \n",
       "1           2                               Madang Airport        Madang   \n",
       "2           3                 Mount Hagen Kagamuga Airport   Mount Hagen   \n",
       "3           4                               Nadzab Airport        Nadzab   \n",
       "4           5  Port Moresby Jacksons International Airport  Port Moresby   \n",
       "\n",
       "            Country  Latitude   Longitude  \n",
       "0  Papua New Guinea -6.081690  145.391998  \n",
       "1  Papua New Guinea -5.207080  145.789001  \n",
       "2  Papua New Guinea -5.826790  144.296005  \n",
       "3  Papua New Guinea -6.569803  146.725977  \n",
       "4  Papua New Guinea -9.443380  147.220001  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airports.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ef1181b0-7b45-4ad2-9105-851571e827c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7698"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(airports)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78930639-62ca-4bd8-bad8-f31bae1cf6d7",
   "metadata": {},
   "source": [
    "There are over 7000 airports in the data and we can use the coordinate information available in the `Latitude` and `Longitude` columns for visualizing them on a map. The coordinates are stored as *{term}`Decimal degrees <Decimal degrees>`*, meaning that the appropriate coordinate reference system for these data is WGS 84 (EPSG:4326).  \n",
    "\n",
    "There is a handy tool in `geopandas` for generating an array of `Point`objects based on x and y coordinates called `.points_from_xy()`. The tool assumes that x coordinates represent longitude and that y coordinates represent latitude. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc3e9360-a8ff-4a5e-9cd5-5a6a27fd63c6",
   "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>Airport ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Goroka Airport</td>\n",
       "      <td>Goroka</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-6.081690</td>\n",
       "      <td>145.391998</td>\n",
       "      <td>POINT (145.39200 -6.08169)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Madang Airport</td>\n",
       "      <td>Madang</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-5.207080</td>\n",
       "      <td>145.789001</td>\n",
       "      <td>POINT (145.78900 -5.20708)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mount Hagen Kagamuga Airport</td>\n",
       "      <td>Mount Hagen</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-5.826790</td>\n",
       "      <td>144.296005</td>\n",
       "      <td>POINT (144.29601 -5.82679)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Nadzab Airport</td>\n",
       "      <td>Nadzab</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-6.569803</td>\n",
       "      <td>146.725977</td>\n",
       "      <td>POINT (146.72598 -6.56980)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Port Moresby Jacksons International Airport</td>\n",
       "      <td>Port Moresby</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>-9.443380</td>\n",
       "      <td>147.220001</td>\n",
       "      <td>POINT (147.22000 -9.44338)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airport ID                                         Name          City  \\\n",
       "0           1                               Goroka Airport        Goroka   \n",
       "1           2                               Madang Airport        Madang   \n",
       "2           3                 Mount Hagen Kagamuga Airport   Mount Hagen   \n",
       "3           4                               Nadzab Airport        Nadzab   \n",
       "4           5  Port Moresby Jacksons International Airport  Port Moresby   \n",
       "\n",
       "            Country  Latitude   Longitude                    geometry  \n",
       "0  Papua New Guinea -6.081690  145.391998  POINT (145.39200 -6.08169)  \n",
       "1  Papua New Guinea -5.207080  145.789001  POINT (145.78900 -5.20708)  \n",
       "2  Papua New Guinea -5.826790  144.296005  POINT (144.29601 -5.82679)  \n",
       "3  Papua New Guinea -6.569803  146.725977  POINT (146.72598 -6.56980)  \n",
       "4  Papua New Guinea -9.443380  147.220001  POINT (147.22000 -9.44338)  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airports[\"geometry\"] = gpd.points_from_xy(\n",
    "    x=airports[\"Longitude\"], y=airports[\"Latitude\"], crs=\"EPSG:4326\"\n",
    ")\n",
    "\n",
    "airports = gpd.GeoDataFrame(airports)\n",
    "airports.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecb3737b-57de-436f-acaa-a903fed516a2",
   "metadata": {},
   "source": [
    "Now we have the point geometries as `shapely`objects in the geometry-column ready to be plotted on a map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1de24cf8-d3a4-43dc-96f1-5576ae6780ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 19,
     "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": [
    "airports.plot(markersize=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afa6079e-df84-4a95-b7d0-456d8917bb8c",
   "metadata": {},
   "source": [
    "_**Figure 6.12**. A basic plot showing the airports from openflights.org._"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "300856c0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Footnotes\n",
    "\n",
    "[^GeoJson]: <https://en.wikipedia.org/wiki/GeoJSON>\n",
    "[^GPKG]: <https://en.wikipedia.org/wiki/GeoPackage>\n",
    "[^KML]: <https://en.wikipedia.org/wiki/Keyhole_Markup_Language>\n",
    "[^openflights]: <https://openflights.org/data.html>\n",
    "[^shp]: <https://en.wikipedia.org/wiki/Shapefile> "
   ]
  }
 ],
 "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
}