mheberger/get_watersheds_mghydro.ipynb

## get_watersheds_mghydro.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "deb82e81",
   "metadata": {},
   "source": [
    "# Using Python and the mghydro.com API to delineate multiple watersheds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c8dcbf",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to use Python and the API for the Global Watersheds web app at mghydro.com to automatically delineate watersheds and save the results on your computer. This is the fastest, easiest way I know to create geodata for watersheds for (almost) anywhere on Earth. \n",
    "\n",
    "For the simplicity of this demo, I put some watershed outlet points in a CSV-formatted string. The outlets are river gages selected at random from the catalog of the [Global Runoff Data Center (GRDC)](https://www.bafg.de/GRDC/EN/). "
   ]
  },
  {
   "cell_type": "code",
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   "id": "ddf67c63",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9999999</td>\n",
       "      <td>False River on Null Island</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3650920</td>\n",
       "      <td>Rio Toritama at Capibaribe, Brazil</td>\n",
       "      <td>-8.012800</td>\n",
       "      <td>-36.057800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4113405</td>\n",
       "      <td>Willow Creek near Willow City, ND, United States</td>\n",
       "      <td>48.580000</td>\n",
       "      <td>-100.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6441107</td>\n",
       "      <td>Rega River at Trzebiatow, Poland</td>\n",
       "      <td>54.063333</td>\n",
       "      <td>15.260833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6337508</td>\n",
       "      <td>Fulda River at Rotenburg, Germany</td>\n",
       "      <td>51.004000</td>\n",
       "      <td>9.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5204170</td>\n",
       "      <td>Namoi River at North Cuerindi, Australia</td>\n",
       "      <td>-30.679000</td>\n",
       "      <td>150.778000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4208325</td>\n",
       "      <td>Toad River above Nonda Creek, Canada</td>\n",
       "      <td>58.855556</td>\n",
       "      <td>-125.380550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6172023</td>\n",
       "      <td>Kasari River at Kasari, Estonia</td>\n",
       "      <td>58.726389</td>\n",
       "      <td>23.996944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4147491</td>\n",
       "      <td>Housatonic River Gaylordsville, CT, United States</td>\n",
       "      <td>41.650000</td>\n",
       "      <td>-73.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1259090</td>\n",
       "      <td>Gaaprivier at Holoog, Namibia</td>\n",
       "      <td>-27.433000</td>\n",
       "      <td>17.967000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                               name        lat  \\\n",
       "0  9999999                         False River on Null Island   0.000000   \n",
       "1  3650920                 Rio Toritama at Capibaribe, Brazil  -8.012800   \n",
       "2  4113405   Willow Creek near Willow City, ND, United States  48.580000   \n",
       "3  6441107                   Rega River at Trzebiatow, Poland  54.063333   \n",
       "4  6337508                  Fulda River at Rotenburg, Germany  51.004000   \n",
       "5  5204170           Namoi River at North Cuerindi, Australia -30.679000   \n",
       "6  4208325               Toad River above Nonda Creek, Canada  58.855556   \n",
       "7  6172023                    Kasari River at Kasari, Estonia  58.726389   \n",
       "8  4147491  Housatonic River Gaylordsville, CT, United States  41.650000   \n",
       "9  1259090                      Gaaprivier at Holoog, Namibia -27.433000   \n",
       "\n",
       "          lng  \n",
       "0    0.000000  \n",
       "1  -36.057800  \n",
       "2 -100.440000  \n",
       "3   15.260833  \n",
       "4    9.720000  \n",
       "5  150.778000  \n",
       "6 -125.380550  \n",
       "7   23.996944  \n",
       "8  -73.490000  \n",
       "9   17.967000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "from io import StringIO\n",
    "import requests\n",
    "import geopandas\n",
    "\n",
    "# Watershed outlet data, corresponding to flow measurement stations, or gages\n",
    "# Note that I purposely put a bad location in here, where the watershed delineation will definitely fail\n",
    "# because the point is over the ocean\n",
    "csv_data = \"\"\"id,name,lat,lng\n",
    "9999999,\"False River on Null Island\",0.0,0.0\n",
    "3650920,\"Rio Toritama at Capibaribe, Brazil\",-8.0128,-36.0578\n",
    "4113405,\"Willow Creek near Willow City, ND, United States\",48.58,-100.44\n",
    "6441107,\"Rega River at Trzebiatow, Poland\",54.06333333,15.26083333\n",
    "6337508,\"Fulda River at Rotenburg, Germany\",51.004,9.72\n",
    "5204170,\"Namoi River at North Cuerindi, Australia\",-30.679,150.778\n",
    "4208325,\"Toad River above Nonda Creek, Canada\",58.855556,-125.38055\n",
    "6172023,\"Kasari River at Kasari, Estonia\",58.726389,23.996944\n",
    "4147491,\"Housatonic River Gaylordsville, CT, United States\",41.65,-73.49\n",
    "1259090,\"Gaaprivier at Holoog, Namibia\",-27.433,17.967\n",
    "\"\"\"\n",
    "\n",
    "csvStringIO = StringIO(csv_data)\n",
    "outlets_df = pandas.read_csv(csvStringIO, sep=\",\")\n",
    "\n",
    "outlets_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3410446f",
   "metadata": {},
   "source": [
    "## The mghydro watersheds API\n",
    "\n",
    "You can find a [short into](https://mghydro.com/?p=70) to using the API on my blog.\n",
    "\n",
    "Below, we'll iterate over our gages DataFrame, and use the latitude and longitude to create a URL to submit to the API. The response from the server will either be a "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eed6e84d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing watershed 9999999 - False River on Null Island\n",
      "{\"message\": \"Sorry, you appear to have entered a point over the ocean (or too close to the coast?). Please try elsewhere.\" }\n",
      "Processing watershed 3650920 - Rio Toritama at Capibaribe, Brazil\n",
      "Processing watershed 4113405 - Willow Creek near Willow City, ND, United States\n",
      "Processing watershed 6441107 - Rega River at Trzebiatow, Poland\n",
      "Processing watershed 6337508 - Fulda River at Rotenburg, Germany\n",
      "Processing watershed 5204170 - Namoi River at North Cuerindi, Australia\n",
      "Processing watershed 4208325 - Toad River above Nonda Creek, Canada\n",
      "Processing watershed 6172023 - Kasari River at Kasari, Estonia\n",
      "Processing watershed 4147491 - Housatonic River Gaylordsville, CT, United States\n",
      "Processing watershed 1259090 - Gaaprivier at Holoog, Namibia\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>outlet_lat</th>\n",
       "      <th>outlet_lng</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1259090</th>\n",
       "      <td>POLYGON ((18.25791 -27.61208, 18.26041 -27.612...</td>\n",
       "      <td>2,250</td>\n",
       "      <td>-27.433</td>\n",
       "      <td>17.967</td>\n",
       "      <td>Gaaprivier at Holoog, Namibia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                                  geometry area_km2  \\\n",
       "id                                                                    \n",
       "1259090  POLYGON ((18.25791 -27.61208, 18.26041 -27.612...    2,250   \n",
       "\n",
       "         outlet_lat  outlet_lng                           name  \n",
       "id                                                              \n",
       "1259090     -27.433      17.967  Gaaprivier at Holoog, Namibia  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterate over the rows in our GeoDataFrame, request the watershed geodata via the API\n",
    "n = len(outlets_df)\n",
    "\n",
    "for i in range(0, n):\n",
    "    # Use basin_id as variable name to avoid confusion, since id() is a Python function \n",
    "    basin_id = outlets_df['id'][i] \n",
    "    name = outlets_df['name'][i]\n",
    "    lat = outlets_df['lat'][i]\n",
    "    lng = outlets_df['lng'][i]\n",
    "    print(\"Processing watershed {} - {}\".format(basin_id, name))\n",
    "    \n",
    "    # Request the Watershed via the API\n",
    "    url = \"https://mghydro.com/app/watershed_api?lat={}&lng={}&precision=high\".format(lat, lng)\n",
    "    r = requests.get(url=url)\n",
    "    \n",
    "    # If everything went well, and the server returned a watershed, the status code will be 200\n",
    "    # If the status code is something else (400 or 404), there was a problem\n",
    "    if r.status_code == 400 or r.status_code == 404:\n",
    "        # There will usually be an error message explaining (briefly) what went wrong\n",
    "        print(r.text)\n",
    "        continue\n",
    "    \n",
    "    # If you are getting a lot of 500 status codes (Internal Server Error), \n",
    "    # please send me an email so I can try to fix it\n",
    "    if r.status_code == 500:\n",
    "        print(\"Server error. Please contact the developer.\")\n",
    "    \n",
    "    # Status code of 200 means everything was OK!\n",
    "    if r.status_code == 200:\n",
    "        # If you want to save the GeoJSON, just write the response text to disk\n",
    "        # (I created a folder called `watersheds_geojson`)\n",
    "        fname = 'watersheds_geojson/{}.json'.format(basin_id)\n",
    "        with open(fname, 'w') as f:\n",
    "            f.write(r.text)\n",
    "\n",
    "        # OK... If you're old school and really like shapefiles, we can use GeoPandas, which has a handy method \n",
    "        # for exporting to a bunch of formats. Exotic formats like feather or parquet are available too. \n",
    "        data = r.json()  # Unpack the GeoJSON string to a Python dictionary\n",
    "\n",
    "        watershed_gdf = geopandas.GeoDataFrame.from_features(data['features'])\n",
    "\n",
    "        # Set the coordinate reference to unprojected lat/lng coordinates (World Geodetic System 1984)\n",
    "        watershed_gdf.set_crs('EPSG:4326', inplace=True)\n",
    "\n",
    "        # Populate some of the fields for the watershed's attribute table using our gage data from the CSV\n",
    "        # (The mghydro app returns geodata only -- it does not contain any names or labels.)\n",
    "        watershed_gdf['id'] = basin_id\n",
    "        watershed_gdf['name'] = name\n",
    "        watershed_gdf.set_index('id', inplace=True)\n",
    "\n",
    "        # Save the watershed as a shapefile in the folder `watersheds_shp`\n",
    "        fname = 'watersheds_shp/{}.shp'.format(basin_id)\n",
    "        watershed_gdf.to_file(fname)\n",
    "    \n",
    "    \n",
    "# Show the GeoDataFrame for the last watershed we processed\n",
    "watershed_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4acaf8",
   "metadata": {},
   "source": [
    "## Map the watershed boundary polygon\n",
    "\n",
    "Here is a quick map. It's not very interesting without styling or a basemap. I'll show how to make \n",
    "something nicer below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6adf7737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 3,
     "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": [
    "watershed_gdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d44bed",
   "metadata": {},
   "source": [
    "# Use the API to add the rivers\n",
    "\n",
    "Again, we will create a separate GeoDataFrame for the rivers in each  basin. The API format is similar to the one for watersheds.\n",
    "\n",
    "The data returned by the rivers API is a little different. In the `FeatureCollection`, there are multiple `Features`. When we put this into a GeoDataFrame, there are multiple rows. Each row is a separate segment, or river reach. Every reach has a unique identifier, its `comid`. This comes from the source dataset, [MERIT-Basins](https://www.reachhydro.org/home/params/merit-basins). The field `sorder`, refers to the [Strahler stream order](https://en.wikipedia.org/wiki/Strahler_number). This field is useful for symbolizing the rivers layer on a map to make it look nice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c27b7d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing basin id: 9999999\n",
      "{\"message\": \"Sorry, you appear to have entered a point over the ocean (or too close to the coast?). Please try elsewhere.\" }\n",
      "Processing basin id: 3650920\n",
      "Processing basin id: 4113405\n",
      "Processing basin id: 6441107\n",
      "Processing basin id: 6337508\n",
      "Processing basin id: 5204170\n",
      "Processing basin id: 4208325\n",
      "Processing basin id: 6172023\n",
      "Processing basin id: 4147491\n",
      "Processing basin id: 1259090\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>sorder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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      ],
      "text/plain": [
       "                                                   geometry  sorder\n",
       "comid                                                              \n",
       "12079807  LINESTRING (18.30333 -27.30333, 18.30417 -27.3...       1\n",
       "12079737  LINESTRING (18.13417 -27.42250, 18.13417 -27.4...       1\n",
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       "12078736  LINESTRING (18.09500 -27.49750, 18.09583 -27.4...       2\n",
       "...                                                     ...     ...\n",
       "12079741  LINESTRING (18.22833 -27.40500, 18.22917 -27.4...       1\n",
       "12079419  LINESTRING (18.33000 -27.26917, 18.33083 -27.2...       2\n",
       "12080093  LINESTRING (18.30667 -27.24667, 18.30583 -27.2...       1\n",
       "12079994  LINESTRING (18.36667 -27.23667, 18.36750 -27.2...       1\n",
       "12079251  LINESTRING (18.30333 -27.30333, 18.30417 -27.3...       3\n",
       "\n",
       "[65 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0, n):\n",
    "    lat = outlets_df['lat'][i]\n",
    "    lng = outlets_df['lng'][i]\n",
    "    basin_id = str(outlets_df['id'][i])\n",
    "    print(\"Processing basin id: \" + basin_id)   \n",
    "    \n",
    "    url = \"https://mghydro.com/app/upstream_rivers_api?lat={}&lng={}&precision=high\".format(lat, lng)\n",
    "    r = requests.get(url=url)\n",
    "    \n",
    "    if r.status_code == 200:\n",
    "        data = r.json()\n",
    "        rivers_gdf = geopandas.GeoDataFrame.from_features(data['features'])\n",
    "        rivers_gdf.set_index('comid', inplace=True)\n",
    "\n",
    "        # I created a folder called `rivers` to keep these files organized\n",
    "        fname = \"rivers/{}.shp\".format(basin_id)\n",
    "        rivers_gdf.to_file(fname)\n",
    "    else:\n",
    "        try:\n",
    "            print(r.text)\n",
    "        except:\n",
    "            print(\"Server returned status code {}\".format(r.status_code))\n",
    "    \n",
    "rivers_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "813bc087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 5,
     "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": [
    "# Make a quick map of the rivers\n",
    "rivers_gdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf23752",
   "metadata": {},
   "source": [
    "## Interactive map for a single watershed & its rivers\n",
    "\n",
    "The maps we made above were not very interesting. We can use the `explore()` method of Geopandas to put the features on a interactive map. GeoPandas is using a Python library called *Folium* which in turn creates a web-style map using the *Leaflet* javascript library. \n",
    "\n",
    "This will map the Gaap River in Namibia. This was the last row in our DataFrame, so the GeoDataFrames for the watershed and rivers is still in memory. (I reused the variable names, so it gets overwritten on each pass through the loop.) \n",
    "\n",
    "Since I want to customize the map and the layers a bit, we'll import the Folium library to access some of its methods. It looks nice to style the river polylines so that higher stream orders are thicker lines. Stream order increases as you move downstream, so in general, higher order streams are bigger and wider rivers and carry more flow. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fdccd556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;html&gt;\n",
       "&lt;head&gt;\n",
       "    \n",
       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
       "    \n",
       "        &lt;script&gt;\n",
       "            L_NO_TOUCH = false;\n",
       "            L_DISABLE_3D = false;\n",
       "        &lt;/script&gt;\n",
       "    \n",
       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
       "    \n",
       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
       "            &lt;style&gt;\n",
       "                #map_0560205d3a96804c9213806b0d749d20 {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
       "                    left: 0.0%;\n",
       "                    top: 0.0%;\n",
       "                }\n",
       "                .leaflet-container { font-size: 1rem; }\n",
       "            &lt;/style&gt;\n",
       "        \n",
       "    \n",
       "                    &lt;style&gt;\n",
       "                        .foliumtooltip {\n",
       "                            \n",
       "                        }\n",
       "                       .foliumtooltip table{\n",
       "                            margin: auto;\n",
       "                        }\n",
       "                        .foliumtooltip tr{\n",
       "                            text-align: left;\n",
       "                        }\n",
       "                        .foliumtooltip th{\n",
       "                            padding: 2px; padding-right: 8px;\n",
       "                        }\n",
       "                    &lt;/style&gt;\n",
       "            \n",
       "    \n",
       "                    &lt;style&gt;\n",
       "                        .foliumtooltip {\n",
       "                            \n",
       "                        }\n",
       "                       .foliumtooltip table{\n",
       "                            margin: auto;\n",
       "                        }\n",
       "                        .foliumtooltip tr{\n",
       "                            text-align: left;\n",
       "                        }\n",
       "                        .foliumtooltip th{\n",
       "                            padding: 2px; padding-right: 8px;\n",
       "                        }\n",
       "                    &lt;/style&gt;\n",
       "            \n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_0560205d3a96804c9213806b0d749d20&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;\n",
       "    \n",
       "    \n",
       "            var map_0560205d3a96804c9213806b0d749d20 = L.map(\n",
       "                &quot;map_0560205d3a96804c9213806b0d749d20&quot;,\n",
       "                {\n",
       "                    center: [-27.39125, 18.27833],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 10,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "            L.control.scale().addTo(map_0560205d3a96804c9213806b0d749d20);\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_c39ff28b001ffd8e4af2d0e872d39b8c = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_0560205d3a96804c9213806b0d749d20);\n",
       "        \n",
       "    \n",
       "            map_0560205d3a96804c9213806b0d749d20.fitBounds(\n",
       "                [[-27.66875, 17.96375], [-27.11375, 18.59291]],\n",
       "                {}\n",
       "            );\n",
       "        \n",
       "    \n",
       "        function geo_json_f9c43289f3b3c31a4c7487f9828705ce_styler(feature) {\n",
       "            switch(feature.id) {\n",
       "                default:\n",
       "                    return {&quot;color&quot;: &quot;red&quot;, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;red&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
       "            }\n",
       "        }\n",
       "        function geo_json_f9c43289f3b3c31a4c7487f9828705ce_highlighter(feature) {\n",
       "            switch(feature.id) {\n",
       "                default:\n",
       "                    return {&quot;fillOpacity&quot;: 0.75};\n",
       "            }\n",
       "        }\n",
       "        function geo_json_f9c43289f3b3c31a4c7487f9828705ce_pointToLayer(feature, latlng) {\n",
       "            var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};\n",
       "            \n",
       "            let style = geo_json_f9c43289f3b3c31a4c7487f9828705ce_styler(feature)\n",
       "            Object.assign(opts, style)\n",
       "            \n",
       "            return new L.CircleMarker(latlng, opts)\n",
       "        }\n",
       "\n",
       "        function geo_json_f9c43289f3b3c31a4c7487f9828705ce_onEachFeature(feature, layer) {\n",
       "            layer.on({\n",
       "                mouseout: function(e) {\n",
       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
       "                        geo_json_f9c43289f3b3c31a4c7487f9828705ce.resetStyle(e.target);\n",
       "                    }\n",
       "                },\n",
       "                mouseover: function(e) {\n",
       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
       "                        const highlightStyle = geo_json_f9c43289f3b3c31a4c7487f9828705ce_highlighter(e.target.feature)\n",
       "                        e.target.setStyle(highlightStyle);\n",
       "                    }\n",
       "                },\n",
       "            });\n",
       "        };\n",
       "        var geo_json_f9c43289f3b3c31a4c7487f9828705ce = L.geoJson(null, {\n",
       "                onEachFeature: geo_json_f9c43289f3b3c31a4c7487f9828705ce_onEachFeature,\n",
       "            \n",
       "                style: geo_json_f9c43289f3b3c31a4c7487f9828705ce_styler,\n",
       "                pointToLayer: geo_json_f9c43289f3b3c31a4c7487f9828705ce_pointToLayer\n",
       "        });\n",
       "\n",
       "        function geo_json_f9c43289f3b3c31a4c7487f9828705ce_add (data) {\n",
       "            geo_json_f9c43289f3b3c31a4c7487f9828705ce\n",
       "                .addData(data)\n",
       "                .addTo(map_0560205d3a96804c9213806b0d749d20);\n",
       "        }\n",
       "            geo_json_f9c43289f3b3c31a4c7487f9828705ce_add({&quot;bbox&quot;: [17.96375, -27.66875, 18.59291, -27.11375], &quot;features&quot;: [{&quot;bbox&quot;: [17.96375, -27.66875, 18.59291, -27.11375], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[18.25791, -27.61208], [18.26041, -27.61208], [18.26041, -27.61125], [18.26125, -27.61125], [18.26125, -27.61041], [18.26208, -27.61041], [18.26208, -27.60958], [18.26375, -27.60958], [18.26375, -27.60875], [18.26625, -27.60875], [18.26625, -27.60708], [18.26708, -27.60708], [18.26708, -27.60458], [18.26791, -27.60458], [18.26791, -27.60375], [18.26875, -27.60375], [18.26875, -27.60208], [18.26958, -27.60208], [18.26958, -27.60125], [18.27041, -27.60125], [18.27041, -27.59875], [18.27125, -27.59875], [18.27125, -27.59708], [18.27208, -27.59708], [18.27208, -27.59625], [18.27458, -27.59625], [18.27458, -27.59541], [18.27541, -27.59541], [18.27541, -27.59458], [18.27625, -27.59458], [18.27625, -27.59375], [18.27708, -27.59375], 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-27.57291], [18.31791, -27.57208], [18.31875, -27.57208], [18.31875, -27.56875], [18.32041, -27.56875], [18.32041, -27.56708], [18.32125, -27.56708], [18.32125, -27.56625], [18.32208, -27.56625], [18.32208, -27.56541], [18.32291, -27.56541], [18.32291, -27.56208], [18.32708, -27.56208], [18.32708, -27.56125], [18.32791, -27.56125], [18.32791, -27.56208], [18.32875, -27.56208], [18.32875, -27.56291], [18.33291, -27.56291], [18.33291, -27.56375], [18.33375, -27.56375], [18.33375, -27.56458], [18.33458, -27.56458], [18.33458, -27.56375], [18.33708, -27.56375], [18.33708, -27.56291], [18.33875, -27.56291], [18.33875, -27.56375], [18.34041, -27.56375], [18.34041, -27.56291], [18.34375, -27.56291], [18.34375, -27.56375], [18.34458, -27.56375], [18.34458, -27.56291], [18.34875, -27.56291], [18.34875, -27.56208], [18.34958, -27.56208], [18.34958, -27.56125], [18.35208, -27.56125], [18.35208, -27.55958], [18.35375, -27.55958], [18.35375, -27.55875], [18.35625, -27.55875], [18.35625, -27.55708], 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-27.64458], [18.04875, -27.64625], [18.04958, -27.64625], [18.04958, -27.64541], [18.05291, -27.64541], [18.05291, -27.64458], [18.05375, -27.64458], [18.05375, -27.64375], [18.05458, -27.64375], [18.05458, -27.64458], [18.05625, -27.64458], [18.05625, -27.64375], [18.06041, -27.64375], [18.06041, -27.64291], [18.06125, -27.64291], [18.06125, -27.64208], [18.06375, -27.64208], [18.06375, -27.64291], [18.06458, -27.64291], [18.06458, -27.64375], [18.06541, -27.64375], [18.06541, -27.64791], [18.06625, -27.64791], [18.06625, -27.64875], [18.06708, -27.64875], [18.06708, -27.64958], [18.06791, -27.64958], [18.06791, -27.65041], [18.07041, -27.65041], [18.07041, -27.65125], [18.07291, -27.65125], [18.07291, -27.65291], [18.07375, -27.65291], [18.07375, -27.65375], [18.07541, -27.65375], [18.07541, -27.65458], [18.07708, -27.65458], [18.07708, -27.65541], [18.07791, -27.65541], [18.07791, -27.65625], [18.07875, -27.65625], [18.07875, -27.65708], [18.07958, -27.65708], [18.07958, -27.65791], [18.08125, -27.65791], [18.08125, -27.65708], [18.08375, -27.65708], [18.08375, -27.65791], [18.08541, -27.65791], [18.08541, -27.65875], [18.08625, -27.65875], [18.08625, -27.65791], [18.08708, -27.65791], [18.08708, -27.65708], [18.08875, -27.65708], [18.08875, -27.65625], [18.08958, -27.65625], [18.08958, -27.65541], [18.09041, -27.65541], [18.09041, -27.65458], [18.09291, -27.65458], [18.09291, -27.65541], [18.09458, -27.65541], [18.09458, -27.65625], [18.09541, -27.65625], [18.09541, -27.65791], [18.09625, -27.65791], [18.09625, -27.65875], [18.09708, -27.65875], [18.09708, -27.65958], [18.09791, -27.65958], [18.09791, -27.66041], [18.09875, -27.66041], [18.09875, -27.66125], [18.09958, -27.66125], [18.09958, -27.66458], [18.09875, -27.66458], [18.09875, -27.66625], [18.09958, -27.66625], [18.09958, -27.66708], [18.10458, -27.66708], [18.10458, -27.66791], [18.10541, -27.66791], [18.10541, -27.66875], [18.10791, -27.66875], [18.10791, -27.66541], [18.10875, -27.66541], [18.10875, -27.66375], [18.10958, -27.66375], [18.10958, -27.66041], [18.11041, -27.66041], [18.11041, -27.65958], [18.11541, -27.65958], [18.11541, -27.66041], [18.11708, -27.66041], [18.11708, -27.66125], [18.11958, -27.66125], [18.11958, -27.66208], [18.12041, -27.66208], [18.12041, -27.66291], [18.12208, -27.66291], [18.12208, -27.66375], [18.12291, -27.66375], [18.12291, -27.66208], [18.12375, -27.66208], [18.12375, -27.66291], [18.12458, -27.66291], [18.12458, -27.66375], [18.13041, -27.66375], [18.13041, -27.66291], [18.13541, -27.66291], [18.13541, -27.66208], [18.13625, -27.66208], [18.13625, -27.65958], [18.13708, -27.65958], [18.13708, -27.65875], [18.13791, -27.65875], [18.13791, -27.65791], [18.13875, -27.65791], [18.13875, -27.65875], [18.13958, -27.65875], [18.13958, -27.65791], [18.14125, -27.65791], [18.14125, -27.65708], [18.14791, -27.65708], [18.14791, -27.65625], [18.14875, -27.65625], [18.14875, -27.65541], [18.15041, -27.65541], [18.15041, -27.65458], [18.15625, -27.65458], [18.15625, -27.65541], [18.15791, -27.65541], [18.15791, -27.65458], [18.16041, -27.65458], [18.16041, -27.65375], [18.16125, -27.65375], [18.16125, -27.65458], [18.16291, -27.65458], [18.16291, -27.65541], [18.16625, -27.65541], [18.16625, -27.65625], [18.16791, -27.65625], [18.16791, -27.65458], [18.16875, -27.65458], [18.16875, -27.65375], [18.16958, -27.65375], [18.16958, -27.65041], [18.17125, -27.65041], [18.17125, -27.64958], [18.17291, -27.64958], [18.17291, -27.64875], [18.17708, -27.64875], [18.17708, -27.64958], [18.17791, -27.64958], [18.17791, -27.65041], [18.17875, -27.65041], [18.17875, -27.64958], [18.17958, -27.64958], [18.17958, -27.64875], [18.18041, -27.64875], [18.18041, -27.64708], [18.18125, -27.64708], [18.18125, -27.64625], [18.18208, -27.64625], [18.18208, -27.64541], [18.18291, -27.64541], [18.18291, -27.64458], [18.18541, -27.64458], [18.18541, -27.64375], [18.18708, -27.64375], [18.18708, -27.64291], [18.18791, -27.64291], [18.18791, -27.64208], [18.18875, -27.64208], [18.18875, -27.64125], [18.19375, -27.64125], [18.19375, -27.64208], [18.19875, -27.64208], [18.19875, -27.64125], [18.19958, -27.64125], [18.19958, -27.64208], [18.20041, -27.64208], [18.20041, -27.64291], [18.20375, -27.64291], [18.20375, -27.64208], [18.20625, -27.64208], [18.20625, -27.64125], [18.20958, -27.64125], [18.20958, -27.64041], [18.21041, -27.64041], [18.21041, -27.64125], [18.21125, -27.64125], [18.21125, -27.64041], [18.21208, -27.64041], [18.21208, -27.63958], [18.21291, -27.63958], [18.21291, -27.63791], [18.21458, -27.63791], [18.21458, -27.63875], [18.21541, -27.63875], [18.21541, -27.63958], [18.21625, -27.63958], [18.21625, -27.64041], [18.21791, -27.64041], [18.21791, -27.63875], [18.21958, -27.63875], [18.21958, -27.63791], [18.22041, -27.63791], [18.22041, -27.63708], [18.21958, -27.63708], [18.21958, -27.63625], [18.22041, -27.63625], [18.22041, -27.63541], [18.22125, -27.63541], [18.22125, -27.63458], [18.22208, -27.63458], [18.22208, -27.63375], [18.22291, -27.63375], [18.22291, -27.63291], [18.22375, -27.63291], [18.22375, -27.63208], [18.22625, -27.63208], [18.22625, -27.63291], [18.22708, -27.63291], [18.22708, -27.63208], [18.22875, -27.63208], [18.22875, -27.63125], [18.23541, -27.63125], [18.23541, -27.63208], [18.23625, -27.63208], [18.23625, -27.63125], [18.23708, -27.63125], [18.23708, -27.62791], [18.23791, -27.62791], [18.23791, -27.62541], [18.23875, -27.62541], [18.23875, -27.62291], [18.24041, -27.62291], [18.24041, -27.62375], [18.24208, -27.62375], [18.24208, -27.62291], [18.24291, -27.62291], [18.24291, -27.62208], [18.24625, -27.62208], [18.24625, -27.62125], [18.24708, -27.62125], [18.24708, -27.61958], [18.25208, -27.61958], [18.25208, -27.61875], [18.25291, -27.61875], [18.25291, -27.61708], [18.25375, -27.61708], [18.25375, -27.61625], [18.25541, -27.61625], [18.25541, -27.61458], [18.25625, -27.61458], [18.25625, -27.61375], [18.25708, -27.61375], [18.25708, -27.61291], [18.25791, -27.61291], [18.25791, -27.61208]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;area_km2&quot;: &quot;2,250&quot;, &quot;id&quot;: 1259090, &quot;name&quot;: &quot;Gaaprivier at Holoog, Namibia&quot;, &quot;outlet_lat&quot;: -27.433, &quot;outlet_lng&quot;: 17.967}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
       "\n",
       "        \n",
       "    \n",
       "    geo_json_f9c43289f3b3c31a4c7487f9828705ce.bindTooltip(\n",
       "    function(layer){\n",
       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
       "    \n",
       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
       "    let fields = [&quot;id&quot;, &quot;area_km2&quot;, &quot;outlet_lat&quot;, &quot;outlet_lng&quot;, &quot;name&quot;];\n",
       "    let aliases = [&quot;id&quot;, &quot;area_km2&quot;, &quot;outlet_lat&quot;, &quot;outlet_lng&quot;, &quot;name&quot;];\n",
       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
       "        String(\n",
       "        fields.map(\n",
       "        (v,i)=&gt;\n",
       "        `&lt;tr&gt;\n",
       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
       "            \n",
       "            &lt;td&gt;${handleObject(layer.feature.properties[v])}&lt;/td&gt;\n",
       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
       "    +&#x27;&lt;/table&gt;&#x27;;\n",
       "    div.innerHTML=table;\n",
       "    \n",
       "    return div\n",
       "    }\n",
       "    ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});\n",
       "                     \n",
       "    \n",
       "        function geo_json_f718ae395ebdc47a4c468f8342f57cc6_styler(feature) {\n",
       "            switch(feature.id) {\n",
       "                case &quot;2&quot;: case &quot;5&quot;: case &quot;9&quot;: case &quot;17&quot;: case &quot;20&quot;: case &quot;21&quot;: case &quot;29&quot;: case &quot;31&quot;: case &quot;32&quot;: case &quot;33&quot;: case &quot;37&quot;: case &quot;43&quot;: case &quot;54&quot;: case &quot;55&quot;: case &quot;56&quot;: case &quot;59&quot;: case &quot;64&quot;: \n",
       "                    return {&quot;color&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 1.5811388300841898};\n",
       "                case &quot;4&quot;: case &quot;6&quot;: case &quot;11&quot;: case &quot;14&quot;: case &quot;15&quot;: case &quot;16&quot;: case &quot;19&quot;: case &quot;25&quot;: case &quot;27&quot;: case &quot;34&quot;: case &quot;40&quot;: case &quot;46&quot;: case &quot;47&quot;: case &quot;53&quot;: case &quot;61&quot;: \n",
       "                    return {&quot;color&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 1.224744871391589};\n",
       "                default:\n",
       "                    return {&quot;color&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 0.7071067811865476};\n",
       "            }\n",
       "        }\n",
       "        function geo_json_f718ae395ebdc47a4c468f8342f57cc6_highlighter(feature) {\n",
       "            switch(feature.id) {\n",
       "                default:\n",
       "                    return {&quot;fillOpacity&quot;: 0.75};\n",
       "            }\n",
       "        }\n",
       "        function geo_json_f718ae395ebdc47a4c468f8342f57cc6_pointToLayer(feature, latlng) {\n",
       "            var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};\n",
       "            \n",
       "            let style = geo_json_f718ae395ebdc47a4c468f8342f57cc6_styler(feature)\n",
       "            Object.assign(opts, style)\n",
       "            \n",
       "            return new L.CircleMarker(latlng, opts)\n",
       "        }\n",
       "\n",
       "        function geo_json_f718ae395ebdc47a4c468f8342f57cc6_onEachFeature(feature, layer) {\n",
       "            layer.on({\n",
       "                mouseout: function(e) {\n",
       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
       "                        geo_json_f718ae395ebdc47a4c468f8342f57cc6.resetStyle(e.target);\n",
       "                    }\n",
       "                },\n",
       "                mouseover: function(e) {\n",
       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
       "                        const highlightStyle = geo_json_f718ae395ebdc47a4c468f8342f57cc6_highlighter(e.target.feature)\n",
       "                        e.target.setStyle(highlightStyle);\n",
       "                    }\n",
       "                },\n",
       "            });\n",
       "        };\n",
       "        var geo_json_f718ae395ebdc47a4c468f8342f57cc6 = L.geoJson(null, {\n",
       "                onEachFeature: geo_json_f718ae395ebdc47a4c468f8342f57cc6_onEachFeature,\n",
       "            \n",
       "                style: geo_json_f718ae395ebdc47a4c468f8342f57cc6_styler,\n",
       "                pointToLayer: geo_json_f718ae395ebdc47a4c468f8342f57cc6_pointToLayer\n",
       "        });\n",
       "\n",
       "        function geo_json_f718ae395ebdc47a4c468f8342f57cc6_add (data) {\n",
       "            geo_json_f718ae395ebdc47a4c468f8342f57cc6\n",
       "                .addData(data)\n",
       "                .addTo(map_0560205d3a96804c9213806b0d749d20);\n",
       "        }\n",
       "            geo_json_f718ae395ebdc47a4c468f8342f57cc6_add({&quot;bbox&quot;: [17.97167, -27.5925, 18.5475, -27.17], &quot;features&quot;: [{&quot;bbox&quot;: [18.30333, -27.37417, 18.40833, -27.30333], &quot;geometry&quot;: {&quot;coordinates&quot;: [[18.30333, -27.30333], [18.30417, -27.30333], [18.305, -27.30333], [18.30583, -27.30417], [18.30667, -27.305], [18.3075, -27.30583], [18.30833, -27.30583], [18.30917, -27.30667], [18.31, -27.3075], [18.31083, -27.30833], [18.31083, -27.30917], [18.31167, -27.31], [18.3125, -27.30917], [18.31333, -27.31], [18.31417, -27.31083], [18.31417, -27.31167], [18.31417, -27.3125], [18.315, -27.3125], [18.31583, -27.31333], [18.31667, -27.31333], [18.3175, -27.31417], [18.31833, -27.315], [18.31917, -27.31583], [18.32, -27.315], [18.32083, -27.31417], [18.32083, -27.31333], [18.32167, -27.31333], [18.3225, -27.31333], [18.32333, -27.31333], [18.32417, -27.31417], [18.325, -27.315], [18.32583, -27.31417], [18.32667, -27.31417], [18.3275, 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&quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [18.30333, -27.30333, 18.31417, -27.28417], &quot;geometry&quot;: {&quot;coordinates&quot;: [[18.30333, -27.30333], [18.30417, -27.3025], [18.305, -27.30167], [18.305, -27.30083], [18.30583, -27.3], [18.30583, -27.29917], [18.30583, -27.29833], [18.30583, -27.2975], [18.305, -27.29667], [18.305, -27.29583], [18.305, -27.295], [18.30583, -27.295], [18.30667, -27.29417], [18.3075, -27.29333], [18.30833, -27.29333], [18.30917, -27.29333], [18.31, -27.29333], [18.31083, -27.2925], [18.31083, -27.29167], [18.31083, -27.29083], [18.31167, -27.29], [18.3125, -27.28917], [18.3125, -27.28833], [18.31167, -27.2875], [18.31083, -27.28667], [18.31167, -27.28583], [18.3125, -27.285], [18.31333, -27.28417], [18.31417, -27.28417]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;64&quot;, &quot;properties&quot;: {&quot;comid&quot;: 12079251, &quot;linewidth&quot;: 1.5811388300841898, &quot;sorder&quot;: 3}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
       "\n",
       "        \n",
       "    \n",
       "    geo_json_f718ae395ebdc47a4c468f8342f57cc6.bindTooltip(\n",
       "    function(layer){\n",
       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
       "    \n",
       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
       "    let fields = [&quot;comid&quot;, &quot;sorder&quot;, &quot;linewidth&quot;];\n",
       "    let aliases = [&quot;comid&quot;, &quot;sorder&quot;, &quot;linewidth&quot;];\n",
       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
       "        String(\n",
       "        fields.map(\n",
       "        (v,i)=&gt;\n",
       "        `&lt;tr&gt;\n",
       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
       "            \n",
       "            &lt;td&gt;${handleObject(layer.feature.properties[v])}&lt;/td&gt;\n",
       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
       "    +&#x27;&lt;/table&gt;&#x27;;\n",
       "    div.innerHTML=table;\n",
       "    \n",
       "    return div\n",
       "    }\n",
       "    ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});\n",
       "                     \n",
       "    \n",
       "            var tile_layer_4605132c9688e7b0150eb4145b11744b = L.tileLayer(\n",
       "                &quot;https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Esri&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_0560205d3a96804c9213806b0d749d20);\n",
       "        \n",
       "    \n",
       "            var layer_control_36bcd35a06935e8668e2ee74fd817710 = {\n",
       "                base_layers : {\n",
       "                    &quot;openstreetmap&quot; : tile_layer_c39ff28b001ffd8e4af2d0e872d39b8c,\n",
       "                    &quot;Esri Satellite&quot; : tile_layer_4605132c9688e7b0150eb4145b11744b,\n",
       "                },\n",
       "                overlays :  {\n",
       "                    &quot;Watershed Boundary&quot; : geo_json_f9c43289f3b3c31a4c7487f9828705ce,\n",
       "                    &quot;River Centerlines&quot; : geo_json_f718ae395ebdc47a4c468f8342f57cc6,\n",
       "                },\n",
       "            };\n",
       "            L.control.layers(\n",
       "                layer_control_36bcd35a06935e8668e2ee74fd817710.base_layers,\n",
       "                layer_control_36bcd35a06935e8668e2ee74fd817710.overlays,\n",
       "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
       "            ).addTo(map_0560205d3a96804c9213806b0d749d20);\n",
       "            tile_layer_4605132c9688e7b0150eb4145b11744b.remove();\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x2acb56c4690>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the Folium library so you can customize the map a bit.\n",
    "import folium\n",
    "\n",
    "folium_map = watershed_gdf.explore(color=\"red\", popup=False, style_kwds={'fill': False}, name=\"Watershed Boundary\")\n",
    "\n",
    "# I like to style rivers with the line width proportional to the square root of the stream order.\n",
    "# To do this, we first add a column to our GeoDataFrame with the line width for each river reach\n",
    "rivers_gdf['linewidth'] = (rivers_gdf['sorder'] - 0.5) ** 0.5\n",
    "\n",
    "# Add the rivers to the map. The style function varies the line width (or weight) by stream order\n",
    "rivers_gdf.explore(\n",
    "    m=folium_map,\n",
    "    name=\"River Centerlines\",\n",
    "    style_kwds=dict(\n",
    "        color='blue',\n",
    "        style_function=lambda x: {\"weight\": x[\"properties\"]['linewidth']}\n",
    "    )\n",
    ")\n",
    "\n",
    "# I like to add the satellite imagery layer to the map\n",
    "satellite_tiles = folium.TileLayer(\n",
    "        tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',\n",
    "        attr = 'Esri',\n",
    "        name = 'Esri Satellite',\n",
    "        overlay = False,\n",
    "        control = True\n",
    "       ).add_to(folium_map)\n",
    "\n",
    "folium.LayerControl().add_to(folium_map)\n",
    "\n",
    "# Display the map\n",
    "folium_map\n",
    "\n",
    "# You could also to save the map to an html file to share or view later (just uncomment the next line).\n",
    "# folium_map.save(\"kasari-map.html\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928969e1",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "In this Jupyter notebook, we saw how to:\n",
    "\n",
    "* use the API at [mghydro.com](https://mghydro.com) to delineate watersheds almost anywhere on Earth\n",
    "* save the results to your computer as GeoJSON files or shapefiles\n",
    "* make nice maps to display and explore your results.\n",
    "\n",
    "There is much more that you can do with this geodata for rivers and watersheds. I also want to give a huge thanks to the developers that created the datasets upon which the app is based: MERIT-Hydro and MERIT-Basins. \n",
    "\n",
    "If you come up with an interesting use for these data, I'd love to hear about it. Please drop me a line at [matt@mghydro.com](mailto:matt@mghydro.com). "
   ]
  }
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