FinalProjectGroup050-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project (change this to your project's title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [ X ] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Climate change has created a drastic negative impact on the Earth and is becoming an issue that will affect us as young people and future generations[1]. Therefore, we are curious to study what groups or demographics of people are reducing climate change in the form of recycling. Overall, we are looking to study what are the characteristics of people who are most likely to reduce climate change by recycling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Athena Hao\n",
    "- Allison Ngo\n",
    "- Karina Sanchez\n",
    "- Lingye Zhuang"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Do communities with higher financial level tend to have higher curbside recycle rates in Buffalo, NY?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recycling is an easy and effective way for normal people to help combat climate change by recycling products so that the materials from those products can be reused in order to reduce excessive/unnecessary waste. This research is important because it can set a foundation for future research to find solutions that will encourage more curbside recycling in communities.In our project we want to research on the factors that influence people’s recycling habits. These factors include age, income, and ethnicity. We can determine what groups of communities recycle the least and target those communities for change.\n",
    "\n",
    "References (include links):\n",
    "- 1)https://www.wwf.org.au/news/blogs/plastic-waste-and-climate-change-whats-the-connection#gs.ecsekh \n",
    "\n",
    "- 2)https://www.colorado.edu/ecenter/2021/02/25/climate-impact-single-use-plastics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this research we are going to conduct a null hypothesis with 5% significance level.\n",
    "\n",
    "Our null hypothesis states that there is no correlation between curbside recycling rates and financial factors of each neighborhood (such as income rates, poverty rates, and unemployment rates) in Buffalo, NY.\n",
    "\n",
    "Our alternative hypothesis states that, among neighborhoods in Buffalo, NY, there is a correlation between curbside recycling rates and financial factors, specifically wealthier neighborhoods have higher curbside recycling rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dataset Name:** Neighborhood and Curbside Recycling Rates\\\n",
    "**Link to the dataset:** https://data.world/buffalony/ug79-xatx/workspace/file?filename=neighborhood-curbside-recycling-rates-1.csv \\\n",
    "**Number of observations:** 624\\\n",
    "**Description:** This dataset analyzes the curbside recycling rate in different neighborhoods in Buffalo, NY. The dataset observes each neighborhood on 12 different dates and provides the recycling rate for each neighborhood on those data.\n",
    "\n",
    "**Dataset Name:** Neighborhood Metrics\\\n",
    "**Link to the dataset:** https://data.world/buffalony/adai-75jt/workspace/file?filename=neighborhood-metrics-1.csv \\\n",
    "**Number of observations:** 35\\\n",
    "**Description:** This is a dataset collected from the 2017 American Community Survey across 35 neighborhoods in Buffalo. It records the average income level, population in each age group, gender, race, and other useful variables of each neighborhood.\n",
    "\n",
    "We will filter out the neighborhoods that exist in both datasets. For these neighborhoods, we will cross-compare each demographic (income, age, etc.) from the second dataset versus the curbside recycle rate from the first dataset. The two datasets are established only 1 year apart, so they are representative of the same period of time in history, and it will be valid to compare them with each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the datasets we acquired both contain information about the neighborhoods in the city of Buffalo, NY, a large number of the neighborhoods' names do not match between datasets. For example, one dataset will list a neighborhood as \"ALLEN\" while the other dataset is listed as \"ALLENTOWN.\"\n",
    "\n",
    "We researched the recent maps of neighborhoods in Buffalo, and noticed there has been a redraw of district boundaries (reference: https://buffalonews.com/news/local/fruit-belt-fights-for-its-name-over-fears-big-tech-is-erasing-it/article_4e4f3089-c594-55ba-b859-694ce1f16bdf.html). Our \"Neighborhood and Curbside Recycling Rates\" dataset turns out to follow the outdated boundaries, while the \"Neighborhood Metrics\" dataset is according to the most recent map.\n",
    "\n",
    "Comparing the old and new maps, we found that the updates made fall into 3 categories:\n",
    "1. The neighborhood maintains the same name in the new map, but its boundaries have been slightly adjusted. In this case, we still consider the neighborhood as the same, and directly match the data in our two datasets for that neighborhood.\n",
    "2. The neighborhood is completely renamed, but the region stays the same. In this case, we use the updated name to represent this neighborhood.\n",
    "3. The new neighborhood is (or roughly is) a combination of several old neighborhoods. In this case, we merge the data of the several old neighborhoods in the \"Neighborhood and Curbside Recycling Rates\" dataset, to match with the combined new neighborhood in the \"Neighborhood Metrics\" dataset.\n",
    "\n",
    "Based on these, we are assuming that the neighborhoods that we matched with each other are equivalent. Any error in this would be potential factors of bias."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=2, style=\"white\")\n",
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframes from datasets\n",
    "\n",
    "# dataframe containing data from Neighborhood Metrics\n",
    "nbMetrics = pd.read_csv('https://query.data.world/s/chl4rxa6a6kaytiiytelownsoejf7z')\n",
    "\n",
    "# dataframe containing data from Neighborhood and Curbside Recycling Rates\n",
    "rcMetrics = pd.read_csv('https://query.data.world/s/iosduz5ijgk2mztfqk7ph26km5mtlg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a) Neighborhood Metrics Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35, 124)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Community</th>\n",
       "      <th>ID</th>\n",
       "      <th>Neighborhood Abbreviation</th>\n",
       "      <th>Labor Force Participation Rate</th>\n",
       "      <th>Employment Rate</th>\n",
       "      <th>Private Employment Rate</th>\n",
       "      <th>Government Employment Rate</th>\n",
       "      <th>Self-Employment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>...</th>\n",
       "      <th>Percent Non-Family Households</th>\n",
       "      <th>Total Living Alone</th>\n",
       "      <th>Percent Living Alone</th>\n",
       "      <th>Total Not Living Alone</th>\n",
       "      <th>Percent Not Living Alone</th>\n",
       "      <th>Total Living Alone Senior</th>\n",
       "      <th>Percent Living Alone Senior</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>New Georeferenced Column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "      <td>1</td>\n",
       "      <td>CN</td>\n",
       "      <td>55.17</td>\n",
       "      <td>93.43</td>\n",
       "      <td>80.41</td>\n",
       "      <td>16.76</td>\n",
       "      <td>2.83</td>\n",
       "      <td>54390</td>\n",
       "      <td>...</td>\n",
       "      <td>59.35</td>\n",
       "      <td>761</td>\n",
       "      <td>78.62</td>\n",
       "      <td>207</td>\n",
       "      <td>21.38</td>\n",
       "      <td>184</td>\n",
       "      <td>19.01</td>\n",
       "      <td>42.875966</td>\n",
       "      <td>-78.877250</td>\n",
       "      <td>(42.87596621, -78.87725)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lower West Side</td>\n",
       "      <td>West</td>\n",
       "      <td>2</td>\n",
       "      <td>LWS</td>\n",
       "      <td>53.92</td>\n",
       "      <td>91.68</td>\n",
       "      <td>79.93</td>\n",
       "      <td>18.35</td>\n",
       "      <td>1.71</td>\n",
       "      <td>18340</td>\n",
       "      <td>...</td>\n",
       "      <td>49.53</td>\n",
       "      <td>1225</td>\n",
       "      <td>85.31</td>\n",
       "      <td>211</td>\n",
       "      <td>14.69</td>\n",
       "      <td>332</td>\n",
       "      <td>23.12</td>\n",
       "      <td>42.892692</td>\n",
       "      <td>-78.885380</td>\n",
       "      <td>(42.89269228, -78.88538022)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West Side</td>\n",
       "      <td>West</td>\n",
       "      <td>3</td>\n",
       "      <td>WS</td>\n",
       "      <td>47.06</td>\n",
       "      <td>88.19</td>\n",
       "      <td>80.25</td>\n",
       "      <td>13.85</td>\n",
       "      <td>5.90</td>\n",
       "      <td>20040</td>\n",
       "      <td>...</td>\n",
       "      <td>52.48</td>\n",
       "      <td>1954</td>\n",
       "      <td>86.61</td>\n",
       "      <td>302</td>\n",
       "      <td>13.39</td>\n",
       "      <td>608</td>\n",
       "      <td>26.95</td>\n",
       "      <td>42.905534</td>\n",
       "      <td>-78.892684</td>\n",
       "      <td>(42.90553428, -78.89268387)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Upper West Side</td>\n",
       "      <td>West</td>\n",
       "      <td>4</td>\n",
       "      <td>UWS</td>\n",
       "      <td>53.18</td>\n",
       "      <td>93.66</td>\n",
       "      <td>84.02</td>\n",
       "      <td>13.57</td>\n",
       "      <td>2.40</td>\n",
       "      <td>31390</td>\n",
       "      <td>...</td>\n",
       "      <td>35.50</td>\n",
       "      <td>803</td>\n",
       "      <td>72.87</td>\n",
       "      <td>299</td>\n",
       "      <td>27.13</td>\n",
       "      <td>265</td>\n",
       "      <td>24.05</td>\n",
       "      <td>42.922925</td>\n",
       "      <td>-78.893404</td>\n",
       "      <td>(42.92292466, -78.89340362)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allentown</td>\n",
       "      <td>West</td>\n",
       "      <td>5</td>\n",
       "      <td>AT</td>\n",
       "      <td>78.49</td>\n",
       "      <td>96.23</td>\n",
       "      <td>84.02</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.82</td>\n",
       "      <td>39060</td>\n",
       "      <td>...</td>\n",
       "      <td>75.58</td>\n",
       "      <td>1119</td>\n",
       "      <td>78.42</td>\n",
       "      <td>308</td>\n",
       "      <td>21.58</td>\n",
       "      <td>80</td>\n",
       "      <td>5.61</td>\n",
       "      <td>42.898553</td>\n",
       "      <td>-78.876092</td>\n",
       "      <td>(42.89855274, -78.87609162)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood Community  ID Neighborhood Abbreviation  \\\n",
       "0          Central   Central   1                        CN   \n",
       "1  Lower West Side      West   2                       LWS   \n",
       "2        West Side      West   3                        WS   \n",
       "3  Upper West Side      West   4                       UWS   \n",
       "4        Allentown      West   5                        AT   \n",
       "\n",
       "   Labor Force Participation Rate  Employment Rate  Private Employment Rate  \\\n",
       "0                           55.17            93.43                    80.41   \n",
       "1                           53.92            91.68                    79.93   \n",
       "2                           47.06            88.19                    80.25   \n",
       "3                           53.18            93.66                    84.02   \n",
       "4                           78.49            96.23                    84.02   \n",
       "\n",
       "   Government Employment Rate  Self-Employment Rate  Median Income  ...  \\\n",
       "0                       16.76                  2.83          54390  ...   \n",
       "1                       18.35                  1.71          18340  ...   \n",
       "2                       13.85                  5.90          20040  ...   \n",
       "3                       13.57                  2.40          31390  ...   \n",
       "4                       13.16                  2.82          39060  ...   \n",
       "\n",
       "   Percent Non-Family Households  Total Living Alone  Percent Living Alone  \\\n",
       "0                          59.35                 761                 78.62   \n",
       "1                          49.53                1225                 85.31   \n",
       "2                          52.48                1954                 86.61   \n",
       "3                          35.50                 803                 72.87   \n",
       "4                          75.58                1119                 78.42   \n",
       "\n",
       "   Total Not Living Alone  Percent Not Living Alone  \\\n",
       "0                     207                     21.38   \n",
       "1                     211                     14.69   \n",
       "2                     302                     13.39   \n",
       "3                     299                     27.13   \n",
       "4                     308                     21.58   \n",
       "\n",
       "   Total Living Alone Senior  Percent Living Alone Senior   Latitude  \\\n",
       "0                        184                        19.01  42.875966   \n",
       "1                        332                        23.12  42.892692   \n",
       "2                        608                        26.95  42.905534   \n",
       "3                        265                        24.05  42.922925   \n",
       "4                         80                         5.61  42.898553   \n",
       "\n",
       "   Longitude     New Georeferenced Column  \n",
       "0 -78.877250     (42.87596621, -78.87725)  \n",
       "1 -78.885380  (42.89269228, -78.88538022)  \n",
       "2 -78.892684  (42.90553428, -78.89268387)  \n",
       "3 -78.893404  (42.92292466, -78.89340362)  \n",
       "4 -78.876092  (42.89855274, -78.87609162)  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original dataset before cleaning\n",
    "print(nbMetrics.shape)\n",
    "nbMetrics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighborhood                      False\n",
       "Community                         False\n",
       "ID                                False\n",
       "Neighborhood Abbreviation         False\n",
       "Labor Force Participation Rate    False\n",
       "                                  ...  \n",
       "Total Living Alone Senior         False\n",
       "Percent Living Alone Senior       False\n",
       "Latitude                          False\n",
       "Longitude                         False\n",
       "New Georeferenced Column          False\n",
       "Length: 124, dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for any null values\n",
    "nbMetrics.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null values in our dataframe so there is no need to remove or handle them for this dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Unnecessary Columns\n",
    "\n",
    "In the Neigborhood Metrics dataset, there ar 124 columns containing different variables such as age, ethnicity, employment, both in absolute numbers and their respective proportions. However, we do not need all of these those columns as they do not all pertaian to our study, so we will drop columns.\n",
    "\n",
    "We will mainly drop columns that contain raw values such as \"White\", \"Black\", \"Asian\", etc. and opt to use the columns that contain their percentages instead, since they are more reliable to use.\n",
    "\n",
    "The columns we will keep, such as median income, poverty rate, percent age, percent ethnicities, all pertain to our research question and are accurate representations of its respective measure. Therefore, we will keep these columns to analyze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35, 15)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Employment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>Percent Male Persons</th>\n",
       "      <th>Percent Female Persons</th>\n",
       "      <th>Percent Age &lt; 24</th>\n",
       "      <th>Percent Age 25 to 44</th>\n",
       "      <th>Percent Age 45 to 64</th>\n",
       "      <th>Percent Age 65 &gt;</th>\n",
       "      <th>Percent White</th>\n",
       "      <th>Percent Black</th>\n",
       "      <th>Percent Latinx</th>\n",
       "      <th>Percent Asian</th>\n",
       "      <th>Percent Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>93.43</td>\n",
       "      <td>54390</td>\n",
       "      <td>19.52</td>\n",
       "      <td>55.75</td>\n",
       "      <td>44.25</td>\n",
       "      <td>23.45</td>\n",
       "      <td>38.40</td>\n",
       "      <td>24.81</td>\n",
       "      <td>13.33</td>\n",
       "      <td>47.89</td>\n",
       "      <td>28.51</td>\n",
       "      <td>20.45</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lower West Side</td>\n",
       "      <td>91.68</td>\n",
       "      <td>18340</td>\n",
       "      <td>46.55</td>\n",
       "      <td>42.78</td>\n",
       "      <td>57.22</td>\n",
       "      <td>38.30</td>\n",
       "      <td>28.04</td>\n",
       "      <td>23.45</td>\n",
       "      <td>10.21</td>\n",
       "      <td>21.86</td>\n",
       "      <td>22.93</td>\n",
       "      <td>50.79</td>\n",
       "      <td>0.43</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West Side</td>\n",
       "      <td>88.19</td>\n",
       "      <td>20040</td>\n",
       "      <td>48.49</td>\n",
       "      <td>51.46</td>\n",
       "      <td>48.54</td>\n",
       "      <td>39.87</td>\n",
       "      <td>30.14</td>\n",
       "      <td>20.06</td>\n",
       "      <td>9.94</td>\n",
       "      <td>31.57</td>\n",
       "      <td>16.66</td>\n",
       "      <td>32.90</td>\n",
       "      <td>14.27</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Upper West Side</td>\n",
       "      <td>93.66</td>\n",
       "      <td>31390</td>\n",
       "      <td>46.96</td>\n",
       "      <td>51.38</td>\n",
       "      <td>48.62</td>\n",
       "      <td>46.49</td>\n",
       "      <td>29.77</td>\n",
       "      <td>17.05</td>\n",
       "      <td>6.69</td>\n",
       "      <td>26.09</td>\n",
       "      <td>28.00</td>\n",
       "      <td>23.36</td>\n",
       "      <td>15.67</td>\n",
       "      <td>6.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allentown</td>\n",
       "      <td>96.23</td>\n",
       "      <td>39060</td>\n",
       "      <td>28.84</td>\n",
       "      <td>54.71</td>\n",
       "      <td>45.29</td>\n",
       "      <td>30.66</td>\n",
       "      <td>43.12</td>\n",
       "      <td>20.19</td>\n",
       "      <td>6.04</td>\n",
       "      <td>76.78</td>\n",
       "      <td>7.73</td>\n",
       "      <td>8.68</td>\n",
       "      <td>2.20</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood  Employment Rate  Median Income  Poverty Rate  \\\n",
       "0          Central            93.43          54390         19.52   \n",
       "1  Lower West Side            91.68          18340         46.55   \n",
       "2        West Side            88.19          20040         48.49   \n",
       "3  Upper West Side            93.66          31390         46.96   \n",
       "4        Allentown            96.23          39060         28.84   \n",
       "\n",
       "   Percent Male Persons  Percent Female Persons  Percent Age < 24  \\\n",
       "0                 55.75                   44.25             23.45   \n",
       "1                 42.78                   57.22             38.30   \n",
       "2                 51.46                   48.54             39.87   \n",
       "3                 51.38                   48.62             46.49   \n",
       "4                 54.71                   45.29             30.66   \n",
       "\n",
       "   Percent Age 25 to 44  Percent Age 45 to 64  Percent Age 65 >  \\\n",
       "0                 38.40                 24.81             13.33   \n",
       "1                 28.04                 23.45             10.21   \n",
       "2                 30.14                 20.06              9.94   \n",
       "3                 29.77                 17.05              6.69   \n",
       "4                 43.12                 20.19              6.04   \n",
       "\n",
       "   Percent White  Percent Black  Percent Latinx  Percent Asian  Percent Other  \n",
       "0          47.89          28.51           20.45           1.68           1.47  \n",
       "1          21.86          22.93           50.79           0.43           3.99  \n",
       "2          31.57          16.66           32.90          14.27           4.59  \n",
       "3          26.09          28.00           23.36          15.67           6.87  \n",
       "4          76.78           7.73            8.68           2.20           4.61  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete all columns after percent other since they're not needed and save as new dataframe\n",
    "nbMetrics = nbMetrics.loc[:, :'Percent Other']\n",
    "\n",
    "# drop unnecessary columns\n",
    "nbMetrics = nbMetrics.drop(columns=['ID', 'Community', 'Neighborhood Abbreviation', 'Labor Force Participation Rate', 'Private Employment Rate',\n",
    "                      'Government Employment Rate', 'Self-Employment Rate', 'Total Persons', 'Male Persons', 'Female Persons', \n",
    "                        'Age < 24', 'Age 25 to 44', 'Age 45 to 64', 'Age 65 >',\n",
    "                      'White', 'Black', 'Latinx', 'Asian', 'Other'])\n",
    "print(nbMetrics.shape)\n",
    "nbMetrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rename Columns\n",
    "\n",
    "We will rename some columns to condense them and make them more understandable to readers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Employment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>Percent Male</th>\n",
       "      <th>Percent Female</th>\n",
       "      <th>Percent Age &lt; 24</th>\n",
       "      <th>Percent Age 25 to 44</th>\n",
       "      <th>Percent Age 45 to 64</th>\n",
       "      <th>Percent Age 65 &gt;</th>\n",
       "      <th>Percent White</th>\n",
       "      <th>Percent Black</th>\n",
       "      <th>Percent Latinx</th>\n",
       "      <th>Percent Asian</th>\n",
       "      <th>Percent Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>93.43</td>\n",
       "      <td>54390</td>\n",
       "      <td>19.52</td>\n",
       "      <td>55.75</td>\n",
       "      <td>44.25</td>\n",
       "      <td>23.45</td>\n",
       "      <td>38.40</td>\n",
       "      <td>24.81</td>\n",
       "      <td>13.33</td>\n",
       "      <td>47.89</td>\n",
       "      <td>28.51</td>\n",
       "      <td>20.45</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LOWER WEST SIDE</td>\n",
       "      <td>91.68</td>\n",
       "      <td>18340</td>\n",
       "      <td>46.55</td>\n",
       "      <td>42.78</td>\n",
       "      <td>57.22</td>\n",
       "      <td>38.30</td>\n",
       "      <td>28.04</td>\n",
       "      <td>23.45</td>\n",
       "      <td>10.21</td>\n",
       "      <td>21.86</td>\n",
       "      <td>22.93</td>\n",
       "      <td>50.79</td>\n",
       "      <td>0.43</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WEST SIDE</td>\n",
       "      <td>88.19</td>\n",
       "      <td>20040</td>\n",
       "      <td>48.49</td>\n",
       "      <td>51.46</td>\n",
       "      <td>48.54</td>\n",
       "      <td>39.87</td>\n",
       "      <td>30.14</td>\n",
       "      <td>20.06</td>\n",
       "      <td>9.94</td>\n",
       "      <td>31.57</td>\n",
       "      <td>16.66</td>\n",
       "      <td>32.90</td>\n",
       "      <td>14.27</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UPPER WEST SIDE</td>\n",
       "      <td>93.66</td>\n",
       "      <td>31390</td>\n",
       "      <td>46.96</td>\n",
       "      <td>51.38</td>\n",
       "      <td>48.62</td>\n",
       "      <td>46.49</td>\n",
       "      <td>29.77</td>\n",
       "      <td>17.05</td>\n",
       "      <td>6.69</td>\n",
       "      <td>26.09</td>\n",
       "      <td>28.00</td>\n",
       "      <td>23.36</td>\n",
       "      <td>15.67</td>\n",
       "      <td>6.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALLENTOWN</td>\n",
       "      <td>96.23</td>\n",
       "      <td>39060</td>\n",
       "      <td>28.84</td>\n",
       "      <td>54.71</td>\n",
       "      <td>45.29</td>\n",
       "      <td>30.66</td>\n",
       "      <td>43.12</td>\n",
       "      <td>20.19</td>\n",
       "      <td>6.04</td>\n",
       "      <td>76.78</td>\n",
       "      <td>7.73</td>\n",
       "      <td>8.68</td>\n",
       "      <td>2.20</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood  Employment Rate  Median Income  Poverty Rate  \\\n",
       "0          CENTRAL            93.43          54390         19.52   \n",
       "1  LOWER WEST SIDE            91.68          18340         46.55   \n",
       "2        WEST SIDE            88.19          20040         48.49   \n",
       "3  UPPER WEST SIDE            93.66          31390         46.96   \n",
       "4        ALLENTOWN            96.23          39060         28.84   \n",
       "\n",
       "   Percent Male  Percent Female  Percent Age < 24  Percent Age 25 to 44  \\\n",
       "0         55.75           44.25             23.45                 38.40   \n",
       "1         42.78           57.22             38.30                 28.04   \n",
       "2         51.46           48.54             39.87                 30.14   \n",
       "3         51.38           48.62             46.49                 29.77   \n",
       "4         54.71           45.29             30.66                 43.12   \n",
       "\n",
       "   Percent Age 45 to 64  Percent Age 65 >  Percent White  Percent Black  \\\n",
       "0                 24.81             13.33          47.89          28.51   \n",
       "1                 23.45             10.21          21.86          22.93   \n",
       "2                 20.06              9.94          31.57          16.66   \n",
       "3                 17.05              6.69          26.09          28.00   \n",
       "4                 20.19              6.04          76.78           7.73   \n",
       "\n",
       "   Percent Latinx  Percent Asian  Percent Other  \n",
       "0           20.45           1.68           1.47  \n",
       "1           50.79           0.43           3.99  \n",
       "2           32.90          14.27           4.59  \n",
       "3           23.36          15.67           6.87  \n",
       "4            8.68           2.20           4.61  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns \n",
    "nbMetrics = nbMetrics.rename(columns={'Percent Male Persons':'Percent Male', 'Percent Female Persons':'Percent Female'})\n",
    "nbMetrics['Neighborhood'] = nbMetrics['Neighborhood'].str.upper()\n",
    "nbMetrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting by neighborhood in ascending order\n",
    "\n",
    "Lastly, we will sort the dataframe by neighborhood names in ascending value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Employment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>Percent Male</th>\n",
       "      <th>Percent Female</th>\n",
       "      <th>Percent Age &lt; 24</th>\n",
       "      <th>Percent Age 25 to 44</th>\n",
       "      <th>Percent Age 45 to 64</th>\n",
       "      <th>Percent Age 65 &gt;</th>\n",
       "      <th>Percent White</th>\n",
       "      <th>Percent Black</th>\n",
       "      <th>Percent Latinx</th>\n",
       "      <th>Percent Asian</th>\n",
       "      <th>Percent Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLENTOWN</td>\n",
       "      <td>96.23</td>\n",
       "      <td>39060</td>\n",
       "      <td>28.84</td>\n",
       "      <td>54.71</td>\n",
       "      <td>45.29</td>\n",
       "      <td>30.66</td>\n",
       "      <td>43.12</td>\n",
       "      <td>20.19</td>\n",
       "      <td>6.04</td>\n",
       "      <td>76.78</td>\n",
       "      <td>7.73</td>\n",
       "      <td>8.68</td>\n",
       "      <td>2.20</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLACK ROCK</td>\n",
       "      <td>87.60</td>\n",
       "      <td>24550</td>\n",
       "      <td>35.42</td>\n",
       "      <td>50.98</td>\n",
       "      <td>49.02</td>\n",
       "      <td>39.22</td>\n",
       "      <td>28.68</td>\n",
       "      <td>21.82</td>\n",
       "      <td>10.28</td>\n",
       "      <td>46.81</td>\n",
       "      <td>11.01</td>\n",
       "      <td>33.91</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BROADWAY FILLMORE</td>\n",
       "      <td>79.11</td>\n",
       "      <td>18940</td>\n",
       "      <td>45.39</td>\n",
       "      <td>48.31</td>\n",
       "      <td>51.69</td>\n",
       "      <td>37.39</td>\n",
       "      <td>25.33</td>\n",
       "      <td>24.81</td>\n",
       "      <td>12.46</td>\n",
       "      <td>18.04</td>\n",
       "      <td>60.96</td>\n",
       "      <td>2.74</td>\n",
       "      <td>13.46</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>93.43</td>\n",
       "      <td>54390</td>\n",
       "      <td>19.52</td>\n",
       "      <td>55.75</td>\n",
       "      <td>44.25</td>\n",
       "      <td>23.45</td>\n",
       "      <td>38.40</td>\n",
       "      <td>24.81</td>\n",
       "      <td>13.33</td>\n",
       "      <td>47.89</td>\n",
       "      <td>28.51</td>\n",
       "      <td>20.45</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CENTRAL PARK</td>\n",
       "      <td>85.42</td>\n",
       "      <td>61940</td>\n",
       "      <td>14.32</td>\n",
       "      <td>50.75</td>\n",
       "      <td>49.25</td>\n",
       "      <td>19.75</td>\n",
       "      <td>36.32</td>\n",
       "      <td>31.29</td>\n",
       "      <td>12.64</td>\n",
       "      <td>77.34</td>\n",
       "      <td>15.23</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Neighborhood  Employment Rate  Median Income  Poverty Rate  \\\n",
       "0          ALLENTOWN            96.23          39060         28.84   \n",
       "1         BLACK ROCK            87.60          24550         35.42   \n",
       "2  BROADWAY FILLMORE            79.11          18940         45.39   \n",
       "3            CENTRAL            93.43          54390         19.52   \n",
       "4       CENTRAL PARK            85.42          61940         14.32   \n",
       "\n",
       "   Percent Male  Percent Female  Percent Age < 24  Percent Age 25 to 44  \\\n",
       "0         54.71           45.29             30.66                 43.12   \n",
       "1         50.98           49.02             39.22                 28.68   \n",
       "2         48.31           51.69             37.39                 25.33   \n",
       "3         55.75           44.25             23.45                 38.40   \n",
       "4         50.75           49.25             19.75                 36.32   \n",
       "\n",
       "   Percent Age 45 to 64  Percent Age 65 >  Percent White  Percent Black  \\\n",
       "0                 20.19              6.04          76.78           7.73   \n",
       "1                 21.82             10.28          46.81          11.01   \n",
       "2                 24.81             12.46          18.04          60.96   \n",
       "3                 24.81             13.33          47.89          28.51   \n",
       "4                 31.29             12.64          77.34          15.23   \n",
       "\n",
       "   Percent Latinx  Percent Asian  Percent Other  \n",
       "0            8.68           2.20           4.61  \n",
       "1           33.91           4.65           3.62  \n",
       "2            2.74          13.46           4.80  \n",
       "3           20.45           1.68           1.47  \n",
       "4            3.01           1.67           2.76  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alphabetize neighborhood column and reset index\n",
    "nbMetrics = nbMetrics.sort_values(by='Neighborhood', ascending=True).reset_index()\n",
    "nbMetrics = nbMetrics.drop(columns=['index'])\n",
    "nbMetrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take a look at the finalized Neighborhood Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Employment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>Percent Male</th>\n",
       "      <th>Percent Female</th>\n",
       "      <th>Percent Age &lt; 24</th>\n",
       "      <th>Percent Age 25 to 44</th>\n",
       "      <th>Percent Age 45 to 64</th>\n",
       "      <th>Percent Age 65 &gt;</th>\n",
       "      <th>Percent White</th>\n",
       "      <th>Percent Black</th>\n",
       "      <th>Percent Latinx</th>\n",
       "      <th>Percent Asian</th>\n",
       "      <th>Percent Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLENTOWN</td>\n",
       "      <td>96.23</td>\n",
       "      <td>39060</td>\n",
       "      <td>28.84</td>\n",
       "      <td>54.71</td>\n",
       "      <td>45.29</td>\n",
       "      <td>30.66</td>\n",
       "      <td>43.12</td>\n",
       "      <td>20.19</td>\n",
       "      <td>6.04</td>\n",
       "      <td>76.78</td>\n",
       "      <td>7.73</td>\n",
       "      <td>8.68</td>\n",
       "      <td>2.20</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLACK ROCK</td>\n",
       "      <td>87.60</td>\n",
       "      <td>24550</td>\n",
       "      <td>35.42</td>\n",
       "      <td>50.98</td>\n",
       "      <td>49.02</td>\n",
       "      <td>39.22</td>\n",
       "      <td>28.68</td>\n",
       "      <td>21.82</td>\n",
       "      <td>10.28</td>\n",
       "      <td>46.81</td>\n",
       "      <td>11.01</td>\n",
       "      <td>33.91</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BROADWAY FILLMORE</td>\n",
       "      <td>79.11</td>\n",
       "      <td>18940</td>\n",
       "      <td>45.39</td>\n",
       "      <td>48.31</td>\n",
       "      <td>51.69</td>\n",
       "      <td>37.39</td>\n",
       "      <td>25.33</td>\n",
       "      <td>24.81</td>\n",
       "      <td>12.46</td>\n",
       "      <td>18.04</td>\n",
       "      <td>60.96</td>\n",
       "      <td>2.74</td>\n",
       "      <td>13.46</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>93.43</td>\n",
       "      <td>54390</td>\n",
       "      <td>19.52</td>\n",
       "      <td>55.75</td>\n",
       "      <td>44.25</td>\n",
       "      <td>23.45</td>\n",
       "      <td>38.40</td>\n",
       "      <td>24.81</td>\n",
       "      <td>13.33</td>\n",
       "      <td>47.89</td>\n",
       "      <td>28.51</td>\n",
       "      <td>20.45</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CENTRAL PARK</td>\n",
       "      <td>85.42</td>\n",
       "      <td>61940</td>\n",
       "      <td>14.32</td>\n",
       "      <td>50.75</td>\n",
       "      <td>49.25</td>\n",
       "      <td>19.75</td>\n",
       "      <td>36.32</td>\n",
       "      <td>31.29</td>\n",
       "      <td>12.64</td>\n",
       "      <td>77.34</td>\n",
       "      <td>15.23</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DELAVAN GRIDER</td>\n",
       "      <td>86.54</td>\n",
       "      <td>28370</td>\n",
       "      <td>29.43</td>\n",
       "      <td>41.81</td>\n",
       "      <td>58.19</td>\n",
       "      <td>34.88</td>\n",
       "      <td>18.54</td>\n",
       "      <td>28.07</td>\n",
       "      <td>18.51</td>\n",
       "      <td>1.85</td>\n",
       "      <td>92.78</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ELLICOTT</td>\n",
       "      <td>84.97</td>\n",
       "      <td>22190</td>\n",
       "      <td>33.49</td>\n",
       "      <td>47.38</td>\n",
       "      <td>52.62</td>\n",
       "      <td>35.60</td>\n",
       "      <td>24.49</td>\n",
       "      <td>27.31</td>\n",
       "      <td>12.60</td>\n",
       "      <td>26.73</td>\n",
       "      <td>47.42</td>\n",
       "      <td>17.97</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ELMWOOD BIDWELL</td>\n",
       "      <td>94.02</td>\n",
       "      <td>56150</td>\n",
       "      <td>18.24</td>\n",
       "      <td>46.86</td>\n",
       "      <td>53.14</td>\n",
       "      <td>41.13</td>\n",
       "      <td>29.73</td>\n",
       "      <td>18.65</td>\n",
       "      <td>10.48</td>\n",
       "      <td>69.85</td>\n",
       "      <td>16.12</td>\n",
       "      <td>6.75</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ELMWOOD BRYANT</td>\n",
       "      <td>93.62</td>\n",
       "      <td>40520</td>\n",
       "      <td>23.95</td>\n",
       "      <td>52.71</td>\n",
       "      <td>47.29</td>\n",
       "      <td>21.83</td>\n",
       "      <td>38.66</td>\n",
       "      <td>22.15</td>\n",
       "      <td>17.36</td>\n",
       "      <td>63.72</td>\n",
       "      <td>15.42</td>\n",
       "      <td>7.88</td>\n",
       "      <td>9.73</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FILLMORE-LEROY</td>\n",
       "      <td>82.79</td>\n",
       "      <td>22910</td>\n",
       "      <td>45.55</td>\n",
       "      <td>47.01</td>\n",
       "      <td>52.99</td>\n",
       "      <td>36.17</td>\n",
       "      <td>26.49</td>\n",
       "      <td>21.45</td>\n",
       "      <td>15.88</td>\n",
       "      <td>10.43</td>\n",
       "      <td>78.58</td>\n",
       "      <td>4.20</td>\n",
       "      <td>2.55</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FIRST WARD</td>\n",
       "      <td>86.73</td>\n",
       "      <td>27880</td>\n",
       "      <td>39.21</td>\n",
       "      <td>41.70</td>\n",
       "      <td>58.30</td>\n",
       "      <td>40.39</td>\n",
       "      <td>21.26</td>\n",
       "      <td>25.82</td>\n",
       "      <td>12.53</td>\n",
       "      <td>78.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FRUIT BELT</td>\n",
       "      <td>89.76</td>\n",
       "      <td>25350</td>\n",
       "      <td>28.03</td>\n",
       "      <td>44.77</td>\n",
       "      <td>55.23</td>\n",
       "      <td>29.01</td>\n",
       "      <td>15.11</td>\n",
       "      <td>30.23</td>\n",
       "      <td>25.65</td>\n",
       "      <td>11.54</td>\n",
       "      <td>82.88</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GENESEE-MOSELLE</td>\n",
       "      <td>80.67</td>\n",
       "      <td>22080</td>\n",
       "      <td>46.99</td>\n",
       "      <td>47.21</td>\n",
       "      <td>52.79</td>\n",
       "      <td>39.01</td>\n",
       "      <td>24.16</td>\n",
       "      <td>26.68</td>\n",
       "      <td>10.14</td>\n",
       "      <td>15.16</td>\n",
       "      <td>75.13</td>\n",
       "      <td>4.43</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GRANT-AMHERST</td>\n",
       "      <td>81.42</td>\n",
       "      <td>24385</td>\n",
       "      <td>47.85</td>\n",
       "      <td>44.37</td>\n",
       "      <td>55.63</td>\n",
       "      <td>45.25</td>\n",
       "      <td>29.49</td>\n",
       "      <td>18.90</td>\n",
       "      <td>6.36</td>\n",
       "      <td>40.13</td>\n",
       "      <td>16.38</td>\n",
       "      <td>27.44</td>\n",
       "      <td>12.07</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HAMLIN PARK</td>\n",
       "      <td>88.14</td>\n",
       "      <td>27860</td>\n",
       "      <td>36.95</td>\n",
       "      <td>45.47</td>\n",
       "      <td>54.53</td>\n",
       "      <td>51.51</td>\n",
       "      <td>18.00</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.82</td>\n",
       "      <td>21.75</td>\n",
       "      <td>71.94</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HOPKINS-TIFFT</td>\n",
       "      <td>90.92</td>\n",
       "      <td>36960</td>\n",
       "      <td>28.07</td>\n",
       "      <td>52.32</td>\n",
       "      <td>47.68</td>\n",
       "      <td>34.87</td>\n",
       "      <td>27.99</td>\n",
       "      <td>25.61</td>\n",
       "      <td>11.53</td>\n",
       "      <td>68.80</td>\n",
       "      <td>6.14</td>\n",
       "      <td>18.63</td>\n",
       "      <td>2.34</td>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KAISERTOWN</td>\n",
       "      <td>92.77</td>\n",
       "      <td>41280</td>\n",
       "      <td>15.14</td>\n",
       "      <td>48.13</td>\n",
       "      <td>51.87</td>\n",
       "      <td>25.97</td>\n",
       "      <td>25.89</td>\n",
       "      <td>33.01</td>\n",
       "      <td>15.14</td>\n",
       "      <td>86.72</td>\n",
       "      <td>4.58</td>\n",
       "      <td>6.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KENFIELD</td>\n",
       "      <td>85.70</td>\n",
       "      <td>26700</td>\n",
       "      <td>37.58</td>\n",
       "      <td>38.64</td>\n",
       "      <td>61.36</td>\n",
       "      <td>43.65</td>\n",
       "      <td>22.24</td>\n",
       "      <td>23.61</td>\n",
       "      <td>10.50</td>\n",
       "      <td>5.53</td>\n",
       "      <td>90.37</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KENSINGTON-BAILEY</td>\n",
       "      <td>89.47</td>\n",
       "      <td>37240</td>\n",
       "      <td>30.88</td>\n",
       "      <td>44.33</td>\n",
       "      <td>55.67</td>\n",
       "      <td>40.30</td>\n",
       "      <td>24.81</td>\n",
       "      <td>24.08</td>\n",
       "      <td>10.80</td>\n",
       "      <td>6.29</td>\n",
       "      <td>87.76</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LOVEJOY</td>\n",
       "      <td>86.95</td>\n",
       "      <td>28780</td>\n",
       "      <td>34.40</td>\n",
       "      <td>46.42</td>\n",
       "      <td>53.58</td>\n",
       "      <td>33.85</td>\n",
       "      <td>28.26</td>\n",
       "      <td>25.04</td>\n",
       "      <td>12.85</td>\n",
       "      <td>76.04</td>\n",
       "      <td>14.54</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LOWER WEST SIDE</td>\n",
       "      <td>91.68</td>\n",
       "      <td>18340</td>\n",
       "      <td>46.55</td>\n",
       "      <td>42.78</td>\n",
       "      <td>57.22</td>\n",
       "      <td>38.30</td>\n",
       "      <td>28.04</td>\n",
       "      <td>23.45</td>\n",
       "      <td>10.21</td>\n",
       "      <td>21.86</td>\n",
       "      <td>22.93</td>\n",
       "      <td>50.79</td>\n",
       "      <td>0.43</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MASTEN PARK</td>\n",
       "      <td>79.84</td>\n",
       "      <td>22520</td>\n",
       "      <td>34.65</td>\n",
       "      <td>41.67</td>\n",
       "      <td>58.33</td>\n",
       "      <td>33.52</td>\n",
       "      <td>22.06</td>\n",
       "      <td>25.61</td>\n",
       "      <td>18.80</td>\n",
       "      <td>4.18</td>\n",
       "      <td>86.66</td>\n",
       "      <td>6.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MLK PARK</td>\n",
       "      <td>84.62</td>\n",
       "      <td>20480</td>\n",
       "      <td>37.04</td>\n",
       "      <td>41.78</td>\n",
       "      <td>58.22</td>\n",
       "      <td>37.01</td>\n",
       "      <td>19.42</td>\n",
       "      <td>25.60</td>\n",
       "      <td>17.98</td>\n",
       "      <td>4.22</td>\n",
       "      <td>93.33</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NORTH PARK</td>\n",
       "      <td>92.54</td>\n",
       "      <td>53870</td>\n",
       "      <td>11.48</td>\n",
       "      <td>49.46</td>\n",
       "      <td>50.54</td>\n",
       "      <td>23.69</td>\n",
       "      <td>38.07</td>\n",
       "      <td>24.96</td>\n",
       "      <td>13.28</td>\n",
       "      <td>72.98</td>\n",
       "      <td>13.01</td>\n",
       "      <td>9.29</td>\n",
       "      <td>2.86</td>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PARKSIDE</td>\n",
       "      <td>95.67</td>\n",
       "      <td>54000</td>\n",
       "      <td>14.03</td>\n",
       "      <td>47.52</td>\n",
       "      <td>52.48</td>\n",
       "      <td>33.40</td>\n",
       "      <td>27.15</td>\n",
       "      <td>27.01</td>\n",
       "      <td>12.44</td>\n",
       "      <td>73.31</td>\n",
       "      <td>15.18</td>\n",
       "      <td>5.53</td>\n",
       "      <td>3.14</td>\n",
       "      <td>2.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PRATT-WILLERT</td>\n",
       "      <td>91.15</td>\n",
       "      <td>26330</td>\n",
       "      <td>30.65</td>\n",
       "      <td>37.24</td>\n",
       "      <td>62.76</td>\n",
       "      <td>29.46</td>\n",
       "      <td>24.31</td>\n",
       "      <td>27.49</td>\n",
       "      <td>18.73</td>\n",
       "      <td>5.97</td>\n",
       "      <td>84.70</td>\n",
       "      <td>4.59</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>RIVERSIDE</td>\n",
       "      <td>95.04</td>\n",
       "      <td>21570</td>\n",
       "      <td>45.32</td>\n",
       "      <td>45.70</td>\n",
       "      <td>54.30</td>\n",
       "      <td>41.35</td>\n",
       "      <td>27.20</td>\n",
       "      <td>23.62</td>\n",
       "      <td>7.83</td>\n",
       "      <td>49.15</td>\n",
       "      <td>13.34</td>\n",
       "      <td>22.49</td>\n",
       "      <td>10.39</td>\n",
       "      <td>4.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SCHILLER PARK</td>\n",
       "      <td>85.49</td>\n",
       "      <td>24020</td>\n",
       "      <td>43.96</td>\n",
       "      <td>44.33</td>\n",
       "      <td>55.67</td>\n",
       "      <td>44.03</td>\n",
       "      <td>24.08</td>\n",
       "      <td>23.35</td>\n",
       "      <td>8.53</td>\n",
       "      <td>10.96</td>\n",
       "      <td>78.86</td>\n",
       "      <td>5.16</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SENECA BABCOCK</td>\n",
       "      <td>82.07</td>\n",
       "      <td>30290</td>\n",
       "      <td>25.86</td>\n",
       "      <td>48.41</td>\n",
       "      <td>51.59</td>\n",
       "      <td>36.78</td>\n",
       "      <td>28.87</td>\n",
       "      <td>26.60</td>\n",
       "      <td>7.75</td>\n",
       "      <td>80.08</td>\n",
       "      <td>1.65</td>\n",
       "      <td>14.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SENECA-CAZENOVIA</td>\n",
       "      <td>87.63</td>\n",
       "      <td>38300</td>\n",
       "      <td>23.60</td>\n",
       "      <td>51.84</td>\n",
       "      <td>48.16</td>\n",
       "      <td>30.64</td>\n",
       "      <td>30.93</td>\n",
       "      <td>25.56</td>\n",
       "      <td>12.87</td>\n",
       "      <td>83.09</td>\n",
       "      <td>6.69</td>\n",
       "      <td>4.76</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SOUTH PARK</td>\n",
       "      <td>94.37</td>\n",
       "      <td>55720</td>\n",
       "      <td>11.89</td>\n",
       "      <td>47.31</td>\n",
       "      <td>52.69</td>\n",
       "      <td>28.72</td>\n",
       "      <td>28.40</td>\n",
       "      <td>28.15</td>\n",
       "      <td>14.73</td>\n",
       "      <td>90.14</td>\n",
       "      <td>3.01</td>\n",
       "      <td>5.60</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>UNIVERSITY HEIGHTS</td>\n",
       "      <td>92.20</td>\n",
       "      <td>42130</td>\n",
       "      <td>35.21</td>\n",
       "      <td>53.63</td>\n",
       "      <td>46.37</td>\n",
       "      <td>49.86</td>\n",
       "      <td>22.63</td>\n",
       "      <td>20.26</td>\n",
       "      <td>7.26</td>\n",
       "      <td>35.88</td>\n",
       "      <td>38.32</td>\n",
       "      <td>6.99</td>\n",
       "      <td>15.97</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>UPPER WEST SIDE</td>\n",
       "      <td>93.66</td>\n",
       "      <td>31390</td>\n",
       "      <td>46.96</td>\n",
       "      <td>51.38</td>\n",
       "      <td>48.62</td>\n",
       "      <td>46.49</td>\n",
       "      <td>29.77</td>\n",
       "      <td>17.05</td>\n",
       "      <td>6.69</td>\n",
       "      <td>26.09</td>\n",
       "      <td>28.00</td>\n",
       "      <td>23.36</td>\n",
       "      <td>15.67</td>\n",
       "      <td>6.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>WEST HERTEL</td>\n",
       "      <td>90.41</td>\n",
       "      <td>25980</td>\n",
       "      <td>38.85</td>\n",
       "      <td>53.58</td>\n",
       "      <td>46.42</td>\n",
       "      <td>37.78</td>\n",
       "      <td>30.35</td>\n",
       "      <td>23.85</td>\n",
       "      <td>8.01</td>\n",
       "      <td>37.78</td>\n",
       "      <td>27.58</td>\n",
       "      <td>24.83</td>\n",
       "      <td>2.87</td>\n",
       "      <td>6.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>WEST SIDE</td>\n",
       "      <td>88.19</td>\n",
       "      <td>20040</td>\n",
       "      <td>48.49</td>\n",
       "      <td>51.46</td>\n",
       "      <td>48.54</td>\n",
       "      <td>39.87</td>\n",
       "      <td>30.14</td>\n",
       "      <td>20.06</td>\n",
       "      <td>9.94</td>\n",
       "      <td>31.57</td>\n",
       "      <td>16.66</td>\n",
       "      <td>32.90</td>\n",
       "      <td>14.27</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Neighborhood  Employment Rate  Median Income  Poverty Rate  \\\n",
       "0            ALLENTOWN            96.23          39060         28.84   \n",
       "1           BLACK ROCK            87.60          24550         35.42   \n",
       "2    BROADWAY FILLMORE            79.11          18940         45.39   \n",
       "3              CENTRAL            93.43          54390         19.52   \n",
       "4         CENTRAL PARK            85.42          61940         14.32   \n",
       "5       DELAVAN GRIDER            86.54          28370         29.43   \n",
       "6             ELLICOTT            84.97          22190         33.49   \n",
       "7      ELMWOOD BIDWELL            94.02          56150         18.24   \n",
       "8       ELMWOOD BRYANT            93.62          40520         23.95   \n",
       "9       FILLMORE-LEROY            82.79          22910         45.55   \n",
       "10          FIRST WARD            86.73          27880         39.21   \n",
       "11          FRUIT BELT            89.76          25350         28.03   \n",
       "12     GENESEE-MOSELLE            80.67          22080         46.99   \n",
       "13       GRANT-AMHERST            81.42          24385         47.85   \n",
       "14         HAMLIN PARK            88.14          27860         36.95   \n",
       "15       HOPKINS-TIFFT            90.92          36960         28.07   \n",
       "16          KAISERTOWN            92.77          41280         15.14   \n",
       "17            KENFIELD            85.70          26700         37.58   \n",
       "18   KENSINGTON-BAILEY            89.47          37240         30.88   \n",
       "19             LOVEJOY            86.95          28780         34.40   \n",
       "20     LOWER WEST SIDE            91.68          18340         46.55   \n",
       "21         MASTEN PARK            79.84          22520         34.65   \n",
       "22            MLK PARK            84.62          20480         37.04   \n",
       "23          NORTH PARK            92.54          53870         11.48   \n",
       "24            PARKSIDE            95.67          54000         14.03   \n",
       "25       PRATT-WILLERT            91.15          26330         30.65   \n",
       "26           RIVERSIDE            95.04          21570         45.32   \n",
       "27       SCHILLER PARK            85.49          24020         43.96   \n",
       "28      SENECA BABCOCK            82.07          30290         25.86   \n",
       "29    SENECA-CAZENOVIA            87.63          38300         23.60   \n",
       "30          SOUTH PARK            94.37          55720         11.89   \n",
       "31  UNIVERSITY HEIGHTS            92.20          42130         35.21   \n",
       "32     UPPER WEST SIDE            93.66          31390         46.96   \n",
       "33         WEST HERTEL            90.41          25980         38.85   \n",
       "34           WEST SIDE            88.19          20040         48.49   \n",
       "\n",
       "    Percent Male  Percent Female  Percent Age < 24  Percent Age 25 to 44  \\\n",
       "0          54.71           45.29             30.66                 43.12   \n",
       "1          50.98           49.02             39.22                 28.68   \n",
       "2          48.31           51.69             37.39                 25.33   \n",
       "3          55.75           44.25             23.45                 38.40   \n",
       "4          50.75           49.25             19.75                 36.32   \n",
       "5          41.81           58.19             34.88                 18.54   \n",
       "6          47.38           52.62             35.60                 24.49   \n",
       "7          46.86           53.14             41.13                 29.73   \n",
       "8          52.71           47.29             21.83                 38.66   \n",
       "9          47.01           52.99             36.17                 26.49   \n",
       "10         41.70           58.30             40.39                 21.26   \n",
       "11         44.77           55.23             29.01                 15.11   \n",
       "12         47.21           52.79             39.01                 24.16   \n",
       "13         44.37           55.63             45.25                 29.49   \n",
       "14         45.47           54.53             51.51                 18.00   \n",
       "15         52.32           47.68             34.87                 27.99   \n",
       "16         48.13           51.87             25.97                 25.89   \n",
       "17         38.64           61.36             43.65                 22.24   \n",
       "18         44.33           55.67             40.30                 24.81   \n",
       "19         46.42           53.58             33.85                 28.26   \n",
       "20         42.78           57.22             38.30                 28.04   \n",
       "21         41.67           58.33             33.52                 22.06   \n",
       "22         41.78           58.22             37.01                 19.42   \n",
       "23         49.46           50.54             23.69                 38.07   \n",
       "24         47.52           52.48             33.40                 27.15   \n",
       "25         37.24           62.76             29.46                 24.31   \n",
       "26         45.70           54.30             41.35                 27.20   \n",
       "27         44.33           55.67             44.03                 24.08   \n",
       "28         48.41           51.59             36.78                 28.87   \n",
       "29         51.84           48.16             30.64                 30.93   \n",
       "30         47.31           52.69             28.72                 28.40   \n",
       "31         53.63           46.37             49.86                 22.63   \n",
       "32         51.38           48.62             46.49                 29.77   \n",
       "33         53.58           46.42             37.78                 30.35   \n",
       "34         51.46           48.54             39.87                 30.14   \n",
       "\n",
       "    Percent Age 45 to 64  Percent Age 65 >  Percent White  Percent Black  \\\n",
       "0                  20.19              6.04          76.78           7.73   \n",
       "1                  21.82             10.28          46.81          11.01   \n",
       "2                  24.81             12.46          18.04          60.96   \n",
       "3                  24.81             13.33          47.89          28.51   \n",
       "4                  31.29             12.64          77.34          15.23   \n",
       "5                  28.07             18.51           1.85          92.78   \n",
       "6                  27.31             12.60          26.73          47.42   \n",
       "7                  18.65             10.48          69.85          16.12   \n",
       "8                  22.15             17.36          63.72          15.42   \n",
       "9                  21.45             15.88          10.43          78.58   \n",
       "10                 25.82             12.53          78.89           0.00   \n",
       "11                 30.23             25.65          11.54          82.88   \n",
       "12                 26.68             10.14          15.16          75.13   \n",
       "13                 18.90              6.36          40.13          16.38   \n",
       "14                 16.67             13.82          21.75          71.94   \n",
       "15                 25.61             11.53          68.80           6.14   \n",
       "16                 33.01             15.14          86.72           4.58   \n",
       "17                 23.61             10.50           5.53          90.37   \n",
       "18                 24.08             10.80           6.29          87.76   \n",
       "19                 25.04             12.85          76.04          14.54   \n",
       "20                 23.45             10.21          21.86          22.93   \n",
       "21                 25.61             18.80           4.18          86.66   \n",
       "22                 25.60             17.98           4.22          93.33   \n",
       "23                 24.96             13.28          72.98          13.01   \n",
       "24                 27.01             12.44          73.31          15.18   \n",
       "25                 27.49             18.73           5.97          84.70   \n",
       "26                 23.62              7.83          49.15          13.34   \n",
       "27                 23.35              8.53          10.96          78.86   \n",
       "28                 26.60              7.75          80.08           1.65   \n",
       "29                 25.56             12.87          83.09           6.69   \n",
       "30                 28.15             14.73          90.14           3.01   \n",
       "31                 20.26              7.26          35.88          38.32   \n",
       "32                 17.05              6.69          26.09          28.00   \n",
       "33                 23.85              8.01          37.78          27.58   \n",
       "34                 20.06              9.94          31.57          16.66   \n",
       "\n",
       "    Percent Latinx  Percent Asian  Percent Other  \n",
       "0             8.68           2.20           4.61  \n",
       "1            33.91           4.65           3.62  \n",
       "2             2.74          13.46           4.80  \n",
       "3            20.45           1.68           1.47  \n",
       "4             3.01           1.67           2.76  \n",
       "5             1.02           0.00           4.34  \n",
       "6            17.97           7.24           0.65  \n",
       "7             6.75           4.42           2.86  \n",
       "8             7.88           9.73           3.25  \n",
       "9             4.20           2.55           4.25  \n",
       "10           19.03           0.00           2.08  \n",
       "11            4.05           0.00           1.52  \n",
       "12            4.43           3.70           1.58  \n",
       "13           27.44          12.07           3.97  \n",
       "14            2.18           2.21           1.92  \n",
       "15           18.63           2.34           4.08  \n",
       "16            6.58           0.00           2.12  \n",
       "17            2.68           0.26           1.16  \n",
       "18            1.46           1.79           2.69  \n",
       "19            5.92           0.64           2.85  \n",
       "20           50.79           0.43           3.99  \n",
       "21            6.49           0.00           2.67  \n",
       "22            1.05           0.00           1.41  \n",
       "23            9.29           2.86           1.85  \n",
       "24            5.53           3.14           2.84  \n",
       "25            4.59           0.92           3.81  \n",
       "26           22.49          10.39           4.62  \n",
       "27            5.16           2.10           2.93  \n",
       "28           14.27           0.00           4.00  \n",
       "29            4.76           2.32           3.13  \n",
       "30            5.60           0.45           0.80  \n",
       "31            6.99          15.97           2.85  \n",
       "32           23.36          15.67           6.87  \n",
       "33           24.83           2.87           6.94  \n",
       "34           32.90          14.27           4.59  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finalized cleaned dataframe for Neighborhood Metrics\n",
    "nbMetrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) Neighborhood and Curbside Recycling Rates Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(624, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NEIGHBORHOOD</th>\n",
       "      <th>DATE</th>\n",
       "      <th>CURBSIDE RECYCLING (IN POUNDS)</th>\n",
       "      <th>CURBSIDE GARBAGE (IN POUNDS)</th>\n",
       "      <th>CURBSIDE RECYCLING RATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABBOTT McKINLEY</td>\n",
       "      <td>07/31/2018</td>\n",
       "      <td>49.18</td>\n",
       "      <td>256.09</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABBOTT McKINLEY</td>\n",
       "      <td>03/31/2018</td>\n",
       "      <td>35.12</td>\n",
       "      <td>182.03</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABBOTT McKINLEY</td>\n",
       "      <td>10/31/2018</td>\n",
       "      <td>46.98</td>\n",
       "      <td>243.91</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBOTT McKINLEY</td>\n",
       "      <td>11/30/2018</td>\n",
       "      <td>42.04</td>\n",
       "      <td>216.08</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABBOTT McKINLEY</td>\n",
       "      <td>05/31/2018</td>\n",
       "      <td>43.48</td>\n",
       "      <td>232.49</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      NEIGHBORHOOD        DATE  CURBSIDE RECYCLING (IN POUNDS)  \\\n",
       "0  ABBOTT McKINLEY  07/31/2018                           49.18   \n",
       "1  ABBOTT McKINLEY  03/31/2018                           35.12   \n",
       "2  ABBOTT McKINLEY  10/31/2018                           46.98   \n",
       "3  ABBOTT McKINLEY  11/30/2018                           42.04   \n",
       "4  ABBOTT McKINLEY  05/31/2018                           43.48   \n",
       "\n",
       "   CURBSIDE GARBAGE (IN POUNDS)  CURBSIDE RECYCLING RATE  \n",
       "0                        256.09                       16  \n",
       "1                        182.03                       16  \n",
       "2                        243.91                       16  \n",
       "3                        216.08                       16  \n",
       "4                        232.49                       16  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original dataset before cleaning\n",
    "print(rcMetrics.shape)\n",
    "rcMetrics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEIGHBORHOOD                      False\n",
       "DATE                              False\n",
       "CURBSIDE RECYCLING (IN POUNDS)    False\n",
       "CURBSIDE GARBAGE (IN POUNDS)      False\n",
       "CURBSIDE RECYCLING RATE           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for any null values in dataframe\n",
    "rcMetrics.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Unnecessary Columns\n",
    "\n",
    "Since we are not interested in the curbside garbage, we will drop that column. Curbside recycling and rate we will keep since they pertain to our research question about recycling rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Curbside recycling (in pounds)</th>\n",
       "      <th>Curbside recycling rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABBOTT MCKINLEY</td>\n",
       "      <td>49.18</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABBOTT MCKINLEY</td>\n",
       "      <td>35.12</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABBOTT MCKINLEY</td>\n",
       "      <td>46.98</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBOTT MCKINLEY</td>\n",
       "      <td>42.04</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABBOTT MCKINLEY</td>\n",
       "      <td>43.48</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood  Curbside recycling (in pounds)  Curbside recycling rate\n",
       "0  ABBOTT MCKINLEY                           49.18                       16\n",
       "1  ABBOTT MCKINLEY                           35.12                       16\n",
       "2  ABBOTT MCKINLEY                           46.98                       16\n",
       "3  ABBOTT MCKINLEY                           42.04                       16\n",
       "4  ABBOTT MCKINLEY                           43.48                       16"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns to match nbMetrics capitalization\n",
    "rcMetrics.columns = map(str.lower, rcMetrics.columns)\n",
    "rcMetrics.columns = map(str.capitalize, rcMetrics.columns)\n",
    "rcMetrics['Neighborhood'] = rcMetrics['Neighborhood'].str.upper()\n",
    "\n",
    "# drop columns that are unnecessary to our study\n",
    "rcMetrics = rcMetrics.drop(columns=['Curbside garbage (in pounds)', 'Date'])\n",
    "rcMetrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by neighborhood\n",
    "\n",
    "Since there are multiple different records of each neighborhoods curbside recyling that are taken at different dates, we want to combine all of the records into one observation per neighborhood and take the mean of all the observations of each neighborhood. For example, for 'ABBOT McKINLEY', the new individual observation for this neighborhood will contain the mean value from the original 12 observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Curbside recycling (in pounds)</th>\n",
       "      <th>Curbside recycling rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABBOTT MCKINLEY</td>\n",
       "      <td>42.654167</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALBRIGHT</td>\n",
       "      <td>23.645000</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALLEN</td>\n",
       "      <td>22.292500</td>\n",
       "      <td>18.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BABCOCK</td>\n",
       "      <td>19.578333</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BLACK ROCK</td>\n",
       "      <td>34.943333</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood  Curbside recycling (in pounds)  Curbside recycling rate\n",
       "0  ABBOTT MCKINLEY                       42.654167                16.666667\n",
       "1         ALBRIGHT                       23.645000                21.500000\n",
       "2            ALLEN                       22.292500                18.833333\n",
       "3          BABCOCK                       19.578333                13.000000\n",
       "4       BLACK ROCK                       34.943333                13.833333"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge rows with the same neighborhood name and take mean of all column values\n",
    "rcMetrics = rcMetrics.groupby('Neighborhood', as_index=False).mean()\n",
    "rcMetrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rebuilding the Neighborhood Column\n",
    "\n",
    "As mentioned earlier in our assumptions, the neighborhood names do not match between datasets, because the \"Neighborhood and Curbside Recycling Rates\" dataset follows an outdated mapping of boundaries, while the \"Neighborhood Metrics\" dataset is according to the updated one. However, we can still match neighborhoods together by refering to their names and geographic areas on maps.\n",
    "\n",
    "In this section, we will rename and combine some neighborhoods in rcMetrics to match the nbMetrics. We used Wikipedia and the University of Buffalo's website (https://library.buffalo.edu/maps/buffalo-wnymaps/buffalo-neighborhoods.html#sb) as our base reference for the current/most recently updated names and geographic boundaries of these neighborhoods. As stated in our assumptions, some geographic boundaries have changed since the time of collecting this data and may not accurately reflect the correct boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Curbside recycling (in pounds)</th>\n",
       "      <th>Curbside recycling rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABBOTT MCKINLEY</td>\n",
       "      <td>42.654167</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALBRIGHT</td>\n",
       "      <td>23.645000</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALLENTOWN</td>\n",
       "      <td>22.292500</td>\n",
       "      <td>18.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BABCOCK</td>\n",
       "      <td>19.578333</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BLACK ROCK</td>\n",
       "      <td>34.943333</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood  Curbside recycling (in pounds)  Curbside recycling rate\n",
       "0  ABBOTT MCKINLEY                       42.654167                16.666667\n",
       "1         ALBRIGHT                       23.645000                21.500000\n",
       "2        ALLENTOWN                       22.292500                18.833333\n",
       "3          BABCOCK                       19.578333                13.000000\n",
       "4       BLACK ROCK                       34.943333                13.833333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace names in dataframe, using website and wikipedia as reference\n",
    "rcMetrics.replace('ALLEN', 'ALLENTOWN', inplace=True)\n",
    "rcMetrics.replace('KENSINGTON', 'KENSINGTON-BAILEY', inplace=True)\n",
    "rcMetrics.replace('M.L.K. PARK', 'MLK PARK', inplace=True)\n",
    "rcMetrics.replace('RIVERSIDE PARK', 'RIVERSIDE', inplace=True)\n",
    "rcMetrics.replace('BRYANT', 'ELMWOOD BRYANT', inplace=True)\n",
    "rcMetrics.replace('GRIDER', 'DELAVAN GRIDER', inplace=True)\n",
    "rcMetrics.replace('LEROY', 'FILLMORE-LEROY', inplace=True)\n",
    "rcMetrics.replace('STARIN CENTRAL', 'CENTRAL PARK',  inplace=True)\n",
    "rcMetrics.replace('WILLERT PARK', 'PRATT-WILLERT', inplace=True)\n",
    "rcMetrics.replace('MILITARY', 'WEST HERTEL', inplace=True)\n",
    "rcMetrics.replace('FRONT PARK', 'WEST SIDE', inplace=True)\n",
    "rcMetrics.replace('MEDICAL PARK', 'FRUIT BELT', inplace=True)\n",
    "\n",
    "rcMetrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging two rows into a new row and drop the merged rows\n",
    "\n",
    "There were some neighborhoods that were initially multiple separate entities, but were found to have combined into one neighborhood based on recent maps of Buffalo. Therefore, we are taking that factor into account and combining some rows into a new row with a new name while taking the mean of the rows values.\n",
    "\n",
    "Since we merged some rows to create new neighborhood observations in the last part, we must delete the individual neighborhoods to avoid duplicate data. For example, Seneca and Cazenovia Park will be dropped from the dataframe once we have combined them into a new row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NORTH DELAWARE + NORTH PARK = new NORTH PARK\n",
    "# create subset of rows for NORTH DELAWARE and NORTH PARK\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'NORTH DELAWARE']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'NORTH PARK']\n",
    "subset = pd.concat([val1, val2])\n",
    "# drop merged rows to avoid duplicate data\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'NORTH DELAWARE'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'NORTH PARK'].index)\n",
    "# create new row based on previous rows' names and mean of their values, append to rcMetrics\n",
    "new_row = {\n",
    "    'Neighborhood': 'NORTH PARK', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# UNIVERSITY + LaSALLE = UNIVERSITY HEIGHTS\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'UNIVERSITY']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'LASALLE']\n",
    "subset = pd.concat([val1, val2])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'UNIVERSITY'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'LASALLE'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'UNIVERSITY HEIGHTS', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# PARK MEADOW + DELAWARE PARK + PARKSIDE = new PARKSIDE\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'PARK MEADOW']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'DELAWARE PARK']\n",
    "val3 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'PARKSIDE']\n",
    "subset = pd.concat([val1, val2, val3])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'PARK MEADOW'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'DELAWARE PARK'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'PARKSIDE'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'PARKSIDE', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# STATE HOSPITAL + FOREST + GRANT FERRY + ALBRIGHT + DELAWARE W. FERRY = ELMWOOD BIDWELL\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'STATE HOSPITAL']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'FOREST']\n",
    "val3 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'GRANT FERRY']\n",
    "val4 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'ALBRIGHT']\n",
    "val5 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'DELAWARE W. FERRY']\n",
    "subset = pd.concat([val1, val2, val3, val4, val5])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'STATE HOSPITAL'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'FOREST'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'GRANT FERRY'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'ALBRIGHT'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'DELAWARE W. FERRY'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'ELMWOOD BIDWELL', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# COLD SPRING + MASTEN PARK + KINGSLEY = new MASTEN PARK\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'COLD SPRING']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'MASTEN PARK']\n",
    "val3 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'KINGSLEY']\n",
    "subset = pd.concat([val1, val2, val3])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'COLD SPRING'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'MASTEN PARK'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'KINGSLEY'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'MASTEN PARK', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# GENESEE MOSELLE + EMERSON = GENESEE-MOSELLE\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'GENESEE MOSELLE']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'EMERSON']\n",
    "subset = pd.concat([val1, val2])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'GENESEE MOSELLE'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'EMERSON'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'GENESEE-MOSELLE', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# LAKEVIEW + COLUMBUS = LOWER WEST SIDE\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'LAKEVIEW']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'COLUMBUS']\n",
    "subset = pd.concat([val1, val2])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'LAKEVIEW'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'COLUMBUS'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'LOWER WEST SIDE', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# JOHNSON + EMSLIE + BROADWAY FILLMORE = new BROADWAY FILLMORE\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'JOHNSON']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'EMSLIE']\n",
    "val3 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'BROADWAY FILLMORE']\n",
    "subset = pd.concat([val1, val2, val3])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'JOHNSON'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'EMSLIE'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'BROADWAY FILLMORE'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'BROADWAY FILLMORE', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# PERRY + SOUTH ELLICOTT = ELLICOTT\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'PERRY']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'SOUTH ELLICOTT']\n",
    "subset = pd.concat([val1, val2])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'PERRY'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'SOUTH ELLICOTT'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'ELLICOTT', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# BABCOCK + VALLEY = SENECA BABCOCK\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'BABCOCK']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'VALLEY']\n",
    "subset = pd.concat([val1, val2])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'BABCOCK'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'VALLEY'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'SENECA BABCOCK', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# ABBOTT McKINLEY + SOUTH ABBOTT = new SOUTH PARK\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'ABBOTT MCKINLEY']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'SOUTH ABBOTT']\n",
    "subset = pd.concat([val1, val2])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'ABBOTT MCKINLEY'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'SOUTH ABBOTT'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'SOUTH PARK', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# SENECA + CAZENOVIA PARK = SENECA-CAZENOVIA\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'SENECA']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'CAZENOVIA PARK']\n",
    "subset = pd.concat([val1, val2])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'SENECA'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'CAZENOVIA PARK'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'SENECA-CAZENOVIA', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# TIFFT + SOUTH PARK + TRIANGLE = HOPKINS-TIFFT\n",
    "val1 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'TIFFT']\n",
    "val2 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'SOUTH PARK']\n",
    "val3 = rcMetrics.loc[rcMetrics['Neighborhood'] == 'TRIANGLE']\n",
    "subset = pd.concat([val1, val2, val3])\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'TIFFT'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'SOUTH PARK'].index)\n",
    "rcMetrics = rcMetrics.drop(rcMetrics.loc[rcMetrics['Neighborhood'] == 'TRIANGLE'].index)\n",
    "new_row = {\n",
    "    'Neighborhood': 'HOPKINS-TIFFT', \n",
    "    'Curbside recycling (in pounds)': subset['Curbside recycling (in pounds)'].mean(), \n",
    "    'Curbside recycling rate': subset['Curbside recycling rate'].mean()}\n",
    "rcMetrics = rcMetrics.append(new_row, ignore_index = True)\n",
    "\n",
    "# alphabetize neighborhood column and reset index\n",
    "rcMetrics = rcMetrics.sort_values(by='Neighborhood', ascending=True).reset_index()\n",
    "rcMetrics = rcMetrics.drop(columns=['index'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the observations in this set have been correctly merged according to the new mapping of districts and reduced to 1 row per neighborhood, so we can accurately merge with nbMetrics. Similar to the last dataset, since these observations rather than recorded individually by each person, there is no need to standarize any of the values. There is also no need to remove or handle null values since there are none in this dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take a look at the finalized Neighborhood and Curbside Recycling Rates dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Curbside recycling (in pounds)</th>\n",
       "      <th>Curbside recycling rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLENTOWN</td>\n",
       "      <td>22.292500</td>\n",
       "      <td>18.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLACK ROCK</td>\n",
       "      <td>34.943333</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BROADWAY FILLMORE</td>\n",
       "      <td>13.594444</td>\n",
       "      <td>11.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CENTRAL PARK</td>\n",
       "      <td>42.293333</td>\n",
       "      <td>20.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DELAVAN GRIDER</td>\n",
       "      <td>27.213333</td>\n",
       "      <td>13.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ELLICOTT</td>\n",
       "      <td>4.876250</td>\n",
       "      <td>13.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ELMWOOD BIDWELL</td>\n",
       "      <td>27.069667</td>\n",
       "      <td>17.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ELMWOOD BRYANT</td>\n",
       "      <td>28.580000</td>\n",
       "      <td>17.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FILLMORE-LEROY</td>\n",
       "      <td>25.184167</td>\n",
       "      <td>13.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FIRST WARD</td>\n",
       "      <td>11.061667</td>\n",
       "      <td>14.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FRUIT BELT</td>\n",
       "      <td>12.385833</td>\n",
       "      <td>13.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GENESEE-MOSELLE</td>\n",
       "      <td>15.965833</td>\n",
       "      <td>11.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HAMLIN PARK</td>\n",
       "      <td>21.532500</td>\n",
       "      <td>12.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HOPKINS-TIFFT</td>\n",
       "      <td>27.361458</td>\n",
       "      <td>15.239583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KAISERTOWN</td>\n",
       "      <td>33.061667</td>\n",
       "      <td>15.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KENFIELD</td>\n",
       "      <td>19.057500</td>\n",
       "      <td>10.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KENSINGTON-BAILEY</td>\n",
       "      <td>41.187500</td>\n",
       "      <td>12.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LOVEJOY</td>\n",
       "      <td>36.294167</td>\n",
       "      <td>12.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LOWER WEST SIDE</td>\n",
       "      <td>10.396667</td>\n",
       "      <td>15.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MASTEN PARK</td>\n",
       "      <td>12.596111</td>\n",
       "      <td>13.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MLK PARK</td>\n",
       "      <td>13.959167</td>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NORTH PARK</td>\n",
       "      <td>52.164583</td>\n",
       "      <td>17.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PARKSIDE</td>\n",
       "      <td>21.670556</td>\n",
       "      <td>19.805556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PRATT-WILLERT</td>\n",
       "      <td>13.288333</td>\n",
       "      <td>12.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RIVERSIDE</td>\n",
       "      <td>52.218333</td>\n",
       "      <td>14.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SCHILLER PARK</td>\n",
       "      <td>36.906667</td>\n",
       "      <td>12.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SENECA BABCOCK</td>\n",
       "      <td>11.279167</td>\n",
       "      <td>13.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SENECA-CAZENOVIA</td>\n",
       "      <td>23.638333</td>\n",
       "      <td>16.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>UNIVERSITY HEIGHTS</td>\n",
       "      <td>31.959167</td>\n",
       "      <td>15.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WEST HERTEL</td>\n",
       "      <td>20.420833</td>\n",
       "      <td>15.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>WEST SIDE</td>\n",
       "      <td>62.533333</td>\n",
       "      <td>14.416667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Neighborhood  Curbside recycling (in pounds)  \\\n",
       "0            ALLENTOWN                       22.292500   \n",
       "1           BLACK ROCK                       34.943333   \n",
       "2    BROADWAY FILLMORE                       13.594444   \n",
       "3         CENTRAL PARK                       42.293333   \n",
       "4       DELAVAN GRIDER                       27.213333   \n",
       "5             ELLICOTT                        4.876250   \n",
       "6      ELMWOOD BIDWELL                       27.069667   \n",
       "7       ELMWOOD BRYANT                       28.580000   \n",
       "8       FILLMORE-LEROY                       25.184167   \n",
       "9           FIRST WARD                       11.061667   \n",
       "10          FRUIT BELT                       12.385833   \n",
       "11     GENESEE-MOSELLE                       15.965833   \n",
       "12         HAMLIN PARK                       21.532500   \n",
       "13       HOPKINS-TIFFT                       27.361458   \n",
       "14          KAISERTOWN                       33.061667   \n",
       "15            KENFIELD                       19.057500   \n",
       "16   KENSINGTON-BAILEY                       41.187500   \n",
       "17             LOVEJOY                       36.294167   \n",
       "18     LOWER WEST SIDE                       10.396667   \n",
       "19         MASTEN PARK                       12.596111   \n",
       "20            MLK PARK                       13.959167   \n",
       "21          NORTH PARK                       52.164583   \n",
       "22            PARKSIDE                       21.670556   \n",
       "23       PRATT-WILLERT                       13.288333   \n",
       "24           RIVERSIDE                       52.218333   \n",
       "25       SCHILLER PARK                       36.906667   \n",
       "26      SENECA BABCOCK                       11.279167   \n",
       "27    SENECA-CAZENOVIA                       23.638333   \n",
       "28  UNIVERSITY HEIGHTS                       31.959167   \n",
       "29         WEST HERTEL                       20.420833   \n",
       "30           WEST SIDE                       62.533333   \n",
       "\n",
       "    Curbside recycling rate  \n",
       "0                 18.833333  \n",
       "1                 13.833333  \n",
       "2                 11.222222  \n",
       "3                 20.750000  \n",
       "4                 13.583333  \n",
       "5                 13.291667  \n",
       "6                 17.633333  \n",
       "7                 17.083333  \n",
       "8                 13.916667  \n",
       "9                 14.833333  \n",
       "10                13.916667  \n",
       "11                11.083333  \n",
       "12                12.666667  \n",
       "13                15.239583  \n",
       "14                15.666667  \n",
       "15                10.916667  \n",
       "16                12.416667  \n",
       "17                12.416667  \n",
       "18                15.708333  \n",
       "19                13.777778  \n",
       "20                10.416667  \n",
       "21                17.458333  \n",
       "22                19.805556  \n",
       "23                12.583333  \n",
       "24                14.166667  \n",
       "25                12.083333  \n",
       "26                13.958333  \n",
       "27                16.125000  \n",
       "28                15.583333  \n",
       "29                15.750000  \n",
       "30                14.416667  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finalized cleaned dataframe for the Neighborhood and Curbside Recycling Rates dataframe\n",
    "rcMetrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## c) Merging Dataframes\n",
    "\n",
    "Now that both datasets have been cleaned, we can merge the two so that all observations are recorded in one dataframe and are grouped by neighborhood name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Employment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>Percent Male</th>\n",
       "      <th>Percent Female</th>\n",
       "      <th>Percent Age &lt; 24</th>\n",
       "      <th>Percent Age 25 to 44</th>\n",
       "      <th>Percent Age 45 to 64</th>\n",
       "      <th>Percent Age 65 &gt;</th>\n",
       "      <th>Percent White</th>\n",
       "      <th>Percent Black</th>\n",
       "      <th>Percent Latinx</th>\n",
       "      <th>Percent Asian</th>\n",
       "      <th>Percent Other</th>\n",
       "      <th>Curbside recycling (in pounds)</th>\n",
       "      <th>Curbside recycling rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLENTOWN</td>\n",
       "      <td>96.23</td>\n",
       "      <td>39060</td>\n",
       "      <td>28.84</td>\n",
       "      <td>54.71</td>\n",
       "      <td>45.29</td>\n",
       "      <td>30.66</td>\n",
       "      <td>43.12</td>\n",
       "      <td>20.19</td>\n",
       "      <td>6.04</td>\n",
       "      <td>76.78</td>\n",
       "      <td>7.73</td>\n",
       "      <td>8.68</td>\n",
       "      <td>2.20</td>\n",
       "      <td>4.61</td>\n",
       "      <td>22.292500</td>\n",
       "      <td>18.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLACK ROCK</td>\n",
       "      <td>87.60</td>\n",
       "      <td>24550</td>\n",
       "      <td>35.42</td>\n",
       "      <td>50.98</td>\n",
       "      <td>49.02</td>\n",
       "      <td>39.22</td>\n",
       "      <td>28.68</td>\n",
       "      <td>21.82</td>\n",
       "      <td>10.28</td>\n",
       "      <td>46.81</td>\n",
       "      <td>11.01</td>\n",
       "      <td>33.91</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.62</td>\n",
       "      <td>34.943333</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BROADWAY FILLMORE</td>\n",
       "      <td>79.11</td>\n",
       "      <td>18940</td>\n",
       "      <td>45.39</td>\n",
       "      <td>48.31</td>\n",
       "      <td>51.69</td>\n",
       "      <td>37.39</td>\n",
       "      <td>25.33</td>\n",
       "      <td>24.81</td>\n",
       "      <td>12.46</td>\n",
       "      <td>18.04</td>\n",
       "      <td>60.96</td>\n",
       "      <td>2.74</td>\n",
       "      <td>13.46</td>\n",
       "      <td>4.80</td>\n",
       "      <td>13.594444</td>\n",
       "      <td>11.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CENTRAL PARK</td>\n",
       "      <td>85.42</td>\n",
       "      <td>61940</td>\n",
       "      <td>14.32</td>\n",
       "      <td>50.75</td>\n",
       "      <td>49.25</td>\n",
       "      <td>19.75</td>\n",
       "      <td>36.32</td>\n",
       "      <td>31.29</td>\n",
       "      <td>12.64</td>\n",
       "      <td>77.34</td>\n",
       "      <td>15.23</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.76</td>\n",
       "      <td>42.293333</td>\n",
       "      <td>20.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DELAVAN GRIDER</td>\n",
       "      <td>86.54</td>\n",
       "      <td>28370</td>\n",
       "      <td>29.43</td>\n",
       "      <td>41.81</td>\n",
       "      <td>58.19</td>\n",
       "      <td>34.88</td>\n",
       "      <td>18.54</td>\n",
       "      <td>28.07</td>\n",
       "      <td>18.51</td>\n",
       "      <td>1.85</td>\n",
       "      <td>92.78</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.34</td>\n",
       "      <td>27.213333</td>\n",
       "      <td>13.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ELLICOTT</td>\n",
       "      <td>84.97</td>\n",
       "      <td>22190</td>\n",
       "      <td>33.49</td>\n",
       "      <td>47.38</td>\n",
       "      <td>52.62</td>\n",
       "      <td>35.60</td>\n",
       "      <td>24.49</td>\n",
       "      <td>27.31</td>\n",
       "      <td>12.60</td>\n",
       "      <td>26.73</td>\n",
       "      <td>47.42</td>\n",
       "      <td>17.97</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.65</td>\n",
       "      <td>4.876250</td>\n",
       "      <td>13.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ELMWOOD BIDWELL</td>\n",
       "      <td>94.02</td>\n",
       "      <td>56150</td>\n",
       "      <td>18.24</td>\n",
       "      <td>46.86</td>\n",
       "      <td>53.14</td>\n",
       "      <td>41.13</td>\n",
       "      <td>29.73</td>\n",
       "      <td>18.65</td>\n",
       "      <td>10.48</td>\n",
       "      <td>69.85</td>\n",
       "      <td>16.12</td>\n",
       "      <td>6.75</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2.86</td>\n",
       "      <td>27.069667</td>\n",
       "      <td>17.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ELMWOOD BRYANT</td>\n",
       "      <td>93.62</td>\n",
       "      <td>40520</td>\n",
       "      <td>23.95</td>\n",
       "      <td>52.71</td>\n",
       "      <td>47.29</td>\n",
       "      <td>21.83</td>\n",
       "      <td>38.66</td>\n",
       "      <td>22.15</td>\n",
       "      <td>17.36</td>\n",
       "      <td>63.72</td>\n",
       "      <td>15.42</td>\n",
       "      <td>7.88</td>\n",
       "      <td>9.73</td>\n",
       "      <td>3.25</td>\n",
       "      <td>28.580000</td>\n",
       "      <td>17.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FILLMORE-LEROY</td>\n",
       "      <td>82.79</td>\n",
       "      <td>22910</td>\n",
       "      <td>45.55</td>\n",
       "      <td>47.01</td>\n",
       "      <td>52.99</td>\n",
       "      <td>36.17</td>\n",
       "      <td>26.49</td>\n",
       "      <td>21.45</td>\n",
       "      <td>15.88</td>\n",
       "      <td>10.43</td>\n",
       "      <td>78.58</td>\n",
       "      <td>4.20</td>\n",
       "      <td>2.55</td>\n",
       "      <td>4.25</td>\n",
       "      <td>25.184167</td>\n",
       "      <td>13.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FIRST WARD</td>\n",
       "      <td>86.73</td>\n",
       "      <td>27880</td>\n",
       "      <td>39.21</td>\n",
       "      <td>41.70</td>\n",
       "      <td>58.30</td>\n",
       "      <td>40.39</td>\n",
       "      <td>21.26</td>\n",
       "      <td>25.82</td>\n",
       "      <td>12.53</td>\n",
       "      <td>78.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>11.061667</td>\n",
       "      <td>14.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FRUIT BELT</td>\n",
       "      <td>89.76</td>\n",
       "      <td>25350</td>\n",
       "      <td>28.03</td>\n",
       "      <td>44.77</td>\n",
       "      <td>55.23</td>\n",
       "      <td>29.01</td>\n",
       "      <td>15.11</td>\n",
       "      <td>30.23</td>\n",
       "      <td>25.65</td>\n",
       "      <td>11.54</td>\n",
       "      <td>82.88</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.52</td>\n",
       "      <td>12.385833</td>\n",
       "      <td>13.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GENESEE-MOSELLE</td>\n",
       "      <td>80.67</td>\n",
       "      <td>22080</td>\n",
       "      <td>46.99</td>\n",
       "      <td>47.21</td>\n",
       "      <td>52.79</td>\n",
       "      <td>39.01</td>\n",
       "      <td>24.16</td>\n",
       "      <td>26.68</td>\n",
       "      <td>10.14</td>\n",
       "      <td>15.16</td>\n",
       "      <td>75.13</td>\n",
       "      <td>4.43</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1.58</td>\n",
       "      <td>15.965833</td>\n",
       "      <td>11.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HAMLIN PARK</td>\n",
       "      <td>88.14</td>\n",
       "      <td>27860</td>\n",
       "      <td>36.95</td>\n",
       "      <td>45.47</td>\n",
       "      <td>54.53</td>\n",
       "      <td>51.51</td>\n",
       "      <td>18.00</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.82</td>\n",
       "      <td>21.75</td>\n",
       "      <td>71.94</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.92</td>\n",
       "      <td>21.532500</td>\n",
       "      <td>12.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HOPKINS-TIFFT</td>\n",
       "      <td>90.92</td>\n",
       "      <td>36960</td>\n",
       "      <td>28.07</td>\n",
       "      <td>52.32</td>\n",
       "      <td>47.68</td>\n",
       "      <td>34.87</td>\n",
       "      <td>27.99</td>\n",
       "      <td>25.61</td>\n",
       "      <td>11.53</td>\n",
       "      <td>68.80</td>\n",
       "      <td>6.14</td>\n",
       "      <td>18.63</td>\n",
       "      <td>2.34</td>\n",
       "      <td>4.08</td>\n",
       "      <td>27.361458</td>\n",
       "      <td>15.239583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KAISERTOWN</td>\n",
       "      <td>92.77</td>\n",
       "      <td>41280</td>\n",
       "      <td>15.14</td>\n",
       "      <td>48.13</td>\n",
       "      <td>51.87</td>\n",
       "      <td>25.97</td>\n",
       "      <td>25.89</td>\n",
       "      <td>33.01</td>\n",
       "      <td>15.14</td>\n",
       "      <td>86.72</td>\n",
       "      <td>4.58</td>\n",
       "      <td>6.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.12</td>\n",
       "      <td>33.061667</td>\n",
       "      <td>15.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KENFIELD</td>\n",
       "      <td>85.70</td>\n",
       "      <td>26700</td>\n",
       "      <td>37.58</td>\n",
       "      <td>38.64</td>\n",
       "      <td>61.36</td>\n",
       "      <td>43.65</td>\n",
       "      <td>22.24</td>\n",
       "      <td>23.61</td>\n",
       "      <td>10.50</td>\n",
       "      <td>5.53</td>\n",
       "      <td>90.37</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.16</td>\n",
       "      <td>19.057500</td>\n",
       "      <td>10.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KENSINGTON-BAILEY</td>\n",
       "      <td>89.47</td>\n",
       "      <td>37240</td>\n",
       "      <td>30.88</td>\n",
       "      <td>44.33</td>\n",
       "      <td>55.67</td>\n",
       "      <td>40.30</td>\n",
       "      <td>24.81</td>\n",
       "      <td>24.08</td>\n",
       "      <td>10.80</td>\n",
       "      <td>6.29</td>\n",
       "      <td>87.76</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.69</td>\n",
       "      <td>41.187500</td>\n",
       "      <td>12.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LOVEJOY</td>\n",
       "      <td>86.95</td>\n",
       "      <td>28780</td>\n",
       "      <td>34.40</td>\n",
       "      <td>46.42</td>\n",
       "      <td>53.58</td>\n",
       "      <td>33.85</td>\n",
       "      <td>28.26</td>\n",
       "      <td>25.04</td>\n",
       "      <td>12.85</td>\n",
       "      <td>76.04</td>\n",
       "      <td>14.54</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2.85</td>\n",
       "      <td>36.294167</td>\n",
       "      <td>12.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LOWER WEST SIDE</td>\n",
       "      <td>91.68</td>\n",
       "      <td>18340</td>\n",
       "      <td>46.55</td>\n",
       "      <td>42.78</td>\n",
       "      <td>57.22</td>\n",
       "      <td>38.30</td>\n",
       "      <td>28.04</td>\n",
       "      <td>23.45</td>\n",
       "      <td>10.21</td>\n",
       "      <td>21.86</td>\n",
       "      <td>22.93</td>\n",
       "      <td>50.79</td>\n",
       "      <td>0.43</td>\n",
       "      <td>3.99</td>\n",
       "      <td>10.396667</td>\n",
       "      <td>15.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MASTEN PARK</td>\n",
       "      <td>79.84</td>\n",
       "      <td>22520</td>\n",
       "      <td>34.65</td>\n",
       "      <td>41.67</td>\n",
       "      <td>58.33</td>\n",
       "      <td>33.52</td>\n",
       "      <td>22.06</td>\n",
       "      <td>25.61</td>\n",
       "      <td>18.80</td>\n",
       "      <td>4.18</td>\n",
       "      <td>86.66</td>\n",
       "      <td>6.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.67</td>\n",
       "      <td>12.596111</td>\n",
       "      <td>13.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MLK PARK</td>\n",
       "      <td>84.62</td>\n",
       "      <td>20480</td>\n",
       "      <td>37.04</td>\n",
       "      <td>41.78</td>\n",
       "      <td>58.22</td>\n",
       "      <td>37.01</td>\n",
       "      <td>19.42</td>\n",
       "      <td>25.60</td>\n",
       "      <td>17.98</td>\n",
       "      <td>4.22</td>\n",
       "      <td>93.33</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.41</td>\n",
       "      <td>13.959167</td>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NORTH PARK</td>\n",
       "      <td>92.54</td>\n",
       "      <td>53870</td>\n",
       "      <td>11.48</td>\n",
       "      <td>49.46</td>\n",
       "      <td>50.54</td>\n",
       "      <td>23.69</td>\n",
       "      <td>38.07</td>\n",
       "      <td>24.96</td>\n",
       "      <td>13.28</td>\n",
       "      <td>72.98</td>\n",
       "      <td>13.01</td>\n",
       "      <td>9.29</td>\n",
       "      <td>2.86</td>\n",
       "      <td>1.85</td>\n",
       "      <td>52.164583</td>\n",
       "      <td>17.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PARKSIDE</td>\n",
       "      <td>95.67</td>\n",
       "      <td>54000</td>\n",
       "      <td>14.03</td>\n",
       "      <td>47.52</td>\n",
       "      <td>52.48</td>\n",
       "      <td>33.40</td>\n",
       "      <td>27.15</td>\n",
       "      <td>27.01</td>\n",
       "      <td>12.44</td>\n",
       "      <td>73.31</td>\n",
       "      <td>15.18</td>\n",
       "      <td>5.53</td>\n",
       "      <td>3.14</td>\n",
       "      <td>2.84</td>\n",
       "      <td>21.670556</td>\n",
       "      <td>19.805556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PRATT-WILLERT</td>\n",
       "      <td>91.15</td>\n",
       "      <td>26330</td>\n",
       "      <td>30.65</td>\n",
       "      <td>37.24</td>\n",
       "      <td>62.76</td>\n",
       "      <td>29.46</td>\n",
       "      <td>24.31</td>\n",
       "      <td>27.49</td>\n",
       "      <td>18.73</td>\n",
       "      <td>5.97</td>\n",
       "      <td>84.70</td>\n",
       "      <td>4.59</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.81</td>\n",
       "      <td>13.288333</td>\n",
       "      <td>12.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RIVERSIDE</td>\n",
       "      <td>95.04</td>\n",
       "      <td>21570</td>\n",
       "      <td>45.32</td>\n",
       "      <td>45.70</td>\n",
       "      <td>54.30</td>\n",
       "      <td>41.35</td>\n",
       "      <td>27.20</td>\n",
       "      <td>23.62</td>\n",
       "      <td>7.83</td>\n",
       "      <td>49.15</td>\n",
       "      <td>13.34</td>\n",
       "      <td>22.49</td>\n",
       "      <td>10.39</td>\n",
       "      <td>4.62</td>\n",
       "      <td>52.218333</td>\n",
       "      <td>14.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SCHILLER PARK</td>\n",
       "      <td>85.49</td>\n",
       "      <td>24020</td>\n",
       "      <td>43.96</td>\n",
       "      <td>44.33</td>\n",
       "      <td>55.67</td>\n",
       "      <td>44.03</td>\n",
       "      <td>24.08</td>\n",
       "      <td>23.35</td>\n",
       "      <td>8.53</td>\n",
       "      <td>10.96</td>\n",
       "      <td>78.86</td>\n",
       "      <td>5.16</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.93</td>\n",
       "      <td>36.906667</td>\n",
       "      <td>12.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SENECA BABCOCK</td>\n",
       "      <td>82.07</td>\n",
       "      <td>30290</td>\n",
       "      <td>25.86</td>\n",
       "      <td>48.41</td>\n",
       "      <td>51.59</td>\n",
       "      <td>36.78</td>\n",
       "      <td>28.87</td>\n",
       "      <td>26.60</td>\n",
       "      <td>7.75</td>\n",
       "      <td>80.08</td>\n",
       "      <td>1.65</td>\n",
       "      <td>14.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>11.279167</td>\n",
       "      <td>13.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SENECA-CAZENOVIA</td>\n",
       "      <td>87.63</td>\n",
       "      <td>38300</td>\n",
       "      <td>23.60</td>\n",
       "      <td>51.84</td>\n",
       "      <td>48.16</td>\n",
       "      <td>30.64</td>\n",
       "      <td>30.93</td>\n",
       "      <td>25.56</td>\n",
       "      <td>12.87</td>\n",
       "      <td>83.09</td>\n",
       "      <td>6.69</td>\n",
       "      <td>4.76</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.13</td>\n",
       "      <td>23.638333</td>\n",
       "      <td>16.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>UNIVERSITY HEIGHTS</td>\n",
       "      <td>92.20</td>\n",
       "      <td>42130</td>\n",
       "      <td>35.21</td>\n",
       "      <td>53.63</td>\n",
       "      <td>46.37</td>\n",
       "      <td>49.86</td>\n",
       "      <td>22.63</td>\n",
       "      <td>20.26</td>\n",
       "      <td>7.26</td>\n",
       "      <td>35.88</td>\n",
       "      <td>38.32</td>\n",
       "      <td>6.99</td>\n",
       "      <td>15.97</td>\n",
       "      <td>2.85</td>\n",
       "      <td>31.959167</td>\n",
       "      <td>15.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WEST HERTEL</td>\n",
       "      <td>90.41</td>\n",
       "      <td>25980</td>\n",
       "      <td>38.85</td>\n",
       "      <td>53.58</td>\n",
       "      <td>46.42</td>\n",
       "      <td>37.78</td>\n",
       "      <td>30.35</td>\n",
       "      <td>23.85</td>\n",
       "      <td>8.01</td>\n",
       "      <td>37.78</td>\n",
       "      <td>27.58</td>\n",
       "      <td>24.83</td>\n",
       "      <td>2.87</td>\n",
       "      <td>6.94</td>\n",
       "      <td>20.420833</td>\n",
       "      <td>15.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>WEST SIDE</td>\n",
       "      <td>88.19</td>\n",
       "      <td>20040</td>\n",
       "      <td>48.49</td>\n",
       "      <td>51.46</td>\n",
       "      <td>48.54</td>\n",
       "      <td>39.87</td>\n",
       "      <td>30.14</td>\n",
       "      <td>20.06</td>\n",
       "      <td>9.94</td>\n",
       "      <td>31.57</td>\n",
       "      <td>16.66</td>\n",
       "      <td>32.90</td>\n",
       "      <td>14.27</td>\n",
       "      <td>4.59</td>\n",
       "      <td>62.533333</td>\n",
       "      <td>14.416667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Neighborhood  Employment Rate  Median Income  Poverty Rate  \\\n",
       "0            ALLENTOWN            96.23          39060         28.84   \n",
       "1           BLACK ROCK            87.60          24550         35.42   \n",
       "2    BROADWAY FILLMORE            79.11          18940         45.39   \n",
       "3         CENTRAL PARK            85.42          61940         14.32   \n",
       "4       DELAVAN GRIDER            86.54          28370         29.43   \n",
       "5             ELLICOTT            84.97          22190         33.49   \n",
       "6      ELMWOOD BIDWELL            94.02          56150         18.24   \n",
       "7       ELMWOOD BRYANT            93.62          40520         23.95   \n",
       "8       FILLMORE-LEROY            82.79          22910         45.55   \n",
       "9           FIRST WARD            86.73          27880         39.21   \n",
       "10          FRUIT BELT            89.76          25350         28.03   \n",
       "11     GENESEE-MOSELLE            80.67          22080         46.99   \n",
       "12         HAMLIN PARK            88.14          27860         36.95   \n",
       "13       HOPKINS-TIFFT            90.92          36960         28.07   \n",
       "14          KAISERTOWN            92.77          41280         15.14   \n",
       "15            KENFIELD            85.70          26700         37.58   \n",
       "16   KENSINGTON-BAILEY            89.47          37240         30.88   \n",
       "17             LOVEJOY            86.95          28780         34.40   \n",
       "18     LOWER WEST SIDE            91.68          18340         46.55   \n",
       "19         MASTEN PARK            79.84          22520         34.65   \n",
       "20            MLK PARK            84.62          20480         37.04   \n",
       "21          NORTH PARK            92.54          53870         11.48   \n",
       "22            PARKSIDE            95.67          54000         14.03   \n",
       "23       PRATT-WILLERT            91.15          26330         30.65   \n",
       "24           RIVERSIDE            95.04          21570         45.32   \n",
       "25       SCHILLER PARK            85.49          24020         43.96   \n",
       "26      SENECA BABCOCK            82.07          30290         25.86   \n",
       "27    SENECA-CAZENOVIA            87.63          38300         23.60   \n",
       "28  UNIVERSITY HEIGHTS            92.20          42130         35.21   \n",
       "29         WEST HERTEL            90.41          25980         38.85   \n",
       "30           WEST SIDE            88.19          20040         48.49   \n",
       "\n",
       "    Percent Male  Percent Female  Percent Age < 24  Percent Age 25 to 44  \\\n",
       "0          54.71           45.29             30.66                 43.12   \n",
       "1          50.98           49.02             39.22                 28.68   \n",
       "2          48.31           51.69             37.39                 25.33   \n",
       "3          50.75           49.25             19.75                 36.32   \n",
       "4          41.81           58.19             34.88                 18.54   \n",
       "5          47.38           52.62             35.60                 24.49   \n",
       "6          46.86           53.14             41.13                 29.73   \n",
       "7          52.71           47.29             21.83                 38.66   \n",
       "8          47.01           52.99             36.17                 26.49   \n",
       "9          41.70           58.30             40.39                 21.26   \n",
       "10         44.77           55.23             29.01                 15.11   \n",
       "11         47.21           52.79             39.01                 24.16   \n",
       "12         45.47           54.53             51.51                 18.00   \n",
       "13         52.32           47.68             34.87                 27.99   \n",
       "14         48.13           51.87             25.97                 25.89   \n",
       "15         38.64           61.36             43.65                 22.24   \n",
       "16         44.33           55.67             40.30                 24.81   \n",
       "17         46.42           53.58             33.85                 28.26   \n",
       "18         42.78           57.22             38.30                 28.04   \n",
       "19         41.67           58.33             33.52                 22.06   \n",
       "20         41.78           58.22             37.01                 19.42   \n",
       "21         49.46           50.54             23.69                 38.07   \n",
       "22         47.52           52.48             33.40                 27.15   \n",
       "23         37.24           62.76             29.46                 24.31   \n",
       "24         45.70           54.30             41.35                 27.20   \n",
       "25         44.33           55.67             44.03                 24.08   \n",
       "26         48.41           51.59             36.78                 28.87   \n",
       "27         51.84           48.16             30.64                 30.93   \n",
       "28         53.63           46.37             49.86                 22.63   \n",
       "29         53.58           46.42             37.78                 30.35   \n",
       "30         51.46           48.54             39.87                 30.14   \n",
       "\n",
       "    Percent Age 45 to 64  Percent Age 65 >  Percent White  Percent Black  \\\n",
       "0                  20.19              6.04          76.78           7.73   \n",
       "1                  21.82             10.28          46.81          11.01   \n",
       "2                  24.81             12.46          18.04          60.96   \n",
       "3                  31.29             12.64          77.34          15.23   \n",
       "4                  28.07             18.51           1.85          92.78   \n",
       "5                  27.31             12.60          26.73          47.42   \n",
       "6                  18.65             10.48          69.85          16.12   \n",
       "7                  22.15             17.36          63.72          15.42   \n",
       "8                  21.45             15.88          10.43          78.58   \n",
       "9                  25.82             12.53          78.89           0.00   \n",
       "10                 30.23             25.65          11.54          82.88   \n",
       "11                 26.68             10.14          15.16          75.13   \n",
       "12                 16.67             13.82          21.75          71.94   \n",
       "13                 25.61             11.53          68.80           6.14   \n",
       "14                 33.01             15.14          86.72           4.58   \n",
       "15                 23.61             10.50           5.53          90.37   \n",
       "16                 24.08             10.80           6.29          87.76   \n",
       "17                 25.04             12.85          76.04          14.54   \n",
       "18                 23.45             10.21          21.86          22.93   \n",
       "19                 25.61             18.80           4.18          86.66   \n",
       "20                 25.60             17.98           4.22          93.33   \n",
       "21                 24.96             13.28          72.98          13.01   \n",
       "22                 27.01             12.44          73.31          15.18   \n",
       "23                 27.49             18.73           5.97          84.70   \n",
       "24                 23.62              7.83          49.15          13.34   \n",
       "25                 23.35              8.53          10.96          78.86   \n",
       "26                 26.60              7.75          80.08           1.65   \n",
       "27                 25.56             12.87          83.09           6.69   \n",
       "28                 20.26              7.26          35.88          38.32   \n",
       "29                 23.85              8.01          37.78          27.58   \n",
       "30                 20.06              9.94          31.57          16.66   \n",
       "\n",
       "    Percent Latinx  Percent Asian  Percent Other  \\\n",
       "0             8.68           2.20           4.61   \n",
       "1            33.91           4.65           3.62   \n",
       "2             2.74          13.46           4.80   \n",
       "3             3.01           1.67           2.76   \n",
       "4             1.02           0.00           4.34   \n",
       "5            17.97           7.24           0.65   \n",
       "6             6.75           4.42           2.86   \n",
       "7             7.88           9.73           3.25   \n",
       "8             4.20           2.55           4.25   \n",
       "9            19.03           0.00           2.08   \n",
       "10            4.05           0.00           1.52   \n",
       "11            4.43           3.70           1.58   \n",
       "12            2.18           2.21           1.92   \n",
       "13           18.63           2.34           4.08   \n",
       "14            6.58           0.00           2.12   \n",
       "15            2.68           0.26           1.16   \n",
       "16            1.46           1.79           2.69   \n",
       "17            5.92           0.64           2.85   \n",
       "18           50.79           0.43           3.99   \n",
       "19            6.49           0.00           2.67   \n",
       "20            1.05           0.00           1.41   \n",
       "21            9.29           2.86           1.85   \n",
       "22            5.53           3.14           2.84   \n",
       "23            4.59           0.92           3.81   \n",
       "24           22.49          10.39           4.62   \n",
       "25            5.16           2.10           2.93   \n",
       "26           14.27           0.00           4.00   \n",
       "27            4.76           2.32           3.13   \n",
       "28            6.99          15.97           2.85   \n",
       "29           24.83           2.87           6.94   \n",
       "30           32.90          14.27           4.59   \n",
       "\n",
       "    Curbside recycling (in pounds)  Curbside recycling rate  \n",
       "0                        22.292500                18.833333  \n",
       "1                        34.943333                13.833333  \n",
       "2                        13.594444                11.222222  \n",
       "3                        42.293333                20.750000  \n",
       "4                        27.213333                13.583333  \n",
       "5                         4.876250                13.291667  \n",
       "6                        27.069667                17.633333  \n",
       "7                        28.580000                17.083333  \n",
       "8                        25.184167                13.916667  \n",
       "9                        11.061667                14.833333  \n",
       "10                       12.385833                13.916667  \n",
       "11                       15.965833                11.083333  \n",
       "12                       21.532500                12.666667  \n",
       "13                       27.361458                15.239583  \n",
       "14                       33.061667                15.666667  \n",
       "15                       19.057500                10.916667  \n",
       "16                       41.187500                12.416667  \n",
       "17                       36.294167                12.416667  \n",
       "18                       10.396667                15.708333  \n",
       "19                       12.596111                13.777778  \n",
       "20                       13.959167                10.416667  \n",
       "21                       52.164583                17.458333  \n",
       "22                       21.670556                19.805556  \n",
       "23                       13.288333                12.583333  \n",
       "24                       52.218333                14.166667  \n",
       "25                       36.906667                12.083333  \n",
       "26                       11.279167                13.958333  \n",
       "27                       23.638333                16.125000  \n",
       "28                       31.959167                15.583333  \n",
       "29                       20.420833                15.750000  \n",
       "30                       62.533333                14.416667  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new dataframe combMetrics that will hold the merged dataframes\n",
    "combMetrics = pd.merge(nbMetrics, rcMetrics, how='inner')\n",
    "combMetrics = combMetrics.sort_values(by='Neighborhood', ascending=True).reset_index().drop(columns='index')\n",
    "combMetrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>Percent Male</th>\n",
       "      <th>Percent Female</th>\n",
       "      <th>Percent Age &lt; 24</th>\n",
       "      <th>Percent Age 25 to 44</th>\n",
       "      <th>Percent Age 45 to 64</th>\n",
       "      <th>Percent Age 65 &gt;</th>\n",
       "      <th>Percent White</th>\n",
       "      <th>Percent Black</th>\n",
       "      <th>Percent Latinx</th>\n",
       "      <th>Percent Asian</th>\n",
       "      <th>Percent Other</th>\n",
       "      <th>Curbside recycling (in pounds)</th>\n",
       "      <th>Curbside recycling rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88.320645</td>\n",
       "      <td>31826.774194</td>\n",
       "      <td>32.502258</td>\n",
       "      <td>47.061613</td>\n",
       "      <td>52.938387</td>\n",
       "      <td>35.834839</td>\n",
       "      <td>26.849355</td>\n",
       "      <td>24.642258</td>\n",
       "      <td>12.672258</td>\n",
       "      <td>41.074194</td>\n",
       "      <td>41.209677</td>\n",
       "      <td>10.976129</td>\n",
       "      <td>3.617097</td>\n",
       "      <td>3.121613</td>\n",
       "      <td>26.031819</td>\n",
       "      <td>14.552800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.656645</td>\n",
       "      <td>11934.846009</td>\n",
       "      <td>10.574998</td>\n",
       "      <td>4.507234</td>\n",
       "      <td>4.507234</td>\n",
       "      <td>7.257322</td>\n",
       "      <td>6.186641</td>\n",
       "      <td>3.565152</td>\n",
       "      <td>4.262253</td>\n",
       "      <td>30.462191</td>\n",
       "      <td>34.694627</td>\n",
       "      <td>11.635131</td>\n",
       "      <td>4.507120</td>\n",
       "      <td>1.338963</td>\n",
       "      <td>13.901914</td>\n",
       "      <td>2.568055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>79.110000</td>\n",
       "      <td>18340.000000</td>\n",
       "      <td>11.480000</td>\n",
       "      <td>37.240000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>19.750000</td>\n",
       "      <td>15.110000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>6.040000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>4.876250</td>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>85.455000</td>\n",
       "      <td>22715.000000</td>\n",
       "      <td>26.945000</td>\n",
       "      <td>44.330000</td>\n",
       "      <td>49.135000</td>\n",
       "      <td>32.030000</td>\n",
       "      <td>23.355000</td>\n",
       "      <td>22.750000</td>\n",
       "      <td>10.175000</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>13.175000</td>\n",
       "      <td>4.125000</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>13.776806</td>\n",
       "      <td>12.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88.140000</td>\n",
       "      <td>27860.000000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>47.210000</td>\n",
       "      <td>52.790000</td>\n",
       "      <td>36.780000</td>\n",
       "      <td>26.490000</td>\n",
       "      <td>24.960000</td>\n",
       "      <td>12.460000</td>\n",
       "      <td>35.880000</td>\n",
       "      <td>22.930000</td>\n",
       "      <td>6.490000</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>2.860000</td>\n",
       "      <td>23.638333</td>\n",
       "      <td>13.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>91.940000</td>\n",
       "      <td>38680.000000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>50.865000</td>\n",
       "      <td>55.670000</td>\n",
       "      <td>40.085000</td>\n",
       "      <td>29.300000</td>\n",
       "      <td>26.640000</td>\n",
       "      <td>14.480000</td>\n",
       "      <td>73.145000</td>\n",
       "      <td>78.720000</td>\n",
       "      <td>16.120000</td>\n",
       "      <td>4.060000</td>\n",
       "      <td>4.040000</td>\n",
       "      <td>34.002500</td>\n",
       "      <td>15.729167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>96.230000</td>\n",
       "      <td>61940.000000</td>\n",
       "      <td>48.490000</td>\n",
       "      <td>54.710000</td>\n",
       "      <td>62.760000</td>\n",
       "      <td>51.510000</td>\n",
       "      <td>43.120000</td>\n",
       "      <td>33.010000</td>\n",
       "      <td>25.650000</td>\n",
       "      <td>86.720000</td>\n",
       "      <td>93.330000</td>\n",
       "      <td>50.790000</td>\n",
       "      <td>15.970000</td>\n",
       "      <td>6.940000</td>\n",
       "      <td>62.533333</td>\n",
       "      <td>20.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Employment Rate  Median Income  Poverty Rate  Percent Male  \\\n",
       "count        31.000000      31.000000     31.000000     31.000000   \n",
       "mean         88.320645   31826.774194     32.502258     47.061613   \n",
       "std           4.656645   11934.846009     10.574998      4.507234   \n",
       "min          79.110000   18340.000000     11.480000     37.240000   \n",
       "25%          85.455000   22715.000000     26.945000     44.330000   \n",
       "50%          88.140000   27860.000000     34.400000     47.210000   \n",
       "75%          91.940000   38680.000000     39.030000     50.865000   \n",
       "max          96.230000   61940.000000     48.490000     54.710000   \n",
       "\n",
       "       Percent Female  Percent Age < 24  Percent Age 25 to 44  \\\n",
       "count       31.000000         31.000000             31.000000   \n",
       "mean        52.938387         35.834839             26.849355   \n",
       "std          4.507234          7.257322              6.186641   \n",
       "min         45.290000         19.750000             15.110000   \n",
       "25%         49.135000         32.030000             23.355000   \n",
       "50%         52.790000         36.780000             26.490000   \n",
       "75%         55.670000         40.085000             29.300000   \n",
       "max         62.760000         51.510000             43.120000   \n",
       "\n",
       "       Percent Age 45 to 64  Percent Age 65 >  Percent White  Percent Black  \\\n",
       "count             31.000000         31.000000      31.000000      31.000000   \n",
       "mean              24.642258         12.672258      41.074194      41.209677   \n",
       "std                3.565152          4.262253      30.462191      34.694627   \n",
       "min               16.670000          6.040000       1.850000       0.000000   \n",
       "25%               22.750000         10.175000      11.250000      13.175000   \n",
       "50%               24.960000         12.460000      35.880000      22.930000   \n",
       "75%               26.640000         14.480000      73.145000      78.720000   \n",
       "max               33.010000         25.650000      86.720000      93.330000   \n",
       "\n",
       "       Percent Latinx  Percent Asian  Percent Other  \\\n",
       "count       31.000000      31.000000      31.000000   \n",
       "mean        10.976129       3.617097       3.121613   \n",
       "std         11.635131       4.507120       1.338963   \n",
       "min          1.020000       0.000000       0.650000   \n",
       "25%          4.125000       0.345000       2.100000   \n",
       "50%          6.490000       2.210000       2.860000   \n",
       "75%         16.120000       4.060000       4.040000   \n",
       "max         50.790000      15.970000       6.940000   \n",
       "\n",
       "       Curbside recycling (in pounds)  Curbside recycling rate  \n",
       "count                       31.000000                31.000000  \n",
       "mean                        26.031819                14.552800  \n",
       "std                         13.901914                 2.568055  \n",
       "min                          4.876250                10.416667  \n",
       "25%                         13.776806                12.625000  \n",
       "50%                         23.638333                13.958333  \n",
       "75%                         34.002500                15.729167  \n",
       "max                         62.533333                20.750000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at statistics of merged dataframe\n",
    "combMetrics.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a) Big Picture\n",
    "\n",
    "We are interested in whether financial factors of a neighborhood is related to its curbside recycle rate. Financial factors that we have data on include income level, proverty rate, and unemployment rate.\n",
    "\n",
    "We want to start off taking a look at which neighborhoods have the highest and lowest curbside recycling rates. Since we hypothesized that wealthier neighborhoods perform better recycling, we are curious to see whether the neighborhoods with top incomes also have the highest recycling rates, and same for lower income neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rank neighborhoods by curbside recycling rates, highest to lowest\n",
    "plt.figure(figsize=(15, 10))\n",
    "neighborhood_rate = sns.barplot (\n",
    "    y = 'Neighborhood',\n",
    "    x = 'Curbside recycling rate',\n",
    "    data = combMetrics,\n",
    "    order = combMetrics.sort_values('Curbside recycling rate', ascending = False).Neighborhood,\n",
    "    color = \"skyblue\"\n",
    ")\n",
    "neighborhood_rate.set_title('Curbside Recycling Rate in Buffulo, NY Neighborhoods')\n",
    "neighborhood_rate.set_yticklabels(neighborhood_rate.get_yticklabels(), fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph displays all of the curbside recycling rates grouped by each neighborhood in Buffalo, NY. According to the graph, the 5 neighborhoods with the highest recycling rates are Central Park, Parkside, Allentwon, Elmwood Bidwell, and North park. The 5 neighborhoods with the least recycle rates are MLK Park, Kenfield, Genesee-miselle, Broadway Fillmore, and Schiller Park."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VV7h06RLq1auHlStX4ty5czh79iz+97//oV27dkhPT0dAQICwT0eRyvz2WL16NVJTU9G8eXPs3LkTFy9exJkzZ5CUlIR9+/bh888/h6mpabHvIdU8DPiJ6K3n4eEBQHVav1KpFK7WF+UX59atW9i+fTvq1q2LrVu3wtXVVTj7X6dOHQwfPlzYoXndunUqdXfs2IHHjx/D2NgYoaGhaN++PYAXAbuHhwfmzZtX7FTDkrRs2RKrVq3CBx98gAYNGghTSt977z2MHz8eAQEBUCqVJV7RKpom2LNnT2hra0NLSwtSqRTffPMNgBfTnkub9ldez549w8SJEzF+/HghQG/QoAGWLl2K+vXrIycnB8ePH1epc+LECRw/fhwikQghISEYNWqUUFdPTw/t2rVDYGCgypX6u3fvYu/evQCAmTNnwtPTE3p6egBevHYbNmxAs2bNkJWVhR9//FHjWLOysuDm5oYpU6YI/VlYWGDFihUQiUS4cOECjh07hvXr10MqlUIkEkFPTw9Dhw4Vloi8uunis2fPsHLlSujo6GD9+vX45JNPhLb19fXh4uKC1atXQyQSYePGjVXy+lemz6Igvrjp7ocPH0ZeXh6aNGmicsWuOo6zLCry+SsPR0dH4dG9e3d06dIF48ePh7a2Nj7//PMK7+T+Llq3bh0yMzPRrFkzbNiwAa1atQLw4m92+PDhQuD466+/Fru8pio0b95c+Dd+5cqV5V5GMnLkSEilUmRnZ2PUqFE4ceIE9PT0sGjRoiqZmaKjo4OAgAAAL2YUPXz4sEz14uLicPToUTRp0gTbtm3D+++/j1q1agEA6tati3HjxmHSpEkoLCzEhg0bytTmv//+i82bNwN4caLE29tbaLNJkyYICQlB48aNS23n2bNnWLNmDQYMGCD8u9u+fXt8//33AIALFy4UOyX8+fPnmDhxIkaPHi303axZM/z4449o2bIlsrOz1WZO7d+/H3///Td0dHSwYcMGODo6QiQSQSQSwd7eHhs2bICuri6uXr2K3377TWO/mZmZmDVrFj755BNhdsB7770HQ0NDlXLm5ub46aef0K5dOwAvPs8ff/wxhg0bBgA4dOiQWrtF4/Xz88P48eOFNs3NzbFixQrY2NigsLBQ7Y4ScrlcWC64bNkyuLi4CLfS7NKlCzZv3ox69eoJGz++rDK/PYouFEyePBlSqVT4nBd9306ePLnEmXFU8zDgJ6K3nouLC/T19XHkyBFh/eyZM2dw9+5ddO7cGa1bty6xvkwmQ2FhIfr06VPsdNy+fftCT08PV69eVfnBVvTlP2zYMNSvX1+t3qBBgyq8u3NJitbtnj17ttgy/fr10zgd1cnJCSKRCLm5uWpXUipLX19fZSnCy+k9evQA8H/Tn4sUnajp0aOHsMlVaY4cOYLCwkKYmppi6NChavm1a9fG2LFjhbLFrSXWtHN248aNhdetuNfQ3t4eANQ2+zp06BAyMzPRtWtXWFpaauzT2toaTZs2RXp6eqlrO8uiMn0OGDAA2trauHnzpsaxFJ0IcHV1VVnHXB3HWRYV+fyVx6NHj4RHWlqaEFxmZGQgPT1dmNpe0ymVShw+fBgAMHr0aI23SRw6dCjMzc2hVCrVgqSqFhAQAF1dXVy6dKncfYlEIixcuBC1atVCSkqK0F6bNm2qbHxubm5o27YtsrOz1U4aFyc8PBzAixkCxsbGxbYLvNgnoiz7JZw6dUpYHqJpfb+uri58fHxKbUcqlUIqlaqld+7cGQ0bNgQAXLt2TWPd2rVrF/s3WjSb5fDhwyonboreU2dnZyEQf1nbtm2FWTwHDhzQ2K+xsTE++uijkg4LwIvPc9FJjJcVBcCv/psfGxuLjIwM6OrqCt85L9PW1sb48eMBvAjwX16TX3TCuGi9/6saNGgAT09PjcdVmd8eRSckynryiWo+ruEnorde3bp18cEHH+DgwYM4fPgwPDw8hCCytKv7AIQN2g4ePCisa9SkaMOyBw8ewMzMDLm5ucKPGltbW411RCIRpFJphTbAyc7Oxs8//4zo6Ghcu3YNz549U9s0raQv7C5dumhM19XVxXvvvYdHjx4hPT293OMqSZs2bYpdr2tubg7gxdWhlxVdbXj//ffL3E9RAGljYyNcEXlV9+7dAby4AnPz5k21H/D6+vrFrvd87733cOvWLY0/LoH/Wyv96rEUfZaSkpLg6OhY7PiLXvf79+8Xu9a1rCrTZ4MGDdC9e3fExsYiMjISnTp1Eso/fPgQZ86cAaA+nb86jrMsKvL5K4+///5b5fnDhw9x8eJFBAUFYdeuXVAoFNixY4faVcOa5s6dO8LVw+L2R9HS0oKdnR327dv32k/4NG7cGMOHD8eOHTsQHByMvn37luvqfIsWLeDp6YktW7agfv36GgO3ytDS0sLEiRMRGBiIPXv2wNfXt9QTwUV/Y1u2bMHu3btLLJuVlYWnT5/ivffeK7Fc0TrxDh06FLuG3cbGpsQ2gOK/W4AXf2cPHjwo9rulc+fOxf6NFn2PPnv2DMnJycIJ+KJxl7QXT/fu3REZGSmU1dTvq3uQaFLcsZmZmQlje1nRZ7t9+/aoV6+exrq2trbQ0dFBfn4+Ll26JHzXlfW41q9fj1u3biEzMxMGBgaV/u3Rq1cvnD9/HsuWLcPt27fRr18/WFtbCzMu6L+HAT8RvRM8PDxw8OBBREREoH///jh8+DB0dXUxcODAUusWnXEv2hyrNEUb5KSnpwtXVYp+DGhSFGiUx8OHD+Hl5aWym76BgQHq1q0LLS0tYaOuksZb0i26im5RqGnX9cqoSJ+PHj0CADRq1KjM/RStZyzptS260vRy+Ze9vFTiVUUnEYpbx1gUTLx6LEWfpezs7FJ38S8qV1mV7dPV1RWxsbHYv38/vvrqK+E12b9/PwoLC9GuXTu1Ex/VcZxl8aY/82ZmZnBycoKlpSVcXFzw119/Ydu2bcIVvZrq5b+nsvwNavr7q2r+/v749ddfcf36dfz2229lOtn7sqKTNLVr1y5TYFheffv2RadOnfDnn39izZo1WLRoUYnli/7Gnj9/XqZlYUXfSyUp2tyxpPXZZfm+qszfWUntv5z35MkTIeAvy7/3RXlPnz6FUqlU+7dd01VwTYo7tuKOqyxj09fXh7GxMR49eqTyt1Ce41IqlUhLS4OBgUGlf3t89tln+PPPP/H7779j165d2LVrF3R0dNClSxf06dMHw4YNU9szh2o2BvxE9E7o2bMn3nvvPfzxxx/YsWMHMjIy4OTkVKYv+cLCQgAv1oKPGjWqysdWkVuTLVq0CLdu3ULTpk3x1VdfoVu3bipXD/755x98+OGHVTnMd1JJa8Mreiutyij6LI0ePVq4N/vb3mffvn0xd+5cPHjwAAkJCcLOzkUb+RVNG67KPmuaBg0aoGfPnoiKisKBAwdqfMD/sje1P0NpTE1N8cknn2Djxo1YvXp1mU72vklFG7p99tlnkMlk8PPzK/GODkV/Y2vXroWzs/ObGma1Ku27sjKfteJmglWVyoztdf0NFfd66unp4ccff8S5c+dw5MgRyOVy/Pnnn8JGiBs3bsTmzZuFfQGo5uMafiJ6J+jo6GDAgAEqG+MUbaxWmqLp2cWtOSxOvXr1hB8RJU2tL+0+uq/Kzc1FdHQ0gBcb+fTt21dtqmDRVfGaoOj1v3fvXpnrFJ3IKanO/fv31cq/bhX9LFVnn4aGhujduzeA/1uzf+fOHSQlJUEkEmkMnKrjON92RTNUXr13edG/ESWt76/Ixp5l9Tr6f/nvqaTlSg8ePFAr/zr5+fnB0NAQd+7cwa+//vpG+iyP999/H127dkVBQQFCQkJKLFv0N3b9+vUq67/oDhIlfSe97nXdZf2ufPkzU5Z/74v2XzA2Nn6jJ3vLMracnBzh7iAVPS6RSCS8f1X128Pa2hrTpk3DL7/8gjNnzmDFihVo3Lgxnjx5gpkzZ5ZYl2oWBvxE9M4omsKZl5eHevXqCRvblabo/rbHjh1DXl5emfvT09MT1oXL5XKNZZRKpcZb8ZQkLS1NOOPfsWNHjWWK7stcExTdGq48t9QqWmt+/vz5YqeyFt2m0cDAoNh7zFe1os9SQkKCMH32XeizaI3+oUOHkJeXJwT+EolE41rj6jjOt13RD/NXp4MXTY19/PhxsVfyLly48NrGVdR/UfBdFf03bdpUaDc+Pl5jmcLCQmEPiJf3hnidTExMhA3h1q5d+1Zuojh58mQAL5bMlLSBZNHfWFVueFj0ffLXX3/h33//1VimuO+yqnLhwoVi/80u+q6sW7cuLCwshPSicRf3WQP+79/74r4zX5eiz/bt27eFfwNelZCQICwFeHl8Rf+fkJBQ7NX4ouNq0aKFsPfB6/jtYWBggIEDB2L+/PkAXuxNUJYljlQzMOAnondG586dMXHiRIwZMwbffPONxp12NRk8eDC0tLTw8OFDtdsBverVjYj69+8PANizZ4/G+3tHRUWVe8M+Q0ND4QrFq5uEAS/O6O/YsaNcbb7Nik7UxMbGlrhp4ss+/PBDaGlp4enTp/jll1/U8rOysrBx40ah7Ouezlmkf//+MDAwQE5ODn744YcSy1bVholV0Wfv3r1hZGSEp0+fIjY2VpjO/+pmfVXZZ03y7Nkz4STcqwFHixYtoKenB6VSiWPHjqnVvX37trDj/etQtP9CSkoKLl68qJYvl8tLvNuHJiKRSFhStG3bNo0B3N69e5GSkgKRSCTsoP4mjBkzBsbGxkhJSSnxtqXVpVu3brC3t4dSqcSqVauKLTd48GAAwMWLF1VuOatJWf/GHB0dhb/bnTt3quXn5+dj69atZWqrorKysrBt2za19KLbyAIv7o7y8lX6os9PTEyMxk35rl69KpwYcXFxeR3DLpajoyMMDQ2Rl5eH0NBQtfyCggKsXbsWwIu7G7y8f0LR74erV68Ks/pe9ujRI+Ez/OpxVea3R0lLCIo27lMqleW6AELvNgb8RPROmTBhAr7++utybdjUunVr4apQSEgI5s2bhzt37gj5//77L2JjYzFt2jRMmjRJpe7IkSPx3nvvIS0tDb6+vrh8+TKAF7MMIiIi8O2338LIyKhcx1CnTh3h6s4333yDv/76C8CLK2anT5+Gl5dXhfYFeFv16tULvXr1glKpRGBgILZv3y7shJybm4u///4bixcvxtGjR4U6TZo0Ee6LvHz5cvzyyy/Cj5ibN2/is88+w+3bt1G7dm18/vnnb+xYTExM8MUXXwAAwsLCMGnSJJWreDk5OZDL5Zg3bx5GjBjx1vSpp6cnBHDBwcG4evUqdHR0iv3xXB3H+TbKz8/HhQsX4O/vj8ePHwOA2u3O9PT0hDXY33//PeRyOQoLC1FYWIhTp07Bx8fnte6O3aRJE+HWiTNmzBBOIubl5eHAgQMICAgodnfxkvj7+8PAwAAPHz7EZ599hhs3bgB48Te7Z88eLFiwAADw8ccfq93aMjk5GWKxGGKxGGFhYZU5PDWGhobw9fUFABw/frxK264qU6ZMAVDy+Hr16oW+ffsCePE9EBwcrDJ9Oz09HUePHsXnn3+OxYsXl6lfQ0ND4btu1apV2L59u7Ch5r179xAYGKi2JKWqGRkZYdWqVdi6davQ9507d/D555/j+vXr0NfXV7td6oABAyAWiwG8uGViXFyc8B14+vRpfPbZZ8jLy0Pbtm0xaNCg1zr+VxkYGGDcuHEAgO3bt+PHH38UZk+kpKTgiy++QGJiIrS0tITZHUWkUil69uwJ4MV7fPDgQWEzvosXL2LMmDFIT09HgwYN1PYXqsxvDzc3N6xYsQJJSUnC96ZSqURSUhK+++47AC/uVlCRfxfo3cRN+4joP2HatGnIzs7G7t27hV1r69SpA21tbTx//lz4cVG0oVmRevXqYeXKlfDz88PFixfh7u4OIyMj5OTkIDc3FxKJBLa2ttiwYUO5xjNjxgyMGjUKV65cgYeHBwwMDFBYWIjs7GwYGxtj4cKFCAgIqLLjr04ikQjLly9HQEAAzpw5gwULFmDRokUwMjLC8+fPhc2rXt0pfvr06bhz5w5iY2Mxe/ZsfPfdd6hdu7ZwskBPTw/Lli17Y9P5i3h5eeH58+cIDg7GwYMHcfDgQdSuXRt6enoqx1PabbnedJ9ubm4ICwsTbjPl4OBQ4trr6jjO6vbqLQifPn0qTNUViUT4/PPPhft1v+zLL7/E6dOncf/+fYwcORK1a9dGYWEhcnJy0KFDB4wePRoLFy58beOeNWuW8O/JoEGDYGBggLy8POTl5aFHjx7o3Llzme8PX6RZs2ZYvnw5Jk+ejDNnzsDFxQV169ZFVlaWcGXQ3t4e33zzzes4pBJ5eXlh69atb+1eJ1ZWVvjggw80zvh42ZIlS1BYWIijR49izZo1WLNmDYyMjKBUKpGRkSGUGzJkSJn7Hj9+PC5cuIBTp05hwYIFWLJkibDzu66uLoKCgjBhwgQAKPMsufJwdnbGv//+i0WLFmHp0qUq/2Zra2vj+++/R7NmzVTq6OnpISQkBD4+Prh79y58fHxQu3ZtAP93d4LGjRsjJCTktYy5NL6+vrh+/TpkMhlWrlyJkJAQGBoa4tmzZ1AqldDS0sKsWbM03kLvhx9+wJgxY/DXX39h0qRJ0NfXh46OjnDSoF69eli9erWwfr9IZX57PH78GOvXr8f69euhra0NIyMj/Pvvv8LfrYmJiXDCjv4beIWfiP4TtLW1MXfuXOzatQuDBg1CkyZNkJeXh+zsbDRu3BgffvghlixZIkzNe5mdnR3Cw8MxYMAA1K9fH7m5uWjSpAkmTpyIrVu3VugHiJWVFX755Rf06dMH9erVQ15eHt577z0MHz4cMpmsxu2eW7duXWzduhVLliyBg4MD6tWrh8zMTJiamsLOzg7ffPON2p4MtWvXxk8//YQFCxZAKpWiVq1ayMrKQpMmTTB06FDs27dPY/D1JowfPx4REREYPnw4WrRoAaVSKRxPr169MHfuXOzdu/et6rN79+4q002Lm85flX2+ax49eqTy0NbWRtOmTeHu7o7du3erzQAq0rRpU+zduxeurq6oX78+CgoK0LBhQ/j7+2P37t3CLeFeFysrK+zatQsffPAB6tati4KCArRs2RJfffUVNmzYUOHb0Dk5OWHfvn0YNmwYmjRpgqysLNSqVQs2Njb47rvvsHHjRo33XC/aTEwkEr2W9f21a9cWrrq+rSZNmlTq5nIGBgZYs2YN1q9fj759+8Lc3BzZ2dnIy8tD8+bN4erqiuDgYMyZM6fM/erp6WH9+vWYPn062rVrB5FIBC0tLXzwwQfYsWOHyj3hX8et2UQiEVatWoUZM2agVatWwp47H3zwAX7++edi767QvHlzREREICAgQOXkb7t27TB+/Hj89ttvb/zkbhFtbW0sWbIEwcHB6NGjB4yMjIR/B11dXbF3716MHDlSY9369evjl19+wfTp09G5c2fo6OggLy8PLVq0gLe3NyIjIyGRSDTWrehvj7Vr12LcuHHo2rUrzMzM8O+//0JXVxdisRifffYZoqKiatxvDCqZSFmT5o0SERERUbXasGEDli9fjn79+iE4OLi6h0MvOX36NEaPHo0mTZrg999/r+7hENEbwCv8RERERFRlzpw5A5FIhPHjx1f3UOgVRRvPOTg4VPNIiOhNYcBPRERERFWioKAACoUCzs7OnDZcDQoKChAYGIiYmBg8f/5cSL969SoCAwNx6tQp6Orqqm0+SUQ1F6f0ExERERHVAPn5+Sr7JhgaGqKgoEDY/E5LSwtz587F8OHDq2uIRPSGMeAnIiIiIqoBlEoldu/ejVOnTuHKlSt4/Pgx8vPzYWpqCqlUCm9v79eykSIRvb0Y8NM7LTs7GxcvXoSpqSm0tbWrezhERERERERvVEFBAVJTU9G5c2fUqlVLJa9i92khektcvHix2FuhEBERERER/Vfs3LkTUqlUJY0BP73Tiu4pvXPnTjRs2LCaR0NERERERDWRsiAXIm296h6GRg8ePMDIkSOF2OhlDPjpnVY0jT8/Zijy61XzYIiIiIiIqEZq4XuzuodQKk1LnHlbPiIiIiIiIqIaiAE/ERERERERUQ3EgL+S9u3bhyFDhkAikaBHjx4YO3Ys5HI5ACAkJASdOnWCRCIRHi9voiAWi+Hm5obCwkIhLSgoCNOnT4dcLhfqWFtbQywWq7Rz7949eHl5oUuXLpBIJOjWrRsmTJiAhw8fqoxPqVTC2dkZAwYMUBu7l5cX9u7dW+oxxsfHo3379kLf/fr1w6+//qrWlq2tLXJzc1XSp0+fjs6dO0MikcDOzg4+Pj64fv26kB8WFoYRI0YIzzMyMuDp6YmJEyciLy+v1LERERERERGRZgz4K2Hz5s1YtGgR/P39ERsbi2PHjuGTTz5BdHS0UMbFxQUKhUJ4FJ0MKPLw4UNERUWptS2VSoU6kZGRAICEhAQhrXHjxgCA2bNnQ6FQ4MiRI8jMzMSSJUtU2klISMCTJ09w584dJCUlVfhYzczMoFAocPbsWUybNg3ffvstrl27BgBITk6GXC6HSCRSOfYivr6+UCgUiImJgbm5OWbOnKmxj/T0dIwePRpNmjRBUFAQdHV1KzxeIiIiIiKi/zoG/BX0/PlzBAcHY/bs2ejbty8MDAygq6sLJycnfP3112Vux9fXFyEhIcjPz6/UeOrWrQtnZ2dcvnxZJT08PBxOTk54//33IZPJKtUHAIhEIvTp0wd169YVAn6ZTAYrKysMHjy4xD5q1aoFFxcXtTECwJMnT+Dt7Y22bdti6dKl0NHhfpJERERERESVwYC/ghQKBXJycvDhhx9Wqp2+ffvC0NAQ4eHhlWonLS0NR44cQbNmzYS0rKwsHDp0CIMGDYKbmxuioqLUptyXV2FhIY4cOYLnz5+jXbt2AICIiAi4ubnBzc0Np06dwqNHjzTWzczMRGRkpMoYgRdX9r28vGBpaYlFixZBS4sfSyIiIiIiospiZFVBT58+hYmJSalXog8ePAipVCo8vLy8VPJFIhEmTZqENWvWVCgYX7BgAWxsbNC9e3ekpaXh22+/FfIOHz4MPT09ODo64oMPPkBBQQFOnDhR7j6AF0sPpFIpunfvjtWrV+OHH35Aq1atIJfLce/ePbi4uKBz585o2rSpsAShyKZNmyCVStG1a1ckJibihx9+UMm/f/8+bt26hY8++ggikahC4yMiIiIiIiJVDPgryNjYGGlpaaVOxe/fvz/kcrnw2L59u1qZ999/H40aNcIvv/xS7nHMmjULiYmJ+O233/Ds2TM8ePBAyJPJZHBxcYGOjg709PTw4YcfVngmgZmZGeRyOc6cOYOIiAgMHDhQ6MPR0RH169cHALi6uqr1MWbMGMjlcvz++++oVasWbt5UvYdl+/bt8dVXX8HPzw+XLl2q0PiIiIiIiIhIFRdKV5BEIoG+vj6OHj2K/v37V7q9yZMn44svvoCrq2uF6ovFYnz++eeYP38+wsPDkZKSgj/++ANJSUk4fPgwgBdT/HNzc/HkyRMhQK+M7OxsHDhwAIWFhXB0dAQA5Obm4tmzZ7h8+TLat2+vUr5x48aYOXMmvv76a3zwwQeoVauWkOft7Y3c3Fz4+Phg+/btwnIBIiIiIiIiqhhe4a8gIyMjBAYGYv78+Th69CiysrKQl5eHEydOqE1ZL4tu3bqhXbt2ldpYz8PDA48fP0Z0dDQiIiLQokULHDx4EDKZDDKZDIcOHYK5ubnKXQHy8/ORk5MjPMpzK7yjR49CW1sbUVFRQh/79++HVCot9jgcHR1hZmamcTaDn58fRo0aBR8fH9y4caPcx09ERERERET/hwF/Jfj4+GD69OlYu3Yt7O3t0bt3b+zcuRN9+vQRyhw4cEC4f33R4/Hjxxrbmzx5Mp4+fVrh8ejp6cHLywtr165FeHg4PvnkE5iamqo8PD09Vabcz507F5aWlsJjxowZZe4vPDwcQ4YMQePGjVX6GDlyJPbt21fscoexY8ciNDRU454FAQEB+PjjjzF69Gj8888/5X8RiIiIiIiICAAgUiqVyuoeBFFFJScnw9nZGVvH5aJhveoeDRERERER1UQtfG+WXqiaFMVE0dHRsLCwUMnjGn6qESyGnVT7cBMREREREVWFwvwcaOnoV/cwyo1T+omIiIiIiIhK8C4G+wADfiIiIiIiIqIaiQE/ERERERERUQ3EgJ+IiIiIiOg/qDA/p7qHQK8ZN+2jGiF5T0/kc5d+IiIiIqIye5t3nqeqwSv8RERERERERDUQA34iIiIiIiKiGuidDfidnJxgaWkJiUQiPObPnw8ACAsLw4gRIzTW8/LyglgsxuXLl1XSx48fD7FYjPj4eDx8+BBisRiPHj0S8n/88UeNab6+vsLzs2fPYtSoUZBIJLCxsYG/vz+uXbum0s+zZ88wZ84cODo6wsrKCm5ubvj111+LPTapVApPT0/s3r0bhYWFxb4eXl5e6NKli9D3yJEj8ffffwv5ISEhmDp1qvBcLBbD2toaEokE3bp1g7e3N/bv3y/kR0ZGYsCAASp9+Pj4aEzbsGGD2rjL+57s3bu32GMjIiIiIiKi8ntnA34AWLduHRQKhfCYPXt2meq1aNECMplMeJ6Wlobz58+jfv36AAAzMzM0b94cCQkJQhm5XI5WrVqppdna2gIAFAoFfH194ezsjJMnTyI6OhpisRgjRozAnTt3AAC5ubkYPXo07t27h59//hlyuRzTpk3D8uXLsXnzZo3HduzYMfj5+eGnn37CzJkzSzyu2bNnQ6FQID4+HnZ2dvjqq69KLB8REQGFQoEDBw5g8ODBmD9/PlavXg0AsLW1xfXr1/HkyRMAQH5+Pi5fvozs7GyVtHPnzkEqlaqNu7zvCREREREREVWtdzrgryg3Nzfs378fBQUFAICoqCj06dMHurq6QhmpVAq5XA4AKCgowKVLlzBq1CiVNIVCIQS7S5cuhbu7O7y9vWFoaAhjY2NMmTIFVlZWCAkJAfAiwL5//z5WrVqFpk2bQldXF7169cKsWbMQHByMjIwMtbEaGRnB2dkZK1euRHh4OK5cuVLq8eno6GDgwIG4fv16mV6P+vXrw8PDA3PnzsX69euRlpYGc3NzNG3aVDjBcenSJbRp0wa2trYqaYWFhejcuXOZ+iEiIiIiIqI35z8Z8Jubm6NNmzY4deoUAEAmk8HDw0OlzKuBbatWrWBvb6+Slp+fD0tLS2RlZUGhUKB///5qfbm4uCAuLg4AEBcXh169esHAwEClTN++fZGTkwOFQlHsmC0tLdGwYUPhhENJcnNzsW/fPlhZWZVa9mXOzs4oKChAUlISANXXICEhAVKpFDY2NippVlZW0NPTK1c/RERERERE9Pq90wF/QEAApFKp8NizZ0+Z67q7uyMiIgI3btzA8+fPIZFIVPJtbW1x9epVpKenIzExEVKpFC1atEBaWpqQVhTspqeno7CwEKampmr9mJqaIi0tDcCLpQOayujo6MDExEQoVxwzMzOkp6cXm79gwQJIpVJ07doVO3bswIQJE8ryUgh0dXVhYmIi9GFrayucYJDL5ULA/3KanZ2dShuVeU+IiIiIiIio6uhU9wAqY82aNXBwcKhQ3b59+2LJkiUwNjbGoEGD1PItLCzQsGFDJCYmIiEhAcOHDwcASCQSIa1o/X7dunWhpaWF1NRUtG7dWqWd1NRUmJiYAABMTEyQmpqq1ld+fj7S0tKEcsVJSUlBvXrF32x+1qxZGDp0KAoLC3H27Fl8/vnn2L59O9q3b1/yi/H/5eXl4cmTJ0Iftra2mDlzJtLT03H+/HksW7YMderUQWpqKtLT03H27Fl4e3urtFGZ94SIiIiIiIiqzjt9hb8yateujV69emH37t1wd3fXWKZo+vq5c+fQtWtXlbTExEQh4DcwMIC1tTUOHjyo1saBAwfQvXt3AICDgwNiYmKQmZmpUubw4cPQ09ODtbV1seNNSkpCSkoKbGxsSj02LS0tSKVSNGvWDLGxsaWWLxIdHQ1tbW1YWloCAJo2bQozMzP88ssvaNSoEerUqQMAsLa2xi+//IJ///23xDETERERERFR9amxAb9SqUROTo7K41VTpkzB9u3bYWFhobENW1tbREREwMzMDIaGhgBeBPwRERHIyMhQCXa//PJLyGQybNu2DRkZGUhPT0dQUBDOnTsnTK13d3dHw4YNMWnSJCQnJyMvLw8nT57EggULMGHCBBgZGamNISMjA8eOHcMXX3yBQYMGQSwWl+n4FQoFrl+/jjZt2pRa9unTp/jtt98wf/58+Pn5qcw0kEql2LJli8pO/DY2NtiyZQs6d+6MWrVqlWk8QMnvSX5+vkp6Xl5emdslIiIiIiIide/0lH5/f39oa2sLzx0cHLBmzRoALwLeoivVRf7880+V5+bm5jA3Ny+2fVtbWzx+/Fjl3vMdOnRAdnY2OnXqhNq1awvpUqkUoaGhWLVqFYKCgiASiSCVSrFr1y60aNECAKCnp4fNmzdjxYoVGDZsGDIyMtC0aVNMmTIFQ4cO1XhsWlpaaNOmDXx8fODp6Vni6zF//nwsWrQIANCgQQNMnjwZ77//frHl3d3dIRKJoKurC7FYjBkzZsDNzU3tNYiMjFSZWSCVSrFkyRJ89NFHam1W9D2ZO3cu5s6dK6S7ublh2bJlJR4vERERERERFU+kVCqV1T0IoopKTk6Gs7Mzto7LRcPitzcgIiIiIqJXtPC9Wd1DoCpQFBNFR0erzV5/p6/wExWxGHay2KUZRERERESkrjA/B1o6+tU9DHqNauwafiIiIiIiIioeg/2ajwE/ERERERERUQ3EgJ+IiIiIiIioBmLAT0RERERE9JYozFe/nThRRXHTPqoRkvf0RD536SciIiKidxx3zqeqxCv8RERERERERDUQA/53lJOTEywtLSGRSODg4IAZM2bg33//BQCEhYVBLBZj//79KnXi4+PRvn17SCQSSCQS9OvXD7/++qtKGbFYjNu3bwvPN27ciB49euDq1asAgHXr1sHJyQkSiQS9evXC5MmThbJeXl7Yu3evxr569eqFSZMmISkpSa0/a2troZxEIsFPP/1UZa8TERERERHRfxWn9L/D1q1bBwcHB6SkpMDX1xc//vgjpk6divDwcBgbG0Mmk2HAgAEqdczMzBATEwOlUomYmBh8/vnnkEgkaNWqlVr7a9euxc8//4zt27ejZcuWCA8PR0REBLZs2YJmzZohNTUVv//+e7Hje7mvlJQU/PLLLxg5ciQ2bNgAe3t7oVxERASaN29edS8MERERERER8Qp/TWBubo6ePXvi6tWruHv3LhISEjB//nycOnUKjx490lhHJBLh/fffR7169fD333+r5QcFBeF///sfdu7ciZYtWwIALly4gB49eqBZs2YAAFNTUwwfPrzU8YlEIjRs2BCTJk3C0KFDsXTp0kocLREREREREZUFA/4a4P79+4iJiUGHDh0gk8nQuXNn9OvXD61bt8a+ffs01iksLER0dDTS0tLUrq4vX74cBw4cwI4dO9C0aVMh3crKChEREQgNDcWFCxdQUFBQ7rF++OGHuHTpEjIzM8tdl4iIiIiIiMqOU/rfYQEBAdDW1oaRkRHef/99+Pv7Y9CgQfjkk08AAK6urggPD4ePj49Q5+HDh5BKpcjOzkZBQQGmT5+Ojh07qrR76tQpDB48GI0bN1ZJd3d3h0gkQlhYGFavXg19fX34+vris88+K/OYzczMoFQq8fz5cxgYGAAABg8eDC2t/zv3FBQUhJ49e5b79SAiIiIiIqL/w4D/HbZmzRo4ODgIzxMTE5GcnIyBAwcCeBHwBwUF4a+//kKHDh0A/N+6+tzcXCxbtgx//PEHRo8erdJuUFAQZs6ciXr16iEwMFAlb9CgQRg0aBDy8vJw9OhRTJs2DR06dChzgP7w4UOIRCIYGRkJaeHh4VzDT0REREREVMU4pb8GkclkUCqV8PDwgKOjI4YNGyakv0pPTw9Tp07FlStXcPToUZW8Fi1aYPPmzdi1axc2bNigsS9dXV24uLigXbt2wg7+ZXHkyBF07NhRuLpPRERERERErwev8NcQOTk5OHDgAObPn4/evXsL6YcPH8aaNWswbdo0tTp6enoYM2YM1qxZgz59+qjktW3bFlu2bIG3tzf09PQwevRohIWFoX79+pBKpTAwMMDJkydx7do1WFpaljg2pVKJhw8fYu/evdi7dy9+/PHHKjlmIiIiIiIiKh4D/hri6NGjqFWrFjw8PKCrqyukf/zxxwgODsbJkyc1XlX/6KOPEBISgt9//x1OTk4qee3bt0doaCjGjBkDfX19vPfee1i3bh2uX7+OgoICNGnSBHPnzoVUKtU4pocPH0IikUCpVMLQ0BBdu3bF9u3bYW1trVKuaG+Al8c8c+bMSrwaREREREREJFIqlcrqHgRRRSUnJ8PZ2Rlbx+WiYb3qHg0RERERUeW08L1Z3UOgd0xRTBQdHQ0LCwuVPK7hJyIiIiIiIqqBOKWfagSLYSfVzmYREREREb1rCvNzoKWjX93DoBqCV/iJiIiIiIjeEgz2qSox4CciIiIiIiKqgRjwExERERERVbPC/JzqHgLVQFzDTzVC8p6eyOcu/URERET0juLu/PQ68Ao/ERERERERUQ3EgJ+IiIiIiIioBmLA/4Y5OTnBwcEBmZmZQtrevXvh5eUlPFcqlQgNDUXfvn1haWmJ3r17Y9myZcjNzRXKTJ8+HZ07d4ZEIoGdnR18fHxw/fp1/Pbbb5BIJJBIJLC0tET79u2F5xKJRBhDXFycyrjCwsIwYsSIYsctFothbW0NiUSCnj174vvvv0dBQYGQHxISArFYjKSkJLV2O3ToAIlEgq5du2LQoEE4duyYkJ+cnAyxWIz8/Hzh2L/77jv0798fKSkp5XlpiYiIiIiI6CUM+KtBQUEBtm3bVmz+ggULsGfPHixZsgRnz57FTz/9hD/++AOTJk1SKefr6wuFQoGYmBiYm5tj5syZGDRoEBQKBRQKBX766SeYmZkJzxUKRaXGHRERAYVCgS1btiAyMhJ79uwB8CJIj4iIgLGxMcLDw9XqWVtbQ6FQQC6X45NPPsEXX3yBZ8+eqZVTKpWYPXs2zpw5gx07dsDc3LxS4yUiIiIiIvovY8BfDXx9fbFp0yaNQe+tW7ewa9cuLFu2DBKJBDo6Omjbti1CQkJw8uRJnD59Wq1OrVq14OLigsuXL7+J4aN169awsbHB1atXAQByuRwPHz7EN998g/3796vMRHiZlpYW3N3dkZmZiVu3bqnkFRYWYvr06bh48SK2bduGBg0avO7DICIiIiIiqtEY8FeDzp07w87ODhs3blTLO336NBo2bAhLS0uV9EaNGsHa2lptKj4AZGZmIjIyEs2aNXttY37ZtWvXkJiYiA4dOgAAwsPD8cEHH2DAgAEAgOPHj2usV1BQgLCwMOjq6qJJkyYqeVOnTsWNGzewdetWmJiYvNbxExERERER/RfwtnzVJDAwECNGjIC3t7dKelpaGkxNTTXWMTU1RVpamvB806ZN2LlzJzIyMtC4cWOsXbu2zP0HBARAW1tbeJ6Xl4eOHTuWWGfw4MHQ1tZGvXr18PHHH+Ojjz5CVlYWDh48iB9++AG6urro168fwsPD0bdvX6He+fPnIZVKkZWVBW1tbfzwww947733VNo+deoUJkyYgLp165b5GIiIiIiIiKh4vMJfTdq1a4fevXtjw4YNKukmJiZITU3VWCc1NVXl6veYMWMgl8vx+++/o1atWrh5s+z37lyzZg3kcrnwmDNnTql1wsPDkZCQgKNHj2LKlCnQ0tLCkSNHoKOjg169egEA3NzccPLkSTx58kSoZ2VlBblcjjNnzsDJyQmJiYlqba9fvx6rV6/G//73vzIfAxERERERERWPAX81CgwMxJ49e1R2o+/evTvu37+vttv9/fv3ce7cOdjb26u107hxY8ycORMLFy5Ednb2ax/3y2QyGTIzM/HBBx/A0dERkyZNQl5eHiIjI9XK1qlTB3PmzEFERAQuXbqkkieRSLBu3TosXLgQ+/bte1PDJyIiIiIiqrEY8Fej5s2bY8CAAdi+fbuQ1rJlS3h6emLq1Kk4d+4cCgoKcPXqVUycOBEODg5wcHDQ2JajoyPMzMzwyy+/vKnhIyUlBadPn8a6desgk8kgk8kQEREBPz8/yGQyjXVMTEwwdOhQrFmzRi3Pzs4Oq1evxuzZs3Hw4MHXPHoiIiIiIqKajQF/NQsICEBmZqZK2uzZs/Hxxx9j2rRpkEgkGDt2LOzs7BASElJiW2PHjkVoaGixu+RXtYiICHTo0AE9evSAqamp8PDy8sLff/+NK1euaKzn7e2NEydOaLyrgKOjI4KCgjBjxgz8/vvvr/sQiIiIiIiIaiyRUqlUVvcgiCoqOTkZzs7O2DouFw3rVfdoiIiIiIgqpoVv2ffjInpZUUwUHR0NCwsLlTzu0k81gsWwk2ofbiIiIiKid0Vhfg60dPSrexhUw3BKPxERERERUTVjsE+vAwN+IiIiIiIiohqIAT8RERERERFRDcSAn4iIiIiojArzc6p7CEREZcZN+6hGSN7TE/ncpZ+IiIheM+6kTkTvEl7hJyIiIiIiIqqB3vqAXywW4/bt2yppISEhmDp1KgAgPj4eYrEY8+bNUykzYsQIhIWFAQDCwsIwYsQIAICvry9WrVql1s/Ro0fh6OiI/Px8TJ8+HZ07d4ZEIhEegwYNAvDiHodisVhId3JywoYNG1Taksvl8PT0hI2NDezs7ODp6YmkpCS1sbzcfvv27WFpaSk8Dw8Ph4ODA548eSK0m5ubCxcXF/z8889q44+Pj0evXr3U0r28vLB3716hTPv27VX6lUgkUCgUamUBICMjA99//z2cnJxgbW2N3r17IzAwUDiW0t6fe/fuqfQjFothbW0tPJfL5Xjw4AEmTpyIbt26wcbGBm5ubsL7RkRERERERBVXI6b0GxgYQCaTwdfXt9R7sQ8ePBgrVqxAYGAgRCKRkP7bb7/Bzc0NOjovXhJfX19MmTKl2HYSEhKgo6ODCxcuwMvLC506dYKjoyMyMjLg7++PuXPnwsXFBXl5eZDL5dDT01NroyjQBgAnJycsWLAADg4OKn0sXLgQy5cvBwCsXbsWpqamGD58eNleGA3MzMwQExNTarnc3Fx4e3ujbt26WLduHVq3bo2cnBzExMTgxIkTsLS0LLWNxo0bqxyjWCxGREQEmjdvLqR5eXmhffv2OHbsGPT09HDlyhWkpqZW7OCIiIiIiIhI8NZf4S8LIyMjDBkyBGvWrCm1bJ8+fZCeng65XC6kpaen49ixY/Dw8Ch33126dEGbNm3w119/AQBu3nyxrsvV1RXa2tqoVasWevTogfbt25e77RkzZiAhIQHHjx/HlStXsHPnTixYsEDlRMXrEhERgZSUFKxZswbt2rWDtrY2DAwM0L9/f0ycOLHK+rl48SKGDBkCAwMD6OjooGPHjnj//ferrH0iIiIiIqL/qhoR8AOAv78/Dh06hBs3bpRYrlatWnBxcYFMJhPSDhw4gFatWlUoKD937hyuXr0qXLVu2bIltLW18fXXX+PEiRNIT08vd5tFjIyMMHfuXMyZMwfffPMNAgIC0KxZswq3Vx5xcXHo0aMHDAwMXms/VlZWmDdvHqKionDv3r3X2hcREREREdF/SY0J+E1NTeHp6Yng4OBSy3p4eODgwYPIzs4GAMhkMgwePFilzKZNmyCVSoXH119/rZLfvXt3WFpaYvjw4fjkk0/Qp08fAIChoSF27doFkUiEb7/9Fvb29vD398ejR48qdFxOTk6wsrJCYWEhRo0aVWLZhw8fqoxZKpUiMTGx1DKZmZlqbaWlpaFBgwbC87/++gtSqRRdu3ZFv379VMoOHjxYpb1X9zQoyapVqyCVSrF27Vo4OzvD3d1dZY8AIiIiIiIiqpi3fg2/trY28vPzVdLy8/Ohq6urVtbPzw8ffvghLl++XGKbUqkU9evXR3R0NCwtLXHx4kWsXr1apcyYMWNKXMP/xx9/QCQSYevWrYiMjEReXp6wTr9169ZYvHgxAOD69euYNm0aFi1ahBUrVpTpmF/Vtm1b6OnpQUur5PMzmtbne3l5lVpGE2NjY5W19B06dIBcLkdcXBxmzZqlUjY8PFxlXX5ISIjaRn7FqVevHqZOnYqpU6fiyZMn+OGHHxAQEICYmJg3snSBiIiIiIiopnrrr/A3atQIycnJKmnJyclo3LixWlkTExN4e3tj5cqVpbbr7u4OmUyGiIgIODo6qlzNLittbW2MGTMG+vr62LVrl8YyrVu3xpAhQ3D16tVyt1+d7O3tERsbq/Hq/+tSv359jBkzBg8fPsTTp0/fWL9EREREREQ10Vsf8A8YMAA//vgjHjx4gMLCQsTFxeH3339Xm1ZexMfHBwqFotS1/B4eHjh9+jT27NlToc36XvbZZ58hNDQUOTk5uH79OjZt2oQHDx4AAO7fv4/IyEhYWVlVqo83zcPDA6amppgwYQKuXLmCgoIC5OTk4OLFi1Xaz9KlS3HlyhXk5+cjIyMDu3fvRvPmzWFiYlKl/RAREREREf3XvPVT+gMCArBq1Sp88sknSE9PR7NmzbBs2TK0a9dOY3lDQ0OMHTsWy5YtK7FdCwsLSCQSXL58Gc7Ozmr5GzduxLZt24Tnenp6iI+P19hW7969Ua9ePezZswd9+/bF+fPnsXnzZjx//hxGRkb44IMP8NVXX5XjqF+fhw8fQiKRqKQtXrxY7QSKvr4+tm3bhuDgYIwbNw5paWkwMTFB586dyzSDoqyys7MxYcIEpKamQl9fH1ZWVvjxxx+rrH0iIiIiIqL/KpFSqVRW9yCIKio5ORnOzs7YOi4XDetV92iIiIiopmvhe7O6h0BEpKIoJoqOjoaFhYVK3ls/pZ+IiIiIiIiIyu+tn9JPVBYWw06qnc0iIiIiqmqF+TnQ0tGv7mEQEZUJr/ATEREREZURg30iepcw4CciIiIiIiKqgRjwExEREdF/WmF+TnUPgYjoteAafqoRkvf0RD536SciIqIK4M77RFRT8Qo/ERERERERUQ3EgJ+IiIiIiIioBmLA/wY4OTkhLi5OeB4VFQVbW1ucOXMGABAfHw+xWIyffvpJpV5ycjLEYjHy8/MBAA8ePMDEiRPRrVs32NjYwM3NDWFhYSplJRKJymP//v0AgOnTp6Nz586QSCSws7ODj48Prl+/jt9++00oa2lpifbt26vULxIWFgY3NzdYWVnB0dERc+bMwbNnzwAADx8+hFgsxqNHj4TyP/74o8Y0X19fYTxisRhJSUlC/u3btyEWiyv/ghMRERERERED/jctPDwc8+fPx/r162FnZwcAkMlkMDY2hkwmK7HutGnT0LBhQxw7dgzx8fFYsmQJ3nvvPZUyCQkJUCgUwmPAgAFCnq+vLxQKBWJiYmBubo6ZM2di0KBBQtmffvoJZmZmKvUBYNOmTVi2bBmmTZsGuVyOX375Bffu3YOPjw9yc3NhZmaG5s2bIyEhQehLLpejVatWamm2trbCc2NjY6xcubKiLyURERERERGVgAH/G/TLL79g8eLFCA0NRdeuXQEAWVlZOHjwIGbPno3bt2/jwoULxda/ePEihgwZAgMDA+jo6KBjx454//33yz2OWrVqwcXFBZcvXy61bEZGBkJCQjBr1iz06tULurq6sLCwwMqVK3Hv3j389ttvAACpVAq5XA4AKCgowKVLlzBq1CiVNIVCAalUKrTt4eGBv//+W5jpQERERERERFWHAf8bsnv3bqxatQpbt25Fly5dhPRDhw6hTp066N+/P3r06IGIiIhi27CyssK8efMQFRWFe/fuVXgsmZmZiIyMRLNmzUote/bsWeTk5KBv374q6XXq1EGvXr2EpQq2trbC1fxLly6hVatWsLe3V0nLz8+HpaWl0EatWrUwbtw4BAUFVfhYiIiIiIiISDMG/G9IbGwsrKys0K5dO5V0mUwGFxcXaGtrw9XVFZGRkcjLy9PYxqpVqyCVSrF27Vo4OzvD3d1dZQ08AHTv3h1SqVR4XL9+XcjbtGkTpFIpunbtisTERPzwww+ljjstLQ0mJibQ0VG/g6OpqSnS0tIAvAj4r169ivT0dCQmJkIqlaJFixZIS0sT0qysrKCnp6fShqenJ+7fv48TJ06UOhYiIiIiIiIqOwb8b8i8efNw69YtzJw5E0qlEgBw//59xMfHw83NDQDg7OyMnJycYoPfevXqYerUqYiKikJsbCw6dOiAgIAAoT0A+OOPPyCXy4VH69athbwxY8ZALpfj999/R61atXDzZun3nDUxMUFaWpqwceDLUlNTYWJiAgCwsLBAw4YNkZiYiISEBNjY2AAAJBKJkPby+v0ienp6GD9+PFatWqVyHERERERERFQ5DPjfkPfeew9btmxBYmIi5s6dCwCIiIhAYWEhPv/8czg6OqJPnz7Izc0tdfM+AKhfvz7GjBmDhw8f4unTp+UaS+PGjTFz5kwsXLgQ2dnZJZaVSCTQ09PD4cOHVdIzMzMRExMDe3t7Ic3GxgYJCQk4d+6csEdBUVpiYqLGgB8AhgwZgoyMDBw5cqRcx0FERERERETFY8D/Bpmbm2Pr1q04efIkFi1aBJlMhgkTJkAmkwmP4OBgHD9+XJgq/7KlS5fiypUryM/PR0ZGBnbv3o3mzZsLV9nLw9HREWZmZvjll19KLGdkZISAgAAsWLAAMTExyMvLQ3JyMiZNmoSGDRvC3d1dKGtra4uIiAiYmZnB0NAQwIuAPyIiAhkZGbC2ttbYh46ODiZMmIDQ0NByHwcRERERERFppr4wm16rRo0aYevWrejfvz/y8/MxcuRI1K9fX8h3dnZG8+bNERUVhd69e6vUzc7OxoQJE5Camgp9fX1YWVnhxx9/VCnz6lX0wMBA+Pj4aBzL2LFj8f3332PEiBFqa+tf5ufnB2NjY/zwww/4559/YGhoiD59+mDZsmUq9WxtbfH48WOVWwF26NAB2dnZ6NSpE2rXrl1sH66urtiwYUO5ZysQERERERGRZiIlF07TOyw5ORnOzs7YOi4XDetV92iIiIjoXdTCt/R9jYiI3lZFMVF0dDQsLCxU8niFn2oEi2En1T7cRERERGVRmJ8DLR396h4GEVGV4xp+IiIiIvpPY7BPRDUVA34iIiIiIiKiGogBPxEREREREVENxICfiIiIiGqkwvyc6h4CEVG14qZ9VCMk7+mJfO7ST0RERC/h7vtE9F/HK/xERERERERENVCNDPidnJxgaWkJiUQiPObPnw8ACAsLw4gRIzTW8/LyglgsxuXLl1XSx48fD7FYjPj4eDx8+BBisRiPHj0S8n/88UeNab6+vsLzs2fPYtSoUZBIJLCxsYG/vz+uXbum0s+zZ88wZ84cODo6wsrKCm5ubvj111+LPTapVApPT0/s3r0bhYWFxb4eXl5e6NKli9D3yJEj8ffffwv5ISEh6NSpk0qbCoUCSqUSI0eOxOrVq1XaCw8PR58+fZCVlSW8pmKxGPv371cpFx8fD7FYjHnz5qmkjxgxAmFhYVi3bp3w/nTp0gUdOnQQng8cOLDY4yEiIiIiIqLS1ciAHwDWrVsHhUIhPGbPnl2mei1atIBMJhOep6Wl4fz586hfvz4AwMzMDM2bN0dCQoJQRi6Xo1WrVmpptra2AACFQgFfX184Ozvj5MmTiI6OhlgsxogRI3Dnzh0AQG5uLkaPHo179+7h559/hlwux7Rp07B8+XJs3rxZ47EdO3YMfn5++OmnnzBz5swSj2v27NlQKBSIj4+HnZ0dvvrqK5V8FxcXKBQK/PHHH+jWrRsmTZoEkUiEhQsXYsuWLbh69SoA4MmTJ1iyZAkWLFiA2rVrA3hxAsDY2FjldStiYGAAmUyG5ORktTx/f3/h/Zk3bx6sra2F51FRUSUeDxEREREREZWsxgb8FeXm5ob9+/ejoKAAABAVFYU+ffpAV1dXKCOVSiGXywEABQUFuHTpEkaNGqWSplAoIJVKAQBLly6Fu7s7vL29YWhoCGNjY0yZMgVWVlYICQkBAEREROD+/ftYtWoVmjZtCl1dXfTq1QuzZs1CcHAwMjIy1MZqZGQEZ2dnrFy5EuHh4bhy5Uqpx6ejo4OBAwfi+vXrxea7ubkhJSUFT548QYsWLeDv74+ZM2eisLAQCxYsQN++fdG9e3cAwN27d5GQkID58+fj1KlTKrMcisY4ZMgQrFmzptSxERERERERUdVhwP8Kc3NztGnTBqdOnQIAyGQyeHh4qJSxtbUVruZfunQJrVq1gr29vUpafn4+LC0tkZWVBYVCgf79+6v15eLigri4OABAXFwcevXqBQMDA5Uyffv2RU5ODhQKRbFjtrS0RMOGDYUTDiXJzc3Fvn37YGVlVWy+TCaDsbEx6tatCwDw8fGBUqlEYGAgzp49qzI7QCaToXPnzujXrx9at26Nffv2qbXp7++PQ4cO4caNG6WOj4iIiIiIiKpGjQ34AwICIJVKhceePXvKXNfd3R0RERG4ceMGnj9/DolEopJva2uLq1evIj09HYmJiZBKpWjRogXS0tKENCsrK+jp6SE9PR2FhYUwNTVV68fU1BRpaWkAXiwd0FRGR0cHJiYmQrnimJmZIT09vdj8BQsWQCqVomvXrtixYwcmTJigkn/w4EFIpVJYWVlh7969CA4Oho7Oi5s4aGtrY9GiRThy5AhmzZoFQ0NDoV5ERARcXV0BAK6urggPD9d4nJ6enggODi7xGIiIiIiIiKjq1NiAf82aNZDL5cJj2LBhZa7bt29f/PHHH9ixYwcGDRqklm9hYYGGDRsiMTERCQkJsLGxAQBIJBIhrWj9ft26daGlpYXU1FS1dlJTU2FiYgIAMDEx0VgmPz8faWlpQrnipKSkoF694u9LN2vWLMjlciQlJWH9+vUIDAxU2Zywf//+kMvliI2NRdu2bfHnn3+q1G/btq3KfwEgMTERycnJwgZ7rq6uuHLlCv766y+1/v38/HDq1Cm1DRGJiIiIiIjo9aixAX9l1K5dG7169cLu3bvh7u6usYyNjQ0SEhJw7tw5dO3aVSUtMTFRCPgNDAxgbW2NgwcPqrVx4MABYS28g4MDYmJikJmZqVLm8OHD0NPTg7W1dbHjTUpKQkpKinDioSRaWlqQSqVo1qwZYmNj1fLr16+PefPmISQkBA8fPiyxLZlMBqVSCQ8PDzg6OgonVTRt3mdiYgJvb2+sXLmy1DESERERERFR5f0nA36lUomcnByVx6umTJmC7du3w8LCQmMbtra2iIiIgJmZmTDF3cbGBhEREcjIyFAJ0L/88kvIZDJs27YNGRkZSE9PR1BQEM6dOydMrXd3d0fDhg0xadIkJCcnIy8vDydPnsSCBQswYcIEGBkZqY0hIyMDx44dwxdffIFBgwZBLBaX6fgVCgWuX7+ONm3aaMxv3bo1evbsidDQ0GLbyMnJwYEDBzB//nzIZDLh8e2332Lfvn3Iz89Xq+Pj4wOFQsG1/ERERERERG+ATnUP4HXx9/eHtra28NzBwUHYKV6hUMDS0lKl/KtT2M3NzWFubl5s+7a2tnj8+DEGDBggpHXo0AHZ2dno1KmTcMs64MWu/qGhoVi1ahWCgoIgEokglUqxa9cutGjRAgCgp6eHzZs3Y8WKFRg2bBgyMjLQtGlTTJkyBUOHDtV4bFpaWmjTpg18fHzg6elZ4usxf/58LFq0CADQoEEDTJ48Ge+//36x5X19feHt7Y1x48bhvffeU8s/evQoatWqBQ8PD5U7GHz88ccIDg7GyZMn1TYgNDQ0xNixY7Fs2bISx0pERERERESVJ1IqlcrqHgRRRSUnJ8PZ2Rlbx+WiYfFbGBAREdF/UAvfm9U9BCKi164oJoqOjlabof6fnNJPREREREREVNPV2Cn99N9iMexksfstEBER0X9TYX4OtHT0q3sYRETVhlf4iYiIiKhGYrBPRP91DPiJiIiIiIiIaiAG/ERERET01ijMV79dMhERVQzX8FONkLynJ/K5Sz8REdE7jzvrExFVHV7hJyIiIiIiIqqBGPATERERERER1UAM+KuRl5cXbG1tkZubK6RNnz4dQUFBGsuLxWLcvn1bLT0sLAwdOnSARCJReaSkpAAAnJyc4ODggMzMTKHO3r174eXlhXv37qnUEYvFsLa2Fp7L5XIAwNmzZzFq1ChIJBLY2NjA398f165dE9rr168f9u/fLzxPTEyEWCxWS5NIJMjPz0dYWBjEYjFCQ0NVjqVXr16Ij48vz8tIREREREREGjDgrybJycmQy+UQiUSIjo6udHvW1tZQKBQqD3NzcyG/oKAA27ZtU6vXuHFjlToAEBERITyXSqVQKBTw9fWFs7MzTp48iejoaIjFYowYMQJ37twBANja2iIhIUFoVy6Xo1WrVmppEokEOjovto4wNjbGTz/9hIyMjEofPxEREREREaliwF9NZDIZrKysMHjwYMhkstfen6+vLzZt2oRnz56Vu+7SpUvh7u4Ob29vGBoawtjYGFOmTIGVlRVCQkIAAFKpVJgNALwI7v38/NTSpFKp8LxVq1aQSCTYsmVLxQ+MiIiIiIiINGLAX00iIiLg5uYGNzc3nDp1Co8ePXqt/XXu3Bl2dnbYuHFjueplZWVBoVCgf//+ankuLi6Ii4sDANjZ2eHq1at4+vQpCgsLcfHiRQwYMADPnj0T0hQKBWxtbVXamDRpErZs2YKnT59W+NiIiIiIiIhIHQP+aiCXy3Hv3j24uLigc+fOaNq0KSIjIyvV5vnz5yGVSoVHnz591MoEBgZix44dePLkSZnbTU9PR2FhIUxNTdXyTE1NkZaWBuDF0oDGjRtDLpfj8uXLaN68OWrVqoWuXbsKaTk5ObCyslJpo0OHDnB0dMRPP/1UziMmIiIiIiKikjDgrwYymQyOjo6oX78+AMDV1RXh4eGVatPKygpyuVx4HD16VK1Mu3bt0Lt3b2zYsKHM7datWxdaWlpITU1Vy0tNTYWJiYnwvGhaf0JCgjB138bGRkizsrKCnp6eWjuBgYHYvXu3xj6IiIiIiIioYnSqewD/NdnZ2Thw4AAKCwvh6OgIAMjNzcWzZ89w+fLl195/YGAgBg8ejDFjxpSpvIGBAaytrXHw4EF0795dJe/AgQMqaba2tvj555/RpEkTDBkyBMCLkwDh4eFo0qSJyvr9l7Vu3Rp9+/bF+vXrK3hURERERERE9CoG/G/Y0aNHoa2tjX379kFXV1dInzx5srB5X2FhIXJycoQ8kUgkXBnPy8tTySva8b6smjdvjgEDBmD79u1o165dmep8+eWXGDt2LFq1aoUhQ4agoKAAmzZtwrlz5/C///1PKCeVSjFnzhzcvXsXCxYsAPBiVkFycjKSk5MxfPjwYvsICAjAoEGDynUsREREREREVDxO6X/DwsPDMWTIEDRu3BimpqbCY+TIkdi3bx/y8/OxYcMGWFpaCg9vb2+h/sCBA1XywsLCAADnzp2DRCJReSQlJWkcQ0BAADIzM8s8ZqlUitDQUBw5cgQ9e/bEBx98gL/++gu7du1CixYthHItW7ZE/fr1YWpqirp16wIAtLS0YGlpiYyMDEgkkmL7aNq0Kdzd3cs1LiIiIiIiIiqeSKlUKqt7EEQVlZycDGdnZ2wdl4uG9ap7NERERFRZLXxvVvcQiIjeKUUxUXR0NCwsLFTyOKWfagSLYSfVPtxERET07inMz4GWjn51D4OIqEbglH4iIiIiemsw2CciqjoM+ImIiIiIiIhqIAb8RERERERERDUQA34iIiIieu0K83NKL0RERFWKm/ZRjZC8pyfyuUs/ERHRW4u77xMRvXm8wk9ERERERERUA72zAb9cLoenpydsbGxgZ2cHT09PJCUlISwsDB06dIBEIlF5pKSkAACcnJzg4OCAzMxMoa29e/fCy8tLeC4Wi2Ftba1S/6effhLyb968icDAQHTr1g02NjZwc3PD5s2bUVBQIJTJzMyERCKBn59fmY7n5MmTGDlyJCQSCbp3745PP/0U0dHRKmXi4+MhFotVxgIAs2fPVjteKysriMViJCQklHpMISEhEIvFOHDggNBmfn4+xGIxkpOThbSzZ89i1KhRkEgksLGxgb+/P65duwYAiIyMhJOTE5RKpcrY8vPzYW9vj2PHjiE+Ph69evVSO/bp06ejY8eOwntERERERERElfdOBvwZGRnw9/fHp59+ijNnziAmJgYTJkyAnp4eAMDa2hoKhULlYW5uLtQvKCjAtm3bSuwjIiJCpX5R4P7PP/9g2LBhaNSoEfbt24fExESsWrUKFy9exL///ivUP3ToEPT09BAbG4uHDx+W2NfBgwcxadIkeHh4ICYmBnFxcQgMDMSxY8dUyslkMhgbG0Mmk6mkz58/X+14+/bti27duqFr166lHhMAGBsbIzg4WOWkxcsUCgV8fX3h7OyMkydPIjo6GmKxGCNGjMCdO3fw4Ycf4tmzZzhz5oxKvZMnT0IkEqFnz54a283MzMShQ4dgZGSEffv2lfg6ERERERERUdm9kwH/zZsv1oC5urpCW1sbtWrVQo8ePdC+ffsy1ff19cWmTZvw7NmzcvcdHBwMiUSCGTNmwMzMDADQqlUrLF++HHXr1hXKhYeHw9PTE2KxuMRAVqlUYvHixRg/fjyGDh0KIyMjaGlpwc7ODgsWLBDKZWVl4eDBg5g9ezZu376NCxcuFNvmrl278Mcff2DFihXQ1tYu03H16NEDurq6+O233zTmL126FO7u7vD29oahoSGMjY0xZcoUWFlZISQkBPr6+nBxcVE7GSGTyeDm5gYdHc3bRRw+fBh169bF+PHj1eoSERERERFRxb2TAX/Lli2hra2Nr7/+GidOnEB6enq56nfu3Bl2dnbYuHFjufs+ffo0+vXrV2KZe/fu4cyZM3Bzc4Obm1uJgeyNGzdw//79Uts8dOgQ6tSpg/79+6NHjx6IiIjQWO7ChQtYunQpgoKC0KBBg1KPp4hIJMKkSZOwevVq5OXlqeRlZWVBoVCgf//+avVcXFwQFxcHAPDw8MChQ4eQnZ0NAHj+/DmOHTsGDw+PYvsNDw/HwIEDMXDgQNy4cQN//vlnmcdMRERERERExXsnA35DQ0Ps2rULIpEI3377Lezt7eHv749Hjx4BAM6fPw+pVCo8+vTpo9ZGYGAgduzYgSdPnmjsY/DgwSptnDx5EgDw9OlTmJqaljg+mUwGsViMNm3aYODAgbh27RouXbqksezTp08BQJgtUFKbLi4u0NbWhqurKyIjI9UC86dPn2LSpEkIDAyEVCot8zEVcXZ2Rv369bF3716V9PT0dBQWFmo8blNTU6SlpQEAbGxs0KBBAxw5cgQAcODAAbRo0QIdOnTQeEz37t1DfHw83Nzc0KBBA9jb2yM8PLzE14GIiIiIiIjK5p0M+AGgdevWWLx4MWJiYrBv3z48fPgQixYtAgBYWVlBLpcLj6NHj6rVb9euHXr37o0NGzZobD88PFyljaI16MbGxkhNTS1xbBEREXBzcwMAmJubw9bWVghkX95gb926dTA2NgaAEtf5379/XwiMgReBeU5ODk6cOCGUUSqVmDZtGjp16gQfH59yHdPLJk+ejHXr1iEn5//ulVu3bl1oaWlpPO7U1FSYmJgIz93d3YUZDRERERg8eHCxxxUREYHWrVsLJwTc3Nw0nsggIiIiIiKi8ntnA/6XtW7dGkOGDMHVq1fLVS8wMBB79uwp1+7w9vb2OHz4cLH5Z8+exa1bt7BhwwY4OjrC0dERSUlJiIqKQn5+vsoGe/7+/mjVqhUaNWpUYpsREREoLCzE559/DkdHR/Tp0we5ubkqSwXWrl2Lf/75B99//32Zj0UTR0dHNG/eHLt27RLSDAwMYG1tjYMHD6qVP3DgALp37y489/DwwB9//AGFQoHz58/D1dW12L5kMhnu3LkjvE7ff/890tLSEBMTU6ljICIiIiIionc04L9+/To2bdqEBw8eAHhxBTwyMhJWVlblaqd58+YYMGAAtm/fXuY6gYGBUCgUWLJkiXDF+/bt25g6dSqePXsGmUwGR0dHREVFQSaTQSaTYd++fcjKytIYyIpEIkyfPh1r167Fr7/+ioyMDBQWFkIul+Pbb78F8CIwnjBhgtCeTCZDcHAwjh8/jrS0NMTFxSE0NBTBwcEwNDQs12ugyeTJkxEaGqqS9uWXX0Imk2Hbtm3IyMhAeno6goKCcO7cOUyYMEEo16RJE3Tt2hVffvklHBwcil3+oFAocOfOHezdu1c4psjISLi6unLzPiIiIiIioiqgeev0t5yhoSHOnz+PzZs34/nz5zAyMsIHH3yAr776CocPH8a5c+cgkUhU6mzduhWWlpZqbQUEBGjcAM/d3R0ikUh4/vHHH2PmzJlo1qwZfv75Z6xcuRKurq7Iz89HkyZNMGTIEOjo6ODAgQNYsmSJWqBbNNXdyclJra/+/fvDwMAA69atw4IFC6Cvr4+2bdvC19cX586dw927dzFy5EjUr19fqOPs7IzmzZsjKioKhw8fRk5ODjw9PdXanjdvHgYNGlTiMb3KxsYGlpaWKicopFIpQkNDsWrVKgQFBUEkEkEqlWLXrl1o0aKFSv3BgwdjxowZmDp1qlrbRcLDw+Hs7AyxWKyS7u3tjU8++QRPnz4VljsQERERERFR+YmUSqWyugdBVFHJyclwdnbG1nG5aFivukdDRERExWnhe7O6h0BEVCMVxUTR0dGwsLBQyXsnp/QTERERERERUcneySn9RK+yGHZS7WwWERERvT0K83OgpaNf3cMgIvpP4RV+IiIiInrtGOwTEb15DPiJiIiIiIiIaiAG/ERERERULoX5OdU9BCIiKgOu4acaIXlPT+Rzl34iIqI3gjvuExG9G3iFn4iIiIiIiKgGYsBPREREREREVAO9cwG/k5MT4uLihOdRUVGwtbXFmTNnIBaLIZFIVB779+8HAEyfPh1isRhJSUlC3du3b0MsFgvPr169ijFjxsDW1hZSqRRDhgzBiRMnAADx8fHo1auXUNbLywtdunTB/fv3hbS4uDg4OTmpjDcqKgpDhw6FtbU17O3tMXToUOzcuRN3795VGadYLIa1tbXwXC6XAwDOnj2LUaNGQSKRwMbGBv7+/rh27ZrQfnx8PMRiMebNm6fS74gRIxAWFqbxNQwLC0OHDh2EvpydnbFr1y61cpmZmZBIJPDz8yvxfQgLC8OIESM09lX0Or18rP7+/nj06BG6deuG+Ph4lfIzZszAF198obEtIiIiIiIiKrt3eg1/eHg4Fi9ejPXr18PMzAwAkJCQAB0dzYdlbGyMlStXYtOmTRrz/f39MWLECKxbtw4AcOHCBSiVymL7NzAwwNq1a/Hdd99pzN+0aRNCQ0Mxe/Zs9OjRA3Xq1MFff/2FjRs3YujQoVAoFEJZsViMiIgING/eXEhTKBTw9fXF5MmTsXbtWuTn52Pz5s1CMN+0aVNhHDKZDL6+vmW+F721tTV2794NAPjzzz/x6aefwtraGh07dhTKHDp0CHp6eoiNjcXDhw+F17i8Zs+ejaFDh6qlz5gxA99++y1+++031KpVC6dPn8bx48cRFRVVoX6IiIiIiIjo/7xzV/iL/PLLL1i8eDFCQ0PRtWvXMtXx8PDA33//jTNnzqjlPXnyBMnJyRg2bBj09PSgp6cHGxsbSKXSYtvz8vJCZGQkbt++rZb3/PlzBAcHY86cOejfvz8MDQ0hEonQsWNHLF++HHp6eqWOd+nSpXB3d4e3tzcMDQ1hbGyMKVOmwMrKCiEhIUI5IyMjDBkyBGvWrCnT6/CqTp06oXXr1rh+/bpKenh4ODw9PSEWi7Fv374KtV0SDw8PtGzZEqtWrUJ2djZmz56NWbNmoX79+lXeFxERERER0X/NOxnw7969G6tWrcLWrVvRpUuXMterVasWxo0bh6CgILU8ExMTNG/eHNOmTcPRo0fx6NGjUtszNzfHsGHDVILvIgqFArm5uXB2di7z+F6WlZUFhUKB/v37q+W5uLioLGsAXsxOOHToEG7cuFHuvpKSknDr1i107txZSLt37x7OnDkDNzc3uLm5QSaTlbvdspg3bx7CwsIwZcoUtG3bFgMHDnwt/RAREREREf3XvJMBf2xsLKysrNCuXTu1vO7du0MqlQqPV69ae3p64v79+8La/CIikQjbtm1DkyZNsHjxYvTo0QMjR47ErVu3ShzLuHHjcOzYMVy9elUlPS0tDSYmJirLCzw9PSGVSmFpaYmEhIQS201PT0dhYSFMTU3V8kxNTZGWlqaW5unpieDg4BLbLXL+/HlIpVJIJBIMHToU7u7uaNGihZAvk8kgFovRpk0bDBw4ENeuXcOlS5fK1ParFixYoPKerFy5Ushr2LAhAgMDcfr0acydO7dC7RMREREREZG6dzLgnzdvHm7duoWZM2eqrbH/448/IJfLhUfr1q1V8vX09DB+/HisWrVKrW7Dhg0xe/ZsHD16FMeOHUPt2rXx9ddflziW+vXr49NPP1ULtI2NjZGWlob8/Hwh7eeff4ZcLoexsTEKCwtLbLdu3brQ0tJCamqqWl5qaipMTEzU0v38/HDq1ClcvnxZJX3gwIFqmwFaWVlBLpdDoVAgNjYWV69exYoVK4Q6ERERcHNzA/BiJoOtrS3Cw8NLHHNxZs2apfKeTJ48WSW/TZs2qFu3boX3CCAiIiIiIiJ172TA/95772HLli1ITEys0FXhIUOGICMjA0eOHCm2TKNGjTBy5EhcuXKl1PZ8fX0RHx+PixcvCmkSiQR6enqIjo4u9/iAFxvxWVtb4+DBg2p5Bw4cQPfu3dXSTUxM4O3trXIFHXhxpwCFQgGFQqFxT4IGDRqgX79+OHbsGIAXdwa4desWNmzYAEdHRzg6OiIpKQlRUVEqJzCIiIiIiIjo7fVOBvzAi6vOW7duxcmTJ7Fo0aJy1dXR0cGECRMQGhoqpKWnpyM4OBi3b99GYWEhnjx5gl9//RXW1taltle3bl34+Phg48aNKmkBAQGYN28eDh48iH///ReFhYX466+/kJWVVaZxfvnll5DJZNi2bRsyMjKQnp6OoKAgnDt3DhMmTNBYx8fHBwqFolxr+dPS0nDkyBG0adMGwIvp/I6OjoiKioJMJoNMJsO+ffuQlZWFmJgYjW0olUrk5OSoPIiIiIiIiKj6vNO35WvUqBG2bt2KTz/9VNhkz9bWVqVMYGAgfHx81Oq6urpiw4YNePr0KQBAV1cXd+/ehY+PD9LS0mBgYIBu3brh22+/LdNYRo0ahW3btqmk+fn5wdzcHKGhofj6669Ru3ZtNG3aFFOnToVEIim1TalUitDQUKxatQpBQUEQiUSQSqXYtWuXynr7lxkaGmLs2LFYtmxZiW2fO3dOGEOtWrVgb2+PmTNnIicnBwcOHMCSJUvU9g9wd3eHTCaDk5OTWnsKhQKWlpYqaX/++ScAYP78+SonZVq2bImwsLBSj5+IiIiIiIgqTqQs6UbzRG+55ORkODs7Y+u4XDSsV92jISIi+m9o4XuzuodARET/X1FMFB0dDQsLC5W8d/oKP1ERi2En1T7cRERE9HoU5udAS0e/uodBRESleGfX8BMRERFR9WCwT0T0bmDAT0RERERERFQDMeAnIiIiIiIiqoEY8BMRERFRiQrzebtdIqJ3ETftoxoheU9P5HOXfiIioteCu/ITEb2beIWfiIiIiIiIqAZiwF9JTk5OiIuLU0kLCwvDiBEjVJ67ubnBysoKjo6OmDNnDp49eybkh4SEoFOnTpBIJJBKpfD09IRCodDYVkZGBjw9PTFx4kTk5eVh+vTpCAoKAvDi/otisRifffaZynimTp2KkJAQ4fm6devg5OQEiUSCXr16YfLkyWrHNXv2bEgkEkgkEnTu3FkYn0QiwdixY4W+8vPzAQDTp09H586dhTISiQT79+8XXiNLS0uVvHXr1gn/36VLF3To0EF4PnDgwIq8FURERERERPQSTul/zTZt2oTQ0FAsXrwY9vb2SElJwbx58+Dj44Pdu3dDT08PAODi4oJly5YhLy8PQUFBmDhxIk6ePKnSVnp6Onx9fdG8eXMsWbIEOjqa377z588jMTERNjY2annh4eGIiIjAli1b0KxZM6SmpuL3339XKzd//nzMnz8fwIsTErdv38ayZcuE/OTkZLU6vr6+mDJlisYxrVu3Dg4ODipp/v7+AF6c1Ni7dy92796tsS4RERERERGVH6/wv0YZGRkICQnBrFmz0KtXL+jq6sLCwgIrV67EvXv38Ntvv6nV0dXVxeDBg5Gamoq0tDQh/cmTJ/D29kbbtm2xdOnSYoN94EXgvXLlSo15Fy5cQI8ePdCsWTMAgKmpKYYPH165AyUiIiIiIqK3DgP+1+js2bPIyclB3759VdLr1KmDXr16qS0FAIDc3FyEhYWhYcOGqF+/PoAXV/a9vLxgaWmJRYsWQUur5Ldt5MiRuHXrlsb2raysEBERgdDQUFy4cAEFBQWVOEIiIiIiIiJ6W3FKfxUICAiAtra28DwvLw8dO3ZEWloaTExMNF6NNzU1xZ9//ik8P3jwII4fPw5dXV20bdsWa9asEfLu37+P3NxcLFq0CCKRqNTx6Ovrw9/fHytXrlSbRu/u7g6RSISwsDCsXr0a+vr68PX1VVv3XxGbNm3Czp07AQDa2tqIj48X8l5+jezs7LB27dpK90dERERERETFY8BfBdasWaMSWBetSTcxMUFaWhry8/PVgv7U1FSYmJgIz/v376+yRv5l7du3R//+/eHn54ctW7agY8eOpY5p2LBh2Lhxo8b1+YMGDcKgQYOQl5eHo0ePYtq0aejQoQNq164NPz8/AEDjxo0RFRVVpuMvMmbMmGLX8L/6GhEREREREdHrxSn9r5FEIoGenh4OHz6skp6ZmYmYmBjY29uXuS1vb2/4+fnBx8cHV65cKbW8rq4uJkyYgFWrVkGpVBZbxsXFBe3atcPVq1chlUqhUCigUCjKHewTERERERHR24UB/2tkZGSEgIAALFiwADExMcjLy0NycjImTZqEhg0bwt3dvVzt+fn5YdSoUfDx8cGNGzdKLe/u7o7c3FycOnVKSAsLC8Px48eRkZGBwsJCnDhxAteuXYOlpWW5j4+IiIiIiIjeXpzS/5r5+fnB2NgYP/zwA/755x8YGhqiT58+WLZsmXBLvvIICAhAbm4uRo8ejR07dpRYVltbGxMnTlSZZm9oaIh169bh+vXrKCgoQJMmTTB37lxIpdJyj4WIiIiIiIjeXiJlcfO9id4BycnJcHZ2xtZxuWhYr7pHQ0REVDO18L1Z3UMgIqJiFMVE0dHRsLCwUMnjlH4iIiIiIiKiGohT+qlGsBh2Uu1sFhEREVWNwvwcaOnoV/cwiIionHiFn4iIiIhKxGCfiOjdxICfiIiIiIiIqAYq05T+Dh06VElnIpEIly5dqpK2iIiIiF7FqedERET/p0wBf1Vt5M8bAtDrkrynJ/K5Sz8R0X8ed5MnIiL6P2UK+KOjozWmnzt3DnPnzoWuri5GjBgBW1tbmJubQ6lUIjU1FQkJCfj555+Rm5uLuXPnwsrKqkoHT0RERERERESalSngb9KkiVra9evX8e2336J169YIDQ1FvXqql1dbtWqFbt26wcvLC2PHjsWsWbPwv//9r2pGTUREREREREQlqvCmfWvWrEFWVhYWLlyoFuy/rF69eliwYAEyMzOxZs2ainZXaXK5HJ6enrCxsYGdnR08PT2RlJQEAAgLC0OHDh0gkUhUHikpKQAAJycnODg4IDMzU2hv79698PLyEp6LxWJYW1ur1P/pp5+E/Js3byIwMBDdunWDjY0N3NzcsHnzZhQUFAhlMjMzIZFI4OfnV+rxvNxft27d8MUXX+DZs2dq5aZPn46OHTsKx1IkJCQEnTp1Esbq4uKCQ4cOqZQpbcy5ublYvnw5evfuDUtLS/Tt2xehoaFqSzdOnjyJkSNHQiKRoHv37vj000+FWSNhYWEYMWKEUDYjIwOenp6YOHEi8vLySn0diIiIiIiISLMKB/xnzpyBoaEh2rVrV2pZsVgMIyMjxMfHV7S7SsnIyIC/vz8+/fRTnDlzBjExMZgwYQL09PSEMtbW1lAoFCoPc3NzIb+goADbtm0rsZ+IiAiV+kWB+z///INhw4ahUaNG2LdvHxITE7Fq1SpcvHgR//77r1D/0KFD0NPTQ2xsLB4+fFjqcRX1Fx0djfT0dISEhKjkZ2Zm4tChQzAyMsK+ffvU6ru4uAhj/eabbzBt2jQ8evSozGMODAzE6dOnsWHDBpw9exY//PAD9uzZg4ULFwp9HDx4EJMmTYKHhwdiYmIQFxeHwMBAHDt2TG086enpGD16NJo0aYKgoCDo6uqW+hoQERERERGRZhUO+J89e4acnByVK9TFyc/PR05ODp4/f17R7irl5s0XG/i4urpCW1sbtWrVQo8ePdC+ffsyt+Hr64tNmzZpvIpemuDgYEgkEsyYMQNmZmYAXix5WL58OerWrSuUCw8Ph6enJ8RiscYAvTiGhoZwcnLC9evXVdIPHz6MunXrYvz48ZDJZCW20bNnT9SpUwf//PNPmcZ8+vRpxMbGIiQkBO3atYOOjg6sra2xdOlS7Ny5E7dv34ZSqcTixYsxfvx4DB06FEZGRtDS0oKdnR0WLFig0v+TJ0/g7e2Ntm3bYunSpdDRKdNqEyIiIiIiIipGhQP+Jk2aIC8vr0yBaWRkJHJzc9G4ceOKdlcpLVu2hLa2Nr7++mucOHEC6enp5W6jc+fOsLOzw8aNG8td9/Tp0+jXr1+JZe7du4czZ87Azc0Nbm5upQboL0tPT0d0dLTapojh4eEYOHAgBg4ciBs3buDPP//UWF+pVOL48ePIy8tDmzZtyjTm2NhYWFlZoVGjRirpVlZWaNiwIU6fPo0bN27g/v37pR57eno6vLy8YGlpiUWLFkFLq8IfSyIiIiIiIvr/KhxZubq6QqlUYt68eSUGp7/99hvmzZsHkUiEQYMGVbS7SjE0NMSuXbsgEonw7bffwt7eHv7+/sL0dQA4f/48pFKp8OjTp49aO4GBgdixYweePHmisZ/BgwertHHy5EkAwNOnT2FqalriGGUyGcRiMdq0aYOBAwfi2rVruHTpUol1ivrr3r077t27B09PTyHv3r17iI+Ph5ubGxo0aAB7e3uEh4er1D948CCkUimsra3x+eefY9y4ccKMg9LGnJaWVmy+qakp0tLS8PTpUwAQZggU5/79+7h16xY++ugjiESiEssSERERERFR2VR43rSfnx9OnDiBpKQkzJgxA0FBQZBKpUJw9/DhQyQmJiIlJQVKpRLW1tbw9fWtsoGXV+vWrbF48WIAL+4wMG3aNCxatAgrVqwA8OLK9O7du0tso127dujduzc2bNiA1q1bq+WHh4ejefPmaunGxsZITU0tse2IiAgMHToUAGBubg5bW1uEh4ejY8eOxdYp6i8vLw+7du3CJ598gv3790NfXx8RERFo3bo1OnToAABwc3PD4sWL8fXXXwtr4/v3749ly5YBAJKTk+Hv7w8jIyN4enqWOmYTExPcvn1bY15qaipMTExgbGwM4MVnoWnTpsW21b59e/Tv3x9+fn7YsmVLicdMREREREREZVPhK/x6enrYsmULPv74Y4hEIqSkpCAqKgpbtmzBli1bEBUVhQcPHkAkEmHo0KHYtGmTyiZ51al169YYMmQIrl69Wu66gYGB2LNnj9qu9yWxt7fH4cOHi80/e/Ysbt26hQ0bNsDR0RGOjo5ISkpCVFQU8vPzS21fV1cXQ4cORXJyMq5cuQLgxYyBO3fuCO19//33SEtLQ0xMjMY2LCws0LNnT2EzvdLG7ODggPPnz+P+/fsq6UlJSbh//z66d++OVq1aoVGjRiW2U8Tb2xt+fn7w8fERjoGIiIiIiIgqrlKLpQ0MDLBgwQIcPXoU06dPx6BBg4QAc9CgQZg+fTqOHj2K7777DgYGBlU15nK7fv06Nm3ahAcPHgB4MYU8MjJSbc17WTRv3hwDBgzA9u3by1wnMDAQCoUCS5YsEa6a3759G1OnTsWzZ88gk8ng6OiIqKgoyGQyyGQy7Nu3D1lZWcUG6C8rKChAWFgYatWqhaZNm0KhUODOnTvYu3ev0F5kZCRcXV2LXX7x4MEDnDp1SljDX9qYHRwcYG9vj4kTJ+Lq1asoKCjAuXPnMHXqVIwYMQItWrSASCTC9OnTsXbtWvz666/IyMhAYWEh5HI5vv32W7Ux+Pn5YdSoUfDx8cGNGzfK/PoSERERERGRuirZCr1x48YYPXp0VTT1WhgaGuL8+fPYvHkznj9/DiMjI3zwwQf46quvhDLnzp2DRCJRqbd161ZYWlqqtRcQEICIiAi1dHd3d5U16B9//DFmzpyJZs2a4eeff8bKlSvh6uqK/Px8NGnSBEOGDIGOjg4OHDiAJUuWqK2Jd3d3h0wmg5OTk8bjKupPJBKhZcuWWL16NYyNjREeHg5nZ2eIxWKV8t7e3vjkk0+EtfUHDhxAdHQ0AKBOnTpwcnLChAkTAKDEMdepUwcAEBISguDgYIwdOxZpaWkwNzfH0KFDMXbsWKHP/v37w8DAAOvWrcOCBQugr6+Ptm3bFru8IyAgALm5uRg9ejR27NiBZs2aaSxHREREREREJRMplUpldQ+CqKKSk5Ph7OyMreNy0bBedY+GiIiqWwvfm9U9BCIiojeqKCaKjo6GhYWFSl6V3ew8KSkJf/75p7CDff369dGpUyeNV8iJqprFsJNqH24iIvrvKczPgZaOfnUPg4iI6K1Q6YB/3759WLlyJe7du6cx38LCApMnT8bAgQMr2xURERFRiRjsExER/Z9KBfxBQUHYsGEDilYFmJubo2HDhgBebAKXkpKCO3fuYOrUqbhy5QqmTJlS+RETERERERERUakqHPD/8ccfWL9+PQBg4MCBmDBhAlq2bKlS5tatWwgJCUFUVBQ2bNgABwcHdOvWrXIjJiIiIiIiIqJSVfi2fDt27IBIJIKXlxeWL1+uFuwDQIsWLbB8+XJ8+umnUCqV5bqVHREREf03FObnVPcQiIiIaqQKX+E/d+4cRCKRcBu3kkyYMAG7du2CQqGoaHdEJUre0xP53KWfiOidxJ31iYiIXo8KX+F/+vQpjIyMUK9e6VGWsbExjIyM8OzZs4p2R0RERERERETlUOGA39jYGM+fP8fTp09LLfv06VM8f/68TCcHqPKcnJwQFxenln727FmMGjUKEokENjY28Pf3x7Vr1wAAkZGRcHJyEjZgLJKfnw97e3scO3YM8fHxaN++PSQSicqjaObG9OnTERQUJNTNzc3F8uXL0bt3b1haWqJv374IDQ2FUqnE1atXYWNjg5s3Va/qeHt7Y/ny5VX9khAREREREf3nVDjgt7a2hlKpxJo1a0otu3r1ahQWFsLa2rqi3VElKRQK+Pr6wtnZGSdPnkR0dDTEYjFGjBiBO3fu4MMPP8SzZ89w5swZlXonT56ESCRCz549AQBmZmZQKBQqD4lEorHPwMBAnD59Ghs2bMDZs2fxww8/YM+ePVi4cCHatm2LMWPGYObMmcJJhr179yIlJaVMy0SIiIiIiIioZBUO+Is24tuxYwemTp2K69evq5W5cOECJkyYgJ07dwob/FH1WLp0Kdzd3eHt7Q1DQ0MYGxtjypQpsLKyQkhICPT19eHi4gKZTKZSTyaTwc3NDTo65dvu4fTp04iNjUVISAjatWsHHR0dWFtbY+nSpdi5cydu376NcePGITMzE7t27cKjR4+wbNkyLFq0CPr6vIcyERERERFRZVU44O/evTvGjRsHpVKJqKgouLq6wtHREUOGDIGrqyu6du2KYcOGITo6GkqlEv7+/rwlXzXJysqCQqFA//791fJcXFyE6f8eHh44dOgQsrOzAQDPnz/HsWPH4OHhUe4+Y2NjYWVlhUaNGqmkW1lZoWHDhjh9+jR0dHSwaNEirFq1CtOmTcOgQYPQtWvX8h8gERERERERqanwLv0AMGXKFLRr1w6rVq3CP//8g8ePH+Px48cqZZo3b45JkyZhwIABlRooVVx6ejoKCwthamqqlmdqaoq0tDQAgI2NDRo0aIAjR47Azc0NBw4cQIsWLdChQweh/MOHDyGVSlXaiImJgYGBgUpaWlqaxv5e7bNjx4746KOPEBUVhbVr11bqOImIiIiIiOj/VCrgB4CBAwdi4MCB+Ouvv/Dnn3/iyZMnAID69eujU6dOKsEiVY+6detCS0sLqampaN26tUpeamoqTExMhOfu7u7CNP6IiAgMHjxYpbyZmRliYmJK7dPExAS3b9/WmPdqn23btkWTJk1Qu3bt8hwWERERERERlaDSAX+RDh06MLh/SxkYGMDa2hoHDx5E9+7dVfIOHDigkubh4YG1a9dCoVDg/PnzWLlyZYX6dHBwwNatW3H//n2Vaf1JSUm4f/++2jiIiIiIiIioalV4DT+93fLy8pCTkyM8vvzyS8hkMmzbtg0ZGRlIT09HUFAQzp07p7IrfpMmTdC1a1d8+eWXcHBwKHZafmkcHBxgb2+PiRMn4urVqygoKMC5c+cwdepUjBgxAi1atKiiIyUiIiIiIiJNquQK/507d3Dw4EFcunRJZUp/x44d0b9/fzRt2rQquqFy+Oyzz1Se+/v7IzQ0FKtWrUJQUBBEIhGkUil27dqlFnwPHjwYM2bMwNSpU9Xaffjwodpt+BYvXox+/foBAEQikZAeEhKC4OBgjB07FmlpaTA3N8fQoUMxduzYKjpKIiIiIiIiKo5IWXQT9ArIzs7GwoUL8euvv0KpVOLVpkQiEUQiET7++GN88803qFWrVqUHTG+vCRMmQCqVYvTo0W+sz+TkZDg7O2PruFw0rPfGuiUioirUwvdmdQ+BiIjonVUUE0VHR8PCwkIlr8JX+AsLCzF+/HicPn0aSqUS5ubmsLOzQ8OGDQEADx48wJkzZ5CSkoK9e/fi7t27CA0NVbkCTDVHSkoKEhMT32iwT0RERERERMWrcMD/66+/Ii4uDvr6+pg5cyaGDh2qFswrlUrs2bMHCxcuRFxcHH799Vd8/PHHlR40vV127tyJ1atXY8iQIWq37HtTLIadVDubRURE74bC/Bxo6ehX9zCIiIhqnApv2hcREQGRSIRZs2Zh2LBhGq/ci0QiDB8+HLNmzYJSqYRMJqvMWOktNXLkSJw+fRrTpk2r7qEQEdE7iME+ERHR61HhgP/KlSvQ0dGBh4dHqWU9PDygo6ODK1euVLQ7IiIiIiIiIiqHCgf82dnZqF27NnR1dUstq6enBwMDA2RnZ1e0OyIiIqoBCvNzqnsIRERE/xkVXsNvZmaGu3fv4vbt22jevHmJZW/evIlnz55xjTW9Nsl7eiKfu/QTEb31uCM/ERHRm1PhK/wODg5QKpWYPXs2cnKKP1ufk5ODOXPmQCQSwcHBoaLdEREREREREVE5VDjg9/Pzg76+Ps6cOYNBgwZh9+7duH79OjIyMvDkyRNcvHgRGzduRN++fZGQkAB9fX34+flV5diJiIiIiIiIqBgVntLftGlTrFy5El988QVu376N+fPnayynVCpRu3ZtrFixAk2bNq3wQKuak5MTHj16BG1tbWhra6NNmzZwd3fH8OHDoaX14jzI9OnTERkZqbJPQdOmTfHbb78hOTkZzv+PvTuPiqr8/wD+HgZHQATBABdEXAIXBEYGF3ApcENFUNM0RVRcSMylXCiX1HAryxQxInMrMzVlcCWVyj0EHfc1UxMXQEEQkWWY+f3hj/v1NiAwYgi+X+dwvt5nu587z3zP6TP3uc/19sb58+dhaFj0x3jr1i107doVgwYNwpw5cwAAs2fPRk5ODj7//HNR20uXLuGdd97B4cOHUatWrSL7FnJ0dISDgwNiYmKEWJcuXYrk5GQsWrSoyFiysrIQHh6Offv2IS0tDbVq1YKzszNGjRoFZ2dnYVxjY2NIJBKYmpqiZ8+emDZtGqRSKQAgICAAffr0wYABAxAfH4/AwEAYGxsDAGrWrAm5XI6goCBhvH+PWWjcuHEYPXo0wsPDERkZCZlMJszB9OnTIZfLnzt3REREREREVDK97/ADwNtvv43t27ejX79+MDU1hVarFf3VrFkT/fv3x/bt2/H222+XV8zlJjIyEiqVCr///jtGjx6N7777DjNmzBC1CQoKgkqlEv62b99e6vFjYmJgbm6O3bt3Iy8vDwDQt29f7Nu3D9nZ2Tpt3377bdSqVavYvs9KSUnBrl27ShVHXl4eAgMDceXKFURGRuLEiRPYvXs3evbsiQMHDujEoVKp8OOPP2L37t3YunVrseNaW1tDpVLh5MmT2Lx5Mxo3biy8oq+oMQv/nl3p4ePjA5VKhT///BNt27bFxIkTS3VNRERERERE9HwvlPADT+94L1iwAAkJCdi3bx82bdqETZs2Yd++fTh+/Djmz5//St3ZL0rNmjXh7e2Nr7/+GtHR0eX2+kClUomJEyfC0NAQv/32GwBALpfD2toae/fuFdoVFBRgx44dolccFtX3WUFBQQgPD4darS4xjpiYGCQnJyMiIgIODg6QSqUwMTFBjx498MEHHxTZp2HDhmjdujUuXrxY4vgSiQR16tTBxIkTMWDAAHzxxRcl9vk3Q0ND+Pr6Ijk5GWlpaWXuT0RERERERGIvnPA/q0GDBnBxcYGLi8srn+QXxdnZGXXq1EFiYuILj5WYmIh79+6hV69e8PHxgVKpFOr8/f1Fx0ePHoVarUanTp1K7FuoW7duMDU1RXR0dImxHD16FB06dICJiUmp47927RpOnDhR4hsY/q1r1664cOGCzgqGkuTl5UGpVKJWrVowMzMrU18iIiIiIiLSVa4Jf1VgbW2NjIwM4Xj16tVQKBTC3/Tp00s1TnR0NDp16gRzc3P07t0bhw4dwoMHDwAAfn5+SEhIwL179wA8vZvfu3dvYa+A5/UtJJFIMHHiRERERBS55P9Z6enpeOONN4TjixcvQqFQoHXr1ujevbuobd++feHq6oqePXuiTZs2eO+990p1vYWsra2h1Wrx6NEj0ZjPfoaHDh0S6mJjY6FQKODi4oItW7Zg+fLlxe6JQERERERERKVXLpnV3bt3cfXqVWRmZpa4xPzZZeuvouTkZJib/++F7iNHjsTkyZPLNEZOTg5iY2MRFhYG4Oky/rp162LHjh0YPnw46tWrB4VCge3bt2PIkCGIi4vDhg0bStX3WZ07d0bdunWxadOm58ZTq1YtpKamCsfNmzdHYmIijh49ipkzZ4raRkdHw87ODnv27MGXX36J7OxsyGSyUl97SkoKJBIJatasKRqzuJUCPXr0wJIlS5CWloYJEybg/PnzaNu2banPR0REREREREV7oYT/9OnTmD9/Ps6ePVvqPq9ywn/mzBkkJyfDzc3thcbZt28fsrKyMHfuXCFxz8zMRExMjJC09+3bF1FRUbCysoKtrS1atmxZ6r7PmjRpEj788EP07t272Hjat2+P8PBwZGdnl2pZv0QiQc+ePREXF4eIiAidjQxLuvYWLVqU6fEBALC0tMTcuXPxzjvvoHfv3rC2ti5TfyIiIiIiIhLTO+E/d+4cAgMDkZubC61Wizp16sDGxqZMd4NfFVlZWUhISMD8+fPRp08fODo6lrpvXl4eCgoKhONq1apBqVSif//+opUBycnJeOedd3D58mU4OjqiW7dumDt3LsLDw0XL5kvT91lt27aFg4MDlEplsW9C8Pf3x88//4zx48cjNDQUTZo0gVqtxrlz5557bWPGjMHAgQMxZswYWFlZFdtOq9UiJSUFW7ZswZYtW/DNN988d9ziNGnSBB07dsSqVavwySef6DUGERERERERPaV3wr9ixQrk5OTAwcEBCxcuFO5QVybBwcGQSqUwMDBA06ZNMWLECAwaNEjU5vvvv8f69euFY5lMhvj4eOH43++MX7RoEY4dO4bo6GhRkmxlZYWOHTtCqVRi+vTpMDExQffu3RETEwNfX18ATxP70vT9t0mTJmHgwIHFXmf16tWxfv16LF++HGPHjkV6ejosLCzg5OSEr7/+uth+jo6OcHd3x/fff4/Q0FCd+pSUFMjlcmi1WpiamqJ169b44Ycf4OrqKmrn5+cHiUQiHL/zzjvFrhoICgpCYGAgxo4di9q1axcbGxERERERET2fRKvVavXp2LZtW2GpuYODQ3nHRVQqSUlJ8Pb2xrqxeahjXnJ7IiKqWPZB1ys6BCIioiqlMCeKi4uDra2tqE7vO/y5ubkwMTFhsk+vBNuBh3S+3ERE9OrRqHNhYFi9osMgIiJ6Lej9Wj47Ozvk5eWVuCs/ERERUSEm+0RERP8dvRP+fv36IT8/H3FxceUZDxERERERERGVA70T/mHDhqFDhw749NNPoVKpyjMmIiIiIiIiInpBpXqGf8WKFUWWt2rVCmfOnMF7770HhUIBJycn1KhR47ljjR8/vuxREhERUaXB5/SJiIheDaVO+J99rdqzCjf5T0hIQGJiYrFjaLVaSCQSJvz0UiRt7gg1d+knInolcCd+IiKiV0OpEn5/f/9iE34iIiIiIiIievWUKuFftGjRy47jtePl5YX79+9DKpUKZbGxscjPz4e3tzfOnz8PQ0NDhIaGYufOnahWrRqqVauGli1bYubMmWjSpAkAIC8vD1999RV2796NR48ewcLCAl26dMEnn3wCuVwujP3kyRPIZDLhfHPnzkWfPn2EepVKhaCgICQkJAhtZs6cid27d+uUGRgYYN68eQCA+Ph4DBs2DFOmTMHo0aOF8QrfBWliYgIAsLCwwKBBgzBmzJgiPwOpVIqmTZvCz88P7777LgwM9N5egoiIiIiIiFDKhL8oCxcuhEQiwbBhw1CvXr3yjOm1ERkZCQ8PD1FZUlKSTrugoCBMnjwZOTk5mDNnDmbMmIGff/4ZABAVFYVz585hy5YtsLa2xu3bt4VHK57dTNHLywthYWE65yvk5OQEjUaD8+fPw9nZGQCQmJgIa2trUVlCQoLosQylUolatWpBqVSKEv5CCQkJMDQ0xNmzZxEQEICWLVvC09NT5zN49OgRjh8/jvnz5+PMmTNYuHBhqT5DIiIiIiIiKpret1F/+OEHbNiwAXXq1CnPeOg5jIyM4OPjg0uXLgllZ8+eRZcuXWBjYwOJRAJbW1v4+/uXeexq1arBxcVF+LHgwYMHyM/PR8+ePUVlN27cgLu7O4CnqwZiY2Mxe/Zs3Lx5E2fPni12/FatWqFp06a4ePFikfU1a9aEt7c3vv76a0RHR+PKlStlvgYiIiIiIiL6H70TfktLSxgbG3Pp9X8oOzsbO3fuhJ2dnVDm4uKCtWvXYsOGDbh8+bKwiaI+3N3dkZCQAODpnXk3Nze4ubmJymxtbYUfeX799VfUqFEDPXr0QIcOHRATE1Ps2KdOncLVq1fRsGHD58bg7OyMOnXqPHcDSCIiIiIiIiqZ3tl669at8ejRI9y9e7c843mthISEQKFQQKFQYNy4ccW2W716NRQKBVq3bo0TJ07g888/F+rGjh2L0aNHY8eOHejfvz86duyI6OhoveJxd3fHyZMnodVqkZiYCIVCAVdXV5w+fVooa9OmjdBeqVTCx8cHUqkUvXv3xs6dO5Gfny8as127dnB2dsa7776L9957D126dCkxDmtra2RkZOh1DURERERERPSU3gn/yJEjIZVKsXjx4vKM57USERGBxMREJCYmYuXKlcW2GzlyJBITE/Hbb7/ByMgI16//73VHUqkUQ4YMwc8//4zExES8//77+OSTT3Dt2rUyx+Pq6orHjx/jypUrSExMhJubG2rUqIE6deoIZQqFAgBw9+5dxMfHw9fXFwDg7e2N3NxcHDhwQDTmn3/+CZVKhenTpyM+Pl7nB4GiJCcnw9yc79gjIiIiIiJ6EXon/K6urvjiiy9w8OBBDB06FPv378eDBw9eaEk5PV+9evUwY8YMzJ8/Hzk5OTr1RkZGGDJkCMzMzPDXX3+Vefzq1aujVatW+OOPP5Camiq8CUChUOCPP/7A5cuXhef3Y2JioNFo8P7778PT0xNdunRBXl4elEqlzrhSqRQjR45E9erV8dNPPz03hjNnziA5ORlubm5ljp+IiIiIiIj+R+9d+ps3by78+8SJEzhx4kSJfSQSCS5cuKDvKQmAp6cnrK2tsWnTJgQGBmLt2rVo3rw5XFxcYGhoiB07duDx48do0aKFXuO7u7tj3bp1aN26tVDm5uaGuXPn4o033hD2D1AqlRg/fjwGDRoktDtz5gwmTpyI9PT0IsceM2YMZs2ahcGDB6N69eqiuqysLCQkJGD+/Pno06cPHB0d9YqfiIiIiIiIntI74dfnTj7v/pePUaNGYeHChRg8eDCMjIywePFi3Lx5ExKJBPb29ggPD0eDBg30Gtvd3R3ffvut6A67m5sbHjx4gF69egF4ugHf7du3MWTIEFhaWgrtvL290bBhQ+zatQtvvfWWzthvvfUWzM3NsXnzZgQEBAAAgoODIZVKYWBggKZNm2LEiBGiHxGIiIiIiIhIPxKtnln47du39Tph/fr19epHVJSkpCR4e3tj3dg81OFj/0RErwT7oOslNyIiIqJyUZgTxcXFwdbWVlSn9x1+Ju5EREREREREry69E36iV4ntwEM6v2YREVHF0KhzYWBYveSGRERE9FKVW8L/4MEDnD9/HmlpaQAAS0tLtGzZErVr1y6vUxAREVElwGSfiIjo1fDCCX9iYiKWLVuGxMTEIusVCgUmTZrE16wRERERERER/YcMXqTzxo0bERgYiMTERGi1WkilUtSuXRu1a9eGVCqFVqtFQkIChg0bhp9//rm8YiYiIqJXmEadW9EhEBEREV7gDv+FCxfw2WefQaPRwM3NDePGjYO7uztkMhkAIC8vD8ePH8fKlStx8uRJfPbZZ3B2dtb7/fBEz5O0uSPU3KWfiOiVwF36iYiIXg163+FfvXo1NBoNfHx88MMPP8DT01NI9gFAJpOhQ4cO+PHHH9G9e3cUFBRgzZo15RI0ERERERERET2f3gl/QkICJBIJPv74YxgYFD+MgYEBPvnkE0gkEhw/flzf0xERERERERFRGeid8KelpcHMzAzW1tYltrWxsYGZmZmwg//rwsvLC0ePHhWVbdu2DYMHD9ZpGxAQAHd3d+Tl5YnKQ0ND4ejoiLi4OFH5/Pnz4ejoiG3btj133MKxt2zZAgCIj4+Ho6Mj5s6dK2ozePBgYax/Cw8PR8uWLSGXy6FQKDBo0CCoVCqhPjs7G3K5HKNHjy7yM3B2doZcLoenpydCQ0Px+PFj0fUtXbpUOL569So6dOiA1atXFxkLERERERERlY7eCb+pqSkeP36M7OzsEttmZ2cjKysLpqam+p6uSktKSkJiYiIkEolOYg8A9vb2UCqVwrFarUZsbCzs7Oz0Op+JiQmUSiWSkpJK3cfHxwcqlQrHjh1D69at8cEHH0Cr1QIAfv31V8hkMhw5cgQpKSk6fSMjI6FSqaBUKnHhwgVERUUVeY6LFy9i2LBhCA4OxsiRI/W6NiIiIiIiInpK74S/RYsWKCgowA8//FBi2/Xr16OgoAAtW7bU93RVmlKphIuLC/r27StK7At5eXnh5MmTyMjIAAAcOnQIjo6OeOONN/Q6X82aNdGvXz9ERESUuW+1atXQt29fpKamIj09HQAQHR2NQYMGwdHRETt27Ci2r5WVFTp06ICLFy/q1J05cwbDhw/Hhx9+iKFDh5Y5LiIiIiIiIhLTO+F/9913odVqsWzZMixduhSPHj3SaZOSkoKFCxdi+fLlkEgkePfdd18o2KoqJiYGvr6+8PX1xeHDh3H//n1RvUwmg5eXF3bt2gXg6Q8E/v7+L3TO4OBg/Prrr/j777/L1C8vLw/btm1DnTp1YGlpiTt37uD48eNC/EX9YFHo3r17OHTokM7KhLNnz2LUqFH4+OOPMWDAAH0uh4iIiIiIiP5F79fydevWDX5+foiJiUFUVBTWrFmDZs2awcbGBnl5ebhz5w5u3LgBtVoNrVaLvn37omvXruUZe6UQEhICqVQqHOfn54teTZiYmIg7d+7Ax8cHlpaWaNCgAXbu3Inhw4eLxvHz88MXX3wBX19fJCQkYPHixdiwYYPecVlZWWHQoEFYvnw5vv766xLbx8bG4o8//kC1atXw5ptvCqsDlEolHB0d0bRpU9SsWRNffPEFLly4ILrGkJAQAE8f7WjXrh0mTJggGvvUqVOoVasWOnXqpPf1EBERERERkZjeCT8ALFq0CE2aNEFUVBSysrJw5swZnTampqYYO3YsgoKCXuRUlVZERAQ8PDyE423btgkb6AFPE2ZPT09YWloCAHr37o3o6GidhF+hUCAtLQ0rV67EW2+9BSMjoxeObfTo0ejatSsuXbpUYtsePXpgyZIlOuUxMTHCXXkbGxu4u7sjOjpalPAXfgbHjx/HRx99hPT0dJiZmQn1Q4YMwT///IORI0di3bp1MDc3f+FrIyIiIiIiet29UMIvkUgwZswYBAQE4MiRIzh//rywE7+lpSVatmwJT09PGBsbl0uwVU1OTg727NkDjUYDT09PAE+XzGdmZuLSpUto1qyZqH2fPn0QERGB9evXl8v5LSwsEBgYWKo7/EU5efIkbty4IazwAIDHjx/jr7/+wvTp02FoKP56tWnTBv369cPixYuxcuVKodzAwABLlizBhAkTEBQUhLVr13KDRyIiIiIiohf0Qgl/IWNjY3Tp0gVdunQpj+FeG/v374dUKsWOHTtQrVo1oXzSpElQKpUIDQ0VtQ8ICIBCoYC7u3uR42m1WuTm5orKqlev/twYRowYAW9vb73iL1ydsHjxYqEsJycHffr0wcGDB+Hl5aXTJzAwEF5eXrh48SKaN28ulFerVg3Lli1DSEgIRo8eje+//x4mJiZ6xUVEREREREQvsGkfvbjo6Gj069cP9erVg5WVlfA3ZMgQ7NixA2q1WtS+Vq1aaN++PSQSSZHjqVQqODs7i/7+Pca/mZqaYtSoUXj48GGZYs/NzcWePXswdOhQUewNGjSAn59fsZv3WVpaws/PT3SHv5BMJsOKFStQvXp1BAcHIycnp0wxERERERER0f9ItIUvU38BOTk5OHLkCC5cuCBa0t+iRQt06NChxLvMRPpKSkqCt7c31o3NQx0++k9E9EqwD7pe0SEQERG9Ngpzori4ONja2orqXmhJv0ajwapVq/Ddd98hKyuryDampqYYPXo0Ro0aBQMDLiggIiIiIiIi+i/onfBrtVpMnDgR+/fvh1arRfXq1dG8eXPY2NhAq9UiNTUVFy5cwKNHj7B06VKcPXsW4eHh5Rk7kcB24CGdX7OIiKhiaNS5MDDk6j4iIqKKpnfC/9NPP2Hfvn0wMDBASEgIhg8frrOz+uPHj7F27VqsXLkS+/fvx08//YT33nvvhYMmIiKiVxeTfSIioleD3mvst2zZAolEgilTpmD8+PFFvkatRo0aCAkJwZQpU6DVakXvnyciIiIiIiKil0fvhP/GjRuQSqWlumM/ePBgGBoa4vp1buJDRERUGWjUuSU3IiIiolea3kv6jY2NodFoYGRkVGJbIyMjmJiYQCqV6ns6oudK2twRau7ST0RUbrjTPhERUeWn9x1+Z2dnZGZm4s6dOyW2vX37NjIzM+Hi4qLv6YiIiIiIiIioDPRO+MeMGQOpVIq5c+ciPz+/2HZqtRrz5s2DoaEhxowZo+/piIiIiIiIiKgM9E743dzc8NVXX+HkyZPo378/lEolbt++DbVaDbVajTt37kCpVKJ///5QqVRYunQpWrduXZ6xv9a8vLxw9OhR4XjXrl1wd3fH8ePH4ejoCLlcLvrbvXs3ACA0NBSOjo44c+aM0PfmzZtwdHQUjgMCAtCqVStRf5VKBQBwdHTEzZs3AQDh4eFo2bKlqJ1CoRDGebbts7Zt24bmzZsLfby8vPDxxx9zjwciIiIiIqJyVKpn+Js3b/7c+qtXr+Ljjz9+bpsJEyZAIpHgwoULpY+OSiU6OhqLFi3Ct99+C2trawBAQkICDA2Lnt5atWrh66+/xurVq4sdc/bs2RgwYECJ5/bx8cGSJUvKHLOrqys2btyIgoIC3L59G6tXr0a/fv2wadMmODg4lHk8IiIiIiIiEivVHX6tVlsufxqN5mVfz2tn06ZNWLRoEVatWlXqFRT+/v64fPkyjh8//pKjK5lUKoWdnR3mzJmDNm3aYMWKFRUdEhERERERUZVQqjv8cXFxLzsO0sPGjRtx4sQJrFu3Ds2aNSt1PyMjI4wdOxZLly7Fxo0bX2KEZdO1a1d89dVXFR0GERERERFRlVCqhL9+/fovOw7Sw5EjR9C2bdsil8C3a9dOdLxp0yY0adJEOB40aBBWr16NAwcOwN7eXqd/WFgYFi9eDABo0KABoqOji4whNjYWf/zxh3DcvHlz/PDDD3pcDWBtbY2MjAy9+hIREREREZFYqRJ+ejXNnTsXK1euxIwZM7BgwQJIJBKh7s8//yz2GX4AkMlkGDduHJYtW1bkXfWZM2eW6hn+Hj166PUMf1GSk5Nhbm5eLmMRERERERG97vTepZ8qXu3atbF27VqcOHECc+bMKXP/fv36ISsrC/v27Sv/4PSwf/9+0S7/REREREREpL8XvsN/7do1/Prrr7h69SoyMzORn59fbFuJRIJ169a96CnpGTY2Nli3bh2GDBmCBQsWYNiwYaXua2hoiPHjx2P+/PkvLb78/Hzk5uaKzvmsgoIC3LlzB2vXrsXx48fx888/v7RYiIiIiIiIXicvlPAvXLgQP/zwg7ALf0meXXJO5adu3bpYt24dhg4divv37wMA3N3dRW0mTJiAESNG6PTt3bs3oqKi8PDhQ73OvWfPHp1NHffv34/atWsDAHr16iWqCwsLg1QqxalTpyCXy6HVamFhYYE2bdrgl19+Ee0zQERERERERPqTaEuTqRdhw4YN+OyzzwAADg4O8Pb2ho2NDapXr/7cfn379tXndERFSkpKgre3N9aNzUMdPv5PRFRu7IOuV3QIREREVAqFOVFcXBxsbW1FdXrf4d+8eTMkEgmGDh2KGTNmvHCQRC/CduAhnS83ERHpT6POhYHh83/EJyIioleb3pv23bhxAwAwceLE8oqFiIiIXhFM9omIiCo/ve/wGxsbo3r16jA1NS3PeIiIiIiIiIioHOh9h9/FxQVZWVlIS0srz3iIiIiIiIiIqBzonfCPGTMGEokE33zzTXnGQ0RERP8xjTq35EZERERU6ei9pN/NzQ1hYWH49NNPkZubizFjxnDTNKowSZs7Qs1d+omI9MId+YmIiKqmUiX83t7exdZJpVJs2bIFW7Zsgbm5OWrUqFFsW4lEgv3795c9SiIiIiIiIiIqk1Il/Ldv3y7VYA8fPsTDhw+LrZdIJKUapzLw8vLC/fv3IZVKYWxsjM6dO2PmzJkIDg7GqVOnYGhoCJlMBnd3d8yePRvW1tZCX61Wiy5duqB69erYvXs3AGDUqFE4ceIEACAvLw8SiQTVqlUDAPj6+mLHjh0AgPz8fGi1WshkMgBPV1qsWrUKAKBWq+Hu7o5169bB2dkZALB9+3ZMnToVW7ZsEZWtXLkSsbGxCA8Px82bN7FkyRIAgKOjI/bu3YuGDRuKrnfbtm3YsmULNm7cqPNZBAQECNdcqG3btoiMjER8fDwCAwNhbGwMALC2tsaYMWPQv39/yOVyof2TJ08gk8kglUoBAHPnzkWfPn3KPjFEREREREQEoJQJ//r16192HJVSZGQkPDw8kJycjKCgIGE/g9mzZ2PAgAHIzMzEpEmTsHjxYnz55ZdCv4SEBKSlpUGtVuPMmTNwdnYWknYACA0NhY2NDSZPniyUzZs3DwB0EvRnGRoawtXVFcePHxeS+8TERDRu3FinzN3dvVw/i8JrLoq1tTUOHjwIrVaLgwcP4v3334dcLodKpRLaeHl5ISwsDB4eHuUaFxERERER0euqVAl/mzZtXnYclZqNjQ06duyIq1evisrNzMzg7e2Nn376SVQeHR0NLy8v5ObmQqlUCol4eVAoFEhMTMSoUaMAPE3uR48ejb1794rKxo4dW27nLC2JRILOnTvD3Nwcly9fRuPGjf/zGIiIiIiIiF4Xeu/ST/9z9+5dHDx4EM2bNxeVp6enY9++fbCzsxPKnjx5gl9//RV9+vSBr68vdu3ahby8vHKLxd3dHSdPnoRGo0FaWhqePHkCHx8fnDlzRij7+++/y/0Of2loNBrExcUhPT1d55EBIiIiIiIiKl9679JPQEhICKRSKWrWrInOnTsjODgYo0ePRlhYGBYtWoSsrCw0a9YMCxYsEPrs3bsXMpkMnp6e0Gg0KCgowIEDB9C1a9dyicnFxQVPnjzBlStXcOvWLbRu3RrGxsawtbUVyurVq4d69eqVy/kKhYWFYfHixcLx0KFDMWnSJABASkoKFAoFcnJyUFBQgNDQULRo0aJcz09ERERERERieif8K1asKFN7mUwGMzMzNGnSBM7Ozqhevbq+p35lREREFPnM+cyZMzFgwABcvnwZwcHBuHfvnpBgK5VK+Pj4CBvcde3aFdHR0Xon/M9u9le40Z2zszMSEhJw69YtKBQKAE839yssexl39wuvuSiFz/Dn5eVhyZIl+PPPPzF8+PByj4GIiIiIiIj+54USfn133Tc1NcV7772HkJAQYbf5qsjR0RHvv/8+5s2bh+joaCQnJ+PPP//EmTNnsHfvXgBPl/jn5eUhLS0NlpaWZT7Hs5v9FVIoFEhISEBSUpKQhLu5uWH79u1ISkrC4MGDX+zC9CSTyTBlyhT06NED+/fvR5cuXSokDiIiIiIioteB3gm/v78/JBIJ4uLikJmZCWNjYzg5OQmvn0tJScH58+eRnZ0Nc3NzvP3223j06BEuXLiAu3fvIioqCufOncN3330HA4Oqu5WAv78/wsPDERcXh2vXrsHe3l7nrQeDBg3Crl27EBAQUC7ndHd3x88//wypVIqmTZsCeJrwz5w5E5mZmSXe4c/Pz0dubq5wXLgaQavVisoBlHmlhkwmw8iRIxEREcGEn4iIiIiI6CXSO+FfuHAhJkyYgOzsbEydOhVDhgyBkZGRqE1ubi42bNiAr776Crm5uYiIiAAA7Ny5EzNnzsTRo0cRExODvn37vthVvMJkMhkCAgKwcuVKZGdnY8iQIbCyshK1GTRoEKKjo8st4ZfL5cjKykLHjh2FVRgWFhawtLSEoaEh7O3tn9u/V69eouOwsDBIpVKoVCqdNwqcP38ewNPXBj67V0GjRo2wbdu2Isfv378/wsPD8dtvv8HLy6usl0dERERERESlINFqtVp9Oq5btw6LFi3CzJkzMWTIkOe23bBhA8LCwkRt161bh4ULF6Jdu3ZYu3atPiEQISkpCd7e3lg3Ng91zCs6GiKiysk+6HpFh0BERER6KsyJ4uLiYGtrK6rTey39tm3bIJVKi92o7VkDBgyAVCrFL7/8IpT17dsXEokEly9f1jcEIiIiIiIiIiqG3kv6//nnH9SoUaNUm+7JZDLUqFEDN27cEMrMzMxgZmaGR48e6RsCkcB24CGdX7OIiKh0NOpcGBhW/rfnEBERkZjed/hlMhkyMzNx9+7dEtveuXMHGRkZqFatmlCm1WqRnZ0NMzMzfUMgIiKicsBkn4iIqGrSO+F3cXEB8PTd72q1uth2BQUF+OyzzyCRSODq6iqUJycnIz8/X2cDOyIiIiIiIiJ6cXon/GPHjoVEIsGBAwfQv39/bNu2DTdu3MDjx4/x+PFj3LhxA1u3bkW/fv3wxx9/QCKRIDg4WOi/f/9+AE9fF0dERET/HY06t+RGREREVOnp/Qy/m5sbFixYgFmzZuHy5cuYMWNGke20Wi2qVauGuXPnonXr1kL548eP4evriz59+ugbApEgaXNHqLlLPxFRqXBXfiIioteD3gk/APj7+8PZ2Rnffvst4uLikJWVJao3NTWFt7c3xowZgyZNmojqxo4d+yKnJiIiIiIiIqLneKGEHwAaN26MxYsXQ6vV4tatW0hPT4dWq4WlpSUaNGgAiURSHnESERERERERURm8cMJfSCKRwM7ODnZ2duU1ZKX07bffIjExEd99951Q1q1bNzRs2FCnbOLEiejVqxccHR1hbGws+nFk3LhxGD16NDIzM7Fw4UIcPHgQ2dnZsLa2Rv/+/dG7d2/06tVLaJ+dnS0a47vvvoNCoRDFFhAQgD59+mDAgAFCWXx8PKZOnYqDBw8KbU6dOgVDw/99Ndq2bYvIyEjEx8cjMDAQxsbGAABra2uMGTMG/fv3h1wuF9o/efIEMpkMUqkUwNONHW/evInIyEjRaxylUikSExMBAI6Ojti7dy8aNmxY1o+ciIiIiIiIilBuCT89pVAoEBUVhYKCAkilUqSmpkKtVuPChQuisps3b4oS8piYmCKT3YULFyI7Oxu7d+9GzZo1cf36dVy9ehX16tWDSqUS2jk6OhY7RlnNnj1b9KPAs6ytrXHw4EFotVocPHgQ77//PuRyuSgWLy8vhIWFwcPDQygLDw+Hj48PlixZ8sLxERERERERUclKlfDfuXPnaWNDQ1hbW4vKyqpevXp69assWrVqBbVajYsXL8LJyQkJCQlo27Ytbt26JSqzs7ODjY1NieOdPXsWkyZNgrn50x3pmjRporMfQkWQSCTo3LkzzM3NcfnyZTRu3LiiQyIiIiIiIqJnlCrh9/b2BvD0ef1du3aJyspCIpHgwoULZe5XmchkMjg7OyMxMRFOTk5ITEyEm5sbrK2tRWX/Xm5fHBcXFyxduhQZGRlwc3ODvb39y72AUtJoNPj999+Rnp7OZfhERERERESvIIPSNNJqtdBqtdBoNDplZfl7tn9V1qZNGyQkJACAkNy7ubmJytq0aSPq07dvXygUCuHv0KFDAIBZs2bB19cXGzZsQK9evdC1a1ccOHBA79jCwsJE5wkODi6xzddffy3UpaSkQKFQwNnZGePHj0doaChatGhRqnPHxsaKxg0ICND7OoiIiIiIiOj5SnWH/9KlS6Uqo6cUCgU2bNiAjIwMpKWlwd7eHm+88QZCQ0ORkZGBq1ev6tzhj46OLvJOuZGREYKDgxEcHIysrCxERUVh0qRJ+P3331GrVq0yxzZz5swiN+17XptnFT7Dn5eXhyVLluDPP//E8OHDS3XuHj168Bl+IiIiIiKi/0ip7vBT2cjlcmRlZWHTpk1o3bo1AMDU1BTW1tbYtGkTrK2t0aBBgzKPa2pqirFjxyI7OxtJSUnlHXaZyGQyTJkyBVeuXMH+/fsrNBYiIiIiIiLSxYT/JTAyMoKTkxPWrl0rupPv5uamU1aSiIgInDlzBnl5ecjNzcX69ethZmaGRo0avYzQy0Qmk2HkyJGIiIgol/Hy8/ORm5sr/BUUFJTLuERERERERK+jcnkt3/3793H8+HHcvXsXT548wfjx48tj2ErN3d0dKpUKbm5uQpmbmxt+/PFHuLu767T38/ODRCIRjt955x3MmDEDEokEn3zyCe7cuQNDQ0M4Ojri22+/RY0aNV5a7PPmzcOCBQuE40aNGmHbtm1Ftu3fvz/Cw8Px22+/wcvL67nj7tmzB3FxcaKy/fv3o3bt2gCAXr16ierCwsKKfbSAiIiIiIiInk+i1Wq1+nbOzc3FggULsHXrVtHd2IsXLwr/zszMRJcuXZCVlYW4uDjUrVv3xSImekZSUhK8vb2xbmwe6phXdDRERJWDfdD1ig6BiIiIyklhThQXFwdbW1tRnd53+NVqNUaPHo2EhAQYGRnB3d0dJ0+eRF5enqidmZkZBg4ciFWrVuHXX38t9QZvRGVhO/CQzpebiIiKplHnwsCwekWHQURERC+Z3s/w//LLLzh+/DgaNWqEnTt3Ys2aNahZs2aRbX18fAAAf/zxh76nIyIionLCZJ+IiOj1oHfCHxMTA4lEglmzZqF+/frPbdusWTNIpVL89ddf+p6OiIiIiIiIiMpA74T/r7/+glQqRZs2bUpsK5VKUbNmTTx8+FDf0xERERERERFRGeid8Ofm5kImk0EqlZaq/ZMnT1C9OpcQEhERvWwadW5Fh0BERESvAL037bOyssKdO3eQlpYGS0vL57Y9c+YMcnNzYWdnp+/piJ4raXNHqLlLPxERAO7CT0RERE/pfYe/cCn/1q1bn9tOo9Hgq6++gkQigaenp76nIyIiIiIiIqIy0PsO/8iRIxETE4NvvvkGjRs3hre3t06ba9euYcGCBfjzzz8hk8kwbNiwFwr2deLl5YX79++LHpmIjY1Ffn4+vL29YWJiAgCwsLDAoEGDMGbMGKGdo6Mj9u7di4YNGwpl4eHhuHnzJpYsWYL4+HhMnToVBw8eRK9evXDnzh0AQE5ODgwNDWFo+PRrMXbsWAQHB4viCg8PR2RkJGQyGQCgTp06mDRpErp37w4AiI+PR2BgIIyNjUX9Vq9eDblcjoCAAPTp0wcDBgwAgDKfn4iIiIiIiEpH74T/zTffxCeffIKwsDCMHz8e9evXR0ZGBgBgwoQJ+Ouvv3D9+tMlhRKJBHPnzkW9evXKJ+rXRGRkJDw8PERlSUlJAICEhAQYGhri7NmzCAgIQMuWLfVaQbFr1y7h3/9Oxovj4+ODJUuWAAAOHTqEkJAQuLm54Y033gAAWFtb4+DBgy/t/ERERERERFQyvZf0A8DQoUOxYsUK1K1bF0lJScjPz4dWq8XevXvx999/Q6vVom7duoiIiEDfvn3LK2Z6RqtWrdC0aVNcvHixQs7fsWNH1KhRA//880+FnJ+IiIiIiIiKpvcd/kJdunSBl5cX4uPjoVKpkJqaCo1GgzfeeAOurq5o3769sESbyt+pU6dw9epVjB079j8/t1arxYEDB5Cfn4+mTZv+5+cnIiIiIiKi4pVLJm5gYID27dujffv25TEc/b+QkBDhGf42bdpg5cqVQl27du2Ql5eH3NxcjBw5El26dPnP4oqNjcUff/yB/Px85OXl4cMPP4SZmZlQn5KSAoVCIepz8OBBYd8BIiIiIiIievl46/0VFhERofMMf6E///wTEokE69atw86dO5Gfny9spCeVSqFWq0Xt1Wo1qlWrVi5x9ejRQ3iGPykpCcHBwahZsyYGDRoEoGzP8BMREREREdHLUeqE/+OPP37hk0kkEixYsOCFx6GnpFIpRo4ciX379uGnn37C8OHDAUDYU6FJkyZC26SkJNjb25d7DLa2tujYsSN+//13IeEnIiIiIiKiilfqhD86OhoSiQRarbbMJynsx4T/5RgzZgxmzZqFwYMHo3r16ujZsye++eYbODo6wtraGn/++Sd+++03bNq0qdzPfe/ePRw+fBidOnUqdR+1Wo3c3Fzh2MDAoNxWHxAREREREdFTpU74e/fuDYlEUuYTpKSkID4+vsz9qPTeeustmJubY/PmzQgICEBISAiWLVuG9957DxkZGbCzs8OSJUvg4OBQLufbs2cP4uLiAAA1atSAl5cXxo8fL9SnpKRALpeL+ixatAjdu3cHAMyZMwdz5swR6nx9fYVHBIiIiIiIiKh8SLT63LIvhfT0dERFReHnn39GTk4OtFotWrZsia1bt76M09FrKikpCd7e3lg3Ng91zCs6GiKiV4N90PWKDoGIiIj+I4U5UVxcHGxtbUV15b5pX1ZWFtasWYN169bh8ePH0Gq1aNq0KSZMmIBu3bqV9+mIiIiIiIiIqAjllvDn5ORg/fr1+P7775GZmQmtVgs7OzuMHz8evr6+ej0OQFRatgMP6fyaRUT0utKoc2FgWL2iwyAiIqIK9sIJf35+Pn7++Wd8++23ePDgAbRaLerWrYtx48ahX79+wnvkiYiI6L/BZJ+IiIiAF0j4NRoNtm7dim+++QZ3796FVqvFG2+8gbFjx+Ldd98V3glPRERERERERP89vRL+HTt2YMWKFfjnn3+g1Wphbm6OUaNGISAgAEZGRuUdIxEREYFL9YmIiKhsypTw79+/H8uWLcNff/0FrVYLU1NTDB8+HMOHD4epqenLipGoREmbO0LNXfqJqIrj7vtERERUFqVO+N955x2cP38eWq0WxsbGGDp0KEaPHg0zM7OXGR8RERERERER6aHUCf+5c+cgkUggkUjQqlUr3LlzB3Pnzi3zCb/88ssy9yEiIiIiIiKisinTkn6tVgsASEhIEB2XlkQiea0Tfi8vL9y/fx9SqRSGhoaQy+WYO3cu6tatCwAIDQ2FjY0NJk+eXOwYoaGh2L59O37//XfY2NiI6g4dOoTIyEhcuHAB1atXR9OmTTFixAh4e3tj27Zt2LJlCzZu3AgAyMrKwqhRo2BlZYWvvvoK1apVE40VEBCAU6dOwdDQEDKZDO7u7pg9ezasra2FNn/99ReWLFmChIQEaDQaODk5YfLkyWjdurXQJi8vD99++y127NiBlJQUWFpaom3btggJCYGtrS0CAgLQp08fDBgwAAAQHx+P8ePHY86cOejVq9eLfeBERERERESvsVIn/H379n2Zcbw2IiMj4eHhgdzcXMyZMwefffYZVq5cWaq+2dnZ+PXXX1GzZk3s2LEDo0aNEupiY2PxySef4OOPP0ZkZCRq1KiBxMREbN++Hd7e3qJxMjIyEBQUhIYNG2Lx4sUwNCz6azB79mwMGDAAmZmZmDRpEhYvXiz8YPPPP/9g8ODBeO+997Bo0SIYGhpi27ZtCAoKwurVqyGXywEAEyZMQHJyMpYsWYIWLVrgyZMn2L59O44dOyYk+YUOHz6MSZMmYeHChejatWupP1MiIiIiIiLSVeqEf+HChS8zjtdO9erV0aNHDyxYsKDUffbu3QszMzOMHDkSW7ZsERJ+rVaLRYsWYdy4caIkuk2bNmjTpo1ojLS0NIwcORLNmzfH/PnzYWBgUOJ5zczM4O3tjZ9++kkoCw8Ph6urq2g1wrBhw3Dt2jUsWbIEGzZswNGjR3H06FH8+uuvwiqGmjVrYsiQITrn+P333zF16lR8+eWX6Ny5c6k/EyIiIiIiIipaydkevRRPnjzB7t274eLiUuo+0dHR6NWrF3r16oW///4b58+fBwD8/fffuHv3Lrp37/7c/hkZGQgICICzszMWLFhQqmQfANLT07Fv3z7Y2dkJZUePHkWPHj102vr4+ODkyZN48uQJjh49CmdnZyHZL85vv/2GqVOnYvny5Uz2iYiIiIiIygkT/v9YSEgIFAoF3NzccOTIEQQFBZWq3507dxAfHw9fX1+88cYbaN++PaKjowEADx8+BADR8/VFuXv3Lm7cuIH+/ftDIpGUeM6wsDC4ubmhXbt2SE9Px6xZs4S69PR0WFlZ6fSxsrKCRqNBZmYmHj58WGSbf4uPj4e9vb3o2X8iIiIiIiJ6MUz4/2MRERFITEzE2bNnMXv2bAQEBCA1NbXEfjExMWjSpAmaN28OAPD19cXOnTuRn5+PWrVqAQBSUlKeO0azZs0wbdo0jB49GhcuXCjxnDNnzsSJEyewfft2ZGZm4t69e0KdhYVFkXGnpqbCwMAAZmZmqFWrVqmubeLEiZDJZAgJCUFeXl6J7YmIiIiIiKhkTPgriFQqRbdu3WBgYIATJ06U2F6pVOLWrVvw9PSEp6cnFi5ciPT0dBw8eBCNGzdG3bp1sXfv3hLHCQwMxOjRozFixAhcuXKlVLE6Ojri/fffx7x584Q3M7Rv3x6xsbE6bffs2QNXV1cYGxvDw8MDZ86cEf1QUBQTExNERUXh0aNHmDBhAvLz80sVFxERERERERWPCX8F0Wq12L9/PzIzM9GkSROhXKPRIDc3V/jLy8uDSqXCrVu3sGXLFiiVSiiVSuzcuRO9e/eGUqmERCJBaGgoVq5cia1btyIrKwsajQaJiYmiZfiFRo8ejWHDhmHEiBH4+++/SxWvv78/Hjx4gLi4OADA+PHjoVKpsHTpUjx8+BBZWVn44YcfEBMTgylTpgAAPDw84OHhgZCQEJw7dw5qtRpZWVnYuHEjfvnlF9H4pqamWLVqFVJSUvDRRx+hoKBA34+WiIiIiIiIUIZd+ql8BAcHQyqVAgDq16+PRYsW4c033xTqo6KiEBUVJRy3bt0ab775Jry9veHo6CgaKzAwEO+99x4ePnyIHj16wMTEBJGRkQgLC0P16tXx5ptvFrtHQOHy+eHDh+PHH38UbchXFJlMhoCAAKxcuRJdunSBvb09fvrpJ3z55Zfw8vKCVquFk5MTVq1aBTc3N6Hf8uXLERkZicmTJyM1NRUWFhbCjwD/ZmZmhtWrV2PYsGGYNm0avvjii1JvLEhERERERERiEm3hGm2iSigpKQne3t5YNzYPdcwrOhoiopfLPuh6RYdAREREr5jCnCguLg62traiOt7hpyrBduAhnS83EVFVo1HnwsCwekWHQURERJUE10sTERFVEkz2iYiIqCyY8BMRERERERFVQUz4iYiIiIiIiKogJvxERESvEI06t6JDICIioiqCm/ZRlZC0uSPU3KWfiKoA7sRPRERE5YV3+ImIiIiIiIiqICb8ZZSYmIhBgwbBzc0Nbdq0waBBg3DmzBmhPiUlBZ988gk6dOgAuVyOHj16YPny5cjOzgYAODo64ubNm6Ixw8PDMWXKFOH42Tb/rnuWl5cXjh49qlMeHx+PZs2aQS6Xi/5UKhUAICAgAK1atYJcLkfbtm0xfvx4pKSkFHvNoaGhcHJyglwuR5s2bTBixAhcu3ZNqL916xaaNWuGOXPm6PR1dHSEq6sr5HI5OnbsiIULF6KgoECoDwgIwJYtW0Sxu7u7Y9euXcXGQ0RERERERCVjwl8GWVlZCA4OxtChQ3H8+HEcPHgQ48ePh0wmAwA8fPgQgwYNQm5uLn7++WeoVCqsWbMGmZmZ+Oeff/7TWK2traFSqUR/crlcqJ89ezZUKhX27duH7OxsLF68+LnjBQUFQaVS4cCBA7C0tMTHH38s1MXExMDc3By7d+9GXl6eTt+YmBioVCr8+OOP2L17N7Zu3VrkOQ4fPoyQkBAsWLAAvXr10vPKiYiIiIiICGDCXybXrz99rrJ3796QSqUwMjJChw4d0KxZMwDAmjVrUKNGDXzxxRewtbUFANStWxczZ84U2rxqzMzM4O3tjUuXLpWqvbGxMXx9fXH16lWhTKlUYuLEiTA0NMRvv/1WbN+GDRuidevWuHjxok7d77//jkmTJuHLL79E165dy34hREREREREJMKEvwwaNWoEqVSK6dOn48CBA8jIyBDVHzt2DF27doWBQeX5WNPT07Fv3z7Y2dmVqv3jx4+xY8cONG/eHMDTRxzu3buHXr16wcfHB0qlsti+165dw4kTJ9CwYUNR+W+//YapU6di+fLl6Ny5s97XQkRERERERP/DXfrLwNTUFD/99BO+++47zJo1C/fv30enTp0QFhaGN954Aw8fPoSVlVWJ4/Tt21f0o0Bubi66d+9errGmpKRAoVCIyg4ePAgTExMAQFhYGBYtWoSsrCw0a9YMCxYseO54q1evxoYNG1C9enW0atUKixYtAgBER0ejU6dOMDc3R+/evTF06FA8ePAAtWvXFvr27dsXGo0GT548Qa9evfDee++Jxo6Pj0fjxo3RunXr8rh0IiIiIiIiAu/wl1mTJk2waNEiHDx4EDt27EBKSoqQLNeqVQupqakljhEdHY3ExEThb8yYMeUep7W1tegciYmJQrIPADNnzsSJEyewfft2ZGZm4t69e88db+TIkUhMTMSRI0cQGRkJOzs75OTkIDY2Fr6+vgAAuVyOunXrYseOHTrXq1KpsHTpUpw+fVrYwLDQxIkTIZPJEBISUuQeAERERERERFR2TPhfQJMmTdCvXz/hefb27dtj37590Gg0FRxZ6Tk6OuL999/HvHnzoNVqy9R33759yMrKwty5c+Hp6QlPT08kJycjJiZGp61EIkHPnj3h6uqKiIgIUZ2JiQmioqLw6NEjTJgwAfn5+S90TURERERERMSEv0yuXbuG1atXC3fD7969i507d8LFxQUAMGLECDx+/BjTp0/H7du3AQDJyclYuHBhqTfFK4pWq0Vubq7w9+xd8Pz8fFGdWq0u8/j+/v548OAB4uLiytRPqVSif//+2LFjB5RKJZRKJTZu3IiLFy/i8uXLRfYZM2YMNm/erLMSwtTUFKtWrUJKSgo++ugj0av7iIiIiIiIqOyY8JeBqakpTp8+jQEDBsDV1RUDBw6Eg4MDQkNDATxd0r9x40YYGhpi4MCBkMvlCAwMRM2aNXU2qiuLnTt3wtnZWfjr0qWLUDdmzBhRXXh4OICnz/DL5XLR36+//lrk+DKZDAEBAVi5cmWpY0pOTsaxY8cQGBgIKysr4c/JyQkdO3YsdvM+R0dHuLu74/vvv9epMzMzw+rVq3Hjxg1MmzatUq2UICIiIiIietVItGVdx030CklKSoK3tzfWjc1DHfOKjoaI6MXZB12v6BCIiIioEinMieLi4oTXwxfiHX4iIiIiIiKiKoiv5aMqwXbgIZ1fs4iIKiONOhcGhtUrOgwiIiKqAniHn4iI6BXCZJ+IiIjKCxN+IiIiIiIioiqICT8REVE506hzKzoEIiIiIj7DT1VD0uaOUHOXfiJ6RXCnfSIiInoV8A4/ERERERERURXEhJ+IiIiIiIioCmLC//+8vLzg7OwMuVwu/J08eRKOjo5Qq9UAgNDQUCxdurTI/o6Ojrh586ZO+bZt2+Do6IiFCxeKyvfv3w9HR0eEhoYKZXl5efjyyy/x1ltvwdnZGd26dcOqVaug1WqFNgEBAWjVqhXkcjnatm2L8ePHIyUlRagPDw9Hy5YtRdehUCiKve6SrsnV1VU01nfffadzHoVCgUGDBkGlUon6Z2Zm4tNPP4WnpydcXFzg6+uLrVu3AgCOHDkCDw8PpKWlia7fx8cHP//8c7HxEhERERERUenwGf5nREZGwsPDQzhOSkoql3Ht7Oywe/duTJ06FYaGTz9ypVIJe3t7UbsJEybg/v37iIqKQuPGjXHu3DlMmzYN9+7dw8yZM4V2s2fPxoABA5CZmYlJkyZh8eLF+PLLL4V6Hx8fLFmypFxij4mJQcOGDYusKzyPWq1GeHg4Jk6ciIMHDwJ4mrwPHz4ctWvXxs8//4w6derg2LFjCA0NRWZmJkaMGIG33noL8+fPF2JfuXIlrKys8O6775ZL7ERERERERK8z3uH/D7zxxhtwcHDA4cOHAQAPHz6ESqWCl5eX0ObYsWM4cuQIwsPD4eDgAENDQ7i6uuKLL77Ahg0bilw9YGZmBm9vb1y6dOk/u5aiGBoawtfXF8nJycId+5iYGNy9exfLli1DgwYNUK1aNXTq1AkzZ87E8uXLkZWVhY8//hgJCQn4448/cOXKFWzYsAFhYWGQSCQVej1ERERERERVARP+/4i/vz+USiUAYNeuXfD29oZMJhPqjxw5AhcXF9StW1fUz8XFRbg7/m/p6enYt28f7OzsXmrsJcnLy4NSqUStWrVgZmYGADh69Cg6deoEExMTUdtu3bohNzcXKpUKNWvWxJw5c/Dpp5/ik08+QUhISIVfCxERERERUVXBhP8ZISEhUCgUUCgUGDduXLmO3bVrVxw/fhyPHj1CTEwM/Pz8RPXp6emwsrIqsq+VlRXS09OF47CwMLi5uaFdu3ZIT0/HrFmzRO1jY2OF61AoFAgICNA77r59+4rGOnTokM55XFxcsGXLFixfvlx4ZKG46zE0NISFhYVwPV5eXnBxcYFGo8GwYcP0jpOIiIiIiIjE+Az/MyIiIl7KM/wAYGRkhM6dO2PlypVIT0+Hm5ub8Lw7AFhYWBS5bB8AUlNTYWFhIRzPnDkTAwYMwOXLlxEcHIx79+6hXr16Qn2PHj2KfIY/MjIS3377LQDA19cX8+bNKzHu6OjoYp/hLzxPWloaJkyYgPPnz6Nt27bC9aSmpur0UavVSE9PF13Pm2++CZlMBgMD/v5ERERERERUXphh/Yf8/f2xZs0anbv7AODh4YHTp0/j7t27ovIzZ87g7t27aNeunU4fR0dHvP/++5g3b55oJ//iBAcHQ6VSQaVSlSrZLy1LS0vMnTsX4eHhwhsDPDw8cPDgQWRnZ4va7t27FzKZDK6uruV2fiIiIiIiItLFhL+MNBoNcnNzhb+8vDyhLj8/X1RXUFAg6tumTRusWbMGQ4cO1RnXw8MD7du3xwcffICrV6+ioKAAp06dwpQpUzB48GCdHf0L+fv748GDB4iLi3sp11RaTZo0QceOHbFq1SoAgJ+fH+rUqYOJEyciKSkJ+fn5OHToEMLCwjB+/HjUrFlT73iJiIiIiIioZFzSX0ZRUVGIiooSjlu3bo2NGzcCAHr16iVqGxYWBqlUKhxLJBK0b9++2LHDw8OxfPlyjBo1Cunp6bCxscGAAQMwatSoYvvIZDIEBARg5cqV6NKlCwBgz549Oj8A7N+/H7Vr1y7zNfn5+Yl2zX/nnXcwY8aMIscJCgpCYGAgxo4di9q1a2PNmjX46quvMHDgQGRlZaFBgwaYPHkyBgwYUOz1EBERERERUfmQaEuzFpzoFZWUlARvb2+sG5uHOuYVHQ0R0VP2QdcrOgQiIiJ6TRTmRHFxcbC1tRXV8Q4/VQm2Aw/pfLmJiCqKRp0LA8PqFR0GERERveb4DD8REVE5Y7JPRERErwIm/ERERERERERVEBN+IiIiIiIioiqICT8REdEL0KhzKzoEIiIioiJx0z6qEpI2d4Sau/QTUQXgjvxERET0quIdfiIiIiIiIqIqiAl/OfPy8oKTkxPS0tJE5X5+fnB0dERSUpKoPDw8HI6Ojjhz5oyoPC8vD4sWLUKnTp0gl8vh5eWFBQsWAADkcrnw16xZMzg7OwvH27dvR3h4OFq2bClqp1AohLEdHR3h6+sLjUYjlC1duhShoaFFXlN8fDyaNWsmjNW9e3ds3bpV1CYgIADu7u7Iy8sTlYeGhsLJyQlyuRxt2rTBiBEjcO3aNaF+27ZtGDx4sHCclZWFQYMG4YMPPkB+fn6xnzMRERERERE9HxP+l6B+/frYtWuXcHz58mXk5OTotNNqtYiJiUGtWrUQHR0tqouKisK5c+ewZcsWnDx5EuvXr0eLFi0AACqVSvirV68eIiMjheM+ffoAAHx8fETtEhMTReOnpKSIYiyJtbU1VCoVTp48ialTp2LWrFn466+/AABJSUlITEyERCJBXFycTt+goCCoVCocPHgQNjY2mDFjRpHnyMjIwPDhw1G/fn0sXboU1apVK3V8REREREREJMaE/yXw8/ODUqkUjpVKJfz9/XXaJSYmIiUlBZ988gl2794tujt+9uxZdOnSBTY2NpBIJLC1tS1yDH0FBQUhPDwcarW6TP0kEgm6dOkCMzMzIeFXKpVwcXFB3759Rdf9b0ZGRvDx8cGlS5d06tLS0hAYGIg333wTX3zxBQwNub0EERERERHRi2DC/xK4uroiKysL165dQ0FBAXbv3i3ceX9WdHQ03n77bfTs2RMA8Mcffwh1Li4uWLt2LTZs2IDLly9Dq9WWa4zdunWDqampzsqCkmg0Guzbtw+PHj2Cg4MDACAmJga+vr7w9fXF4cOHcf/+/SL7ZmdnY+fOnbCzsxOVZ2RkICAgAM7OzliwYAEMDPi1JCIiIiIielHMrF6Swrv8R44cQePGjWFjYyOqf/LkCWJjY+Hr64tq1aqhe/fuouR77NixGD16NHbs2IH+/fujY8eOZUrOY2NjoVAohL+AgABRvUQiwcSJExEREaHz3H1RUlJSoFAo0K5dO6xYsQKff/45GjdujMTERNy5cwc+Pj5wcnJCgwYNsHPnTlHf1atXQ6FQoHXr1jhx4gQ+//xzUf3du3dx48YN9O/fHxKJpNTXSERERERERMXjuumXxM/PD0OHDkVSUhL8/Px06vft2wdDQ0N06tQJAODr64sRI0YgLS0NlpaWkEqlGDJkCIYMGYKcnBxs3boVn3zyCZydndGkSZMSz9+jRw8sWbLkuW06d+6MunXrYtOmTSWOZ21tjYMHD+qUK5VKeHp6wtLSEgDQu3dvREdHY/jw4UKbkSNHYvLkybhz5w5GjRqF69evo1mzZkJ9s2bN0KNHD4wePRpr164V9iogIiIiIiIi/fEO/0tSv3592Nra4sCBA+jWrZtOvVKpRHZ2Nt5++214enpi4sSJyM/P17k7Djx99n3IkCGi5+bLy6RJkxAZGVnkpoIlycnJwZ49e5CQkABPT094enpi3bp1uHTpUpHP6derVw8zZszA/Pnzdc4XGBiI0aNHY8SIEbhy5Yre10NERERERERP8Q7/SzR//nxkZGTAxMREtDlecnIyjh07hu+++w6Ojo5C+bp166BUKjFs2DCsXbsWzZs3h4uLCwwNDbFjxw48fvy43O9+t23bFg4ODlAqlXj77bfL1Hf//v2QSqXYsWOHaEf9SZMmQalUFvmaP09PT1hbW2PTpk0IDAwU1Y0ePRp5eXkYMWIEfvjhBzRu3Fi/iyIiIiIiIiLe4X+Z7Ozs0KpVK53ymJgYNG/eHB06dICVlZXwFxAQgMuXL+PKlSswMjLC4sWL4enpiXbt2mHDhg0IDw9HgwYNSnXuPXv2QC6Xi/4ePHhQZNtJkybh4cOHZb6+6Oho9OvXD/Xq1RNdx5AhQ7Bjx45i3wAwatQorFq1qsi9A0JCQvDOO+9g+PDh+Oeff8ocExERERERET0l0Zb39u9E/6GkpCR4e3tj3dg81DGv6GiI6HVkH3S9okMgIiKi11hhThQXFwdbW1tRHe/wExEREREREVVBfIafqgTbgYd0fs0iIvovaNS5MDCsXtFhEBEREengHX4iIqIXwGSfiIiIXlVM+ImIiIiIiIiqICb8REREZaBR51Z0CERERESlwmf4qUpI2twRau7ST0T/Ae7KT0RERJUF7/ATERERERERVUFM+ImIiIiIiIiqIC7pr+S8vLxw//59SKVSoaxv375wcnLCli1bsHHjRp0+AQEB6NOnDwYMGCAqT0pKgre3N86fPw9Dw6dfjTNnziA8PBwqlQoGBgaws7PD4MGD0b9/fwBAZmYmvvzyS+zfvx9ZWVmws7PD8OHDhXq5XC6M/+TJE8hkMiFWCwsLpKenAwBycnJgaGgonHfs2LEIDg4ur4+JiIiIiIjotcOEvwqIjIyEh4eHqGzbtm0vPK5KpcLIkSPx/vvvY/HixbCwsMD58+fx3XffoX///sjLy8Pw4cNRu3Zt/Pzzz6hTpw6OHTuG0NBQZGZmYsSIEVCpVMJ4Xl5eCAsL04kVKP5HCCIiIiIiItIPl/RTsT7//HP4+/tjzJgxsLS0hEQigZOTE5YtWwYAiImJwd27d7Fs2TI0aNAA1apVQ6dOnTBz5kwsX74cWVlZFXwFREREREREry8m/FSkJ0+e4NSpU+jevXuxbY4ePYpOnTrBxMREVN6tWzfk5uaK7u4TERERERHRf4tL+quAkJAQ0TP806ZNE56F11dmZiY0Gg2srKyKbZOeng4nJyedckNDQ9Hz+URERERERPTfY8JfBURERJT7M/xmZmYwMDBAamoqmjRpUmQbCwsLpKam6pSr1Wqkp6fDwsLihWIgIiIiIiIi/XFJPxXJ2NgYrq6u2Lt3b7FtPDw8cPDgQWRnZ4vK9+7dC5lMBldX15ccJRERERERERWHCX8VptVqkZubK/orpFarReX5+fk6/adOnYro6GisWrVKWJ5/6dIlTJ48GQDg5+eHOnXqYOLEiUhKSkJ+fj4OHTqEsLAwjB8/HjVr1vxvLpSIiIiIiIh0cEl/FRAcHCx6ht/DwwPe3t5QqVRwdnYWtT1//jwAYM6cOZgzZ45Q7uvri0mTJonatm7dGuvWrcPy5cvxzTffQCqVomHDhhgyZAgAQCaTYc2aNfjqq68wcOBAZGVloUGDBpg8eTJfr0dERERERFTBJFqtVlvRQRDpKykpCd7e3lg3Ng91zCs6GiJ6HdgHXa/oEIiIiIgEhTlRXFwcbG1tRXW8w09Vgu3AQzpfbiKil0GjzoWBYfWKDoOIiIioRHyGn4iIqAyY7BMREVFlwYSfiIiIiIiIqApiwk9ERERERERUBTHhJyIiKiWNOrfkRkRERESvCG7aR1VC0uaOUHOXfiJ6ybhDPxEREVUmvMNPREREREREVAVV2Tv8u3btwtq1a3H16lUYGxvD1tYW/v7+eO+99/Dxxx9j586dqFatmtC+QYMG2L59u/AOw86dOyMqKkqonzJlCho2bIgPPvgA8fHxCAwMhLGxseicq1evhlwux9WrV7Fw4UKcPXsWWq0WdnZ2mDhxIjp37lxi34CAAJw6dQqGhv+bmrZt2yIyMrLI6/Ty8kJKSgoOHjwIS0tLodzPzw+XLl0SvYvx5MmT+Prrr3H27FkYGBjA3d0dU6ZMQdOmTYV+kZGR2Lx5M9LT01GzZk20bt0aX3/9NQA8N7b4+HhMnToVBw8e1IkxNDQUNjY2mDx5sk6do6MjjI2NIZFIhLJx48Zh9OjRRV4vERERERERlU6VTPhXr16NVatWYfbs2ejQoQNq1KiBixcv4vvvv8eAAQMAAEFBQUUmoIVOnz6NEydOwM3Nrch6a2vrIpNbAAgODsbgwYOFJL0w8S9NXwCYPXu2EGdp1K9fH7t27UJAQAAA4PLly8jJyRG1UalUCAoKwqRJk7By5Uqo1WqsWbMGgwcPxrZt29CgQQNER0cjJiYGa9euhZ2dHVJTU/Hbb7+9UGylERMTg4YNG5brmERERERERK+7Krek/9GjR1i+fDk+/fRT9OjRA6amppBIJGjRogW+/PJLyGSyUo0TFBQk3Nkui7S0NCQlJWHgwIGQyWSQyWRwc3ODQqEo81il5efnB6VSKRwrlUr4+/uL2nzxxRfw8/NDYGAgTE1NUatWLUyePBkuLi4IDw8H8PSHiQ4dOsDOzg4AYGVlhXffffelxU1EREREREQvT5VL+FUqFfLy8uDt7f1C4wwZMgQ3btzA0aNHy9TPwsICDRs2xNSpU7F//37cv3//heIoDVdXV2RlZeHatWsoKCjA7t270adPH6H+yZMnUKlU6NGjh05fHx8f4RpdXFwQExODVatW4ezZsygoKHjpsRMREREREdHLUeUS/vT0dFhYWIieMx80aBAUCgWcnZ2RkJAA4Omyf4VCIfxNnz5dNE716tURHBxc7F3+lJQUUX+FQoHs7GxIJBKsX78e9evXx6JFi9ChQwfhx4OS+hYKCwsT1ZVmpUHhXf4jR46gcePGsLGxEeoyMjKg0WhgZWWl08/Kygrp6enCGDNnzsThw4cREBAADw8P0T4G+sZWkr59+4rGPHTo0AuPSURERERE9Lqrcs/w16pVC+np6VCr1ULS//PPPwMAOnXqBI1GAwAYOXLkc5/hB4CBAwfi+++/13mOHXj+c/h16tTB7NmzAQB3797FrFmzMH36dGzatKnEvgAwc+bMIp+THzVqFE6cOAEAmDt3ruguvp+fH4YOHYqkpCT4+fmJ+pmZmcHAwACpqalo0qSJqC41NRUWFhbCcZ8+fdCnTx/k5+dj//79mDp1Kpo3b46OHTs+N7YXER0dzWf4iYiIiIiIylmVu8Mvl8shk8kQFxf3wmNVq1YN48ePx7Jly0Sb7pVF3bp1MWTIEFy5cuWF41m1ahVUKhVUKpUo2Qeebtxna2uLAwcOoFu3bqI6ExMTuLq6IjY2VmfMPXv2oF27djrl1apVg4+PDxwcHHD16tUXjp2IiIiIiIj+W1Uu4TczM0NISAjmzp2L2NhYPH78GBqNBhcvXsSTJ0/KPJ6fnx/y8vJw+PDhUrXPyMjA8uXLcfPmTWg0GqSlpWHr1q1wdXUt87nLav78+Vi3bh1MTEx06j766CMolUqsX78eWVlZyMjIwNKlS3Hq1CmMHz8eALBt2zb88ccfyMrKgkajwYEDB/DXX3/B2dm51DHk5uaK/gp/KNFoNKLyvLy88rloIiIiIiIiKlKVW9IPAKNHj4aNjQ1WrVqF6dOnw9jYGA0aNMCUKVMgl8sRHR2N77//HuvXrxf6yGQyxMfH64wllUrxwQcf6Cz/T0lJgVwuF5UtWrQIHTt2xO3btzFixAikp6fDxMQEbdu2xaxZs0rs2717dwDAvHnzsGDBAqGuUaNG2LZtW4nXXbi7flEUCgVWrVqFZcuWYenSpZBIJFAoFPjpp59gb28PADA1NUVkZKSw+V/9+vUxZ84c0RsGnhdbcnKyzo8De/fuBQBERUWJ9gNo3bo1Nm7cCODpjyoSiUSoe+eddzBjxowSr5eIiIiIiIiKJ9Hqu1ad6BWQlJQEb29vrBubhzrmFR0NEVV19kHXKzoEIiIiIpHCnCguLg62traiuiq3pJ+IiIiIiIiIquiSfnr92A48pPNrFhFRedOoc2FgWL2iwyAiIiIqFd7hJyIiKiUm+0RERFSZMOEnIiIiIiIiqoKY8BMRET1Do86t6BCIiIiIygWf4acqIWlzR6i5Sz8RlQPuxE9ERERVBe/wExEREREREVVBTPiJiIiIiIiIqiAu6a8EvLy8cP/+fUilUpiYmKBjx46YNWsWatSogdDQUNjY2GDcuHHw9PREeHg42rdvL+q/YMEC3Lt3D8uXLxeNVahv376YPXs2tm3bhhkzZsDIyAgSiQS2traYPHky3n77baHtli1b8P333yM5ORnGxsZwcnLCV199BVNTUyGWyZMnIykpCd7e3jAxMQEAGBsbo1WrVhg2bBg8PT2LvLZ/x0NERERERET6Y8JfSURGRsLDwwOpqakICgpCVFQUJk+eLNRXr14dPXv2RExMjCjhLygowK5du/DZZ5/pjFUUV1dXbNy4ERqNBps3b8aHH36IAwcOwMzMDMePH8fSpUuxatUqtGjRAg8fPsTvv//+3LgTEhJgaGiI1NRU7N69G+PHj8esWbPQr1+/UsVDRERERERE+uGS/krGysoKHTp0wMWLF3Xq/P398euvv+LJkydC2eHDh6HRaNCpU6cyncfAwAB+fn7Izs7GjRs3AABnz56Fq6srWrRoAQCoVasW+vbtC1NT01LFHRgYiPHjx2PJkiXQaDRlioeIiIiIiIjKhgl/JXPv3j0cOnQIdnZ2OnWtW7eGtbU19u7dK5TFxMSgd+/eMDQs22KOgoICbNu2DdWqVUP9+vUBAC4uLjh8+DCWL1+OEydOIC8vr8zxd+vWDQ8ePMD169wFm4iIiIiI6GXikv5KIiQkBACQnZ2Ndu3aYcKECUW28/PzQ0xMDPz8/JCVlYW4uDhs3LhRZ6xnn5mfNm0aBg4cCAA4ffo0FAoFnjx5AqlUis8//xy1a9cGACgUCoSHh2Pjxo1Yv349CgoKMHDgQEybNk003vNYW1sDAB4+fFiqeIiIiIiIiEg/TPgriYiICHh4eOD48eP46KOPkJ6eDjMzM512/v7+iIiIQHJysrASoHAJ/r/HKoqLiws2btyIx48fY8aMGThx4gR69uwp1Hfu3BmdO3eGRqNBfHw8Jk6ciEaNGmHQoEGluo7k5GQATx8HKE08REREREREpB8u6a9k2rRpg379+mHx4sVF1terVw9ubm7Yvn07YmJi4O/vr9d5atSogU8//RQxMTG4cOGCTr2BgQHat2+Pdu3a4erVq6Ued9++fahduzYaNWqkV1xERERERERUOkz4K6HAwEAcPXq0yI37gKevtduwYQNUKhV8fX31Po+FhQUGDBiAiIgIAMD+/fuxa9cuZGRkQKvV4syZMzh+/DhcXFxKHOv+/fv48ccfsWLFCnz44YcwMOBXj4iIiIiI6GXikv5KyNLSEn5+fli5ciVq1KihU9+9e3fMmzcP7dq1E56Zf1ZwcLDomXkPDw8hqf+3wMBAdOnSBZcuXYK5uTlWrFiBefPmIS8vD9bW1ggKCkKfPn2KjdXd3R1arRbGxsZwcnLCsmXLdN4YUJZ4iIiIiIiIqHQkWq1WW9FBEOkrKSkJ3t7eWDc2D3XMKzoaIqoK7IP4FhEiIiKqPApzori4ONja2orqeIefqgTbgYd0vtxERPrQqHNhYFi9osMgIiIiemF8kJqIiOgZTPaJiIioqmDCT0RERERERFQFMeEnIiIiIiIiqoKY8BMRUZWkUedWdAhEREREFYqb9lGVkLS5I9TcpZ+InsHd9omIiOh1xzv8RERERERERFUQE/5KyMvLC05OTkhLSxOV+/n5wdHREUlJSQCA0NBQLF26tMgxHB0dcfPmTeH4+++/R4cOHXD16lWdttu2bUPz5s0hl8vRunVr+Pn54ffffxfqtVotvL290bNnT52+AQEBaNWqFeRyOdq2bYvx48cjJSVFqA8PD8eUKVOE4+TkZPTo0QNhYWHQarWl/ESIiIiIiIjo35jwV1L169fHrl27hOPLly8jJydHr7FWrlyJdevW4YcffsCbb75ZZBtXV1eoVCokJibinXfewaRJk/Dw4UMAQEJCAtLS0nDr1i2cOXNGp+/s2bOhUqmwb98+ZGdnY/HixUWe4/bt2xgyZAi8vLwwc+ZMSCQSva6HiIiIiIiImPBXWn5+flAqlcKxUqmEv79/mcdZunQpfvnlF2zYsAGNGjUqsb2BgQH69++PnJwc3Lp1CwAQHR0NLy8vdO7cWRTTv5mZmcHb2xuXLl3Sqfvnn38wdOhQ9O7dG9OmTSvzdRAREREREZEYE/5KytXVFVlZWbh27RoKCgqwe/du9OnTp0xjfPnll9izZw9+/PFHNGjQoFR91Go1tmzZAhMTE9jb2+PJkyf49ddf0adPH/j6+mLXrl3Iy8srsm96ejr27dsHOzs7UXlSUhKGDBmCd999F5MmTSrTNRAREREREVHRuEt/JVZ4l9/d3R2NGzeGjY1NmfofPnwYffv2Rb169Upse/r0aSgUCkilUjRs2BARERGoWbMmYmJiIJPJ4OnpCY1Gg4KCAhw4cABdu3YV+oaFhWHRokXIyspCs2bNsGDBAtHYV65cgYGBQZF7ABAREREREZF+eIe/EvPz88POnTsRHR0NPz+/MvdfunQpfv31VyxfvrzEti4uLkhMTER8fDw2b94MDw8PAE8fJfDx8YGhoSFkMhm6du2K6OhoUd+ZM2fixIkT2L59OzIzM3Hv3j1RvZeXF/r374/AwEDcvn27zNdBREREREREuniHvxKrX78+bG1tceDAAcyfP7/M/e3t7bFmzRoEBATAyMgIY8aMKVP/e/fu4c8//8SZM2ewd+9eAMCTJ0+Ql5eHtLQ0WFpaito7Ojri/fffx7x58xAdHS3alO/jjz9GXl4eAgMDsWHDhjKvViAiIiIiIiIx3uGv5ObPn49169bBxMSkyHqNRoPc3Fzh79/P17/55ptYu3Ytvv/+e6xdu7ZM546JiYG9vT1iY2OhVCqhVCrx66+/wsbGRvQGgWf5+/vjwYMHiIuL06mbPXs22rVrh8DAQNy/f79MsRAREREREZEYE/5Kzs7ODq1atSq2PioqCs7OzsJfYGCgTptmzZph1apViIiIwMaNG0t97ujoaLz33nuwsrIS/Q0aNEhnWX8hmUyGgIAArFy5UqdOIpFg3rx5cHZ2xogRI5CWllbqWIiIiIiIiEhMotVqtRUdBJG+kpKS4O3tjXVj81DHvKKjIaJXiX3Q9YoOgYiIiOilK8yJ4uLiYGtrK6rjHX4iIiIiIiKiKoib9lGVYDvwkM6vWUT0etOoc2FgWL2iwyAiIiKqMLzDT0REVRKTfSIiInrdMeEnIiIiIiIiqoKY8BMRUaWmUedWdAhEREREryQ+w09VQtLmjlBzl36i1xJ34yciIiIqGu/wExEREREREVVBTPiJiIiIiIiIqiAu6X8Fffvtt0hMTMR3330nlHXr1g0NGzbUKZs4cSJ69eoFR0dHGBsbQyKRCPXjxo3D6NGjkZmZiYULF+LgwYPIzs6GtbU1+vfvj969e6NXr15C++zsbNEY3333HRQKhSi2q1evYuHChTh79iy0Wi3s7OwwceJEdO7cGfHx8Zg6dSoOHjwIAAgICMCpU6dgaGgIiUQCe3t79OjRA8OHD4dMJgMAhIeHIzIyUjgGAKlUisTExHL8RImIiIiIiF4/TPhfQQqFAlFRUSgoKIBUKkVqairUajUuXLggKrt586YoIY+JiUHDhg11xlu4cCGys7Oxe/du1KxZE9evX8fVq1dRr149qFQqoZ2jo2OxYxQKDg7G4MGDERkZCQBC4l+c2bNnY8CAAcjOzsbZs2exYMECHDlyBGvXrhV+WPDx8cGSJUvK/DkRERERERFR8bik/xXUqlUrqNVqXLx4EQCQkJCAtm3bolGjRqIyOzs72NjYlDje2bNn4evrC3NzcxgYGKBJkybo0aNHmeNKS0tDUlISBg4cCJlMBplMBjc3N51VAEUxMTFB27Zt8c033+DUqVP4448/ynx+IiIiIiIiKj0m/K8gmUwGZ2dnYVl7YmIi3Nzc4ObmJiorTaINAC4uLli6dCm2bt2KGzdu6B2XhYUFGjZsiKlTp2L//v24f/9+mceoV68enJycuGSfiIiIiIjoJWPC/4pq06YNEhISAPwvuXdzcxOVtWnTRtSnb9++UCgUwt+hQ4cAALNmzYKvry82bNiAXr16oWvXrjhw4ECZY5JIJFi/fj3q16+PRYsWoUOHDhgyZEiZf0SwtrZGRkaGcBwbGyuKOyAgoMyxERERERERkRif4X9FKRQKbNiwARkZGUhLS4O9vT3eeOMNhIaGIiMjA1evXtW5wx8dHV3k8/dGRkYIDg5GcHAwsrKyEBUVhUmTJuH3339HrVq1yhRXnTp1MHv2bADA3bt3MWvWLEyfPh2bNm0q9RjJycmQy+XCcY8ePfgMPxERERERUTnjHf5XlFwuR1ZWFjZt2oTWrVsDAExNTWFtbY1NmzbB2toaDRo0KPO4pqamGDt2LLKzs5GUlPRCMdatWxdDhgzBlStXSt3n7t27OH/+fKkfRyAiIiIiIiL9MOF/RRkZGcHJyQlr164VJcdubm46ZSWJiIjAmTNnkJeXh9zcXKxfvx5mZmZo1KhRmWLKyMjA8uXLcfPmTWg0GqSlpWHr1q1wdXUtse+TJ09w/PhxjBs3Ds7OzujcuXOZzk1ERERERERlwyX9rzB3d3eoVCq4ubkJZW5ubvjxxx/h7u6u097Pz0941R0AvPPOO5gxYwYkEgk++eQT3LlzB4aGhnB0dMS3336LGjVqlCmeatWq4fbt2xgxYgTS09OFnfdnzZpVbJ958+ZhwYIFAICGDRuie/fuGDlyJAwM/vdb0549exAXFyfqt3//ftSuXbtM8REREREREdH/SLTPe4k60SsuKSkJ3t7eWDc2D3XMKzoaIqoI9kHXKzoEIiIiogpTmBPFxcXB1tZWVMc7/FQl2A48pPPlJqLXg0adCwPD6hUdBhEREdErh8/wExFRpcZkn4iIiKhoTPiJiIiIiIiIqiAm/ERERERERERVEBN+IiJ6JWjUuRUdAhEREVGVwk37qEpI2twRau7ST1Spcbd9IiIiovLFO/xEREREREREVdArl/B7eXnB2dkZcrkc7u7uGDNmDO7evSvUh4aGwsnJCXK5HG3atMGIESNw7do10Rj37t3DRx99hLZt28LV1RXvvPMOfv/9d51z3bp1C82aNcOcOXN06hwdHeHq6gq5XI62bdsiMDAQu3fvFup37tyJnj17ivqMGDGiyLKoqCjheNu2bXB0dBTGysnJQbdu3aBUKkX9VqxYgUGDBkGj0Tz3Myr8S05ORlJSEhwdHaFWq4XPaunSpTr9C6/v5s2bOuWF8S1cuFBUvn//fjg6OiI0NFQoy8vLw5dffom33noLzs7O6NatG1atWgWtViu0CQgIQKtWrYTPcfz48UhJSRHqw8PD0bJlS9G1KBSKImMmIiIiIiKi0nvlEn4AiIyMhEqlwuHDh1G7dm189tlnovqgoCCoVCocPHgQNjY2mDFjhlD38OFDvPfee5DJZNi5cyf+/PNPDB8+HB999BFiY2NF48TExMDc3By7d+9GXl6eThwxMTFQqVTYs2cP+vbti3nz5mHFihUAAHd3d1y7dg1paWkAALVajUuXLiEnJ0dUdurUKVECGx0djVq1agkJvpGREebPn4+FCxfi/v37AIBr165hzZo1mD9/PgwMip6iws+o8M/GxqYsH/Fz2dnZYffu3cIPBwCgVCphb28vajdhwgQcO3YMUVFROHnyJD7//HNs3rwZ8+fPF7WbPXs2VCoV9u3bh+zsbCxevFhU7+PjI7qWxMTEcrsWIiIiIiKi19UrmfAXql69Onr06KFzB7+QkZERfHx8cOnSJaFs7dq1MDExwfz582FlZQUjIyP07t0bwcHBWLx4sejus1KpxMSJE2FoaIjffvut2DgsLS3h7++POXPm4Ntvv0V6ejpsbGzQoEEDJCQkAAAuXLiApk2bwt3dXVSm0Wjg5OQEALh9+zYSEhIwb948HD58WEjw3d3d0bNnT8ybNw9arRYzZ87E2LFj0aRJkxf7APX0xhtvwMHBAYcPHwbw9EcUlUoFLy8voc2xY8dw5MgRhIeHw8HBAYaGhnB1dcUXX3yBDRs2FLl6wMzMDN7e3qL5IiIiIiIiopfjlU74nzx5gt27d8PFxaXI+uzsbOzcuRN2dnZC2dGjR9GtWzedO+M+Pj64c+cOrl9/uilUYmIi7t27h169esHHx0dnSX1RvL29UVBQgDNnzgCAKLlPSEiAQqGAm5ubqMzFxQUymQzA0x8YnJyc0L17dzRp0gQ7duwQxp4yZQrOnTuHDz74ALm5uQgKCirlp/Ry+Pv7C5/Jrl274O3tLVwHABw5cgQuLi6oW7euqJ+Liwvq1KmDY8eO6YyZnp6Offv2ieaLiIiIiIiIXo5XMuEPCQkRkucjR47oJL+rV6+GQqFA69atceLECXz++edCXXp6OqysrHTGtLa2FuqBp0vrO3XqBHNzc/Tu3RuHDh3CgwcPnhtXtWrVYGFhgYyMDABPE/7C5eeJiYlCzM+WtWnTRugfExOD3r17AwB69+6N6Ohooa5GjRqYPXs29u3bhwULFkAqlZbqM1IoFBg3btxz2+qja9euOH78OB49eoSYmBj4+fmJ6ov7nAHAyspK+JwBICwsDG5ubmjXrh3S09Mxa9YsUfvY2FjhWhQKBQICAsr9eoiIiIiIiF43r2TCHxERgcTERJw9exazZ89GQEAAUlNThfqRI0ciMTERv/32G4yMjIS79gBgYWEhaluocKM4CwsL5OTkIDY2Fr6+vgAAuVyOunXriu64FyU/Px9paWkwN3/6/jd3d3dcvnwZGRkZOH36NFxdXdGkSROkpqYiIyMDJ0+eFJ7fP3HiBJKSktCrVy8ATxP+K1eu4OLFi8L4TZs2Ff1vaT6jxMRErFy5ssT2ZWVkZITOnTtj5cqVSE9Ph5ubm6i+uM8ZAFJTU2FhYSEcz5w5EydOnMD27duRmZmJe/fuidr36NFDuJbExET88MMP5X49REREREREr5tXMuEvJJVKheX5J06c0KmvV68eZsyYgfnz5yMnJwcA0L59e+zdu1dnd/s9e/agbt26aNSoEfbt24esrCzMnTsXnp6e8PT0RHJyMmJiYp4bT1xcHKRSKZydnQEADRo0gLW1NTZt2oS6deuiRo0aAABXV1ds2rQJjx8/hqurK4Cny/m1Wi38/f3h6emJgQMHCuWvKn9/f6xZs0bn7j4AeHh44PTp06I3KADAmTNncPfuXbRr106nj6OjI95//31hrwIiIiIiIiJ6eV7phF+r1WL//v3IzMwsdgM7T09PIekGgOHDhyMrKwszZsxAamoqcnNzsXPnTkRGRmLatGmQSCRQKpXo378/duzYAaVSCaVSiY0bN+LixYu4fPmyzjkePnyI7du3Y968eRg9erTo7rVCocDatWtFO/G7ublh7dq1cHJygpGREXJzc7Fnzx7MmzdPOJ9SqcSsWbOwY8cO0W745U2j0SA3N1f4e/ZtBPn5+aK6goICUd82bdpgzZo1GDp0qM64Hh4eaN++PT744ANcvXoVBQUFOHXqFKZMmYLBgwfr7OhfyN/fHw8ePEBcXFy5XicRERERERGJGVZ0AEUJDg4WnmGvX78+Fi1ahDfffLPY9qNGjcLChQsxePBgWFhY4KeffsKSJUvQq1cv5OXloUmTJvj888/RpUsXJCcn49ixY4iOjhY9g25lZYWOHTtCqVRi+vTpAAA/Pz9IJBJUq1YNjo6O+Pjjj4XHAAq5u7tj586doiXvCoUCixcvRv/+/QE8fYe9kZER/P39Ua1aNaHdO++8g+XLl+PQoUN4++23X/yDK0JUVBSioqKE49atW2Pjxo0AIDxeUCgsLEy0d4BEIkH79u2LHTs8PBzLly/HqFGjhDcXDBgwAKNGjSq2j0wmQ0BAAFauXIkuXboAeLr64t8/AOzfvx+1a9cu/YUSERERERGRiETLtdVUiSUlJcHb2xvrxuahjnlFR0NEL8I+6HrJjYiIiIhIpDAniouLg62trajulV7ST0RERERERET6eSWX9BOVle3AQzq/ZhFR5aJR58LAsHpFh0FERERUZfAOPxERvRKY7BMRERGVLyb8RERERERERFUQE34iIiIiIiKiKogJPxER/Wc06tyKDoGIiIjotcFN+6hKSNrcEWq+lo/olcdX7xERERH9d3iHn4iIiIiIiKgKqtIJv5eXF44ePVpk3cmTJzFs2DDI5XK4ubkhODgYf/31l1DfvXt37N69Wzg+ceIEHB0ddcrkcjnUajW2bduG5s2bQy6Xi/6Sk5OFWJydnSGXy+Hp6YnQ0FA8fvy4yNh27tyJnj17ispGjBhRZFlUVJTO+IV/8+bNAwDk5eVh0aJF6NSpE+RyOby8vLBgwQIAELVv1qyZaIzt27frxHbv3j188MEHaNu2Ldzc3ODr64tt27YBAJKSkuDo6Ai1Wg0ACA0NhZOTkzBe79698eWXX+LRo0fCeCV9bkRERERERKSf13JJv0qlQlBQECZNmoSVK1dCrVZjzZo1GDx4MLZt24YGDRrA3d0dCQkJQpKdmJiIxo0b65TJ5XIYGj79GF1dXbFx48ZizxsZGQkPDw+kpqYiKCgIUVFRmDx5sk47d3d3XLt2DWlpabC0tIRarcalS5dgbGwsKjt16hQ++OADnfH/LSoqCufOncOWLVtgbW2N27dvIzExUfgsCnl5eSEsLKzIMQpNnToVzZo1w++//w6ZTIYrV64gNTW12PZBQUGYPHkycnNzcfnyZXzxxRcYPHgwNm/eDBMTk1J9bkRERERERFR2VfoOf3G++OIL+Pn5ITAwEKampqhVqxYmT54MFxcXhIeHAwAUCoWQFANPk/vRo0frlCkUijKf38rKCh06dMDFixeLrLexsUGDBg2QkJAAALhw4QKaNm0q/AhRWKbRaODk5FTi+c6ePYsuXbrAxsYGEokEtra28Pf3L3PcAHDu3Dn069cPJiYmMDQ0RIsWLdC5c+cS+1WvXh3Ozs745ptv8PDhQ2FVABEREREREb0cr13C/+TJE6hUKvTo0UOnzsfHR3gEoE2bNrh69SoePnwIjUaDc+fOoWfPnsjMzBTKVCoV3N3dyxzDvXv3cOjQIdjZ2RXb5tnkPiEhAQqFAm5ubqIyFxcXyGSyEs/n4uKCtWvXYsOGDbh8+TK0Wm2ZY352rLlz52LXrl24c+dOmfubmprCw8ND9MMJERERERERlb/Xbkl/RkYGNBoNrKysdOqsrKyQnp4OAKhXrx7q1auHxMRE1KtXDw0bNoSRkRFat24tlOXm5sLFxUXof/r0adEd/1q1amH//v3CcUhICAAgOzsb7dq1w4QJE4qN093dHevXrwfwdCXB0KFDUadOHfz0009CWZs2bUR9QkJCIJVKheNp06Zh4MCBGDt2LMzNzbFjxw4sXLgQtWrVwkcffYS+ffuW+nMrtGzZMnz33XdYuXIl/v77bzg4OOCzzz6Ds7NzqcewtrbG+fPnheOSPjciIiIiIiIqu9cu4TczM4OBgQFSU1PRpEkTUV1qaiosLCyE48Jl/XXr1hUSUjc3N6Hs33fYXVxcnvssekREBDw8PHD8+HF89NFHSE9Ph5mZWZFt3d3dMWPGDGRkZOD06dNYsmQJatSogdTUVGRkZODkyZMIDAwscvx/k0qlGDJkCIYMGYKcnBxs3boVn3zyCZydnXU+g5KYm5tjypQpmDJlCtLS0vD5558jJCQEBw8eLPUYycnJMDf/3zv0SvrciIiIiIiIqOxeuyX9JiYmcHV1RWxsrE7dnj170K5dO+G4cFn9iRMnhIRfoVDolJVVmzZt0K9fPyxevLjYNg0aNIC1tTU2bdqEunXrokaNGgCebnC3adMmPH78GK6urmU+t5GREYYMGQIzMzPRWwn0YWlpiZEjRyIlJQUPHz4sVZ/Hjx/j2LFjen92REREREREVDpVPuHPz89Hbm6u8KdWq/HRRx9BqVRi/fr1yMrKQkZGBpYuXYpTp05h/PjxQl+FQoGLFy/i+PHjaN26NQDAwcEBSUlJiI+P1+v5/UKBgYE4evRosRv3FZ5/7dq1ouTYzc0Na9euhZOTE4yMjEp1rrVr1yI+Ph45OTlQq9WIjo7G48eP0aJFizLH/cUXX+DKlStQq9XIysrCxo0b0bBhQ9HKiKLk5eXh3LlzCAkJgZmZGfr161fmcxMREREREVHpVfkl/WPGjBEdBwcHY/LkyVi1ahWWLVuGpUuXQiKRQKFQ4KeffoK9vb3QtlGjRrC0tISFhYWw9N7AwADOzs44evQo5HK5aOxTp07plK1bt67I59stLS3h5+eHlStXCm8G+Dd3d3fs3LkTbm5uQplCocDixYvRv39/nfbBwcGiZ/g9PDwQEREBIyMjLF68GDdv3oREIoG9vT3Cw8PRoEGDYj614uXk5GD8+PFITU1F9erV4eLigm+++abY9t9//z3Wr18PrVaL+vXr46233sLy5cuFV/IBZfvciIiIiIiIqHQk2hfZsp2ogiUlJcHb2xvrxuahjnnJ7YmoYtkHXa/oEIiIiIiqlMKcKC4uDra2tqK6Kr+kn4iIiIiIiOh1VOWX9FPVVlBQAAAw7LQFhnXqVHA0RFSSWzf/hkQqK7khEREREZXKvXv3APwvN3oWE36q1FJTUwEAQ4YMqeBIiIiIiIiIKk5qaioaNmwoKuMz/FSp5eTk4Ny5c7CyshJtWEhERERERPQ6KCgoQGpqapFvcmPCT0RERERERFQFcdM+IiIiIiIioiqICT8RERERERFRFcSEn4iIiIiIiKgKYsJPREREREREVAUx4SciIiIiIiKqgpjwExEREREREVVBTPiJiIiIiIiIqiDDig6ASB87duzAxo0bcfnyZWg0GjRq1Aj9+/fH4MGDYWDA37H+S3///TcOHTqEs2fP4ty5c7hx4wa0Wi2WLVuGHj16PLevvvNYWfq9DvLz85GYmIgDBw7g5MmTuHPnDh4+fAgLCwvI5XIMGTIEbdu2LbZ/ZZlLfgeK98MPPyAxMRFXrlxBWloasrKyULNmTTRr1gx9+/ZFnz59IJFIiuxbWeaR8182X331Fb799lsAwLRp0xAUFFRku8oyj5z/koWGhiI6OrrY+kaNGiE2NrbIusoyn/welCwnJwc//PADYmNjcfPmTeTn56N27dpwcnJCYGAg3NzcdPpUlnmszPMv0Wq12ooOgqgs5s6di59++gnVq1dH+/btYWhoiGPHjuHx48fo2rUrli1bBqlUWtFhvjbmz5+P9evX65SXlPDrO4+Vpd/r4ujRoxgxYgQAwMrKCi1btoSxsTGuXbuGK1euAADGjRuHiRMn6vStLHPJ78DzderUCWlpaXjzzTdhY2MDY2Nj3LlzB6dPn4ZWq4W3tzdWrFih8x9ElWUeOf9lc+bMGQwaNAgajQZarbbYhL+yzCPnv3QKE/7WrVujYcOGOvVWVlb46KOPdMory3zye1CyW7duISgoCDdv3kTt2rXh4uICmUyG27dv49KlSxg3bhzGjRsn6lNZ5rHSz7+WqBKJjY3VOjg4aD09PbXXr18XylNTU7U+Pj5aBwcH7dq1aysuwNfQ5s2btYsXL9bu2rVLe/PmTe3QoUO1Dg4O2j179hTbR995rCz9XidHjx7VfvDBB9qEhASdul27dmmbN2+udXBw0B47dkxUV1nmkt+BkiUkJGgfP36sU37lyhWth4eH1sHBQfvLL7+I6irLPHL+yyY3N1fbs2dPbYcOHbTjxo3TOjg4aFetWqXTrrLMI+e/9KZPn651cHDQbt26tdR9Kst88ntQssePH2u7dOmidXBw0H799dfavLw8UX1aWpr277//FpVVlnmsCvPPhJ8qlb59+2odHBy00dHROnXx8fHC/yELCgr+++BIq9VqS5Xw6zuPlaUf/c8nn3yidXBw0H788cei8soyl/wOvJgVK1ZoHRwctB9++KGovLLMI+e/bD7//HOtg4ODNi4uTkgAi0r4K8s8cv5LT5+Ev7LMJ78HJVuyZInWwcFBO23atFL3qSzzWBXm/9V+4IDoGffu3cP58+dRrVq1IpeKt2nTBjY2NkhNTcWpU6f++wCpVPSdx8rSj8RatGgBAEhOThbKKstc8jvw4gwNn24VJJPJhLLKMo+c/7I5ffo01qxZg969e8PLy6vYdpVlHjn/L1dlmU9+D0qWl5eHzZs3AwDGjBlTqj6VZR6ryvwz4adK48KFCwCAN998E0ZGRkW2adWqFQDg4sWL/1lcVDb6zmNl6UdiN27cAPD0+c1ClWUu+R14Mbdu3cLPP/8MAKIEsLLMI+e/9HJzczF9+nSYm5tjxowZz21bWeaR86+f+Ph4LFy4ELNmzcLXX3+NQ4cOQaPR6LSrLPPJ70HJzp8/j4cPH6Ju3bpo0qQJTp48ia+++gqzZ8/G8uXLoVKpdPpUlnmsKvPPXfqp0khKSgIA1KtXr9g2devWFbWlV4++81hZ+tH/pKamCrs2d+vWTSivLHPJ70DZbN26FQkJCcjPz0dycjJUKhU0Gg3Gjh2Lrl27Cu0qyzxy/ktv6dKluH79OpYuXQpLS8vntq0s88j5149SqdQpa9q0Kb766is4OjoKZZVlPvk9KFnhBr0NGzYs8m0NERER6N69Oz7//HMhaa4s81hV5p8JP1Ua2dnZAABjY+Ni29SoUQMA8Pjx4/8kJio7feexsvSjp9RqNaZOnYpHjx6hffv2oju8lWUu+R0om5MnT4r+Q8/Q0BATJ04U3uJQqLLMI+e/dE6ePIl169ahS5cu6NmzZ4ntK8s8cv7LplmzZpg5cybat2+PevXqISsrCxcuXMDSpUtx6dIljBgxAtHR0bCxsQFQeeaT34OSZWRkAAASExNRUFCAkSNHYvDgwahVqxYSEhIwd+5c/Prrr6hRowYWLlwIoPLMY1WZfy7pp0pD+/9vkCzufc5UOeg7j5WlHz316aef4tixY6hbty6++OILUV1lmUt+B8pm/vz5uHz5Mk6fPo1du3Zh2LBhWLFiBQYOHCjaw6GyzCPnv2Q5OTn4+OOPYWpqik8//bRUfSrLPHL+y2b48OEICAhA06ZNYWJiAmtra7z11lvYsmULXF1d8eDBA3z77bdC+8oyn/welKzwkQ21Wo133nkH06dPh52dHczMzODt7Y2IiAhIJBIolUrcunULQOWZx6oy/0z4qdIo/AWt8Ne2ohT+ulbYll49+s5jZelHQFhYGH755RdYWVlh7dq1ouf3gcozl/wO6MfIyAhNmzbF9OnT8eGHH+LSpUv47LPPhPrKMo+c/5J99dVXuHHjBkJDQ2FtbV2qPpVlHjn/5UMmkwkbuR04cEAoryzzye9ByZ697oEDB+rUt2rVCi1btoRGo0F8fLyoz6s+j1Vl/rmknyqN+vXrAwDu3LlTbJt79+6J2tKrR995rCz9XneLFi3CDz/8AEtLS6xduxb29vY6bSrLXPI78OL69euHxYsX4/fff0d+fj6qVatWaeaR81+y/fv3w8DAAEqlUufZ7b///hsAsHHjRvzxxx+ws7PD/PnzK808cv7LT+PGjQGI39ZSWeaT34OSPXvdtra2RbaxtbXFuXPncP/+fVGfV30eq8r8M+GnSqPw9V5Xr15FTk5Okbtlnj17FgDQvHnz/zQ2Kj1957Gy9Hudff7551izZg1q1aqFNWvWoGnTpkW2qyxzye/AizMzM4OhoSHUajUyMjLwxhtvVJp55PyXjkajwfHjx4utv3XrFm7duoXMzEwAlWceOf/l5+HDhwDEd0Ary3zye1Cyli1bCv9OT08vcuPO9PR0AICJiQmAyjOPVWX+uaSfKo26deuiZcuWyM/PR2xsrE798ePHce/ePVhZWUEul1dAhFQa+s5jZen3ulqyZAm+//57mJubY82aNWjWrFmxbSvLXPI78OISEhKgVqthZmYGCwsLAJVnHjn/Jfvtt99w+fLlIv/69u0LAJg2bRouX76MmJgYAJVnHjn/5WfPnj0AACcnDbzc5gAAEhBJREFUJ6Gssswnvwcls7GxgYuLCwDgzz//1KnPyMgQXm9X+B2oLPNYZeZfS1SJ7NmzR+vg4KD19PTU3rhxQyi/f/++tmfPnloHBwft2rVrKzBCGjp0qNbBwUG7Z8+eYtvoO4+Vpd/rZunSpVoHBwetQqHQnj17tlR9Kstc8jvwfAkJCdqYmBhtbm6uTl1iYqLW29tb6+DgoF20aJGorrLMI+dff9OnT9c6ODhoV61apVNXWeaR8186Fy5c0P72229atVotKs/Pz9euXr1a26xZM62Dg4P24MGDovrKMp/8HpQsLi5O6+DgoG3fvr32woULQnlOTo520qRJWgcHB23fvn21Go1GqKss81gV5l+i1f7/9oNElcScOXOwceNGVK9eHR4eHjA0NMSxY8eQlZWFLl26YPny5ZBKpRUd5mvj/PnzmDt3rnD8119/4fHjx7C3t4e5ublQvnnzZlE/feexsvR7XcTFxWHcuHEAnv5y/+abbxbZrnHjxsLGTYUqy1zyO1C8bdu24eOPP4aZmRlatGiBN954A48fP8atW7fw119/AQDeeustLFu2TGcpZGWZR86/fgrfxz1t2jQEBQXp1FeWeeT8l2z//v0ICQlBrVq1YG9vDxsbGzx+/BhXrlxBSkoKDAwM8OGHH2L06NE6fSvLfPJ7ULLFixdj9f+1d+dBVdX/H8ef9wJXA71SuCOa2Uij5pKK1mROSC6lgpk5jblMJmmLpI6GVDOZ26Q5Y6Fmht9S1EzKJbRIXJpGEZXFrSwTQ5TU6wYSi6Dc3x/MPT+Qy+bXr8bl9fgLzvl8znmfc+4/78/5fN7nP//Bw8ODLl264O3tzZEjR7DZbDRr1ozVq1eXq+tTW55jbX/+SvilVoqNjWXt2rWcOHGC4uJiHnroIYYPH85LL72E2ayVKnfT/v37GTNmTJXt/vjjj3Lbbvc51pZ+dYEj4atKQEAA0dHR5bbXlmep34BzZ86cYePGjSQlJZGRkcHVq1ex2+00adKETp06MXToUIKCgirsX1ueo55/zVWV8EPteY56/pU7c+YMq1ev5ujRo2RmZpKVlYXJZKJ58+Z0796dUaNGlZnOf6va8jz1O6hafHw80dHRHD9+nPz8fFq2bElgYCChoaFO1/ZD7XmOtfn5K+EXERERERERcUH/7uEIEREREREREbktSvhFREREREREXJASfhEREREREREXpIRfRERERERExAUp4RcRERERERFxQUr4RURERERERFyQEn4RERERERERF6SEX0RERP41IiMj8ff3Jzw8vNw+f39//P39OXv27D2ITEREpPZRwi8iIuLCwsPDjUS5Y8eOXL58udL2O3bsMNr7+/uzcePGuxSp6xk9enSFgxciIiJ3gxJ+ERGROuLGjRvExsZW2mbz5s13J5jb0LZtW9q2bYuHh8e9DkVERKRWUMIvIiJSB7Rs2RKALVu2VNgmKyuLn3/+GU9PT7y9ve9SZNUXFxdHXFwczZo1u9ehiIiI1ApK+EVEROqArl270rp1a3777Tf+/PNPp222bdtGUVERAwYMoF69enc5QhEREbnT3O91ACIiInJ3BAcHExkZyebNm5k+fXq5/Y63/8HBwSQkJFR5vF27dhETE8ORI0fIzs6mYcOGdOnShVGjRtGnT58K+506dYolS5awb98+cnNzadmyJYMHDyY0NLTS8/n7+wOwc+dOWrVqZWy/efMmBw8eZMeOHaSkpHD+/HmuXbuGt7c3Xbp04eWXX+bxxx93eszRo0dz4MAB5s+fz7PPPssXX3zB1q1bOXfuHF5eXvTu3ZuwsDAefPDBKu9HTQQGBpKZmcnq1avx9/fns88+Iz4+HpvNxv3330/fvn2ZPHkyTZs2rfAY586dY9WqVezZs4fMzEwAWrRoQdeuXRk6dCi9e/cu1ycjI4OoqCj27t2LzWajfv36tG/fnpCQEJ5//nnc3NzK9Sl9j/r378+yZcvYvn07NpuNJk2aMHToUCZOnGgMEu3bt4+oqCiOHTvG9evX6dixI1OmTKFHjx4VXktubi7R0dHEx8eTnp5OYWEhLVq04KmnnmL8+PG0aNGiprdYRERQwi8iIlJnOBL+2NhYpk2bhtn8/xP9/vrrLw4fPkyLFi3o1atXpccpKipi5syZZeoBNGjQgCtXrrB79252797N+PHjmTFjRrm+Bw8eZMKECeTn5xv9zp49S2RkJHv27CEgIKDG15WWlsbYsWON/y0WCx4eHly8eJEdO3awY8cOpkyZwsSJEys8xj///MNLL73Eb7/9hsViwWw2c+XKFX744QcSEhKIiYmhdevWNY6tKufPn2fmzJlkZmZy3333YTKZsNlsxMTEkJCQwKZNm2jUqFG5fj/99BMzZsygoKAAgHr16uHm5kZaWhppaWkkJiaya9euMn12795NWFgY169fB6Bhw4bk5+eTlJREUlISP/zwA0uXLsXT09NprNeuXWPEiBGcOnUKT09PiouLOXv2LMuWLeP48eMsX76ctWvXMnv2bEwmE56ensbxx40bx6pVq+jevXu546alpTFhwgRj0MLd3R2LxcLp06eJjo7m+++/57PPPnPaV0REKqcp/SIiInWEn58fjz32GBcuXCAxMbHMPkexviFDhpQZCHBm4cKFxMbG4uvry6JFi0hJSSE5OZmUlBQ+/PBDGjRowMqVK9m6dWuZftnZ2YSFhZGfn0/Hjh3ZsmULycnJpKam8tFHH/H777+zbt26Gl+Xh4cHAwcOZPny5ezdu5cjR46QmppKQkICYWFhuLm5sXjxYg4fPlzhMSIjI8nOziYqKopDhw6RmprK2rVrad68OVlZWSxatKjGcVXHnDlzsFqtrF+/3jjvsmXLsFqtZGZm8vnnn5frk5qaytSpUykoKKBXr17ExMRw+PBhUlNT2bdvH0uXLi03aJORkcHUqVO5fv06AQEB/PjjjyQlJRnPzGKxkJCQwNy5cyuMdcmSJdjtdtauXUtqaiopKSnMmTMHd3d3du/ezdKlS5k/fz6hoaHs37+f5ORkdu3aRbdu3SgqKmL+/PnljpmTk2Mk+0FBQWzatMm4lp07dxIcHEx2djaTJ0/m2rVr//0NFxGpY5Twi4iI1CEhISFA2Wr8drvdeFvv2F+R9PR0oqOjsVqtrFq1isGDB+Pl5QWAl5cXI0eOZPbs2QAsX768TN81a9Zw+fJlvL29iYqK4pFHHgFKEvaQkBBmzZpFTk5Oja+pbdu2fPLJJzz99NM0btwYk8kEgI+PD6+//jpvvPEGdrud9evXV3iMwsJCvvzyS/r06YObmxtms5kePXoQEREBlCxfKCwsrHFsVbFYLHz55Zd069YNKHm73a9fPyZNmgSUvMm/1bx587hx4wY9e/Zk5cqVdO7c2bjmBx54gKCgoHLJ9fLly8nLy6N169asWLGChx56yDj/yJEjee+99wD47rvvOH36tNNY8/Pz+fzzz42p+RaLhREjRhAcHAzAp59+ypAhQ5g6dSpWqxXAGBQymUwcPXqUv//+u8wxo6KiyMzMpF+/fixZsoQOHTrg7l4yAbVVq1YsWLCAvn37cunSJWJiYmp4d0VERAm/iIhIHTJo0CDq1atHfHw8eXl5ABw4cIDMzEw6depEu3btKu2/efNmiouLCQoKws/Pz2mb/v37Y7FY+PPPP7HZbMZ2R/L64osv8sADD5TrN3ToUHx9fW/30ioUGBgIQEpKSoVtBgwYQJs2bZz2NZlMFBYWkpGRccdje/HFF7n//vvLbQ8KCgLg7NmzxnOCkunvR44cAWD69OnV+kSh3W5n+/btAIwbN4777ruvXJsRI0bQrFkz7Ha700EGgIEDBzq9R0888YTx92uvvVZuv6+vr9Hv1oKRjoGncePGGYMWt3ruuecAqlVXQkREytIafhERkTrEarXy9NNPExcXx/bt2wkJCTGSrqre7kPJdHIo+UTeL7/8UmG7GzduACVr1Js2bUphYSEnT54EoGfPnk77mEwmevToYazlromCggLWr1/Pzp07OXnyJNeuXTNicCg9+HCrRx991Ol2Dw8PfHx8uHTpEtnZ2TWOqyoVnbf0pwdzcnKMdfWOZQmOgoTVcebMGWPmREX1GcxmMwEBAcTGxvLrr786bdO+fXun2318fICSOgLOBgQcbdLT08vcw3PnznH+/HkAwsLCKlxKUlRUZLQXEZGaUcIvIiJSx4SEhBAXF8eWLVsYOHAg27dvx8PDw3iTWpmLFy8CkJeXV+bNc0Ucxfmys7O5efMmQKWV50snutVls9kYPXo06enpxjZPT0+sVitms5mbN29y9erVSuN1LEtwxlF9/tYBhDuhovOW/iyiI+EFuHTpEkCNqtZfuXLF+Luy+9u8efNy7Utr0qSJ0+2ORL30copbOar/l76Hjt9SZecszVGgUEREqk8Jv4iISB3Tp08ffHx8SExMZM2aNfzzzz8EBgY6nWZ/q+LiYgDeffddxowZc8djs9vtNe4zb9480tPT8fPzY8aMGfTq1atMZfuMjAyeeeaZOxlmrfW/qENwuxy/JYDk5GQaNGhwD6MREXFNWsMvIiJSx7i7u/Pss89SXFzM4sWLAYzCa1Vp3LgxgDE9v7oaNWpkvOWtbGp96be+1VFYWMjOnTsB+Pjjj+nfv3+5z9g53oq7Asf9r8n09tIDOZUtl3BMr6/OwM+d4FgKADX/PYmISPUo4RcREamDHOv1i4qKaNSokVHYripdu3YFSr7pXnqqeVUsFgsPP/wwAElJSU7b2O12Dh48WO1jAly9etV4a92hQwenbVyp2Jtj3X5WVhaHDh2qVh8/Pz+jav7+/fudtikuLubAgQMAdOzY8b8PtJpxOQYwHEUFRUTkzlLCLyIiUgd16tSJt956i1deeYWIiAgsFku1+g0bNgyz2YzNZnP6jfjSbi1yN3DgQAA2bNhAVlZWufbbtm2rccG+Bg0aGOvG//jjj3L7bTYba9asqdEx/83atWtH586dAVi4cGG1Bl1MJpOxpGH16tVGXYXSYmJiuHDhAiaTiQEDBtzZoCsxbNgwAL7++mvS0tIqbGe322/rk40iInWdEn4REZE66s033+Sdd96pVnV+h3bt2jF27FgAIiMjmTVrFmfOnDH25+bmsnfvXqZPn05YWFiZvqNGjcLHx4erV68yfvx4fv/9d6BklsGWLVt4//33adiwYY2uwcvLy5h1EBERwfHjx4GSN9b79u1j9OjRt1UX4N8sPDwcd3d3kpKSePXVVzl69Kix78qVK2zbto1p06aV6TNx4kQ8PT2x2WyEhoZy6tQpoGRJxIYNG5gzZw4AL7zwQoWV9v8XQkND8fPzIy8vj5dffplNmzaRm5tr7D937hwbNmzg+eefJz4+/q7FJSLiKlS0T0RERGpk+vTpFBQU8PXXX7Nu3TrWrVuHl5cXbm5u5OTkGAl2QEBAmX6NGjVi8eLFTJgwgWPHjhEcHEzDhg25fv06hYWFdOvWjZ49e7JixYoaxTNz5kzGjBnDiRMnCAkJwdPTk+LiYgoKCvD29mbu3Lm88cYbd+z677Xu3buzYMECwsPDSUxM5IUXXqB+/fqYzWbjSwS+vr5l+rRu3ZpFixbx9ttvc+DAAQYNGoTVaiU/P9+YJfD4448TERFxV6/FarWycuVKJk2aRFpaGuHh4URERGC1WikoKChTmb+iLwCIiEjFlPCLiIhIjbi5ufHBBx8wZMgQ1q9fT3JyMhcvXqSoqIiWLVvSoUMHgoKC6NevX7m+AQEBbNq0icjISBITE8nNzcXX15fBgwczYcKEGif7ULKu/ZtvviEyMpKDBw+Sl5dH06ZNefLJJ5k0aZLxOUBX8txzz9G5c2e++uor9uzZw/nz5zGbzbRr147HHnvMaRHGwMBAYmNjiYqKYu/evdhsNurXr0/nzp0JCQlh+PDhRmHFu6lNmzZs3ryZb7/9lri4OE6cOEFOTg716tXD39+fgIAABgwYQPfu3e96bCIitZ3J7mrz3EREREREREREa/hFREREREREXJESfhEREREREREXpIRfRERERERExAUp4RcRERERERFxQUr4RURERERERFyQEn4RERERERERF6SEX0RERERERMQFKeEXERERERERcUFK+EVERERERERckBJ+ERERERERERekhF9ERERERETEBf0fhC1fqjaEUiIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rank neighborhoods by curbside income level, highest to lowest\n",
    "plt.figure(figsize=(15, 10))\n",
    "neighborhood_income = sns.barplot (\n",
    "    y = 'Neighborhood',\n",
    "    x = 'Median Income',\n",
    "    data = combMetrics,\n",
    "    order = combMetrics.sort_values('Median Income', ascending = False).Neighborhood,\n",
    "    color = \"orange\"\n",
    ")\n",
    "neighborhood_income.set_title('Median income level in Buffulo, NY Neighborhoods')\n",
    "neighborhood_income.set_yticklabels(neighborhood_income.get_yticklabels(), fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph displays the household median income grouped by each neighborhood in Buffalo, NY. If we look at the neighborhoods with the top incomes among all, we will notice that the 4 most wealthy neighborhoods are also among the top-five in terms of curbside recycling rate (Central Park, Elmwood Bidwell, Parkside, North Park). Now we turn to the least wealthy neighborhoods at the bottom of the graph, among the 6 neighborhoods with the lowest income, 3 of them are also among the bottom-five in the curbside recycling rate barplot (Broadway Fillmore, MLK Park, Genesee-moselle.\n",
    "\n",
    "This discovery seems to support our hypothesis. To observe the relationships between financial factors and curbside recycling rates more indepth, we will create scattorplots to better visualize them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) Relationship\n",
    "\n",
    "We are looking into three financial factors among the neighborhoods: median income level, employment rate, and poverty rate. These three variables depict the economic status of the neighborhoods from three different angles, so if we can find considerable relationship and correlation in all three aspects, that would be a strong support to our hypothesis.\n",
    "\n",
    "First we want to test the relationships. Is there a relationship between financial factors and curbside recycling rate? If so, what does the relationship look like?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# median income vs recycle rate\n",
    "data_1 = combMetrics[['Curbside recycling rate', 'Median Income' ]]\n",
    "data_1.columns = ['Recycle_Rate', 'Median_Income']\n",
    "\n",
    "sns.lmplot (\n",
    "    data = data_1,\n",
    "    x = 'Median_Income',\n",
    "    y = 'Recycle_Rate',\n",
    "    scatter_kws ={'color': 'orange'},\n",
    "    line_kws={'color': 'skyblue'},\n",
    "    fit_reg = True,\n",
    "    height=6, aspect=2, x_jitter=.5, y_jitter=.5\n",
    ")\n",
    "ax = plt.gca()\n",
    "ax.set_title(\"Recyling Rate Based off of Median Income\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This scatterplot shows the relationship between the Median Income vs the Curbside Recycling Rate of neighborhoods in Buffalo. The best fitting linear regrassion suggests a likely relationship between curbside recycling rate and income level. We see that the line has a positive slope, indicating a positive relationship, that is higher income neighborhoods practice better curbside recycling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Employment Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Recyling Rate Based off of Employment Rate')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# employment rate vs recycle rate\n",
    "data_emp = combMetrics[['Curbside recycling rate', 'Employment Rate']]\n",
    "data_emp.columns = [ 'Recycle_Rate', 'Employment_Rate']\n",
    "\n",
    "sns.lmplot (\n",
    "    data = data_emp,\n",
    "    x = 'Employment_Rate',\n",
    "    y = 'Recycle_Rate',\n",
    "    scatter_kws ={'color': 'green'},\n",
    "    line_kws={'color': 'skyblue'},\n",
    "    fit_reg = True,\n",
    "    height=6, aspect=2, x_jitter=.5, y_jitter=.5\n",
    ")\n",
    "ax = plt.gca()\n",
    "ax.set_title(\"Recyling Rate Based off of Employment Rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employment Rate vs Curbside Recycling Rate plot also displays a positive slope, similar to the income factor above. The linear regression line here goes from ~12% to ~18%, which is slightly flatter compared to the ~12% to ~20% regression line in the graph above. At the same time, more datapoints seem to lie far from the best fitting line. One neighborhood with middle level of employment rate even holds the highest curbside recycling rate, as shown by the outlying datapoint in the middle top. These suggest that the relationship between curbside recycling rate and employment rate might be weaker than the one with income level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Poverty Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Recyling Rate Based off of Poverty Rate')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# proverty rate vs recycle rate\n",
    "data_pov = combMetrics[['Curbside recycling rate', 'Poverty Rate']]\n",
    "data_pov.columns = [ 'Recycle_Rate', 'Poverty_Rate']\n",
    "\n",
    "sns.lmplot (\n",
    "    data = data_pov,\n",
    "    x = 'Poverty_Rate',\n",
    "    y = 'Recycle_Rate',\n",
    "    scatter_kws ={'color': 'red'},\n",
    "    line_kws={'color': 'skyblue'},\n",
    "    fit_reg = True,\n",
    "    height=6, aspect=2, x_jitter=.5, y_jitter=.5\n",
    ")\n",
    "ax = plt.gca()\n",
    "ax.set_title(\"Recyling Rate Based off of Poverty Rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This scatterplot shows the relationship between Poverty Rate and Curbside Recycling Rate of neighborhoods in Buffalo. As shown by the linear regression here, as poverty rate increases from 10% to 50%, curbside recycling rate drops by approximately 7%. The negative slope indicates a negative relationship, that is as poverty rate in a neighborhood increases, curbside recycling rate decreases. If such a decrease continues on for even higher proverty rate, then neighborhoods with 100% proverty rate might almost not do recycle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## c) Correlation\n",
    "\n",
    "Now that we see there is very likely a relationship between financial level and curbside recycling rate, we also want to know how strong the correlation is.\n",
    "\n",
    "Before we analysis the correlation between financial factors vs curbside recycling rate, let's first check the ditribution of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram to see the distribution of our data\n",
    "fig, axs = plt.subplots(1, 4, figsize=(30, 6))\n",
    "sns.histplot(data=combMetrics, x=\"Curbside recycling rate\", kde=True, color=\"skyblue\", ax=axs[0])\n",
    "sns.histplot(data=combMetrics, x=\"Median Income\", kde=True, color=\"orange\", ax=axs[1])\n",
    "sns.histplot(data=combMetrics, x=\"Poverty Rate\", kde=True, color=\"red\", ax=axs[2])\n",
    "sns.histplot(data=combMetrics, x=\"Employment Rate\", kde=True, color=\"green\", ax=axs[3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histograms above show the distribution of our data on curbside recycling rate and the three financial factors that we are looking into. We notice that the data on curbside recycling rate, proverty rate, and employment rate are only slightly skewed, but the data on median income is highly skewed to the right. Therefore, we will include a Spearman's correlation test besides the normal Pearson's correlation when checking the correlation of median income vs. curbside recycling rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median Income\n",
    "\n",
    "The following are the Pearson's correlation, Spearman's correlation, and OLS results based on the relationship between Median Income and Curbside Recycling Rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7887587803414294"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson's r of median income vs recycle rate\n",
    "combMetrics['Median Income'].corr(combMetrics['Curbside recycling rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6178665179063303"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spearman's rho of median income vs recycle rate\n",
    "combMetrics['Median Income'].corr(combMetrics['Curbside recycling rate'], method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Recycle   R-squared:                       0.622\n",
      "Model:                            OLS   Adj. R-squared:                  0.609\n",
      "Method:                 Least Squares   F-statistic:                     47.75\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):           1.36e-07\n",
      "Time:                        23:48:44   Log-Likelihood:                -57.631\n",
      "No. Observations:                  31   AIC:                             119.3\n",
      "Df Residuals:                      29   BIC:                             122.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      9.1512      0.833     10.983      0.000       7.447      10.855\n",
      "Income         0.0002   2.46e-05      6.910      0.000       0.000       0.000\n",
      "==============================================================================\n",
      "Omnibus:                        0.179   Durbin-Watson:                   1.555\n",
      "Prob(Omnibus):                  0.914   Jarque-Bera (JB):                0.316\n",
      "Skew:                           0.156   Prob(JB):                        0.854\n",
      "Kurtosis:                       2.616   Cond. No.                     9.80e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.8e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# OLS table of median income vs recycle rate\n",
    "data_income = combMetrics[['Median Income', 'Curbside recycling rate']]\n",
    "data_income.columns = ['Income', 'Recycle']\n",
    "outcome, predictors = patsy.dmatrices('Recycle ~ Income', data_income)\n",
    "mod_log = sm.OLS (outcome, predictors)\n",
    "fit_res = mod_log.fit()\n",
    "print (fit_res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pearson's correlation states that there is a very strong correlation of almost +0.8 between Curbside Recycling Rate and Median Income. But because the data of Median Income is highly skewed, we also want to double check with the Spearman correlation. The Spearman correlation also shows at least a moderate correlation of +0.6. Also as seen in the OLS table, the p-value is smaller than 0.01, which means it is very unlikely to get our result by chance alone, leading us to reject the null hypothesis. So we can conclude that there is a significant correlation between Curbside Recycling Rate and Median Income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Employment Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5975470693488579"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson's r of employment rate vs recycle rate\n",
    "combMetrics['Employment Rate'].corr(combMetrics['Curbside recycling rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Recycle   R-squared:                       0.357\n",
      "Model:                            OLS   Adj. R-squared:                  0.335\n",
      "Method:                 Least Squares   F-statistic:                     16.11\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):           0.000386\n",
      "Time:                        23:48:45   Log-Likelihood:                -65.870\n",
      "No. Observations:                  31   AIC:                             135.7\n",
      "Df Residuals:                      29   BIC:                             138.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    -14.5521      7.262     -2.004      0.055     -29.405       0.301\n",
      "Employment     0.3295      0.082      4.013      0.000       0.162       0.497\n",
      "==============================================================================\n",
      "Omnibus:                       14.153   Durbin-Watson:                   1.841\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               17.296\n",
      "Skew:                           1.155   Prob(JB):                     0.000175\n",
      "Kurtosis:                       5.838   Cond. No.                     1.71e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.71e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# OLS table of employment rate vs recycle rate\n",
    "data_emp = combMetrics[['Employment Rate', 'Curbside recycling rate']]\n",
    "data_emp.columns = ['Employment', 'Recycle']\n",
    "outcome, predictors = patsy.dmatrices('Recycle ~ Employment', data_emp)\n",
    "mod_log = sm.OLS (outcome, predictors)\n",
    "fit_res = mod_log.fit()\n",
    "print (fit_res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the Pearson's correlation, there is about a moderate correlation of about +0.6 between Employment Rate of the neighborhoods and their Curbside Recycling Rate. Again, the p-value is smaller than 0.01, so we can reject the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Poverty Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6562128487081829"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson's r of proverty rate vs recycle rate\n",
    "combMetrics['Poverty Rate'].corr(combMetrics['Curbside recycling rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Poverty   R-squared:                       0.431\n",
      "Model:                            OLS   Adj. R-squared:                  0.411\n",
      "Method:                 Least Squares   F-statistic:                     21.93\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):           6.11e-05\n",
      "Time:                        23:48:45   Log-Likelihood:                -107.86\n",
      "No. Observations:                  31   AIC:                             219.7\n",
      "Df Residuals:                      29   BIC:                             222.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     71.8271      8.523      8.428      0.000      54.396      89.258\n",
      "Recycle       -2.7022      0.577     -4.683      0.000      -3.882      -1.522\n",
      "==============================================================================\n",
      "Omnibus:                        1.354   Durbin-Watson:                   1.355\n",
      "Prob(Omnibus):                  0.508   Jarque-Bera (JB):                1.293\n",
      "Skew:                           0.422   Prob(JB):                        0.524\n",
      "Kurtosis:                       2.464   Cond. No.                         86.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# OLS table of proverty rate vs recycle rate\n",
    "data_pov = combMetrics[['Poverty Rate', 'Curbside recycling rate']]\n",
    "data_pov.columns = ['Poverty', 'Recycle']\n",
    "outcome, predictors = patsy.dmatrices('Poverty ~ Recycle', data_pov)\n",
    "mod_log = sm.OLS (outcome, predictors)\n",
    "fit_res = mod_log.fit()\n",
    "print (fit_res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is about a strong correlation of about -0.65 between Poverty Rate of the neighborhoods and their Curbside Recycling Rate. The correlation value is negative, meaning their is a negative relationship, just as we discovered in the relationship section above. According to the OLS table, the p-value is smaller than 0.01, leading us to reject the null hypothesis.\n",
    "\n",
    "In conclusion, all 3 financial factors we have display at least a moderate correlation with curbside recycling rate in Buffalo. Generally speaking, the higher income level and employment rate a neighborhood has, or the lower poverty rate it suffers, then it is more likely that it owns higher curbside recycling rate. This observation strongly support our hypothesis that, among neighborhoods in Buffalo, NY, there is a correlation between curbside recycling rates and financial factors, and wealthier neighborhoods practice recycling better.\n",
    "\n",
    "Comparing between the correlation of the 3 financial factors, we can see that poverty rate is the most correlated to curbside recycling rate. Income level displays a strong correlation , but since the data for it is highly skewed, research needs to be done with more normalized datasets to be sure. Employment rate seems to predict curbside recycling rate less well compared to the other two factors, but there is still a moderate correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## d) Surprising Finding About Gender\n",
    "\n",
    "What other factors might influence, or at least correlate to curbside recycling rate? As we played with different variables in the dataset and tried to answer this question, we found something unexpected about the relationship between Gender Ratio and Curbside Recycling Rate. Gender ratio in a neighborhood seems to effect its curbside recycling rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gender (male ratio) vs recycle rate\n",
    "data_male = combMetrics[['Curbside recycling rate', 'Percent Male']]\n",
    "data_male.columns = ['Recycle_rate', 'Percent_Male' ]\n",
    "\n",
    "sns.lmplot (\n",
    "    data = data_male,\n",
    "    x = 'Percent_Male',\n",
    "    y = 'Recycle_rate',\n",
    "    scatter_kws ={'color': 'purple'},\n",
    "    line_kws={'color': 'skyblue'},\n",
    "    fit_reg = True,\n",
    "    height=6, aspect=2, x_jitter=.5, y_jitter=.5\n",
    ")\n",
    "ax = plt.gca()\n",
    "ax.set_title(\"Recyling Rate Based off of Percentage of Males Present\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           Recycle_rate   R-squared:                       0.299\n",
      "Model:                            OLS   Adj. R-squared:                  0.275\n",
      "Method:                 Least Squares   F-statistic:                     12.36\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):            0.00146\n",
      "Time:                        23:48:45   Log-Likelihood:                -67.213\n",
      "No. Observations:                  31   AIC:                             138.4\n",
      "Df Residuals:                      29   BIC:                             141.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept       -0.1063      4.188     -0.025      0.980      -8.671       8.459\n",
      "Percent_Male     0.3115      0.089      3.516      0.001       0.130       0.493\n",
      "==============================================================================\n",
      "Omnibus:                        3.202   Durbin-Watson:                   1.929\n",
      "Prob(Omnibus):                  0.202   Jarque-Bera (JB):                2.229\n",
      "Skew:                           0.652   Prob(JB):                        0.328\n",
      "Kurtosis:                       3.153   Cond. No.                         504.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# OLS table of gender (male) vs curbside recycling rate\n",
    "outcome, predictors = patsy.dmatrices('Recycle_rate ~ Percent_Male', data_male)\n",
    "mod_log = sm.OLS (outcome, predictors)\n",
    "fit_res = mod_log.fit()\n",
    "print (fit_res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why would gender effect recycling rate? In fact, even if we are told that curbside recycling rate differs significantly by gender, from the widely held stereotype of male and female, we are likely to expect that neighborhoods with more male residents would care less about cleaning and recycling, and thus have lower curbside recycling rate. Yet surprisingly, our data seems to tell the opposite. As seen in the scatterplot above, neighborhoods in Buffalo with higher percentage of male residents actually perform better curbside recycling on average. The p-value of 0.01 further indicates that this finding is statistically significant.\n",
    "\n",
    "We have thought of two possible explanations to this phenomenon:\n",
    "1. Maybe male residents purchase more recyclable containers, such as boxes of beer and cans of soda. While female residents purchase less of those and reuse things when they can, so most of their garbbage are unrecyclable, such as food waste.\n",
    "2. Income level might be a confounder here. As shown in the plot below, there is a slightly positive relationship between male ratio and median income of a neighborhood, that is neighborhoods with more male residents are also the wealthier ones. Since income level is strongly correlated with curbside recycling rate as we analyzed above, it could make sense that males who earn more are performing better recycling too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graoh to support possible explanation 2 above\n",
    "income_male = combMetrics[['Percent Male', 'Median Income']]\n",
    "income_male.columns = ['male', 'income']\n",
    "sns.lmplot (\n",
    "    data = income_male,\n",
    "    x = 'male',\n",
    "    y = 'income',\n",
    "    scatter_kws = {'color': 'purple'},\n",
    "    line_kws = {'color': 'blue'},\n",
    "    fit_reg = True,\n",
    "    height=6, aspect=2, x_jitter=.5, y_jitter=.5\n",
    ")\n",
    "ax = plt.gca()\n",
    "ax.set_title(\"Median Income vs Males Present\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why did we choose the two datasets? \n",
    "\n",
    "This is the most ideal dataset we found for our research. It shows the rate of recycling grouped by income rates, unemployment rates, and poverty rates, along with some other factors such as gender, age, and ethnicity.\n",
    "\n",
    "### How we made sure the community boundaries we had are consistent between the two datasets\n",
    "\n",
    "We have found a map for the region with the most recent names for the neighborhoods, and we have matched the names on the combined dataset with the names on the map for consistency. However, since none of our group members are actually from the area, there are still possibilities that the neighborhood names are not accurate.\n",
    "\n",
    "### Potential Bias in Our Research\n",
    "\n",
    "Ethnicity bias: Since we are only making observations on one city, Buffalo NY, there might be potential bias on the percentage/composition of different racial backgrounds. The observations we have might be hard to make generalizations to the whole U.S. population.\n",
    "\n",
    "### How did we handle the privacy/bias concerns\n",
    "\n",
    "Since the datasets are on a public website and both of them use curbside recycling inside of the actual recycling rate, there are less privacy concerns to privacy issues. None of the datasets include personal information regarding residents in those neighborhoods, so there’s no concern of leaking personal information in our project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our project, we started by merging and renaming the datasets we found. To ensure the names of each neighborhood is accurate, we found a map with the latest names for each of the neighborhoods in Buffalo, NY. Then we used the combined dataset called comMetrics to make graphs showing the income and financial factors of each neighborhood. Through the graphs, we realize there could be some relationships between financial factors and curbside recycling rates like what we stated in the null hypothesis. Therefore, we decided to make an OLS chart showing all the statistical aspects of our dataset.\n",
    "\n",
    "Interestingly, the p-values for the three graphs: Recycling Rate Based Off of Median Income, Recycling Rate Based off of Employment Rate, and Recycling Rate Base off of Poverty Rate are all 0.000, which means there is a strong statistical significance between these values. For curbside recycling rate and median income, the Pearson Correlation is 79%, which shows there is a strong positive correlation between the median income and curbside recycling rate per neighborhood. For curbside recycling rate and employment rate, there is a Pearson Correlation of 60%, which shows there is a positive correlation between these two variables in Buffalo, NY. Then, for curbside recycling rate and poverty rate, there is a Pearson correlation of -66%, which shows a negative correlation between the two variables. This means that as the poverty rate increases in the neighborhood, the curbside recycling rate is going to drop.\n",
    "\n",
    "Since we are using null hypothesis testing here for our research, at the 5% significance level, since the p-values are all equal to 0.000 in our research, it is sufficient enough to reject the null hypothesis. Therefore, we are confident to say there exists a correlation between curbside recycling rates and financial factors in each neighborhood of Buffalo, NY.\n",
    "\n",
    "We are aware that there are some other variables which might contribute to the curbside recycling levels in neighborhoods of Buffalo, NY., but for our research we are only going to focus on the relationship between financial factors and curbside recycling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Athena Hao: Helped with writing the overview, background, and prior work. Also helped with writing the ethics & privacy, and conclusion.\n",
    "- Allison Ngo: Helped with data cleaning, such as merging the two datasets and renaming them.\n",
    "- Karina Sanchez: Helped with EDA, such as making the ols chart and the plots.\n",
    "- Lingye Zhuang: Helped with both data cleaning and EDA, such as writing the analysis for each graph. "
   ]
  }
 ],
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