FinalProject_Group099_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Does the use of public transport in California decrease the number and/or severity of car crashes? \n",
    "\n",
    "# Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [ X ] YES - make available\n",
    "* [  ] NO - keep private\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Nicole Kim\n",
    "- Rikako Ono\n",
    "- Geena Limfat\n",
    "- MyungJoo Kim\n",
    "- Elizaveta Beltyukova"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abstract\n",
    "\n",
    "In this study, we investigate the relationship between public transportation usage and the frequency of car accidents in California cities, as well as the severity of car accidents and public transportation usage. We collected data from the US Census Bureau, and merged them into a single dataset with 58 observations. From there, we used linear regression to see if public transporation usage had a significant effect on accident occurence rates and average accident severity.  \n",
    "In our analysis, we found that there was a significant positive linear relationship between public transportation usage (\"transit_usage\") and car accident occurence (\"accident_rate\"), as well as an insignificant positive linear relationship between public transportation usage (\"transit_usage\") and average accident severity (\"avg_severity\"). This opposes our initial hypothesis, and we conclude that further research is needed on confounding variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does higher usage of public transportation in a city affect the number of car crashes in that city? \n",
    "\n",
    "Furthermore, does higher use of public transportation lessen the severity of car accidents?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Over the years, advancements in car safety, including structural design improvements and technological developments, have enhanced vehicle safety.\n",
    "Despite these progressions, the frequency of car accidents has increased significantly in recent years.\n",
    "In the United States alone, there were 6,102,936 police-reported vehicle accidents in 2021<a name=\"cite_ref-2\"></a>[<sup>2</sup>](#cite_note-2).\n",
    "Various factors contribute to car crashes in the US, such as distracted driving, speeding, drunk driving, reckless driving, and tailgating.\n",
    "Notably, more than a third (36%) of all fatal crashes involve alcohol<a name=\"cite_ref-2\"></a>[<sup>2</sup>](#cite_note-2).\n",
    "\n",
    "These behaviors collectively contribute to the high rate of car accidents in the US, underlining the importance of addressing these issues through awareness campaigns, preventive measures, and effective policies to enhance road safety.\n",
    "\n",
    "Public transportation serves as a vital component of urban areas by offering consistent transportation services through modes like buses, streetcars, light rail, ferries, and subways.\n",
    "This mode of transportation is essential for diverse groups such as older adults, individuals with disabilities, and commuters, particularly in major cities where public transit is prevalent. \n",
    "Public transportation not only provides a means of travel but also contributes to health and equity in various ways.\n",
    "Research indicates that public transportation can reduce traffic accidents and air pollution while increasing physical activity and improving access to essential services like medical care, healthy food, employment opportunities, and social connections.<a name=\"cite_ref-3\"></a>[<sup>3</sup>](#cite_note-3). \n",
    "Despite these benefits, it remains unexplored whether the utilization of public transportation correlates with the severity of car crashes in an area.\n",
    "\n",
    "To investigate the relationship between public transport usage and the number and severity of car crashes in an area, one could explore how increased public transportation utilization might lead to reduced traffic congestion and fewer private vehicles on the road. \n",
    "However, it’s also possible that less traffic congestion could encourage drivers to travel at higher speeds, thus being more likely to cause a more severe/fatal accident in the area.\n",
    "Another factor to consider is that with increased public transport use, there could be more pedestrians, and therefore potentially more car-caused pedestrian injuries or deaths. \n",
    "\n",
    "1. <a name=\"cite_note-1\"></a> [^](#cite_ref-1) [Evaluating Public Transportation Health Benefits. (n.d.).](https://www.vtpi.org/tran_health.pdf) \n",
    "2. <a name=\"cite_note-2\"></a> [^](#cite_ref-2) [Moore, T. (2024, January 16). Fatal car crash statistics 2024. USA Today.](https://www.usatoday.com/money/blueprint/auto-insurance/fatal-car-crash-statistics/#:~:text=There%20are%20nearly%2043%2C000%20fatal,accidents%20in%20the%20United%20States.&text=Of%20those%2C%2039%2C508%20were%20fatal) \n",
    "3. <a name=\"cite_note-3\"></a> [^](#cite_ref-3) [Top 25 causes of car accidents: Exploring the major factors. GJEL Accident Attorneys. (2023, November 24).](https://www.gjel.com/car-accident-lawyers/top-causes-car-accidents ) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Null Hypothesis: The use of public transportation does not affect the number of car crashes that occur in a city. It also does not affect the severity of the accident.\n",
    "\n",
    "Alternate Hypothesis: We hypothesize that a higher use of public transportation lessens the chance of an accident occurring. Additionally, higher public transporation use rates will lead to a lower average severity of a car accident.  \n",
    "A higher usage of public transportation will result in fewer vehicles on the road (due to the number of people on shared rides or on a specialized path such as trains) which can reduce congestion and lower the probability of accidents. Public transportation also utilizes professional drivers who go through proper licensing to operate the mode of transportation that they are in charge of and may adhere more strictly to traffic regulations and safety protocols which can reduce the likelihood of accidents. We also believe that cities where more people are encouraged to make use of public transportation when in an unsuitable state of driving will have less reckless drivers on the road, leading to increased road safety."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data overview\n",
    "\n",
    "- Dataset #1\n",
    "  - Dataset Name: US Accidents (2016 - 2023)\n",
    "  - Link to the dataset: https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents?select=US_Accidents_March23.csv\n",
    "  - Number of observations: 7,728,394\n",
    "  - Number of variables: 46\n",
    "- Dataset #2\n",
    "  - Dataset Name: Highest Public Transit Usage Cities in California\n",
    "  - Link to the dataset: https://www.homearea.com/rankings/place-in-ca/percent_using_public_transportation/#:~:text=The%20California%20percent%20using%20public,year%20saw%20several%20big%20changes\n",
    "  - Number of observations: 62\n",
    "  - Number of variables: 3\n",
    "- Dataset #3\n",
    "  - Dataset Name: City and Town Population Totals: 2010-2019\n",
    "  - Link to the dataset: https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-cities-and-towns.html\n",
    "  - Number of observations: 490\n",
    "  - Number of variables: 13\n",
    "\n",
    "The first dataset refers to the US accident, encompassing 7,728,394 observations recorded between 2016 and 2023.\n",
    "The main features in this dataset include  the name of City, start time, description of the accident, and severity.\n",
    "The second dataset focuses  on public transportation usage in California, specifically in 62 cities in CA. This dataset comprises exactly 62 observations, each corresponding to a different city.\n",
    "The third dataset is the population of various California cities, which we will use to find per capita accident rates to ensure Dataset #1 and Dataset #2 have the same units of measurement.\n",
    "\n",
    "We decided to merge a subset of the first dataset comprised of accident data from California in 2017 with the second dataset, which details public transportation usage across 62 cities in California, to measure the correlation between the US car accident rate and public transportation usage in California during 2017. This lets us directly assess how public transportation affects car accident rates within the cities in California.\n",
    "This combined dataset allows a comprehensive analysis of the relationship between public transportation use, and various factors influencing car accidents in California.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "### Dataset #1\n",
    "\n",
    "This dataset is somewhat clean. The dataset is consistent and uniform throughout. \n",
    "In terms of pre-processing, we noticed that there was a small issue with completeness, as there was some missing data. \n",
    "As the dataset's description states, “There is missing data for certain days, which could be due to network connectivity issues during data collection.” We will handle this issue by removing these missing inputs. Additionally, we will attempt to make the file smaller so that it can be cleaned and uploaded to GitHub. \n",
    "\n",
    "For the correct values that do remain, we are still confident that they are accurate and reliable. In the description, it states, “The data was collected from APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks.” Because APIs have low latency, real-time data processing, and quick response times, we can conclude that the data is precise. We can also conclude from this excerpt that the sources are credible and trustworthy. We have also confirmed that the dataset included at least a year’s worth of data to observe trends for appropriate time frames. \n",
    "\n",
    "To make our data usable, we first converted the ‘Start_Time’ column into a datetime object, so that we can filter the crash data by year (2017). Within the to_datetime function, we specified errors = ‘coerce’ to better handle improper formatted or mixed date strings in the dataframe. Then, we filtered the state to just be California, and the year to just be 2017 (using dt.year). Then, we selected the columns ‘Start_Time’, ‘Severity’, ‘City’, and ‘Description’, and sorted the dataframe by Start_Time, while also resetting the index.  \n",
    " \n",
    "\n",
    "\n",
    "### Dataset #2\n",
    "\n",
    "This dataset is very clean. This is mainly because we created our own data set. Thus, we had full control over the quality of the data set and over what variables, observations, and units of measurement were included. In terms of pre-processing, we made sure all the data was consistent and complete. We cleaned the data and checked that it included at least a year’s worth of data to observe trends for appropriate time frames. We checked that it was consistent with our other data set. \n",
    "\n",
    "We ensured the credibility of the data by making sure it was taken from a reputable source like the U.S. Census Bureau American Community Survey. Unfortunately, the data may have small holes in precision, as survey participation is often voluntary. However, the source states that certain cities “were ranked the previous year but did not have sufficient data or population for the most recent rankings.” From this, we can conclude that the source has only published results that have solid data to support them, thus making the research conducted accurate.\n",
    "\n",
    "To get the data into a usable format we had to take research and statistics from various websites related to our study.\n",
    "We then had to transform the file type twice. First, we inputted data into an Excel spreadsheet for ease of viewing and editing.\n",
    "Second, we converted the file into a CSV to be read by pandas.\n",
    "\n",
    "\n",
    "\n",
    "### Dataset #3\n",
    "\n",
    "This dataset is not readable. The headers are spread across multiple rows, and has many null values. The bottom few rows also included source information and compliance information that is not relevant to the columns that they reside in. However, since we pulled this information directly from the US Census website, we believe it is a trustworthy source of information regarding population.\n",
    "\n",
    "To make the data usable for our purposes, we cut the first few rows, replaced them with the correct column header names, and reformatted columns (such as removing California from the city names). We then picked out the columns we need, city name and population in 2017, to be merged into the rest of the data to normalize values based on population.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.mode.copy_on_write = True\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [10, 6]\n",
    "\n",
    "import seaborn as sns\n",
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset #1 - US Accidents (2016 - 2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the original dataset was too large to be uploaded on GitHub, we cleaned the data locally with the following code:\n",
    "\n",
    "\n",
    "```python\n",
    "df = pd.read_csv('US_Accidents_March23.csv')\n",
    "\n",
    "# create a subset of the dataset with relevant year 2017, which we will be using for the rest of the project\n",
    "df['Start_Time'] = pd.to_datetime(df['Start_Time'], errors='coerce')\n",
    "\n",
    "accidents_df = df[df['State'] == 'CA'][df['Start_Time'].dt.year == 2017]\n",
    "accidents_df = accidents_df.get(['Start_Time', 'Severity', 'City', 'Description']).sort_values(by='Start_Time').reset_index(drop=True)\n",
    "\n",
    "accidents_df.to_csv(r\"~\\AccidentData.csv\", index=False)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Start_Time</th>\n",
       "      <th>Severity</th>\n",
       "      <th>City</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:03:31</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Norwalk</td>\n",
       "      <td>Accident on I-5 Northbound at Exits 120 120A B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 00:09:26</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Lynwood</td>\n",
       "      <td>Accident on I-710 Southbound at Exits 12 12A 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 00:09:52</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Hesperia</td>\n",
       "      <td>Accident on I-15 Northbound at Exit 138 Oak Hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 00:10:14</td>\n",
       "      <td>0.50</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>Accident on CA-110 Southbound at Glenarm St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 00:11:14</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Colton</td>\n",
       "      <td>Accident on I-10 Eastbound at Exit 72 I-215.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start_Time  Severity      City  \\\n",
       "0  2017-01-01 00:03:31      0.75   Norwalk   \n",
       "1  2017-01-01 00:09:26      0.75   Lynwood   \n",
       "2  2017-01-01 00:09:52      0.75  Hesperia   \n",
       "3  2017-01-01 00:10:14      0.50  Pasadena   \n",
       "4  2017-01-01 00:11:14      0.75    Colton   \n",
       "\n",
       "                                         Description  \n",
       "0  Accident on I-5 Northbound at Exits 120 120A B...  \n",
       "1  Accident on I-710 Southbound at Exits 12 12A 1...  \n",
       "2  Accident on I-15 Northbound at Exit 138 Oak Hi...  \n",
       "3       Accident on CA-110 Southbound at Glenarm St.  \n",
       "4       Accident on I-10 Eastbound at Exit 72 I-215.  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_df = pd.read_csv('AccidentData.csv')\n",
    "\n",
    "# normalize severity to be on a scale of 0-1 instead of 1-4 to make comparing easier later on\n",
    "accident_df['Severity'] = accident_df['Severity']/4\n",
    "\n",
    "accident_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset #2 - Highest Public Transit Usage Cities in California"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>City</th>\n",
       "      <th>Percent Using Transit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>0.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>0.227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Berkeley</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daly City</td>\n",
       "      <td>0.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Lakewood</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>San Buenaventura (Ventura)</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Irvine</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Visalia</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Napa</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          City  Percent Using Transit\n",
       "0                San Francisco                  0.347\n",
       "1                      Oakland                  0.227\n",
       "2                     Berkeley                  0.221\n",
       "3                    Daly City                  0.207\n",
       "4                      Alameda                  0.185\n",
       "..                         ...                    ...\n",
       "57                    Lakewood                  0.008\n",
       "58  San Buenaventura (Ventura)                  0.008\n",
       "59                      Irvine                  0.008\n",
       "60                     Visalia                  0.006\n",
       "61                        Napa                  0.005\n",
       "\n",
       "[62 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "City = ['San Francisco', 'Oakland', 'Berkeley', 'Daly City', 'Alameda', 'San Leandro', 'Richmond', 'Pleasanton', 'San Mateo', 'Concord', 'San Ramon', 'Hayward', 'Los Angeles', 'Mountain View', 'Fremont', 'Antioch', 'Sunnyvale', 'Pittsburg', 'East Los Angeles', 'Redwood City', 'Vallejo', 'Pasadena', 'Long Beach', 'El Cajon', 'San Jose', 'Santa Clara', 'Santa Ana', 'Inglewood', 'South Gate', 'San Diego', 'Downey', 'Sacramento', 'Chula Vista', 'Santa Clarita', 'Glendale', 'Anaheim', 'Escondido', 'Riverside', 'Santa Rosa', 'Oceanside', 'Santa Barbara', 'Elk Grove', 'Palmdale', 'San Bernardino', 'Fullerton', 'Fresno', 'Moreno Valley', 'Pomona', 'Huntington Beach', 'Santa Maria', 'Lancaster', 'Ontario', 'Garden Grove', 'Corona', 'Bakersfield', 'Stockton', 'Rancho Cucamonga', 'Lakewood', 'San Buenaventura (Ventura)', 'Irvine', 'Visalia', 'Napa']\n",
    "Percent_Used = [0.347, 0.227, 0.221, 0.207, 0.185, 0.149, 0.121, 0.105, 0.103, 0.099, 0.098, 0.098, 0.089, 0.088, 0.081, 0.079, 0.076, 0.071, 0.066, 0.066, 0.061, 0.055, 0.055, 0.053, 0.05, 0.049, 0.048, 0.047, 0.046, 0.04, 0.038, 0.038, 0.036, 0.034, 0.033, 0.031, 0.03, 0.03, 0.026, 0.023, 0.023, 0.021, 0.021, 0.021, 0.021, 0.02, 0.019, 0.018, 0.018, 0.018, 0.016, 0.015, 0.014, 0.012, 0.011, 0.011, 0.011, 0.008, 0.008, 0.008, 0.006, 0.005]\n",
    "\n",
    "transit_df = pd.DataFrame({'City':City, 'Percent Using Transit':Percent_Used})\n",
    "\n",
    "transit_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>City</th>\n",
       "      <th>avg_severity</th>\n",
       "      <th>num_accidents</th>\n",
       "      <th>Percent Using Transit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>0.546612</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Antioch</td>\n",
       "      <td>0.514423</td>\n",
       "      <td>208</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>0.534253</td>\n",
       "      <td>562</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkeley</td>\n",
       "      <td>0.694248</td>\n",
       "      <td>565</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>0.662577</td>\n",
       "      <td>326</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Concord</td>\n",
       "      <td>0.540720</td>\n",
       "      <td>528</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Corona</td>\n",
       "      <td>0.576827</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Daly City</td>\n",
       "      <td>0.640288</td>\n",
       "      <td>278</td>\n",
       "      <td>0.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Downey</td>\n",
       "      <td>0.696450</td>\n",
       "      <td>845</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  avg_severity  num_accidents  Percent Using Transit\n",
       "0      Alameda      0.500000              6                  0.185\n",
       "1      Anaheim      0.546612           2022                  0.031\n",
       "2      Antioch      0.514423            208                  0.079\n",
       "3  Bakersfield      0.534253            562                  0.011\n",
       "4     Berkeley      0.694248            565                  0.221\n",
       "5  Chula Vista      0.662577            326                  0.036\n",
       "6      Concord      0.540720            528                  0.099\n",
       "7       Corona      0.576827           1793                  0.012\n",
       "8    Daly City      0.640288            278                  0.207\n",
       "9       Downey      0.696450            845                  0.038"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To better compare the datasets, we will merge them by city name.\n",
    "\n",
    "accident_df_new = accident_df.groupby('City').agg(avg_severity=('Severity', \"mean\"), num_accidents=('Description', np.count_nonzero)).reset_index()\n",
    "accidents_vs_transit = pd.merge(accident_df_new, transit_df, on='City')\n",
    "accidents_vs_transit.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset #3 - City and Town Population Totals: 2010-2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>table with row headers in column A and column headers in rows 3 through 4 (leading dots indicate sub-parts)</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Annual Estimates of the Resident Population fo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Geographic Area</td>\n",
       "      <td>2010-04-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Population Estimate (as of July 1)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Census</td>\n",
       "      <td>Estimates Base</td>\n",
       "      <td>2010</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelanto city, California</td>\n",
       "      <td>31765</td>\n",
       "      <td>31760</td>\n",
       "      <td>31661</td>\n",
       "      <td>31864.0</td>\n",
       "      <td>31088.0</td>\n",
       "      <td>31206.0</td>\n",
       "      <td>32522.0</td>\n",
       "      <td>32961.0</td>\n",
       "      <td>33249.0</td>\n",
       "      <td>33952.0</td>\n",
       "      <td>34070.0</td>\n",
       "      <td>34049.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agoura Hills city, California</td>\n",
       "      <td>20330</td>\n",
       "      <td>20336</td>\n",
       "      <td>20338</td>\n",
       "      <td>20475.0</td>\n",
       "      <td>20544.0</td>\n",
       "      <td>20591.0</td>\n",
       "      <td>20648.0</td>\n",
       "      <td>20740.0</td>\n",
       "      <td>20665.0</td>\n",
       "      <td>20572.0</td>\n",
       "      <td>20411.0</td>\n",
       "      <td>20222.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  table with row headers in column A and column headers in rows 3 through 4 (leading dots indicate sub-parts)  \\\n",
       "0  Annual Estimates of the Resident Population fo...                                                            \n",
       "1                                    Geographic Area                                                            \n",
       "2                                                NaN                                                            \n",
       "3                          Adelanto city, California                                                            \n",
       "4                      Agoura Hills city, California                                                            \n",
       "\n",
       "            Unnamed: 1      Unnamed: 2                          Unnamed: 3  \\\n",
       "0                  NaN             NaN                                 NaN   \n",
       "1  2010-04-01 00:00:00             NaN  Population Estimate (as of July 1)   \n",
       "2               Census  Estimates Base                                2010   \n",
       "3                31765           31760                               31661   \n",
       "4                20330           20336                               20338   \n",
       "\n",
       "   Unnamed: 4  Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2      2011.0      2012.0      2013.0      2014.0      2015.0      2016.0   \n",
       "3     31864.0     31088.0     31206.0     32522.0     32961.0     33249.0   \n",
       "4     20475.0     20544.0     20591.0     20648.0     20740.0     20665.0   \n",
       "\n",
       "   Unnamed: 10  Unnamed: 11  Unnamed: 12  \n",
       "0          NaN          NaN          NaN  \n",
       "1          NaN          NaN          NaN  \n",
       "2       2017.0       2018.0       2019.0  \n",
       "3      33952.0      34070.0      34049.0  \n",
       "4      20572.0      20411.0      20222.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df = pd.read_excel('SUB-IP-EST2019-ANNRES-06.xlsx')\n",
    "\n",
    "population_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>2</th>\n",
       "      <th>City</th>\n",
       "      <th>population_2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelanto</td>\n",
       "      <td>33952.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agoura Hills</td>\n",
       "      <td>20572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>79037.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albany</td>\n",
       "      <td>20076.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alhambra</td>\n",
       "      <td>84878.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2          City  population_2017\n",
       "0      Adelanto          33952.0\n",
       "1  Agoura Hills          20572.0\n",
       "2       Alameda          79037.0\n",
       "3        Albany          20076.0\n",
       "4      Alhambra          84878.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The code does not have proper column markers, and has excess information. We will fix that with the following:\n",
    "\n",
    "# Fix column values\n",
    "col_values = population_df.iloc[2]\n",
    "col_values.iloc[0] = population_df.iloc[1].iloc[0]\n",
    "population_df.columns = col_values\n",
    "\n",
    "# Simplify rows and columns\n",
    "population_df = population_df[3:]\n",
    "population_df = population_df.reset_index(drop=True).get(['Geographic Area', 2017.0]).rename(columns={'Geographic Area': 'City', 2017.0:'population_2017'})\n",
    "population_df['City'] = population_df['City'].str.title().str.replace(', California', '').str.replace(' City', '')\n",
    "population_df.dropna(inplace=True)\n",
    "\n",
    "    \n",
    "population_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>num_accidents</th>\n",
       "      <th>population_2017</th>\n",
       "      <th>accident_rate</th>\n",
       "      <th>avg_severity</th>\n",
       "      <th>transit_usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>79037.0</td>\n",
       "      <td>0.759138</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>2022</td>\n",
       "      <td>350476.0</td>\n",
       "      <td>57.692966</td>\n",
       "      <td>0.546612</td>\n",
       "      <td>310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Antioch</td>\n",
       "      <td>208</td>\n",
       "      <td>111494.0</td>\n",
       "      <td>18.655712</td>\n",
       "      <td>0.514423</td>\n",
       "      <td>790.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>562</td>\n",
       "      <td>377940.0</td>\n",
       "      <td>14.870085</td>\n",
       "      <td>0.534253</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkeley</td>\n",
       "      <td>565</td>\n",
       "      <td>122164.0</td>\n",
       "      <td>46.249304</td>\n",
       "      <td>0.694248</td>\n",
       "      <td>2210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>326</td>\n",
       "      <td>269046.0</td>\n",
       "      <td>12.116887</td>\n",
       "      <td>0.662577</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Concord</td>\n",
       "      <td>528</td>\n",
       "      <td>129592.0</td>\n",
       "      <td>40.743256</td>\n",
       "      <td>0.540720</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Corona</td>\n",
       "      <td>1793</td>\n",
       "      <td>167310.0</td>\n",
       "      <td>107.166338</td>\n",
       "      <td>0.576827</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Downey</td>\n",
       "      <td>845</td>\n",
       "      <td>112426.0</td>\n",
       "      <td>75.160550</td>\n",
       "      <td>0.696450</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>El Cajon</td>\n",
       "      <td>371</td>\n",
       "      <td>103468.0</td>\n",
       "      <td>35.856497</td>\n",
       "      <td>0.602426</td>\n",
       "      <td>530.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  num_accidents  population_2017  accident_rate  avg_severity  \\\n",
       "0      Alameda              6          79037.0       0.759138      0.500000   \n",
       "1      Anaheim           2022         350476.0      57.692966      0.546612   \n",
       "2      Antioch            208         111494.0      18.655712      0.514423   \n",
       "3  Bakersfield            562         377940.0      14.870085      0.534253   \n",
       "4     Berkeley            565         122164.0      46.249304      0.694248   \n",
       "5  Chula Vista            326         269046.0      12.116887      0.662577   \n",
       "6      Concord            528         129592.0      40.743256      0.540720   \n",
       "7       Corona           1793         167310.0     107.166338      0.576827   \n",
       "8       Downey            845         112426.0      75.160550      0.696450   \n",
       "9     El Cajon            371         103468.0      35.856497      0.602426   \n",
       "\n",
       "   transit_usage  \n",
       "0         1850.0  \n",
       "1          310.0  \n",
       "2          790.0  \n",
       "3          110.0  \n",
       "4         2210.0  \n",
       "5          360.0  \n",
       "6          990.0  \n",
       "7          120.0  \n",
       "8          380.0  \n",
       "9          530.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge population_df into the dataset 'accidents_vs_transit' to calculate accident rates and transit usage rates\n",
    "clean_df = pd.merge(accidents_vs_transit, population_df, on='City')\n",
    "\n",
    "# create variable \"accident_rate\", which describes the number of accidents that occur for every 10,000 residents in each city.\n",
    "clean_df = clean_df.assign(accident_rate = clean_df['num_accidents']/clean_df['population_2017'] * 10000)\n",
    "\n",
    "# create variable \"transit_usage\", which describes the number of people who use public transportation for every 10,000 residents in a city to normalize the units with \"accident_rate\"\n",
    "clean_df = clean_df.assign(transit_usage = clean_df['Percent Using Transit'] * 10000)\n",
    "\n",
    "\n",
    "# shrink the dataset down to only relevant columns\n",
    "clean_df = clean_df.get(['City', 'num_accidents', 'population_2017', 'accident_rate', 'avg_severity', 'transit_usage'])\n",
    "\n",
    "# Look at some values in our cleaned dataframe\n",
    "clean_df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through cleaning the data, it can be observed that the length of our dataset shortened to 58 observations. This is due to some of the values not being in the other datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results\n",
    "\n",
    "## Exploratory Data Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by examining the distribution of the variables in question: car crashes that occur in California, alongside transit use rates and population, to visualize if there are any similarities.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=clean_df, x='accident_rate', y='City', color=\"#c74b2c\")\n",
    "sns.color_palette(\"Set2\")\n",
    "plt.xlabel('Crashes per 10,000 people')\n",
    "plt.yticks(fontsize = '6')\n",
    "plt.ylabel('City')\n",
    "plt.title('Car Crashes in California Cities per 10,000 Populants (2017)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We chose to make this next bar graph because we wanted a clean visual representation of the next dataset. We wished to easily reference which cities had the lowest and highest usage of public transportation in CA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a bar plot representing the public transporation use rates in California cities\n",
    "sns.barplot(data=clean_df, x='transit_usage', y='City', color=\"#23b8cf\")\n",
    "plt.xlabel('Public Transportation Use per 10,000 People')\n",
    "plt.yticks(fontsize = '6')\n",
    "plt.ylabel('City')\n",
    "plt.title('Public Transportation Use Rates by California City (2017)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expect that population *may* be a confounding variable. To see if there might be similar trends, we will plot the population of each California city in our dataset in a bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a barplot representing the population of each California City\n",
    "sns.barplot(data=clean_df, x='population_2017', y='City', color='#61c27b')\n",
    "plt.xlabel('Population')\n",
    "plt.yticks(fontsize = '6')\n",
    "plt.ylabel('City')\n",
    "plt.title('Population of California Cities (2017)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seem to be similarities in the plots, such as where peaks are located. In the next sections (EDA), we will explore how each of these variables (Transit Use Rates and Population) affect car crash rates in Californian cities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Part 1: Exploring relationships with \"Transit Usage\"__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we use scatterplots to visually see if there is a relationship between transit usage and our two variables of interest, number of accidents and car crash severity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ins>Scatterplot of Accident Rates and Transit Usage (by city in California):</ins>  \n",
    "We chose to study “accident_rate” and “transit_usage'' because we wanted to see if our hypothesis was supported: The higher use of public transportation lessens the number of accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIjCAYAAAAJLyrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACAQUlEQVR4nO3dd3jTVf//8VfKaAttU1Zpi1AKlCkyZYMKCCgquABvlCGCKAi4GN4yb5XlQJQhLsTb2y2oqAwZosiGggyxIGVJQYG2FMpoe35/8Gu+hK6kJE3SPh/X1esi53zyyTv5JCXvnnPex2KMMQIAAAAAOMzP0wEAAAAAgK8hkQIAAAAAJ5FIAQAAAICTSKQAAAAAwEkkUgAAAADgJBIpAAAAAHASiRQAAAAAOIlECgAAAACcRCIFAAAAAE4ikQLgcqtXr5bFYtEXX3zh6VAccvz4cd13330qV66cLBaLZsyY4emQ7PTr109Vq1bN87j4+HhZLBbNnz/f7TGhYHFtnXP77bdr4MCBng6jSJo7d66qVKmiCxcueDoUwO1IpAAfNX/+fFksFgUEBOjo0aNZ+m+++WZdf/31HojM9zz55JNaunSpxowZow8//FBdunTJ8z6JiYkKCAiQxWLRnj17CiBK7zF79myf/kJ/8803y2Kx5PkzYcIET4eaq++//96pGKtWrao77rgj277NmzcXmkRt7dq1WrZsmUaNGuXpUOykpKRo/Pjx6tKli8qWLZvn671nzx516dJFQUFBKlu2rB566CH9/fffDj/er7/+qjZt2qhUqVIKDw/XsGHDlJKSkuW4CxcuaNSoUYqMjFRgYKCaN2+u5cuX5/uc/fr108WLF/XWW285HCvgq4p7OgAA1+bChQuaMmWK3njjDU+H4rNWrlypbt266ZlnnnH4Pp9//rksFovCw8P10Ucf6YUXXnBbfG+//bYyMjLcdn5nzZ49W+XLl1e/fv08HUq+/Pvf/9Yjjzxiu71p0ybNnDlTzz33nOrUqWNrv+GGGzwRXraioqKUmpqqEiVK2Nq+//57zZo1y+sTvoI2ffp0dejQQTVq1PB0KHb++ecfTZo0SVWqVFGDBg20evXqHI89cuSI2rVrJ6vVqpdeekkpKSl6+eWX9dtvv2njxo0qWbJkro8VGxurDh06qE6dOnr11Vd15MgRvfzyy4qLi9MPP/xgd2y/fv30xRdfaMSIEYqJidH8+fN1++23a9WqVWrTpo3T5wwICFDfvn316quv6oknnpDFYsnfCwb4ABIpwMc1bNhQb7/9tsaMGaPIyEhPh1Ogzp49q9KlS1/zeU6cOKHQ0FCn7vPf//5Xt99+u6KiovS///3PrYnUlV+ece1uvfVWu9sBAQGaOXOmbr31Vt1888053s9V77f8yBx9Ru5OnDih7777TnPnzvXI4+f2HomIiNCxY8cUHh6uzZs368Ybb8zxPC+99JLOnj2rLVu2qEqVKpKkZs2a6dZbb9X8+fM1aNCgXON47rnnVKZMGa1evVohISGSLo9IDhw4UMuWLVOnTp0kSRs3btQnn3yi6dOn2/6Q1KdPH11//fUaOXKkfv31V6fPKUk9evTQtGnTtGrVKrVv3z6vlw3wWUztA3zcc889p/T0dE2ZMiXX43JbY3H1NKYJEybIYrHojz/+0IMPPiir1aoKFSpo7NixMsbo8OHD6tatm0JCQhQeHq5XXnkl28dMT0/Xc889p/DwcJUuXVp33XWXDh8+nOW4DRs2qEuXLrJarSpVqpRuuukmrV271u6YzJh2796tf/3rXypTpozdX0uz8+eff+r+++9X2bJlVapUKbVo0ULfffedrT9zeqQxRrNmzbJN6crLoUOH9PPPP6tXr17q1auXDhw4YPeF40r//e9/1axZM5UqVUplypRRu3bttGzZMrtjfvjhB910000KDg5WSEiIbrzxRv3vf/+z9We3RioxMVH9+vWT1WpVaGio+vbtq8TExGxj+P3333XfffepbNmyCggIUNOmTfXNN9/YHZP5Wqxdu1ZPPfWUKlSooNKlS+vuu++2m05UtWpV7dq1Sz/99JPt9cpMPi5duqSJEycqJiZGAQEBKleunNq0aZPjNCHp/6aUffDBB1n6li5dKovFosWLF0uSzpw5oxEjRqhq1ary9/dXWFiYbr31Vm3dujXH8+dXbu+3HTt2qF+/fqpWrZoCAgIUHh6uhx9+WCdPnsz2HPv27VO/fv0UGhoqq9Wq/v3769y5c3bHLl++XG3atFFoaKiCgoJUq1YtPffcc7b+qz+//fr106xZsyTJbjqiKyUkJKh///667rrr5O/vr4iICHXr1k3x8fG2Y77++mt17dpVkZGR8vf3V/Xq1fWf//xH6enpWc43a9YsVatWTYGBgWrWrJl+/vln3XzzzVmS1wsXLmj8+PGqUaOG/P39VblyZY0cOdKhNTffffed0tLS1LFjR7v2zPf3mjVr9Oijj6pcuXIKCQlRnz59dPr06Szn+eGHH9S2bVuVLl1awcHB6tq1q3bt2mV3TL9+/RQUFKT9+/fr9ttvV3BwsHr37p1jbP7+/goPD8/zOUjSl19+qTvuuMOWRElSx44dVbNmTX322We53jc5OVnLly/Xgw8+aEt4pMsJUlBQkN39v/jiCxUrVswuMQsICNCAAQO0bt062+9rZ84pSU2aNFHZsmX19ddfO/R8AV/FiBTg46Kjo9WnTx+9/fbbGj16tEtHpXr27Kk6depoypQp+u677/TCCy+obNmyeuutt9S+fXtNnTpVH330kZ555hndeOONateund39X3zxRVksFo0aNUonTpzQjBkz1LFjR8XGxiowMFDS5Wl1t912m5o0aaLx48fLz89P77//vtq3b6+ff/5ZzZo1szvn/fffr5iYGL300ksyxuQY+/Hjx9WqVSudO3dOw4YNU7ly5fTBBx/orrvu0hdffKG7775b7dq104cffqiHHnpIt956q/r06ePQ6/Lxxx+rdOnSuuOOOxQYGKjq1avro48+UqtWreyOmzhxoiZMmKBWrVpp0qRJKlmypDZs2KCVK1fa/no7f/58Pfzww6pXr57GjBmj0NBQbdu2TUuWLNG//vWvbB/fGKNu3brpl19+0eDBg1WnTh0tXLhQffv2zXLsrl271Lp1a1WqVEmjR49W6dKl9dlnn6l79+768ssvdffdd9sd/8QTT6hMmTIaP3684uPjNWPGDA0dOlSffvqpJGnGjBl64oknFBQUpH//+9+SpIoVK0q6nDhMnjxZjzzyiJo1a6bk5GRt3rxZW7duzTIKlKlp06aqVq2aPvvssyzxf/rppypTpow6d+4sSRo8eLC++OILDR06VHXr1tXJkyf1yy+/aM+ePWrcuHGu1yy/snu/LV++XH/++af69++v8PBw7dq1S/PmzdOuXbu0fv36LAlNjx49FB0drcmTJ2vr1q165513FBYWpqlTp0q6fI3uuOMO3XDDDZo0aZL8/f21b9++LH9MuNKjjz6qv/76S8uXL9eHH37olud+7733ateuXXriiSdUtWpVnThxQsuXL9ehQ4dsif38+fMVFBSkp556SkFBQVq5cqXGjRun5ORkTZ8+3XauOXPmaOjQoWrbtq2efPJJxcfHq3v37ipTpoyuu+4623EZGRm666679Msvv2jQoEGqU6eOfvvtN7322mv6448/tGjRolxj/vXXX1WuXDlFRUVl2z906FCFhoZqwoQJ2rt3r+bMmaODBw/aCuRI0ocffqi+ffuqc+fOmjp1qs6dO6c5c+aoTZs22rZtm90fNdLS0tS5c2e1adNGL7/8skqVKpW/F/sKR48e1YkTJ9S0adMsfc2aNdP333+f6/1/++03paWlZbl/yZIl1bBhQ23bts3Wtm3bNtWsWdMuOcp8HOnydL7KlSs7dc5MjRs3zvU9DBQKBoBPev/9940ks2nTJrN//35TvHhxM2zYMFv/TTfdZOrVq2e7feDAASPJvP/++1nOJcmMHz/ednv8+PFGkhk0aJCtLS0tzVx33XXGYrGYKVOm2NpPnz5tAgMDTd++fW1tq1atMpJMpUqVTHJysq39s88+M5LM66+/bowxJiMjw8TExJjOnTubjIwM23Hnzp0z0dHR5tZbb80S0wMPPODQ6zNixAgjyfz888+2tjNnzpjo6GhTtWpVk56ebvf8hwwZ4tB5jTGmfv36pnfv3rbbzz33nClfvry5dOmSrS0uLs74+fmZu+++2+6xMp+3McYkJiaa4OBg07x5c5OamprtMcYY07dvXxMVFWW7vWjRIiPJTJs2zdaWlpZm2rZtm+Uad+jQwdSvX9+cP3/e7tytWrUyMTExtrbM91PHjh3tHvvJJ580xYoVM4mJiba2evXqmZtuuinL69KgQQPTtWvXLO15GTNmjClRooQ5deqUre3ChQsmNDTUPPzww7Y2q9Xq1HVy1Oeff24kmVWrVtnacnu/nTt3Lkvbxx9/bCSZNWvWZDnHlc/BGGPuvvtuU65cOdvt1157zUgyf//9d44xZvf5HTJkiHHmv/GoqKgcr8+mTZvszn/69GkjyUyfPj3Xc2b3Wjz66KOmVKlStvfchQsXTLly5cyNN95o9xmZP3++kWT3Xvrwww+Nn5+f3efWGGPmzp1rJJm1a9fmGk+bNm1MkyZNsrRnvr+bNGliLl68aGufNm2akWS+/vprY8zl3xGhoaFm4MCBdvdPSEgwVqvVrr1v375Gkhk9enSuMWXn6tc7u74FCxZk6Xv22WeNJLvP89Uy389Xvhcz3X///SY8PNx2u169eqZ9+/ZZjtu1a5eRZObOnev0OTMNGjTIBAYG5hgnUBgwtQ8oBKpVq6aHHnpI8+bN07Fjx1x23isX5BcrVkxNmzaVMUYDBgywtYeGhqpWrVr6888/s9y/T58+Cg4Ott2+7777FBERYfuLamxsrOLi4vSvf/1LJ0+e1D///KN//vlHZ8+eVYcOHbRmzZosRRYGDx7sUOzff/+9mjVrZjf9LygoSIMGDVJ8fLx2797t2ItwlR07dui3337TAw88YGt74IEH9M8//2jp0qW2tkWLFikjI0Pjxo2Tn5/9r9rMv3wvX75cZ86c0ejRo7Osf8ltmtb333+v4sWL67HHHrO1FStWTE888YTdcadOndLKlSvVo0cPnTlzxvb6njx5Up07d1ZcXFyWio+DBg2ye+y2bdsqPT1dBw8ezOulUWhoqHbt2qW4uLg8j71Sz549denSJX311Ve2tmXLlikxMVE9e/a0O/+GDRv0119/OXX+a5Hd+y1zNFWSzp8/r3/++UctWrSQpGynGV59jrZt2+rkyZNKTk6WJNv6vK+//tpriooEBgaqZMmSWr16dbZT3648LlPme6xt27Y6d+6cfv/9d0mXp2+ePHlSAwcOVPHi/zcRpnfv3ipTpozd+T7//HPVqVNHtWvXtr1f//nnH9s6m1WrVuUa98mTJ7Oc80qDBg2yW3P42GOPqXjx4rbfScuXL1diYqLtM535U6xYMTVv3jzbx7/yc+gKqampki5PBbxa5u+JzGPyc/8r75uamurQ4zhzzkxlypRRampqlmmsQGFCIgUUEs8//7zS0tLyXCvljCvn50uS1WpVQECAypcvn6U9uy9bMTExdrctFotq1KhhW2OR+YW7b9++qlChgt3PO++8owsXLigpKcnuHNHR0Q7FfvDgQdWqVStLe2ZVNkcSg+z897//VenSpVWtWjXt27dP+/btU0BAgKpWraqPPvrIdtz+/fvl5+enunXr5niu/fv3S5LTZeoPHjyoiIgIBQUF2bVf/Xz37dsnY4zGjh2b5fUdP368pMuL86909TXP/FKa25fpTJMmTVJiYqJq1qyp+vXr69lnn9WOHTvyvF+DBg1Uu3Zt2/RB6fK0vvLly9stVJ82bZp27typypUrq1mzZpowYUK2CbwrZfd+O3XqlIYPH66KFSsqMDBQFSpUsB139ftVyvs17dmzp1q3bq1HHnlEFStWVK9evfTZZ595JKnKTKL9/f01depU/fDDD6pYsaLatWunadOmKSEhwe74Xbt26e6775bValVISIgqVKigBx98UNL/vRaZn7Wrq+gVL148y9q/uLg47dq1K8v7tWbNmpKyvl+zY3KZ8nv176SgoCBFRERk+Z3Uvn37LDEsW7Ysy+MXL17cbmqiK2Qmp9mtCTt//rzdMfm5/5X3DQwMdOhxnDlnpszrQNU+FGaskQIKiWrVqunBBx/UvHnzNHr06Cz9Of1nlt2i8EzFihVzqE3K/ctLTjK/KE6fPl0NGzbM9pirk4XcvkC4mzFGH3/8sc6ePZttgnTixAmlpKRkidlTMl/fZ555xrbO6GpXf7m9luvbrl077d+/X19//bWWLVumd955R6+99prmzp1rN7qZnZ49e+rFF1/UP//8o+DgYH3zzTd64IEH7EYwevToobZt22rhwoVatmyZpk+frqlTp+qrr77Sbbfdlmd8+ZHd+61Hjx769ddf9eyzz6phw4YKCgpSRkaGunTpkm3yk9drGhgYqDVr1mjVqlX67rvvtGTJEn366adq3769li1bluP9nZXTyIEk26jBlSOjI0aM0J133qlFixZp6dKlGjt2rCZPnqyVK1eqUaNGSkxM1E033aSQkBBNmjRJ1atXV0BAgLZu3apRo0blKxHMyMhQ/fr19eqrr2bbX7ly5VzvX65cOYeS/tweX7q8Tiq7whBXvh+lywnn1SPO1yoiIkKSsp1dcOzYMZUtWzbbkSFH73/lOtqIiIhs9yHMvG/msc6cM9Pp06dVqlQpj/7OBtyNRAooRJ5//nn997//tS1iv1LmX8GvruyW35EZR1w9xcsYo3379tn256levbokKSQkJEuVrWsVFRWlvXv3ZmnPnG6U02L03Pz00086cuSIJk2aZLffkHT5S8OgQYO0aNEiPfjgg6pevboyMjK0e/fuHJPEzOe/c+dOp/a8iYqK0ooVK7IkbVc/32rVqkm6XD7dla9vbn9hLlu2rPr376/+/fsrJSVF7dq104QJExxKpCZOnKgvv/xSFStWVHJysnr16pXluIiICD3++ON6/PHHdeLECTVu3Fgvvvii2xKpq50+fVorVqzQxIkTNW7cOFu7s9MZr+bn56cOHTqoQ4cOevXVV/XSSy/p3//+t1atWpXjtXP2L/1RUVE5TmnNfO9c/bmoXr26nn76aT399NOKi4tTw4YN9corr+i///2vVq9erZMnT+qrr76yKzRz4MCBLI8rXR4hveWWW2ztaWlpio+Pt9uvq3r16tq+fbs6dOiQr5GM2rVr68svv8yxPy4uzi6GlJQUHTt2TLfffrvt8SUpLCzM5b+THFWpUiVVqFBBmzdvztK3cePGHH+fZLr++utVvHhxbd68WT169LC1X7x4UbGxsXZtDRs21KpVq5ScnGxXcGLDhg22fmfPmenAgQNZfk8ChQ1T+4BCpHr16nrwwQf11ltvZZmCExISovLly2vNmjV27bNnz3ZbPAsWLNCZM2dst7/44gsdO3bM9qW3SZMmql69ul5++WWlpKRkuf+VZbeddfvtt2vjxo1at26dre3s2bOaN2+eqlatmuuUu5xkTut79tlndd9999n9DBw4UDExMbbpfd27d5efn58mTZqU5S/zmSMRnTp1UnBwsCZPnmybSnP1MTk9t7S0NM2ZM8fWlp6enmVT5rCwMN1888166623sv1Lcn5f39KlS2dbav3q8t9BQUGqUaOGQ2Wr69Spo/r16+vTTz/Vp59+qoiICLsv5+np6VmmzYWFhSkyMtLu/P/8849+//13t63LyBwduvr6zJgxI9/nPHXqVJa2zC+wub12mfsV5VT2/mq33367jhw5kqXy3YULF2yVBDOrH547dy7Le7J69eoKDg62xZTda3Hx4sUsv1OaNm2qcuXK6e2331ZaWpqt/aOPPsoyetSjRw8dPXpUb7/9dpb4U1NTdfbs2VyfY8uWLXX69Okcp3zOmzdPly5dst2eM2eO0tLSbL+TOnfurJCQEL300kt2x2W6lt9Jzrj33nu1ePFiu+0iVqxYoT/++EP3339/rve1Wq3q2LGj/vvf/9r9/v3www+VkpJid//77rtP6enpmjdvnq3twoULev/999W8eXPbCKAz58y0devWLJVMgcKGESmgkPn3v/+tDz/8UHv37lW9evXs+h555BFNmTJFjzzyiJo2bao1a9bojz/+cFssZcuWVZs2bdS/f38dP35cM2bMUI0aNTRw4EBJl/8K/8477+i2225TvXr11L9/f1WqVElHjx7VqlWrFBISom+//TZfjz169Gh9/PHHuu222zRs2DCVLVtWH3zwgQ4cOKAvv/zS6ek4Fy5c0Jdffqlbb701x41R77rrLr3++us6ceKEatSooX//+9/6z3/+o7Zt2+qee+6Rv7+/Nm3apMjISE2ePFkhISF67bXX9Mgjj+jGG2+07Ve0fft2nTt3Ltu9lSTpzjvvVOvWrTV69GjFx8erbt26+uqrr7JdnzNr1iy1adNG9evX18CBA1WtWjUdP35c69at05EjR7R9+3anXgfpcgI8Z84cvfDCC6pRo4bCwsLUvn171a1bVzfffLNtD5nNmzfbypU7omfPnho3bpxtH5srr9GZM2d03XXX6b777lODBg0UFBSkH3/8UZs2bbLbx+zNN9/UxIkTtWrVqlw3182vkJAQ23qhS5cuqVKlSlq2bFmWURhnTJo0SWvWrFHXrl0VFRWlEydOaPbs2bruuuty3SutSZMmkqRhw4apc+fOKlasWLajeJkGDRqk9957T/fff78efvhhNWrUSCdPntSnn36qnTt3asGCBSpZsqQk6Y8//lCHDh3Uo0cP1a1bV8WLF9fChQt1/Phx22O0atVKZcqUUd++fTVs2DBZLBZ9+OGHWZLMkiVLasKECXriiSfUvn179ejRQ/Hx8Zo/f76qV69uN/L00EMP6bPPPtPgwYO1atUqtW7dWunp6fr999/12WefaenSpdmWBc/UtWtXFS9eXD/++GO2m9ZevHjR9rz27t2r2bNnq02bNrrrrrskXb6+c+bM0UMPPaTGjRurV69eqlChgg4dOqTvvvtOrVu31ptvvpnj4+flzTffVGJioq1gyrfffqsjR45Iurz1gNVqlXR5f8DPP/9ct9xyi4YPH66UlBRNnz5d9evXV//+/e3OmbnO7Mr9vV588UW1atVKN910kwYNGqQjR47olVdeUadOndSlSxfbcc2bN9f999+vMWPG2H5vffDBB4qPj9e7775r9ziOnlOStmzZolOnTqlbt275fq0An+CJUoEArt2V5c+vllmW98ry58ZcLlU8YMAAY7VaTXBwsOnRo4c5ceJEjuXPry7H3LdvX1O6dOksj3d1qfXM8ucff/yxGTNmjAkLCzOBgYGma9eu5uDBg1nuv23bNnPPPfeYcuXKGX9/fxMVFWV69OhhVqxYkWdMudm/f7+57777TGhoqAkICDDNmjUzixcvznKcHCh//uWXXxpJ5t13383xmNWrV9uVdzfGmPfee880atTI+Pv7mzJlypibbrrJLF++3O5+33zzjWnVqpUJDAw0ISEhplmzZubjjz+29V9d/twYY06ePGkeeughExISYqxWq3nooYfMtm3bsi2pvH//ftOnTx8THh5uSpQoYSpVqmTuuOMO88UXX9iOyen9lHktrywNnpCQYLp27WqCg4Ptyle/8MILplmzZiY0NNQEBgaa2rVrmxdffNGu3HRu4uLijCQjyfzyyy92fRcuXDDPPvusadCggQkODjalS5c2DRo0MLNnz7Y7LvN9cmW8ecmt/Hl277cjR46Yu+++24SGhhqr1Wruv/9+89dffzn8Ocp8rQ8cOGCMMWbFihWmW7duJjIy0pQsWdJERkaaBx54wPzxxx+2+2RX/jwtLc088cQTpkKFCsZisThUCv306dPmySefNNHR0aZEiRImJCTE3HLLLeaHH36wO+6ff/4xQ4YMMbVr1zalS5c2VqvVNG/e3Hz22Wd2x61du9a0aNHCBAYGmsjISDNy5EizdOnSbK/BzJkzTVRUlPH39zfNmjUza9euNU2aNDFdunSxO+7ixYtm6tSppl69erbPTZMmTczEiRNNUlJSns/xrrvuMh06dLBry3zNf/rpJzNo0CBTpkwZExQUZHr37m1OnjyZ5RyrVq0ynTt3Nlar1QQEBJjq1aubfv36mc2bN9uOyen3YW6ioqJs7/GrfzLfD5l27txpOnXqZEqVKmVCQ0NN7969TUJCQpZzli9f3rRo0SJL+88//2xatWplAgICTIUKFcyQIUPstqPIlJqaap555hkTHh5u/P39zY033miWLFmSbfyOnnPUqFGmSpUqdlspAIWRxZh8rBAHAAC4BhkZGapQoYLuueeebKfy5dfPP/+sm2++Wb///rutSt/8+fPVv39/bdq0KdcRLV+ze/du1atXT4sXL1bXrl09HY6ky6P3VatW1ejRozV8+HBPhwO4FWukAACAW50/fz7LlL8FCxbo1KlTLp+C2bZtW3Xq1EnTpk1z6Xm90apVq9SyZUuvSaIk6f3331eJEiUc3vMP8GWMSAEAALdavXq1nnzySd1///0qV66ctm7dqnfffVd16tTRli1bbGuz3KWwjkgB8CyKTQAAALeqWrWqKleurJkzZ+rUqVMqW7as+vTpoylTprg9iQIAd2FECgAAAACcxBopAAAAAHASiRQAAAAAOIk1UrpcgvWvv/5ScHCw3caAAAAAAIoWY4zOnDmjyMhIu83hr0YiJemvv/5S5cqVPR0GAAAAAC9x+PBhXXfddTn2k0hJCg4OlnT5xQoJCfFwNAAAAAA8JTk5WZUrV7blCDkhkZJs0/lCQkJIpAAAAADkueSHYhMAAAAA4CQSKQAAAABwEokUAAAAADiJRAoAAAAAnEQiBQAAAABOIpECAAAAACeRSAEAAACAk0ikAAAAAMBJJFIAAAAA4CQSKQAAAABwEokUAAAAADiJRAoAAAAAnEQiBQAAAABOIpECAAAAACcV93QAQG5Sj8br/LHDCoioosBKUZ4OBwAAAJDk4RGpNWvW6M4771RkZKQsFosWLVpk67t06ZJGjRql+vXrq3Tp0oqMjFSfPn30119/2Z3j1KlT6t27t0JCQhQaGqoBAwYoJSWlgJ8JXC3tTJL2jBus7Y/eqb0THtf2R+/QnnGDlZaS5OnQAAAAAM8mUmfPnlWDBg00a9asLH3nzp3T1q1bNXbsWG3dulVfffWV9u7dq7vuusvuuN69e2vXrl1avny5Fi9erDVr1mjQoEEF9RTgJnHTRykpdr1dW1LsesVNG+WhiAAAAID/YzHGGE8HIUkWi0ULFy5U9+7dczxm06ZNatasmQ4ePKgqVapoz549qlu3rjZt2qSmTZtKkpYsWaLbb79dR44cUWRkpEOPnZycLKvVqqSkJIWEhLji6eAapB6N1/ZH78yxv8Fbi5nmBwAAALdwNDfwqWITSUlJslgsCg0NlSStW7dOoaGhtiRKkjp27Cg/Pz9t2LAhx/NcuHBBycnJdj/wHuePHc6j/1ABRQIAAABkz2cSqfPnz2vUqFF64IEHbJlhQkKCwsLC7I4rXry4ypYtq4SEhBzPNXnyZFmtVttP5cqV3Ro7nBMQkfv1CIioUkCRAAAAANnziUTq0qVL6tGjh4wxmjNnzjWfb8yYMUpKSrL9HD6c+wgIClZgpaqyNm4t+RWz7/ArJmvj1kzrAwAAgMd5fSKVmUQdPHhQy5cvt5unGB4erhMnTtgdn5aWplOnTik8PDzHc/r7+yskJMTuB94lZuRUWRu2sGuzNmyhmJFTPRQRAAAA8H+8eh+pzCQqLi5Oq1atUrly5ez6W7ZsqcTERG3ZskVNmjSRJK1cuVIZGRlq3ry5J0KGixQPsqrOpLlKPXpQ548dYh8pAAAAeBWPJlIpKSnat2+f7faBAwcUGxursmXLKiIiQvfdd5+2bt2qxYsXKz093bbuqWzZsipZsqTq1KmjLl26aODAgZo7d64uXbqkoUOHqlevXg5X7IN3C6wURQIFAAAAr+PR8uerV6/WLbfckqW9b9++mjBhgqKjo7O936pVq3TzzTdLurwh79ChQ/Xtt9/Kz89P9957r2bOnKmgoCCH46D8OQAAAADJ8dzAa/aR8iQSKQAAAABSId1HCgAAAAC8AYkUAAAAADiJRAoAAAAAnEQiBQAAAABOIpECAAAAACeRSAEAAACAk0ikAAAAAMBJJFIAAAAA4CQSKQAAAABwEokUAAAAADiJRAoAAAAAnEQiBQAAAABOIpECAAAAACeRSAEAAACAk0ikAAAAAMBJJFIAAAAA4CQSKQAAAABwEokUAAAAADiJRAoAAAAAnEQiBQAAAABOIpECAAAAACeRSAEAAACAk0ikAAAAAMBJJFIAAAAA4CQSKQAAAABwEokUAAAAADiJRAoAAAAAnEQiBQAAAABOIpECAAAAACeRSAEAAACAk0ikAAAAAMBJJFIAAAAA4CQSKQAAAABwEokUAAAAADipuKcDgGukHo3X+WOHFRBRRYGVojwdDgAAAFCokUj5uLQzSYqbPkpJW9fa2qyNWytm5FQVD7J6MDIAAACg8GJqn4+Lmz5KSbHr7dqSYtcrbtooD0UEAAAAFH4kUj4s9Wj85ZGojHT7jox0JW1dq9SjBz0TGAAAAFDIkUj5sPPHDufRf6iAIgEAAACKFhIpHxYQUTmP/ioFFAkAAABQtJBI+bDASlVlbdxa8itm3+FXTNbGraneBwAAALgJiZSPixk5VdaGLezarA1bKGbkVA9FBAAAABR+lD/3ccWDrKozaa5Sjx7U+WOH2EcKAAAAKAAkUoVEYKUoEigAAACggDC1DwAAAACcRCIFAAAAAE4ikQIAAAAAJ5FIAQAAAICTSKQAAAAAwEkkUgAAAADgJBIpAAAAAHASiRQAAAAAOIlECgAAAACcRCIFAAAAAE4ikQIAAAAAJ5FIAQAAAICTSKQAAAAAwEkkUgAAAADgJBIpAAAAAHCSRxOpNWvW6M4771RkZKQsFosWLVpk12+M0bhx4xQREaHAwEB17NhRcXFxdsecOnVKvXv3VkhIiEJDQzVgwAClpKQU4LMAAAAAUNR4NJE6e/asGjRooFmzZmXbP23aNM2cOVNz587Vhg0bVLp0aXXu3Fnnz5+3HdO7d2/t2rVLy5cv1+LFi7VmzRoNGjSooJ4CAAAAgCLIYowxng5CkiwWixYuXKju3btLujwaFRkZqaefflrPPPOMJCkpKUkVK1bU/Pnz1atXL+3Zs0d169bVpk2b1LRpU0nSkiVLdPvtt+vIkSOKjIx06LGTk5NltVqVlJSkkJAQtzw/AAAAAN7P0dzAa9dIHThwQAkJCerYsaOtzWq1qnnz5lq3bp0kad26dQoNDbUlUZLUsWNH+fn5acOGDTme+8KFC0pOTrb7AQAAAABHeW0ilZCQIEmqWLGiXXvFihVtfQkJCQoLC7PrL168uMqWLWs7JjuTJ0+W1Wq1/VSuXNnF0QMAAAAozIo7e4cLFy5ow4YNOnjwoM6dO6cKFSqoUaNGio6Odkd8bjFmzBg99dRTttvJyckkUwAAAAAc5nAitXbtWr3++uv69ttvdenSJVmtVgUGBurUqVO6cOGCqlWrpkGDBmnw4MEKDg6+5sDCw8MlScePH1dERISt/fjx42rYsKHtmBMnTtjdLy0tTadOnbLdPzv+/v7y9/e/5hgBAAAAFE0OTe2766671LNnT1WtWlXLli3TmTNndPLkSR05ckTnzp1TXFycnn/+ea1YsUI1a9bU8uXLrzmw6OhohYeHa8WKFba25ORkbdiwQS1btpQktWzZUomJidqyZYvtmJUrVyojI0PNmze/5hgAAAAAIDsOjUh17dpVX375pUqUKJFtf7Vq1VStWjX17dtXu3fv1rFjxxx68JSUFO3bt892+8CBA4qNjVXZsmVVpUoVjRgxQi+88IJiYmIUHR2tsWPHKjIy0lbZr06dOurSpYsGDhyouXPn6tKlSxo6dKh69erlcMU+AAAAAHCWR8ufr169WrfcckuW9r59+2r+/Pkyxmj8+PGaN2+eEhMT1aZNG82ePVs1a9a0HXvq1CkNHTpU3377rfz8/HTvvfdq5syZCgoKcjgOyp8DAAAAkBzPDZxOpNLS0rRr1y5bVbzw8HDVrVs3x9EqX0AiBQAAAEByPDdwuNhERkaGxo0bp1mzZikpKcmuz2q1aujQoZo4caL8/Ly2ojoAAAAAuITDidTo0aM1f/58TZkyRZ07d7bt73T8+HEtW7ZMY8eO1cWLFzV16lS3BQsAAAAA3sDhqX3h4eH64IMP1Llz52z7ly5dqj59+uj48eMuDbAgMLUPAAAAgOR4buDwPLwzZ87kWgkvIiJCZ8+edS5KAAAAAPBBDidSN998s5555hn9888/Wfr++ecfjRo1SjfffLMrYwMAAAAAr+TwGqm5c+fq9ttvV0REhOrXr2+3Ruq3335T3bp1tXjxYrcFCgAAAADewqny5xkZGVq6dKnWr19vV/68ZcuW6tSpk89W7GONFAAAAADJjftIFUYkUgAAAAAkN+wjlWnjxo1at26d3YhUq1atdOONN+Y/WgAAAADwIQ4nUidOnNC9996rtWvXqkqVKnZrpJ588km1bt1aX375pcLCwtwWLAAAAAB4A4cXNT3++ONKT0/Xnj17FB8frw0bNmjDhg2Kj4/Xnj17lJGRoSFDhrgzVgAAAADwCg6vkQoODtaaNWvUqFGjbPu3bNmim2++WWfOnHFpgAWBNVIAAAAAJDdsyOvv76/k5OQc+8+cOSN/f3/nogQAAAAAH+RwItWzZ0/17dtXCxcutEuokpOTtXDhQvXv318PPPCAW4IEAAAAAG/icLGJV199VRkZGerVq5fS0tJUsmRJSdLFixdVvHhxDRgwQC+//LLbAgUAAAAAb+H0PlLJycnavHmzjh8/Luly+fMmTZr49Noi1kgBAAAAkNy4j1RISIjat29/TcEBAAAAgC9zKpH6559/9N5772W7IW+/fv1UoUIFtwQJAAAAAN7E4WITmzZtUs2aNTVz5kxZrVa1a9dO7dq1k9Vq1cyZM1W7dm1t3rzZnbECAAAAgFdweI1UixYt1KBBA82dO1cWi8WuzxijwYMHa8eOHVq3bp1bAnUn1kgBAAAAkNywRmr79u2aP39+liRKkiwWi5588skcN+sFAAAAgMLE4al94eHh2rhxY479GzduVMWKFV0SFAAAAAB4M4dHpJ555hkNGjRIW7ZsUYcOHWxJ0/Hjx7VixQq9/fbb7CMFAAAAoEhwOJEaMmSIypcvr9dee02zZ89Wenq6JKlYsWJq0qSJ5s+frx49ergtUAAAAADwFk5vyCtJly5d0j///CNJKl++vEqUKOHywAoSxSYAAAAASG7ckFeSSpQooYiIiHwHBwAAAAC+zOFiE454+OGH9eGHH7rylAAAAADgdVyaSP35558aO3asGjZs6MrTAgAAAIBXydfUvpysXr1akrR7925XnhYAAAAAvIpLR6Qy1a1b1x2nBQAAAACv4NSI1MWLF7Vo0SKtW7dOCQkJki5v1NuqVSt169ZNJUuWdEuQAAAAAOBNHB6R2rdvn+rUqaO+fftq27ZtysjIUEZGhrZt26Y+ffqoXr162rdvnztjBQAAAACv4PA+UrfeeqtKly6tBQsWZKmnnpycrD59+ig1NVVLly51S6DuxD5SAAAAACQ37CO1du1abdy4MduThYSE6D//+Y+aN2+ev2gBAAAAwIc4PLUvNDRU8fHxOfbHx8crNDTUBSEBAAAAgHdzeETqkUceUZ8+fTR27Fh16NBBFStWlCQdP35cK1as0AsvvKAnnnjCbYECAAAAgLdweI2UJE2dOlWvv/66EhISZLFYJEnGGIWHh2vEiBEaOXKk2wJ1J9ZIAQAAAJAczw2cSqQyHThwwK78eXR0dP4j9QIkUgAAAAAkNxSbuFJ0dLTPJ08AAAAAkF8OF5vIy+HDh/Xwww+76nQAAAAA4LVclkidOnVKH3zwgatOBwAAAABey+Gpfd98802u/X/++ec1BwMAAAAAvsDhRKp79+6yWCzKrTZFZiU/AAAAACjMHJ7aFxERoa+++koZGRnZ/mzdutWdcQIAAACA13A4kWrSpIm2bNmSY39eo1UAAAAAUFg4PLXv2Wef1dmzZ3Psr1GjhlatWuWSoAAAAADAm+VrQ97Chg15AQAAAEhu3pAXviP1aLzOHzusgIgqCqwU5elwAAAAgEKBRKqQSjuTpLjpo5S0da2tzdq4tWJGTlXxIKsHIwMAAAB8n8s25IV3iZs+Skmx6+3akmLXK27aKA9FBAAAABQeJFKFUOrR+MsjURnp9h0Z6UraulapRw96JjAgF6lH43V688+8PwEAgE9wamrfpUuX9Oijj2rs2LGKjo52V0y4RuePHc6j/1Cu66VYV4WCxDRUAADgi5wakSpRooS+/PJLd8UCFwmIqJxHf5Vs29POJGnPuMHa/uid2jvhcW1/9A7tGTdYaSlJ7ggTkMQ0VAAA4JucntrXvXt3LVq0yA2hwFUCK1WVtXFrya+YfYdfMVkbt85xlIkvtChoTEMFAAC+yumqfTExMZo0aZLWrl2rJk2aqHTp0nb9w4YNc1lwyL+YkVMVN+2q6VINWyhm5NRsj7d9ob3aFV9omeYHV7vWaagAAACe4nQi9e677yo0NFRbtmzRli1b7PosFguJlJcoHmRVnUlzlXr0oM4fO5Tneie+0MIT8jsNFQAAwNOcTqQOHDjgjjjgJoGVohxKgPhCC0/InIaaFLvefnqfXzFZG7YgeQcAAF4r3+XPL168qL179yotLc2V8cBD8ruuCrhWMSOnytqwhV1bbtNQs0PpdAAAUNAsxhjjzB3OnTunJ554Qh988IEk6Y8//lC1atX0xBNPqFKlSho9erRbAnWn5ORkWa1WJSUlKSQkxNPheExaSlLWdVWUoUYBcXQa6pUonQ4AAFzN0dzA6URq+PDhWrt2rWbMmKEuXbpox44dqlatmr7++mtNmDBB27Ztu+bgCxqJlL38fKEFPGHPuME5TgusM2mu5wIDAAA+y9HcwOmpfYsWLdKbb76pNm3ayGKx2Nrr1aun/fv35y/aHKSnp9s2/w0MDFT16tX1n//8R1fmfsYYjRs3ThEREQoMDFTHjh0VFxfn0jiKmsBKUSrTtC1JFLwapdMBAIAnOZ1I/f333woLC8vSfvbsWbvEyhWmTp2qOXPm6M0339SePXs0depUTZs2TW+88YbtmGnTpmnmzJmaO3euNmzYoNKlS6tz5846f/68S2MB4F0cqTQJAADgLk4nUk2bNtV3331nu52ZPL3zzjtq2bKl6yKT9Ouvv6pbt27q2rWrqlatqvvuu0+dOnXSxo0bJV0ejZoxY4aef/55devWTTfccIMWLFigv/76K9dNgy9cuKDk5GS7HwC+hUqTAADAk5xOpF566SU999xzeuyxx5SWlqbXX39dnTp10vvvv68XX3zRpcG1atVKK1as0B9//CFJ2r59u3755Rfddtttki6XYk9ISFDHjh1t97FarWrevLnWrVuX43knT54sq9Vq+6lcOfcvZAC8D5UmAQCAJzmdSLVp00axsbFKS0tT/fr1tWzZMoWFhWndunVq0qSJS4MbPXq0evXqpdq1a6tEiRJq1KiRRowYod69e0uSEhISJEkVK1a0u1/FihVtfdkZM2aMkpKSbD+HD+c+RQj5Q0lquJsrSqcDAADkh9Mb8kpS9erV9fbbb7s6liw+++wzffTRR/rf//6nevXqKTY2ViNGjFBkZKT69u2b7/P6+/vL39/fhZHiSpSkRkEpHmRVnUlzqTQJAAAKXL4SqfT0dC1cuFB79uyRJNWtW1fdunVT8eL5Ol2Onn32WduolCTVr19fBw8e1OTJk9W3b1+Fh4dLko4fP66IiAjb/Y4fP66GDRu6NBY4Lm76qMslqa+QFLtecdNGUZIabhFYKYoECgAAFCinp/bt2rVLNWvWVN++fbVw4UItXLhQffv2VUxMjHbu3OnS4M6dOyc/P/sQixUrpoyMDElSdHS0wsPDtWLFClt/cnKyNmzY4PLCF3AMJakBAABQFDg9hPTII4+oXr162rx5s8qUKSNJOn36tPr166dBgwbp119/dVlwd955p1588UVVqVJF9erV07Zt2/Tqq6/q4YcflnS5YuCIESP0wgsvKCYmRtHR0Ro7dqwiIyPVvXt3l8UBxzlSkpqRAwAAAPg6pxOp2NhYuyRKksqUKaMXX3xRN954o0uDe+ONNzR27Fg9/vjjOnHihCIjI/Xoo49q3LhxtmNGjhyps2fPatCgQUpMTFSbNm20ZMkSBQQEuDQWOIaS1AAAACgKnE6katasqePHj6tevXp27SdOnFCNGjVcFpgkBQcHa8aMGZoxY0aOx1gsFk2aNEmTJk1y6WMjfzJLUifFrref3udXTNaGLRiNAgAAQKHg9BqpyZMna9iwYfriiy905MgRHTlyRF988YVGjBihqVOnssktKEkNAACAQs9ijDHO3OHK4g8Wi0WSlHmKK29bLBalp6dnPYEXSk5OltVqVVJSkkJCQjwdTqFBSWoAAAD4GkdzA6en9q1ateqaAkPRQUlqAAAAFFZOJ1I33XSTO+IAAAAAAJ/h9BopAAAAACjqnB6RAgpS6tF4nT92mHVWAAAA8CokUvBKaWeSFDd9lJK2rrW1WRu3VszIqSoeZPVgZAAAAABT++Cl4qaPurwX1RWSYtcrbtooD0UEAAAA/B+nE6n27dsrMTExS3tycrLat2/viphQxKUejb88EpVxVfn8jHQlbV2r1KMHPRMYAAAA8P85nUitXr1aFy9ezNJ+/vx5/fzzzy4JCkXb+WOH8+g/VECRwFulHo3X6c0/k1QDAACPcXiN1I4dO2z/3r17txISEmy309PTtWTJElWqVMm10aFICoionEd/lQKKBN6GtXMAAMBbOJxINWzYUBaLRRaLJdspfIGBgXrjjTdcGhyKpsBKVWVt3PryGqkrp/f5FZO1YYt8Ve+j+l/hkNvauTqT5nooKgAAUBQ5nEgdOHBAxhhVq1ZNGzduVIUKFWx9JUuWVFhYmIoVK+aWIFH0xIycqrhpV408NGyhmJFTnToPIxiFh23t3NWuWDtHkgwAAAqKw4lUVNTlLygZGRluCwbIVDzIqjqT5ir16EGdP3Yo3yNJjGAUHo6snSORAgAABSVf+0jFxcVp1apVOnHiRJbEaty4cS4JDJCkwEpR+f5yzAhG4cLaOQAA4E2cTqTefvttPfbYYypfvrzCw8NlsVhsfRaLhUQKXoMRjMLFHWvnAAAA8svpROqFF17Qiy++qFGj2BgV3o0RjMLHVWvnAAAArpXTidTp06d1//33uyMWwKUYwSh8XLV2DgAA4Fo5vSHv/fffr2XLlrkjFsDlYkZOlbVhC7s2RjB8X2ClKJVp2pYkCgAAeIzTI1I1atTQ2LFjtX79etWvX18lSpSw6x82bJjLggOuFSMYAAAAcAeLMcY4c4fo6OicT2ax6M8//7zmoApacnKyrFarkpKSFBIS4ulwAAAAAHiIo7mB0yNSBw4cuKbAAAAAAMDXOb1GCgAAAACKOqdHpB5++OFc+9977718BwMA8G6pR+N1/thh1hsCAIq8fJU/v9KlS5e0c+dOJSYmqn379i4LDADgPdLOJClu+lV7eDVurZiRU1U8yOrByAAA8AynE6mFCxdmacvIyNBjjz2m6tWruyQoAIB3iZs+6vKebFdIil2vuGmjVGfSXA9FBQCA57hkjZSfn5+eeuopvfbaa644HQDAi6Qejb88EnXlxtaSlJGupK1rlXr0oGcCAwDAg1xWbGL//v1KS0tz1ekAAF7i/LHDefQfKqBIAADwHk5P7XvqqafsbhtjdOzYMX333Xfq27evywIDAHiHgIjKefRXKaBIAADwHk4nUtu2bbO77efnpwoVKuiVV17Js6IfAMD3BFaqKmvj1pfXSF05vc+vmKwNW1C9DwBQJFmMMcbTQXiao7sXA0BRlZaSpLhpVO0DABR+juYGTo9IZfr777+1d+9eSVKtWrVUoUKF/J4KAODligdZVWfSXKUePajzxw6xjxQAoMhzOpE6e/asnnjiCS1YsEAZGRmSpGLFiqlPnz564403VKpUKZcHCQDwDoGVokigAABQPqr2PfXUU/rpp5/07bffKjExUYmJifr666/1008/6emnn3ZHjAAAAADgVZxeI1W+fHl98cUXuvnmm+3aV61apR49eujvv/92ZXwFgjVSAAAAACTHcwOnR6TOnTunihUrZmkPCwvTuXPnnD0dAAAAAPgcpxOpli1bavz48Tp//rytLTU1VRMnTlTLli1dGhwAAAAAeCOni028/vrr6ty5s6677jo1aNBAkrR9+3YFBARo6dKlLg8QAAAAALxNvvaROnfunD766CP9/vvvkqQ6deqod+/eCgwMdHmABYE1UgAAAAAkN+8jVapUKQ0cODDfwQEAAACAL3N6jdTkyZP13nvvZWl/7733NHXqVJcEBQAAAADezOlE6q233lLt2rWztNerV09z5851SVAAAAAA4M2cTqQSEhIUERGRpb1ChQo6duyYS4ICAAAAAG/mdCJVuXJlrV27Nkv72rVrFRkZ6ZKgAAAArpZ6NF6nN/+s1KMHPR0KADhfbGLgwIEaMWKELl26pPbt20uSVqxYoZEjR+rpp592eYAAAKBoSzuTpLjpo5S09f/+kGtt3FoxI6eqeJDVg5EBKMqcTqSeffZZnTx5Uo8//rguXrwoSQoICNCoUaM0ZswYlwcIAACKtrjpo5QUu96uLSl2veKmjVKdSazPBuAZ+dpHSpJSUlK0Z88eBQYGKiYmRv7+/q6OrcCwjxQAAN4p9Wi8tj96Z479Dd5arMBKUQUYEYDCzq37SElSUFCQbrzxxvzeHQAAIE/njx3Oo/8QiRQAj3Co2MTgwYN15MgRh0746aef6qOPPrqmoAAAACQpIKJyHv1VCigSALDn0IhUhQoVVK9ePbVu3Vp33nmnmjZtqsjISAUEBOj06dPavXu3fvnlF33yySeKjIzUvHnz3B03AAAoAgIrVZW1cevLa6Qy0v+vw6+YrA1bMBoFwGMcXiN1/PhxvfPOO/rkk0+0e/duu77g4GB17NhRjzzyiLp06eKWQN2JNVIAAHivtJQkxU2jah+AguFobpCvYhOnT5/WoUOHlJqaqvLly6t69eqyWCzXFLAnkUjBEalH43X+2GEFRFThL6AA4AGpRw/q/LFD/B4G4FZuLTZRpkwZlSlTJt/BAb6E/UsAwDsEVooigQLgNRwqNgEUZbntXwIAAICiiUQKyEXq0fjLI1FXLnCWpIx0JW1dq9SjBz0TGAAAADyKRArIhSP7lwAAAKDoIZECcsH+JQAAAMiO04lUamqqzp07Z7t98OBBzZgxQ8uWLXNpYIA3yNy/RH7F7Dv8isnauDWLngEAAIoopxOpbt26acGCBZKkxMRENW/eXK+88oq6deumOXPmuDxAwNNiRk6VtWELuzZrwxaKGTnVQxEBAADA05xOpLZu3aq2bdtKkr744gtVrFhRBw8e1IIFCzRz5kyXB3j06FE9+OCDKleunAIDA1W/fn1t3rzZ1m+M0bhx4xQREaHAwEB17NhRcXFxLo8DRVfxIKvqTJqrBm8tVq0Js9XgrcWqM2kupc8BAACKMKcTqXPnzik4OFiStGzZMt1zzz3y8/NTixYtdPCgayuYnT59Wq1bt1aJEiX0ww8/aPfu3XrllVfs9rCaNm2aZs6cqblz52rDhg0qXbq0OnfurPPnz7s0FiCwUpTKNG3LdD4AAAA4vyFvjRo1tGjRIt19991aunSpnnzySUnSiRMnct35Nz+mTp2qypUr6/3337e1RUdH2/5tjNGMGTP0/PPPq1u3bpKkBQsWqGLFilq0aJF69erl0ngKm9Sj8Tp/7DA7xAMAAABOcnpEaty4cXrmmWdUtWpVNW/eXC1btpR0eXSqUaNGLg3um2++UdOmTXX//fcrLCxMjRo10ttvv23rP3DggBISEtSxY0dbm9VqVfPmzbVu3bocz3vhwgUlJyfb/RQlaWeStGfcYG1/9E7tnfC4tj96h/aMG6y0lCRPhwYAAAD4BKcTqfvuu0+HDh3S5s2btWTJElt7hw4dNGPGDFfGpj///FNz5sxRTEyMli5dqscee0zDhg3TBx98IElKSEiQJFWsWNHufhUrVrT1ZWfy5MmyWq22n8qVcy9xXdjETR+lpNj1dm1JsesVN22UhyICAAAAfIvTidTDDz+s0qVLq1GjRvLz+7+716tXT1OnuraKWUZGhho3bqyXXnpJjRo10qBBgzRw4EDNnTv3ms47ZswYJSUl2X4OH85909XCJPVovJK2rpUy0u07MtKVtHWtUo+6dp0bAAAAUBg5nUh98MEHSk1NzdKemppqK4vuKhEREapbt65dW506dXTo0CFJUnh4uCTp+PHjdsccP37c1pcdf39/hYSE2P0UFeeP5Z40nj92qIAiAQAAAHyXw4lUcnKykpKSZIzRmTNn7NYXnT59Wt9//73CwsJcGlzr1q21d+9eu7Y//vhDUVGXCyNER0crPDxcK1assItzw4YNtrVbsBcQkfs0xoCIKgUUCQAAAOC7HK7aFxoaKovFIovFopo1a2bpt1gsmjhxokuDe/LJJ9WqVSu99NJL6tGjhzZu3Kh58+Zp3rx5tsccMWKEXnjhBcXExCg6Olpjx45VZGSkunfv7tJYCovASlVlbdz68hqpK6f3+RWTtWELt1TvozogAAAAChuLMcY4cuBPP/0kY4zat2+vL7/8UmXLlrX1lSxZUlFRUYqMjHR5gIsXL9aYMWMUFxen6OhoPfXUUxo4cKCt3xij8ePHa968eUpMTFSbNm00e/bsbJO9nCQnJ8tqtSopKalITPNLS0lS3LRRl9dK/X/Wxq0VM3KqSzeZTTuTdLmwhZsfBwAAAHAVR3MDhxOpTAcPHlTlypXtCk34uqKWSGVKPXpQ548dcttI0Z5xg3Mc+aoz6doKhgAAAADu4Ghu4PSGvFFRUUpMTNTGjRt14sQJZWRk2PX36dPH+WjhEYGVotw21c5WHfBqV1QHZJofAAAAfJXTidS3336r3r17KyUlRSEhIbJYLLY+i8VCIgVJjlUHJJECAACAr3J6ft7TTz+thx9+WCkpKUpMTNTp06dtP6dOnXJHjPBBVAd0j9Sj8Tq9+Wf2+wIAAPAwp0ekjh49qmHDhqlUqVLuiAeFhCeqAxZmFO4AAADwLk6PSHXu3FmbN292RywoZGJGTpW1YQu7NmvDFooZOdVDEfmuuOmjLielV0iKXa+4aaM8FBEAAEDR5vSIVNeuXfXss89q9+7dql+/vkqUKGHXf9ddd7ksOPi24kFW1Zk01+3VAQs7CncAAAB4H6cTqcw9nCZNmpSlz2KxKD09PUs7ijZ3VgcsCry1cAcbLQMAgKLM6UTq6nLnANzL2wp3sF4LAAAgH2ukrnT+/HlXxQEgB5mFO+RXzL7Dr5isjVsX+GgQ67UAAADykUilp6frP//5jypVqqSgoCD9+eefkqSxY8fq3XffdXmAALyncIdtvVbGVVN4r1ivBQAAUBQ4PbXvxRdf1AcffKBp06bZ1ktJ0vXXX68ZM2ZowIABLg0QgPcU7vDW9VoAAAAFzekRqQULFmjevHnq3bu3ihX7v6lGDRo00O+//+7S4ADYC6wUpTJN23osWfG29VoAAACe4nQidfToUdWoUSNLe0ZGhi5duuSSoAB4J29brwUAAOApTidSdevW1c8//5yl/YsvvlCjRo1cEhQA7+Ut67UAAAA8yek1UuPGjVPfvn119OhRZWRk6KuvvtLevXu1YMECLV682B0xAvAi3rJeCwAAwJMsxhjj7J1+/vlnTZo0Sdu3b1dKSooaN26scePGqVOnTu6I0e2Sk5NltVqVlJSkkJAQT4cDAAAAwEMczQ3ylUgVNiRSAAAAACTHc4Nr2pAXAAAAAIoih9ZIlSlTRhaLxaETnjp16poCAgAAAABv51AiNWPGDNu/T548qRdeeEGdO3dWy5YtJUnr1q3T0qVLNXbsWLcECQAAAADexOk1Uvfee69uueUWDR061K79zTff1I8//qhFixa5Mr4CwRopAAAAAJIb10gtXbpUXbp0ydLepUsX/fjjj86eDgAAAAB8jtOJVLly5fT1119naf/6669Vrlw5lwQFAAAAAN7M6Q15J06cqEceeUSrV69W8+bNJUkbNmzQkiVL9Pbbb7s8QAAAAADwNk4nUv369VOdOnU0c+ZMffXVV5KkOnXq6JdffrElVsi/1KPxOn/ssAIiqiiwUpSnwwEAAACQDTbklXcUm0g7k6S46aOUtHWtrc3auLViRk5V8SCrR2ICAAAAihqXFptITk62+3duP8ifuOmjlBS73q4tKXa94qaN8lBEuFrq0Xid3vyzUo8e9HQoAAAA8DCHN+Q9duyYwsLCFBoamu3mvMYYWSwWpaenuzzIwi71aLzdSJRNRrqStq5V6tGDTPPzIEYLAQAAcDWHEqmVK1eqbNmykqRVq1a5NaCi6Pyxw3n0HyKR8qDcRgvrTJrroagAAADgSQ4lUjfddFO2/4ZrBERUzqO/SgFFgqsxWghHUSgGAICixemqfe+//76CgoJ0//3327V//vnnOnfunPr27euy4IqKwEpVZW3c+vKoR8YVUyP9isnasIXXfikrCl8cGS1EXpj6CQBA0eT0hryTJ09W+fLls7SHhYXppZdecklQRVHMyKmyNmxh12Zt2EIxI6d6KKKcpZ1J0p5xg7X90Tu1d8Lj2v7oHdozbrDSUpI8HZrLMVqIvFAoBgCAosnpEalDhw4pOjo6S3tUVJQOHTrkkqCKouJBVtWZNFepRw/q/LFDXj3KU5TWDPnqaCEKBlM/AQAoupwekQoLC9OOHTuytG/fvl3lypVzSVBFWWClKJVp2tZrv3zZvjhmXFWd8YovjoWNL40WomA5MvUTAAAUTk6PSD3wwAMaNmyYgoOD1a5dO0nSTz/9pOHDh6tXr14uDxDepSiuGfKl0UIULKZ+AgBQdDmdSP3nP/9RfHy8OnTooOLFL989IyNDffr0YY1UEVCUvzgGVooigYIdpn4CAFB0WYwxJj93jIuLU2xsrAIDA1W/fn1FRfnuF4bk5GRZrVYlJSUpJCTE0+F4vT3jBuf4xbGwrZEC8pKWkqS4aVTtAwCgsHA0N8h3IlWYkEg5hy+OQFZM/QScVxS20QDge9yWSN17771q1qyZRo2yL+07bdo0bdq0SZ9//nn+IvYgEqn84YsjACA/2H8NgDdzNDdwumrfmjVrdPvtt2dpv+2227RmzRpnTwcf5u0VBgEA3on91wAUBk4nUikpKSpZsmSW9hIlSig5OdklQQEAgMKpKG6jAaBwcjqRql+/vj799NMs7Z988onq1q3rkqAAAEDhxP5rAAoLp8ufjx07Vvfcc4/279+v9u3bS5JWrFihjz/+2CfXRwHu4guLqH0hRgCFS1HeRgNA4eJ0InXnnXdq0aJFeumll/TFF18oMDBQN9xwg3788UfddNNN7ogR8Cm+sIjaF2IEUDix/xqAwsKl5c937typ66+/3lWnKzBU7YMr+cI+W74QI4DCi200AHgzR3MDp0ekrnbmzBl9/PHHeuedd7Rlyxalp6fnfSegkLItor7aFYuoPf3XVl+IEUDhVjzIqjqT5rKNBgCf5nSxiUxr1qxRnz59FBERoZdfflnt27fX+vXr874jUIj5wiJqX4gRQNHANhoAfJlTI1IJCQmaP3++3n33XSUnJ6tHjx66cOGCFi1aRMW+QoyCBI7zhUXUvhAjAACAt3N4ROrOO+9UrVq1tGPHDs2YMUN//fWX3njjDXfGBg9LO5OkPeMGa/ujd2rvhMe1/dE7tGfcYKWlJHk6NK+VuYhafsXsO/yKydq4tVckor4QIwAAgLdzOJH64YcfNGDAAE2cOFFdu3ZVsWLF8r4TfBo7z+dPzMipsjZsYddmbdhCMSOneiiirHwhRgAAAG/m8NS+X375Re+++66aNGmiOnXq6KGHHlKvXr3cGRs8iIIE+ecLi6h9IUYAAABv5vCIVIsWLfT222/r2LFjevTRR/XJJ58oMjJSGRkZWr58uc6cOePOOFHAKEhw7XxhEbUvxAgAAOCNnK7aV7p0aT388MP65Zdf9Ntvv+npp5/WlClTFBYWprvuussdMcIDKEgAAAAA5Czf5c8lqVatWpo2bZqOHDmijz/+2FUxwQtQkAAAAADImcUYYzwdhKc5untxUcPO8wAAAChqHM0NnNpHCkULBQkAAACA7JFIIU+BlaLylUCxkS/yi/cOAADwdiRScLm0M0mX96BiSiCcxHsHAAD4CoeKTTRu3FinT5+WJE2aNEnnzp1za1DwbWzki/zivQMAAHyFQ4nUnj17dPbsWUnSxIkTlZKS4tagcjJlyhRZLBaNGDHC1nb+/HkNGTJE5cqVU1BQkO69914dP37cI/Hhio18M9LtO67YyBfIDu8dAADgSxya2tewYUP1799fbdq0kTFGL7/8soKCgrI9dty4cS4NMNOmTZv01ltv6YYbbrBrf/LJJ/Xdd9/p888/l9Vq1dChQ3XPPfdo7dq1OZwJ7uTIRr7etuaF9TjewRffOwAAoOhyKJGaP3++xo8fr8WLF8tiseiHH35Q8eJZ72qxWNySSKWkpKh37956++239cILL9jak5KS9O677+p///uf2rdvL0l6//33VadOHa1fv14tWrRweSzInS9t5Mt6HO/iS+8dAAAAhxKpWrVq6ZNPPpEk+fn5acWKFQoLC3NrYFcaMmSIunbtqo4dO9olUlu2bNGlS5fUsWNHW1vt2rVVpUoVrVu3LsdE6sKFC7pw4YLtdnJysvuCL2IyN/JNil1vP0XLr5isDVt41YhCbutx6kya66Goii5feu8AAAA4tEbqShkZGQWaRH3yySfaunWrJk+enKUvISFBJUuWVGhoqF17xYoVlZCQkOM5J0+eLKvVavupXDn3v4R7g9Sj8Tq9+WefWCcSM3KqrA3tk1hrwxaKGTnVQxFlxXoc7+QL7x0AAAApn+XP9+/frxkzZmjPnj2SpLp162r48OGqXr26S4M7fPiwhg8fruXLlysgIMBl5x0zZoyeeuop2+3k5GSvTaZ8cfqZL2zky3oc13HlGjNfeO8AAABI+Uikli5dqrvuuksNGzZU69atJUlr165VvXr19O233+rWW291WXBbtmzRiRMn1LhxY1tbenq61qxZozfffFNLly7VxYsXlZiYaDcqdfz4cYWHh+d4Xn9/f/n7+7ssTnfy5eln+d3ItyCwHufauTPJ9+b3DgAAgJSPRGr06NF68sknNWXKlCzto0aNcmki1aFDB/322292bf3791ft2rU1atQoVa5cWSVKlNCKFSt07733SpL27t2rQ4cOqWXLli6Lw1Ns08+udsX0M75s5g/rca6dLyf5AAAA18rpRGrPnj367LPPsrQ//PDDmjFjhitisgkODtb1119v11a6dGmVK1fO1j5gwAA99dRTKlu2rEJCQvTEE0+oZcuWhaJiH9PP3Ctm5FTFTbtqRIX1OA4hyQcAAEWd04lUhQoVFBsbq5iYGLv22NjYAi1Ckem1116Tn5+f7r33Xl24cEGdO3fW7NmzCzwOd2D6mXuxHif/SPIBAEBR53QiNXDgQA0aNEh//vmnWrVqJenyGqmpU6faFXBwl9WrV9vdDggI0KxZszRr1iy3P3ZBY/pZVu7YPJf1OM4jyQcAAEWd04nU2LFjFRwcrFdeeUVjxoyRJEVGRmrChAkaNmyYywMs6ph+dpkvVi8szEjyAQBAUWcxxpj83vnMmTOSLq9l8mXJycmyWq1KSkpSSEiIp8PJVlGffrZn3OAcv7RT2MAz0lKSsib5JLcAAMDHOZob5GsfqUy+nkD5kqI8/YzCBt6JNWYAAKAou6ZECigInips4I71WIVRUU7yAQBA0UUiBa/nrsIGOSVKrMcCAABAXkik4PVcXdggr0SJjWYBAACQFz9nDr506ZI6dOiguLg4d8UDZCtm5FRZG9pvspzf6oW5JUq29VhXJmyS3XosAAAAwKkRqRIlSmjHjh3uigXIkasKG+RVuCJ555Zc789GswAAAJCcHJGSpAcffFDvvvuuO2IB8hRYKUplmrbNdzKTV+EKKffdANhoFgAAAFI+1kilpaXpvffe048//qgmTZqodOnSdv2vvvqqy4IDXC2vwhUh19/IRrMAAADIk9OJ1M6dO9W4cWNJ0h9//GHXZ7FYXBMV4CaOFK6IGTk160az+VyPBQAAgMLJYozJfS5TEeDo7sUoHNJSkrImStmUN2ejWQAAgKLH0dwg34nUvn37tH//frVr106BgYEyxvjsiBSJVNFEogQAAICrOZobOD217+TJk+rRo4dWrVoli8WiuLg4VatWTQMGDFCZMmX0yiuvXFPgQEEJrBRFAgUAAIB8cbpq35NPPqkSJUro0KFDKlWqlK29Z8+eWrJkiUuDAwAAAABv5PSI1LJly7R06VJdd911du0xMTE6eJDNSgEAAAAUfk6PSJ09e9ZuJCrTqVOn5O/v75KgAAAAAMCbOZ1ItW3bVgsWLLDdtlgsysjI0LRp03TLLbe4NDgAAAAA8EZOT+2bNm2aOnTooM2bN+vixYsaOXKkdu3apVOnTmnt2rV5nwAAAAAAfJzTI1LXX3+9/vjjD7Vp00bdunXT2bNndc8992jbtm2qXr26O2IEAAAAAK/ChrxiHykAAAAAl7ltHylJOn36tN59913t2bNHklS3bl31799fZcuWzV+0AAAAAOBDnJ7at2bNGlWtWlUzZ87U6dOndfr0ac2cOVPR0dFas2aNO2IEAAAAAK/i9NS++vXrq2XLlpozZ46KFSsmSUpPT9fjjz+uX3/9Vb/99ptbAnUnpvYBAAAAkBzPDZwekdq3b5+efvppWxIlScWKFdNTTz2lffv25S9aAAAAAPAhTidSjRs3tq2NutKePXvUoEEDlwQFAAAAAN7MoWITO3bssP172LBhGj58uPbt26cWLVpIktavX69Zs2ZpypQp7okSAAAAALyIQ2uk/Pz8ZLFYlNehFotF6enpLguuoLBGCgAAAIDk4vLnBw4ccFlgAAAAAODrHEqkoqKi3B0HAAAAAPiMfG3I+9dff+mXX37RiRMnlJGRYdc3bNgwlwQGoGhJPRqv88cOKyCiigIr8ccbAADg3ZxOpObPn69HH31UJUuWVLly5WSxWGx9FouFRAqAU9LOJClu+iglbV1ra7M2bq2YkVNVPMjqwcgAAABy5vSGvJUrV9bgwYM1ZswY+fk5XT3dK1FsAvCcPeMGKyl2vZRxRaEav2KyNmyhOpPmei4wAABQJLltQ95z586pV69ehSaJAuA5qUfjL49EZVxV7TMjXUlb1yr16EHPBAYAAJAHp7OhAQMG6PPPP3dHLHCh1KPxOr35Z76IFlG+cv3PHzucR/+hAooEAADAOU6vkZo8ebLuuOMOLVmyRPXr11eJEiXs+l999VWXBQfnsd6kaPO16x8QUTmP/ioFFAkAAIBz8pVILV26VLVq1ZKkLMUm4Flx00ddXm9yhaTY9YqbNor1JkWAr13/wEpVZW3cOsc1UlTvAwAA3srpROqVV17Re++9p379+rkhHFwL23qTq12x3oQvpoWXr17/mJFTFTftqlG0hi0UM3KqB6MCAADIndOJlL+/v1q3bu2OWHCNHFlv4o1fpOEavnr9iwdZVWfSXKUePajzxw6xjxQAAPAJThebGD58uN544w13xIJrxHqTos3Xr39gpSiVadqWJAoAAPgEp0ekNm7cqJUrV2rx4sWqV69elmITX331lcuCQ95Sj8br/LHD//+v+K5Zb2J/zsL1pbYwPzfWGwEAABQcpxOp0NBQ3XPPPe6IBU7IqTpb9OP/1oHZL+ZrvYmvVXxzRmF+bldivREAAEDBsBhjjKeD8DRHdy/2JnvGDc5x5CG/603yOqcvK8zPLTusNwIAAMgfR3MDp0ek4HmOVmdz5gu0r1Z8c0Rhfm45cfb6AwAAwDlOJ1LR0dG57hf1559/XlNAyJs7qrP5asU3RxTm5wYAAADPcDqRGjFihN3tS5cuadu2bVqyZImeffZZV8WFXLijOpuvV3zLTWF+bgAAAPAMpxOp4cOHZ9s+a9Ysbd68+ZoDQt7cUZ2tMFd8K8zPDQAAAJ7h9D5SObntttv05Zdfuup0yEPMyKmyNmxh13at1dnccc6cpB6N1+nNPyv16EGXnzs7BfncAAAAUPi5rGrftGnTNHv2bMXHx7vidAXKF6v2ZXJHdTZ3VnzzdBlyqtkBAAAgN26r2teoUSO7YhPGGCUkJOjvv//W7Nmz8xct8s091dncVxE/bvqoy1PsrpAUu15x00YVSBlyqtkBAADAFZxOpLp3725328/PTxUqVNDNN9+s2rVruyouOCj1aLzOHzvskhEWd48WFcUy5AAAACicnE6kxo8f74444CR3JD3uHi2iDDkAAAAKC5cVm0DByi3pyQ/baNGVVe0ku9Gia0UZcgAAAFytoIuQuYrDI1J+fn65bsQrSRaLRWlpadccFHLnjilyBTFa5Iky5K6c+ggAAADX8XQRsmvlcCK1cOHCHPvWrVunmTNnKiMjwyVBIXfuSHoKarQoZuRUxU276gPjhjLkvv7BBAAAKOw8XYTsWjmcSHXr1i1L2969ezV69Gh9++236t27tyZNmuTS4JA9Vyc9maM2QXUbK+X37W4dLSoeZFWdSXPdXobc1z+YAAAAhVlhKELmdLEJSfrrr780fvx4ffDBB+rcubNiY2N1/fXXuzo25MBVU+SyG7UpHhKqtORE2213bVrrzjLkheGDCQAAUJgVhiJkTiVSSUlJeumll/TGG2+oYcOGWrFihdq2beuu2JALV0yRy27UJi3ljILrNlZkj0d8dl1RYfhgAgAAFGaFoQiZw4nUtGnTNHXqVIWHh+vjjz/OdqofCs61TpHLbdTmzO6tPptESYXjgwkAAFCYeaIImas5XP589OjROn/+vGrUqKEPPvhA99xzT7Y/rjR58mTdeOONCg4OVlhYmLp37669e/faHXP+/HkNGTJE5cqVU1BQkO69914dP37cpXF4s8BKUSrTtK1bqvT5qswPpvyK2Xf4FZO1cWuf+GACAAAUdjEjp8rasIVdm7uWlbiDwyNSffr0ybP8uav99NNPGjJkiG688UalpaXpueeeU6dOnbR7926VLl1akvTkk0/qu+++0+effy6r1aqhQ4fqnnvu0dq12Yy2wCavUZuLJ0/49FqigqoOCAAAgPwpqCJk7mIxxhhPB+Gov//+W2FhYfrpp5/Url07JSUlqUKFCvrf//6n++67T5L0+++/q06dOlq3bp1atGiRxxkvS05OltVqVVJSkkJCQtz5FLzKnnGDsw6nXsXXS4b76gcTAAAAnuFobuDw1D5vkJSUJEkqW7asJGnLli26dOmSOnbsaDumdu3aqlKlitatW5fjeS5cuKDk5GS7n6Iou+HUq2WWDPdV+Z36CAAAAOTGZxKpjIwMjRgxQq1bt7aVWk9ISFDJkiUVGhpqd2zFihWVkJCQ47kmT54sq9Vq+6lcOfdpboVV5nBqg7cWK3ro+OwPuqJkOAAAAIDLfCaRGjJkiHbu3KlPPvnkms81ZswYJSUl2X4OH8698EJhF1gpSiXLV8z1GF8uPoH/k3o0Xqc3/0xiDAAAcI3ytSFvQRs6dKgWL16sNWvW6LrrrrO1h4eH6+LFi0pMTLQblTp+/LjCw8NzPJ+/v7/8/f3dGbLPoWR44Zbd5su+vv4NAADAk7x6RMoYo6FDh2rhwoVauXKloqOj7fqbNGmiEiVKaMWKFba2vXv36tChQ2rZsmVBh+vTKBleuGW3+bKvr38DAADwJK9OpIYMGaL//ve/+t///qfg4GAlJCQoISFBqampkiSr1aoBAwboqaee0qpVq7Rlyxb1799fLVu2dLhiH/6Pr9fyR/Zsmy9fXZ2R9W8AAAD55tVT++bMmSNJuvnmm+3a33//ffXr10+S9Nprr8nPz0/33nuvLly4oM6dO2v27NkFHGnhkFMt/9Sj8Trz+w5KiPsoRzZf5roCAAA4x6sTKUe2uAoICNCsWbM0a9asAoioaAisFKXASlFKO5N0ea8p1tX4NNa/AQAAuJ5XT+2DZ7GupnBg/RsAAIDrkUghW6yrKVxY/wYAAOBaXj21D57DuprCJaf1bwAAAMgfEilki3U1hVPm+jcAAABcG6b2IVusqwEAAAByRiKFHLGuBgAAAMgeU/uQI9bVAAAAANkjkfJxqUfjdf7YYbcmOayrAQAAAOyRSPmotDNJl/d5YrNcAAAAoMCxRspHsVkuAAAA4DkkUj6IzXIBAAAAzyKR8kGObJYLAAAAwH1IpHwQm+UCAAAAnkUi5YPYLBcAAADwLBIpH8VmuQAAAIDnUP7cR7FZLgAAAOA5JFI+IqeNd9ksFwAAACh4JFJerqhtvJtTwggAAAB4ExIpL5fbxrt1Js31UFSuV9QSRgAAAPg2ik14saK08W5uCSMAAADgbUikvFhR2Xi3KCWMAAAAKBxIpLxYUdl4t6gkjAAAACg8SKS8WEFvvJt6NF6nN/9c4CNARSVhBAAAQOFBsQkvFzNyquKmXVWEwcUb73q60ENmwpgUu95+ep9fMVkbtqB6HwAAALyOxRhjPB2EpyUnJ8tqtSopKUkhISGeDidb7tx4d8+4wTkmMQVVGTAtJSlrwkjVPgAAABQwR3MDRqR8hLs23rUVerjaFYUeCmJEqHiQVXUmzXVrwggAAAC4ColUEedIoYeCTGjclTACAAAArkQi5aVSj8br/LHDbh2ZST0ar4snj+d6DIUeAAAAgKxIpLxMQRR+yO4xsiigQg8FkTACAAAArkYi5WXipo+6XPjhCkmx6xU3bZTLCj9k9xhXc3VlwKt5ulIgAAAAcC1IpLyIuws/pB6NV/Jvm3MdiYp+YoJCrm/q9tGhgkgYAQAAAHchkfIi7ir84NBUvv+vZLmwApnO5w2VAgEAAID88vN0APg/ARGV8+jPX+EHR6byXetjOMORhBEAAADwZiRSXiSwUlVZG7eW/IrZd/gVk7Vx63yN0thGf67cbDc71/AYznJXwggAAAAUFBIpLxMzcqqsDVvYtV1L4Ye8Rn9c8RjOckfCCAAAABQk1kh5meJBVtWZNFepRw/q/LFD11wWPK/Rn4IqLnG1mJFTFTftqqp9BZjMAQAAANeCRMpLBVaKcklykzn6kxS73n56n18xBde+QSXLhV3zY+SHqxNGAAAAoCBZjDHG00F4WnJysqxWq5KSkhQSEuLpcFwuLSUpy+hP8ZBQpSUn2m6zhxMAAADgeG5AIqXCn0hlyhz9OfrZO0r5fXuWESprwxbs4QQAAIAizdHcgGITRUhgpSgFRFRWyu6tWav4XbGHEwAAAIDckUgVMezhBAAAAFw7Eqkihj2cAAAAgGtHIlXEsIcTAAAAcO1IpIogV2/6CwAAABQ17CNVBLGHEwAAAHBtSKSKMFdt+gsAAAAUNUztAwAAAAAnkUgBAAAAgJNIpAAAAADASSRSAAAAAOAkEikAAAAAcBKJFAAAAAA4iUQKAAAAAJxEIgUAAAAATiKRAgAAAAAnkUgBAAAAgJNIpAAAAADAScU9HQAAAIVN6tF4nT92WAERVRRYKcrT4QAA3IBECgAAF0k7k6S46aOUtHWtrc3auLViRk5V8SCrByMDALhaoZnaN2vWLFWtWlUBAQFq3ry5Nm7c6OmQAABFTNz0UUqKXW/XlhS7XnHTRnkoIgCAuxSKROrTTz/VU089pfHjx2vr1q1q0KCBOnfurBMnTng6NABAEZF6NP7ySFRGun1HRrqStq5V6tGDngkMAOAWhSKRevXVVzVw4ED1799fdevW1dy5c1WqVCm99957ng4NAFBEnD92OI/+QwUUCQCgIPh8InXx4kVt2bJFHTt2tLX5+fmpY8eOWrduXbb3uXDhgpKTk+1+AAC4FgERlfPor1JAkQAACoLPJ1L//POP0tPTVbFiRbv2ihUrKiEhIdv7TJ48WVar1fZTuXLu//kBAJCXwEpVZW3cWvIrZt/hV0zWxq2p3gcAhYzPJ1L5MWbMGCUlJdl+Dh/OfToGAACOiBk5VdaGLezarA1bKGbkVA9FBABwF58vf16+fHkVK1ZMx48ft2s/fvy4wsPDs72Pv7+//P39CyI8AEARUjzIqjqT5ir16EGdP3aIfaQAoBDz+RGpkiVLqkmTJlqxYoWtLSMjQytWrFDLli09GBkAoKgKrBSlMk3bkkQBQCHm8yNSkvTUU0+pb9++atq0qZo1a6YZM2bo7Nmz6t+/v6dDAwAAAFAIFYpEqmfPnvr77781btw4JSQkqGHDhlqyZEmWAhQAAAAA4AoWY4zxdBCelpycLKvVqqSkJIWEhHg6HAAAAAAe4mhu4PNrpAAAAACgoJFIAQAAAICTSKQAAAAAwEkkUgAAAADgJBIpAAAAAHASiRQAAAAAOIlECgAAAACcRCIFAAAAAE4ikQIAAAAAJ5FIAQAAAICTSKQAAAAAwEnFPR2ANzDGSJKSk5M9HAkAAAAAT8rMCTJzhJyQSEk6c+aMJKly5coejgQAAACANzhz5oysVmuO/RaTV6pVBGRkZOivv/5ScHCwLBaLWx4jOTlZlStX1uHDhxUSEuKWx0D+cG28F9fGe3FtvBfXxntxbbwX18Z7eeLaGGN05swZRUZGys8v55VQjEhJ8vPz03XXXVcgjxUSEsIH1EtxbbwX18Z7cW28F9fGe3FtvBfXxnsV9LXJbSQqE8UmAAAAAMBJJFIAAAAA4CQSqQLi7++v8ePHy9/f39Oh4CpcG+/FtfFeXBvvxbXxXlwb78W18V7efG0oNgEAAAAATmJECgAAAACcRCIFAAAAAE4ikQIAAAAAJ5FIAQAAAICTSKQKwKxZs1S1alUFBASoefPm2rhxo6dDKvQmTJggi8Vi91O7dm1b//nz5zVkyBCVK1dOQUFBuvfee3X8+HG7cxw6dEhdu3ZVqVKlFBYWpmeffVZpaWkF/VR83po1a3TnnXcqMjJSFotFixYtsus3xmjcuHGKiIhQYGCgOnbsqLi4OLtjTp06pd69eyskJEShoaEaMGCAUlJS7I7ZsWOH2rZtq4CAAFWuXFnTpk1z91PzeXldm379+mX5HHXp0sXuGK6N602ePFk33nijgoODFRYWpu7du2vv3r12x7jqd9jq1avVuHFj+fv7q0aNGpo/f767n55Pc+Ta3HzzzVk+N4MHD7Y7hmvjenPmzNENN9xg27S1ZcuW+uGHH2z9fGY8J69r49OfGQO3+uSTT0zJkiXNe++9Z3bt2mUGDhxoQkNDzfHjxz0dWqE2fvx4U69ePXPs2DHbz99//23rHzx4sKlcubJZsWKF2bx5s2nRooVp1aqVrT8tLc1cf/31pmPHjmbbtm3m+++/N+XLlzdjxozxxNPxad9//73597//bb766isjySxcuNCuf8qUKcZqtZpFixaZ7du3m7vuustER0eb1NRU2zFdunQxDRo0MOvXrzc///yzqVGjhnnggQds/UlJSaZixYqmd+/eZufOnebjjz82gYGB5q233iqop+mT8ro2ffv2NV26dLH7HJ06dcruGK6N63Xu3Nm8//77ZufOnSY2NtbcfvvtpkqVKiYlJcV2jCt+h/3555+mVKlS5qmnnjK7d+82b7zxhilWrJhZsmRJgT5fX+LItbnpppvMwIED7T43SUlJtn6ujXt888035rvvvjN//PGH2bt3r3nuuedMiRIlzM6dO40xfGY8Ka9r48ufGRIpN2vWrJkZMmSI7XZ6erqJjIw0kydP9mBUhd/48eNNgwYNsu1LTEw0JUqUMJ9//rmtbc+ePUaSWbdunTHm8hdMPz8/k5CQYDtmzpw5JiQkxFy4cMGtsRdmV39Zz8jIMOHh4Wb69Om2tsTEROPv728+/vhjY4wxu3fvNpLMpk2bbMf88MMPxmKxmKNHjxpjjJk9e7YpU6aM3bUZNWqUqVWrlpufUeGRUyLVrVu3HO/DtSkYJ06cMJLMTz/9ZIxx3e+wkSNHmnr16tk9Vs+ePU3nzp3d/ZQKjauvjTGXvxQOHz48x/twbQpOmTJlzDvvvMNnxgtlXhtjfPszw9Q+N7p48aK2bNmijh072tr8/PzUsWNHrVu3zoORFQ1xcXGKjIxUtWrV1Lt3bx06dEiStGXLFl26dMnuutSuXVtVqlSxXZd169apfv36qlixou2Yzp07Kzk5Wbt27SrYJ1KIHThwQAkJCXbXwmq1qnnz5nbXIjQ0VE2bNrUd07FjR/n5+WnDhg22Y9q1a6eSJUvajuncubP27t2r06dPF9CzKZxWr16tsLAw1apVS4899phOnjxp6+PaFIykpCRJUtmyZSW57nfYunXr7M6ReQz/Pznu6muT6aOPPlL58uV1/fXXa8yYMTp37pytj2vjfunp6frkk0909uxZtWzZks+MF7n62mTy1c9McbeevYj7559/lJ6ebnfhJalixYr6/fffPRRV0dC8eXPNnz9ftWrV0rFjxzRx4kS1bdtWO3fuVEJCgkqWLKnQ0FC7+1SsWFEJCQmSpISEhGyvW2YfXCPztczutb7yWoSFhdn1Fy9eXGXLlrU7Jjo6Oss5MvvKlCnjlvgLuy5duuiee+5RdHS09u/fr+eee0633Xab1q1bp2LFinFtCkBGRoZGjBih1q1b6/rrr5ckl/0Oy+mY5ORkpaamKjAw0B1PqdDI7tpI0r/+9S9FRUUpMjJSO3bs0KhRo7R371599dVXkrg27vTbb7+pZcuWOn/+vIKCgrRw4ULVrVtXsbGxfGY8LKdrI/n2Z4ZECoXSbbfdZvv3DTfcoObNmysqKkqfffYZv+gAB/Xq1cv27/r16+uGG25Q9erVtXr1anXo0MGDkRUdQ4YM0c6dO/XLL794OhRcJadrM2jQINu/69evr4iICHXo0EH79+9X9erVCzrMIqVWrVqKjY1VUlKSvvjiC/Xt21c//fSTp8OCcr42devW9enPDFP73Kh8+fIqVqxYlqowx48fV3h4uIeiKppCQ0NVs2ZN7du3T+Hh4bp48aISExPtjrnyuoSHh2d73TL74BqZr2Vun5Hw8HCdOHHCrj8tLU2nTp3iehWwatWqqXz58tq3b58kro27DR06VIsXL9aqVat03XXX2dpd9Tssp2NCQkL4g1Mecro22WnevLkk2X1uuDbuUbJkSdWoUUNNmjTR5MmT1aBBA73++ut8ZrxATtcmO770mSGRcqOSJUuqSZMmWrFiha0tIyNDK1assJsXCvdLSUnR/v37FRERoSZNmqhEiRJ212Xv3r06dOiQ7bq0bNlSv/32m92XxOXLlyskJMQ2FI1rFx0drfDwcLtrkZycrA0bNthdi8TERG3ZssV2zMqVK5WRkWH7ZduyZUutWbNGly5dsh2zfPly1apVi6ljLnTkyBGdPHlSERERkrg27mKM0dChQ7Vw4UKtXLkyy9RIV/0Oa9mypd05Mo/h/6ec5XVtshMbGytJdp8brk3ByMjI0IULF/jMeKHMa5Mdn/rMuLWUBcwnn3xi/P39zfz5883u3bvNoEGDTGhoqF3lEbje008/bVavXm0OHDhg1q5dazp27GjKly9vTpw4YYy5XAa1SpUqZuXKlWbz5s2mZcuWpmXLlrb7Z5ba7NSpk4mNjTVLliwxFSpUoPx5Ppw5c8Zs27bNbNu2zUgyr776qtm2bZs5ePCgMeZy+fPQ0FDz9ddfmx07dphu3bplW/68UaNGZsOGDeaXX34xMTExdiW2ExMTTcWKFc1DDz1kdu7caT755BNTqlQpSmznIbdrc+bMGfPMM8+YdevWmQMHDpgff/zRNG7c2MTExJjz58/bzsG1cb3HHnvMWK1Ws3r1artywOfOnbMd44rfYZnlgp999lmzZ88eM2vWLEo55yGva7Nv3z4zadIks3nzZnPgwAHz9ddfm2rVqpl27drZzsG1cY/Ro0ebn376yRw4cMDs2LHDjB492lgsFrNs2TJjDJ8ZT8rt2vj6Z4ZEqgC88cYbpkqVKqZkyZKmWbNmZv369Z4OqdDr2bOniYiIMCVLljSVKlUyPXv2NPv27bP1p6ammscff9yUKVPGlCpVytx9993m2LFjdueIj483t912mwkMDDTly5c3Tz/9tLl06VJBPxWft2rVKiMpy0/fvn2NMZdLoI8dO9ZUrFjR+Pv7mw4dOpi9e/fanePkyZPmgQceMEFBQSYkJMT079/fnDlzxu6Y7du3mzZt2hh/f39TqVIlM2XKlIJ6ij4rt2tz7tw506lTJ1OhQgVTokQJExUVZQYOHJjlj0BcG9fL7ppIMu+//77tGFf9Dlu1apVp2LChKVmypKlWrZrdYyCrvK7NoUOHTLt27UzZsmWNv7+/qVGjhnn22Wft9sQxhmvjDg8//LCJiooyJUuWNBUqVDAdOnSwJVHG8JnxpNyuja9/ZizGGOPeMS8AAAAAKFxYIwUAAAAATiKRAgAAAAAnkUgBAAAAgJNIpAAAAADASSRSAAAAAOAkEikAAAAAcBKJFAAAAAA4iUQKAAAAAJxEIgUAhZjFYtGiRYs8HUaBu3jxomrUqKFff/3V06GgALRo0UJffvmlp8MAUMSQSAGAC1gsllx/JkyY4JG4jh07pttuu02SFB8fL4vFotjY2Fzvs3r1alksFiUmJmbpq1q1qmbMmOH6QF1s7ty5io6OVqtWrTwdip0JEyaoYcOGng4jixdffFGtWrVSqVKlFBoamu0xhw4dUteuXVWqVCmFhYXp2WefVVpaWq7nPXXqlHr37q2QkBCFhoZqwIABSklJsTtmx44datu2rQICAlS5cmVNmzYty3k+//xz1a5dWwEBAapfv76+//57u/7nn39eo0ePVkZGhnNPHACuAYkUALjAsWPHbD8zZsxQSEiIXdszzzxjO9YYk+cXUFcJDw+Xv79/gTyWtzDG6M0339SAAQM8HYpXye19d/HiRd1///167LHHsu1PT09X165ddfHiRf3666/64IMPNH/+fI0bNy7Xx+zdu7d27dql5cuXa/HixVqzZo0GDRpk609OTlanTp0UFRWlLVu2aPr06ZowYYLmzZtnO+bXX3/VAw88oAEDBmjbtm3q3r27unfvrp07d9qOue2223TmzBn98MMPzrwkAHBtDADApd5//31jtVptt1etWmUkme+//940btzYlChRwqxatcrs27fP3HXXXSYsLMyULl3aNG3a1CxfvtzuXFFRUebFF180/fv3N0FBQaZy5crmrbfesvVfuHDBDBkyxISHhxt/f39TpUoV89JLL9n6JZmFCxfa/n3lz0033ZRt/Jnxnj59OktfVFSUee211xx67FdeecVcf/31plSpUua6664zjz32mDlz5ozd+ebNm2euu+46ExgYaLp3725eeeUVu9fOGGMWLVpkGjVqZPz9/U10dLSZMGGCuXTpUg6vvjGbNm0yfn5+Jjk52a798OHDplevXqZMmTKmVKlSpkmTJmb9+vW2/tmzZ5tq1aqZEiVKmJo1a5oFCxbY+g4cOGAkmW3bttnaTp8+bSSZVatW2b1uP/74o2nSpIkJDAw0LVu2NL///rsx5vL74upr8P7772f7HPr27Wu6detmJkyYYMqXL2+Cg4PNo48+ai5cuGA7Jj093bz00kumatWqJiAgwNxwww3m888/t/Xn9L7LzdXv3Uzff/+98fPzMwkJCba2OXPmmJCQELuYrrR7924jyWzatMnW9sMPPxiLxWKOHj1qjLn8mpcpU8buHKNGjTK1atWy3e7Ro4fp2rWr3bmbN29uHn30Ubu2/v37mwcffDDX5wcArsSIFAAUkNGjR2vKlCnas2ePbrjhBqWkpOj222/XihUrtG3bNnXp0kV33nmnDh06ZHe/V155RU2bNtW2bdv0+OOP67HHHtPevXslSTNnztQ333yjzz77THv37tVHH32kqlWrZvv4GzdulCT9+OOPOnbsmL766qtrej55Pbafn59mzpypXbt26YMPPtDKlSs1cuRIW//atWs1ePBgDR8+XLGxsbr11lv14osv2j3Gzz//rD59+mj48OHavXu33nrrLc2fPz/LcVffp2bNmgoODra1paSk6KabbtLRo0f1zTffaPv27Ro5cqRtKtjChQs1fPhwPf3009q5c6ceffRR9e/fX6tWrXL6dfn3v/+tV155RZs3b1bx4sX18MMPS5J69uypp59+WvXq1bONVPbs2TPH86xYsUJ79uzR6tWr9fHHH+urr77SxIkTbf2TJ0/WggULNHfuXO3atUtPPvmkHnzwQf30009257n6fZcf69atU/369VWxYkVbW+fOnZWcnKxdu3bleJ/Q0FA1bdrU1taxY0f5+flpw4YNtmPatWunkiVL2p137969On36tO2Yjh072p27c+fOWrdunV1bs2bN9PPPP+fr+QFAvng6kwOAwianEalFixbled969eqZN954w3Y7KirK7q/sGRkZJiwszMyZM8cYY8wTTzxh2rdvbzIyMrI9n64YkcpuVCU7jo5I5fXYV/v8889NuXLlbLd79uyZZaShd+/edq9dhw4d7Ea5jDHmww8/NBERETk+zvDhw0379u3t2t566y0THBxsTp48me19WrVqZQYOHGjXdv/995vbb7/dGOP8iFSm7777zkgyqampxhhjxo8fbxo0aJBj7Jn69u1rypYta86ePWtrmzNnjgkKCjLp6enm/PnzplSpUubXX3+1u9+AAQPMAw88YBePI++7TDmNSA0cONB06tTJru3s2bO2Ea/svPjii6ZmzZpZ2itUqGBmz55tjDHm1ltvNYMGDbLr37Vrl5Fkdu/ebYwxpkSJEuZ///uf3TGzZs0yYWFhdm1ff/218fPzM+np6bk/SQBwEUakAKCAXPmXeenyKMkzzzyjOnXqKDQ0VEFBQdqzZ0+WEakrRxEsFovCw8N14sQJSVK/fv0UGxurWrVqadiwYVq2bJn7n8j/l9dj//jjj+rQoYMqVaqk4OBgPfTQQzp58qTOnTsnSdq7d6+aNWtmd5+rb2/fvl2TJk1SUFCQ7WfgwIE6duyY7TxXS01NVUBAgF1bbGysGjVqpLJly2Z7nz179qh169Z2ba1bt9aePXvyfiGucuX1ioiIkCTb9XJGgwYNVKpUKdvtli1bKiUlRYcPH9a+fft07tw53XrrrXavzYIFC7R//36781z9viusAgMDlZGRoQsXLng6FABFRHFPBwAARUXp0qXtbj/zzDNavny5Xn75ZdWoUUOBgYG67777dPHiRbvjSpQoYXfbYrHYpqQ1btxYBw4c0A8//KAff/xRPXr0UMeOHfXFF1/kO86QkBBJUlJSUpYKbomJibJarXk+dnx8vO644w499thjevHFF1W2bFn98ssvGjBggC5evGiXIOQmJSVFEydO1D333JOl7+pkKVP58uX122+/2bUFBgY69Hg58fO7/HdHY4yt7dKlS9kee+X1slgskuTyanKZle++++47VapUya7v6uIiV7/v8iM8PNw2NTTT8ePHbX053efqBDItLU2nTp2y3Sc8PNx2npzOm9MxVz/uqVOnVLp06Wu+1gDgKEakAMBD1q5dq379+unuu+9W/fr1FR4ervj4eKfPExISop49e+rtt9/Wp59+qi+//FKnTp3KclzmOpT09PRczxcTEyM/Pz9t2bLFrv3PP/9UUlKSatasmedjb9myRRkZGXrllVfUokUL1axZU3/99Zfd+WrVqqVNmzbZtV19u3Hjxtq7d69q1KiR5Sczublao0aN9Pvvv9slPTfccINiY2OzfV0kqU6dOlq7dq1d29q1a1W3bl1JUoUKFSRdrs6YKa8y8tkpWbJknq9/pu3btys1NdV2e/369QoKClLlypVVt25d+fv769ChQ1lel8qVKzsdV15atmyp3377zS4xWr58uUJCQmyvUXb3SUxMtHsfrVy5UhkZGWrevLntmDVr1tglpcuXL1etWrVUpkwZ2zErVqywO/fy5cvVsmVLu7adO3eqUaNG1/ZEAcAJjEgBgIfExMToq6++0p133imLxaKxY8c6PXLx6quvKiIiQo0aNZKfn58+//xzhYeHZ7sXUFhYmAIDA7VkyRJdd911CggIsI0uXSk4OFiPPPKInn76aRUvXlz169fX4cOHNWrUKLVo0cK2N1Nuj12jRg1dunRJb7zxhu68806tXbtWc+fOtXucJ554Qu3atdOrr76qO++8UytXrtQPP/xgG8WRpHHjxumOO+5QlSpVdN9998nPz0/bt2/Xzp079cILL2T7mtxyyy1KSUnRrl27dP3110uSHnjgAb300kvq3r27Jk+erIiICG3btk2RkZFq2bKlnn32WfXo0UONGjVSx44d9e233+qrr77Sjz/+KOnyiFaLFi00ZcoURUdH68SJE3r++eedulbS5X24Dhw4oNjYWF133XUKDg7OsTz9xYsXNWDAAD3//POKj4/X+PHjNXToUPn5+Sk4OFjPPPOMnnzySWVkZKhNmzZKSkrS2rVrFRISor59+zoV16FDh3Tq1CkdOnRI6enptiSxRo0aCgoKUqdOnVS3bl099NBDmjZtmhISEvT8889ryJAhOcZfp04ddenSRQMHDtTcuXN16dIlDR06VL169VJkZKQk6V//+pcmTpyoAQMGaNSoUdq5c6def/11vfbaa7bzDB8+XDfddJNeeeUVde3aVZ988ok2b95sVyJdulxkpFOnTk49bwC4Jp5epAUAhU1OxSauLt5w4MABc8stt5jAwEBTuXJl8+abb5qbbrrJDB8+3HbMlcUdMjVo0MCMHz/eGHO5fHjDhg1N6dKlTUhIiOnQoYPZunWr7VhdUWzCGGPefvttU7lyZePn55dj+XNjjElNTTXjx483tWvXNoGBgSY6OtoMGjTI/P3337Zj8nrsV1991URERJjAwEDTuXNns2DBgiyvw7x580ylSpVs5c9feOEFEx4ebhfLkiVLTKtWrUxgYKAJCQkxzZo1M/PmzcsxdmMul8wePXq0XVt8fLy59957TUhIiClVqpRp2rSp2bBhg60/t/Lnxlwu592yZUsTGBhoGjZsaJYtW5ZtsYkrn9+2bduMJHPgwAFjjDHnz5839957rwkNDXWo/Pm4ceNMuXLlTFBQkBk4cKA5f/687ZiMjAwzY8YMU6tWLVOiRAlToUIF07lzZ/PTTz/lGE9O+vbtm6U0+5XPLfP1u+2220xgYKApX768efrpp+3K0GcW5LjyPidPnjQPPPCACQoKMiEhIaZ///5ZSuBv377dtGnTxvj7+5tKlSqZKVOmZInvs88+MzVr1jQlS5Y09erVM999951d/5EjR0yJEiXM4cOH83yuAOAqFmOumPsAAIAHDRw4UL///vs1l7HesWOHbr31Vu3fv19BQUEuiq7g9OvXT4mJiVq0aJGnQ3HYqlWrdM899+jPP/+0TcsrKKNGjdLp06ezjFIBgDuxRgoA4DEvv/yytm/frn379umNN97QBx984PS0tOzccMMNmjp1qg4cOOCCKOGI77//Xs8991yBJ1HS5Wmr//nPfwr8cQEUbYxIAQA8pkePHlq9erXOnDmjatWq6YknntDgwYM9HZbH+eKIFAAUNSRSAAAAAOAkpvYBAAAAgJNIpAAAAADASSRSAAAAAOAkEikAAAAAcBKJFAAAAAA4iUQKAAAAAJxEIgUAAAAATiKRAgAAAAAn/T92SFklqxz5nwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot that shows the relationship between transit usage and number of accidents in California cities\n",
    "accident_scatter = clean_df.plot.scatter(x='transit_usage', y='accident_rate', color='#c74b2c')\n",
    "accident_scatter.set_title('Number of Accidents vs. Transit Usage (per 10,000)')\n",
    "accident_scatter.set_xlabel('Transit Usage (count per 10,000)')\n",
    "accident_scatter.set_ylabel('Number of Accidents (count per 10,000)')\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "What we noticed:\n",
    "1. The two variables seem to have an overall moderate, positive, linear distribution. There seems to be one data cluster closer to the origin, and some outliers for both variables (relatively extreme accident rates and public transportation usage rates). \n",
    "2. The most prominent outlier belongs to the city of San Francisco. The very low accident rate outlier belongs to Alameda. There are two more outliers in between those points, Oakland and Berkeley. It’s interesting to note that all of those points are cities in the San Francisco Bay Area, which is an area of moderate to high transport use due to BART and CalTrain systems. Also, locals in San Francisco tend to use the bus or trolley to get around. There are also two outliers that are prominent due to their high accident rates: Corona and Sacramento. Those outliers might be explained by the dense population and busy streets. Overall, there are obvious outliers on the graph, but they make logical sense when taking into consideration each city.  \n",
    "3. Based on the graph and analysis of its distributions, it seems like there is a moderate positive linear relationship between “accident_rate” and “transit_usage”  in cities of California. We will continue to explore this in the following Part 2.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ins>Scatterplot of Accident Severity and Transit Usage (by city in California):</ins>  \n",
    "We chose to study “Accident Severity” and “Transit Usage” because we wanted to see if our hypothesis was supported: Accidents are less severe when there is a higher percentage of people using transit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot that shows the relationship between transit usage and number of accidents in California cities\n",
    "severity_scatter = clean_df.plot.scatter(x='transit_usage', y='avg_severity', color='#23b8cf')\n",
    "severity_scatter.set_title('Transit Usage (per 10,000) vs Severity of Accidents')\n",
    "severity_scatter.set_xlabel('Transit Usage (count per 10,000)')\n",
    "severity_scatter.set_ylabel('Severity of Accidents')\n",
    "\n",
    "# Show the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we noticed:\n",
    "1. We can observe that there are two data clusters (high severity and low severity). We can also observe that most of the points are close to the origin (small in x-value). We can say that the graph looks similar to a positive linear relationship, but the strength is too weak. It almost feels like it needs to have two lines drawn instead of a singular one. Thus, the distribution of the points is not clear enough to conclude that there is a positive relationship between “Accident Severity” and “Transit Usage.”\n",
    "2. There are a few outliers in the graph. Similar to the previous graph, the high “Transit Usage” outlier belongs to San Francisco. The very low “Accident Severity” outlier belongs to Alameda. The reasoning for the most extreme outlier can be supported. San Francisco is an outlier due to its higher usage of public transportation such as the metro, cable cars, Muni buses, and more. Because it is a popular tourist location and the city is notorious for it being a hassle to park, it makes sense that many people would rather utilize transit than drive or rent personal vehicles. Upon further research, we decided that the Alameda outlier could be justified with project “Vision Zero.” According to the official government website, “Vision Zero is an international movement that aims to reduce traffic deaths and life-changing injuries to zero, while increasing safe, healthy, equitable mobility for all”<a name=\"cite_ref-4\"></a>[<sup>4</sup>](#cite_note-4).\n",
    "3. Based on the graph, it seems as though there is little correlation between “Accident Severity” and “Transit Usage.” We will continue to explore this in Part 3.\n",
    "\n",
    " <a name=\"cite_note-4\"></a>  [^](#cite_ref-3) [Vision Zero](https://www.alamedaca.gov/Departments/Planning-Building-and-Transportation/Transportation/Vision-Zero) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Part 2.1: Exploring relationships with \"Accident Rate\"__\n",
    "In this section, we will run a linear regression model to see if there is a relationship between public transportation usage and the amount of accidents that occur in a city. We begin by removing outliers and examining if there is a trend in our data. We chose to study \"Accident Rate\" and \"Percent Using Transit\" applied with a log10 transformation to shift the values away from being centered near zero when put on the log scale.\n",
    "\n",
    "\n"
   ]
  },
  {
   "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>accident_rate</th>\n",
       "      <th>transit_usage</th>\n",
       "      <th>transit_log10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.759138</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>3.267195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.692966</td>\n",
       "      <td>310.0</td>\n",
       "      <td>2.491502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.655712</td>\n",
       "      <td>790.0</td>\n",
       "      <td>2.897682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.870085</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.041787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.249304</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>3.344412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accident_rate  transit_usage  transit_log10\n",
       "0       0.759138         1850.0       3.267195\n",
       "1      57.692966          310.0       2.491502\n",
       "2      18.655712          790.0       2.897682\n",
       "3      14.870085          110.0       2.041787\n",
       "4      46.249304         2210.0       3.344412"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a subset of clean_df for column of interest for linear regression model\n",
    "sub_col_acc_transit = ['accident_rate','transit_usage']\n",
    "sub_accident_rate = clean_df[sub_col_acc_transit]\n",
    "\n",
    "# Apply a log-10 transformation for less skewed dataset\n",
    "\n",
    "sub_accident_rate['transit_log10'] = np.log10(sub_accident_rate['transit_usage']+0.1)\n",
    "sub_accident_rate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>accident_rate</th>\n",
       "      <th>transit_usage</th>\n",
       "      <th>population_2017</th>\n",
       "      <th>transit_log10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.759138</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>79037.0</td>\n",
       "      <td>3.267195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.692966</td>\n",
       "      <td>310.0</td>\n",
       "      <td>350476.0</td>\n",
       "      <td>2.491502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.655712</td>\n",
       "      <td>790.0</td>\n",
       "      <td>111494.0</td>\n",
       "      <td>2.897682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.870085</td>\n",
       "      <td>110.0</td>\n",
       "      <td>377940.0</td>\n",
       "      <td>2.041787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.249304</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>122164.0</td>\n",
       "      <td>3.344412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accident_rate  transit_usage  population_2017  transit_log10\n",
       "0       0.759138         1850.0          79037.0       3.267195\n",
       "1      57.692966          310.0         350476.0       2.491502\n",
       "2      18.655712          790.0         111494.0       2.897682\n",
       "3      14.870085          110.0         377940.0       2.041787\n",
       "4      46.249304         2210.0         122164.0       3.344412"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_col_2 = ['accident_rate','transit_usage']\n",
    "sub_accident_rate_2 = clean_df[sub_col_2]\n",
    "\n",
    "# Apply a log-10 transformation for less skewed dataset\n",
    "\n",
    "sub_accident_rate_2['transit_log10'] = np.log10(sub_accident_rate_2['transit_usage']+0.1)\n",
    "\n",
    "# filter out the outliers from 'accident_rate'\n",
    "x = 2\n",
    "mean = sub_accident_rate_2['accident_rate'].mean()\n",
    "std = sub_accident_rate_2['accident_rate'].std()\n",
    "filtered_accidents  = sub_accident_rate_2[(sub_accident_rate_2['accident_rate'] > (mean - x * std)) & (sub_accident_rate_2['accident_rate']< (mean + x * std))]\n",
    "filtered_accidents\n",
    "\n",
    "filtered_accidents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ins>Linear Regression of Accident Rate and Percent Using Transit:</ins>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and axes objects for subplots\n",
    "sns.regplot(x='transit_log10', y='accident_rate', data=filtered_accidents, fit_reg=True, color='#c74b2c')\n",
    "plt.xlabel('transit_log10')\n",
    "plt.ylabel('accident rate')\n",
    "plt.title('Relationship between Accident Rate and Transit Usage')\n",
    "\n",
    "# Show the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "1. The best fit line slopes upwards, which implies a positive linear relationship between public transportation use rates and the rate at which accidents occur. This goes against our initial hypothesis that transit usage has a negative effect on accident occurrence.\n",
    "2. We observe that the points are scattered. There may be little correlation between the two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Part 2.2: Finding evidence for correlation with Accident Rate__\n",
    "\n",
    "<ins>OLS Model of Accident Rate and Percent Using Transit:</ins>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          accident_rate   R-squared:                       0.079\n",
      "Model:                            OLS   Adj. R-squared:                  0.062\n",
      "Method:                 Least Squares   F-statistic:                     4.662\n",
      "Date:                Tue, 19 Mar 2024   Prob (F-statistic):             0.0353\n",
      "Time:                        11:12:49   Log-Likelihood:                -252.79\n",
      "No. Observations:                  56   AIC:                             509.6\n",
      "Df Residuals:                      54   BIC:                             513.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept        -1.7641     19.091     -0.092      0.927     -40.040      36.512\n",
      "transit_log10    15.7699      7.304      2.159      0.035       1.127      30.413\n",
      "==============================================================================\n",
      "Omnibus:                        2.216   Durbin-Watson:                   1.940\n",
      "Prob(Omnibus):                  0.330   Jarque-Bera (JB):                2.153\n",
      "Skew:                           0.428   Prob(JB):                        0.341\n",
      "Kurtosis:                       2.564   Cond. No.                         19.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome,predictors= patsy.dmatrices('accident_rate ~ transit_log10',filtered_accidents)\n",
    "model = sm.OLS(outcome,predictors)\n",
    "res_log = model.fit()\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through our summary, we can observe the following:\n",
    "1. The p-value is 0.035, which is less than our significance level a = 0.05. This is statistically significant, and allows us to reject our null hypothesis that public transportation usage does not affect accident rates.\n",
    "2. R-squared is 0.079, which is much closer to 0 than 1. This means that our plot is scattered, and the variance cannot be explained by transit usage alone.\n",
    "<br>\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Part 3.1: Exploring relationships with \"Average Severity\"__\n",
    "To address our second question on if higher public transportation usage leads to less severe accidents, we will see if there is a linear relationship between public transportation usage and car crash severity. To do this, we applied a log 10 transformation on our transit usage rates variable to shift the values away from being centered near zero when put on the log scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>avg_severity</th>\n",
       "      <th>transit_usage</th>\n",
       "      <th>population_2017</th>\n",
       "      <th>transit_log10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>79037.0</td>\n",
       "      <td>3.267195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546612</td>\n",
       "      <td>310.0</td>\n",
       "      <td>350476.0</td>\n",
       "      <td>2.491502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.514423</td>\n",
       "      <td>790.0</td>\n",
       "      <td>111494.0</td>\n",
       "      <td>2.897682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.534253</td>\n",
       "      <td>110.0</td>\n",
       "      <td>377940.0</td>\n",
       "      <td>2.041787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.694248</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>122164.0</td>\n",
       "      <td>3.344412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_severity  transit_usage  population_2017  transit_log10\n",
       "0      0.500000         1850.0          79037.0       3.267195\n",
       "1      0.546612          310.0         350476.0       2.491502\n",
       "2      0.514423          790.0         111494.0       2.897682\n",
       "3      0.534253          110.0         377940.0       2.041787\n",
       "4      0.694248         2210.0         122164.0       3.344412"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a subset of clean_df for column of interest for linear regression model\n",
    "sub_col_sev_transit = ['avg_severity','transit_usage']\n",
    "sub_sev_rate = clean_df[sub_col_sev_transit]\n",
    "\n",
    "# Apply a log-10 transformation for less skewed dataset\n",
    "\n",
    "sub_sev_rate['transit_log10'] = np.log10(sub_sev_rate['transit_usage']+0.1)\n",
    "sub_sev_rate.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ins>Linear Regression of Average Severity and Percent Using Transit:</ins>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "\n",
    "\n",
    "sns.regplot(x='transit_log10', y='avg_severity', data=sub_sev_rate, fit_reg=True, ax=ax, label='Average Severity')\n",
    "# sns.regplot(x='transit_log10', y='pop_comp', data=sub_sev_rate, fit_reg=True, ax=ax, label='population_2017')\n",
    "ax.set(ylabel='avg_severity', xlabel='transit_log10')\n",
    "\n",
    "\n",
    "plt.title('Relationship between Average Severity Rates and transit_usage')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "1. There is a high level or variance, with many points being far from the best-fit line.\n",
    "2. There are no obvious outliers, since the data is scattered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Part 3.2: Finding evidence for correlation with Average Severity__\n",
    "<ins>OLS Model of Average Severity  and Percent Using Transit:</ins>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           avg_severity   R-squared:                       0.057\n",
      "Model:                            OLS   Adj. R-squared:                  0.040\n",
      "Method:                 Least Squares   F-statistic:                     3.388\n",
      "Date:                Tue, 19 Mar 2024   Prob (F-statistic):             0.0710\n",
      "Time:                        11:12:51   Log-Likelihood:                 77.801\n",
      "No. Observations:                  58   AIC:                            -151.6\n",
      "Df Residuals:                      56   BIC:                            -147.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept         0.4927      0.054      9.164      0.000       0.385       0.600\n",
      "transit_log10     0.0380      0.021      1.841      0.071      -0.003       0.079\n",
      "==============================================================================\n",
      "Omnibus:                       19.259   Durbin-Watson:                   1.650\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                3.872\n",
      "Skew:                           0.028   Prob(JB):                        0.144\n",
      "Kurtosis:                       1.735   Cond. No.                         19.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome,predictors= patsy.dmatrices('avg_severity ~ transit_log10', sub_sev_rate)\n",
    "model = sm.OLS(outcome,predictors)\n",
    "res_log = model.fit()\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through our summary, we can observe the following:\n",
    "1. The p-value is 0.071, which is greater than our significance level a = 0.05. This is statistically insignificant, so we fail to reject our null hypothesis that public transportation usage lessens accident severity.\n",

    "2. R-squared is incredibly close to 0, which shows us that very little of the variance in data can be explained by our model.\n",
    "<br>\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first linear regression model addressed our question: does increased public transportation usage lead to lower accident rates? From running an OLS model, we found that our p-value was 0.035, which is less than our significance level of 0.05.   This is statistically significant so we can reject the null hypothesis that public transportation usage does not affect the accident rate. However, it should be noted that R-squared is 0.079, which means very little of the variance in the accident rates can be explained by transit usage alone. Because our regression line was upwards sloping, which implies an increase in transit usage leads to an increase in accident rates, we can say that our alternate hypothesis was not supported by our research.  \n",
    "<br>\n",
    "The second set of analyses explored our second research question: does increased public transportation usage lessen the severity of accidents? From the linear regression model, we found that we cannot reject our null hypothesis for Accident Severity since the p-value is 0.071 which is bigger than 0.05. This is statistically insignificant so we cannot reject the null hypothesis of public transportation affecting the accident severity. Additionally, our  R-squared value is 0.040 which is closer to 0, meaning it overall has a poor fit.  \n",
    "<br>\n",
    "Ultimately, our research concluded that there is no significant change in accident rate or severity as caused by increased public transportation usage. There are many other confounding variables that we failed to find initially, which may allow us to better understand the effect of public transportation usage on accident occurrence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There should not be an issue with any biases from the source of collection as the dataset that we are exmaining the data from takes the data from multiple APIs which broadcast traffic data which is captured from various sources, including the US and state departments of transportation, law enforcement agencies, trafic cameras, and trafic sensors within road network. This should allow us to avoid any biases as there are many sources of information that went into this dataset which can validate each other. Additionally, as some of the data is taken in from traffic cameras and sensors, the data is inputted by a machine/algorithm which will remove human biases.\n",
    "\n",
    "However, there may be biases in which accidents were reported to the government as there may be racial/ethical biases when it came to reporting the accidents to the government.\n",
    "This may exclude certain demographics of people or target others, but as we are not focusing on race/demographics, it should not affect our data analysis as much. Additionally, since the data is vastly spread across the country and over the time frame of 2016 - 2013, the data should even out itself.\n",
    "\n",
    "One factor we may fail to fully consider is the location and its impact on each accident.\n",
    "For example, there may be weather conditions or other variables that we are unable to record that may be the main reason for the accident, yet we concluded a different reasoning with the information we analyzed.\n",
    "However, this should still be acceptable as the main goal of our hypothesis and research is to find out how certain factors consistently result in an accident.\n",
    "In other words, our focus is more on concluding if there will be an accident if a certain variable is present, rather than which variable is the most crucial out of all variables to lead to an accident.\n",
    "\n",
    "\n",
    "We will detect these specific biases before analysis by thoroughly discussing our research question before finding datasets and identifying any additional variables that would skew our data and affect our question.\n",
    "During data analysis, we will identify any points in the data that do not match up with the rest of the data and conduct additional research to identify whether those specific data points are abnormalities or whether they are biases that we did not take into account before.\n",
    "Then, we will explain the abnormalities/biases in our analysis and discuss how this affects our data.\n",
    "After we have already written our analysis of the data, we will proofread our conclusion for any other biases that may stand out and if they do occur, we will revise and address them again.\n",
    "Additionally, we may conduct a peer review (if allowed) with another group so we can have unbiased feedback.\n",
    "\n",
    "We will not be avoiding anyone's privacy when collecting data about the public transportation usage rates as the rates are generalized to the entire city and should not be exposing any indivdual person's privacy. To avoid violating anyone's privacy who was involved in the accident, we may remove/recode any information that can identify the individual(s) involved in the accident. This includes any distinguishing features about the vehicles involved in the accident. \n",
    "\n",
    "Our question will not bring up any ethical concerns as it does not dive into the demographics of any of the people involved in the study. It only takes into account the public rates of public transportation usage and car accident data. Our question will not have any ethical implications.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discussion and Conclusion\n",
    "\n",
    "There is an abundance of research done on common negative behaviors that lead to car crashes such as distracted driving, speeding, drunk driving, reckless driving, and tailgating; despite that, our extensive search did not tell us much about the effect of public transportation usage on car crashes. There was, however, research that hinted towards many benefits of public transportation, such as lessening traffic congestion. This led us to believe that a higher use of public transportation would create a “domino effect”: public transportation would indicate fewer vehicles on the road, which could mean fewer reckless drivers, which could mean fewer car crashes. We also believe that public transportation drivers are better equipped to handle accidents, as they have more training so they can better react to dangerous situations, leading us to wonder if higher public transportation usage can also lessen the average severity of car crashes. Through our background research, we hypothesized that public transportation could serve as a remedy to those issues since it gives an option for unfit drivers to leave the wheel and let professionals, who go through rigorous training to get the appropriate license, to take to the roads instead. Thus, we predicted that a higher utilization of public transportation could reduce the number of accidents on the road as well as the severity of the accident.  \n",
    "<br>Through our analysis, we investigated if increased public transportation usage can lead to less car accident occurrences and lessened severity, and found that there was not enough evidence to point to public transportation usage alone leading to less accidents. While both of the linear regression models we created for comparing transit usage to accident rates and severity had low p-values, at 0.035 and 0.071 respectively, there was a high level of variance and positive linear relationship which went against our alternative hypothesis that a higher rate of public transportation usage would lead to lower accident rates. Because of this, we noticed through our analysis that all of public transportation, accident occurrence, and accident severity, were positively associated with population; as a result, public transportation usage, accident occurrence, and accident severity were moderately positively associated. We expected that changing accident counts to rates would allow us to disregard population in our analysis, but it had a much higher impact on accident rates itself than expected.  \n",
    "<br>Limitations to our project included data clarity and abundance of confounding variables.  Although it was not difficult to find accident information itself online, they were usually not linked to each other in any way, or missed vital information that would allow us to group and merge the datasets. Additionally, due to our project hinging on public transportation usage rates in California, we were only able to compare data points for the cities that were included on the website that we sourced our data from.  \n",
    "<br> Although our group was not able to form a significant conclusion, we hope that our question can lead to further research being done in a better way; if results support our hypothesis, it can be a call for people to utilize public transportation, and, if not, urge city officials to increase funding for our transportation and make it more efficient and accessible for all.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions\n",
    "\n",
    "Speficy who did what.  This should be pretty granular, perhaps bullet points, no more than a few sentences per person.\n",
    "\n",
    "Elizaveta Beltyukova\n",
    "- wrote analysis for the first scatterplot \n",
    "- wrote background & prior work \n",
    "- helped with format \n",
    "- did part of the analysis/results section \n",
    "\n",
    "Rikako Ono\n",
    "- wrote our initial hypothesis and research question\n",
    "- cleaned the data\n",
    "- reformatted and cleaned graphs\n",
    "- write and reformat analysis for respective figures\n",
    "\n",
    "Nicole Kim\n",
    "- wrote data cleaning set #1 and #2\n",
    "- wrote analysis for the second scatterplot\n",
    "- wrote part of the analysis/results section\n",
    "- wrote part of conclusion\n",
    "\n",
    "MyungJoo Kim \n",
    "- visualized linear regression plots and a bar graph\n",
    "- analyzed the visualization with OLS models \n",
    "- helped writing part of the data analysis \n",
    "- wrote the summary of the results\n",
    "\n",
    "Geena Limfat\n",
    "- finalized hypothesis and research question\n",
    "- created scatterplots and second linear regression plot\n",
    "- wrote abstract\n",
    "- recorded final presentation video\n",
    "\n",
    "All teammates contributed to the final slideshow used for the video and proof read the final project.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.5 ('COGS118B_WI24')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  },
  "vscode": {
   "interpreter": {
    "hash": "d32b38566dea232296ad015676f5eb17bd884ce0b9315fa59364119c2a8275d7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}