rough_draft.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our project we have decided to dive deep into the issue of climate change and the rise in pollution levels over the past decade. Our generation is already dealing with the negative effects of climate change and it is something that will be an issue that we must deal with for most likely the rest of our lives. While we are aware of how prevalent the climate change issue is in our world, we want to analyze how much climate change has actually become an issue, specifically in New York state where we are both currently students of Cornell University. These issues are ones that weigh heavily on many of our consciences and is something that affects our everyday decisions. In our analysis we want to see if over the years, as pollution levels rise and climate change becomes more rampant, are people making more sustainable choices to fight this reality. We will look at recycling rates and bike usage to quantify these sustainable efforts to help fight the climate change that is occuring. While we know these small individual actions will not completely save the planet, we want to see if there is positive change occuring in our society to fight against our current reality in whatever ways an individual can because small changes can lead to big movements.\n",
    "\n",
    "For our data sources we use a dataset to examine the pollution levels in New York called, ghgp_data_by_year.csv which shows pollution levels across New  York organized by cities, industry sectors, and total emissions by year. The other dataset we have includes recycling rates in New York City where pollution is a major concern which is called recycling-diversion-and-capture-rates.csv which gives us the date, the borough, the recycling diversion rate, and the recycling capture rate for the years of 2010 and 2016. The other datasets we have include bike usage by Citi Bike in New York City. Here we have the date, the borough, and the bike’s usage information such as the birth year, gender, and type of user, and the time duration of the bike rental. For our overall research question, we are interested in how an increase in pollution levels over the last couple decades have affected public sentiment towards environmental activism and sustainable practices. We are interested in better understanding the relationship between climate change throughout the years and sentiment towards environmental activism and sustainable practices such as recycling and bike usage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pollution dataset\n",
    "For the dataset on greenhouse gas emissions, each observation is of a facility that emits greenhouse gases. The majority of the facilities include power plants and chemical production centers. The attributes for each of these observations include FRS ID, facility name, city, state, zip code, address, county, latitude, longitude, NAICS code, industry subparts, industry sector, and emission for the years 2011 through 2018.  This dataset was created by the Environmental Protection Agency (EPA) in order to monitor greenhouse gas emissions for specific facilities across industries over time. Since the EPA sets environmental regulations, this data was likely collected in order to better understand how much pollution certain facilities contribute to the environment in order to determine a plan for regulating these industries. Given this, each facility likely provides the EPA with this data and is aware of the data collection that is occurring. The raw data can be found on our github page under the csv file that is named ghgp_data_by_year or our cleaned dataset version called ghg_data_ny."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recycling dataset\n",
    "For the dataset on recycling rates in New York City, we have divided the data set into two dividing by the two years present in the dataset including 2010 and 2016. The attributes for this dataset include Zone, District, Fiscal Month Number, Fiscal Year, Month Name, Diversion Rate, and Capture Rate. This dataset was created by \n",
    "The raw data can be found on our github page under the csv file that is named recycling-diversion-and-capture-rates or our cleaned dataset versions called recycling_2010 and recycling_2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biking dataset\n",
    "For the dataset on bike usage in New York City, we got our dataset from Citi Bike which has data on bike usage for every month and year since 2013. We have chosen to examine two datasets for the years of 2013 and 2016 to most closely resemble the years for the recycling datasets. The attributes for this dataset include Zone, Start Station Latitude, Start Station Longitude, tripduration, starttime, stoptime, start station id, end station id, end station name, end station latitude, end station longitude, bikeid, usertype, birthyear, and gender . The raw data can be found on our github page under the csv file that is named 201306-citibike-tripdata.csv and 201606-citibike-tripdata.csv or our cleaned samples of the dataset called biking_2013.csv and biking_2016.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-Registration Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our two analyses we would like to understand how both individuals and local government have been able to carry out and influence sustainable human actions and behaviors over time as pollution levels change. Using our first two datasets, the goal of our first hypothesis test is to understand if a higher percentage of waste is being recycled in New York City over time across multiple districts. By comparing this against our dataset that maps pollution levels for NYC and another for the whole country, we are interested in whether the trends in recycling match that of pollution levels.  We will use a linear regression model with our x input being the pollution levels and seeing if there is a positive correlation with the y output of recycling. We would like to do a similar analysis with the biking and use a linear regression model with our x input in this case also being pollution levels, but this time comparing that with our y output of bike usage and see if there is a positive correlation. This is an appropriate method to address our research question because we want to see if people’s knowledge of a rise in pollution levels (the x variable) affect people’s participation in sustainable practices (recycling and bike usage as the y variables).\n",
    "\n",
    "For our second hypothesis test, we plan on using Citi bike data from NYC since biking is more sustainable than other forms of transportation. We are interested in seeing how pollution levels might correlate with higher uses of the bikes, greater construction of bike routes on the part of the government, and if there are certain correlations between the location of where bike routes are established and the sustainable behaviors of the people. We plan on creating a multivariable Linear Regression model where we will group by the 5 boroughs of NYC and use measures of recycling such as the diversion rate (the percent of total waste that is recycled) and capture rate (the total recyclable waste that is sent to be recycled) as our X inputs to predict the number of bike routes in each area, the Y input. For this model, we’ll also have to create a new dataframe where we join the recycling data and the biking data. This model is appropriate because it will help us see if there is a correlation between how much people are biking and how much they are recycling to understand if people are engaging in environmentally sustainable behaviors. This will help us address our research question as it will help explain the sentiment towards environmental activism and how it varies between the NYC boroughs so that we can provide more context for our previous model which directly compares pollution levels to environmental activism. \n",
    "\n",
    "These two tests will hopefully help us understand if government actions like constructing bike routes might be driving responses (which might lead to higher Citi bike use) to rising pollution levels or if individuals are taking action of their own and are becoming more environmentally conscious (this can be inferred from the recycling data analysis) as they become aware of increasing pollution levels. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy import stats\n",
    "from scipy.stats import poisson, ttest_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Pollution Levels in NY\n",
    "Before analyzing how attitudes towards sustainable practices have changed from 2010 to 2016 in New York, we wanted to understand how pollution levels have changed over time and who were the largest contributors to pollution both from the persepctive of which cities were contributing the most to pollution and which industries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>71702.38</td>\n",
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       "      <td>127808.87</td>\n",
       "      <td>132095.46</td>\n",
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       "      <td>Greene</td>\n",
       "      <td>42.27</td>\n",
       "      <td>...</td>\n",
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       "      <td>1381328.03</td>\n",
       "      <td>1487213.14</td>\n",
       "      <td>1514867.54</td>\n",
       "      <td>1285191.24</td>\n",
       "      <td>1026373.20</td>\n",
       "      <td>1376177.50</td>\n",
       "      <td>2133936.99</td>\n",
       "      <td>1566947.61</td>\n",
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       "      <th>3</th>\n",
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       "      <td>11211</td>\n",
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       "      <td>40.72</td>\n",
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       "   Unnamed: 0  facility_id        frs_id                    facility_name  \\\n",
       "0           0      1000077  110000774797        REPUBLIC STEEL LACKAWANNA   \n",
       "1           1      1000093  110000324435  Momentive Performance Materials   \n",
       "2           2      1000101  110019468057        Athens Generating Company   \n",
       "3           3      1000108  110012158707                        North 1st   \n",
       "4           4      1000111  110012158734                 Vernon Boulevard   \n",
       "\n",
       "               city state  zip_code                     address       county  \\\n",
       "0          BLASDELL    NY     14219         S3049 LAKESHORE RD.  ERIE COUNTY   \n",
       "1         WATERFORD    NY     12188       260 HUDSON RIVER ROAD     Saratoga   \n",
       "2            ATHENS    NY     12015         9300 U S HIGHWAY 9W       Greene   \n",
       "3          BROOKLYN    NY     11211  NORTH 1ST & 47-79 RIVER ST        Kings   \n",
       "4  LONG ISLAND CITY    NY     11427           42 30 VERNON BLVD       Queens   \n",
       "\n",
       "   latitude  ...  industy_subparts  industry_sector        2018        2017  \\\n",
       "0     42.80  ...                 C           Metals    78801.40    76235.01   \n",
       "1     42.82  ...               CTT       OtherWaste   134663.45   131363.56   \n",
       "2     42.27  ...                 D     Power Plants  1381328.03  1487213.14   \n",
       "3     40.72  ...                CD     Power Plants    31069.94    22460.32   \n",
       "4     40.75  ...                CD     Power Plants    35221.82    28073.30   \n",
       "\n",
       "         2016        2015        2014        2013        2012        2011  \n",
       "0    61447.47    73193.02    77355.81    71702.38    80619.20    72355.73  \n",
       "1   125491.33   126818.78   127808.87   132095.46   120944.74   142529.89  \n",
       "2  1514867.54  1285191.24  1026373.20  1376177.50  2133936.99  1566947.61  \n",
       "3    26369.45    18559.93    20816.32    26629.45    26775.50    45473.83  \n",
       "4    28262.00    27000.90    26232.55    43133.84    38257.51    44820.68  \n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_ny = pd.read_csv('ghg_ny.csv')\n",
    "ghg_ny.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changes in Pollution from 2010 to 2016\n",
    "For our first visuallization, we wanted to see if there is any relationship between the pollution levels in 2010 and 2016. We chose these two years because all of our 5 datasets have a large data sample for 2016 and and we chose 2010 so that there was a large enough time difference between the two years to see noticeable trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"2011\", y=\"2016\", data=ghg_ny)\n",
    "plt.xlabel(\"Pollution Levels in 2011\")\n",
    "plt.ylabel(\"Pollution Levels in 2016\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this graph, we notice two key ideas. The first is that the axis scales for 2016 have pollution rates that are higher than the highest ones in 2011 by 500,000. This shows that there has been a large increase in pollution levels from 2011 to 2016. This graph also shows a proportional relationship where as pollution levels increase in 2011, pollution levels increase proportionately in 2016. This relationship reveals that the highest polluters in 2011 continue to be the highest polluters in 2016 despite new regulations from the Environmental Protection Agency (EPA) on power and chemical plants such as the Mercury and Air Toxics Standards of 2014. We test the signifiance of these findings later in the report."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pollution Level by Industry\n",
    "In the previous graph, we noticed that the points seem to be grouped in two clusters - one seems to occur below 0.5e6 levels on pollution in both 2011 and 2016 and the other cluster seems to start at 0.6e6 pollution level in 2011 and 2016 and increases more rapidly in 2016 than the first cluster. The dataset contains several different variables that could correlate with these two clusters such as industry sector or zip code. We graphed these variables and industry sector seems to follow the trends most closely of the clusters we noticed initiatilly. On the next graph, you can see the graph we created where we color coded the clusters. The first cluster refers to the green and red points while the second cluster refers to the brown points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"2011\", y=\"2016\", hue=\"industry_sector\", data=ghg_ny)\n",
    "plt.xlabel(\"Pollution Levels in 2011\")\n",
    "plt.ylabel(\"Pollution Levels in 2016\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After color coding the points in the graph based on industry sector, there were a few key ideas that we came across that influenced the next steps of our analysis. At first, based on our initial plots, thought there were two industries based on the cluster patterns we saw. However, this graph showed us that there were 13 different industry sectors within dataset, but only a few were prevalent such as power plants (the brown colored points). To limit the number of industries so that it fit the cluster pattern, we wanted to calculate which industries had the highest counts in the dataset and focus on those. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power Plants                         76\n",
       "Waste                                36\n",
       "Other                                32\n",
       "Minerals                              9\n",
       "Petroleum and Natural Gas Systems     7\n",
       "Metals                                6\n",
       "Pulp and Paper                        5\n",
       "OtherSuppliers of CO2                 2\n",
       "Chemicals                             2\n",
       "MetalsWaste                           1\n",
       "Pulp and PaperWaste                   1\n",
       "OtherSuppliers of CO2Waste            1\n",
       "OtherWaste                            1\n",
       "Name: industry_sector, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_ny[\"industry_sector\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After using the value counts function, we found that the most prevalent industries in this dataset were power plants, waste, and other. Since waste and other have a very small difference in the number of counts, we decided to cluster our dataset by 3 groups. We chose not to include the other industry sectors in our analysis since their counts were too low and too mixed in with our top three groups to be able to identify clear trends and patterns. We used KMeans clustering in order to do this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  87235.63477987,   88936.0527044 ],\n",
       "       [1901039.43888889, 1806430.52      ],\n",
       "       [1055539.39181818,  606134.08272727]])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## initialize a KMeans object\n",
    "clustering = KMeans(n_clusters = 3, random_state = 15)\n",
    "\n",
    "## perform the KMeans algorithm on the pollution for 2011 and 2016\n",
    "clustering.fit(ghg_ny[[\"2011\", \"2016\"]])\n",
    "\n",
    "##finding the cluster centers\n",
    "clustering.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>Unnamed: 0</th>\n",
       "      <th>facility_id</th>\n",
       "      <th>frs_id</th>\n",
       "      <th>facility_name</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>address</th>\n",
       "      <th>county</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>industry_sector</th>\n",
       "      <th>2018</th>\n",
       "      <th>2017</th>\n",
       "      <th>2016</th>\n",
       "      <th>2015</th>\n",
       "      <th>2014</th>\n",
       "      <th>2013</th>\n",
       "      <th>2012</th>\n",
       "      <th>2011</th>\n",
       "      <th>cluster_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1000077</td>\n",
       "      <td>110000774797</td>\n",
       "      <td>REPUBLIC STEEL LACKAWANNA</td>\n",
       "      <td>BLASDELL</td>\n",
       "      <td>NY</td>\n",
       "      <td>14219</td>\n",
       "      <td>S3049 LAKESHORE RD.</td>\n",
       "      <td>ERIE COUNTY</td>\n",
       "      <td>42.80</td>\n",
       "      <td>...</td>\n",
       "      <td>Metals</td>\n",
       "      <td>78801.40</td>\n",
       "      <td>76235.01</td>\n",
       "      <td>61447.47</td>\n",
       "      <td>73193.02</td>\n",
       "      <td>77355.81</td>\n",
       "      <td>71702.38</td>\n",
       "      <td>80619.20</td>\n",
       "      <td>72355.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1000093</td>\n",
       "      <td>110000324435</td>\n",
       "      <td>Momentive Performance Materials</td>\n",
       "      <td>WATERFORD</td>\n",
       "      <td>NY</td>\n",
       "      <td>12188</td>\n",
       "      <td>260 HUDSON RIVER ROAD</td>\n",
       "      <td>Saratoga</td>\n",
       "      <td>42.82</td>\n",
       "      <td>...</td>\n",
       "      <td>OtherWaste</td>\n",
       "      <td>134663.45</td>\n",
       "      <td>131363.56</td>\n",
       "      <td>125491.33</td>\n",
       "      <td>126818.78</td>\n",
       "      <td>127808.87</td>\n",
       "      <td>132095.46</td>\n",
       "      <td>120944.74</td>\n",
       "      <td>142529.89</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1000101</td>\n",
       "      <td>110019468057</td>\n",
       "      <td>Athens Generating Company</td>\n",
       "      <td>ATHENS</td>\n",
       "      <td>NY</td>\n",
       "      <td>12015</td>\n",
       "      <td>9300 U S HIGHWAY 9W</td>\n",
       "      <td>Greene</td>\n",
       "      <td>42.27</td>\n",
       "      <td>...</td>\n",
       "      <td>Power Plants</td>\n",
       "      <td>1381328.03</td>\n",
       "      <td>1487213.14</td>\n",
       "      <td>1514867.54</td>\n",
       "      <td>1285191.24</td>\n",
       "      <td>1026373.20</td>\n",
       "      <td>1376177.50</td>\n",
       "      <td>2133936.99</td>\n",
       "      <td>1566947.61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1000108</td>\n",
       "      <td>110012158707</td>\n",
       "      <td>North 1st</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>11211</td>\n",
       "      <td>NORTH 1ST &amp; 47-79 RIVER ST</td>\n",
       "      <td>Kings</td>\n",
       "      <td>40.72</td>\n",
       "      <td>...</td>\n",
       "      <td>Power Plants</td>\n",
       "      <td>31069.94</td>\n",
       "      <td>22460.32</td>\n",
       "      <td>26369.45</td>\n",
       "      <td>18559.93</td>\n",
       "      <td>20816.32</td>\n",
       "      <td>26629.45</td>\n",
       "      <td>26775.50</td>\n",
       "      <td>45473.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1000111</td>\n",
       "      <td>110012158734</td>\n",
       "      <td>Vernon Boulevard</td>\n",
       "      <td>LONG ISLAND CITY</td>\n",
       "      <td>NY</td>\n",
       "      <td>11427</td>\n",
       "      <td>42 30 VERNON BLVD</td>\n",
       "      <td>Queens</td>\n",
       "      <td>40.75</td>\n",
       "      <td>...</td>\n",
       "      <td>Power Plants</td>\n",
       "      <td>35221.82</td>\n",
       "      <td>28073.30</td>\n",
       "      <td>28262.00</td>\n",
       "      <td>27000.90</td>\n",
       "      <td>26232.55</td>\n",
       "      <td>43133.84</td>\n",
       "      <td>38257.51</td>\n",
       "      <td>44820.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  facility_id        frs_id                    facility_name  \\\n",
       "0           0      1000077  110000774797        REPUBLIC STEEL LACKAWANNA   \n",
       "1           1      1000093  110000324435  Momentive Performance Materials   \n",
       "2           2      1000101  110019468057        Athens Generating Company   \n",
       "3           3      1000108  110012158707                        North 1st   \n",
       "4           4      1000111  110012158734                 Vernon Boulevard   \n",
       "\n",
       "               city state  zip_code                     address       county  \\\n",
       "0          BLASDELL    NY     14219         S3049 LAKESHORE RD.  ERIE COUNTY   \n",
       "1         WATERFORD    NY     12188       260 HUDSON RIVER ROAD     Saratoga   \n",
       "2            ATHENS    NY     12015         9300 U S HIGHWAY 9W       Greene   \n",
       "3          BROOKLYN    NY     11211  NORTH 1ST & 47-79 RIVER ST        Kings   \n",
       "4  LONG ISLAND CITY    NY     11427           42 30 VERNON BLVD       Queens   \n",
       "\n",
       "   latitude  ...  industry_sector        2018        2017        2016  \\\n",
       "0     42.80  ...           Metals    78801.40    76235.01    61447.47   \n",
       "1     42.82  ...       OtherWaste   134663.45   131363.56   125491.33   \n",
       "2     42.27  ...     Power Plants  1381328.03  1487213.14  1514867.54   \n",
       "3     40.72  ...     Power Plants    31069.94    22460.32    26369.45   \n",
       "4     40.75  ...     Power Plants    35221.82    28073.30    28262.00   \n",
       "\n",
       "         2015        2014        2013        2012        2011  cluster_label  \n",
       "0    73193.02    77355.81    71702.38    80619.20    72355.73              0  \n",
       "1   126818.78   127808.87   132095.46   120944.74   142529.89              0  \n",
       "2  1285191.24  1026373.20  1376177.50  2133936.99  1566947.61              1  \n",
       "3    18559.93    20816.32    26629.45    26775.50    45473.83              0  \n",
       "4    27000.90    26232.55    43133.84    38257.51    44820.68              0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create cluster labels\n",
    "cluster_labels = clustering.labels_\n",
    "cluster_labels[0:5]\n",
    "ghg_ny['cluster_label'] = cluster_labels\n",
    "ghg_ny.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After initializing a KMeans object, we performed the KMeans algorithm on the pollution levels for 2010 and for 2016 in order to create cluster labels that had the predicted industry. We added these cluster labels to the ghg_ny dataframe and then, tested the accuracy of the algorithm visually using the plot below. The colors of the points indicate the actual industry that produced pollution and the styles of the points indicate the industry predicted by the algorithm. In the legend of the graph, 0 represents Power Plants, 1 represents Waste, and 2 representes Other. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"2011\", y=\"2016\", hue=\"industry_sector\", style=\"cluster_label\", data=ghg_ny)\n",
    "plt.xlabel(\"Pollution Levels in 2011\")\n",
    "plt.ylabel(\"Pollution Levels in 2016\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating the plot, it seems that the clustering algorithm was not that accurate as several of the Power Plant points were identified to be a mix of all three of the industries (We complete a more thorough analysis of the statistical significance of these findings later in the report). This reveals that the clusters are likely not correlated with industry sector and that pollution levels contributed varies a lot within an industry. Some likely causes for this variation could be variables that were not measured by the dataset such as size of the factory or differing environmental regulations across cities. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top Contributors to Pollution by City\n",
    "After looking at industry, we were able to understand how pollution varies within and across industries, but we were still unable to fully explain what was causing some of the higher values of pollution in some of the power plants. To further explore this question, we decided to look at what the highest pollution levels were, which cities were overall producting the most pollution, and which cities were consisently polluting the most from 2010-2018. The graph compares pollution levels in 2010 and 2016, but is color coded by city this time instead of industry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"2011\", y=\"2016\", hue=\"city\", data=ghg_ny)\n",
    "plt.xlabel(\"Pollution Levels in 2011\")\n",
    "plt.ylabel(\"Pollution Levels in 2016\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the graph, it seems that some of the top polluters are in Astoria and New York as indicated by the orange and brown colored points. To get a more comprehensive understanding of which factories and cities are the top polluters, we decided to see how pollution levels in these cities across the entire timespan of the dataset (2011-2018). In the next few steps, we use the function, nlargest, to calculate the largest polluters and track their greenhouse gas emissions over time in a new dataframe titled top_polluters."
   ]
  },
  {
   "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>counts_2012</th>\n",
       "      <th>counts_2011</th>\n",
       "      <th>counts_2013</th>\n",
       "      <th>counts_2014</th>\n",
       "      <th>counts_2015</th>\n",
       "      <th>counts_2016</th>\n",
       "      <th>counts_2017</th>\n",
       "      <th>counts_2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASTORIA</th>\n",
       "      <td>4764387.02</td>\n",
       "      <td>4042716.00</td>\n",
       "      <td>4380823.02</td>\n",
       "      <td>4675385.27</td>\n",
       "      <td>4466886.67</td>\n",
       "      <td>4155855.99</td>\n",
       "      <td>3778604.39</td>\n",
       "      <td>4217072.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW YORK</th>\n",
       "      <td>2622599.36</td>\n",
       "      <td>2735569.11</td>\n",
       "      <td>2803733.68</td>\n",
       "      <td>2662647.29</td>\n",
       "      <td>2675021.50</td>\n",
       "      <td>2643367.57</td>\n",
       "      <td>2610288.32</td>\n",
       "      <td>2682403.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSWEGO</th>\n",
       "      <td>2422483.93</td>\n",
       "      <td>1500633.06</td>\n",
       "      <td>2346473.42</td>\n",
       "      <td>2683641.65</td>\n",
       "      <td>2847435.29</td>\n",
       "      <td>2086964.18</td>\n",
       "      <td>1941340.78</td>\n",
       "      <td>2319907.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          counts_2012  counts_2011  counts_2013  counts_2014  counts_2015  \\\n",
       "city                                                                        \n",
       "ASTORIA    4764387.02   4042716.00   4380823.02   4675385.27   4466886.67   \n",
       "NEW YORK   2622599.36   2735569.11   2803733.68   2662647.29   2675021.50   \n",
       "OSWEGO     2422483.93   1500633.06   2346473.42   2683641.65   2847435.29   \n",
       "\n",
       "          counts_2016  counts_2017  counts_2018  \n",
       "city                                             \n",
       "ASTORIA    4155855.99   3778604.39   4217072.28  \n",
       "NEW YORK   2643367.57   2610288.32   2682403.80  \n",
       "OSWEGO     2086964.18   1941340.78   2319907.68  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_pollutors = pd.DataFrame()\n",
    "top_pollutors[\"counts_2012\"] = ghg_ny.groupby(\"city\")[\"2012\"].sum().nlargest(3)\n",
    "top_pollutors[\"counts_2011\"] = ghg_ny.groupby(\"city\")[\"2011\"].sum()\n",
    "top_pollutors[\"counts_2013\"] = ghg_ny.groupby(\"city\")[\"2013\"].sum()\n",
    "top_pollutors[\"counts_2014\"] = ghg_ny.groupby(\"city\")[\"2014\"].sum()\n",
    "top_pollutors[\"counts_2015\"] = ghg_ny.groupby(\"city\")[\"2015\"].sum()\n",
    "top_pollutors[\"counts_2016\"] = ghg_ny.groupby(\"city\")[\"2016\"].sum()\n",
    "top_pollutors[\"counts_2017\"] = ghg_ny.groupby(\"city\")[\"2017\"].sum()\n",
    "top_pollutors[\"counts_2018\"] = ghg_ny.groupby(\"city\")[\"2018\"].sum()\n",
    "top_pollutors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    " \n",
    "# set height of bar\n",
    "bars1 = top_pollutors.iloc[0]\n",
    "bars2 = top_pollutors.iloc[1]\n",
    "bars3 = top_pollutors.iloc[2]\n",
    " \n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(bars1))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='Astoria')\n",
    "plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label='New York')\n",
    "plt.bar(r3, bars3, color='#2d7f5e', width=barWidth, edgecolor='white', label='Oswego')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('Year', fontweight='bold')\n",
    "plt.ylabel('Pollution Level', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(bars1))], ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018'])\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating this graph, we found out that the top three polluters in New York state have consistently been Astoria, New York, and Oswego. Although Astoria surpasses New York and Oswego levels all eight years, we decided to focus the rest of our analysis on New York (NYC) since there was more data available on environmental activism and sustainability initiatives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recycling in 2010 vs. 2016\n",
    "There are two csv files for recycling data. One file is for 2010 and the other file is for 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>District</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN01</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>29.3</td>\n",
       "      <td>54.4</td>\n",
       "      <td>70.2</td>\n",
       "      <td>58.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN02</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>26.5</td>\n",
       "      <td>44.3</td>\n",
       "      <td>79.0</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN03</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN04</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>23.1</td>\n",
       "      <td>37.4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>45.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN05</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>23.8</td>\n",
       "      <td>41.1</td>\n",
       "      <td>66.6</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       Zone District  Fiscal Month Number  Fiscal Year  \\\n",
       "0           0  Manhattan     MN01                   11         2010   \n",
       "1           1  Manhattan     MN02                   11         2010   \n",
       "2           2  Manhattan     MN03                   11         2010   \n",
       "3           3  Manhattan     MN04                   11         2010   \n",
       "4           4  Manhattan     MN05                   11         2010   \n",
       "\n",
       "  Month Name  Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "0        May            29.3                                          54.4   \n",
       "1        May            26.5                                          44.3   \n",
       "2        May            13.1                                          47.5   \n",
       "3        May            23.1                                          37.4   \n",
       "4        May            23.8                                          41.1   \n",
       "\n",
       "   Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  \n",
       "0                                    70.2          58.2  \n",
       "1                                    79.0          52.7  \n",
       "2                                    39.5          44.0  \n",
       "3                                    73.0          45.9  \n",
       "4                                    66.6          47.3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recycling_2010 = pd.read_csv('recycling_2010.csv')\n",
    "recycling_2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>District</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>14.7</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>43.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN02</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>16.5</td>\n",
       "      <td>30.2</td>\n",
       "      <td>49.8</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN03</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>10.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN04</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>12.4</td>\n",
       "      <td>30.6</td>\n",
       "      <td>46.4</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN05</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>9.1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      Zone District  Fiscal Month Number  Fiscal Year Month Name  \\\n",
       "0          59  Brooklyn    BKN01                    1         2016       July   \n",
       "1          60  Brooklyn    BKN02                    1         2016       July   \n",
       "2          61  Brooklyn    BKN03                    1         2016       July   \n",
       "3          62  Brooklyn    BKN04                    1         2016       July   \n",
       "4          63  Brooklyn    BKN05                    1         2016       July   \n",
       "\n",
       "   Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "0            14.7                                          44.0   \n",
       "1            16.5                                          30.2   \n",
       "2            10.1                                          29.0   \n",
       "3            12.4                                          30.6   \n",
       "4             9.1                                          23.5   \n",
       "\n",
       "   Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  \n",
       "0                                    43.2          43.6  \n",
       "1                                    49.8          36.0  \n",
       "2                                    32.8          30.8  \n",
       "3                                    46.4          37.8  \n",
       "4                                    33.0          27.9  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recycling_2016 = pd.read_csv('recycling_2016.csv')\n",
    "recycling_2016.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two files for Citibike data. One is for 2013 (the earliest year recorded) and the other is for 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>416821</td>\n",
       "      <td>663</td>\n",
       "      <td>2013-06-25 08:23:45</td>\n",
       "      <td>2013-06-25 08:34:48</td>\n",
       "      <td>393</td>\n",
       "      <td>E 5 St &amp; Avenue C</td>\n",
       "      <td>293.0</td>\n",
       "      <td>Lafayette St &amp; E 8 St</td>\n",
       "      <td>40.730207</td>\n",
       "      <td>-73.991026</td>\n",
       "      <td>16733</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>305402</td>\n",
       "      <td>1452</td>\n",
       "      <td>2013-06-21 07:00:30</td>\n",
       "      <td>2013-06-21 07:24:42</td>\n",
       "      <td>349</td>\n",
       "      <td>Rivington St &amp; Ridge St</td>\n",
       "      <td>522.0</td>\n",
       "      <td>E 51 St &amp; Lexington Ave</td>\n",
       "      <td>40.757148</td>\n",
       "      <td>-73.972078</td>\n",
       "      <td>19616</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>525696</td>\n",
       "      <td>719</td>\n",
       "      <td>2013-06-29 09:27:43</td>\n",
       "      <td>2013-06-29 09:39:42</td>\n",
       "      <td>349</td>\n",
       "      <td>Rivington St &amp; Ridge St</td>\n",
       "      <td>369.0</td>\n",
       "      <td>Washington Pl &amp; 6 Ave</td>\n",
       "      <td>40.732241</td>\n",
       "      <td>-74.000264</td>\n",
       "      <td>17302</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>290404</td>\n",
       "      <td>561</td>\n",
       "      <td>2013-06-20 16:08:52</td>\n",
       "      <td>2013-06-20 16:18:13</td>\n",
       "      <td>411</td>\n",
       "      <td>E 6 St &amp; Avenue D</td>\n",
       "      <td>502.0</td>\n",
       "      <td>Henry St &amp; Grand St</td>\n",
       "      <td>40.714215</td>\n",
       "      <td>-73.981346</td>\n",
       "      <td>17235</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>473951</td>\n",
       "      <td>943</td>\n",
       "      <td>2013-06-27 08:18:32</td>\n",
       "      <td>2013-06-27 08:34:15</td>\n",
       "      <td>317</td>\n",
       "      <td>E 6 St &amp; Avenue B</td>\n",
       "      <td>442.0</td>\n",
       "      <td>W 27 St &amp; 7 Ave</td>\n",
       "      <td>40.746647</td>\n",
       "      <td>-73.993915</td>\n",
       "      <td>17940</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Zone  start station latitude  start station longitude   index  \\\n",
       "0           8   1.0                   40.72                   -73.98  416821   \n",
       "1          16   1.0                   40.72                   -73.98  305402   \n",
       "2          24   1.0                   40.72                   -73.98  525696   \n",
       "3          32   1.0                   40.72                   -73.98  290404   \n",
       "4          40   1.0                   40.72                   -73.98  473951   \n",
       "\n",
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           663  2013-06-25 08:23:45  2013-06-25 08:34:48               393   \n",
       "1          1452  2013-06-21 07:00:30  2013-06-21 07:24:42               349   \n",
       "2           719  2013-06-29 09:27:43  2013-06-29 09:39:42               349   \n",
       "3           561  2013-06-20 16:08:52  2013-06-20 16:18:13               411   \n",
       "4           943  2013-06-27 08:18:32  2013-06-27 08:34:15               317   \n",
       "\n",
       "        start station name  end station id         end station name  \\\n",
       "0        E 5 St & Avenue C           293.0    Lafayette St & E 8 St   \n",
       "1  Rivington St & Ridge St           522.0  E 51 St & Lexington Ave   \n",
       "2  Rivington St & Ridge St           369.0    Washington Pl & 6 Ave   \n",
       "3        E 6 St & Avenue D           502.0      Henry St & Grand St   \n",
       "4        E 6 St & Avenue B           442.0          W 27 St & 7 Ave   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  birthyear  \\\n",
       "0             40.730207             -73.991026   16733  Subscriber     1957.0   \n",
       "1             40.757148             -73.972078   19616  Subscriber     1987.0   \n",
       "2             40.732241             -74.000264   17302  Subscriber     1982.0   \n",
       "3             40.714215             -73.981346   17235  Subscriber     1964.0   \n",
       "4             40.746647             -73.993915   17940  Subscriber     1984.0   \n",
       "\n",
       "   gender  \n",
       "0       2  \n",
       "1       2  \n",
       "2       1  \n",
       "3       2  \n",
       "4       1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2013 = pd.read_csv('biking_2013.csv')\n",
    "biking_2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>900435</td>\n",
       "      <td>160</td>\n",
       "      <td>6/20/2016 06:22:53</td>\n",
       "      <td>6/20/2016 06:25:33</td>\n",
       "      <td>3070</td>\n",
       "      <td>McKibbin St &amp; Manhattan Ave</td>\n",
       "      <td>3071</td>\n",
       "      <td>Boerum St &amp; Broadway</td>\n",
       "      <td>40.705381</td>\n",
       "      <td>-73.949765</td>\n",
       "      <td>17226</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>750686</td>\n",
       "      <td>1474</td>\n",
       "      <td>6/17/2016 00:03:24</td>\n",
       "      <td>6/17/2016 00:27:59</td>\n",
       "      <td>3074</td>\n",
       "      <td>Montrose Ave &amp; Bushwick Ave</td>\n",
       "      <td>3129</td>\n",
       "      <td>Queens Plaza North &amp; Crescent St</td>\n",
       "      <td>40.751102</td>\n",
       "      <td>-73.940737</td>\n",
       "      <td>25447</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>747914</td>\n",
       "      <td>634</td>\n",
       "      <td>6/16/2016 21:44:55</td>\n",
       "      <td>6/16/2016 21:55:30</td>\n",
       "      <td>3081</td>\n",
       "      <td>Graham Ave &amp; Grand St</td>\n",
       "      <td>3068</td>\n",
       "      <td>Humboldt St &amp; Varet St</td>\n",
       "      <td>40.703172</td>\n",
       "      <td>-73.940636</td>\n",
       "      <td>19282</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>895781</td>\n",
       "      <td>1249</td>\n",
       "      <td>6/19/2016 21:02:22</td>\n",
       "      <td>6/19/2016 21:23:11</td>\n",
       "      <td>411</td>\n",
       "      <td>E 6 St &amp; Avenue D</td>\n",
       "      <td>446</td>\n",
       "      <td>W 24 St &amp; 7 Ave</td>\n",
       "      <td>40.744876</td>\n",
       "      <td>-73.995299</td>\n",
       "      <td>20052</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>1022077</td>\n",
       "      <td>443</td>\n",
       "      <td>6/22/2016 11:02:31</td>\n",
       "      <td>6/22/2016 11:09:55</td>\n",
       "      <td>150</td>\n",
       "      <td>E 2 St &amp; Avenue C</td>\n",
       "      <td>293</td>\n",
       "      <td>Lafayette St &amp; E 8 St</td>\n",
       "      <td>40.730287</td>\n",
       "      <td>-73.990765</td>\n",
       "      <td>18622</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Zone  start station latitude  start station longitude    index  \\\n",
       "0           0   3.0                   40.71                   -73.94   900435   \n",
       "1           2   3.0                   40.71                   -73.94   750686   \n",
       "2           4   3.0                   40.71                   -73.94   747914   \n",
       "3           6   1.0                   40.72                   -73.98   895781   \n",
       "4          14   1.0                   40.72                   -73.98  1022077   \n",
       "\n",
       "   tripduration           starttime            stoptime  start station id  \\\n",
       "0           160  6/20/2016 06:22:53  6/20/2016 06:25:33              3070   \n",
       "1          1474  6/17/2016 00:03:24  6/17/2016 00:27:59              3074   \n",
       "2           634  6/16/2016 21:44:55  6/16/2016 21:55:30              3081   \n",
       "3          1249  6/19/2016 21:02:22  6/19/2016 21:23:11               411   \n",
       "4           443  6/22/2016 11:02:31  6/22/2016 11:09:55               150   \n",
       "\n",
       "            start station name  end station id  \\\n",
       "0  McKibbin St & Manhattan Ave            3071   \n",
       "1  Montrose Ave & Bushwick Ave            3129   \n",
       "2        Graham Ave & Grand St            3068   \n",
       "3            E 6 St & Avenue D             446   \n",
       "4            E 2 St & Avenue C             293   \n",
       "\n",
       "                   end station name  end station latitude  \\\n",
       "0              Boerum St & Broadway             40.705381   \n",
       "1  Queens Plaza North & Crescent St             40.751102   \n",
       "2            Humboldt St & Varet St             40.703172   \n",
       "3                   W 24 St & 7 Ave             40.744876   \n",
       "4             Lafayette St & E 8 St             40.730287   \n",
       "\n",
       "   end station longitude  bikeid    usertype  birthyear  gender  \n",
       "0             -73.949765   17226  Subscriber     1952.0       1  \n",
       "1             -73.940737   25447  Subscriber     1991.0       1  \n",
       "2             -73.940636   19282  Subscriber     1981.0       1  \n",
       "3             -73.995299   20052  Subscriber     1980.0       1  \n",
       "4             -73.990765   18622  Subscriber     1991.0       1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2016 = pd.read_csv('biking_2016.csv')\n",
    "biking_2016.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Capture Rate 2010\n",
    "In this dataset, the capture rate represents the percent of the total waste that is sent to the plant that actually ends up being recycled. The closer the capture rate is to 100, the more efficient the recycling plant is. Ideally, a plant should have a capture rate of 70%, but a more realistic capture rate of 50% is also considered to be very efficient. In the following analyses, we analyze the probability of reaching a capture rate of 50% and 70% in NYC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following histogram shows the distribution of capture rates for each of the districts of NYC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Histogram for 2010\n",
    "plt.hist(recycling_2010[\"Capture Rate\"], bins=18)\n",
    "plt.xlabel(\"Capture Rate in 2010\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to predict the probabilities of reaching a capture rate of 50% and 70%, we calculated the overall mean capture rate in 2010 and used it to approximate a poisson distribution of 1000 samples. We chose to model this scenario using a poisson distribution because there more than two outcomes and each outcome does not have an equal chance of occurring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Average Capture Rate for 2010\n",
    "mean_2010 = round(recycling_2010[[\"Capture Rate\"]].mean(),2)\n",
    "draws_2010 = np.random.poisson(mean_2010, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Capture Rate for 2010:  43.22\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Capture Rate for 2010: \", mean_2010[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph below shows the poisson distribution that is an approximation of the previous histogram. The graph also has two lines - the red line marks which values fall after a 50% capture rate and the green line marks which values fall after a 70% capture rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram for draws_2010\n",
    "plt.hist(draws_2010, bins=18)\n",
    "plt.xlabel(\"Sample Value Based on The Mean Capture Rate for 2010\")\n",
    "plt.ylabel(\"Frequency at Which Sample Value Occurs\")\n",
    "plt.axvline(color='r', linestyle=\"dotted\", x=50, label=\"Realistic Capture Rate\")\n",
    "plt.axvline(color='g', linestyle=\"dotted\", x=70, label=\"Ideal Capture Rate\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of capturing more than 50% of recycled waste is: 13.501%\n"
     ]
    }
   ],
   "source": [
    "## Probability of Capturing 50% in 2010\n",
    "rate50_2010 = round(float(poisson.sf(50, mean_2010)*100),4)\n",
    "print(\"The probability of capturing more than 50% of recycled waste is: \"+ str(rate50_2010)+\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of capturing more than 70% of recycled waste is: 0.0066%\n"
     ]
    }
   ],
   "source": [
    "## Probability of Capturing 70% in 2010\n",
    "rate70_2010 = round(float(poisson.sf(70, mean_2010)*100),4)\n",
    "print(\"The probability of capturing more than 70% of recycled waste is: \"+ str(rate70_2010)+\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the histogram and the probabilities, in 2010 the probability of a district having a capture rate over 50 was 13.5% and having one over 70 was 0.0066%. These probabilities are extremely low and could be attributed to a variety of reasons such as not having enough facilitites to recycle all waste, individuals recycling waste that cannot actually be recycled at the plant, or not having specialized recycling facilities that handle wastes like CD's that cannot be recycled in a typical recycling plant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Capture Rate 2016\n",
    "The following histogram shows the distribution of capture rates for each of the districts of NYC in 2016. The analyses follows the same steps that we did for the analysis of the capture rate in 2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Histogram for 2016\n",
    "plt.hist(recycling_2016[\"Capture Rate\"], bins=18)\n",
    "plt.xlabel(\"Capture Rate in 2016\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the poisson distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Average Capture Rate for 2016\n",
    "mean_2016 = round(recycling_2016[[\"Capture Rate\"]].mean(),2)\n",
    "draws_2016 = np.random.poisson(mean_2016, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Capture Rate for 2016:  42.02\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Capture Rate for 2016: \", mean_2016[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphing the poisson approximation and the realistic (50) vs. ideal (70) capture rates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IVAMuAgr++qq6HdjuvV8oIl8CLQDrw8IYgE2bYNeuyJKfO3cuAwYMoGrVqhx++OGceuqpQPHdN4P7kb/jjjsKOro788wzA6efaF+XXHIJVapU4eijj6Z58+ZJB7Qp7Mgjj+SEE04AYMaMGcyYMYPjjjsOgM2bN/P555/TtWvXfbbr0aMH69at45BDDim4fZSfn8+wYcPIzs6matWqxQ4DOmPGDJYuXVpQL5OXl8fnn3+etKtxPzZtdx3mHXTgQYG29/NI6tmqentsRlV/EpFzgECFAtAT+F98WwcRaQj8qKq7RKQ5rsX0VwH3b8z+Z80a5pzyVEE7hbSqaczJnFPwcc20mnvN161ed6/5BjUb7DV/WG1/XWfHK6o76eK6bwZ3a+e1116jffv2ZGVlMSe+6+4itG7dmoULFxYUOH735afb62rVqu1VXxDfFXWtuAaBqsrw4cO56qrkd8hnz55NrVq1yMzM5K677uLvf/87Dz/8MIceeihLlixh9+7dxY75oKo8+uijoQrK4qzZ5MZwbnlgsHYKfuoUqorIgbEZEakBHJhg/dh6LwIfAi1FZJWI/Nb7qD/7VjB3BZaKyBJgKnC1DeJjTJz0dPeKSNeuXZk8eTK7du1i7dq1BSORFdd9M7hBcRo1akR+fj6TJk1Kmsbw4cP54x//WNDz6fbt2xkzZkzSfb388svs3r2bL7/8kq+++oqWLVvu0+V1eno6ixYtAmDRokXF3qo588wzeeaZZ9jsPem1evXqvepPCqtRowajR49mwoQJ/Pjjj+Tl5dGoUSOqVKnC888/zy7v6q5wPGeeeSZPPPEE+fn5gBu17Zdfwo2DUPBd66WTXi898PZ+rhQmAu+KyLO47i6uBBJ3lA6o6oBilmcWsewV3COqxpiiHJj0PKxMXXjhhfz73/+mbdu2tGjRgm7dugFwwAEHMHXqVK677jry8vLYuXMnN9xwA61bt+bee+/l+OOP58gjj6Rt27ZFjpEQ75xzzmHdunX07NkTVUVEuPJKN8ZXon21bNmSbt26sW7dOsaOHUv16tXp0aMHI0eOJCMjg+HDh3PxxRczYcIEMjIy6Ny5My1atCgyhjPOOIMVK1Zw4oknAu6R3IkTJ3LIIYcUG3ejRo0YMGAAjz/+OEOHDuXiiy/m5ZdfpkePHgVXIe3ataNatWq0b9+ezMxMrr/+enJzc+nQoQOqSsOGDQvGiQjrwGrh8krSrrMBROQs3G0fAWao6r9CpVpKrOvs/Y91nb3HXl0eb9zopnWKfP6i0srMzKRXr1706RN5T/4VxsbtLq/UOdDllVLvOltEmnnrzQZWqKrd6zemvHmPeVqhYJJZu8nllVihUFLFFgreI6FPAZ2AbNxVQnsRWQj8VlU3BkrRGFNypfBUyv4oKysr6hAqnGb1wuWVRBXNY4D/Ar9W1YtU9ULgKOBT4LFQqRpjfCm4vXvAAe5lTBIHVDuAA6q5vOKneqCwRLePTi5cKawuhXtE5POiNzHGlJbq1auzYcMG6tevj8TqFAJ2+2Aqj7xteYC7fbRhw4ZiH4stTqJCYd+HfY0x5aZx48asWrWK9evXQ2yA+sNK3r7AVC6x1uqH1T6M6tWr07hx4xJtn6hQ+EBE7gLu1bhrEBG5E/goQKzGmBJIS0vb08L14IPd1AoFk8TBm11eCdJAERIXCtcCTwNfeOMeKHAcsBj4bYLtjDGlzQoD41PQwiCm2ELBe7qor4gchesMT4BbVfXLUCkaY0ruTdeRHb17RxuHqfBinR72bhksryRtp+AVAlYQGBOlhx5yUysUTBIPfejySpkVCsaYCiDgKGem8pl6Sbi8YoWCMamgQYOoIzApokHNcHnFTy+piMgpInKF976h1/WFMaa8vPqqexmTxKsrXi0YXCkIP30f3Y3r6qIl8CyQhus59eTAqRpjSsbrQpqLLoo2DlPhjfnY5ZWLjg2WV/zcProQ9yjqIgBVXSMiwYb0McYE8/rrUUdgUsTr/cPlFT+Fwg5VVRFRABGplWwDY0wps+4tjE91q4fLK37qFF4SkSeBeiIyGJgFjA+VqjGmZKZMcS9jkpiybApTlgXPK37aKYwSkdOBjbh6hbtUdWbgFI0xJffEE27ar1+0cZgK74kFLq/0axMsr/h6JNUrBEpUEIjIM0Av4HtVbeMtGwEMBtZ7q92uqm97nw3HdZ+xC7iuoozuZkyF8PbbUUdgUsTbA8PlFT9PH23C9XsEcADu6aNfVDXZsD5ZuHEXJhRa/rCqjiqURiugP9AaOByYJSItVHVX0m9gTGVQs2bUEZgUUTMtXF7xc/toryeNROQC4Dc+tpsrIuk+4zgfmKyq24GvReQLL40PfW5vzP5t4kQ3HTQo2jhMhTdxqcsrg9oFyyu+Gq/FU9XXgFMDpeYME5GlIvKMiHj9AXMEsDJunVXesn2IyBARWSAiC9avX1/UKsbsf556yr2MSeKpRU/x1KLgecXP7aP4FhBVcA3ZSj7Gm/MEcK+3/b3AQ8CVFD2gT5FpqOo4YBxAp06dgsZhTGqZac92GH9mXhour/ipaI7vam8nkIu73VNiqrou9l5ExgPTvdlVQJO4VRsDa4KkYcx+KS0t6ghMikirGi6v+KlTuCJUCnFEpJGqrvVmLwSWee/fAF4Qkb/jKpqPBj4prXSNSXlZWW6amRllFCYFZGVnAZCZkRlo+2ILBRF5lAS3iVT1ukQ7FpEXge5AAxFZBdwNdBeRDG+/ucBV3r6Wi8hLwH9xVyPX2JNHxsSxQsH4VGaFArAg0B49qjqgiMVPJ1j/L8BfwqRpzH5rzpyoIzApYk7mnFDbJxqO87lQezbGGJNy/Dx91BC4FTdOc/XYclUN81iqMaYkxnvdjQ0eHG0cpsIbv9DllcEdg+UVP+0UJgErgGbAn3F1AfMDpWaMCcY6xDM+TVk+hSnLy7BDPKC+qj4tIter6nvAeyLyXuAUjTElN2tW1BGYFDHrsnB5xU+hkO9N14rIubj2A41DpWqMMaZCSvRIapqq5gP3iUhd4GbgUaAOcGM5xWeMAfjHP9x06NBo4zAV3j/mu7wytHOwvJKoTmG11+p4C7BRVZepag9V7aiqbwRKzRgTzJtvupcxSbz52Zu8+VnwvJLo9tGxQB/gTmCCiEwFXlTVjwOnZowJ5p//jDoCkyL+OTBcXin2SkFVN6jqk6raA9eN9dfAaBH5UkSskZkxxuyHfHWdraprcK2RnwA2Ab8ry6CMMYU88oh7GZPEIx89wiMfBc8rCQsFEakuIn1F5FXgS+A0YDiu0zpjTHl59133MiaJd79+l3e/Dp5XEj199ALQE5gLvAD8n6puC5ySMSa4N+zZDuPPGwPC5ZVEFc3/Aq5S1U2hUjDGGJMyrEM8Y1LBqFFuesst0cZhKrxR81xeueWkYHnFT4tmY0zUPvww6ghMivhwVbi8YoWCManglVeijsCkiFcuCZdXkj6SKiI1ReROr3UzInK0iPQKlaoxxpgKyU87hWeB7cCJ3vwq4L4yi8gYs6+RI93LmCRG/mckI/8TPK/4uX10lKr2E5EBAKq6VUQk2UYi8gzQC/heVdt4yx4EegM7cO0erlDVn0UkHTdmQ463+UeqenVJv4wJLv22t0LvI3fkuaUQiSlSdnbUEZgUkf1duLzip1DYISI1AAUQkaNwVw7JZAGPARPils0EhqvqThH5G64h3K3eZ1+qaobfwI2pVCZPjjoCkyIm9wmXV/zcProbeAdoIiKTgHeBPybbSFXnAj8WWjZDVXd6sx9h4zIYY0yFkvRKQVVnisgi4ARAgOtV9YdSSPtKIH7MuGYishjYCNyhqu+XQhrG7B/uvddN77wz2jhMhXfvey6v3NktWF5J1M1Fh0KL1nrTpiLSVFUXBUrR7ftPwE7c+M+xfTdV1Q0i0hF4TURaq+rGIrYdAgwBaNq0adAQjEktOTnJ1zEGyNkQLq8kulJ4KMFnCpwaJEERuRxXAX2aqiqAqm7Hq6dQ1YUi8iXQAliwT8Kq44BxAJ06ddIgMRiTciZOjDoCkyImXhQuryTq5qJHqD0XQUTOwlUsd1PVLXHLGwI/quouEWkOHA18VdrpG2OMSSxpnYKIVAeGAqfgrhDeB8Ym6zFVRF4EugMNRGQVrsJ6OHAgMNN7qjX26GlX4B4R2QnsAq5W1R+L3LExldFdd7npPfdEG4ep8O6a7fLKPT2C5RU/j6ROwA2s86g3PwB4HuibaCNVHVDE4qeLWfcVwNrxG1OclSujjsCkiJUbw+UVP4VCS1VtHzc/W0SWhErVGFMyzz4bdQQmRTx7fri84qedwmIROSE2IyLHAx+EStUYY0yF5OdK4XjgMhH51ptvCqwQkU8BVdV2ZRadMcYZPtxN778/2jhMhTd8lssr9/cMllf8FApnBdqzMab0bNgQdQQmRWzYGi6v+GnR/I2IHAw0iV8/TOM1Y0wJjRsXdQQmRYzrHS6v+Hkk9V4gE9eraayxWODGa8akAus11lRWfm4fXYLrPntHWQdjjClGbGzm2FjNxhTjlhkur4w6I1he8VMoLAPqAd8HSsEYE97WrVFHYFLE1vxwecVPoXA/7rHUZcSNo6Cq54VK2Rjj3+OPRx2BSRGPnxsur/gpFJ4D/gZ8CuwOlZoxxpgKzU+h8IOqjinzSIwxxbvhBjcdPTraOEyFd8M7Lq+MPitYXvFTKCwUkfuBN9j79pE9kmqMMfsZP4XCcd70hLhl9kiqMeXJrhCMT0GvEGL8NF4r9XEVjClLpdHGwJjKys+VAiJyLtAaqB5bpqrWsbsx5eWaa9zUnkIySVzzlssrQZ9C8tOieSxQE+gBPAX0AT4JlJoxJpgaNaKOwKSIGmnh8oqfK4WTVLWdiCxV1T+LyEPAq6FSNcaUjLVkNj4Fbckc42c8hVjzuC0icjiQDzQLlaoxxpgKyc+VwnQRqQc8CCzCPXk0vkyjMsbsbcgQN7XeUk0SQ950eSVob6l+nj6613v7iohMB6qral6y7UTkGaAX8L2qtvGW/QqYAqQDucAlqvqT99lw4LfALuA6Vf1Xib+NMRVIqfa0Wr9+6H2ZyqF+jXB5RVS16A9EOgMrVfU7b/4y4GLgG2CEqv6YcMciXYHNwIS4QuEB4EdVHSkitwEHq+qtItIKeBH4DXA4MAtooaq7EqXRqVMnXbBggf9va4plj3FWTNb9tikLIrJQVTsV9VmiOoUngR3eDroCI4EJQB6Q9LpEVecChQuO83F9KeFNL4hbPllVt6vq18AXuALCGGNMOUpUKFSNuxroB4xT1VdU9U7g1wHTO1RV1wJ400O85UcAK+PWW+Ut24eIDBGRBSKyYP369QHDMCbFXHGFexmTxBWvX8EVrwfPK4nqFKqKSDVV3QmcBgzxuV0QUsSyIu9rqeo4vCuVTp06FX3vy5j9TZMmUUdgUkSTOuHySqIf9xeB90TkB9xjqe8DiMivcbeQglgnIo1Uda2INGLPwD2rcGNAxzQG1gRMw5j9zz3WgYDx554e4fJKsbePVPUvwM1AFnCK7qmRrgJcGzC9N4DLvfeXA6/HLe8vIgeKSDPgaKzVtDHGlLuEt4FU9aMiln3mZ8ci8iLQHWggIquAu3GV1S+JyG+Bb4G+3j6Xi8hLwH+BncA1yZ48MqZSGTTITSdOjDYOU+ENetXllYkXBcsrpV03UEBVBxTz0WnFrP8X4C9lFY8xKa1ly6gjMCmiZf1weaXMCgVjTCm6886oIzAp4s5u4fKKn76PjDHGVBJJCwURuUhEPheRPBHZKCKbRGRjeQRnjPH07+9exiTRf2p/+k8Nnlf83D56AOitqisCp2KMCScjI+oITIrIOCxcXvFTKKyzAsGYiN12W9QRmBRx2ynh8kqxhYKIXOS9XSAiU4DXgO2xz1XVBtoxxpj9TKIrhd5x77cAZ8TNKzb6mjHl5+KL3fSVV6KNw1R4F7/k8sorlwTLK8UWCqpqvW8ZU1GceGLUEZgUcWLjcHklaZ2CiDwHXK+qP3vzBwMPqeqVoVI2xvh3yy1RR2BSxC0nhcsrftoptIsVCADeSGnHhUrVGGNMheSnUKjiXR0ABUNqWktoY8rTeee5lzFJnPfieZz3YvC84ufH/SFgnohM9eb7YtooGz8AABuxSURBVH0UGVO+TiuyyzBj9nFas3B5JWmhoKoTRGQBcCpuMJyLVPW/oVI1xpTM9ddHHYFJEdefEC6vJGqnUEdVN3q3i74DXoj77FdxQ3UaY4zZTyS6UngB6AUsZO+hMcWbb16GcRlj4p19tpv+85/RxmEqvLMnubzyz4HB8kqidgq9vGmzQHs2xpSe3r2Tr2MM0LtFuLzi6ykiETkCODJ+fVWdGyplY4x/Q4dGHYFJEUM7h8srfhqv/Q3ohxsqMzZEpgJWKBhjzH7Gz5XCBUBLVd2edE0fRKQlMCVuUXPgLqAeMBhY7y2/XVXfLo00jUl5PXu66axZ0cZhKryeE1xemXVZsLzip1D4CkgjrofUMFQ1B8gAEJGqwGpgGnAF8LCqjiqNdIzZr/TrF3UEJkX0ax0uryR6JPVR3G2iLUC2iLzL3l1nXxcqZec04EtV/UZESmF3xuynBg+OOgKTIgZ3DJdXEl0pLPCmC4E3QqVSvP7Ai3Hzw0TkMi/tm71+lvYiIkOAIQBNmzYto7CMMaZySlQo5AHzVPX7skhYRA4AzgOGe4ueAO7FXZ3ci+teY5+eWFV1HDAOoFOnTlr4c2P2S927u+mcOVFGYVJA96zuAMzJnBNo+0SFwiDgcRHZAnwAzAM+UNXlgVLa19nAIlVdBxCbAojIeGB6KaVjTOrLzIw6ApMiMjMyQ22fqPFaHwARSQdO8l5XiUhTYL6qnhMqZRhA3K0jEWmkqmu92QuBZSH3b8z+wwoF41OZFQoxqporItWBGt4r9j4wEakJnA5cFbf4ARHJwN0+yi30mTGVW36+m6alRRuHqfDyd7m8klY1WF5J9PTR7cCJQEMgB/gIeAwYoqq7itvOD1XdAtQvtOzSMPs0Zr92+uluanUKJonTn3d5pSzqFC4DNuPu7c8DPlbVvECpGGPC+d3voo7ApIjfdQiXVxLVKRzjdZt9EtAduE1EagNLcE8lPRsqZWOMf4MGRR2BSRGD2oXLKwnrFLwxE6aLyDtAR6Ar7l7/lYAVCsaUly1b3LRmzWjjMBXelnyXV2qmBcsrieoUzsNdJZwMtAaW424j3exNjTHl5RzvYT+rUzBJnDPJ5ZWyqFPIxP34/xFYqKo7AqVgjAnv97+POgKTIn7fKVxeSVSncFGoPRtjSo91iGd86tcmXF6pUkpxGGPKUl6eexmTRN62PPK2Bc8rvkZeM8ZE7Pzz3dTqFEwS5092eaUs6hQAEJHrVfWRZMuMMWXoutLoqd5UBtcdHy6v+LlSuBwoXABkFrHMGFNWLrIqPuPPRceGyyuJHkkdAPwf0ExE4sdTOAjYECpVY0zJ/PCDmzZoEG0cpsL7YYvLKw1qBssria4U5gFrgQa4sQ1iNgFLA6VmjAmmTx83tToFk0Sfl1xeKfU6BVX9BvgG1ymeMSZKN98cdQQmRdx8Yri84qei+QTgUeBY4ACgKvCLqtYJlbIxJqn0297y3nlPj3/wVrHrFid35LmlF5Cp8Hq37B1qez/tFB7DDYjzOW4chd/hCgljTDlpuPknGm7eZ8hyY/bx3ebv+G7zd4G399VOQVW/EJGq3jgKz4qI9X1kTDl69I2/AdD//0ZGHImp6PpP7Q+UYTsFYIuIHABki8gDuMrnWoFSM8YE8sQJfaMOwaSI2065LdT2fgqFS3G3mYYBNwJNgItDpWqMKZH3mneMOgSTIs769VmhtvczRvM33tttwJ9DpeYRkVzco627gJ2q2skb0GcKkI4bo/kSVbWbqD7sqYw0+6tGG9cDsLZOw4gjMRXdyryVADSp2yTQ9lF2iNdDVTNUtZM3fxvwrqoeDbzrzRtjgIenP8TD0x9KvqKp9C6ddimXTgs+5H1F6hDvfNywnwDPAXOAW6MKxpiK5NGT+kcdgkkRd3S9I9T2ftoptFHVZaFS2ZcCM0REgSdVdRxwqKquBVDVtSJySDHxDAGGADRt2rSUwzKmYvogPSPqEEyK6Nm8Z6jt/VwpjPWePsoCXlDVn0Ol6Jysqmu8H/6ZIvI/vxt6Bcg4gE6dOmkpxGJMhdfkZ/fc+cp6h0UcianovvrpKwCaH9w80PZJ6xRU9RRgIO6powUi8oKInB4otT37XONNvwemAb8B1olIIwBv+n2YNIzZnzz49mgefHt01GGYFHDl61dy5etXBt7eb+O1z0XkDmABMAY4TkQEuF1VXy1JgiJSC6iiqpu892cA9wBv4LrpHulNXy/Jfo3Znz18ysCoQzAp4s/dwz0k6qdOoR1wBXAuMBPoraqLRORw4EOgRIUCcCgwzZUpVMPdknpHROYDL4nIb4FvAWutY4zn46Ztow7BpIhu6d1Cbe/nSuExYDzuqmBrbKFXJ1Diam5V/QpoX8TyDcBpJd1fqrM2BsaP5htWAfBV/cYRR2IqupwfcgBo2aBloO39FArnAFu9fo8QkSpAdVXdoqrPB0rVGFMif/3XY4D1fWSSu2r6VUDZ9n00C+gJbPbmawIzgJMCpWiMKbEHul4eeNuwV6PW9XZq+etpfw21vZ9CobqqxgoEVHWziNQMlaoxpkQWNT426hBMijipSbjzdT/dXPwiIh1iMyLSEdiaYH1jTClrsT6XFutzow7DpIBl3y9j2ffB2xv7uVK4AXhZRNZ4842AfoFTNMaU2D0zxwJWp2CSG/b2MKAM6xRUdb6IHAO0BAT4n6rmB0rNGBPIX3sEb4xkKpcHT38w1PZ+O8TrjOvSuhqu4RqqOiFUysYY35Y2ahF1CCZFdD6ic6jt/TReex44CsjGjX8ArkM7KxSMKSet1rn+bP57aLD+bEzlkf1dNgAZhwXrRNHPlUInoJWqWudzxkTkrnfHAVanYJK74Z0bgLJtp7AMOAw3NrMxJgL3nDYk6hBMihh9VriOE/0UCg2A/4rIJ8D22EJVPS9UysYY3+y2kfEr6G2jGD+FwohQKRhjQmu39jPAKpxNcvNXzweCVzj7eST1PRE5EjhaVWd5rZmrBkrNGBPI7bOfAaxOwST3h5l/AMqwTkFEBuOGv/wV7imkI4CxVMIeTY2Jyl2nXx11CCZFPHbOY6G293P76BrcyGgfQ8GAO0WOn2yMKRufNUyPOgSTItoc0ibU9n76PtquqjtiMyJSDddOwRhTTjqsWkGHVSuiDsOkgHkr5zFv5bzA2/u5UnhPRG4HanhjMw8F3gycojGmxP449znA6hRMcre/eztQtu0UbgN+C3wKXAW8DTwVKDVjTCC3nzks6hBMiniy15Ohtvfz9NFu3HCc40Ol5BGRJrguMg4DdgPjVPURERkBDAbWe6verqpvl0aaxqQ6G4bT+BV0GM4YP08ffU0RdQiqGrQ1zU7gZlVdJCIHAQtFZKb32cOqOirgfo3Zbx3/7acAfNy0bcSRmIruvdz3AOiW3i3Q9n77PoqpDvTFPZ4aiKquxesyQ1U3icgK3GOuxphi3PifSYDVKZjk7p5zN1C24ylsKLRotIj8B7grUIpxRCQdOA73uOvJwDARuQxYgLua+ClsGsbsD/5wzg1Rh2BSxDPnPxNqez+3jzrEzVbBXTkcFCpVt9/awCvADaq6UUSeAO7F3aq6F3gI2GdkEREZgmtMR9OmTcOGYUxKWFnvsKhDMCmi+cHh+snyc/voobj3O4Fc4JIwiYpIGq5AmKSqrwKo6rq4z8cD04vaVlXHAeMAOnXqZO0lTKVwcq7rI/+D9HCdnZn936yvZgHQs3nPQNv7uX3UI9CeiyEiAjwNrFDVv8ctb+TVNwBciOuy2xgDXDtvMmCFgknuvrn3AWVYKIjITYk+j/9h9+lk4FLgUxHJ9pbdDgwQkQzc7aNcXJsIYwxwY6+bow7BpIjnL3w+1PZ+nz7qDLzhzfcG5gIrgySoqv8BpIiPrE2CMcVYW6dh1CGYFNGkbpNQ2/sdZKeDqm4C8BqZvayqvwuVsjHGt25fLQTgveYdI47EVHTvfPEOAGf9+qxA2/spFJoCO+LmdwDpgVIzxgTy+49eBqxQMMmN/I9ry1KWhcLzwCciMg13v/9CXDcVxphycu15t0YdgkkRk/tMDrW9n6eP/iIi/wS6eIuuUNXFoVI1xpTI+toHRx2CSRGH1Q7XpsXPeAoANYGNqvoIsEpEmoVK1RhTIqd98TGnffFx1GGYFPBmzpu8mRN8dAM/j6TejXsCqSXwLJAGTMQ9WmqMKQeDP5kGwLu/Pj7iSExF99CHrr1x75a9A23vp07hQlz/RIsAVHWN17upMaac/P6C4VGHYFLE1EumhtreT6GwQ1VVRBRARGqFStEYU2I/1awbdQgmRTSo2SDU9n4KhZdE5EmgnogMxnVSVyoD7hhj/Dkzx425+6+WJ5V72um3vRV6H7kjzy2FSIwfr654FYCLjr0o0PYJCwWvn6IpwDHARly9wl2qOjPRdsaY0nXFQtehQBSFQmmwgqX8jPl4DFBGhYJ32+g1Ve0IWEFQSGlkdGP8GHzxnVGHYFLE6/1fD7W9n9tHH4lIZ1WdHyolY0xgmw60qjzjT93q4eqf/BQKPYCrRSQX+AXXmZ2qartQKVcAdqZvUkWvFXMBmH5s14gjMRXdlGVTAOjXpl+g7YstFESkqap+C5wdaM/GmFIzaLHrRNgKBZPMEwueAMqgUABew/WO+o2IvKKqFwdKwRgTWmbfEVGHYFLE2wPDjUKQqFCIH/Mg3KCfxphQtqVVjzoEkyJqptUMtX2iQkGLeW+MKWcXLJ8NwGutS3V03JRij7X6M3HpRAAGtRsUaPtEhUJ7EdmIu2Ko4b2HPRXNdQKlaIwpsf5L/gVU7kLB+PPUoqeAMigUVLVqsJCMMaVtUL/7og7BpIiZl4ZrUua36+xyIyJniUiOiHwhIrdFHY8xFcHOqtXYWdXPE+SmskurmkZa1bTA21eoQkFEqgKP4x6DbQUMEJFW0UZlTPT6fDqLPp/OijoMkwKysrPIys4KvH1FO/X4DfCFqn4FICKTgfOB/0YalTERixUIU9v2jDiS1LY/NVgtrtI8ViBkZmQG2q+oVpwHi0SkD3CWqv7Om78UOF5Vh8WtMwQY4s22BHLKPdDS1wD4IeogKhg7JvuyY1I0Oy77SnZMjlTVhkV9UNGuFKSIZXuVWqo6DhhXPuGUDxFZoKqdoo6jIrFjsi87JkWz47KvMMekQtUpAKuAJnHzjYE1EcVijDGVTkUrFOYDR4tIMxE5AOgPvBFxTMYYU2lUqNtHqrpTRIYB/wKqAs+o6vKIwyoP+9XtsFJix2RfdkyKZsdlX4GPSYWqaDbGGBOtinb7yBhjTISsUDDGGFPACoVyJCLVReQTEVkiIstF5M/e8l+JyEwR+dybHhx1rOVNRKqKyGIRme7N2zERyRWRT0UkW0QWeMsq9XERkXoiMlVE/iciK0TkxMp8TESkpZc/Yq+NInJDmGNihUL52g6cqqrtgQzgLBE5AbgNeFdVjwbe9eYrm+uBFXHzdkycHqqaEffMeWU/Lo8A76jqMUB7XJ6ptMdEVXO8/JEBdAS2ANMIc0xU1V4RvICawCLgeFyr7Ebe8kZATtTxlfOxaOxl3FOB6d6ySn1MvO+dCzQotKzSHhegDvA13gMydkz2OT5nAB+EPSZ2pVDOvNsk2cD3wExV/Rg4VFXXAnjTQ6KMMQKjgT8Cu+OWVfZjAq41/wwRWeh17wKV+7g0B9YDz3q3Gp8SkVpU7mMSrz/wovc+8DGxQqGcqeoudZd6jYHfiEibqGOKkoj0Ar5X1YVRx1IBnayqHXC9Bl8jIl2jDihi1YAOwBOqehzwC5XoVlEiXmPf84CXw+7LCoWIqOrPwBzgLGCdiDQC8KbfRxhaeTsZOE9EcoHJwKkiMpHKfUwAUNU13vR73H3i31C5j8sqYJV3dQ0wFVdIVOZjEnM2sEhV13nzgY+JFQrlSEQaikg9730NoCfwP1xXHpd7q10OvB5NhOVPVYeramNVTcdd/v5bVQdRiY8JgIjUEpGDYu9x94uXUYmPi6p+B6wUkZbeotNw3epX2mMSZwB7bh1BiGNiLZrLkYi0A57DdeFRBXhJVe8RkfrAS0BT4Fugr6r+GF2k0RCR7sAtqtqrsh8TEWmOuzoAd9vkBVX9ix0XyQCeAg4AvgKuwPtfovIek5rASqC5quZ5ywLnEysUjDHGFLDbR8YYYwpYoWCMMaaAFQrGGGMKWKFgjDGmgBUKxhhjClih4JOI/Mnr2XSp1xvh8WWc3hwR8TXwtohkisiLhZY1EJH1InJggm0eK6VY43vz/FREzi+N/SZIb3Mp7+9Pcb1M7op7f52IZIlInxD7zhWR9wstyxaRZeEjT5r2ZSKyzMu3/xWRWwLuJ0NEzint+OL2H8s/S0XkPRE5Msn66SLyfwHSuc7rWXVSwDgzROTDuN+BfnGfNRORj71eSad4LYwRkWO8bbYXPv5F9fgaJK7SZoWCD94fqxfQQVXb4RqdrYw2qr28CpzuPa8c0wd4Q1W3l1MMPbzuO/oAY8opzVKhqn/RPT1Nbo29V9XS+h4HiUgTABE5tpT2mZCInA3cAJyhqq1xLX/zAu4uAyhRoSBOSX5fenj/W3OAO5Ksmw6UuFAAhgLnqOpAPyuLSOHhircAl3nH8yxgdKwxKvA34GF1vZL+BPzWW/4jcB0wqogkiurxNXJWKPjTCPgh9gOrqj/EuiAQkbtEZL53RjZORMRbPkdEHhaRud5ZQGcRedU7k7jPWyfdO0t4zjvzmFrohx1vvTO8s41FIvKyiNSO/1xVNwJzgd5xi/sDL4pIb+8MZrGIzBKRQ4vY/15nw/Fn4iLyB+/7LRVv/Ick6uD+KWLbvyauQ7fl4nXqJq5TwCzvmH0qIjd6y48SkXe89d8XkWO85c287z9fRO4tLmERucnb5zIRucFblu4d//FeDDPEtSYvia4iMk9Evip0nPwem5eA2FnlXi1PvWPxYNx+rvKW1xaRd72/ecHVVwm+z3BcQ8BYVxnbVHW8t4/BXnpLROSVWJ7z/iZjvWP/mYj08s547wH6ibvC6SciI+LPer3jnR4X2z9wPQA3CZB/PgSOiPuu73vHYJGInOStMxLo4sVzY3HHMJ6IjMV1qPeGt82vvLy5VEQ+EtewFO+7jRORGcCE+H2o6meq+rn3fg2u64iGIiK4Hn6neqs+B1zgrfe9qs4H8gvFUwfoCjztrbfD6/omelF395oKL6A2kA18BvwD6Bb32a/i3j8P9PbezwH+5r2/HliDK1wOxPXhUh93xqO4js8AnsH9I8e27wQ0wP3g1/KW3wrcVUSMfYFp3vvDvfSqAgezp5Hi74CHvPeZwGPe+yygT9y+NuuernjHAYI7gZgOdC0i7VzgU1w3DFuAXoWPD1DD+7w+rt/3mXHr1POm7wJHe++Px3V5Aa7J/mXe+2ti8RWKoaMXQy3v77UcOM47xjuBDG+9l4BBCf7WmwvNZ+E6GasCtAK+CHBsWgDzvPnF3n6WefNDgDu89wcCC4BmuFbMdbzlDYAvvLR8fR/cGWrdYr5j/bj39wHXxn3Xd7zvczQun1aPzyveeiPw8qk3v8yLKx3X0+0JAY5RA+/9aGCI974mUN17fzSwwHvfHa+L9UTHMEk6jwJ3e+9PBbLjvttCoEaS34Tf4M7sq8T+PnGfNYn9fRMcswzgE++YL8a10q5V2r9dQV52peCDqm7G/egMwXXdO0VEMr2Pe3hn4p/iMlfruE3f8KafAstVda26q42vcBkHYKWqfuC9nwicUij5E3A/Ih+I63L7cqCoe67TgVO8M5BLgKmqugvXG+u/vPj+UCi+ZM7wXotxZ37H4P45i9JDVdsAbYHH4q5mrhORJcBH3nc+Gvf9m4vIoyJyFrDRW/8k4GXvez6JK0TBdZoXO7t+vpj0T8EVir94f69XgS7eZ1+rarb3fiHux6skXlPV3ar6XyB2pVWSY/Mj8JOI9Mf9kGyJ++wM4DLvO3+MKzSPxv2Q/lVElgKzcGfPsbTDfp823hn4p8BA9s4TL3nf9XPc3+mYEu77G1X9yHtfkmM0W0S+x92afcFblgaM9+J8Gfd/UJTijmEip+DlJVX9N1BfROp6n72hqluL21BcB3PPA1eo6m7c36qwZF1FVNgeXwvfMzPF8H5g5wBzvEx6uYhMxl05dFLVlSIyAndmFRO7n7877n1sPnbsC2eewvOCO6sekCS+rSLyDnAh7tbRjd5HjwJ/V9U3xPUtNKKIzXfi3Ur0LoUPiEv7flV9MlHaheL4UkTWAa282xI9gRNVdYuIzMGd+f0kIu2BM3Fn/pfg7n//rO6+fpG7TpJ0Uf+YMfHHfhfuqqUk4reXuGlJjs0U4HHcWXc8wZ2p/2uvhe6koyHQUVXzxfUiG8tbfr7PctyJzL+L+CwLuEBVl3jpdI/7LFl+hLj84onP87/Efw38H6Me3rZZuNtVN+Hy8Drc/fYqwLZiti3yGCaR6If8lyI+cxu5k663cFcmscLvB6CeiFRT1Z24E7E1SdIvqsfXClEo2JWCD+LGQY0/88gAvmHPP8MP3plukKdUmsqepw4GAP8p9PlHwMki8msvlpoi0qKYfb2I+2c61NsOoC6w2nt/eVEb4S6rO3rvz8edoQH8C7gydtYvIkeISMLBOrzPm+GOT13gJ69AOAZ31YOINACqqOorwJ24CvyNwNci0tdbR7yCA+ADXEEH7sy2KHOBC7zjUwtXOL5fzLqloaTHZhrwgLdd4f38XkTSvP208OKvixtnIl9EelD01WEi9wMPiMhh3n4PFJHrvM8OAtZ6aRY+nn1FpIqIHIW7B58DbPK2icnFneUiIh1wf++ilOgYeWfnN+DO+n+FOwZrvbPxS3G3QykinuKOYSJz8b67d7L0g5cHi+XVr0wDJqhqwbgF6u4HzWbP/3/SXkm1+B5fI2dXCv7UBh4V96TBTtz93SGq+rOIjMfdHsoF5gfY9wrcVceTwOfAE/Efqup672zuRdnzeOkduPqNwmbgKrme9jIquCuDl0VkNa6gKOofeDzwuoh8gruv/4uX9gxxT8t86C4g2AwMoui+2WeLyC5cgXKbqq7zrlyu9m6B5LCnoDoCN3pW7KRkuDcdCDwhInd4+5kMLMHVybwgItcDrxSRNqq6SESycPdpAZ5S1cUikl7U+mGV8NigqptwT6jgrR/zFO72zyLvKm09rpJyEvCmiCzA1Wf9r4TxvS3uoYJZ3n4VV2cFriD+GFdwf8reP7A5wHu4E4urVXWbiMwGbvNuz9yP+xvEbtfMp+i8WOJj5G2zVtzj1dfgrsJf8U4UZrPnDH4psNO7LZmFe4onnX2PYSIjcHlwKe52XnEnTPEuwVUO14+7fZzp3cq7FZgs7iGSxXgVyF6hvAD3AMZucQ9AtPIKoGuBSV5hE+vxNXLWS2qEvB+s6d69eGMi5RWq01V1arJ1zf7Lbh8ZY4wpYFcKxhhjCtiVgjHGmAJWKBhjjClghYIxxpgCVigYY4wpYIWCMcaYAv8P7drra7o9AUIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram for draws_2016\n",
    "plt.hist(draws_2016, bins=18)\n",
    "plt.xlabel(\"Sample Value Based on The Mean Capture Rate for 2016\")\n",
    "plt.ylabel(\"Frequency at Which Sample Value Occurs\")\n",
    "plt.axvline(color='r', linestyle=\"dotted\", x=50, label=\"Realistic Capture Rate\")\n",
    "plt.axvline(color='g', linestyle=\"dotted\", x=70, label=\"Ideal Capture Rate\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of capturing more than 50% of recycled waste is: 9.8094%\n"
     ]
    }
   ],
   "source": [
    "## Probability of Capturing 50% in 2016\n",
    "rate50_2016 = round(float(poisson.sf(50, mean_2016)*100),4)\n",
    "print(\"The probability of capturing more than 50% of recycled waste is: \"+ str(rate50_2016)+\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of capturing more than 70% of recycled waste is: 0.0029%\n"
     ]
    }
   ],
   "source": [
    "## Probability of Capturing 70% in 2016\n",
    "rate70_2016 = round(float(poisson.sf(70, mean_2016)*100),4)\n",
    "print(\"The probability of capturing more than 70% of recycled waste is: \"+ str(rate70_2016)+\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probabilities for reaching a realistic or ideal capture rate in 2016 were even lower than the ones for 2010, which shows that NYC's capture rate has decreased over time. This decrease in the capture rate could suggest that a decrease in environmental activism or it could be due to other factors that were listed before such as individuals recycling items that cannot be recycled. In the next steps of our analyses, we explore what might have contributed to a decrease in the capture rate by grouping the data by borough instead of looking at the overall values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Capture Rate by Borough in 2010 and 2016\n",
    "This bar graph shows the capture rate by borough in NYC in 2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(recycling_2010[\"Zone\"], recycling_2010[\"Capture Rate\"])\n",
    "plt.xlabel('Borough of NYC')\n",
    "plt.ylabel('Recycling Capture Rate in 2010')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This bar graph shows the capture rate by borough in NYC in 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(recycling_2016[\"Zone\"], recycling_2016[\"Capture Rate\"])\n",
    "plt.xlabel('Borough of NYC')\n",
    "plt.ylabel('Recycling Capture Rate in 2016')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From 2010 to 2016 the capture rates for Brooklyn, Queens, and Staten Island stay the same. The capture rate for the Bronx increases, but the capture rate for Manhattan decreases. Baesd on these charts, it becomes more clear that the state of recycling did not change across all of New York City, but instead depended on the borough. The Bronx was able to increase efficiency which could have been through more efficient recycling plants or even through more awareness among individuals who have improved their recycling practices. The overall decrease in the capture rate from 2010 and 2016 as we saw from the previous analyses can be attributed to the decrease in the capture rate in Manhattan. Since Manhattan is the second largest borough in NYC (after Brooklyn) with a population of 1.62 million, a change in the capture rate in Manhattan affected the overall rate more than an increase in the capture rate in the Bronx since it is a smaller area with a smaller population, and therefore, a smaller impact. In this section, we used recycling capture rates as a measure of sustainable behaviors. In the next sections, we explore biking as a sustainable behavior by trying to find if the decrease in recycling in Manhattan correlates with a decrease in other sustainable behaviors including biking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Sustainable Behaviors (Biking vs. Recycling)\n",
    "In this section we compare the number of Citibike trips in 2013 and 2016 by borough to see if they follow the same trends as what we found in the previous analysis with recycling capture rates. If we see the same trends, we can use the data to further understand how sustainable behavior in NYC has changed over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next two cells, we count the number of bike trips per borough. The first dataframe is for 2013 and the second dataframe is for 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>boro_counts</th>\n",
       "      <th>Zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>706</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>91</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     boro_counts       Zone\n",
       "1.0          706  Manhattan\n",
       "3.0           91   Brooklyn"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boro_counts_2013 = pd.DataFrame(biking_2013[\"Zone\"].value_counts())\n",
    "boro_counts_2013 = boro_counts_2013.rename(columns={\"Zone\":\"boro_counts\"})\n",
    "boro_counts_2013[\"Zone\"] = [\"Manhattan\", \"Brooklyn\"]\n",
    "boro_counts_2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>boro_counts</th>\n",
       "      <th>Zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>740</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>73</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>16</td>\n",
       "      <td>Queens</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     boro_counts       Zone\n",
       "1.0          740  Manhattan\n",
       "3.0           73   Brooklyn\n",
       "4.0           16     Queens"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boro_counts_2016 = pd.DataFrame(biking_2016[\"Zone\"].value_counts())\n",
    "boro_counts_2016 = boro_counts_2016.rename(columns={\"Zone\":\"boro_counts\"})\n",
    "boro_counts_2016[\"Zone\"] = [\"Manhattan\", \"Brooklyn\",\"Queens\"]\n",
    "boro_counts_2016.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two bar graphs below are a graphical representation of the dataframes from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o6uDMzKyxynQ0fxU4IyLWA0g6FvgB4KRgZtbHlOlT2NKeEJIn2DngzMzM+pBOzxQknZ8mV0n6ITCXrE/hAmB5A2IzM7MGq3f56JzC9GbgnWl6KzBw99XNzGxf12lSiIiLGhmImZk1X5m7j44BPgGMLq5fpnS2mZntW8rcfbQA+BbZKOZXqw3HzMyaqUxS+F1EfL3ySMzMrOnKJIWvSfoccBfZc5cBiIgHKovKzMyaokxSOAn4C+Bd7Lx8FGnezMz6kDJJ4QPA6yPilaqDMTOz5iozovlh4KiqAzEzs+Yrc6YwFHhc0nJ27VPwLalmZn1MmaTwucqjMDOzXqHM8xSWNCIQMzNrvjIjml9g5zOZDwQOAF6KiCOqDMzMzBqvy47miDg8Io5IfwcDHwSu6Wo7SSMl3S1pjaRVki5J7YMkLZK0Lr0OLGwzXdJ6SWslndWTN2ZmZt1X5u6jXUTEAsqNUdgOfCoijgdOBaZKGkP2zOfFEdECLE7zpGUTgROA8cBMSQO6G5+Zme25MpePzi/M7ge0svNyUqciYhOwKU2/IGkNMByYAJyeVpsN3ANcltrnRMQ2YIOk9cA44L6S78XMzHqozN1HxecqbAeeJPsCL03SaOBNwFJgaEoYRMQmSUPSasOB+wubtaW2jvuaAkwBGDVqVHfCMDOzLpS5+6hHz1WQdBhwC/DJiHheUqer1jp8jXhmAbMAWltbuzxjMTOz8uo9jvOzdbaLiLiiq51LOoAsIdwYEbem5s2ShqWzhGHsfN5zGzCysPkIYGNXxzAzs72nXkfzSzX+ACaT9QHUpeyU4FvAmoi4urBoITApTU8Cbiu0T5R0UHqwTwuwrOT7MDOzvaDe4zi/2j4t6XDgEuAiYA7w1c62K3g7WXXVRyU9lNr+DrgSmCtpMvA0cEE63ipJc4HVZH0XUyNiR7ffkZmZ7bG6fQqSBgGXAn9OdqfQKRHxXJkdR8RPqd1PAHBmJ9vMAGaU2b+Zme199foUvgKcT9ape1JEvNiwqMzMrCnq9Sl8Cngt8A/ARknPp78XJD3fmPDMzKyR6vUpdHu0s5mZ7dv8xW9mZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmucqSgqTrJW2R9FihbZCkRZLWpdeBhWXTJa2XtFbSWVXFZWZmnavyTOEGYHyHtmnA4ohoARaneSSNASYCJ6RtZkoaUGFsZmZWQ2VJISLuBX7VoXkCMDtNzwbOK7TPiYhtEbEBWA+Mqyo2MzOrrdF9CkMjYhNAeh2S2ocDzxTWa0ttu5E0RdIKSSu2bt1aabBmZv1Nb+loVo22qLViRMyKiNaIaB08eHDFYZmZ9S+NTgqbJQ0DSK9bUnsbMLKw3ghgY4NjMzPr9/Zv8PEWApOAK9PrbYX270m6Gngt0AIsa3BsZr3O6Gk/aHYI1ks9eeXZley3sqQg6SbgdOBoSW3A58iSwVxJk4GngQsAImKVpLnAamA7MDUidlQVm5mZ1VZZUoiICztZdGYn688AZlQVj5mZda23dDSbmVkv4KRgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyvS4pSBovaa2k9ZKmNTseM7P+pFclBUkDgGuB9wJjgAsljWluVGZm/UevSgrAOGB9RDwREa8Ac4AJTY7JzKzf2L/ZAXQwHHimMN8GvLW4gqQpwJQ0+6KktQ2Kra87Gvhls4PoLfSlZkdgNfgzWtDDz+jrOlvQ25KCarTFLjMRs4BZjQmn/5C0IiJamx2HWWf8GW2M3nb5qA0YWZgfAWxsUixmZv1Ob0sKy4EWScdIOhCYCCxsckxmZv1Gr7p8FBHbJX0c+DEwALg+IlY1Oaz+wpfkrLfzZ7QBFBFdr2VmZv1Cb7t8ZGZmTeSkYGZmOSeFXk5SSPpuYX5/SVsl3dGDfb7YzfVPl/THhfnzPNLc6pG0Q9JDkh6W9EDx89PD/X5E0jU12j8v6W/2xjH6OyeF3u8l4ERJh6T5dwM/b3AMpwPF/9TnkZUhMevMyxExNiJOBqYDX+y4QiprY72Mk8K+4UfA2Wn6QuCm9gWSxkn6L0kPptc3pvaPSLpV0p2S1kn6cnGHkmakX3H3Sxqa2s6RtDTt6z8kDZU0GvgY8Nfpl987gXOBr6T5YyV9VNLytL9bJL0m7e8GSV9PcT0h6U8r/ney3ukI4DnIzzrvlvQ94FFJB0v6tqRH0+fujLRezfYiSWdLuk/S0YW2YyU9UJhvkbQyTT8p6fJ05vKopOOqfuP7IieFfcMcYKKkg4E/ApYWlj0OnBYRbwI+C3yhsGws8CHgJOBDktoHBh4K3J9+xd0LfDS1/xQ4Ne1rDvC3EfEk8E3g/6VffkvIxo58Os3/DLg1It6S9rcGmFyIYRjwDuD9wJV74d/C9g2HpB8NjwPXAVcUlo0D/j4ixgBTASLiJLIfPLPT57yzdgAkfQCYBrwvIvLSF+nz+BtJY1PTRcANhWP/MiJOAb4B+HJTDb1qnILVFhGPpF/sFwI/7LD4SLL/MC1kJUEOKCxbHBG/AZC0mqzeyTPAK0B7n8RKsktSkI0g/76kYcCBwIaSIZ4o6Z+Ao4DDyMaZtFsQEa8Cq9vPSKxfeDkixgJIehvwHUknpmXLIqL9s/UO4F8AIuJxSU8Bb6jTDnAG0Aq8JyKer3Hs64CLJF1K9qNoXGHZrel1JXB+z99m3+MzhX3HQuAqCpeOkiuAuyPiROAc4ODCsm2F6R3s/BHw+9g5QKXY/i/ANenX2cUd9lXPDcDH03aX14mhVm0r6+Mi4j6yYnaDU9NLhcWdfSbqfVaeAA5nZ5Lo6Bay8vvvB1ZGxLOFZe2fx+Ln3gqcFPYd1wP/GBGPdmg/kp0dzx/p4TGK+5pUaH+B7D9hZ/OHA5skHQD8eQ9jsD4mXbsfADxbY/G9pM+MpDcAo4C1ddoBniL7lf8dSSd03GFE/I7sbPUbwLf35nvpD5wU9hER0RYRX6ux6MvAFyX9f7L/eD3xeeBmST9h1xLFtwMfSNeI/4Ssv+HTqQPwWOAzZP0ci8j6OMza+xQeAr4PTIqIHTXWmwkMkPRoWu8jEbGtTjsAEbGWLGncnD6DHd1Idjn1rr36rvoBl7kwsz4njVk4MiI+0+xY9jW+pmZmfYqk+cCxwLuaHcu+yGcKZmaWc5+CmZnlnBTMzCznpGBmZjknBdvnVVWRcw9juaEnNZ4k/ZWkNZJu7NB+urKKuecU2u5I7V+Q9KVC++tSramjUm2seyWtlfS4pOvaa1OZ1eKkYH1BlxU5O9MLK3X+JVk9n1qDANuAv6/RfgUwQdLxaf5rZGNHDgJuBi6LiDcCxwN3suvAQ7NdOClYX1OsyClJX5H0WKqK+aHUXrZS5y61+9t/mafpyZL+W9I9kv5Nu9b4P62ryrCSLk1xPSbpk6ntm8DrgYWS/rrGZg+TFXt7d7ExIl4GLgVmSnovcHhE3EhWVG52KjNBZOZFxOZu/ptaP+JxCtYXHJJGzh5MVpW1/f7088kqxZ5MVntnuaR707JxwIkRsUHSpyCryJlKMtyVSivUJOm1ZL/ETyEr+fGfZF/Y7dorwx5HVrNqXoft30xWvfOtZDV+lkpaEhEfkzQeOKNY+bODf0p/i4qNEfFDSZOB76RjA5wIzO7sfZjV4jMF6wvaLx8dB4wnq4kjsi/HmyJiR/p1vAR4S9qmY6XO70JWkZOstk6nSYEsoSyJiF9FxO/JLtEULYiIVyNiNVCrMuw7gPkR8VJEvEhWufNPyrzRiPgJQCo30tG1wPJUAsJsjzgpWJ/SoSJnvUqbZSp1bmfX/yPt1V+7qvbaVWXYnlaLnUHtvoVX01+7VcCbe3gs62ecFKxP6VCR816yhwsNkDQYOA1YVmOzzipyPgmMlbRfekBRe13+ZcA7JQ2UtD/wwW6GeS9wnqTXSDoU+ADwk7IbR8RdwECyy2L1XANMkvTW9gZJH5b0v7oZr/Uj7lOwvqC9TwGyX+GTImJHqoHzNrLr/UH2JLlfaPfHMM4Evpkqcm4nVeRMlWc3AI8CjwEPAETEzyV9gawy7EZgNfCbssFGxAOSbmBngrouIh7s5nueAdzWxXE2S5oIXCVpCNlZxL3sfNCM2W5c+8hsD0g6LCJeTGcK84HrI2J+s+My6ylfPjLbM59PZyePkZ1NLGhyPGZ7hc8UzMws5zMFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOz3P8A+bzB02dhOUgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(boro_counts_2013[\"Zone\"], boro_counts_2013[\"boro_counts\"])\n",
    "plt.xlabel('Borough of NYC')\n",
    "plt.ylabel('Number of Bikers 2013')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PA++q0P4ScEIn27QALSXiNjOzGihz8dpVwHERsQpA0oHAT4GqScHMzPqfMmMK69sTQvI0mweHzcxsAOm0pyDprDS5VNKdwByyMYWzya5qNjOzAaba4aPTCtPrgA+k6TZgr5pFZGZmDdNpUoiI8+sZiJmZNV6Zs4/2Bz5JVtU0X9+ls83MBp4yZx/NBb5HdhXzptqGY2ZmjVQmKbwREd+qeSRmZtZwZZLC1ZIuBe4hu0cCABFR5p4KZmbWj5RJCocB/wc4ns2HjyLNm5nZAFImKZwJHFAsn21mZgNTmSuaHwP2rHUgZmbWeGV6CsOApyQ9zJZjCj4l1cxsgCmTFC6teRRmZtYnlLmfwsJ6BGJmZo1X5ormP7L5Xsk7AjsAr0XE7rUMzMzM6q9MT2G34rykM4DxNYvIzMwapszZR1uIiLn4GgUzswGpzOGjswqz2wHNbD6cZGZmA0iZnsJphcfJwB+BCV1tJGk/SfdKWi5pqaRPpfa9Jc2XtDI971XYZpqkVZJWSDq5e2/JzMy6q8yYQnfvq/Am8JmIeETSbsBiSfOB84AFETFd0lRgKnCJpDHARGAs8Fbg55IOioiN3Xx9MzPbRtVux/mlKttFRFxWbccR8QLwQpr+o6TlwAiyXsaxabVZwH3AJal9dkRsAJ6RtIpsQPuBUu/EzMx6rNrho9cqPAAmk32JlyZpFPAu4CFgWEoY7Ylj37TaCGB1YbM1qa3jvqZIapXU2tbWti1hmJlZF6rdjvOq9ul0+OdTwPnAbOCqzrbrSNKuwI+BiyLiFUmdrlopjApxzQRmAjQ3N3vA28ysF1UdaE6Dwl8FHidLIEdExCURsb7MziXtQJYQboyIW1PzOknD0/LhQPu+1gD7FTYfCawt/U7MzKzHOk0Kkr4BPEx2ttFhEfHliHi57I6VdQm+ByyPiG8WFs0DJqXpScDthfaJkoam+0KPBhaVfidmZtZj1c4++gxZVdQvAJ8vHPYR2UBzV2Uujia7Oc8Tkpaktn8GpgNzJE0GngfOJtvhUklzgGVkZy5d6DOPzMzqq9qYwjZf7dxh+19SeZwA4IROtmkBWnryumZm1n09+uI3M7OBxUnBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlqtZUpB0naT1kp4stO0tab6klel5r8KyaZJWSVoh6eRaxWVmZp2rZU/hBuCUDm1TgQURMRpYkOaRNAaYCIxN28yQNKSGsZmZWQU1SwoRcT/wuw7NE4BZaXoWcEahfXZEbIiIZ4BVwPhaxWZmZpXVe0xhWES8AJCe903tI4DVhfXWpLatSJoiqVVSa1tbW02DNTMbbPrKQLMqtEWlFSNiZkQ0R0RzU1NTjcMyMxtc6p0U1kkaDpCe16f2NcB+hfVGAmvrHJuZ2aBX76QwD5iUpicBtxfaJ0oaKml/YDSwqM6xmZkNetvXaseSbgKOBfaRtAa4FJgOzJE0GXgeOBsgIpZKmgMsA94ELoyIjbWKzczMKqtZUoiIcztZdEIn67cALbWKx8zMutZXBprNzKwPqFlPway3jZr600aHMGA9O/3URodgfYR7CmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc73UzCzmvE9MGqnVvfAcE/BzMxyfS4pSDpF0gpJqyRNbXQ8ZmaDSZ9KCpKGAN8GPgiMAc6VNKaxUZmZDR59KikA44FVEfF0RPwZmA1MaHBMZmaDRl8baB4BrC7MrwHeU1xB0hRgSpp9VdKKOsXWaPsALzY6iLJ0RaMj6BP6zWfmzwvoR58X9Pgze3tnC/paUlCFtthiJmImMLM+4fQdklojornRcVh5/sz6F39emb52+GgNsF9hfiSwtkGxmJkNOn0tKTwMjJa0v6QdgYnAvAbHZGY2aPSpw0cR8aakTwB3A0OA6yJiaYPD6isG3SGzAcCfWf/izwtQRHS9lpmZDQp97fCRmZk1kJOCmZnlnBR6SFJI+kFhfntJbZLu6ME+X93G9Y+V9D8L82f4SvBtI2mjpCWSHpP0SPHv2cP9nifpmgrtX5b0T73xGgaSRkq6XdJKSU9LukbS0EbH1R85KfTca8ChknZO8ycC/13nGI4Fil9iZ5CVCbHyXo+IcRFxODAN+FrHFVIZFutjJAm4FZgbEaOB0cDOwNcbGlg/5aTQO34GtNexPRe4qX2BpPGSfi3p0fR8cGo/T9Ktku5Kv262+AcsqSX9an1Q0rDUdpqkh9K+fi5pmKRRwMeBT6dfuh8ATge+keYPlPQxSQ+n/f1Y0lvS/m6Q9K0U19OSPlLjv1N/sTvwMuS9sHsl/RB4QtJOkq6X9ET6HI5L61VsL5J0qqQHJO1TaDtQ0iOF+dGSFqfpZyV9JfVcnpB0SK3feD91PPBGRFwPEBEbgU8Dfy/pE8WemqQ7JB2bpk9Kn8cjkm6WtGtqP1LSQkmLJd0taXhqv0/SFZIWSfovSe9P7WNT2xJJj0saXd+337ucFHrHbGCipJ2AdwIPFZY9BRwTEe8CvgRcXlg2DjgHOAw4R1L7hXu7AA+mX633Ax9L7b8Ejkr7mg18LiKeBb4L/N/0S3ch2bUdn03zvwFujYh3p/0tByYXYhgOvA/4EDC9F/4W/dXO6T/1U8C1wGWFZeOBz0fEGOBCgIg4jOwHwKz0uXfWDoCkM4GpwN9GRF5KIX0+f5A0LjWdD9xQeO0XI+II4DuADzdVNhZYXGyIiFeAZ+nktPuUmL8A/E36+7YCF0vaAfg34CMRcSRwHdBS2HT7iBgPXARcmto+DlwdEeOAZrKLcPutPnWdQn8VEY+nX+znAnd2WLwH2RfEaLKSHTsUli2IiD8ASFpGVo9kNfBnoH1MYjHZISnIrvD+UfrlsiPwTMkQD5X0VWBPYFey60DazY2ITcCy9h7JIPV6+k+NpPcC35d0aFq2KCLa/9bvI/vSICKekvQccFCVdoDjyL4sTkpfVh1dC5wv6WKyHwnjC8tuTc+LgbN6/jYHJNGhHE6hvTNHkR1i/VV29IkdgQeAg4FDgfmpfQjwQmG74ucxKk0/AHxe0kiyH2Aru/Uu+gj3FHrPPOBKCoeOksuAeyPiUOA0YKfCsg2F6Y1sTtJ/ic0XkBTb/w24Jv0avaDDvqq5AfhE2u4rVWKo9p9o0IiIB8iKozWlptcKizv7G1X72z0N7MbmJNHRj8nKxX8IWBwRLxWWtX8+xX8HtqWlZEk3J2l3YBjwElt+z7X/2xcwP/Wmx0XEmIiYnNqXFtoPi4iTCttv9XlExA/JDtm+Dtwt6fhefn915aTQe64D/iUinujQvgebB57P6+FrFPc1qdD+R7Ivnc7mdwNeSF3j/93DGAa8dOx+CNkXSkf3k/6Gkg4C3gasqNIO8BzZr/zvSxrbcYcR8QZZ7+07wPW9+V4GiQXAWyT9PeQnBFwFXEPWmx4nabt0eLa9F/YgcLSkv07bvCV9biuAptRbRNIOlT6zIkkHAE9HxLfIfhy+s9ffYR05KfSSiFgTEVdXWPR14GuSfkX2RdMTXwZulvQLtizx+xPgzHRM/P1k4w2fTQOeBwJfJBvnmE82xmFbax9TWAL8CJiUBiw7mgEMkfREWu+8iNhQpR2AiFhBljRuTp9JRzeSHQK5p1ff1SCQetVnAh+RtJIsmW+KiBbgV2SJ4QmynvwjaZs2sh9pN0l6nCxJHJLu4/IR4ApJjwFL2PLMvkrOAZ5M/3YOAb7fu++wvlzmwqwPUHbNwh4R8cVGx9LfKbvG5CbgrIhY3NX6tiUnBbMGk3QbcCBwfPHMJLNGcFIwM7OcxxTMzCznpGBmZjknBTMzyzkpWL+nGlU47WYsN/SkhpSkf5S0XNKNHdqPVVaR97RC2x2p/XJJVxTa366sltWeympv3S9phaSnJF2rVPvKrBInBRsIuqxw2hn1vcqn/0BWH6nSRYZrgM9XaL8MmCDpHWn+arJrU4YCNwOXRMTBwDuAu9jywkazLTgp2EBTrHAqSd+Q9GSqMnpOai9b+XSLeyFoywqbk1OlzPsk/bu2vGfCMeqi8qyki1NcT0q6KLV9FzgAmCfp0xU2e4yseN6JxcaIeB24GJgh6YPAbhFxI1mRvlmpbAeRuSUi1m3j39QGEddSsYFg53Q16U5kVV/ba8+cRVaJ9nCyWkYPS7o/LRsPHBoRz0j6DGQVTlOJi3tSyYOKJL2V7Jf4EWQlRf6T7Au7XXvl2UPIyh7c0mH7I8mqob6HrNbOQ5IWRsTHJZ0CHFfleoWvpsf8YmNE3ClpMtnVtO9LzYcCszp7H2aVuKdgA0H74aNDgFPIagyJ7MvxpojYmH4dLwTenbbpWPn0B5BVOCWrVdRpUiBLKAsj4ncR8ReyQzRFcyNiU0QsIyvK1tH7gNsi4rWIeJWs8ub7y7zRiPgFQCpn0tG3gYdTSQ2zbnFSsAGlQ4XTapVLy1Q+fZPOK2xW01Xl2Z5Wo22h8tjCpvRotxQ4soevZYOMk4INKB0qnN5PdvOiIZKagGOARRU266zC6bNUrrC5CPiApL0kbQ98eBvDvB84I1Xm3IWsmNsvym4cEfcAe5EdFqvmGmCSpPe0N0j6qKT/sY3x2iDiMQUbCNrHFCD7FT4pIjammkLvJTveH2R3qvuttr6t5Qzgu6nC6ZukCqepsm17hc0n2Vxh878lXU5WeXYtsAz4Q9lgI+IRSTewOUFdGxGPbuN7bgFu7+J11kmaCFwpaV+yXsT9bL5RjNlWXPvIrBsk7RoRr6aewm3AdRFxW6PjMuspHz4y654vp97Jk2S9ibkNjsesV7inYGZmOfcUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMcv8f/KiIP+JR5sUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(boro_counts_2016[\"Zone\"], boro_counts_2016[\"boro_counts\"])\n",
    "plt.xlabel('Borough of NYC')\n",
    "plt.ylabel('Number of Bikers 2016')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these graphs, we are able to see that the number of bike trips in Manhattan and Brooklyn remained almost the same in 2013 and 2016. However, there was an increase in the number of bike trips in Queens. There are a few reasons that this could have happened. Since there are no trips in Queens in 2013, it is likely that there were no bike stations contructed in Queens since that is the first year the Citibike was implemented. Customers might have used the bike in Queens but could not return or pick up a bike from Queens. Our dataset only shows the start and end location (where the bike was returned and where it was picked up), based on the station. Based on this, new stations were likely constructed in Queens by 2016. One reason we might not have seen a change in Manhattan and Brooklyn from 2013 to 2016 could be because we are working with a smaller sample from the full dataset since each orginal dataset had 2 million observations and we were unable to process and work with such large datasets. These plots might have showed more of a difference had we been able to process the entire dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at this analysis in comparison to the analysis on the recycling capture rates, we do not see the same trends in sustainable behaviors. For recycling, in Manhattan, we saw a decrease in the capture rate but the same number of trips for bikers. Given that none of the trends match up for boroughs, it is difficult to determine a relationship between recycling and biking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pollution Levels vs. Citibike Trips\n",
    "In this section, we were interested in looking at how pollution levels and the number of biking trips using the Citibike have changed over the years. Using the Citibike operations reports, we constructed a new dataframe that contains the total yearly pollution (we did this by summing the columns in ghg_ny) and biker counts from 2013 to 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sum of pollution levels for each year from 2011-2018: \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>Year</th>\n",
       "      <th>total_pollution</th>\n",
       "      <th>biker_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>39270240.62</td>\n",
       "      <td>618572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>39988530.62</td>\n",
       "      <td>1035478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>39958269.61</td>\n",
       "      <td>941117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>37066181.97</td>\n",
       "      <td>1460303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>31824056.02</td>\n",
       "      <td>1731155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  total_pollution  biker_counts\n",
       "0  2013      39270240.62        618572\n",
       "1  2014      39988530.62       1035478\n",
       "2  2015      39958269.61        941117\n",
       "3  2016      37066181.97       1460303\n",
       "4  2017      31824056.02       1731155"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = 2013\n",
    "total_sums = np.zeros(6)\n",
    "for i in range(6):\n",
    "    year_str = str(year)\n",
    "    total_sums[i] = ghg_ny[year_str].sum()\n",
    "    year = year+1\n",
    "total_sums = pd.DataFrame(total_sums)\n",
    "total_sums[\"total_pollution\"] = total_sums\n",
    "total_sums[\"Year\"] = [\"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\"]\n",
    "total_sums = total_sums[[\"Year\", \"total_pollution\"]]\n",
    "total_sums[\"biker_counts\"] = [618572, 1035478, 941117, 1460303, 1731155, 1952950]\n",
    "print(\"The sum of pollution levels for each year from 2011-2018: \")\n",
    "total_sums.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We created this plot to see individually understand the trends for how the number of bikers has changed and how total polluion has changed from 2013 to 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(total_sums['Year'], total_sums[\"total_pollution\"], label=\"Total Pollution\")\n",
    "plt.plot(total_sums['Year'], total_sums[\"biker_counts\"], label=\"Number of Bikers\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For pollution levels, we see a decrease from 2013 to 2017, but an increase from there into 2018. Overall though, there is a decrease in the pollution levels. The number of Citibike trips has steadily increased from 2013 to 2018. While this could be a predictor of increased environmentally sustainable behavior, this increase in the number of trips could be due to a variety of other factors such as the growth in the Citibike program as more people find out about it and an increased convenience in using Citibike as new stations were constructed in places such as Queens (as per previous plots). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='total_pollution', y=\"biker_counts\", data=total_sums)\n",
    "plt.xlabel(\"Total Pollution\")\n",
    "plt.ylabel(\"Number of Bikers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we compare the total pollution levels to the number of bikers, we see a inversely proportional relationship. As the total pollution increase, we see a decrease in the number of bike trips (even though the overall trend is an increase as per the last plot). This seems contradictory to the previous plot at first, but can help explain how the levels of pollution in NYC, even though they are decreasing, are so high that increased sustainable behaviors like biking are unable to offset the differences. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of Significance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recycling Capture Rate in 2010 vs. 2016\n",
    "In this section, we used a t-test to see if the decrease in the capture rate from 2010 to 2016 was statistically significant. We did this by using the mean and standard deviation to contruct a normal distribution to approximate the sample from above and calculated the p value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Find the standard deviation for 2010 and 2016\n",
    "std_2010 = recycling_2010[[\"Capture Rate\"]].std()\n",
    "std_2016 = recycling_2016[[\"Capture Rate\"]].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the slope is:  -0.0588\n"
     ]
    }
   ],
   "source": [
    "##Create the distributions for 2010 and 2016 \n",
    "x_2010 = pd.DataFrame(np.random.normal(mean_2010,std_2010, size=1000))\n",
    "y_2016 = pd.DataFrame(np.random.normal(mean_2016,std_2016, size=1000))\n",
    "recycling_model = LinearRegression().fit(x_2010,y_2016)\n",
    "print(\"the slope is: \", round(float(recycling_model.coef_),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is:  0.0237\n",
      "The test statistic is: 2.26\n"
     ]
    }
   ],
   "source": [
    "recycle_val = stats.ttest_ind(x_2010, y_2016)\n",
    "print(\"The p-value is: \", round(float(recycle_val[1]),4))\n",
    "print(\"The test statistic is:\", round(float(recycle_val[0]),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, our p-value was 0.02. Since the p-value is less than 0.05, this indicates that at a 95% confidence level, there is a statistically significant difference between the capture rates in 2010 and in 2016. This means that the decrease that we saw in capture rate from 2010 to 2016 likely occurred due to some other factor other than random chance. Based on our analysis from earlier, this decrease could have been due to decreased efficiency at the recycling plant or a decrease in sustainable recycling behaviors on the part of the general public. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pollution Levels vs. Citibike Trips\n",
    "In order to test whether the inversely proportional relationship was statistically significant, we created a Linear Regression model and fit the data with the total pollution sum and the counts of the number of bike rides. From this Linear Regression model, we were able to determine the slope, intercept, and r squared value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope for total pollution levels in NYC vs. number of Citi bike trips is: -0.130\n",
      "The intercept for total pollution levels in NYC vs. number of Citi bike trips is: 6096814.740\n",
      "The r squared value for total pollution levels in NYC vs. number of Citi bike trips is: 0.737\n"
     ]
    }
   ],
   "source": [
    "pollution_bike_model = LinearRegression().fit(total_sums[['total_pollution']],total_sums[['biker_counts']])\n",
    "pollution_bike_slope = pollution_bike_model.coef_[0][0]\n",
    "pollution_bike_intercept = pollution_bike_model.intercept_[0]\n",
    "pollution_bike_corr = pollution_bike_model.score(total_sums[['total_pollution']],total_sums[['biker_counts']])\n",
    "print(\"The slope for total pollution levels in NYC vs. number of Citi bike trips is: {:.3f}\".format(pollution_bike_slope))\n",
    "print(\"The intercept for total pollution levels in NYC vs. number of Citi bike trips is: {:.3f}\".format(pollution_bike_intercept))\n",
    "print(\"The r squared value for total pollution levels in NYC vs. number of Citi bike trips is: {:.3f}\".format(pollution_bike_corr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slope shows us that as the pollution levels increase by 1, the total number of bike trips decreases by 0.130. Since the r squared value, based on the linear regression model, was 0.737 we can see that the relationship between these two variables is quite strong and that the pollution levels is a good predictor of the number of bike trips. This shows us that the data is statistically significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pollution in 2011 vs. 2016\n",
    "In this section, we test the statistical significance of the relationship between pollution levels in 2011 vs. the pollution levels in 2016. In order to do this, we split the dataset using train test split, train a linear regression model using the training set, and then test the accuracy of the linear regression model on the testing set. We then determine the accuracy of the training and testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the training set for the NY pollution data:       Unnamed: 0  facility_id        frs_id                      facility_name  \\\n",
      "71           71      1002160  110004541434            GUARDIAN INDUSTRIES LLC   \n",
      "31           31      1000771  110000582398                          Northport   \n",
      "138         138      1006042  110007384106      TGP Station 245 West Winfield   \n",
      "151         151      1006910  110019768526      OSWEGO CO ENERGY RECOVERY FAC   \n",
      "120         120      1004894  110004513571  FULTON COUNTY DEPT OF SOLID WASTE   \n",
      "\n",
      "              city state  zip_code               address           county  \\\n",
      "71          GENEVA    NY     14456          50 FORGE AVE   ONTARIO COUNTY   \n",
      "31       NORTHPORT    NY     11768     EATON'S NECK ROAD          Suffolk   \n",
      "138  WEST WINFIELD    NY     13491        457 BURROWS RD  HERKIMER COUNTY   \n",
      "151         Fulton    NY     13069  2801 STATE ROUTE 481    OSWEGO COUNTY   \n",
      "120      JOHNSTOWN    NY     12095            847 MUD RD    FULTON COUNTY   \n",
      "\n",
      "     latitude  ...                    industry_sector        2018       2017  \\\n",
      "71      42.88  ...                           Minerals    94019.34  139846.26   \n",
      "31      40.92  ...                       Power Plants  1622224.51  992247.85   \n",
      "138     42.87  ...  Petroleum and Natural Gas Systems    46683.83   50941.22   \n",
      "151     43.35  ...                              Waste    29101.30   27699.04   \n",
      "120     43.01  ...                              Waste    32826.00   14486.80   \n",
      "\n",
      "           2016        2015        2014        2013        2012        2011  \\\n",
      "71    140136.16   138709.86   136448.02   133326.23   142014.85   140496.21   \n",
      "31   1481120.55  1836382.11  1656151.69  1886742.79  1952018.63  2111284.68   \n",
      "138    62102.88    51755.70    49018.48    58759.90    58561.81    50696.57   \n",
      "151    29446.17    28739.81    16900.58    31039.33    30640.70    30979.61   \n",
      "120    18165.10    17503.60    25088.60    16805.15    25042.40    24403.75   \n",
      "\n",
      "     cluster_label  \n",
      "71               0  \n",
      "31               1  \n",
      "138              0  \n",
      "151              0  \n",
      "120              0  \n",
      "\n",
      "[5 rows x 23 columns]\n",
      "This is the test set for the NY pollution data:       Unnamed: 0  facility_id        frs_id  \\\n",
      "26           26      1000763  110008054265   \n",
      "21           21      1000482  110008035704   \n",
      "107         107      1004057  110007155121   \n",
      "112         112      1004412  110000808083   \n",
      "36           36      1000781  110019644679   \n",
      "\n",
      "                            facility_name       city state  zip_code  \\\n",
      "26             Narrows Generating Station   BROOKLYN    NY     11232   \n",
      "21               ReEnergy Black River LLC  FORT DRUM    NY     13602   \n",
      "107  DANC SOLID WASTE MANAGEMENT FACILITY     RODMAN    NY     13682   \n",
      "112             GE GLOBAL RESEARCH CENTER  NISKAYUNA    NY     12309   \n",
      "36    CON ED-EAST 60TH STREET STEAM PLANT  MANHATTAN    NY     10022   \n",
      "\n",
      "                              address            county  latitude  ...  \\\n",
      "26                53RD ST AND 1ST AVE             Kings     40.65  ...   \n",
      "21   4515 Euphrates River Valley Road  JEFFERSON COUNTY     44.04  ...   \n",
      "107                23400 NY ROUTE 177         JEFFERSON     43.83  ...   \n",
      "112               ONE RESEARCH CIRCLE       SCHENECTADY     42.83  ...   \n",
      "36               514 EAST 60TH STREET   NEW YORK COUNTY     40.76  ...   \n",
      "\n",
      "     industry_sector       2018       2017       2016       2015       2014  \\\n",
      "26      Power Plants   66469.00   23171.91   72685.02   57026.62   25653.40   \n",
      "21      Power Plants   72777.15   63353.55   52544.56   29193.71   37231.60   \n",
      "107            Waste   47585.15   33742.15   42644.35   54085.75   99117.70   \n",
      "112            Other   24947.51   23855.53   26807.34   30498.56   28621.62   \n",
      "36      Power Plants  112792.08  100345.22  116696.76  136345.94  158933.49   \n",
      "\n",
      "          2013       2012       2011  cluster_label  \n",
      "26   112345.07   99636.85  107077.43              0  \n",
      "21    21442.40     106.20      98.10              0  \n",
      "107   72084.65   52527.45   46922.15              0  \n",
      "112   28317.17   26429.64   27274.08              0  \n",
      "36   174569.99  204689.43  221298.62              0  \n",
      "\n",
      "[5 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = train_test_split(ghg_ny, test_size = 0.20, random_state = 15)\n",
    "print (\"This is the training set for the NY pollution data: \", train_set.head())\n",
    "print (\"This is the test set for the NY pollution data: \", test_set.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope for NY pollution levels in 2011 vs. the pollution levels in 2016: 0.659\n",
      "The intercept for NY pollution levels in 2011 vs. the pollution levels in 2016: 46633.421\n",
      "The r squared for NY pollution levels in 2011 vs. the pollution levels in 2016: 0.563\n"
     ]
    }
   ],
   "source": [
    "pollution_model = LinearRegression().fit(train_set[[\"2011\"]],train_set[[\"2016\"]])\n",
    "pollution_slope = pollution_model.coef_[0][0]\n",
    "pollution_intercept = pollution_model.intercept_[0]\n",
    "pollution_corr = pollution_model.score(train_set[[\"2011\"]],train_set[[\"2016\"]])\n",
    "print(\"The slope for NY pollution levels in 2011 vs. the pollution levels in 2016: {:.3f}\".format(pollution_slope))\n",
    "print(\"The intercept for NY pollution levels in 2011 vs. the pollution levels in 2016: {:.3f}\".format(pollution_intercept))\n",
    "print(\"The r squared for NY pollution levels in 2011 vs. the pollution levels in 2016: {:.3f}\".format(pollution_corr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percent accuracy of the training set is 56.27%\n"
     ]
    }
   ],
   "source": [
    "score_train = pollution_model.score(train_set[['2011']], train_set['2016'])\n",
    "print(\"The percent accuracy of the training set is \" + \"{:.2%}\".format(score_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percent accuracy of the testing set is 80.29%\n"
     ]
    }
   ],
   "source": [
    "score_test = pollution_model.score(test_set[['2011']], test_set['2016'])\n",
    "print(\"The percent accuracy of the testing set is \" + \"{:.2%}\".format(score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through this test, we are able to find that the training set had an accuracy of 56% while the testing set had an accuracy of 80%. This shows that the data is somewhat predictive as we have a correlationof 0.563, but it does not have as strong a correlation as we saw before. This means that there is a correlation between pollution levels in 2011 and 2016, but it may not be as strong."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Pollution Levels by Industry\n",
    "In this section, we test the accuracy of the KMeans clustering by determining if the predicted industries (based on the KMeans clustering algorithm) correctly identifies the industry that the dataset originally indicated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>cluster_label</th>\n",
       "      <th>predicted_industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Power Plants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Waste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_label predicted_industry\n",
       "0              0       Power Plants\n",
       "1              1              Waste\n",
       "2              2              Other"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry_lookup = pd.DataFrame({\"cluster_label\": [0, 1, 2],\n",
    "                                        \"predicted_industry\": [\"Power Plants\", \"Waste\", \"Other\"]})\n",
    "industry_lookup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>facility_id</th>\n",
       "      <th>frs_id</th>\n",
       "      <th>facility_name</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>address</th>\n",
       "      <th>county</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>2018</th>\n",
       "      <th>2017</th>\n",
       "      <th>2016</th>\n",
       "      <th>2015</th>\n",
       "      <th>2014</th>\n",
       "      <th>2013</th>\n",
       "      <th>2012</th>\n",
       "      <th>2011</th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>predicted_industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1000077</td>\n",
       "      <td>110000774797</td>\n",
       "      <td>REPUBLIC STEEL LACKAWANNA</td>\n",
       "      <td>BLASDELL</td>\n",
       "      <td>NY</td>\n",
       "      <td>14219</td>\n",
       "      <td>S3049 LAKESHORE RD.</td>\n",
       "      <td>ERIE COUNTY</td>\n",
       "      <td>42.80</td>\n",
       "      <td>...</td>\n",
       "      <td>78801.40</td>\n",
       "      <td>76235.01</td>\n",
       "      <td>61447.47</td>\n",
       "      <td>73193.02</td>\n",
       "      <td>77355.81</td>\n",
       "      <td>71702.38</td>\n",
       "      <td>80619.20</td>\n",
       "      <td>72355.73</td>\n",
       "      <td>0</td>\n",
       "      <td>Power Plants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1000093</td>\n",
       "      <td>110000324435</td>\n",
       "      <td>Momentive Performance Materials</td>\n",
       "      <td>WATERFORD</td>\n",
       "      <td>NY</td>\n",
       "      <td>12188</td>\n",
       "      <td>260 HUDSON RIVER ROAD</td>\n",
       "      <td>Saratoga</td>\n",
       "      <td>42.82</td>\n",
       "      <td>...</td>\n",
       "      <td>134663.45</td>\n",
       "      <td>131363.56</td>\n",
       "      <td>125491.33</td>\n",
       "      <td>126818.78</td>\n",
       "      <td>127808.87</td>\n",
       "      <td>132095.46</td>\n",
       "      <td>120944.74</td>\n",
       "      <td>142529.89</td>\n",
       "      <td>0</td>\n",
       "      <td>Power Plants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1000108</td>\n",
       "      <td>110012158707</td>\n",
       "      <td>North 1st</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>11211</td>\n",
       "      <td>NORTH 1ST &amp; 47-79 RIVER ST</td>\n",
       "      <td>Kings</td>\n",
       "      <td>40.72</td>\n",
       "      <td>...</td>\n",
       "      <td>31069.94</td>\n",
       "      <td>22460.32</td>\n",
       "      <td>26369.45</td>\n",
       "      <td>18559.93</td>\n",
       "      <td>20816.32</td>\n",
       "      <td>26629.45</td>\n",
       "      <td>26775.50</td>\n",
       "      <td>45473.83</td>\n",
       "      <td>0</td>\n",
       "      <td>Power Plants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1000111</td>\n",
       "      <td>110012158734</td>\n",
       "      <td>Vernon Boulevard</td>\n",
       "      <td>LONG ISLAND CITY</td>\n",
       "      <td>NY</td>\n",
       "      <td>11427</td>\n",
       "      <td>42 30 VERNON BLVD</td>\n",
       "      <td>Queens</td>\n",
       "      <td>40.75</td>\n",
       "      <td>...</td>\n",
       "      <td>35221.82</td>\n",
       "      <td>28073.30</td>\n",
       "      <td>28262.00</td>\n",
       "      <td>27000.90</td>\n",
       "      <td>26232.55</td>\n",
       "      <td>43133.84</td>\n",
       "      <td>38257.51</td>\n",
       "      <td>44820.68</td>\n",
       "      <td>0</td>\n",
       "      <td>Power Plants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1000112</td>\n",
       "      <td>110019827286</td>\n",
       "      <td>23rd and 3rd</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>11232</td>\n",
       "      <td>730 3rd Avenue</td>\n",
       "      <td>Kings</td>\n",
       "      <td>40.66</td>\n",
       "      <td>...</td>\n",
       "      <td>70705.29</td>\n",
       "      <td>52698.66</td>\n",
       "      <td>66797.35</td>\n",
       "      <td>67399.25</td>\n",
       "      <td>40722.20</td>\n",
       "      <td>102599.22</td>\n",
       "      <td>68984.74</td>\n",
       "      <td>100591.83</td>\n",
       "      <td>0</td>\n",
       "      <td>Power Plants</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  facility_id        frs_id                    facility_name  \\\n",
       "0           0      1000077  110000774797        REPUBLIC STEEL LACKAWANNA   \n",
       "1           1      1000093  110000324435  Momentive Performance Materials   \n",
       "2           3      1000108  110012158707                        North 1st   \n",
       "3           4      1000111  110012158734                 Vernon Boulevard   \n",
       "4           5      1000112  110019827286                     23rd and 3rd   \n",
       "\n",
       "               city state  zip_code                     address       county  \\\n",
       "0          BLASDELL    NY     14219         S3049 LAKESHORE RD.  ERIE COUNTY   \n",
       "1         WATERFORD    NY     12188       260 HUDSON RIVER ROAD     Saratoga   \n",
       "2          BROOKLYN    NY     11211  NORTH 1ST & 47-79 RIVER ST        Kings   \n",
       "3  LONG ISLAND CITY    NY     11427           42 30 VERNON BLVD       Queens   \n",
       "4          BROOKLYN    NY     11232              730 3rd Avenue        Kings   \n",
       "\n",
       "   latitude  ...       2018       2017       2016       2015       2014  \\\n",
       "0     42.80  ...   78801.40   76235.01   61447.47   73193.02   77355.81   \n",
       "1     42.82  ...  134663.45  131363.56  125491.33  126818.78  127808.87   \n",
       "2     40.72  ...   31069.94   22460.32   26369.45   18559.93   20816.32   \n",
       "3     40.75  ...   35221.82   28073.30   28262.00   27000.90   26232.55   \n",
       "4     40.66  ...   70705.29   52698.66   66797.35   67399.25   40722.20   \n",
       "\n",
       "        2013       2012       2011  cluster_label  predicted_industry  \n",
       "0   71702.38   80619.20   72355.73              0        Power Plants  \n",
       "1  132095.46  120944.74  142529.89              0        Power Plants  \n",
       "2   26629.45   26775.50   45473.83              0        Power Plants  \n",
       "3   43133.84   38257.51   44820.68              0        Power Plants  \n",
       "4  102599.22   68984.74  100591.83              0        Power Plants  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry_predict = pd.merge(ghg_ny, industry_lookup, on=\"cluster_label\")\n",
    "industry_predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percent accuracy of predicting industry based on 2011 and 2016 pollution levels is: 39.58%\n"
     ]
    }
   ],
   "source": [
    "num_correct = 0\n",
    "for row in range(len(industry_predict)):\n",
    "    if (industry_predict.iloc[row]['industry_sector'] == industry_predict.iloc[row][\"predicted_industry\"]):\n",
    "        num_correct = num_correct + 1\n",
    "accuracy = (num_correct/(len(industry_predict)-35))\n",
    "print(\"The percent accuracy of predicting industry based on 2011 and 2016 pollution levels is: \" + \"{:.2%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removing the industries that did not fall under Power Plants, Waste, and Other (the top three industries we clustered by), we got a prediction accuracy of 39%. As we saw with the pollution analysis for 2011 vs. 2016, this tells us that there is a relationship between the two variables but it may not be that strong. Including other variables might help lead to a stronger accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretations and Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, we see a few major trends in the datasets. From 2011 to 2018, we see an overall decrease in the pollution levels, but if we look at just NYC, which has been consistently in the top three polluters in New York state, the greenhouse gas emission levles have been relatively stable throughout the years and do not seem to increase nor decrease. By analyzing the recycling capture rate, we see a statistically significant decrease in the capture rate from 2010 to 2016, which shows a decrease in the efficiency of the recycling process which could either be due to lower engagement with sustainable practices in the general public or due to lower efficiency in the recycling plants themselves. However, this decrease can be attributed to changes in Manhattan as capture rates stay the same in Queens, Brooklyn, and Staten Island, and even increase in the Bronx. Lastly, from the Citibike dataset, we see that there is an increase in the number of bike trips from 2013 to 2018, which could be due to a number of factors including the construction of new drop-off and pick up stations in Queens, the growth of the Citibike program, or an increase in sustainable behavior. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we put all of these trends together, we see that there has been a mixed response to climate change from the general public. From the biking data, we see that the public and individuals are engaging more with sustainable behabiors by using transportation like bikes in place of fossil-fuel-dependent transportation options. However, from the recycling data, we see that perhaps on the government side (the government runs the recycling programs in NYC), that there is a decrease in engagement with sustainable behaviors as pollution decreases. This could be due to economic reasons or due to issues that NYC might be facing. In terms of the overall impact of these sustainable behaviors, in the graph where we compare pollution levels to number of bike trips, we see that even as the number of bike trips increases yearly, the pollution levels are so much greater, even though they are decreasing, that the benefits of sustainable transportation are almost negligible. The number of bikers are enough to offset pollution levels at the scales that we are seeing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our research question, we are interested in the relationship between climate change throughout the years and sentiment towards environmental activism and sustainable practices in the general public. This dataset, however, shows pollution levels by specific facilities and industries and not the general public. Because the dataset doesn’t reflect the entire population of who we’re trying to analyze, the trends we find might not be generalizable to the entire public and it might be difficult to bring the dataset of greenhouse gas emissions and recycling and bike usage together exactly. Since this data was collected by the EPA likely to regulate emissions, the emissions over time for each observation might be affected by certain EPA regulations. Another limitation includes the fact that the bike and recycling data is only for New York City and so we are not able to generalize our finding to all of New York State."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - https://www.epa.gov/mats/regulatory-actions-final-mercury-and-air-toxics-standards-mats-power-plants\n",
    " - https://www.citibikenyc.com/system-data/operating-reports\n",
    " - https://www.citibikenyc.com/system-data\n",
    " - https://python-graph-gallery.com/11-grouped-barplot/\n",
    " - https://data.world/city-of-ny/gaq9-z3hz\n",
    " - https://data.world/city-of-ny/7vsa-caz7\n",
    " - https://worldpopulationreview.com/boroughs/manhattan-population\n",
    " - https://portal.311.nyc.gov/article/?kanumber=KA-02013"
   ]
  }
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