Finding Zones for a New Restaurant Using Clustering (V0).ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:purple\">Clustering: </span><span>Finding Restaurant Clusters for Retail Site Selection</span>\n",
    "\n",
    "![clustering](img/restaurants_clustering_v2.gif \"Density-based Clustering\")\n",
    "\n",
    "\n",
    "## An Example of Spatial Analysis in ArcGIS Using Density-based Clustering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prospecting a successful retail location requires a thorough understanding of where people shop, dine, and linger for entertainment in a study area. One useful way to start making sense of the vast quantities of retail business information and start finding the locations that matter is to use spatial clustering to find locations with a dense retail presence. \n",
    "\n",
    "This example starts with retail business data in Pittsburgh, PA and leverages a Spatial Statistics tool in ArcGIS named [Density-based Clustering](http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/densitybasedclustering.htm) to separate the dense retail location data into clusters and noise. These clusters can then be the target of further analysis and potential decisions for your organization. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# [Spatial Analysis Methodology](http://www.esri.com/library/books/the-language-of-spatial-analysis.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 1. Ask questions: \n",
    "Formulate hypotheses and spatial\n",
    "questions.\n",
    "\n",
    "### 2. Explore the data: \n",
    "Examine the data quality,\n",
    "completeness, and measurement limitations (scale\n",
    "and resolution) to determine the level of analysis and\n",
    "interpretation that can be supported.\n",
    "\n",
    "### 3. Analyze and model: \n",
    "Break the problem down into\n",
    "solvable components that can be modeled. Quantify\n",
    "and evaluate the spatial questions.\n",
    "\n",
    "### 4. Interpret the results: \n",
    "Evaluate and analyze the results\n",
    "in the context of the question posed, data limitations,\n",
    "accuracy, and other implications.\n",
    "\n",
    "### 5. Repeat as necessary: \n",
    "Spatial analysis is a continuous\n",
    "and iterative process that often leads to further\n",
    "questions and refinements.\n",
    "\n",
    "### 6. Present the results: \n",
    "The best information and\n",
    "analysis becomes increasingly valuable when it can be\n",
    "effectively presented and shared with a larger audience.\n",
    "\n",
    "### 7. Make a decision: \n",
    "\n",
    "Spatial analysis and GIS are used to support the \n",
    "decision-making process. A successful spatial analysis \n",
    "process often leads to the understanding necessary to \n",
    "drive decisions and action."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# <span style=\"color:purple\">1. Ask Questions</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Where are the locations in Pittsburgh, PA with dense restaurant retail presence?\n",
    "\n",
    "#### Where are the clusters of restaurants with the highest average revenue? \n",
    "\n",
    "#### Are there clusters of restaurants whose collective revenue is greater than expected?\n",
    "\n",
    "#### Is there a relationship between average restaurant cluster revenue and demographics?\n",
    "\n",
    "#### Is there a relationship between average restaurant cluster revenue and transportation access? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Hypotheses: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Where are the locations in Pittsburgh, PA with dense restaurant retail presence?\n",
    "\n",
    "- Dense restaurant clusters in Pittsburgh will likely be located in the CBD.\n",
    "\n",
    "- Dense restaurant clusters in Pittsburgh will likely be located in near proximity to intersections of heavily trafficked roads.\n",
    "\n",
    "### Where are the clusters of restaurants with the highest average revenue? \n",
    "\n",
    "- Clusters with the highest average revenue will likely be situated near downtown Pittsburgh.\n",
    "- Clusters with the highest average revenue will likely be situated in neighborhoods with higher average income levels. \n",
    "- Clusters with the highest average revenue will likely be situated along major thoroughfares and near major public transit routes. \n",
    "\n",
    "### Are there clusters of restaurants whose collective revenue is greater than expected (i.e. revenue outliers)?\n",
    "\n",
    "- There may be clusters with collective revenue outlier values in neighborhoods with a growing population. \n",
    "- There may be clusters with collective revenue outlier values in areas that recently underwent major redevelopment.  \n",
    "\n",
    "### Is there a relationship between average restaurant cluster revenue and demographics?\n",
    "\n",
    "- When considering service areas for each restaurant, there is likely positive correlation between socioeconomic status and average cluster revenue. \n",
    "\n",
    "### Is there a relationship between average restaurant cluster revenue and transportation access? \n",
    "\n",
    "- When considering service areas for each restaurant, there is likely positive correlation between average cluster revenue and proximity of the cluster to major thoroughfares. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:purple\">2. Explore the Data</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Preliminary Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Import the needed modules, including ArcPy, the ArcGIS API for Python, and other useful modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import arcpy\n",
    "import arcgis\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Set reference paths to locally available data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Path to File Geodatabase containing available data\n",
    "file_geodatabase = \"data//retail.gdb\"\n",
    "\n",
    "# Path to retail business data\n",
    "businesses_fc = file_geodatabase + \"//us_businesses_pittsburgh\"\n",
    "restaurants_fc = file_geodatabase + \"//restaurants_pittsburgh\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Optional step: Parse businesses feature class by NAICS code to retrieve restaurants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Exploring data via Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
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       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>5.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>92.4219</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8894393.918535315, 'y': 4844038.0720063...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>403988914</td>\n",
       "      <td>RIVERSIDE DINER</td>\n",
       "      <td>PENN ST</td>\n",
       "      <td>POINT MARION</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15474</td>\n",
       "      <td>1234</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>4.0</td>\n",
       "      <td></td>\n",
       "      <td>t</td>\n",
       "      <td>A</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8894449.578280712, 'y': 4828144.5492451...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>422091394</td>\n",
       "      <td>CONNIE PIZZAS-N-PIES</td>\n",
       "      <td>PENN ST</td>\n",
       "      <td>POINT MARION</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15474</td>\n",
       "      <td>1235</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>4.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8894349.390739, 'y': 4828101.11945761, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>426019153</td>\n",
       "      <td>BETTY &amp; NAN'S</td>\n",
       "      <td>FILBERT ORIENT RD</td>\n",
       "      <td>NEW SALEM</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15468</td>\n",
       "      <td>1148</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8889384.541449623, 'y': 4858693.5948817...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>427143428</td>\n",
       "      <td>MULLEN'S IRISH HUB</td>\n",
       "      <td>MAIN ST</td>\n",
       "      <td>REPUBLIC</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15475</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8892067.341177732, 'y': 4860566.9961773...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>459189593</td>\n",
       "      <td>APPLE ANNIE'S</td>\n",
       "      <td>CHURCH ST</td>\n",
       "      <td>POINT MARION</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15474</td>\n",
       "      <td>1267</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8894371.654637156, 'y': 4828303.7934168...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>614075166</td>\n",
       "      <td>PIZZA HUT</td>\n",
       "      <td>NATIONAL PIKE</td>\n",
       "      <td>GRINDSTONE</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15442</td>\n",
       "      <td>1106</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8890931.882371644, 'y': 4867235.6875473...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>647664945</td>\n",
       "      <td>LARDIN HOUSE INN</td>\n",
       "      <td>MCCLELLANDTOWN RD</td>\n",
       "      <td>MASONTOWN</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15461</td>\n",
       "      <td>2508</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>3.0</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8893926.376673985, 'y': 4846474.3255170...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>831827324</td>\n",
       "      <td>DOUBLE T BAR &amp; GRILL</td>\n",
       "      <td>MCCLELLANDTOWN RD</td>\n",
       "      <td>MC CLELLANDTOWN</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15458</td>\n",
       "      <td>1101</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>3.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8891688.854909034, 'y': 4848693.5754889...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>831898861</td>\n",
       "      <td>GEORGIO'S SUBS &amp; PIZZAS</td>\n",
       "      <td>HIGH ST</td>\n",
       "      <td>BROWNSVILLE</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15417</td>\n",
       "      <td>2164</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8894282.599044519, 'y': 4867758.9117359...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>893928549</td>\n",
       "      <td>SUBWAY</td>\n",
       "      <td>RR 1 BOX</td>\n",
       "      <td>MASONTOWN</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15461</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8895752.01632299, 'y': 4844589.07715973...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>946970027</td>\n",
       "      <td>MC DONALD'S</td>\n",
       "      <td>ROBERTS RD</td>\n",
       "      <td>GRINDSTONE</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15442</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>K</td>\n",
       "      <td>e</td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8890542.26415387, 'y': 4867584.50060682...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>968687756</td>\n",
       "      <td>SUBWAY</td>\n",
       "      <td>N MAIN ST</td>\n",
       "      <td>POINT MARION</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15474</td>\n",
       "      <td>1219</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8894438.44633163, 'y': 4828390.65494555...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>118605138</td>\n",
       "      <td>PIZZA HUT</td>\n",
       "      <td>DANIEL DR</td>\n",
       "      <td>UNIONTOWN</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15401</td>\n",
       "      <td>8002</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8878608.814740825, 'y': 4851827.4774533...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>118621994</td>\n",
       "      <td>GOAL POST RESTAURANT &amp; LOUNGE</td>\n",
       "      <td>N MORGANTOWN ST</td>\n",
       "      <td>FAIRCHANCE</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15436</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8878163.536777662, 'y': 4840703.6915741...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>226915429</td>\n",
       "      <td>TWINS TREATS</td>\n",
       "      <td>N MORGANTOWN ST</td>\n",
       "      <td>FAIRCHANCE</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15436</td>\n",
       "      <td>1038</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>5.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8878085.613134094, 'y': 4840689.1967055...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>248313066</td>\n",
       "      <td>RUBY TUESDAY</td>\n",
       "      <td>MATTHEW DR</td>\n",
       "      <td>UNIONTOWN</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15401</td>\n",
       "      <td>8417</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>y</td>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8878230.328472134, 'y': 4852364.4023489...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>4997</td>\n",
       "      <td>514253285</td>\n",
       "      <td>GARDA'S</td>\n",
       "      <td>STATE ROUTE 66</td>\n",
       "      <td>FORD CITY</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16226</td>\n",
       "      <td>4425</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>47.0</td>\n",
       "      <td></td>\n",
       "      <td>I</td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8853773.436344847, 'y': 4971437.4882425...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>4998</td>\n",
       "      <td>544208978</td>\n",
       "      <td>MEL'S PIZZERIA</td>\n",
       "      <td>BUTLER RD</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>2170</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>35.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8853662.11685406, 'y': 4984401.09493216...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>4999</td>\n",
       "      <td>544209794</td>\n",
       "      <td>ATTABOYZ</td>\n",
       "      <td>S JEFFERSON ST</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>2409</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8852081.380084794, 'y': 4984915.8789673...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>5000</td>\n",
       "      <td>618872824</td>\n",
       "      <td>THERESA'S ROYLE ALLEGHENY</td>\n",
       "      <td>BUTLER RD</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>4403</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>20.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8853817.96414117, 'y': 4984960.00457681...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>5001</td>\n",
       "      <td>970387031</td>\n",
       "      <td>VOCELLI PIZZA</td>\n",
       "      <td>S WATER ST</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>2434</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8852148.171779273, 'y': 4984812.9199878...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>5002</td>\n",
       "      <td>986413763</td>\n",
       "      <td>HOTSPOT HOTEL</td>\n",
       "      <td>N GRANT AVE</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>1490</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>5.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8852103.64398295, 'y': 4985577.78406131...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5002</th>\n",
       "      <td>5003</td>\n",
       "      <td>003260080</td>\n",
       "      <td>CADET RESTAURANT</td>\n",
       "      <td>STATE ROUTE 422</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>3546</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8848886.51069902, 'y': 4983724.56292264...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5003</th>\n",
       "      <td>5004</td>\n",
       "      <td>118944875</td>\n",
       "      <td>MARSHA'S PLACE</td>\n",
       "      <td>STALEYS COURTS RD</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>3718</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>3.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8848474.628583089, 'y': 4986019.0790697...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>5005</td>\n",
       "      <td>252108709</td>\n",
       "      <td>TWIN PINES TAVERN</td>\n",
       "      <td>MCGREGOR RD</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>3746</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8848519.156379411, 'y': 4986033.7892470...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>5006</td>\n",
       "      <td>427537805</td>\n",
       "      <td>VILLA ROSA ITALIAN RESTAURANT</td>\n",
       "      <td></td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td>I</td>\n",
       "      <td>A</td>\n",
       "      <td>Postal</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8849008.962138897, 'y': 4984062.8230652...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>5007</td>\n",
       "      <td>970386413</td>\n",
       "      <td>SUPER SHOOTERS SPORTS BAR</td>\n",
       "      <td>US ROUTE 422</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>4045</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>5.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8844578.446405323, 'y': 4980945.3908082...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>5008</td>\n",
       "      <td>118959865</td>\n",
       "      <td>CHARLEY'S PLACE RESTAURANT</td>\n",
       "      <td>W MAIN ST</td>\n",
       "      <td>RURAL VALLEY</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16249</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8830830.489292355, 'y': 4982856.9057250...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5008</th>\n",
       "      <td>5009</td>\n",
       "      <td>118959998</td>\n",
       "      <td>MAMA'S PIZZA &amp; SOFT SERVE</td>\n",
       "      <td>W MAIN ST</td>\n",
       "      <td>RURAL VALLEY</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16249</td>\n",
       "      <td></td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8829249.752523094, 'y': 4982783.3789990...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5009</th>\n",
       "      <td>5010</td>\n",
       "      <td>316965193</td>\n",
       "      <td>SANDWICH HOUSE</td>\n",
       "      <td>RR 422</td>\n",
       "      <td>ELDERTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15736</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>5.0</td>\n",
       "      <td></td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>StreetName</td>\n",
       "      <td>M</td>\n",
       "      <td>85.7031</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8832500.281654252, 'y': 4967384.3794728...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5010</th>\n",
       "      <td>5011</td>\n",
       "      <td>400143168</td>\n",
       "      <td>VALLEY GETAWAY</td>\n",
       "      <td>STATE ROUTE 85</td>\n",
       "      <td>KITTANNING</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16201</td>\n",
       "      <td>8167</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8832544.809450574, 'y': 4983092.1949702...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5011</th>\n",
       "      <td>5012</td>\n",
       "      <td>401360941</td>\n",
       "      <td>TINAS LOG CABIN</td>\n",
       "      <td>RR 422</td>\n",
       "      <td>ELDERTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15736</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>Postal</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8832266.510723587, 'y': 4967457.7904193...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5012</th>\n",
       "      <td>5013</td>\n",
       "      <td>402545577</td>\n",
       "      <td>MITZI'S PLACE</td>\n",
       "      <td>E MAIN ST</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16222</td>\n",
       "      <td></td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8821056.638000706, 'y': 4994687.2178030...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5013</th>\n",
       "      <td>5014</td>\n",
       "      <td>403546354</td>\n",
       "      <td>FOX'S PIZZA DEN</td>\n",
       "      <td>WHITE OAK RD</td>\n",
       "      <td>RURAL VALLEY</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16249</td>\n",
       "      <td>3619</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>=</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8825865.640002979, 'y': 4977152.8767819...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5014</th>\n",
       "      <td>5015</td>\n",
       "      <td>415340501</td>\n",
       "      <td>FROSTY'S FROZEN DELIGHTS</td>\n",
       "      <td>WHITE OAK RD</td>\n",
       "      <td>RURAL VALLEY</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16249</td>\n",
       "      <td>3619</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>StreetAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8825865.640002979, 'y': 4977152.8767819...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5015</th>\n",
       "      <td>5016</td>\n",
       "      <td>415340502</td>\n",
       "      <td>TINA'S LOG CABIN RESTAURANT</td>\n",
       "      <td>RT 422</td>\n",
       "      <td>ELDERTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15736</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>StreetName</td>\n",
       "      <td>M</td>\n",
       "      <td>97.0625</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8832500.281654252, 'y': 4967384.3794728...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5016</th>\n",
       "      <td>5017</td>\n",
       "      <td>417708702</td>\n",
       "      <td>KEYSTONE INN</td>\n",
       "      <td>RR 2</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16222</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>Postal</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8820889.658764523, 'y': 4993892.1948145...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5017</th>\n",
       "      <td>5018</td>\n",
       "      <td>425562338</td>\n",
       "      <td>COUNTRY PALACE</td>\n",
       "      <td>FAIRVIEW RD</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16222</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>3.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>Postal</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8820889.658764523, 'y': 4993892.1948145...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5018</th>\n",
       "      <td>5019</td>\n",
       "      <td>461808859</td>\n",
       "      <td>SHAFFER'S PIZZA</td>\n",
       "      <td>ELKHORN RD</td>\n",
       "      <td>MAYPORT</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16240</td>\n",
       "      <td></td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>Postal</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8817895.16446218, 'y': 5017062.80294019...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5019</th>\n",
       "      <td>5020</td>\n",
       "      <td>607247178</td>\n",
       "      <td>SUBWAY</td>\n",
       "      <td>3RD ST</td>\n",
       "      <td>RURAL VALLEY</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16249</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>StreetName</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8820166.082074365, 'y': 4979622.2535225...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5020</th>\n",
       "      <td>5021</td>\n",
       "      <td>607251600</td>\n",
       "      <td>BUZZARD'S PIZZA &amp; SUBS</td>\n",
       "      <td>STATE ROUTE 1037</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16222</td>\n",
       "      <td>3914</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8830785.96149604, 'y': 4990447.84429529...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5021</th>\n",
       "      <td>5022</td>\n",
       "      <td>613174333</td>\n",
       "      <td>MILLER'S HOAGIES</td>\n",
       "      <td>STATE ROUTE 422</td>\n",
       "      <td>ELDERTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15736</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>5.0</td>\n",
       "      <td></td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>97.0625</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8832500.281654252, 'y': 4967281.6050732...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>5023</td>\n",
       "      <td>614159796</td>\n",
       "      <td>DIMAIO RISTORANTE</td>\n",
       "      <td>W MAIN ST</td>\n",
       "      <td>RURAL VALLEY</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16249</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td>I</td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8830875.017088676, 'y': 4982842.2003355...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5023</th>\n",
       "      <td>5024</td>\n",
       "      <td>647054824</td>\n",
       "      <td>OLD TYME DINER</td>\n",
       "      <td>KITTANNING AVE</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16222</td>\n",
       "      <td></td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>8.0</td>\n",
       "      <td></td>\n",
       "      <td>t</td>\n",
       "      <td>B</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8821646.631301912, 'y': 4994333.8662458...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5024</th>\n",
       "      <td>5025</td>\n",
       "      <td>690613609</td>\n",
       "      <td>FOX'S PIZZA DEN</td>\n",
       "      <td>US ROUTE 422</td>\n",
       "      <td>SHELOCTA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15774</td>\n",
       "      <td>2235</td>\n",
       "      <td>72251115</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>=</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8830062.384805882, 'y': 4964184.1976627...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5025</th>\n",
       "      <td>5026</td>\n",
       "      <td>872614821</td>\n",
       "      <td>FINAL SCORE SPORTS SALOON</td>\n",
       "      <td>STATE ROUTE 85</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>16222</td>\n",
       "      <td>2820</td>\n",
       "      <td>72251117</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>4.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>PointAddress</td>\n",
       "      <td>M</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>INFOGROUP</td>\n",
       "      <td>{'x': -8822381.339941151, 'y': 4982798.0842999...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5026 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      OBJECTID     LOCNUM                         CONAME             STREET  \\\n",
       "0            1  402036338                      RUDNICK'S        TEN MILE RD   \n",
       "1            2  387410335                  RIVERSIDE INN           FRONT ST   \n",
       "2            3  831505334      RIVIERA RESTAURANT LOUNGE           FRONT ST   \n",
       "3            4  907890941        KUKI CHINESE RESTAURANT           E 3RD ST   \n",
       "4            5  309912996                        DOLFI'S          RIVER AVE   \n",
       "5            6  439157900                      PIZZA HUT         REDWOOD ST   \n",
       "6            7  118627041                  JANET'S DINER  MCCLELLANDTOWN RD   \n",
       "7            8  214799900                   CHINA GARDEN  MCCLELLANDTOWN RD   \n",
       "8            9  217577758   SPORTY'S PIZZA OVEN & GRILLE            PENN ST   \n",
       "9           10  269742656                         SUBWAY         ROBERTS RD   \n",
       "10          11  300212255     JOHNNY'S FAMILY RESTAURANT             DUNLAP   \n",
       "11          12  316965672                         SAULIE          S MAIN ST   \n",
       "12          13  379284433                  COUNTRY PIZZA      THOMPSON 2 RD   \n",
       "13          14  403076167                        PAESANO          S MAIN ST   \n",
       "14          15  403988914                RIVERSIDE DINER            PENN ST   \n",
       "15          16  422091394           CONNIE PIZZAS-N-PIES            PENN ST   \n",
       "16          17  426019153                  BETTY & NAN'S  FILBERT ORIENT RD   \n",
       "17          18  427143428             MULLEN'S IRISH HUB            MAIN ST   \n",
       "18          19  459189593                  APPLE ANNIE'S          CHURCH ST   \n",
       "19          20  614075166                      PIZZA HUT      NATIONAL PIKE   \n",
       "20          21  647664945               LARDIN HOUSE INN  MCCLELLANDTOWN RD   \n",
       "21          22  831827324           DOUBLE T BAR & GRILL  MCCLELLANDTOWN RD   \n",
       "22          23  831898861        GEORGIO'S SUBS & PIZZAS            HIGH ST   \n",
       "23          24  893928549                         SUBWAY           RR 1 BOX   \n",
       "24          25  946970027                    MC DONALD'S         ROBERTS RD   \n",
       "25          26  968687756                         SUBWAY          N MAIN ST   \n",
       "26          27  118605138                      PIZZA HUT          DANIEL DR   \n",
       "27          28  118621994  GOAL POST RESTAURANT & LOUNGE    N MORGANTOWN ST   \n",
       "28          29  226915429                   TWINS TREATS    N MORGANTOWN ST   \n",
       "29          30  248313066                   RUBY TUESDAY         MATTHEW DR   \n",
       "...        ...        ...                            ...                ...   \n",
       "4996      4997  514253285                        GARDA'S     STATE ROUTE 66   \n",
       "4997      4998  544208978                 MEL'S PIZZERIA          BUTLER RD   \n",
       "4998      4999  544209794                       ATTABOYZ     S JEFFERSON ST   \n",
       "4999      5000  618872824      THERESA'S ROYLE ALLEGHENY          BUTLER RD   \n",
       "5000      5001  970387031                  VOCELLI PIZZA         S WATER ST   \n",
       "5001      5002  986413763                  HOTSPOT HOTEL        N GRANT AVE   \n",
       "5002      5003  003260080               CADET RESTAURANT    STATE ROUTE 422   \n",
       "5003      5004  118944875                 MARSHA'S PLACE  STALEYS COURTS RD   \n",
       "5004      5005  252108709              TWIN PINES TAVERN        MCGREGOR RD   \n",
       "5005      5006  427537805  VILLA ROSA ITALIAN RESTAURANT                      \n",
       "5006      5007  970386413      SUPER SHOOTERS SPORTS BAR       US ROUTE 422   \n",
       "5007      5008  118959865     CHARLEY'S PLACE RESTAURANT          W MAIN ST   \n",
       "5008      5009  118959998      MAMA'S PIZZA & SOFT SERVE          W MAIN ST   \n",
       "5009      5010  316965193                 SANDWICH HOUSE             RR 422   \n",
       "5010      5011  400143168                 VALLEY GETAWAY     STATE ROUTE 85   \n",
       "5011      5012  401360941                TINAS LOG CABIN             RR 422   \n",
       "5012      5013  402545577                  MITZI'S PLACE          E MAIN ST   \n",
       "5013      5014  403546354                FOX'S PIZZA DEN       WHITE OAK RD   \n",
       "5014      5015  415340501       FROSTY'S FROZEN DELIGHTS       WHITE OAK RD   \n",
       "5015      5016  415340502    TINA'S LOG CABIN RESTAURANT             RT 422   \n",
       "5016      5017  417708702                   KEYSTONE INN               RR 2   \n",
       "5017      5018  425562338                 COUNTRY PALACE        FAIRVIEW RD   \n",
       "5018      5019  461808859                SHAFFER'S PIZZA         ELKHORN RD   \n",
       "5019      5020  607247178                         SUBWAY             3RD ST   \n",
       "5020      5021  607251600         BUZZARD'S PIZZA & SUBS   STATE ROUTE 1037   \n",
       "5021      5022  613174333               MILLER'S HOAGIES    STATE ROUTE 422   \n",
       "5022      5023  614159796              DIMAIO RISTORANTE          W MAIN ST   \n",
       "5023      5024  647054824                 OLD TYME DINER     KITTANNING AVE   \n",
       "5024      5025  690613609                FOX'S PIZZA DEN       US ROUTE 422   \n",
       "5025      5026  872614821      FINAL SCORE SPORTS SALOON     STATE ROUTE 85   \n",
       "\n",
       "                 CITY STATE    STATE_NAME    ZIP  ZIP4     NAICS  \\\n",
       "0            MARIANNA    PA  Pennsylvania  15345  1080  72251117   \n",
       "1       FREDERICKTOWN    PA  Pennsylvania  15333        72251117   \n",
       "2       FREDERICKTOWN    PA  Pennsylvania  15333        72251117   \n",
       "3           BETHLEHEM    PA  Pennsylvania  18015  1306  72251117   \n",
       "4           MASONTOWN    PA  Pennsylvania  15461  1551  72251117   \n",
       "5           MASONTOWN    PA  Pennsylvania  15461        72251115   \n",
       "6           MASONTOWN    PA  Pennsylvania  15461  2504  72251117   \n",
       "7           MASONTOWN    PA  Pennsylvania  15461  2508  72251117   \n",
       "8        POINT MARION    PA  Pennsylvania  15474  1235  72251115   \n",
       "9          GRINDSTONE    PA  Pennsylvania  15442  1105  72251117   \n",
       "10           REPUBLIC    PA  Pennsylvania  15475        72251117   \n",
       "11       POINT MARION    PA  Pennsylvania  15474  1224  72251117   \n",
       "12           REPUBLIC    PA  Pennsylvania  15475        72251115   \n",
       "13          MASONTOWN    PA  Pennsylvania  15461  2044  72251115   \n",
       "14       POINT MARION    PA  Pennsylvania  15474  1234  72251117   \n",
       "15       POINT MARION    PA  Pennsylvania  15474  1235  72251115   \n",
       "16          NEW SALEM    PA  Pennsylvania  15468  1148  72251117   \n",
       "17           REPUBLIC    PA  Pennsylvania  15475        72251117   \n",
       "18       POINT MARION    PA  Pennsylvania  15474  1267  72251117   \n",
       "19         GRINDSTONE    PA  Pennsylvania  15442  1106  72251115   \n",
       "20          MASONTOWN    PA  Pennsylvania  15461  2508  72251117   \n",
       "21    MC CLELLANDTOWN    PA  Pennsylvania  15458  1101  72251117   \n",
       "22        BROWNSVILLE    PA  Pennsylvania  15417  2164  72251117   \n",
       "23          MASONTOWN    PA  Pennsylvania  15461        72251117   \n",
       "24         GRINDSTONE    PA  Pennsylvania  15442        72251117   \n",
       "25       POINT MARION    PA  Pennsylvania  15474  1219  72251117   \n",
       "26          UNIONTOWN    PA  Pennsylvania  15401  8002  72251115   \n",
       "27         FAIRCHANCE    PA  Pennsylvania  15436        72251117   \n",
       "28         FAIRCHANCE    PA  Pennsylvania  15436  1038  72251117   \n",
       "29          UNIONTOWN    PA  Pennsylvania  15401  8417  72251117   \n",
       "...               ...   ...           ...    ...   ...       ...   \n",
       "4996        FORD CITY    PA  Pennsylvania  16226  4425  72251117   \n",
       "4997       KITTANNING    PA  Pennsylvania  16201  2170  72251115   \n",
       "4998       KITTANNING    PA  Pennsylvania  16201  2409  72251117   \n",
       "4999       KITTANNING    PA  Pennsylvania  16201  4403  72251117   \n",
       "5000       KITTANNING    PA  Pennsylvania  16201  2434  72251115   \n",
       "5001       KITTANNING    PA  Pennsylvania  16201  1490  72251117   \n",
       "5002       KITTANNING    PA  Pennsylvania  16201  3546  72251117   \n",
       "5003       KITTANNING    PA  Pennsylvania  16201  3718  72251117   \n",
       "5004       KITTANNING    PA  Pennsylvania  16201  3746  72251115   \n",
       "5005       KITTANNING    PA  Pennsylvania  16201        72251117   \n",
       "5006       KITTANNING    PA  Pennsylvania  16201  4045  72251117   \n",
       "5007     RURAL VALLEY    PA  Pennsylvania  16249        72251117   \n",
       "5008     RURAL VALLEY    PA  Pennsylvania  16249        72251115   \n",
       "5009         ELDERTON    PA  Pennsylvania  15736        72251117   \n",
       "5010       KITTANNING    PA  Pennsylvania  16201  8167  72251117   \n",
       "5011         ELDERTON    PA  Pennsylvania  15736        72251117   \n",
       "5012           DAYTON    PA  Pennsylvania  16222        72251115   \n",
       "5013     RURAL VALLEY    PA  Pennsylvania  16249  3619  72251115   \n",
       "5014     RURAL VALLEY    PA  Pennsylvania  16249  3619  72251117   \n",
       "5015         ELDERTON    PA  Pennsylvania  15736        72251117   \n",
       "5016           DAYTON    PA  Pennsylvania  16222        72251117   \n",
       "5017           DAYTON    PA  Pennsylvania  16222        72251117   \n",
       "5018          MAYPORT    PA  Pennsylvania  16240        72251115   \n",
       "5019     RURAL VALLEY    PA  Pennsylvania  16249        72251117   \n",
       "5020           DAYTON    PA  Pennsylvania  16222  3914  72251115   \n",
       "5021         ELDERTON    PA  Pennsylvania  15736        72251117   \n",
       "5022     RURAL VALLEY    PA  Pennsylvania  16249        72251117   \n",
       "5023           DAYTON    PA  Pennsylvania  16222        72251117   \n",
       "5024         SHELOCTA    PA  Pennsylvania  15774  2235  72251115   \n",
       "5025           DAYTON    PA  Pennsylvania  16222  2820  72251117   \n",
       "\n",
       "                            ...                         HDBRCH  EMPNUM FRNCOD  \\\n",
       "0                           ...                                    6.0          \n",
       "1                           ...                                    5.0          \n",
       "2                           ...                                    5.0          \n",
       "3                           ...                                    3.0          \n",
       "4                           ...                                   10.0          \n",
       "5                           ...                              2    23.0      M   \n",
       "6                           ...                                    5.0          \n",
       "7                           ...                                    2.0          \n",
       "8                           ...                                    2.0          \n",
       "9                           ...                              2    15.0      R   \n",
       "10                          ...                                    1.0          \n",
       "11                          ...                                    1.0          \n",
       "12                          ...                                    4.0          \n",
       "13                          ...                                    5.0          \n",
       "14                          ...                                    4.0          \n",
       "15                          ...                                    4.0          \n",
       "16                          ...                                    6.0          \n",
       "17                          ...                                    6.0          \n",
       "18                          ...                                    9.0          \n",
       "19                          ...                              2    17.0      M   \n",
       "20                          ...                                    3.0          \n",
       "21                          ...                                    3.0          \n",
       "22                          ...                                    9.0          \n",
       "23                          ...                              2     7.0      R   \n",
       "24                          ...                              2    50.0      K   \n",
       "25                          ...                              2    11.0      R   \n",
       "26                          ...                              2    18.0      M   \n",
       "27                          ...                                    6.0          \n",
       "28                          ...                                    5.0          \n",
       "29                          ...                              2    45.0      y   \n",
       "...                         ...                            ...     ...    ...   \n",
       "4996                        ...                                   47.0          \n",
       "4997                        ...                                   35.0          \n",
       "4998                        ...                                    2.0          \n",
       "4999                        ...                                   20.0          \n",
       "5000                        ...                              2    16.0          \n",
       "5001                        ...                                    5.0          \n",
       "5002                        ...                                    6.0          \n",
       "5003                        ...                                    3.0          \n",
       "5004                        ...                                    1.0          \n",
       "5005                        ...                                    6.0          \n",
       "5006                        ...                                    5.0          \n",
       "5007                        ...                                    6.0          \n",
       "5008                        ...                                    1.0          \n",
       "5009                        ...                                    5.0          \n",
       "5010                        ...                                    2.0          \n",
       "5011                        ...                                    6.0          \n",
       "5012                        ...                                    1.0          \n",
       "5013                        ...                              2    10.0      =   \n",
       "5014                        ...                                    6.0          \n",
       "5015                        ...                                    6.0          \n",
       "5016                        ...                                    6.0          \n",
       "5017                        ...                                    3.0          \n",
       "5018                        ...                                    6.0          \n",
       "5019                        ...                              2     4.0      R   \n",
       "5020                        ...                                    1.0          \n",
       "5021                        ...                                    5.0          \n",
       "5022                        ...                                    6.0          \n",
       "5023                        ...                                    8.0          \n",
       "5024                        ...                              2    10.0      =   \n",
       "5025                        ...                                    4.0          \n",
       "\n",
       "      ISCODE SQFTCODE       LOC_NAME STATUS     SCORE     SOURCE  \\\n",
       "0          3        A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "1                   A  StreetAddress      M   90.0938  INFOGROUP   \n",
       "2                   A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "3          C        A   PointAddress      M   86.8281  INFOGROUP   \n",
       "4                   B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "5                   B     StreetName      T   90.5313  INFOGROUP   \n",
       "6          t        A   PointAddress      M  100.0000  INFOGROUP   \n",
       "7          C        A   PointAddress      M  100.0000  INFOGROUP   \n",
       "8                   A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "9          D        B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "10                  A  StreetAddress      M   91.3906  INFOGROUP   \n",
       "11                  A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "12                  A  StreetAddress      M   90.0938  INFOGROUP   \n",
       "13                  A   PointAddress      M   92.4219  INFOGROUP   \n",
       "14         t        A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "15                  A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "16         P        A   PointAddress      M  100.0000  INFOGROUP   \n",
       "17         M        A   PointAddress      M  100.0000  INFOGROUP   \n",
       "18                  B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "19                  B   PointAddress      M  100.0000  INFOGROUP   \n",
       "20         2        A   PointAddress      M  100.0000  INFOGROUP   \n",
       "21                  A   PointAddress      M  100.0000  INFOGROUP   \n",
       "22         D        B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "23         D        B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "24         e        B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "25         D        B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "26                  B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "27                  A   PointAddress      M  100.0000  INFOGROUP   \n",
       "28                  A   PointAddress      M  100.0000  INFOGROUP   \n",
       "29         3        B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "...      ...      ...            ...    ...       ...        ...   \n",
       "4996       I        B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "4997                B   PointAddress      M  100.0000  INFOGROUP   \n",
       "4998                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "4999                B   PointAddress      M  100.0000  INFOGROUP   \n",
       "5000                B   PointAddress      M  100.0000  INFOGROUP   \n",
       "5001                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5002                A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "5003                A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "5004                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5005       I        A         Postal      M  100.0000  INFOGROUP   \n",
       "5006                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5007                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5008                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5009       D        A     StreetName      M   85.7031  INFOGROUP   \n",
       "5010                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5011                A         Postal      M  100.0000  INFOGROUP   \n",
       "5012                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5013                B  StreetAddress      M  100.0000  INFOGROUP   \n",
       "5014                A  StreetAddress      M  100.0000  INFOGROUP   \n",
       "5015                A     StreetName      M   97.0625  INFOGROUP   \n",
       "5016                A         Postal      M  100.0000  INFOGROUP   \n",
       "5017                A         Postal      M  100.0000  INFOGROUP   \n",
       "5018                A         Postal      M  100.0000  INFOGROUP   \n",
       "5019       D        A     StreetName      M  100.0000  INFOGROUP   \n",
       "5020                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5021       D        A   PointAddress      M   97.0625  INFOGROUP   \n",
       "5022       I        A   PointAddress      M  100.0000  INFOGROUP   \n",
       "5023       t        B   PointAddress      M  100.0000  INFOGROUP   \n",
       "5024                B   PointAddress      M  100.0000  INFOGROUP   \n",
       "5025                A   PointAddress      M  100.0000  INFOGROUP   \n",
       "\n",
       "                                                  SHAPE  \n",
       "0     {'x': -8919964.005570533, 'y': 4867758.9117359...  \n",
       "1     {'x': -8905258.700836735, 'y': 4865826.0263644...  \n",
       "2     {'x': -8905314.36058214, 'y': 4866727.02381835...  \n",
       "3     {'x': -8904590.783891981, 'y': 4868224.0230725...  \n",
       "4     {'x': -8896241.822082484, 'y': 4844052.5717511...  \n",
       "5     {'x': -8895774.28022115, 'y': 4844531.07518083...  \n",
       "6     {'x': -8894527.501924265, 'y': 4845430.1438411...  \n",
       "7     {'x': -8893759.397437794, 'y': 4846532.3388230...  \n",
       "8     {'x': -8894338.258789917, 'y': 4828101.1194576...  \n",
       "9     {'x': -8890542.26415387, 'y': 4867729.84300147...  \n",
       "10    {'x': -8892056.209228657, 'y': 4860596.0439408...  \n",
       "11    {'x': -8894672.217262294, 'y': 4827768.1640337...  \n",
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       "15    {'x': -8894349.390739, 'y': 4828101.11945761, ...  \n",
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       "24    {'x': -8890542.26415387, 'y': 4867584.50060682...  \n",
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       "29    {'x': -8878230.328472134, 'y': 4852364.4023489...  \n",
       "...                                                 ...  \n",
       "4996  {'x': -8853773.436344847, 'y': 4971437.4882425...  \n",
       "4997  {'x': -8853662.11685406, 'y': 4984401.09493216...  \n",
       "4998  {'x': -8852081.380084794, 'y': 4984915.8789673...  \n",
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       "5003  {'x': -8848474.628583089, 'y': 4986019.0790697...  \n",
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       "5005  {'x': -8849008.962138897, 'y': 4984062.8230652...  \n",
       "5006  {'x': -8844578.446405323, 'y': 4980945.3908082...  \n",
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       "5010  {'x': -8832544.809450574, 'y': 4983092.1949702...  \n",
       "5011  {'x': -8832266.510723587, 'y': 4967457.7904193...  \n",
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       "5013  {'x': -8825865.640002979, 'y': 4977152.8767819...  \n",
       "5014  {'x': -8825865.640002979, 'y': 4977152.8767819...  \n",
       "5015  {'x': -8832500.281654252, 'y': 4967384.3794728...  \n",
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       "5017  {'x': -8820889.658764523, 'y': 4993892.1948145...  \n",
       "5018  {'x': -8817895.16446218, 'y': 5017062.80294019...  \n",
       "5019  {'x': -8820166.082074365, 'y': 4979622.2535225...  \n",
       "5020  {'x': -8830785.96149604, 'y': 4990447.84429529...  \n",
       "5021  {'x': -8832500.281654252, 'y': 4967281.6050732...  \n",
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       "5023  {'x': -8821646.631301912, 'y': 4994333.8662458...  \n",
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       "5025  {'x': -8822381.339941151, 'y': 4982798.0842999...  \n",
       "\n",
       "[5026 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a spatial dataframe (python table with geometry) out of the restaurants feature class\n",
    "restaurants_sdf = arcgis.features.SpatialDataFrame.from_featureclass(restaurants_fc)\n",
    "restaurants_sdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OBJECTID',\n",
       " 'LOCNUM',\n",
       " 'CONAME',\n",
       " 'STREET',\n",
       " 'CITY',\n",
       " 'STATE',\n",
       " 'STATE_NAME',\n",
       " 'ZIP',\n",
       " 'ZIP4',\n",
       " 'NAICS',\n",
       " 'SIC',\n",
       " 'SALESVOL',\n",
       " 'HDBRCH',\n",
       " 'EMPNUM',\n",
       " 'FRNCOD',\n",
       " 'ISCODE',\n",
       " 'SQFTCODE',\n",
       " 'LOC_NAME',\n",
       " 'STATUS',\n",
       " 'SCORE',\n",
       " 'SOURCE',\n",
       " 'SHAPE']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get list of attributes (columns)\n",
    "restaurants_sdf.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OBJECTID        int64\n",
       "LOCNUM         object\n",
       "CONAME         object\n",
       "STREET         object\n",
       "CITY           object\n",
       "STATE          object\n",
       "STATE_NAME     object\n",
       "ZIP            object\n",
       "ZIP4           object\n",
       "NAICS          object\n",
       "SIC            object\n",
       "SALESVOL      float64\n",
       "HDBRCH         object\n",
       "EMPNUM        float64\n",
       "FRNCOD         object\n",
       "ISCODE         object\n",
       "SQFTCODE       object\n",
       "LOC_NAME       object\n",
       "STATUS         object\n",
       "SCORE         float64\n",
       "SOURCE         object\n",
       "SHAPE          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types for each column\n",
    "restaurants_sdf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>SALESVOL</th>\n",
       "      <th>EMPNUM</th>\n",
       "      <th>SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5026.000000</td>\n",
       "      <td>5026.000000</td>\n",
       "      <td>5026.000000</td>\n",
       "      <td>5026.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2513.500000</td>\n",
       "      <td>802.645444</td>\n",
       "      <td>17.216076</td>\n",
       "      <td>99.608847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1451.025557</td>\n",
       "      <td>1130.667265</td>\n",
       "      <td>24.190230</td>\n",
       "      <td>1.855258</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>85.703100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1257.250000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2513.500000</td>\n",
       "      <td>374.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3769.750000</td>\n",
       "      <td>888.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>100.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "      <td>14035.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          OBJECTID      SALESVOL       EMPNUM        SCORE\n",
       "count  5026.000000   5026.000000  5026.000000  5026.000000\n",
       "mean   2513.500000    802.645444    17.216076    99.608847\n",
       "std    1451.025557   1130.667265    24.190230     1.855258\n",
       "min       1.000000      0.000000     1.000000    85.703100\n",
       "25%    1257.250000    280.000000     6.000000   100.000000\n",
       "50%    2513.500000    374.000000     8.000000   100.000000\n",
       "75%    3769.750000    888.000000    19.000000   100.000000\n",
       "max    5026.000000  14035.000000   300.000000   100.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics on numeric fields\n",
    "restaurants_sdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Restaurants in Pittsburgh by Sales Volume: \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CONAME</th>\n",
       "      <th>SALESVOL</th>\n",
       "      <th>STREET</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STATE_NAME</th>\n",
       "      <th>ZIP</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEESECAKE FACTORY</td>\n",
       "      <td>14035.0</td>\n",
       "      <td>ROSS PARK MALL DR</td>\n",
       "      <td>PITTSBURGH</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EAT'N PARK</td>\n",
       "      <td>12865.0</td>\n",
       "      <td>PARK MANOR DR</td>\n",
       "      <td>PITTSBURGH</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEESECAKE FACTORY</td>\n",
       "      <td>12631.0</td>\n",
       "      <td>S 27TH ST</td>\n",
       "      <td>PITTSBURGH</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M I FRIDAY</td>\n",
       "      <td>11695.0</td>\n",
       "      <td>PERRY HWY</td>\n",
       "      <td>PITTSBURGH</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NANA</td>\n",
       "      <td>11695.0</td>\n",
       "      <td>WATERFRONT DR</td>\n",
       "      <td>PITTSBURGH</td>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>15222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CONAME  SALESVOL             STREET        CITY STATE  \\\n",
       "0  CHEESECAKE FACTORY   14035.0  ROSS PARK MALL DR  PITTSBURGH    PA   \n",
       "1          EAT'N PARK   12865.0      PARK MANOR DR  PITTSBURGH    PA   \n",
       "2  CHEESECAKE FACTORY   12631.0          S 27TH ST  PITTSBURGH    PA   \n",
       "3          M I FRIDAY   11695.0          PERRY HWY  PITTSBURGH    PA   \n",
       "4                NANA   11695.0      WATERFRONT DR  PITTSBURGH    PA   \n",
       "\n",
       "     STATE_NAME    ZIP  \n",
       "0  Pennsylvania  15237  \n",
       "1  Pennsylvania  15205  \n",
       "2  Pennsylvania  15203  \n",
       "3  Pennsylvania  15229  \n",
       "4  Pennsylvania  15222  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the restaurants with the highest sales revenue by sorting on the 'SALESVOL' column\n",
    "top_sales_sdf = restaurants_sdf.sort_values(by=['SALESVOL'], ascending=False).reset_index()\n",
    "print(\"Top 5 Restaurants in Pittsburgh by Sales Volume: \")\n",
    "\n",
    "top_sales_sdf[['CONAME', 'SALESVOL', 'STREET', 'CITY', 'STATE', 'STATE_NAME', 'ZIP',]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>11.0</td>\n",
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       "      <td>324.500000</td>\n",
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       "      <th>15146</th>\n",
       "      <td>1013.027778</td>\n",
       "      <td>1179.398148</td>\n",
       "      <td>25.222222</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15636</th>\n",
       "      <td>704.900000</td>\n",
       "      <td>1173.800000</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15626</th>\n",
       "      <td>552.888889</td>\n",
       "      <td>1166.444444</td>\n",
       "      <td>24.944444</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15601</th>\n",
       "      <td>442.476190</td>\n",
       "      <td>1146.115646</td>\n",
       "      <td>24.510204</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16066</th>\n",
       "      <td>4682.950000</td>\n",
       "      <td>1144.462500</td>\n",
       "      <td>24.475000</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16046</th>\n",
       "      <td>4741.843750</td>\n",
       "      <td>1128.031250</td>\n",
       "      <td>24.125000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090</th>\n",
       "      <td>3512.531915</td>\n",
       "      <td>1127.255319</td>\n",
       "      <td>24.106383</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15479</th>\n",
       "      <td>860.500000</td>\n",
       "      <td>1122.500000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15229</th>\n",
       "      <td>3240.884615</td>\n",
       "      <td>1098.769231</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16001</th>\n",
       "      <td>4844.804348</td>\n",
       "      <td>1097.347826</td>\n",
       "      <td>23.467391</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15442</th>\n",
       "      <td>25.250000</td>\n",
       "      <td>1075.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15139</th>\n",
       "      <td>1379.125000</td>\n",
       "      <td>1063.812500</td>\n",
       "      <td>22.750000</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15219</th>\n",
       "      <td>2715.515152</td>\n",
       "      <td>1054.181818</td>\n",
       "      <td>22.545455</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15314</th>\n",
       "      <td>2938.444444</td>\n",
       "      <td>1044.333333</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18015</th>\n",
       "      <td>1838.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15043</th>\n",
       "      <td>4414.333333</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15112</th>\n",
       "      <td>1344.571429</td>\n",
       "      <td>193.142857</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15684</th>\n",
       "      <td>338.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>233.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15082</th>\n",
       "      <td>4124.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>233.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15311</th>\n",
       "      <td>4067.500000</td>\n",
       "      <td>186.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>236.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15671</th>\n",
       "      <td>248.500000</td>\n",
       "      <td>186.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>236.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15019</th>\n",
       "      <td>4391.500000</td>\n",
       "      <td>186.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>236.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15367</th>\n",
       "      <td>3452.500000</td>\n",
       "      <td>186.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>236.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15632</th>\n",
       "      <td>668.400000</td>\n",
       "      <td>186.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15089</th>\n",
       "      <td>1001.571429</td>\n",
       "      <td>179.857143</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16222</th>\n",
       "      <td>5019.833333</td>\n",
       "      <td>178.833333</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15697</th>\n",
       "      <td>552.000000</td>\n",
       "      <td>177.200000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15637</th>\n",
       "      <td>833.750000</td>\n",
       "      <td>174.750000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>243.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15458</th>\n",
       "      <td>40.666667</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>244.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15475</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>244.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15464</th>\n",
       "      <td>187.500000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>246.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15923</th>\n",
       "      <td>206.500000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>246.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15621</th>\n",
       "      <td>355.500000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15680</th>\n",
       "      <td>386.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15646</th>\n",
       "      <td>253.333333</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15004</th>\n",
       "      <td>4437.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15276</th>\n",
       "      <td>3788.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15014</th>\n",
       "      <td>864.333333</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15432</th>\n",
       "      <td>1486.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>255.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15688</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>255.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16020</th>\n",
       "      <td>4863.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>255.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15055</th>\n",
       "      <td>3810.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>255.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15104</th>\n",
       "      <td>1434.600000</td>\n",
       "      <td>83.600000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16229</th>\n",
       "      <td>4947.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          OBJECTID     SALESVOL     EMPNUM  Zip Ave. Sales Rank\n",
       "ZIP                                                            \n",
       "15231  4293.818182  1862.272727  39.818182                  1.0\n",
       "15275  3861.695652  1801.652174  38.521739                  2.0\n",
       "15482   950.000000  1637.000000  35.000000                  3.0\n",
       "15084  1093.789474  1524.842105  32.605263                  4.0\n",
       "15205  3716.203540  1422.796460  30.424779                  5.0\n",
       "15225  3822.666667  1387.333333  29.666667                  6.0\n",
       "15120  1779.529412  1294.686275  29.647059                  7.0\n",
       "15236  2419.912281  1274.105263  27.245614                  8.0\n",
       "15672   626.588235  1268.058824  27.117647                  9.0\n",
       "15044  2320.352941  1257.029412  26.882353                 10.0\n",
       "15237  3122.731481  1256.546296  26.907407                 11.0\n",
       "15624   324.500000  1227.500000  26.250000                 12.0\n",
       "15238  1542.074074  1218.370370  26.055556                 13.0\n",
       "15639   604.000000  1216.000000  26.000000                 14.0\n",
       "15061  4519.340909  1204.068182  25.750000                 15.0\n",
       "15241  3504.424242  1181.757576  25.272727                 16.0\n",
       "15146  1013.027778  1179.398148  25.222222                 17.0\n",
       "15636   704.900000  1173.800000  25.100000                 18.0\n",
       "15626   552.888889  1166.444444  24.944444                 19.0\n",
       "15601   442.476190  1146.115646  24.510204                 20.0\n",
       "16066  4682.950000  1144.462500  24.475000                 21.0\n",
       "16046  4741.843750  1128.031250  24.125000                 22.0\n",
       "15090  3512.531915  1127.255319  24.106383                 23.0\n",
       "15479   860.500000  1122.500000  24.000000                 24.0\n",
       "15229  3240.884615  1098.769231  23.500000                 25.0\n",
       "16001  4844.804348  1097.347826  23.467391                 26.0\n",
       "15442    25.250000  1075.500000  23.000000                 27.0\n",
       "15139  1379.125000  1063.812500  22.750000                 28.0\n",
       "15219  2715.515152  1054.181818  22.545455                 29.0\n",
       "15314  2938.444444  1044.333333  22.333333                 30.0\n",
       "...            ...          ...        ...                  ...\n",
       "18015  1838.000000   210.000000   4.500000                230.0\n",
       "15043  4414.333333   202.000000   4.333333                231.0\n",
       "15112  1344.571429   193.142857   4.142857                232.0\n",
       "15684   338.000000   187.000000   4.000000                233.5\n",
       "15082  4124.000000   187.000000   4.000000                233.5\n",
       "15311  4067.500000   186.500000   4.000000                236.5\n",
       "15671   248.500000   186.500000   4.000000                236.5\n",
       "15019  4391.500000   186.500000   4.000000                236.5\n",
       "15367  3452.500000   186.500000   4.000000                236.5\n",
       "15632   668.400000   186.400000   4.000000                239.0\n",
       "15089  1001.571429   179.857143   3.857143                240.0\n",
       "16222  5019.833333   178.833333   3.833333                241.0\n",
       "15697   552.000000   177.200000   3.800000                242.0\n",
       "15637   833.750000   174.750000   3.750000                243.0\n",
       "15458    40.666667   171.000000   3.666667                244.5\n",
       "15475    14.000000   171.000000   3.666667                244.5\n",
       "15464   187.500000   163.000000   3.500000                246.5\n",
       "15923   206.500000   163.000000   3.500000                246.5\n",
       "15621   355.500000   140.000000   3.000000                250.0\n",
       "15680   386.000000   140.000000   3.000000                250.0\n",
       "15646   253.333333   140.000000   3.000000                250.0\n",
       "15004  4437.000000   140.000000   3.000000                250.0\n",
       "15276  3788.000000   140.000000   3.000000                250.0\n",
       "15014   864.333333   124.000000   2.666667                253.0\n",
       "15432  1486.000000    93.000000   2.000000                255.5\n",
       "15688   569.000000    93.000000   2.000000                255.5\n",
       "16020  4863.000000    93.000000   2.000000                255.5\n",
       "15055  3810.000000    93.000000   2.000000                255.5\n",
       "15104  1434.600000    83.600000   1.800000                258.0\n",
       "16229  4947.000000    46.000000   1.000000                259.0\n",
       "\n",
       "[259 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the top five zip codes by average restaurant sales volume\n",
    "\n",
    "# Use groupby object to make calculations by Zip Code\n",
    "zip_groupby = restaurants_sdf.groupby(restaurants_sdf['ZIP'])\n",
    "# Determine means for numeric columns by zip\n",
    "zip_mean_sales_df = zip_groupby.mean().sort_values(by=['SALESVOL'], ascending=False)\n",
    "# Calculate Zip ranks by average sales volume\n",
    "zip_mean_sales_df['Zip Ave. Sales Rank'] = zip_mean_sales_df['SALESVOL'].rank(ascending=False)\n",
    "# Drop the unneeded 'SCORE' field\n",
    "del zip_mean_sales_df['SCORE']\n",
    "\n",
    "zip_mean_sales_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring data via maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter password: ········\n"
     ]
    }
   ],
   "source": [
    "# Connect to ArcGIS Online\n",
    "gis = arcgis.gis.GIS(\"https://esrifederal.maps.arcgis.com\", username=\"Anieto_esrifederal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03db3da8bd7547fc817bffd5b83f7033",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create map widget\n",
    "restaurants_map = gis.map(\"Pittsburgh\")\n",
    "restaurants_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants_sdf.plot(kind='map',\n",
    "                     map_widget=restaurants_map,\n",
    "                     marker_size=3, \n",
    "                     pallette='Greys_r',\n",
    "                     symbol_style='o',\n",
    "                     outline_color='Greys_r',\n",
    "                     line_width=.5,\n",
    "                     cstep=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO - Change view example to published service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9da8de828f814e9e9bfe54621518c2d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the highest five selling restaurants\n",
    "top_five_map = gis.map(\"Pittsburgh\")\n",
    "top_sales_sdf[['CONAME', 'SALESVOL', 'STREET', 'CITY', 'STATE', 'STATE_NAME', 'ZIP','SHAPE']].head().plot(kind='map', map_widget=top_five_map)\n",
    "top_five_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b3d0c3ed23e4e539c8373675ed8854f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the lowest five selling restaurants\n",
    "bot_five_map = gis.map(\"Pittsburgh\")\n",
    "bot_sales_sdf = restaurants_sdf.sort_values(by=['SALESVOL'], ascending=True).reset_index()\n",
    "bot_sales_sdf[['CONAME', 'SALESVOL', 'STREET', 'CITY', 'STATE', 'STATE_NAME', 'ZIP','SHAPE']].head().plot(kind='map', map_widget=bot_five_map)\n",
    "bot_five_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Exploring data via charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This section will leverage plotting libraries in Python for now. When a common web charting specification is made available across ArcGIS, we could integrate an explanation of that approach in this section. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Import needed plotting modules\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a sales histogram\n",
    "sales_histogram = restaurants_sdf.hist(column=\"SALESVOL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an employee count histogram\n",
    "empnum_histogram = restaurants_sdf.hist(column=\"EMPNUM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BXxDdvPhh4EfZDU2kOJw4+1F++swbbNndyE+feYMTZz+a75BEOixVUqlw95atT64A7nP3fweuRDsCi3Ta7b9eR+tx5eZQLlKIUiWV+E0bzwOWA2h+RSQzHlm3tV3lIl1dqqSywsweMrPvA/2AFQBmNhjQfIpIJ116SuL5kWTlIl1dqqTyVaKlxK8DH3X3plB+AnBjFuMSKQo3fvyUI/4SloRykUKUKqmc5O4L3P1O4O2WQnf/I9HDskSkk16bcwmfH/9eBh9bzufHv5fX5lyS75BEOizVhpLPu/tprV8nOi4U2lBSckX3lEh3ku6GkqnuU7EkrxMdi0gQv6PwwqpaThp0NI9/7Zz8BiWSAymfUZ/kdaJjkaJX19DIPSv/fMSOwq9s+yvL12tFl3R/qXoqlWZ2F1GvpOU14XhI8stEis+StW8ya3E1Bw8m/v/WE+u3aRhMur1USeXrca9bT0RoYkIkqGtoZNbiavY1Jb+FK9lOwyLdSZtJxd3vT3bOzL6T+XBEClNt/V7KSkrYd8T98ZGTBh2tXooUhVQ9lbb8HXBtpgIRKRSJVnVV9qugqfnwhNKz1LjolBOY/OH3KKFI0ehMUunw6i8z6wvcA5xCNOH/98ArwEJgONHNln/n7vVmZsD3gYuBd4DPufvz4efMAL4RfuxtbfWsRDIh2aqu/r3LmTt1DNctrqaspISm5mbmTh3D5LGaepTikuohXcclO0XnlhR/H/itu19uZj2Bo4AbgOXuPsfMZgOzgVnARcCo8DUOuBsYF2K7CYgRJaY1ZrbU3es7EZdIUomeE9+yqmvC6BOYPHYI40cOoLZ+L5X9KvTwLClKqXoqa4j+wU6UQJoSlKVkZscAZwOfAwjPZNlvZlOAc0K1+4GniZLKFGC+R3dprjKzvmHvsXOAZe6+M/zcZcAk4MGOxCWSSrLnxMev6urfu1zJRIpaqon6EVl4zxOBHcB/mtmHiRLXNcAgd98S3neLmR0f6g8BNsddXxvKkpUfwcxmAjMBhg0blrmWSLf3899vZEn1FqaMGczE0YNYWFV7RB2t6hJ5V7vnVMzsfcA04NPu3pFd73oApwFfcffVYQfk2W29ZYKyZL2nhDcIuPs8YB5E27S0L1wpVh+++bfs3ncQgOder+fYXqVHPCdeq7pEDpdWUgnDTZ8CPgOMAf4V+HQH37MWqHX31eF4EVFS2WZmg0MvZTCwPa7+0LjrK4G3Qvk5rcqf7mBMIof5+e83HkooLXbvO8jXJ76XwX0rtKeXSBKpJuo/T5Q8KokeLfwPwBJ3/1ZH39Ddt5rZZjM7yd1fASYA68PXDGBO+L4kXLIU+LKZLSCaqN8dEs/jwLfNrF+oNxG4vqNxiVy78HmWvbydC04+njfq9yWss6R6C7/4f2cpmYgkkaqn8kPgD8Bn3L0KwMwyMXz0FeCBsPLrNaLHE5cAD5nZVcAm4JOh7mNEy4lriJYUXwng7jvN7FbguVDvlpZJe5H2Gh73XPhFf9yStN6UMYNzEY5IwUqVVN5D9I/7d81sEFFvpayzb+rua4mWArc2IUFdB65O8nPuA+7rbDxSnOoaGqmt38s9/70h4flSg/htvI7tVcoVZ2Vj7YpI95Fq9dfbRPeF3G1mlUQT9NvN7GXgV+5+Qw5iFMm4B1a9wbd+/RJlpSX8df/BhHV6l5fy9YknHVr9pYQiklqqOZW/ATa7+1Z3rzWzHcCb4breuQhQJNMeWPUGNz68DoD9BxMnFIALTj6eK84aoWQi0g6pnqfyE2A/gJmdTbTq636ie0vek93QRDKvrqGRbz2yPq263/lUwT3YVCTvUs2plMZNfn8KmOfui4HFZrY2u6GJZMZPntrAw9VbuGzMYM4cOZCepcb+A4fX6VlqXHjyQFbW1HHByccroYh0UMqkYmY93P0A0ST6zHZcK5J3J934KI1hhOvlLXsof/JVrOTIDvpNH/8gnz3zvTmOTqT7STX89SDw32a2BNgL/A7AzEYCu7Mcm0infOWBNYcSSovGgzDxA8fTq6yEo8tL6dmjhNs/cYoSikiGpFr9dbuZLQcGA0+E5b0QJaOvZDs4kY6qa2jk1y8mfib8hrf/yjOzztNuwiJZkHIIy91XJSh7NTvhiHROzbY9rN28i15lpZSReCvty8YM1m7CIlmieRHpNr758IvMX7Xp0HGiHUd7lcIXzh2Vu6BEikyqORWRglCzbc9hCQWiLat7lr67BcTHP3QCf7r9kpzHJlJM1FORgnXKNx+lYT/07gk3Tx6TsM4NF5/MqcOO09yJSI4oqUjBqWto5PTbnjx03LAfrl1UnbDuR0cOZOSgPrkKTaToafhLCsqStW8ellDitf7DPP0jw5RQRHJMPRUpGFUb6/inhck3cmgGnvza2azdvIuxQ/sqoYjkgZKKFITWK7sS6d0TRg7qo2QikkdKKtJl1TU0cu3Ctax+o4539qd+Nty6W7SySyTflFSkS1qy9k2uWZDenqUlwGtzlFBEugIlFelyzv/OCmre3puy3tXnnMgnTq3UcJdIF6KkIl1K/LPi2zL9I8P4+qSTsxyNiLRX3pYUm1mpmf3RzB4JxyPMbLWZbTCzhWbWM5SXh+OacH543M+4PpS/YmYX5qcl0hl1DY28sHkXdQ2NTPruUynrx4Yey5NfO5tbpnwoB9GJSHvls6dyDfAycEw4vgO4090XmNmPgauAu8P3encfaWbTQr1PmdloYBrwQaKnUD5pZu939+TPh5Uu5Ru/epEHVm+iZwlYaQn7mppTXrPo6o/mIDIR6ai89FTMrBK4BLgnHBtwHrAoVLkfuCy8nhKOCecnhPpTgAXu3ujuG4Ea4IzctEA6K3brE/x89SYcaGwmZUK5/NTBvK7JeJEuL189le8B1wEtM6z9gV3hCZMAtcCQ8HoIsBnA3Q+Y2e5QfwgQvy1//DXSRdU1NPKNX1Xz9l+P3JS+vCRKMK0pmYgUjpwnFTO7FNju7mvM7JyW4gRVPcW5tq5p/Z4zCY9CHjZsWLvilcw47Vu/Yefetnsj+5thzTfO57Pzfs+ftr/DB44/it/+07k5ilBEMiEfPZXxwGQzuxjoRTSn8j2gr5n1CL2VSuCtUL8WGArUmlkP4FhgZ1x5i/hrDuPu84B5ALFYLPVddJJR6a7o+uy4YfTvXa5EIlLAcj6n4u7Xu3uluw8nmmhf4e6fBZ4CLg/VZgBLwuul4ZhwfkV4rPFSYFpYHTYCGAU8m6NmSJrSTSgDji7jtk9oRZdIoetK96nMAhaY2W3AH4F7Q/m9wM/MrIaohzINwN1fMrOHgPXAAeBqrfzqOuoaGvn4f/wurbpXjBumhCLSTVj0n/7iEYvFvKqqKt9hdFt1DY1cctdKtv5lf8q6/3zB+/lMGPISka7NzNa4eyxVva7UU5ECVrNtD99b9gqPrNuWVv013zhfyUSkG1JSkU5LZ1v6eFoiLNJ9KalIh9U1NHLGbU+S7kTWiQOOYsW1Wtkl0p0pqUiH3P7Ien76PxvTqntMrxLum3EGsRH9sxyViOSbkoq0S822PVx+9zPs2pf+Qrvqmy/KYkQi0pUoqUjaPnDDo+xLvefjIX8z7Bh+8aWPZS8gEelylFQkpZptezj/zpVp13/ya2frwVkiRUpJRdp05X3P8tSrO9Kur5VdIsVNSUUSSnd7lRa9SuBP31ZCESl2SipyhPYklA+e0JtHv/q3WYxGRAqJkooc0t65k0VfOFPLhEXkMEoqAsA/Pvg8S1/YknZ9zZ2ISCJKKkVu+fqtXDV/TVp1S4HrLnw/Xzh3VHaDEpGCpaRSxD52x3I21+9Lq+65Jw3gP68cl+WIRKTQKakUoeXrt3L1A2tI96Z43XciIulSUiky7VnZZUB5WQnrt/xFSUVE0pLzxwlLfixfv7Xd9544sK+pmesWV1PX0JidwESkW1FPpQiMu30Z2/akfhJjiz7lPdjTeODQcVlJCbX1e/VQLRFJSUmlG2tvz2TIMT1Z+o9nM/6OFYeVNzU3U9mvIpOhiUg3lfPhLzMbamZPmdnLZvaSmV0Tyo8zs2VmtiF87xfKzczuMrMaM6s2s9PiftaMUH+Dmc3IdVu6svYklOOO6sHrcy7hmRsuoH/vcuZOHUOvshL6lPegV1kJc6eOUS9FRNKSj57KAeCf3f15M+sDrDGzZcDngOXuPsfMZgOzgVnARcCo8DUOuBsYZ2bHATcBMaLh/zVmttTd63Peoi7k4ec389WHqtOu/72/G8Nlpw09rGzy2CGMHzmA2vq9VParUEIRkbTlPKm4+xZgS3i9x8xeBoYAU4BzQrX7gaeJksoUYL67O7DKzPqa2eBQd5m77wQIiWkS8GDOGtPFnHrL49S/cyB1xaCtu+L79y5XMhGRdsvrnIqZDQdOBVYDg0LCwd23mNnxodoQYHPcZbWhLFl50WnXXfEGC2dqzy4RyY68JRUz6w0sBr7q7n8xs6RVE5R5G+WJ3msmMBNg2LBh7Q+2Czv91ieo+2tTWnUH9enJ6hsvyHJEIlLM8nKfipmVESWUB9z9l6F4WxjWInzfHsprgfhB/0rgrTbKj+Du89w95u6xgQMHZq4hefSTpzYwfPajaSeUe6efroQiIlmX856KRV2Se4GX3f27caeWAjOAOeH7krjyL5vZAqKJ+t1heOxx4Nstq8SAicD1uWhDvrVnZdfw/hU8/fXzshiNiMi78jH8NR74v8CLZrY2lN1AlEweMrOrgE3AJ8O5x4CLgRrgHeBKAHffaWa3As+Fere0TNp3R8vXb+WJ9dtYWFWb9jX3Tj+dCaNPyGJUIiKHs2hRVfGIxWJeVVWV7zDSVrNtD1N+sJI0R7kO0fNORCSTzGyNu8dS1dMd9V1Ye++Ib6GEIiL5oqTSBZ0950k27WrfBo6nDTuGX37pY1mKSEQkPUoqXUxHeid63omIdBVKKl2EhrpEpDtQUukC2vvgrNjwfkwZM5grzhqRvaBERDpASSWPzp27nI0703tGPMD1F76fL5w7KosRiYh0jpJKnrR3uEvDXCJSCJRUcuyTP/odz236S9r1b5s8WsNcIlIwlFRySL0TEenulFRyYNTsR2nPDfETPzCAeZ8bl7V4RESyRUkly9Q7EZFioqSSJe1NJsP6lrNy9vlZikZEJDeUVLJAvRMRKVZKKhnUkbvilVBEpDtRUskQ9U5ERJRUOq29yeSE3mWs+sbELEUjIpJfSiqdoN6JiMjhlFQ6QHMnIiKJKam0k3onIiLJFXxSMbNJwPeBUuAed5+TjfdpbzKpKIWXb1dCEZHiUtBJxcxKgR8CFwC1wHNmttTd12fyfdqbUNJ5EmNdQyO19Xup7FdB/97lKesAKesXm3R+hyKSWwWdVIAzgBp3fw3AzBYAU4CMJZWOzJ+cf+dKpn9kGLdM+VDC80vWvsmsxdWUlZTQ1NzM3KljmDx2SNI6+w4cxN2pKOuRtH6xSed3KCK5V5LvADppCLA57rg2lOXd/D9sombbniPK6xoambW4mn1NzexpPMC+pmauW1xNXUNj0jpNB50DzSStX2zS+R2KSH4UelKxBGV+RCWzmWZWZWZVO3bsyEFYkbWbdx1RVlu/l7KSw3/tZSUl1NbvbbNOW/WLTTq/QxHJj0JPKrXA0LjjSuCt1pXcfZ67x9w9NnDgwHa9QWdWb40d2veIssp+FTQ1Nx9W1tTcfGjeJFmdtuoXm3R+hyKSH4WeVJ4DRpnZCDPrCUwDlmb6TTqSWKZ/ZFjCyfr+vcuZO3UMvcpK6FPeg15lJcydOuawiebWdcpKjR4lJK1fbNL5HYpIfpj7EaNFBcXMLga+R7Sk+D53v72t+rFYzKuqqjr1nnUNjbz01m7AOKqshNfr3jnUK1m7eRdjh/bV6q8c0OovkdwxszXuHktZr9CTSntlIqmIiBSbdJNKoQ9/iYhIF6KkIiIiGaOkIiIiGaOkIiIiGaOkIiIiGVN0q7/MbAfwRgcuHQC8neFw8kVt6Zq6S1u6SztAbYn3XndPefd40SWVjjKzqnSW0xUCtaVr6i5t6S7tALWlIzT8JSIiGaOkIiIiGaOkkr55+Q4gg9SWrqm7tKW7tAPUlnbTnIqIiGSMeioiIpIxSiopmNkkM3vFzGrMbHa+42kvM3vYRwWNAAAF40lEQVTdzF40s7VmVhXKjjOzZWa2IXzvl+84EzGz+8xsu5mtiytLGLtF7gqfU7WZnZa/yI+UpC03m9mb4bNZG3bcbjl3fWjLK2Z2YX6iTszMhprZU2b2spm9ZGbXhPKC+2zaaEvBfTZm1svMnjWzF0JbvhXKR5jZ6vC5LAyPCcHMysNxTTg/PCOBuLu+knwRbaf/Z+BEoCfwAjA633G1sw2vAwNalc0FZofXs4E78h1nktjPBk4D1qWKHbgY+A3R00DPBFbnO/402nIzcG2CuqPDn7VyYET4M1ia7zbExTcYOC287gO8GmIuuM+mjbYU3GcTfr+9w+syYHX4fT8ETAvlPwa+GF5/CfhxeD0NWJiJONRTadsZQI27v+bu+4EFwJQ8x5QJU4D7w+v7gcvyGEtS7r4S2NmqOFnsU4D5HlkF9DWzwbmJNLUkbUlmCrDA3RvdfSNQQ/RnsUtw9y3u/nx4vQd4GRhCAX42bbQlmS772YTfb0M4LAtfDpwHLArlrT+Xls9rETDBzBI9or1dlFTaNgTYHHdcS9t/4LoiB54wszVmNjOUDXL3LRD9pQKOz1t07Zcs9kL9rL4choTuixuGLJi2hCGTU4n+V1zQn02rtkABfjZmVmpma4HtwDKintQudz8QqsTHe6gt4fxuoH9nY1BSaVuirF1oy+XGu/tpwEXA1WZ2dr4DypJC/KzuBt4HjAW2AP8eyguiLWbWG1gMfNXd/9JW1QRlXao9CdpSkJ+Nux9097FAJVEP6uRE1cL3rLRFSaVttcDQuONK4K08xdIh7v5W+L4d+BXRH7RtLcMP4fv2/EXYbsliL7jPyt23hX8EmoGf8u4wSpdvi5mVEf0j/IC7/zIUF+Rnk6gthfzZALj7LuBpojmVvmbWI5yKj/dQW8L5Y0l/iDYpJZW2PQeMCqsnehJNZi3Nc0xpM7OjzaxPy2tgIrCOqA0zQrUZwJL8RNghyWJfCkwPK43OBHa3DMV0Va3mFT5B9NlA1JZpYXXOCGAU8Gyu40smjLvfC7zs7t+NO1Vwn02ythTiZ2NmA82sb3hdAZxPNEf0FHB5qNb6c2n5vC4HVniYte+UfK9Y6OpfRCtXXiUam7wx3/G0M/YTiVaqvAC81BI/0bjpcmBD+H5cvmNNEv+DREMPTUT/q7oqWexEXfkfhs/pRSCW7/jTaMvPQqzV4S/44Lj6N4a2vAJclO/4W7Xlo0TDJNXA2vB1cSF+Nm20peA+G2AM8McQ8zrgm6H8RKLEVwP8AigP5b3CcU04f2Im4tAd9SIikjEa/hIRkYxRUhERkYxRUhERkYxRUhERkYxRUhERkYxRUhHpADM7GLeD7VoLO1ib2dNmtil+DyUze9jMGsLr4Wa2N1yz3sx+bGYlodzN7Ctx1/3AzD4X93NjceeGW9jx2MzOCddeFXf+1FB2bdZ/GSJxlFREOmavu4+N+5oTd24XMB4g3IzWevPEP3u0lcYYol1vWzb42w5c07I1eTu9CHwq7nga0f1JIjmlpCKSeQuI/lEH+D/ALxNV8mgTv98DI0PRDqKbBmckqp/CJqCXmQ0KvaRJRNvNi+SUkopIx1S0Gv6K7yUsB842s1LCcyoS/QAzOwqYQNTLaDEH+OdwbXstAj4JnAU8DzR24GeIdEqP1FVEJIG9YQgrkYPA/xANR1W4++utHlPxvrA9uQNL3P03LU/dc/eNZvYs8JlWPzPR1hetyx4iSmAfINoW5qz0myOSGUoqItmxgGhX6JsTnPtzGwkJ4NtEvY6VcWV1QPxjn48D3o6/yN23mlkTcAFwDUoqkgca/hLJjt8B/0rUY2gXd/8TsB64NK74aeCKuFVlM4h2n23tm8Asdz/Y3vcVyQT1VEQ6piIMYbX4rbvPbjnwaKfW73Ti599OtONsi3lEw1ovmJkDVcD1rS9y99934j1FOk27FIuISMZo+EtERDJGSUVERDJGSUVERDJGSUVERDJGSUVERDJGSUVERDJGSUVERDJGSUVERDLmfwHi+tLVqyYfggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of sales vs employee number\n",
    "scatterplot = restaurants_sdf[['EMPNUM', 'SALESVOL']].plot(kind='scatter', x='EMPNUM', y='SALESVOL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9978171595302142"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the correlation between employee count at restaurant and sales volume at restaurant\n",
    "restaurants_sdf['EMPNUM'].corr(restaurants_sdf['SALESVOL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top five Zip Codes by average sales volume: ['15231', '15275', '15482', '15084', '15205']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1edd45dd4e0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a box plot of the top five zip codes by sales volume\n",
    "\n",
    "# Get top five zip codes by average sales volume\n",
    "top_five_zips_list = zip_mean_sales_df.index[zip_mean_sales_df['Zip Ave. Sales Rank'] <= 5].tolist()\n",
    "print(\"Top five Zip Codes by average sales volume: \"+str(top_five_zips_list))\n",
    "\n",
    "# Create subset table for the named cities\n",
    "subset_sdf = restaurants_sdf.loc[restaurants_sdf['ZIP'].isin(top_five_zips_list)]\n",
    "\n",
    "\n",
    "restaurants_pivots = subset_sdf.pivot_table(columns='ZIP', values='SALESVOL')\n",
    "restaurants_pivots.plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To determine which analysis questions our data can support, let's recall our listed questions and assess how applicable the data currently is:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Where are the locations in Pittsburgh, PA with dense restaurant retail presence?\n",
    "\n",
    "Assessment: The data appears to have accurate restaurant locations and can be used in conjunction with analysis tools answer this question.\n",
    "\n",
    "#### Where are the clusters of restaurants with the highest average revenue? \n",
    "\n",
    "Assessment: The data appears sufficient quality for sales volume data for each restaurant, and could be used to answer this question.\n",
    "\n",
    "#### Are there clusters of restaurants whose collective revenue is greater than expected?\n",
    "\n",
    "Assessment: Since the two components mentioned above (location and sales revenue per restaurant) appear to be of good enough quality, this question is a matter of using the two components and can likely be addressed through this data. \n",
    "\n",
    "#### Is there a relationship between average restaurant cluster revenue and demographics?\n",
    "\n",
    "Assessment: We need demographic information and a method to connect each restaurant to related geographic features with accurate demographic data, as well as more specific information on which demographics we want to study relationships for. This question is proposed for a progression version of this analysis. \n",
    "\n",
    "#### Is there a relationship between average restaurant cluster revenue and transportation access? \n",
    "\n",
    "Assessment: In a similar way to the previous question, we need transportation information and a robust method to associate restaurant patronage with nearby transportation features. This question is proposed for a future version of this analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- Examine the data quality: Check for null values, check for outliers.\n",
    "- Examine location quality: Make a few cells that confirm/reject the quality of geometry for the data. \n",
    "- Trash the box plot example and determine a better way to start exploring different neighborhood distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:purple\">3. Analyze and model:</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's recall our analysis questions that survived our exploration of the data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Where are the locations in Pittsburgh, PA with dense restaurant retail presence?\n",
    "\n",
    "#### 2. Where are the clusters of restaurants with the highest average revenue? \n",
    "\n",
    "#### 3. Are there clusters of restaurants whose collective revenue is greater than expected?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These three questions depend on finding locations with dense restaurant retail presence, or clusters based on location. Thankfully, a tool is available to help us find these clusters:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Density-based Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![clustering](img/dbclustering_01.png \"Density-based Clustering\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The [Density-based Clustering tool](http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/how-density-based-clustering-works.htm) works by detecting areas where points are concentrated and where they are separated by areas that are empty or sparse. Points that are not part of a cluster are labeled as noise.\n",
    "\n",
    "This tool uses unsupervised machine learning clustering algorithms which automatically detect patterns based purely on spatial location and the distance to a specified number of neighbors. These algorithms are considered unsupervised because they do not require any training on what it means to be a cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Three clustering algorithms:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The Density-based Clustering tool provides three different Clustering Methods with which to find clusters in your point data:\n",
    "\n",
    "- _Defined distance (DBSCAN)_ — Uses a specified distance to separate dense clusters from sparser noise. The DBSCAN algorithm is the fastest of the clustering methods, but is only appropriate if there is a very clear Search Distance to use, and that works well for all potential clusters. This requires that all meaningful clusters have similar densities.\n",
    "\n",
    "- _Self-adjusting (HDBSCAN)_ — Uses a range of distances to separate clusters of varying densities from sparser noise. The HDBSCAN algorithm is the most data-driven of the clustering methods, and thus requires the least user input.\n",
    "\n",
    "- _Multi-scale (OPTICS)_ — Uses the distance between neighboring features to create a reachability plot which is then used to separate clusters of varying densities from noise. The OPTICS algorithm offers the most flexibility in fine-tuning the clusters that are detected, though it is computationally intensive, particularly with a large Search Distance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Option 1: Run locally and load into web map widget \n",
    "#### Issue: Need to determine how to load symbology or specified renderer when working with spatial dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data//retail.gdb//Restaurants_HDBSCAN'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run Density-based Clustering on the restaurants feature class\n",
    "if arcpy.Exists(\"{0}//{1}_HDBSCAN\".format(file_geodatabase, \"Restaurants\")):\n",
    "    clusters_fc = \"{0}//{1}_HDBSCAN\".format(file_geodatabase, \"Restaurants\")\n",
    "else:\n",
    "    clusters_fc = arcpy.stats.DensityBasedClustering(restaurants_fc, \"{0}//{1}_HDBSCAN\".format(file_geodatabase, \"Restaurants\"), \"HDBSCAN\", 5)\n",
    "clusters_fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'x': -79.91629999999996, 'y': 39.849200000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'x': -79.91209999999994, 'y': 39.852500000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'x': -79.90089999999994, 'y': 39.858700000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "      <td>0.790149</td>\n",
       "      <td>0.209851</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'x': -79.89399999999995, 'y': 39.866300000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>{'x': -79.89919999999995, 'y': 39.739100000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>56</td>\n",
       "      <td>0.357273</td>\n",
       "      <td>0.642727</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>{'x': -79.86509999999997, 'y': 40.012300000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>{'x': -79.87869999999997, 'y': 39.963200000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>0.498830</td>\n",
       "      <td>0.501170</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>{'x': -79.90219999999991, 'y': 39.736800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011129</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>{'x': -79.89079999999994, 'y': 39.958200000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'x': -79.89969999999995, 'y': 39.849100000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>{'x': -79.90019999999996, 'y': 39.739400000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>{'x': -79.89929999999997, 'y': 39.739100000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>{'x': -79.85470000000001, 'y': 39.950100000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>{'x': -79.87879999999991, 'y': 39.963000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>{'x': -79.89949999999995, 'y': 39.740500000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>56</td>\n",
       "      <td>0.344194</td>\n",
       "      <td>0.655806</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>{'x': -79.86859999999996, 'y': 40.008900000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>0.838014</td>\n",
       "      <td>0.161986</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'x': -79.89549999999997, 'y': 39.865900000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>0.508461</td>\n",
       "      <td>0.491539</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'x': -79.87539999999991, 'y': 39.881200000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "      <td>0.561879</td>\n",
       "      <td>0.438121</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>{'x': -79.89869999999992, 'y': 40.012500000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>{'x': -79.91189999999993, 'y': 39.852900000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "      <td>0.343456</td>\n",
       "      <td>0.656544</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>{'x': -79.86509999999997, 'y': 40.011300000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>{'x': -79.90009999999992, 'y': 39.741100000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255119</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>{'x': -79.75789999999992, 'y': 39.902800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>{'x': -79.75390000000002, 'y': 39.826100000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>{'x': -79.7531999999999, 'y': 39.8260000000001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>63</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>{'x': -79.75449999999996, 'y': 39.906500000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>4997</td>\n",
       "      <td>4997</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103226</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>{'x': -79.53479999999993, 'y': 40.722100000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>4998</td>\n",
       "      <td>4998</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175061</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>{'x': -79.53379999999999, 'y': 40.810300000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>4999</td>\n",
       "      <td>4999</td>\n",
       "      <td>11</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>{'x': -79.51959999999997, 'y': 40.813800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.191985</td>\n",
       "      <td>0.808015</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>{'x': -79.53519999999997, 'y': 40.814100000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>11</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>{'x': -79.5202, 'y': 40.81310000000006, 'spati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>5002</td>\n",
       "      <td>5002</td>\n",
       "      <td>11</td>\n",
       "      <td>0.336729</td>\n",
       "      <td>0.663271</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>{'x': -79.51979999999995, 'y': 40.818300000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5002</th>\n",
       "      <td>5003</td>\n",
       "      <td>5003</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>{'x': -79.49089999999991, 'y': 40.805700000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5003</th>\n",
       "      <td>5004</td>\n",
       "      <td>5004</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>{'x': -79.48719999999993, 'y': 40.821300000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>5005</td>\n",
       "      <td>5005</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>{'x': -79.48759999999999, 'y': 40.821400000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>5006</td>\n",
       "      <td>5006</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>{'x': -79.49199999999995, 'y': 40.808000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>5007</td>\n",
       "      <td>5007</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109885</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>{'x': -79.45219999999993, 'y': 40.786800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>5008</td>\n",
       "      <td>5008</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.32869999999996, 'y': 40.799800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5008</th>\n",
       "      <td>5009</td>\n",
       "      <td>5009</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.31449999999998, 'y': 40.799300000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5009</th>\n",
       "      <td>5010</td>\n",
       "      <td>5010</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'x': -79.34369999999993, 'y': 40.694500000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5010</th>\n",
       "      <td>5011</td>\n",
       "      <td>5011</td>\n",
       "      <td>2</td>\n",
       "      <td>0.999882</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.34409999999997, 'y': 40.801400000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5011</th>\n",
       "      <td>5012</td>\n",
       "      <td>5012</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'x': -79.34159999999994, 'y': 40.695000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5012</th>\n",
       "      <td>5013</td>\n",
       "      <td>5013</td>\n",
       "      <td>2</td>\n",
       "      <td>0.712546</td>\n",
       "      <td>0.287454</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.24089999999995, 'y': 40.880200000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5013</th>\n",
       "      <td>5014</td>\n",
       "      <td>5014</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.28409999999998, 'y': 40.761000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5014</th>\n",
       "      <td>5015</td>\n",
       "      <td>5015</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.28409999999998, 'y': 40.761000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5015</th>\n",
       "      <td>5016</td>\n",
       "      <td>5016</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'x': -79.34369999999993, 'y': 40.694500000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5016</th>\n",
       "      <td>5017</td>\n",
       "      <td>5017</td>\n",
       "      <td>2</td>\n",
       "      <td>0.721675</td>\n",
       "      <td>0.278325</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.23940000000002, 'y': 40.874800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5017</th>\n",
       "      <td>5018</td>\n",
       "      <td>5018</td>\n",
       "      <td>2</td>\n",
       "      <td>0.721675</td>\n",
       "      <td>0.278325</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.23940000000002, 'y': 40.874800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5018</th>\n",
       "      <td>5019</td>\n",
       "      <td>5019</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.421425</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>{'x': -79.21249999999998, 'y': 41.032000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5019</th>\n",
       "      <td>5020</td>\n",
       "      <td>5020</td>\n",
       "      <td>2</td>\n",
       "      <td>0.786247</td>\n",
       "      <td>0.213753</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.2329, 'y': 40.777800000000106, 'spat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5020</th>\n",
       "      <td>5021</td>\n",
       "      <td>5021</td>\n",
       "      <td>2</td>\n",
       "      <td>0.967406</td>\n",
       "      <td>0.032594</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.32829999999997, 'y': 40.851400000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5021</th>\n",
       "      <td>5022</td>\n",
       "      <td>5022</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'x': -79.34369999999993, 'y': 40.693800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>5023</td>\n",
       "      <td>5023</td>\n",
       "      <td>2</td>\n",
       "      <td>0.997590</td>\n",
       "      <td>0.002410</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.32909999999998, 'y': 40.799700000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5023</th>\n",
       "      <td>5024</td>\n",
       "      <td>5024</td>\n",
       "      <td>2</td>\n",
       "      <td>0.761452</td>\n",
       "      <td>0.238548</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.24619999999996, 'y': 40.877800000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5024</th>\n",
       "      <td>5025</td>\n",
       "      <td>5025</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'x': -79.32179999999995, 'y': 40.672700000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5025</th>\n",
       "      <td>5026</td>\n",
       "      <td>5026</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>{'x': -79.2528, 'y': 40.79940000000009, 'spati...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5026 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      OBJECTID  SOURCE_ID  CLUSTER_ID      PROB   OUTLIER  EXEMPLAR  \\\n",
       "0            1          1          -1  0.000000  0.027113         0   \n",
       "1            2          2          -1  0.000000  0.023014         0   \n",
       "2            3          3          -1  0.000000  0.005891         0   \n",
       "3            4          4          56  0.173037  0.826963         0   \n",
       "4            5          5          36  1.000000  0.000000         1   \n",
       "5            6          6          36  1.000000  0.000000         1   \n",
       "6            7          7          36  1.000000  0.000000         1   \n",
       "7            8          8          36  0.790149  0.209851         0   \n",
       "8            9          9           7  1.000000  0.000000         1   \n",
       "9           10         10          56  0.357273  0.642727         0   \n",
       "10          11         11          35  1.000000  0.000000         1   \n",
       "11          12         12           7  0.498830  0.501170         0   \n",
       "12          13         13          -1  0.000000  0.011129         0   \n",
       "13          14         14          36  1.000000  0.000000         1   \n",
       "14          15         15           7  1.000000  0.000000         1   \n",
       "15          16         16           7  1.000000  0.000000         1   \n",
       "16          17         17          35  1.000000  0.000000         1   \n",
       "17          18         18          35  1.000000  0.000000         1   \n",
       "18          19         19           7  1.000000  0.000000         1   \n",
       "19          20         20          56  0.344194  0.655806         0   \n",
       "20          21         21          36  0.838014  0.161986         0   \n",
       "21          22         22          36  0.508461  0.491539         0   \n",
       "22          23         23          56  0.561879  0.438121         0   \n",
       "23          24         24          36  1.000000  0.000000         1   \n",
       "24          25         25          56  0.343456  0.656544         0   \n",
       "25          26         26           7  1.000000  0.000000         1   \n",
       "26          27         27          -1  0.000000  0.255119         0   \n",
       "27          28         28          40  1.000000  0.000000         1   \n",
       "28          29         29          40  1.000000  0.000000         1   \n",
       "29          30         30          63  1.000000  0.000000         1   \n",
       "...        ...        ...         ...       ...       ...       ...   \n",
       "4996      4997       4997          -1  0.000000  0.103226         0   \n",
       "4997      4998       4998          -1  0.000000  0.175061         0   \n",
       "4998      4999       4999          11  1.000000  0.000000         1   \n",
       "4999      5000       5000          11  0.191985  0.808015         0   \n",
       "5000      5001       5001          11  1.000000  0.000000         1   \n",
       "5001      5002       5002          11  0.336729  0.663271         0   \n",
       "5002      5003       5003          10  1.000000  0.000000         1   \n",
       "5003      5004       5004          10  1.000000  0.000000         1   \n",
       "5004      5005       5005          10  1.000000  0.000000         1   \n",
       "5005      5006       5006          10  1.000000  0.000000         1   \n",
       "5006      5007       5007          -1  0.000000  0.109885         0   \n",
       "5007      5008       5008           2  1.000000  0.000000         1   \n",
       "5008      5009       5009           2  1.000000  0.000000         1   \n",
       "5009      5010       5010           3  1.000000  0.000000         1   \n",
       "5010      5011       5011           2  0.999882  0.000118         0   \n",
       "5011      5012       5012           3  1.000000  0.000000         1   \n",
       "5012      5013       5013           2  0.712546  0.287454         0   \n",
       "5013      5014       5014           2  1.000000  0.000000         1   \n",
       "5014      5015       5015           2  1.000000  0.000000         1   \n",
       "5015      5016       5016           3  1.000000  0.000000         1   \n",
       "5016      5017       5017           2  0.721675  0.278325         0   \n",
       "5017      5018       5018           2  0.721675  0.278325         0   \n",
       "5018      5019       5019          -1  0.000000  0.421425         0   \n",
       "5019      5020       5020           2  0.786247  0.213753         0   \n",
       "5020      5021       5021           2  0.967406  0.032594         0   \n",
       "5021      5022       5022           3  1.000000  0.000000         1   \n",
       "5022      5023       5023           2  0.997590  0.002410         0   \n",
       "5023      5024       5024           2  0.761452  0.238548         0   \n",
       "5024      5025       5025           3  1.000000  0.000000         1   \n",
       "5025      5026       5026           2  1.000000  0.000000         1   \n",
       "\n",
       "      STABILITY  COLOR_ID                                              SHAPE  \n",
       "0           0.0        -1  {'x': -80.12939999999999, 'y': 40.012500000000...  \n",
       "1           0.0        -1  {'x': -79.99729999999992, 'y': 39.999199999999...  \n",
       "2           0.0        -1  {'x': -79.99779999999998, 'y': 40.005400000000...  \n",
       "3           0.0         7  {'x': -79.99129999999997, 'y': 40.015700000000...  \n",
       "4           0.0         4  {'x': -79.91629999999996, 'y': 39.849200000000...  \n",
       "5           0.0         4  {'x': -79.91209999999994, 'y': 39.852500000000...  \n",
       "6           0.0         4  {'x': -79.90089999999994, 'y': 39.858700000000...  \n",
       "7           0.0         4  {'x': -79.89399999999995, 'y': 39.866300000000...  \n",
       "8           0.0         8  {'x': -79.89919999999995, 'y': 39.739100000000...  \n",
       "9           0.0         7  {'x': -79.86509999999997, 'y': 40.012300000000...  \n",
       "10          0.0         3  {'x': -79.87869999999997, 'y': 39.963200000000...  \n",
       "11          0.0         8  {'x': -79.90219999999991, 'y': 39.736800000000...  \n",
       "12          0.0        -1  {'x': -79.89079999999994, 'y': 39.958200000000...  \n",
       "13          0.0         4  {'x': -79.89969999999995, 'y': 39.849100000000...  \n",
       "14          0.0         8  {'x': -79.90019999999996, 'y': 39.739400000000...  \n",
       "15          0.0         8  {'x': -79.89929999999997, 'y': 39.739100000000...  \n",
       "16          0.0         3  {'x': -79.85470000000001, 'y': 39.950100000000...  \n",
       "17          0.0         3  {'x': -79.87879999999991, 'y': 39.963000000000...  \n",
       "18          0.0         8  {'x': -79.89949999999995, 'y': 39.740500000000...  \n",
       "19          0.0         7  {'x': -79.86859999999996, 'y': 40.008900000000...  \n",
       "20          0.0         4  {'x': -79.89549999999997, 'y': 39.865900000000...  \n",
       "21          0.0         4  {'x': -79.87539999999991, 'y': 39.881200000000...  \n",
       "22          0.0         7  {'x': -79.89869999999992, 'y': 40.012500000000...  \n",
       "23          0.0         4  {'x': -79.91189999999993, 'y': 39.852900000000...  \n",
       "24          0.0         7  {'x': -79.86509999999997, 'y': 40.011300000000...  \n",
       "25          0.0         8  {'x': -79.90009999999992, 'y': 39.741100000000...  \n",
       "26          0.0        -1  {'x': -79.75789999999992, 'y': 39.902800000000...  \n",
       "27          0.0         3  {'x': -79.75390000000002, 'y': 39.826100000000...  \n",
       "28          0.0         3  {'x': -79.7531999999999, 'y': 39.8260000000001...  \n",
       "29          0.0         2  {'x': -79.75449999999996, 'y': 39.906500000000...  \n",
       "...         ...       ...                                                ...  \n",
       "4996        0.0        -1  {'x': -79.53479999999993, 'y': 40.722100000000...  \n",
       "4997        0.0        -1  {'x': -79.53379999999999, 'y': 40.810300000000...  \n",
       "4998        0.0         7  {'x': -79.51959999999997, 'y': 40.813800000000...  \n",
       "4999        0.0         7  {'x': -79.53519999999997, 'y': 40.814100000000...  \n",
       "5000        0.0         7  {'x': -79.5202, 'y': 40.81310000000006, 'spati...  \n",
       "5001        0.0         7  {'x': -79.51979999999995, 'y': 40.818300000000...  \n",
       "5002        0.0         5  {'x': -79.49089999999991, 'y': 40.805700000000...  \n",
       "5003        0.0         5  {'x': -79.48719999999993, 'y': 40.821300000000...  \n",
       "5004        0.0         5  {'x': -79.48759999999999, 'y': 40.821400000000...  \n",
       "5005        0.0         5  {'x': -79.49199999999995, 'y': 40.808000000000...  \n",
       "5006        0.0        -1  {'x': -79.45219999999993, 'y': 40.786800000000...  \n",
       "5007        0.0         6  {'x': -79.32869999999996, 'y': 40.799800000000...  \n",
       "5008        0.0         6  {'x': -79.31449999999998, 'y': 40.799300000000...  \n",
       "5009        0.0         1  {'x': -79.34369999999993, 'y': 40.694500000000...  \n",
       "5010        0.0         6  {'x': -79.34409999999997, 'y': 40.801400000000...  \n",
       "5011        0.0         1  {'x': -79.34159999999994, 'y': 40.695000000000...  \n",
       "5012        0.0         6  {'x': -79.24089999999995, 'y': 40.880200000000...  \n",
       "5013        0.0         6  {'x': -79.28409999999998, 'y': 40.761000000000...  \n",
       "5014        0.0         6  {'x': -79.28409999999998, 'y': 40.761000000000...  \n",
       "5015        0.0         1  {'x': -79.34369999999993, 'y': 40.694500000000...  \n",
       "5016        0.0         6  {'x': -79.23940000000002, 'y': 40.874800000000...  \n",
       "5017        0.0         6  {'x': -79.23940000000002, 'y': 40.874800000000...  \n",
       "5018        0.0        -1  {'x': -79.21249999999998, 'y': 41.032000000000...  \n",
       "5019        0.0         6  {'x': -79.2329, 'y': 40.777800000000106, 'spat...  \n",
       "5020        0.0         6  {'x': -79.32829999999997, 'y': 40.851400000000...  \n",
       "5021        0.0         1  {'x': -79.34369999999993, 'y': 40.693800000000...  \n",
       "5022        0.0         6  {'x': -79.32909999999998, 'y': 40.799700000000...  \n",
       "5023        0.0         6  {'x': -79.24619999999996, 'y': 40.877800000000...  \n",
       "5024        0.0         1  {'x': -79.32179999999995, 'y': 40.672700000000...  \n",
       "5025        0.0         6  {'x': -79.2528, 'y': 40.79940000000009, 'spati...  \n",
       "\n",
       "[5026 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe from the density-based clustering output\n",
    "clusters_sdf = arcgis.features.SpatialDataFrame.from_featureclass(clusters_fc, sr = arcpy.SpatialReference(4326))\n",
    "clusters_sdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=d96efefc81b9479598bff089db97b963' target='_blank'>\n",
       "                        <img src='http://static.arcgis.com/images/desktopapp.png' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=d96efefc81b9479598bff089db97b963' target='_blank'><b>Restaurant Clusters v1</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://esrifederal.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by Anieto_esrifederal\n",
       "                        <br/>Last Modified: August 16, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Restaurant Clusters v1\" type:Feature Layer Collection owner:Anieto_esrifederal>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Publish the density-based clustering output to the GIS\n",
    "fc = gis.content.import_data(clusters_sdf, title=\"Restaurant Clusters v1\", tags=\"Restaurants; Clusters\")\n",
    "fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.005891</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8905314.36058214, 'y': 4866727.02381835...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.826963</td>\n",
       "      <td>0.173037</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8904590.783891981, 'y': 4868224.0230725...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8896241.822082486, 'y': 4844052.5717511...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8895774.280221151, 'y': 4844531.0751808...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894527.501924269, 'y': 4845430.1438411...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.209851</td>\n",
       "      <td>0.790149</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8893759.397437792, 'y': 4846532.3388230...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894338.25878992, 'y': 4828101.11945760...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.642727</td>\n",
       "      <td>0.357273</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8890542.264153872, 'y': 4867729.8430014...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8892056.209228659, 'y': 4860596.0439408...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.501170</td>\n",
       "      <td>0.498830</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894672.217262296, 'y': 4827768.1640337...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.011129</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8893403.175067255, 'y': 4859869.8753424...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894393.918535313, 'y': 4844038.0720063...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894449.578280715, 'y': 4828144.5492451...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894349.390739001, 'y': 4828101.1194576...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8889384.541449621, 'y': 4858693.5948817...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8892067.341177735, 'y': 4860566.9961773...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894371.65463716, 'y': 4828303.79341687...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.655806</td>\n",
       "      <td>0.344194</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8890931.882371644, 'y': 4867235.6875472...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.161986</td>\n",
       "      <td>0.838014</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8893926.376673985, 'y': 4846474.3255170...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0.491539</td>\n",
       "      <td>0.508461</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8891688.854909038, 'y': 4848693.5754889...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.438121</td>\n",
       "      <td>0.561879</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894282.599044522, 'y': 4867758.9117359...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8895752.016322993, 'y': 4844589.0771597...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.656544</td>\n",
       "      <td>0.343456</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8890542.264153872, 'y': 4867584.5006068...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8894438.446331633, 'y': 4828390.6549455...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.255119</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8878608.81474083, 'y': 4851827.47745330...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8878163.53677766, 'y': 4840703.69157409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8878085.613134097, 'y': 4840689.1967055...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8878230.328472132, 'y': 4852364.4023488...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>4997</td>\n",
       "      <td>0.103226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4997</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8853773.43634485, 'y': 4971437.48824258...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>4998</td>\n",
       "      <td>0.175062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4998</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8853662.11685406, 'y': 4984401.09493216...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4999</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8852081.380084794, 'y': 4984915.8789673...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.808015</td>\n",
       "      <td>0.191985</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8853817.96414117, 'y': 4984960.00457681...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5001</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8852148.171779271, 'y': 4984812.9199878...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5002</td>\n",
       "      <td>0.663271</td>\n",
       "      <td>0.336729</td>\n",
       "      <td>5002</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8852103.643982952, 'y': 4985577.7840613...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5002</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5003</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8848886.510699024, 'y': 4983724.5629226...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5003</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5004</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8848474.628583092, 'y': 4986019.0790697...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5005</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8848519.156379411, 'y': 4986033.7892470...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5006</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8849008.962138899, 'y': 4984062.8230652...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>5007</td>\n",
       "      <td>0.109885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5007</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8844578.446405325, 'y': 4980945.3908082...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5008</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8830830.489292359, 'y': 4982856.9057250...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5008</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5009</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8829249.752523093, 'y': 4982783.3789990...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5009</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5010</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8832500.281654255, 'y': 4967384.3794728...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5010</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5011</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.999883</td>\n",
       "      <td>5011</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8832544.809450574, 'y': 4983092.1949702...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5011</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5012</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8832266.51072359, 'y': 4967457.79041935...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5012</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5013</td>\n",
       "      <td>0.287454</td>\n",
       "      <td>0.712546</td>\n",
       "      <td>5013</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8821056.638000706, 'y': 4994687.2178030...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5013</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5014</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8825865.640002977, 'y': 4977152.8767819...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5014</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5015</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8825865.640002977, 'y': 4977152.8767819...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5015</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5016</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8832500.281654255, 'y': 4967384.3794728...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5016</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5017</td>\n",
       "      <td>0.278325</td>\n",
       "      <td>0.721675</td>\n",
       "      <td>5017</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8820889.65876452, 'y': 4993892.19481454...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5017</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5018</td>\n",
       "      <td>0.278325</td>\n",
       "      <td>0.721675</td>\n",
       "      <td>5018</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8820889.65876452, 'y': 4993892.19481454...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5018</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>5019</td>\n",
       "      <td>0.421425</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5019</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8817895.16446218, 'y': 5017062.80294019...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5019</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5020</td>\n",
       "      <td>0.213753</td>\n",
       "      <td>0.786247</td>\n",
       "      <td>5020</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8820166.082074363, 'y': 4979622.2535225...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5020</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5021</td>\n",
       "      <td>0.032594</td>\n",
       "      <td>0.967406</td>\n",
       "      <td>5021</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8830785.961496038, 'y': 4990447.8442952...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5021</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5022</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5022</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8832500.281654255, 'y': 4967281.6050732...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5023</td>\n",
       "      <td>0.002410</td>\n",
       "      <td>0.997590</td>\n",
       "      <td>5023</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8830875.017088676, 'y': 4982842.2003355...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5023</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5024</td>\n",
       "      <td>0.238548</td>\n",
       "      <td>0.761452</td>\n",
       "      <td>5024</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8821646.631301915, 'y': 4994333.8662458...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5024</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5025</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8830062.38480588, 'y': 4964184.19766278...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5025</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5026</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8822381.33994115, 'y': 4982798.08429993...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5026 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CLUSTER_ID  COLOR_ID  EXEMPLAR  OBJECTID   OUTLIER      PROB  SOURCE_ID  \\\n",
       "0             -1        -1         0         1  0.027113  0.000000          1   \n",
       "1             -1        -1         0         2  0.023014  0.000000          2   \n",
       "2             -1        -1         0         3  0.005891  0.000000          3   \n",
       "3             56         7         0         4  0.826963  0.173037          4   \n",
       "4             36         4         1         5  0.000000  1.000000          5   \n",
       "5             36         4         1         6  0.000000  1.000000          6   \n",
       "6             36         4         1         7  0.000000  1.000000          7   \n",
       "7             36         4         0         8  0.209851  0.790149          8   \n",
       "8              7         8         1         9  0.000000  1.000000          9   \n",
       "9             56         7         0        10  0.642727  0.357273         10   \n",
       "10            35         3         1        11  0.000000  1.000000         11   \n",
       "11             7         8         0        12  0.501170  0.498830         12   \n",
       "12            -1        -1         0        13  0.011129  0.000000         13   \n",
       "13            36         4         1        14  0.000000  1.000000         14   \n",
       "14             7         8         1        15  0.000000  1.000000         15   \n",
       "15             7         8         1        16  0.000000  1.000000         16   \n",
       "16            35         3         1        17  0.000000  1.000000         17   \n",
       "17            35         3         1        18  0.000000  1.000000         18   \n",
       "18             7         8         1        19  0.000000  1.000000         19   \n",
       "19            56         7         0        20  0.655806  0.344194         20   \n",
       "20            36         4         0        21  0.161986  0.838014         21   \n",
       "21            36         4         0        22  0.491539  0.508461         22   \n",
       "22            56         7         0        23  0.438121  0.561879         23   \n",
       "23            36         4         1        24  0.000000  1.000000         24   \n",
       "24            56         7         0        25  0.656544  0.343456         25   \n",
       "25             7         8         1        26  0.000000  1.000000         26   \n",
       "26            -1        -1         0        27  0.255119  0.000000         27   \n",
       "27            40         3         1        28  0.000000  1.000000         28   \n",
       "28            40         3         1        29  0.000000  1.000000         29   \n",
       "29            63         2         1        30  0.000000  1.000000         30   \n",
       "...          ...       ...       ...       ...       ...       ...        ...   \n",
       "4996          -1        -1         0      4997  0.103226  0.000000       4997   \n",
       "4997          -1        -1         0      4998  0.175062  0.000000       4998   \n",
       "4998          11         7         1      4999  0.000000  1.000000       4999   \n",
       "4999          11         7         0      5000  0.808015  0.191985       5000   \n",
       "5000          11         7         1      5001  0.000000  1.000000       5001   \n",
       "5001          11         7         0      5002  0.663271  0.336729       5002   \n",
       "5002          10         5         1      5003  0.000000  1.000000       5003   \n",
       "5003          10         5         1      5004  0.000000  1.000000       5004   \n",
       "5004          10         5         1      5005  0.000000  1.000000       5005   \n",
       "5005          10         5         1      5006  0.000000  1.000000       5006   \n",
       "5006          -1        -1         0      5007  0.109885  0.000000       5007   \n",
       "5007           2         6         1      5008  0.000000  1.000000       5008   \n",
       "5008           2         6         1      5009  0.000000  1.000000       5009   \n",
       "5009           3         1         1      5010  0.000000  1.000000       5010   \n",
       "5010           2         6         0      5011  0.000118  0.999883       5011   \n",
       "5011           3         1         1      5012  0.000000  1.000000       5012   \n",
       "5012           2         6         0      5013  0.287454  0.712546       5013   \n",
       "5013           2         6         1      5014  0.000000  1.000000       5014   \n",
       "5014           2         6         1      5015  0.000000  1.000000       5015   \n",
       "5015           3         1         1      5016  0.000000  1.000000       5016   \n",
       "5016           2         6         0      5017  0.278325  0.721675       5017   \n",
       "5017           2         6         0      5018  0.278325  0.721675       5018   \n",
       "5018          -1        -1         0      5019  0.421425  0.000000       5019   \n",
       "5019           2         6         0      5020  0.213753  0.786247       5020   \n",
       "5020           2         6         0      5021  0.032594  0.967406       5021   \n",
       "5021           3         1         1      5022  0.000000  1.000000       5022   \n",
       "5022           2         6         0      5023  0.002410  0.997590       5023   \n",
       "5023           2         6         0      5024  0.238548  0.761452       5024   \n",
       "5024           3         1         1      5025  0.000000  1.000000       5025   \n",
       "5025           2         6         1      5026  0.000000  1.000000       5026   \n",
       "\n",
       "      STABILITY                                              SHAPE  \n",
       "0             0  {'x': -8919964.005570533, 'y': 4867758.9117359...  \n",
       "1             0  {'x': -8905258.700836737, 'y': 4865826.0263644...  \n",
       "2             0  {'x': -8905314.36058214, 'y': 4866727.02381835...  \n",
       "3             0  {'x': -8904590.783891981, 'y': 4868224.0230725...  \n",
       "4             0  {'x': -8896241.822082486, 'y': 4844052.5717511...  \n",
       "5             0  {'x': -8895774.280221151, 'y': 4844531.0751808...  \n",
       "6             0  {'x': -8894527.501924269, 'y': 4845430.1438411...  \n",
       "7             0  {'x': -8893759.397437792, 'y': 4846532.3388230...  \n",
       "8             0  {'x': -8894338.25878992, 'y': 4828101.11945760...  \n",
       "9             0  {'x': -8890542.264153872, 'y': 4867729.8430014...  \n",
       "10            0  {'x': -8892056.209228659, 'y': 4860596.0439408...  \n",
       "11            0  {'x': -8894672.217262296, 'y': 4827768.1640337...  \n",
       "12            0  {'x': -8893403.175067255, 'y': 4859869.8753424...  \n",
       "13            0  {'x': -8894393.918535313, 'y': 4844038.0720063...  \n",
       "14            0  {'x': -8894449.578280715, 'y': 4828144.5492451...  \n",
       "15            0  {'x': -8894349.390739001, 'y': 4828101.1194576...  \n",
       "16            0  {'x': -8889384.541449621, 'y': 4858693.5948817...  \n",
       "17            0  {'x': -8892067.341177735, 'y': 4860566.9961773...  \n",
       "18            0  {'x': -8894371.65463716, 'y': 4828303.79341687...  \n",
       "19            0  {'x': -8890931.882371644, 'y': 4867235.6875472...  \n",
       "20            0  {'x': -8893926.376673985, 'y': 4846474.3255170...  \n",
       "21            0  {'x': -8891688.854909038, 'y': 4848693.5754889...  \n",
       "22            0  {'x': -8894282.599044522, 'y': 4867758.9117359...  \n",
       "23            0  {'x': -8895752.016322993, 'y': 4844589.0771597...  \n",
       "24            0  {'x': -8890542.264153872, 'y': 4867584.5006068...  \n",
       "25            0  {'x': -8894438.446331633, 'y': 4828390.6549455...  \n",
       "26            0  {'x': -8878608.81474083, 'y': 4851827.47745330...  \n",
       "27            0  {'x': -8878163.53677766, 'y': 4840703.69157409...  \n",
       "28            0  {'x': -8878085.613134097, 'y': 4840689.1967055...  \n",
       "29            0  {'x': -8878230.328472132, 'y': 4852364.4023488...  \n",
       "...         ...                                                ...  \n",
       "4996          0  {'x': -8853773.43634485, 'y': 4971437.48824258...  \n",
       "4997          0  {'x': -8853662.11685406, 'y': 4984401.09493216...  \n",
       "4998          0  {'x': -8852081.380084794, 'y': 4984915.8789673...  \n",
       "4999          0  {'x': -8853817.96414117, 'y': 4984960.00457681...  \n",
       "5000          0  {'x': -8852148.171779271, 'y': 4984812.9199878...  \n",
       "5001          0  {'x': -8852103.643982952, 'y': 4985577.7840613...  \n",
       "5002          0  {'x': -8848886.510699024, 'y': 4983724.5629226...  \n",
       "5003          0  {'x': -8848474.628583092, 'y': 4986019.0790697...  \n",
       "5004          0  {'x': -8848519.156379411, 'y': 4986033.7892470...  \n",
       "5005          0  {'x': -8849008.962138899, 'y': 4984062.8230652...  \n",
       "5006          0  {'x': -8844578.446405325, 'y': 4980945.3908082...  \n",
       "5007          0  {'x': -8830830.489292359, 'y': 4982856.9057250...  \n",
       "5008          0  {'x': -8829249.752523093, 'y': 4982783.3789990...  \n",
       "5009          0  {'x': -8832500.281654255, 'y': 4967384.3794728...  \n",
       "5010          0  {'x': -8832544.809450574, 'y': 4983092.1949702...  \n",
       "5011          0  {'x': -8832266.51072359, 'y': 4967457.79041935...  \n",
       "5012          0  {'x': -8821056.638000706, 'y': 4994687.2178030...  \n",
       "5013          0  {'x': -8825865.640002977, 'y': 4977152.8767819...  \n",
       "5014          0  {'x': -8825865.640002977, 'y': 4977152.8767819...  \n",
       "5015          0  {'x': -8832500.281654255, 'y': 4967384.3794728...  \n",
       "5016          0  {'x': -8820889.65876452, 'y': 4993892.19481454...  \n",
       "5017          0  {'x': -8820889.65876452, 'y': 4993892.19481454...  \n",
       "5018          0  {'x': -8817895.16446218, 'y': 5017062.80294019...  \n",
       "5019          0  {'x': -8820166.082074363, 'y': 4979622.2535225...  \n",
       "5020          0  {'x': -8830785.961496038, 'y': 4990447.8442952...  \n",
       "5021          0  {'x': -8832500.281654255, 'y': 4967281.6050732...  \n",
       "5022          0  {'x': -8830875.017088676, 'y': 4982842.2003355...  \n",
       "5023          0  {'x': -8821646.631301915, 'y': 4994333.8662458...  \n",
       "5024          0  {'x': -8830062.38480588, 'y': 4964184.19766278...  \n",
       "5025          0  {'x': -8822381.33994115, 'y': 4982798.08429993...  \n",
       "\n",
       "[5026 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve data from the published service \n",
    "clusters_sdf = arcgis.features.SpatialDataFrame.from_layer(fc.layers[0])\n",
    "clusters_sdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cced8856b1f4d4b98f9e8e224a84721",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters_map = gis.map(\"Pittsburgh\")\n",
    "clusters_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_sdf.plot(kind='map', map_widget=clusters_map, renderer_type='u', col=\"COLOR_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# arcgis.mapping.generate_renderer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# clusters_map.add_layer(fc, {\"renderer\":\"unique\", \"field_name\": \"COLOR_ID\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Option 2 (Temporary): Use previously run analysis to work with inherent symbology "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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' 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       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=2b0b02a450eb4b14974af6dc55be6591' target='_blank'><b>HDBSCAN_restaurants_5mfc</b>\n",
       "                        </a>\n",
       "                        <br/>HDBSCAN restaurant clusters<img src='https://esrifederal.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by albe9057@esri.com_esrifederal\n",
       "                        <br/>Last Modified: August 16, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"HDBSCAN_restaurants_5mfc\" type:Feature Layer Collection owner:albe9057@esri.com_esrifederal>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_item = gis.content.search(\"HDBSCAN_restaurants_5mfc\", item_type=\"feature service\")[0]\n",
    "cluster_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b4ac8a55889476296df5a779f349326",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters_map = gis.map(\"Pittsburgh\")\n",
    "clusters_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Option 3? - Run Portal/AGOL Density-based Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Directional Distribution (Standard Deviational Ellipse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ellipses](img/ellipses_01.png \"Standard Deviational Ellipses\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While you can get a sense of the clusters by running Density-based Clustering and drawing output cluster features on a map, calculating the standard deviational ellipse makes the trend clear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Option 1: Run locally and load into web map widget \n",
    "#### Issue: Need to determine how to load symbology or specified renderer when working with spatial dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "clusters_map.add_layer(cluster_item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Option 2 (Temporary): Use previously run analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "                       <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=a375853383814e0db00f3865da21b266' target='_blank'>\n",
       "                        <img 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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=a375853383814e0db00f3865da21b266' target='_blank'><b>Ellipses_HDBSCAN_restaurants_5mfc</b>\n",
       "                        </a>\n",
       "                        <br/>Ellipses for HDBSCAN restaurants 5mfc<img src='https://esrifederal.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by albe9057@esri.com_esrifederal\n",
       "                        <br/>Last Modified: August 16, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Ellipses_HDBSCAN_restaurants_5mfc\" type:Feature Layer Collection owner:albe9057@esri.com_esrifederal>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ellipses_item = gis.content.search(\"Ellipses_HDBSCAN_restaurants_5mfc\", item_type=\"feature service\")[0]\n",
    "ellipses_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abc0e1ab08f94a8fbbbf3db7801c3e79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ellipses_map = gis.map(\"Pittsburgh\")\n",
    "ellipses_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Add layers to the map\n",
    "ellipses_map.add_layer(cluster_item)\n",
    "ellipses_map.add_layer(ellipses_item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# <span style=\"color:purple\">4. Interpret the Results:</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's recall our analysis questions and stated hypotheses:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Where are the locations in Pittsburgh, PA with dense restaurant retail presence?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Stated Hypotheses:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- Dense restaurant clusters in Pittsburgh will likely be located in the CBD.\n",
    "\n",
    "- Dense restaurant clusters in Pittsburgh will likely be located in near proximity to intersections of heavily trafficked roads."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Assessment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Where are the clusters of restaurants with the highest average revenue? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Stated Hypotheses:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- Clusters with the highest average revenue will likely be situated near downtown Pittsburgh.\n",
    "- Clusters with the highest average revenue will likely be situated in neighborhoods with higher average income levels. \n",
    "- Clusters with the highest average revenue will likely be situated along major thoroughfares and near major public transit routes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Assessment:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Are there clusters of restaurants whose collective revenue is greater than expected (i.e. revenue outliers)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Stated Hypotheses:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- There may be clusters with collective revenue outlier values in neighborhoods with a growing population. \n",
    "- There may be clusters with collective revenue outlier values in areas that recently underwent major redevelopment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Assessment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Next Steps:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- Assess whether the outputs help address each analysis question.\n",
    "- Address assumptions and issues in the current iteration of the analysis. \n",
    "- Determine which refinements are needed for the next iteration. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# <span style=\"color:purple\">5. Repeat as Necessary:</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- Run different methods of Density-based Clustering (OPTICS, DBSCAN)\n",
    "- Determine how different parameter inputs for a single clustering method alter the results\n",
    "- Assess the three questions with each iteration. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# <span style=\"color:purple\">6. Present the Results:</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- Publish the final output layers.\n",
    "- Design a web map to contain the final information products that answer each question. \n",
    "- Optional: Design and launch a story map using the Python API to contain the narrative and explanation of the analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# <span style=\"color:purple\">7. Make a Decision:</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- To be determined."
   ]
  }
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