LCA_Blog_Part3-Unsupervised_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code from parts 1 and 2 included for continuity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1 - Retrieve the Data from the API\n",
    "\n",
    "Now that we understand how the API request is constructed, let’s start working with our Python script. First, we need to load all of the necessary libraries.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To communicate with the API and dynamically request data for different combinations of state FIPS codes, early years, and late years, we need to employ the Requests library. Requests is a library designed to allow users to send HTTP requests with Python. In the function below, we use the method .get to request data from API and load it into a Response object r. The Requests library includes a built in JSON decoder to read the JSON data that is returned and load it into a Pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataframe(fips,early_year,late_year):\n",
    "    \"\"\"\n",
    "    Submits request for data to the API and creates formatted pandas dataframe for use with clustering algorithms\n",
    "    \n",
    "    fips = string defining the 2-digit FIPS code for your state you of interest\n",
    "    early_year = string defining the 1st date of change period (1996, 2001, 2006 or 2010)\n",
    "    late_year = string defining the 2nd date of change period (1996, 2001, 2006 or 2010)\n",
    "    \"\"\"\n",
    "    # Format inputs for url\n",
    "    fips = \"'{}'\".format(fips)\n",
    "    early_year = \"'{}'\".format(early_year)\n",
    "    late_year = \"'{}'\".format(late_year)\n",
    "    \n",
    "    #API request for Land Cover data in JSON format\n",
    "    url = \"https://coast.noaa.gov/opendata/LandCover/api/v1/distributionOfChangeGainsLossesByLandcovers?$filter=startswith(geoId, {0}) and length(geoId) eq 5 and earlyYear eq {1} and lateYear eq {2}\".format(fips, early_year, late_year)\n",
    "    \n",
    "    r = requests.get(url, headers={'Accept': 'application/json'})\n",
    "    data = r.json()\n",
    "    lca_df = pd.DataFrame(data['value'])\n",
    "    \n",
    "    # Set index to the FIPS code and drop Early and Late Year Fields\n",
    "    lca_df_index = lca_df.set_index('geoId').drop(['earlyYear', 'lateYear'], axis=1)\n",
    "    \n",
    "    return lca_df_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will execute the function. The C-CAP Atlas has county level land cover change statistics organized by FIPS code and dates of change. C-CAP has mapped land cover for the years 1996, 2001, 2006 and 2010 using imagery acquired by the Landsat suite of satellites. This means we can look at changes in land cover that have occurred over multiple time series such as coarse intervals like 1996-2010 or we can look at finer scale changes over a 5 year period such as 2001-2006.  For this example, again we will select the state of Connecticut (FIPS code:9) and request land cover changes occurring from 2001-2010. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CT_2001_2010 = create_dataframe('09',2001,2010)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For k-means example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "NC_2006_2010 = create_dataframe('37',2006,2010)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s confirm our new object contains the correct data using the method .head( ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgrAreaGain</th>\n",
       "      <th>AgrAreaLoss</th>\n",
       "      <th>BarAreaGain</th>\n",
       "      <th>BarAreaLoss</th>\n",
       "      <th>EmwAreaGain</th>\n",
       "      <th>EmwAreaLoss</th>\n",
       "      <th>ForAreaGain</th>\n",
       "      <th>ForAreaLoss</th>\n",
       "      <th>GrsAreaGain</th>\n",
       "      <th>GrsAreaLoss</th>\n",
       "      <th>...</th>\n",
       "      <th>LIDAreaGain</th>\n",
       "      <th>LIDAreaLoss</th>\n",
       "      <th>OSDAreaGain</th>\n",
       "      <th>OSDAreaLoss</th>\n",
       "      <th>SscbAreaGain</th>\n",
       "      <th>SscbAreaLoss</th>\n",
       "      <th>WdwAreaGain</th>\n",
       "      <th>WdwAreaLoss</th>\n",
       "      <th>WtrAreaGain</th>\n",
       "      <th>WtrAreaLoss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>09001</th>\n",
       "      <td>0.31</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>0.43</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.67</td>\n",
       "      <td>-7.58</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>...</td>\n",
       "      <td>4.33</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>3.61</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>0.49</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09003</th>\n",
       "      <td>0.71</td>\n",
       "      <td>-6.39</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-10.56</td>\n",
       "      <td>1.06</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>6.25</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>6.91</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09005</th>\n",
       "      <td>0.60</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-3.89</td>\n",
       "      <td>1.36</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09007</th>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-4.09</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>1.72</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>1.37</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.69</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09009</th>\n",
       "      <td>0.36</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>0.49</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-8.45</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>...</td>\n",
       "      <td>4.05</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>3.93</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AgrAreaGain  AgrAreaLoss  BarAreaGain  BarAreaLoss  EmwAreaGain  \\\n",
       "geoId                                                                    \n",
       "09001         0.31        -1.64         0.43        -0.20         0.06   \n",
       "09003         0.71        -6.39         0.48        -0.19         0.14   \n",
       "09005         0.60        -1.03         0.31        -0.06         0.29   \n",
       "09007         0.30        -0.92         0.30        -0.09         0.12   \n",
       "09009         0.36        -1.92         0.49        -0.27         0.38   \n",
       "\n",
       "       EmwAreaLoss  ForAreaGain  ForAreaLoss  GrsAreaGain  GrsAreaLoss  ...  \\\n",
       "geoId                                                                   ...   \n",
       "09001        -0.16         0.67        -7.58         0.41        -0.30  ...   \n",
       "09003        -0.19         0.23       -10.56         1.06        -0.17  ...   \n",
       "09005        -0.10         0.20        -3.89         1.36        -0.22  ...   \n",
       "09007        -0.09         0.11        -4.09         0.24        -0.16  ...   \n",
       "09009        -0.13         0.20        -8.45         0.75        -0.59  ...   \n",
       "\n",
       "       LIDAreaGain  LIDAreaLoss  OSDAreaGain  OSDAreaLoss  SscbAreaGain  \\\n",
       "geoId                                                                     \n",
       "09001         4.33        -0.49         3.61        -1.77          0.49   \n",
       "09003         6.25        -0.45         6.91        -1.83          0.95   \n",
       "09005         0.77        -0.06         0.97        -0.20          0.84   \n",
       "09007         1.72        -0.11         1.37        -0.37          0.69   \n",
       "09009         4.05        -0.43         3.93        -1.55          0.71   \n",
       "\n",
       "       SscbAreaLoss  WdwAreaGain  WdwAreaLoss  WtrAreaGain  WtrAreaLoss  \n",
       "geoId                                                                    \n",
       "09001         -0.86         0.05        -0.36         0.05        -0.20  \n",
       "09003         -1.95         0.08        -0.81         0.06        -0.26  \n",
       "09005         -0.36         0.13        -0.22         0.12        -0.28  \n",
       "09007         -0.47         0.05        -0.32         0.08        -0.15  \n",
       "09009         -1.01         0.06        -0.42         0.15        -0.48  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CT_2001_2010.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get a list of the column names by using the following code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AgrAreaGain',\n",
       " 'AgrAreaLoss',\n",
       " 'BarAreaGain',\n",
       " 'BarAreaLoss',\n",
       " 'EmwAreaGain',\n",
       " 'EmwAreaLoss',\n",
       " 'ForAreaGain',\n",
       " 'ForAreaLoss',\n",
       " 'GrsAreaGain',\n",
       " 'GrsAreaLoss',\n",
       " 'HIDAreaGain',\n",
       " 'HIDAreaLoss',\n",
       " 'LIDAreaGain',\n",
       " 'LIDAreaLoss',\n",
       " 'OSDAreaGain',\n",
       " 'OSDAreaLoss',\n",
       " 'SscbAreaGain',\n",
       " 'SscbAreaLoss',\n",
       " 'WdwAreaGain',\n",
       " 'WdwAreaLoss',\n",
       " 'WtrAreaGain',\n",
       " 'WtrAreaLoss']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(NC_2006_2010.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Cleaning the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of this blog post is to introduce some ways we can use the Pandas and GeoPandas libraries to prepare the data we retrieved from the Land Cover Atlas API and get it ready for exploratory analysis with unsupervised machine learning algorithms.  Additionally, we will acquire spatial data from the U.S. Census to be enable us to visualize the results.  To get started, let's import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import requests\n",
    "import zipfile\n",
    "import io\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have loaded all of the necessary libraries,  we are going to need a shapefile representing the county boundaries for the entire U.S. Fortunately, we can easily download a TIGER/Line shapefile from our buddies at the U.S. Census Bureau.  We want to incorporate this data set into our project for 2 purposes:\n",
    "\n",
    "1. Add county names to our Land Cover change DataFrame\n",
    "2. Visualize the results of our exploratory analysis spatially\n",
    "\n",
    "We will talk more about visualization in the next blog post. In the meantime, we can use the function below to download the TIGER/Line shapefile from Census using Requests, unzip it using the Zipfile library and import the shapefile into a GeoPandas GeoDataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_shapefile(url, directory):\n",
    "    \"\"\"\n",
    "    Download a shapefile in .zip format from a ftp site. Unzips shapefile and \n",
    "    loads it into a GeoPandas GeoDataFrame\n",
    "    \"\"\"          \n",
    "    print('Downloading shapefile...')\n",
    "        \n",
    "    # Split on the URL on rightmost / and take everything on the right side of that\n",
    "    # Replace .zip file extension with .shp\n",
    "    shapefile = url.rsplit('/', 1)[-1].replace(\".zip\",\".shp\")\n",
    "    \n",
    "    r = requests.get(url)\n",
    "    # Keep contents of the response object as bytes in an in-memory buffer using IO\n",
    "    zipfile.ZipFile(io.BytesIO(r.content)).extractall(directory)\n",
    "            \n",
    "    # Read the shapefile into GeoPandas GeoDataframe   \n",
    "    nation = gpd.read_file(os.path.join(directory,shapefile))\n",
    "    \n",
    "    return nation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the function, downloading the data to the directory of your choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading shapefile...\n"
     ]
    }
   ],
   "source": [
    "url = \"https://www2.census.gov/geo/tiger/TIGER2017/COUNTY/tl_2017_us_county.zip\"\n",
    "us_counties = download_shapefile(url,\"C:\\\\Projects\\\\Cluster\") # Be sure to rename the directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with GeoPandas Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GeoPandas is an excellent open source library which facilitates working with spatial data in Python. It does this by leveraging the capabilities of the Pandas and Shapely libraries.  Since we have access to all of the operations available in Pandas, let's go ahead and inspect the attributes of our GeoPandas GeoDataFrame using the .info( ) method. This will provide us with a summary of the GeoDataFrame with information such as column data types and non-null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 3233 entries, 0 to 3232\n",
      "Data columns (total 18 columns):\n",
      "STATEFP     3233 non-null object\n",
      "COUNTYFP    3233 non-null object\n",
      "COUNTYNS    3233 non-null object\n",
      "GEOID       3233 non-null object\n",
      "NAME        3233 non-null object\n",
      "NAMELSAD    3233 non-null object\n",
      "LSAD        3233 non-null object\n",
      "CLASSFP     3233 non-null object\n",
      "MTFCC       3233 non-null object\n",
      "CSAFP       1231 non-null object\n",
      "CBSAFP      1899 non-null object\n",
      "METDIVFP    113 non-null object\n",
      "FUNCSTAT    3233 non-null object\n",
      "ALAND       3233 non-null int64\n",
      "AWATER      3233 non-null int64\n",
      "INTPTLAT    3233 non-null object\n",
      "INTPTLON    3233 non-null object\n",
      "geometry    3233 non-null object\n",
      "dtypes: int64(2), object(16)\n",
      "memory usage: 454.7+ KB\n"
     ]
    }
   ],
   "source": [
    "us_counties.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27f181a7198>"
      ]
     },
     "execution_count": 9,
     "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": [
    "us_counties.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll admit it's not the most visually appealing map but this provides us with a quick way to verify the geography is correct. \n",
    "\n",
    "Now that we have created a GeoPandas Dataframe, let's leverage some more of those tools available in the Pandas library. Let’s create a new GeoDataFrame that consists of a single coastal state. This can be performed using the .loc method which allows us to access groups of rows and columns. Here we will make our selection based on a state’s FIPS codes to make a new GeoDataFrame.\n",
    "\n",
    "Now that we have made our selection, let's do some cleaning. First things first. Let's eliminate some redundancy. You may have noticed when inspecting the attributes of the GeoDataFrame that there are 2 columns containing county names, NAME and NAMELSAD. Let's use the .drop() method to remove the column called NAME. We also will use the .rename method to rename the GEOID and NAMELSAD columns. Renaming GEOID to geoId is required to enable joining with the land cover data we retrieved from the API. To reduce the lines of code and number of variables required, the methods are chained together on one line. This is a great feature within Python but be careful not to abuse it! See the function below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_state(fips, nation):\n",
    "    \"\"\"\n",
    "    Creates new GeoDataFrame based on FIPS code\n",
    "    \n",
    "    fips:the 2-digit FIPS code for your state you of interest\n",
    "    nation: GeoDataFrame of census counties\n",
    "    \"\"\"\n",
    "      \n",
    "    counties = nation.loc[nation['STATEFP'] == '{0}'.format(fips)].squeeze()\n",
    "    counties = counties.drop(columns=['NAME']).rename(columns={'GEOID': 'geoId','NAMELSAD': 'name'})\n",
    "        \n",
    "    #return counties\n",
    "    return counties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will execute the function. For this example we will select the state of Connecticut (FIPS code: 09). Make sure the FIPS code is provided as a String!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "counties_gdf = select_state('09', us_counties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the K-means example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "counties_gdf = select_state('37', us_counties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to confirm our new object contains changes we made to the columns, let’s use the Pandas method .columns to view a full list of the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['STATEFP', 'COUNTYFP', 'COUNTYNS', 'geoId', 'name', 'LSAD', 'CLASSFP',\n",
       "       'MTFCC', 'CSAFP', 'CBSAFP', 'METDIVFP', 'FUNCSTAT', 'ALAND', 'AWATER',\n",
       "       'INTPTLAT', 'INTPTLON', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties_gdf.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the modifications are present. While we are at it. Let's use .plot( ) again to check our geography. If everything looks good, we are going to set the GeoDataFrame aside for use in the next blog post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27f18189978>"
      ]
     },
     "execution_count": 26,
     "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": [
    "counties_gdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may have noticed earlier when you inspected the DataFrame of land cover change data that the table only includes FIPS codes. When it comes time to make visualizations of our analysis is may be helpful to be able use the actual county names as labels. To do this, let’s create another Pandas DataFrame with county names and FIPs codes for our state of interest using the GeoPandas GeoDataFrame we created in the last section.  We will accomplish this by using square brackets [ ] to select specific columns (geoId & name) from the GeoDataFrame. To make the upcoming merge process work, we'll need to set the resulting DataFrame's index to the FIPS code ('geoId') using the .set_index( ) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_fips_table(dataframe):\n",
    "    \"\"\"\n",
    "    Creates table of FIPS codes and county names for joining to Land Cover DataFrame\n",
    "    \n",
    "    dataframe: Geopandas dataframe of State of interest\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    county_attributes = pd.DataFrame(dataframe)\n",
    "    fips_names = county_attributes[['geoId','name']].set_index('geoId')\n",
    "    \n",
    "    return fips_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's execute the function.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fips_df = create_fips_table(counties_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, let’s confirm our new object contains the correct data using the method .head( )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>09007</th>\n",
       "      <td>Middlesex County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09011</th>\n",
       "      <td>New London County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09009</th>\n",
       "      <td>New Haven County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09013</th>\n",
       "      <td>Tolland County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09003</th>\n",
       "      <td>Hartford County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name\n",
       "geoId                   \n",
       "09007   Middlesex County\n",
       "09011  New London County\n",
       "09009   New Haven County\n",
       "09013     Tolland County\n",
       "09003    Hartford County"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fips_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_dataframes(dataframe,fips_names_df):\n",
    "    \"\"\"\n",
    "    Merges county names to land cover dataframe based on FIPS code\n",
    "    \n",
    "    dataframe: DataFrame with Land Cover change data\n",
    "    fips_names_df: DataFrame with FIPS codes and County Names\n",
    "    \"\"\"\n",
    "    fips_names_index_df = pd.merge(dataframe, fips_names_df,left_index=True,right_index=True).set_index('name')\n",
    "        \n",
    "    return fips_names_index_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the function below, we use the Pandas method .merge( ) to perform a database-style join of  the county FIPS and names DataFrame with LC Change Dataframe we created from the API. We also use the .set_index( ) method to set the names column as the index of the resulting DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "CT_2001_2010_index = merge_dataframes(CT_2001_2010, fips_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, let’s confirm our new object contains the correct data using the method .info( )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 8 entries, Fairfield County to Windham County\n",
      "Data columns (total 22 columns):\n",
      "AgrAreaGain     8 non-null float64\n",
      "AgrAreaLoss     8 non-null float64\n",
      "BarAreaGain     8 non-null float64\n",
      "BarAreaLoss     8 non-null float64\n",
      "EmwAreaGain     8 non-null float64\n",
      "EmwAreaLoss     8 non-null float64\n",
      "ForAreaGain     8 non-null float64\n",
      "ForAreaLoss     8 non-null float64\n",
      "GrsAreaGain     8 non-null float64\n",
      "GrsAreaLoss     8 non-null float64\n",
      "HIDAreaGain     8 non-null float64\n",
      "HIDAreaLoss     8 non-null float64\n",
      "LIDAreaGain     8 non-null float64\n",
      "LIDAreaLoss     8 non-null float64\n",
      "OSDAreaGain     8 non-null float64\n",
      "OSDAreaLoss     8 non-null float64\n",
      "SscbAreaGain    8 non-null float64\n",
      "SscbAreaLoss    8 non-null float64\n",
      "WdwAreaGain     8 non-null float64\n",
      "WdwAreaLoss     8 non-null float64\n",
      "WtrAreaGain     8 non-null float64\n",
      "WtrAreaLoss     8 non-null float64\n",
      "dtypes: float64(22)\n",
      "memory usage: 1.4+ KB\n"
     ]
    }
   ],
   "source": [
    "CT_2001_2010_index.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the C-CAP Land Cover Atlas using Machine Learning and Python Part 3: Exploring the Data with Unsupervised Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s get started by applying hierarchical clustering to the land cover change data we retrieved and cleaned for the state of Connecticut. First thing we need to do is import the required packages from the SciPy library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import linkage, dendrogram, fcluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below creates a dendrogram from our land cover change dataframe for the years 2001-2010. First, we run SciPy's linkage routine that does the clustering and save the output to the variable mergings. Mergings is then input into the dendrogram routine to plot the hierarchical clustering. The resulting clusters are labeled with their county names.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dendrogram(df, title):\n",
    "    \"\"\"\n",
    "    df: The formatted dataframe to be used for hierarchical clustering\n",
    "    title(String): Title of the resulting dendrogram \n",
    "    \"\"\"\n",
    "    mergings = linkage(df, method='complete')\n",
    "    \n",
    "    # Set up plot size and insert the title\n",
    "    plt.figure(figsize=(12,10))\n",
    "    plt.title(f'{title}')\n",
    "    \n",
    "    # Create the dendrogram\n",
    "    dendrogram(mergings, labels=df.index, leaf_rotation=90, leaf_font_size=13)\n",
    "    \n",
    "    #Display the plot\n",
    "    plt.show()\n",
    "    \n",
    "    return mergings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the function to see the resulting dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dendro_plot =make_dendrogram(CT_2001_2010_index, \"Hierarchical Clustering Dendrogram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To take our exploration a step further, we can extract the labels at a specific distance (Y axis) and join them to the counties GeoDataFrame. This will allow us to map the results and visualize any spatial patterns that may exist. The function below facilitates the extraction of cluster labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we need to import the required packages from the SciPy library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn import preprocessing, metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, let's subset our data to a new dataframe. The harvested forest would be captured in Forest loss (ForAreaLoss) column (along with other types of loss such as residential development) while the regenerating herbaceous and woody vegetation would be highlighted in the Grassland gain (GrsAreaGain)  and Scrub Shrub gain (SscbAreaGain) columns. We will name the new dataframe to clustered NC_2006_2010_silv.  We will also apply a method from Pandas that we have not previously used. Since the C-CAP Land Cover Atlas reports losses as a negative value, let's add the .abs() method to the end of our subset request ensuring all values are represented as a positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "NC_2006_2010_silv = NC_2006_2010[['ForAreaLoss','GrsAreaGain','SscbAreaGain']].abs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, let's check the contents of our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>ForAreaLoss</th>\n",
       "      <th>GrsAreaGain</th>\n",
       "      <th>SscbAreaGain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37013</th>\n",
       "      <td>45.31</td>\n",
       "      <td>30.65</td>\n",
       "      <td>45.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37015</th>\n",
       "      <td>33.17</td>\n",
       "      <td>18.37</td>\n",
       "      <td>43.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37017</th>\n",
       "      <td>21.12</td>\n",
       "      <td>15.18</td>\n",
       "      <td>21.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37019</th>\n",
       "      <td>20.97</td>\n",
       "      <td>14.62</td>\n",
       "      <td>21.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37029</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ForAreaLoss  GrsAreaGain  SscbAreaGain\n",
       "geoId                                        \n",
       "37013        45.31        30.65         45.76\n",
       "37015        33.17        18.37         43.12\n",
       "37017        21.12        15.18         21.04\n",
       "37019        20.97        14.62         21.79\n",
       "37029         0.91         0.71          0.67"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NC_2006_2010_silv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plots are a commonly used tool for data exploration. They allow us to graphically view the relationship between 2 or more variables.  Before performing any clustering, let's inspect the distribution of the observations in our dataframe. To do this, we need to call the toolkits collection from Matplotlib. Mplot3d will provide us with the tools we need for generating a 3D scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below will generate a 3D scatter plot of our 3 columns of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d_plot(df, x, y, z):\n",
    "    \"\"\"\n",
    "    Generates 3D scatterplot based on 3 user specified columns\n",
    "    df = Dataframe containing data to be plotted\n",
    "    x = Column to represent the X-axis\n",
    "    y = Column to represent the Y-axis\n",
    "    z = Column to represent the z-axis\n",
    "    \"\"\"\n",
    "    dplot = plt.figure().gca(projection='3d')\n",
    "    dplot.scatter(df[x],df[y], df[z])\n",
    "    dplot.set_xlabel(f\"{x}\")\n",
    "    dplot.set_ylabel(f\"{y}\")\n",
    "    dplot.set_zlabel(f\"{z}\")\n",
    "    \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And execute it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot = d_plot(NC_2006_2010_silv,'ForAreaLoss','GrsAreaGain','SscbAreaGain')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting the optimal number of clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below runs the K-means numerous times based on the range of clusters defined by the user and provides plots for the inertia as well as the silhouette score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmeans_cluster(df, min, max):\n",
    "    \"\"\"\n",
    "    Runs a user-defined range of k-means clusters and plots inertia to help user determine optimal number of clusters\n",
    "    \n",
    "    df: Name of pandas dataframe being used for cluster analysis\n",
    "    min: Minimum number of clusters\n",
    "    max: Maximum number of clusters\n",
    "    \n",
    "    \"\"\"\n",
    "    max_cluster_model = max + 1\n",
    "    clusters = range(min, max_cluster_model)\n",
    "    inertias = []\n",
    "    sil_scores = []\n",
    "    \n",
    "    for k in clusters:\n",
    "        # Create a KMeans instance with k clusters: model\n",
    "        model = KMeans(n_clusters=k)\n",
    "    \n",
    "        # Fit model to samples\n",
    "        model.fit(df)\n",
    "        \n",
    "        # Determine the cluster labels of the points: labels\n",
    "        labels = model.predict(df)\n",
    "                \n",
    "        # Append the inertia to the list of inertias\n",
    "        inertias.append(model.inertia_)\n",
    "        \n",
    "        # Append the silhouette scores to the list of scores\n",
    "        sil_scores.append(metrics.silhouette_score(df, labels, metric='euclidean'))\n",
    "        \n",
    "   \n",
    "    # Plot inertias\n",
    "    plt.plot(clusters, inertias, '-o')\n",
    "    plt.xlabel('Number of clusters, k')\n",
    "    plt.ylabel('Inertia')\n",
    "    plt.xlim(min, max)\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    # Plot Silhouette Scores\n",
    "    plt.plot(clusters, sil_scores, '-o')\n",
    "    plt.xlabel('Number of clusters, k')\n",
    "    plt.ylabel('Avg Silhoette Score')\n",
    "    plt.xlim(min, max)\n",
    "   \n",
    "    plt.show()\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the function and let's review the resulting graphs. In the example below, we run the clustering algorithm nine times using the range of two through 10 for the number of clusters k. We start with two because you cannot generate a Silhouette score  based on one cluster and will result in an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "NC_clusters = kmeans_cluster(NC_2006_2010_silv,2,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have performed some clustering, lets write a function that will generate a scatter plot with the data points color coded by their associated cluster.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_plot(df, n, x, y, z):\n",
    "    \"\"\"\n",
    "    Takes labels from k-means clustering and joins them to Geopandas DataFrame\n",
    "    \n",
    "    df: Name of pandas dataframe being used for cluster analysis\n",
    "    n: number of clusters for k-means clustering\n",
    "    x = Column to represent the X-axis\n",
    "    y = Column to represent the Y-axis\n",
    "    z = Column to represent the z-axis\n",
    "    \"\"\"\n",
    "    \n",
    "    # Create a KMeans instance with X # of clusters: model\n",
    "    model = KMeans(n)\n",
    "\n",
    "    # Fit model to points\n",
    "    model.fit(df)\n",
    "\n",
    "    # Determine the cluster labels of new_points: labels\n",
    "    labels = model.predict(df)\n",
    "    \n",
    "    # Rescale cluster labels to start with the number 1\n",
    "    # labels=optimal_cluster\n",
    "    map_labels = []\n",
    "\n",
    "    for i in labels:\n",
    "      i = i + 1\n",
    "      map_labels.append(i)\n",
    "    \n",
    "    #Scatterplot\n",
    "    dplot = plt.figure().gca(projection='3d')\n",
    "    dplot.scatter(df[x],df[y], df[z], c=map_labels, s=50, cmap='Paired')\n",
    "    dplot.set_xlabel(f\"{x}\")\n",
    "    dplot.set_ylabel(f\"{y}\")\n",
    "    dplot.set_zlabel(f\"{z}\")\n",
    "    \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "scatter = cluster_plot(NC_2006_2010_silv,3,'ForAreaLoss','GrsAreaGain','SscbAreaGain')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By visualizing the resulting clusters with the scatter plot, we can see that blue and orange clusters represent low and moderate forest loss and regrowth while the brown cluster is associated with more extreme forest loss. Another way we may be able to gain additional insight is to review the spatial distribution for our resulting clusters. The function below, takes the labels from a k-means out and maps them so we can look at their spatial distribution.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_clusters(df, n, gdf):\n",
    "    \"\"\"\n",
    "    Takes labels from k-means clustering and joins them to Geopandas DataFrame\n",
    "    \n",
    "    df: Name of pandas dataframe being used for cluster analysis\n",
    "    n: number of clusters for k-means clustering\n",
    "    gdf: geodataframe representing the geography of interest\n",
    "    \"\"\"\n",
    "    \n",
    "    # Create a KMeans instance with X # of clusters: model\n",
    "    model = KMeans(n)\n",
    "\n",
    "    # Fit model to points\n",
    "    model.fit(df)\n",
    "\n",
    "    # Determine the cluster labels of new_points: labels\n",
    "    labels = model.predict(df)\n",
    "    \n",
    "    # Rescale cluster labels to start with the number 1\n",
    "    map_labels = []\n",
    "\n",
    "    for i in labels:\n",
    "        i = i + 1\n",
    "        map_labels.append(i)\n",
    "    \n",
    "    # Create dictionary of cluster labels and convert to a dataframe\n",
    "    labels_dict = {'label':map_labels}\n",
    "    labels_df = pd.DataFrame(labels_dict, index=df.index)\n",
    "\n",
    "    #Join the labels to the geodataframe and fill the NaN values for non-coastal counties\n",
    "    counties_cluster = gdf.join(labels_df, on='geoId').fillna(0)\n",
    "    #Remove the non-coastal counties (Counties with a 0 label value)\n",
    "    counties_cluster = counties_cluster[counties_cluster.label != 0]\n",
    "    \n",
    "    #Plot the map\n",
    "    cluster_map = counties_cluster.plot(cmap='Paired',column='label')\n",
    "    cluster_map.set_axis_off()\n",
    "   \n",
    "    return counties_cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the function so the map can be displayed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "map = map_clusters(NC_2006_2010_silv, 3, counties_gdf)"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}