15. Geospatial Analysis/tutorial/01. your-first-map.ipynb

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    "# Introduction\n",
    "\n",
    "In this micro-course, you'll learn about different methods to wrangle and visualize **geospatial data**, or data with a geographic location.\n",
    "\n",
    "<center>\n",
    "<img src=\"https://i.imgur.com/v6ZUGgI.png\"><br/>\n",
    "</center>\n",
    "\n",
    "Along the way, you'll offer solutions to several real-world problems like:\n",
    "- Where should a global non-profit expand its reach in remote areas of the Philippines?\n",
    "- How do purple martins, a threatened bird species, travel between North and South America?  Are the birds travelling to conservation areas?\n",
    "- Which areas of Japan could potentially benefit from extra earthquake reinforcement?\n",
    "- Which Starbucks stores in California are strong candidates for the next [Starbucks Reserve Roastery](https://www.forbes.com/sites/garystern/2019/01/22/starbucks-reserve-roastery-its-spacious-and-trendy-but-why-is-starbucks-slowing-down-expansion/#6cb80d4a1bc6) location?\n",
    "- Does New York City have sufficient hospitals to respond to motor vehicle collisions?  Which areas of the city have gaps in coverage?\n",
    "\n",
    "You'll also visualize crime in the city of Boston, examine health facilities in Ghana, explore top universities in Europe, and track releases of toxic chemicals in the United States.\n",
    "\n",
    "In this first tutorial, we'll quickly cover the pre-requisites that you'll need to complete this micro-course.  And, if you'd like to review more deeply, we recommend the **[Pandas micro-course](https://www.kaggle.com/learn/pandas)**.  \n",
    "\n",
    "We'll also get started with visualizing our first geospatial dataset!\n",
    "\n",
    "# Reading data\n",
    "\n",
    "The first step is to read in some geospatial data!  To do this, we'll use the [GeoPandas](http://geopandas.org/) library."
   ]
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   "source": [
    "import geopandas as gpd"
   ]
  },
  {
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   "source": [
    "There are many, many different geospatial file formats, such as [shapefile](https://en.wikipedia.org/wiki/Shapefile), [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON), [KML](https://en.wikipedia.org/wiki/Keyhole_Markup_Language), and [GPKG](https://en.wikipedia.org/wiki/GeoPackage).  We won't discuss their differences in this micro-course, but it's important to mention that:\n",
    "- shapefile is the most common file type that you'll encounter, and \n",
    "- all of these file types can be quickly loaded with the `gpd.read_file()` function.\n",
    "\n",
    "The next code cell loads a shapefile containing information about forests, wilderness areas, and other lands under the care of the [Department of Environmental Conservation](https://www.dec.ny.gov/index.html) in the state of New York.  "
   ]
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       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>FACILITY</th>\n",
       "      <th>CLASS</th>\n",
       "      <th>UMP</th>\n",
       "      <th>DESCRIPTIO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>URL</th>\n",
       "      <th>SOURCE</th>\n",
       "      <th>UPDATE_</th>\n",
       "      <th>OFFICE</th>\n",
       "      <th>ACRES</th>\n",
       "      <th>LANDS_UID</th>\n",
       "      <th>GREENCERT</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FOR PRES DET PAR</td>\n",
       "      <td>CFP</td>\n",
       "      <td>HANCOCK FP DETACHED PARCEL</td>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>None</td>\n",
       "      <td>DELAWARE COUNTY DETACHED PARCEL</td>\n",
       "      <td>4</td>\n",
       "      <td>DELAWARE</td>\n",
       "      <td>http://www.dec.ny.gov/</td>\n",
       "      <td>DELAWARE RPP</td>\n",
       "      <td>5/12</td>\n",
       "      <td>STAMFORD</td>\n",
       "      <td>738.620192</td>\n",
       "      <td>103</td>\n",
       "      <td>N</td>\n",
       "      <td>2.990365e+06</td>\n",
       "      <td>7927.662385</td>\n",
       "      <td>POLYGON ((486093.245 4635308.586, 486787.235 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FOR PRES DET PAR</td>\n",
       "      <td>CFP</td>\n",
       "      <td>HANCOCK FP DETACHED PARCEL</td>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>None</td>\n",
       "      <td>DELAWARE COUNTY DETACHED PARCEL</td>\n",
       "      <td>4</td>\n",
       "      <td>DELAWARE</td>\n",
       "      <td>http://www.dec.ny.gov/</td>\n",
       "      <td>DELAWARE RPP</td>\n",
       "      <td>5/12</td>\n",
       "      <td>STAMFORD</td>\n",
       "      <td>282.553140</td>\n",
       "      <td>1218</td>\n",
       "      <td>N</td>\n",
       "      <td>1.143940e+06</td>\n",
       "      <td>4776.375600</td>\n",
       "      <td>POLYGON ((491931.514 4637416.256, 491305.424 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>FOR PRES DET PAR</td>\n",
       "      <td>CFP</td>\n",
       "      <td>HANCOCK FP DETACHED PARCEL</td>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>None</td>\n",
       "      <td>DELAWARE COUNTY DETACHED PARCEL</td>\n",
       "      <td>4</td>\n",
       "      <td>DELAWARE</td>\n",
       "      <td>http://www.dec.ny.gov/</td>\n",
       "      <td>DELAWARE RPP</td>\n",
       "      <td>5/12</td>\n",
       "      <td>STAMFORD</td>\n",
       "      <td>234.291262</td>\n",
       "      <td>1780</td>\n",
       "      <td>N</td>\n",
       "      <td>9.485476e+05</td>\n",
       "      <td>5783.070364</td>\n",
       "      <td>POLYGON ((486000.287 4635834.453, 485007.550 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>FOR PRES DET PAR</td>\n",
       "      <td>CFP</td>\n",
       "      <td>GREENE COUNTY FP DETACHED PARCEL</td>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>GREENE</td>\n",
       "      <td>http://www.dec.ny.gov/</td>\n",
       "      <td>GREENE RPP</td>\n",
       "      <td>5/12</td>\n",
       "      <td>STAMFORD</td>\n",
       "      <td>450.106464</td>\n",
       "      <td>2060</td>\n",
       "      <td>N</td>\n",
       "      <td>1.822293e+06</td>\n",
       "      <td>7021.644833</td>\n",
       "      <td>POLYGON ((541716.775 4675243.268, 541217.579 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>FOREST PRESERVE</td>\n",
       "      <td>AFP</td>\n",
       "      <td>SARANAC LAKES WILD FOREST</td>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>SARANAC LAKES</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>ESSEX</td>\n",
       "      <td>http://www.dec.ny.gov/lands/22593.html</td>\n",
       "      <td>DECRP, ESSEX RPP</td>\n",
       "      <td>12/96</td>\n",
       "      <td>RAY BROOK</td>\n",
       "      <td>69.702387</td>\n",
       "      <td>1517</td>\n",
       "      <td>N</td>\n",
       "      <td>2.821959e+05</td>\n",
       "      <td>2663.909932</td>\n",
       "      <td>POLYGON ((583896.043 4909643.187, 583891.200 4...</td>\n",
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       "   OBJECTID          CATEGORY UNIT                          FACILITY  \\\n",
       "0         1  FOR PRES DET PAR  CFP        HANCOCK FP DETACHED PARCEL   \n",
       "1         2  FOR PRES DET PAR  CFP        HANCOCK FP DETACHED PARCEL   \n",
       "2         3  FOR PRES DET PAR  CFP        HANCOCK FP DETACHED PARCEL   \n",
       "3         4  FOR PRES DET PAR  CFP  GREENE COUNTY FP DETACHED PARCEL   \n",
       "4         6   FOREST PRESERVE  AFP         SARANAC LAKES WILD FOREST   \n",
       "\n",
       "         CLASS            UMP                       DESCRIPTIO  REGION  \\\n",
       "0  WILD FOREST           None  DELAWARE COUNTY DETACHED PARCEL       4   \n",
       "1  WILD FOREST           None  DELAWARE COUNTY DETACHED PARCEL       4   \n",
       "2  WILD FOREST           None  DELAWARE COUNTY DETACHED PARCEL       4   \n",
       "3  WILD FOREST           None                             None       4   \n",
       "4  WILD FOREST  SARANAC LAKES                             None       5   \n",
       "\n",
       "     COUNTY                                     URL            SOURCE UPDATE_  \\\n",
       "0  DELAWARE                  http://www.dec.ny.gov/      DELAWARE RPP    5/12   \n",
       "1  DELAWARE                  http://www.dec.ny.gov/      DELAWARE RPP    5/12   \n",
       "2  DELAWARE                  http://www.dec.ny.gov/      DELAWARE RPP    5/12   \n",
       "3    GREENE                  http://www.dec.ny.gov/        GREENE RPP    5/12   \n",
       "4     ESSEX  http://www.dec.ny.gov/lands/22593.html  DECRP, ESSEX RPP   12/96   \n",
       "\n",
       "      OFFICE       ACRES  LANDS_UID GREENCERT    SHAPE_AREA    SHAPE_LEN  \\\n",
       "0   STAMFORD  738.620192        103         N  2.990365e+06  7927.662385   \n",
       "1   STAMFORD  282.553140       1218         N  1.143940e+06  4776.375600   \n",
       "2   STAMFORD  234.291262       1780         N  9.485476e+05  5783.070364   \n",
       "3   STAMFORD  450.106464       2060         N  1.822293e+06  7021.644833   \n",
       "4  RAY BROOK   69.702387       1517         N  2.821959e+05  2663.909932   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((486093.245 4635308.586, 486787.235 4...  \n",
       "1  POLYGON ((491931.514 4637416.256, 491305.424 4...  \n",
       "2  POLYGON ((486000.287 4635834.453, 485007.550 4...  \n",
       "3  POLYGON ((541716.775 4675243.268, 541217.579 4...  \n",
       "4  POLYGON ((583896.043 4909643.187, 583891.200 4...  "
      ]
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   "source": [
    "# Read in the data\n",
    "full_data = gpd.read_file(\"../input/geospatial-learn-course-data/DEC_lands/DEC_lands/DEC_lands.shp\")\n",
    "\n",
    "# View the first five rows of the data\n",
    "full_data.head()"
   ]
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   "source": [
    "As you can see in the \"CLASS\" column, each of the first five rows corresponds to a different forest.  \n",
    "\n",
    "For the rest of this tutorial, consider a scenario where you'd like to use this data to plan a weekend camping trip.  Instead of relying on crowd-sourced reviews online, you decide to create your own map.  This way, you can tailor the trip to your specific interests. \n",
    "\n",
    "# Prerequisites\n",
    "\n",
    "To view the first five rows of the data, we used the `head()` method.  You may recall that this is also what we use to preview a Pandas DataFrame.  In fact, every command that you can use with a DataFrame will work with the data!  \n",
    "\n",
    "This is because the data was loaded into a (GeoPandas) **GeoDataFrame** object that has all of the capabilities of a (Pandas) DataFrame."
   ]
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       "geopandas.geodataframe.GeoDataFrame"
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    "type(full_data)"
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   "source": [
    "For instance, if we don't plan to use all of the columns, we can select a subset of them.  (_To review other methods for selecting data, check out [this tutorial](https://www.kaggle.com/residentmario/indexing-selecting-assigning/) from the Pandas micro-course_.)"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "data = full_data.loc[:, [\"CLASS\", \"COUNTY\", \"geometry\"]].copy()"
   ]
  },
  {
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   },
   "source": [
    "We use the `value_counts()` method to see a list of different land types, along with how many times they appear in the dataset. (_To review this (and related methods), check out [this tutorial](https://www.kaggle.com/residentmario/summary-functions-and-maps) from the Pandas micro-course._)"
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "WILD FOREST                   965\n",
       "INTENSIVE USE                 108\n",
       "PRIMITIVE                      60\n",
       "WILDERNESS                     52\n",
       "ADMINISTRATIVE                 17\n",
       "UNCLASSIFIED                    7\n",
       "HISTORIC                        5\n",
       "PRIMITIVE BICYCLE CORRIDOR      4\n",
       "CANOE AREA                      1\n",
       "Name: CLASS, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "# How many lands of each type are there?\n",
    "data.CLASS.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ea04e2",
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    "You can also use `loc` (and `iloc`) and `isin` to select subsets of the data.  (_To review this, check out [this tutorial](https://www.kaggle.com/residentmario/indexing-selecting-assigning/) from the Pandas micro-course._)"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLASS</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>DELAWARE</td>\n",
       "      <td>POLYGON ((486093.245 4635308.586, 486787.235 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>DELAWARE</td>\n",
       "      <td>POLYGON ((491931.514 4637416.256, 491305.424 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>DELAWARE</td>\n",
       "      <td>POLYGON ((486000.287 4635834.453, 485007.550 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>GREENE</td>\n",
       "      <td>POLYGON ((541716.775 4675243.268, 541217.579 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WILD FOREST</td>\n",
       "      <td>ESSEX</td>\n",
       "      <td>POLYGON ((583896.043 4909643.187, 583891.200 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CLASS    COUNTY                                           geometry\n",
       "0  WILD FOREST  DELAWARE  POLYGON ((486093.245 4635308.586, 486787.235 4...\n",
       "1  WILD FOREST  DELAWARE  POLYGON ((491931.514 4637416.256, 491305.424 4...\n",
       "2  WILD FOREST  DELAWARE  POLYGON ((486000.287 4635834.453, 485007.550 4...\n",
       "3  WILD FOREST    GREENE  POLYGON ((541716.775 4675243.268, 541217.579 4...\n",
       "4  WILD FOREST     ESSEX  POLYGON ((583896.043 4909643.187, 583891.200 4..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select lands that fall under the \"WILD FOREST\" or \"WILDERNESS\" category\n",
    "wild_lands = data.loc[data.CLASS.isin(['WILD FOREST', 'WILDERNESS'])].copy()\n",
    "wild_lands.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f86e5f",
   "metadata": {
    "papermill": {
     "duration": 0.016355,
     "end_time": "2021-11-09T00:09:19.328041",
     "exception": false,
     "start_time": "2021-11-09T00:09:19.311686",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you're not familiar with the commands above, you are encouraged to bookmark this page for reference, so you can look up the commands as needed.  (_Alternatively, you can take the [Pandas micro-course](https://www.kaggle.com/learn/pandas)._)  We'll use these commands throughout this micro-course to understand and filter data before creating maps.\n",
    "\n",
    "# Create your first map!\n",
    "\n",
    "We can quickly visualize the data with the `plot()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e50160e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:09:19.363721Z",
     "iopub.status.busy": "2021-11-09T00:09:19.362727Z",
     "iopub.status.idle": "2021-11-09T00:09:20.026186Z",
     "shell.execute_reply": "2021-11-09T00:09:20.026665Z"
    },
    "papermill": {
     "duration": 0.682839,
     "end_time": "2021-11-09T00:09:20.026841",
     "exception": false,
     "start_time": "2021-11-09T00:09:19.344002",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wild_lands.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e435a963",
   "metadata": {
    "papermill": {
     "duration": 0.018149,
     "end_time": "2021-11-09T00:09:20.063026",
     "exception": false,
     "start_time": "2021-11-09T00:09:20.044877",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Every GeoDataFrame contains a special \"geometry\" column.  It contains all of the geometric objects that are displayed when we call the `plot()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fde0dc03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:09:20.104639Z",
     "iopub.status.busy": "2021-11-09T00:09:20.103898Z",
     "iopub.status.idle": "2021-11-09T00:09:20.109175Z",
     "shell.execute_reply": "2021-11-09T00:09:20.109778Z"
    },
    "papermill": {
     "duration": 0.029266,
     "end_time": "2021-11-09T00:09:20.109945",
     "exception": false,
     "start_time": "2021-11-09T00:09:20.080679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    POLYGON ((486093.245 4635308.586, 486787.235 4...\n",
       "1    POLYGON ((491931.514 4637416.256, 491305.424 4...\n",
       "2    POLYGON ((486000.287 4635834.453, 485007.550 4...\n",
       "3    POLYGON ((541716.775 4675243.268, 541217.579 4...\n",
       "4    POLYGON ((583896.043 4909643.187, 583891.200 4...\n",
       "Name: geometry, dtype: geometry"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the first five entries in the \"geometry\" column\n",
    "wild_lands.geometry.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc95a1f",
   "metadata": {
    "papermill": {
     "duration": 0.017702,
     "end_time": "2021-11-09T00:09:20.145668",
     "exception": false,
     "start_time": "2021-11-09T00:09:20.127966",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "While this column can contain a variety of different datatypes, each entry will typically be a **Point**, **LineString**, or **Polygon**.\n",
    "\n",
    "![](https://i.imgur.com/N1llefr.png)\n",
    "\n",
    "The \"geometry\" column in our dataset contains 2983 different Polygon objects, each corresponding to a different shape in the plot above.\n",
    "\n",
    "In the code cell below, we create three more GeoDataFrames, containing campsite locations (**Point**), foot trails (**LineString**), and county boundaries (**Polygon**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e96b7d57",
   "metadata": {
    "_kg_hide-input": false,
    "execution": {
     "iopub.execute_input": "2021-11-09T00:09:20.185761Z",
     "iopub.status.busy": "2021-11-09T00:09:20.184727Z",
     "iopub.status.idle": "2021-11-09T00:09:21.617516Z",
     "shell.execute_reply": "2021-11-09T00:09:21.616900Z"
    },
    "papermill": {
     "duration": 1.454045,
     "end_time": "2021-11-09T00:09:21.617668",
     "exception": false,
     "start_time": "2021-11-09T00:09:20.163623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Campsites in New York state (Point)\n",
    "POI_data = gpd.read_file(\"../input/geospatial-learn-course-data/DEC_pointsinterest/DEC_pointsinterest/Decptsofinterest.shp\")\n",
    "campsites = POI_data.loc[POI_data.ASSET=='PRIMITIVE CAMPSITE'].copy()\n",
    "\n",
    "# Foot trails in New York state (LineString)\n",
    "roads_trails = gpd.read_file(\"../input/geospatial-learn-course-data/DEC_roadstrails/DEC_roadstrails/Decroadstrails.shp\")\n",
    "trails = roads_trails.loc[roads_trails.ASSET=='FOOT TRAIL'].copy()\n",
    "\n",
    "# County boundaries in New York state (Polygon)\n",
    "counties = gpd.read_file(\"../input/geospatial-learn-course-data/NY_county_boundaries/NY_county_boundaries/NY_county_boundaries.shp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17c5264",
   "metadata": {
    "papermill": {
     "duration": 0.017983,
     "end_time": "2021-11-09T00:09:21.654572",
     "exception": false,
     "start_time": "2021-11-09T00:09:21.636589",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we create a map from all four GeoDataFrames.  \n",
    "\n",
    "The `plot()` method takes as (optional) input several parameters that can be used to customize the appearance.  Most importantly, setting a value for `ax` ensures that all of the information is plotted on the same map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a25515f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:09:21.695134Z",
     "iopub.status.busy": "2021-11-09T00:09:21.694170Z",
     "iopub.status.idle": "2021-11-09T00:09:23.003737Z",
     "shell.execute_reply": "2021-11-09T00:09:23.004281Z"
    },
    "papermill": {
     "duration": 1.331747,
     "end_time": "2021-11-09T00:09:23.004460",
     "exception": false,
     "start_time": "2021-11-09T00:09:21.672713",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a base map with county boundaries\n",
    "ax = counties.plot(figsize=(10,10), color='none', edgecolor='gainsboro', zorder=3)\n",
    "\n",
    "# Add wild lands, campsites, and foot trails to the base map\n",
    "wild_lands.plot(color='lightgreen', ax=ax)\n",
    "campsites.plot(color='maroon', markersize=2, ax=ax)\n",
    "trails.plot(color='black', markersize=1, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870f0db2",
   "metadata": {
    "papermill": {
     "duration": 0.02127,
     "end_time": "2021-11-09T00:09:23.047140",
     "exception": false,
     "start_time": "2021-11-09T00:09:23.025870",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It looks like the northeastern part of the state would be a great option for a camping trip!\n",
    "\n",
    "# Your turn\n",
    "\n",
    "This feels complex at first, but you've already learned enough to do important analysis. See for yourself as you **[identify remote areas](https://www.kaggle.com/kernels/fork/5832167)** of the Philippines where a non-profit can expand its operations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be14eee1",
   "metadata": {
    "papermill": {
     "duration": 0.021346,
     "end_time": "2021-11-09T00:09:23.089967",
     "exception": false,
     "start_time": "2021-11-09T00:09:23.068621",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/geospatial-analysis/discussion) to chat with other learners.*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 15.594518,
   "end_time": "2021-11-09T00:09:23.721658",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-11-09T00:09:08.127140",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}