FinalProject_group143.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OVERVIEW #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can all agree that housing plays an essential role when it comes to settling down and starting a family and while looking for housing, people take many different preferences into consideration. We wanted to investigate these population preferences across San Diego County; our process will be breaking down the current housing map out of the area. In addition, we will be considering and highlighting the attractions and accomodations that greatly influence peoples’ decisions to settle in certain areas of San Diego County. Specifically we wanted to consider the factors of race, age, income, and family size when it came to choosing an area to live and through this research, we hope to underline the psychology and nuances of the housing preferences and ultimately bring about insights of how these factors interact."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NAMES ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Danny Wang ####\n",
    "#### Nikhil Ranjan ####\n",
    "#### Andre Lopez ####\n",
    "#### Ezra Chung ####\n",
    "#### HongSeok Oh ####"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over the past decade how has the characterization of the greater San Diego has changed in the socio-economic values of people? In particular how do factors such as age, income, racial diversity influence of movement within certain parts of San Diego? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background and Prior Work#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we were thinking of a question that we wanted to answer, our main criteria was that the question should center around something that we all associated with. Coming from different stories on how we entered the San Diego region, we felt that it was best to delve into a topic that resonated in our minds as we entered the coming school year. Furthermore, housing is also a staple in the acclimation process of an individual to a new environment.\n",
    "\n",
    "\n",
    "#### While looking into housing, particularly in the San Diego region, we found multiple censuses on the population of San Diego and we began to find patterns through time on how populations settled and preferred certain locations. Business managers, schools, and apartment complexes have used these censuses to develop an idea on where to set up their business. These changes in the population, among many other impacting factors, have continuously altered the formation and opportunities of San Diego over the past decade. Current data shows impacts of individual comparisons, including income and location, and its effects on where individuals and families decide to live. Thus, as residents of San Diego, it has become part of our interest to discover what areas cater best to specific demographics of our considering factors of race, age, and income. Our data and research will create an accurate model of the current housing market of San Diego County which will be essential for renters and buyers because it will help them map out the different options and offerings of each area. In addition, this model will simplify the housing hunting process for students. ####\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) https://www.globest.com/2018/06/29/san-diegos-population-growth-is-slowing/?slreturn=20190509142758\n",
    "#### 2) https://www.voiceofsandiego.org/neighborhoods/how-san-diegos-changing-in-three-charts/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since we want to observe the changes of socio-economic environment of the San Diego County, we will mainly explore and hypothesize the distribution of population based on income, age, and race. From all the data we will gather, we should be able to predict correlations between where a certain group of demographics live and why they live there. Knowing that San Diego is the largest city in the county, we predict that the city will have the highest median income as well as being the most populated. In addition, we believe that the majority of Chula Vista residents consist of older, retired people. Overall in the county, we anticipate that the percentage of Caucasians will decrease because of increased immigration of other ethnicities including Asians and Hispanics.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### American FactFinder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_00_PL_GCTPL.CY07&prodType=table\n",
    "#### This dataset contains over 1,000 data points pertaining to the demographic distribution of San Diego County. Variables of the dataset include the different races residing in SD County which are indexed by census tracts taken over different areas of San Diego County. We took data from 2000 and 2010's consensuses to show the changes if the county over the decade.\n",
    "#### Dataset is culmination of data from 2000 to 2017. Contains most information for San Diego County needed for this project. Will be used to combine with later years to find correlations and changes. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demographic Profiles of 2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/demographics/FINAL_2016_Demographic_Profiles_1.30.18.pdf\n",
    "#### This dataset contains over 10,000 observations on the age distribution of San Diego County further broken down into the cities of the county. This set also has displays the occupation distribution of San Diego County from 2012 to 2016 allowing us to better represent the county over the past years. The sources of these observations is from the Health and Human Services Agency of San Diego County.\n",
    "#### This demographic data includes basic population characteristics like age, gender, race, and education. Also important data on Income and Poverty which includes labor force, poverty level, and renter occupancy.\n",
    "#### Although this data set represents data from 2012 to 2016, we still want as much data available to display the differences and relations. So we would combine more data from other years but the same categories in respect to a demographic context to show year over year. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income Breakdown\n",
    "#### https://www.deptofnumbers.com/income/california/san-diego/ \n",
    "#### This site has about 100 observations of the incomes in San Diego County over the past year and compares them to California and US averages. We utilized the \"Historical Inflation Adjusted Median Household Income for San Diego\" chart in intro of anaylsis to layout our factors we are analyzing in our hypothesis. The source of the data is from the Department of Numbers, a public dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demographic Breakdown\n",
    "#### https://www.sandag.org/uploads/publicationid/publicationid_2001_20213.pdf\n",
    "#### This source has graphs and charts of over 500 observations ranging from total populaion of San Diego County, population change, and demographic stats. We used the data of San Diego different demographic population numbers provided in one of the charts listed on the site. The source of this data is from the San Diego Association of Governments (SANDAG)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pylab as pl\n",
    "import matplotlib.pyplot as plot\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning And Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With the scope of our analysis limited to the factors of age, income, education, diversity and housing, we will first display the data of each factor on the broadest scale. Modeled below, we show the increase of income in San Diego County from 2006 to 2016. We note the dip that started around 2008 with the housing crisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Income ($)')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = pd.read_excel('historical_InflationSD.xlsx')\n",
    "graph1 = df1.plot.line(x = \"Year\", y = \"Income\",title = \"Graph 1: Change in Income SD County 2006 to 2016\",figsize=(7,7))\n",
    "plot.ylabel('Income ($)',fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In the next graph, we plotted the population of each city in a 5 year estimate from 2012 to 2016. We can see that San Diego is the largest city in SD County."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Population')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = pd.read_excel('SDcitiesPopulations.xlsx')\n",
    "df2 = df2.drop([17])    #drop Unincorporated entry \n",
    "graph2 = df2.plot.bar(x = \"Cities\", y = \"Population\", title = \"Graph 2: 5 Year Estimate of SD County Cities Populations\", legend = False ,figsize=(7,7))\n",
    "plot.ylabel('Population',fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In our final graph, we display the age breakdown of the cities of San Diego County from 2012 to 2016. We note that the largest age group appears to be ages 25-44."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = pd.read_excel('SDcountyAgeBreakdown.xlsx')\n",
    "df3 = df3.drop(columns = ['0-4N', '5-14N', '15-24N', '25-44N','45-64N', '65+N']) #Remove number columns, only want percentagesdf3 = \n",
    "df3 = df3.rename(columns={'0-4P': 'Ages 0-4', '5-14P': 'Ages 5-14', '15-24P': 'Ages 15-24', '25-44P': 'Ages 25-44','45-64P': 'Ages 45-64', '65+P': 'Ages 65+'})\n",
    "graph3 = df3.plot(kind = 'bar', title = \"Graph 3: Age Breakdown of SD County 2012 to 2016\", stacked = True, x = \"Cities\", figsize=(7,7)).legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "plot.ylabel('Percentage',fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning For Change in Population and Housing Units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This sheet was taken from the Census Bureau and SANDAG. Only the datasheet from the Census Bureau was cleaned. We first dropped a column we didn't need, no real quantifiable data. We then only filtered the data that pertained to 'San Diego County.' Lastly we took out any small values that were not going to help with visualization and then created a final dataframe with the population count. \n",
    "\n",
    "#### The graph below did not need to have its sheet cleaned, as it only had 5 data points representing the amount of building permits were approved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           City    type variable  Population Count  Year\n",
      "0  San Diego County, California  county    count         3138265.0  2013\n",
      "2  San Diego County, California  county    count         3100500.0  2012\n",
      "4  San Diego County, California  county    count         3060849.0  2011\n",
      "6  San Diego County, California  county    count         3022468.0  2010\n",
      "8  San Diego County, California  county    count         2987543.0  2009\n",
      "   Year  Units\n",
      "0  2013   3132\n",
      "1  2014   2115\n",
      "2  2015   4403\n",
      "3  2016   4459\n",
      "4  2017   5865\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "populationdf = pd.read_excel('PopulationOverTheYears.xlsx')\n",
    "populationdf.columns = ['id', 'City','type','variable','Population Count','Year']\n",
    "populationdf = populationdf.drop(['id'], axis=1)\n",
    "\n",
    "populationdf = populationdf[populationdf['City'] == 'San Diego County, California']\n",
    "populationdf['Population Count'] = populationdf['Population Count'].astype(float)\n",
    "popChangedf = populationdf[populationdf['Population Count'] < 5]\n",
    "populationdf2 = populationdf[populationdf['variable'] == 'count']\n",
    "\n",
    "graph6 = populationdf2.plot.line(x='Year',y='Population Count',title = \"Graph 4: Population Change 2009 to 2013\",figsize = (8,8),marker ='o',ylim=(2800000,3400000),grid=True)\n",
    "plot.ylabel('Population',fontsize=12)\n",
    "print(populationdf2) # population san diego\n",
    "\n",
    "#----------------------------------------------------------------------------------------------------------------------#\n",
    "housingdf = pd.read_excel('Housing.xlsx')\n",
    "housingdf.columns = ['Year','Units']\n",
    "graph7 = housingdf.plot.line(x='Year',y='Units',title = \"Graph 5: Number of Housing Units 2013 to 2017\",figsize = (8,8),marker ='o',grid=True)\n",
    "plot.ylabel('Number of Units',fontsize=12)\n",
    "print(housingdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### These graphs are connected to each other. We wanted to show the influx of people coming into San Diego. As we see over the years, there was a steady but large increase of people moving into San Diego. The graph below the population graph shows the amount of building permits(units) were approved to buiild more housing. This visualization just shows there more people moving here and we want to see where they prefer to settle down. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning For Income Region Breakdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This dataset was retrieved from SANDAG. The excel was practicall clean as it directly pertained to Income. All we did was drop the NaN values, labeled the columns. Deleted specific cities and only plotted the averages of the major regions as it still got main point across"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Median Household Income\n",
      "Region                                       \n",
      "Central Region                          53523\n",
      "East Region                             66663\n",
      "North Central Region                    87963\n",
      "North Coastal Region                    76602\n",
      "North Inland Region                     79116\n",
      "South Region                            62418\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "incomeregdf = pd.read_excel('IncomeRegion.xlsx')\n",
    "\n",
    "incomeregdf = incomeregdf.dropna()\n",
    "\n",
    "incomeregdf.columns =['Region', 'Median Household Income']\n",
    "incomeregdf = incomeregdf[incomeregdf['Region'].isin(['Central Region','East Region', 'North Central Region', 'North Coastal Region', 'North Inland Region', 'South Region'])]\n",
    "incomeregdf.set_index('Region', inplace=True)\n",
    "graph10 = incomeregdf.plot(kind='barh', title = \"Graph 6: Household Income Distribution\",figsize=(10,7), fontsize=13);\n",
    "graph10.set_xlabel(\"Median Household Income\",fontsize=12)\n",
    "graph10.set_ylabel(\"Region\",fontsize=12)\n",
    "print(incomeregdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This data shows the average Median Household Income in San Diego. This data was collected from 2012 to 2016. Although we can see that the bars are close enough, in a monetary context, there is definitely a disparity in income in these regions. Just from this graph we can tell certain people of certain incomes, stay in a certain region."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning For Job/Occupation Region Breakdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This data was also retrieved from SANDAG. This dataset was also pretty organized. The only cleaning we had to do was with organizing the columns and setting the index. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Agriculture  Construction  Manufacturing  Wholesale  \\\n",
      "Region                                                                      \n",
      "Central Region              0.006         0.052          0.067      0.017   \n",
      "East Region                 0.005         0.080          0.076      0.022   \n",
      "North Central Region        0.003         0.036          0.097      0.023   \n",
      "North Coastal Region        0.015         0.053          0.116      0.032   \n",
      "North Inland Region         0.022         0.067          0.116      0.029   \n",
      "South Region                0.004         0.058          0.083      0.029   \n",
      "\n",
      "                      Retail  Transportation  Information  Finance  \\\n",
      "Region                                                               \n",
      "Central Region         0.104           0.039        0.022    0.054   \n",
      "East Region            0.130           0.049        0.018    0.060   \n",
      "North Central Region   0.086           0.030        0.030    0.074   \n",
      "North Coastal Region   0.112           0.031        0.024    0.065   \n",
      "North Inland Region    0.103           0.033        0.024    0.061   \n",
      "South Region           0.130           0.054        0.016    0.056   \n",
      "\n",
      "                      Scientific  Educational  Entertainment  Other  \n",
      "Region                                                               \n",
      "Central Region             0.153        0.208          0.161  0.065  \n",
      "East Region                0.122        0.208          0.115  0.057  \n",
      "North Central Region       0.183        0.231          0.111  0.046  \n",
      "North Coastal Region       0.155        0.191          0.111  0.052  \n",
      "North Inland Region        0.154        0.202          0.096  0.053  \n",
      "South Region               0.105        0.221          0.112  0.053  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jobregdf = pd.read_excel('JobByRegion.xlsx')\n",
    "\n",
    "jobregdf.columns =['Region', 'Agriculture','Construction','Manufacturing','Wholesale','Retail','Transportation','Information','Finance','Scientific','Educational','Entertainment','Other']\n",
    "jobregdf = jobregdf[jobregdf['Region'].isin(['Central Region','East Region', 'North Central Region', 'North Coastal Region', 'North Inland Region', 'South Region'])]\n",
    "jobregdf.set_index('Region', inplace=True)\n",
    "graph4 = jobregdf.plot(kind = 'bar', title = \"Graph 7: Occupation Breakdown\", stacked = True,figsize=(7,7)).legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "plot.ylabel('Percentage',fontsize=12)\n",
    "print(jobregdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This graph is a representation of breakdown of the major regions of San Diego in relation to Occupation/Job. As we see each bar is broken down by occupation. The more bar taken up by a certain color, the more percentage of occupation in that region. As we see, each region has its own characteristics. Central has more agriculture, while North Central has more Educational and Scientific. This visualization helps us understand how these cities are broken down by jobs, and similarities on jobs and where those people live."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning for Race By Region Sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This graph was retrieved from SANDAG. Unfortunately I had to clean the data in Excel as I had trouble retrieving the data from the pdf to excel. I had to manually enter the input into this excel file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8c\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Hispanic(#)</th> \n",
       "        <th class=\"col_heading level0 col1\" >White(#)</th> \n",
       "        <th class=\"col_heading level0 col2\" >Black(#)</th> \n",
       "        <th class=\"col_heading level0 col3\" >API(#)</th> \n",
       "        <th class=\"col_heading level0 col4\" >Other(#)</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Geography</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8clevel0_row0\" class=\"row_heading level0 row0\" >Central Region</th> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow0_col0\" class=\"data row0 col0\" >218611</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow0_col1\" class=\"data row0 col1\" >150434</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow0_col2\" class=\"data row0 col2\" >56193</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow0_col3\" class=\"data row0 col3\" >68029</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow0_col4\" class=\"data row0 col4\" >15843</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8clevel0_row1\" class=\"row_heading level0 row1\" >East Region</th> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow1_col0\" class=\"data row1 col0\" >132450</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow1_col1\" class=\"data row1 col1\" >277696</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow1_col2\" class=\"data row1 col2\" >27723</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow1_col3\" class=\"data row1 col3\" >24290</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow1_col4\" class=\"data row1 col4\" >22443</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8clevel0_row2\" class=\"row_heading level0 row2\" >North Central Region</th> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow2_col0\" class=\"data row2 col0\" >100701</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow2_col1\" class=\"data row2 col1\" >361985</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow2_col2\" class=\"data row2 col2\" >21687</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow2_col3\" class=\"data row2 col3\" >122313</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow2_col4\" class=\"data row2 col4\" >26977</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8clevel0_row3\" class=\"row_heading level0 row3\" >North Coastal Region</th> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow3_col0\" class=\"data row3 col0\" >149464</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow3_col1\" class=\"data row3 col1\" >314572</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow3_col2\" class=\"data row3 col2\" >17019</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow3_col3\" class=\"data row3 col3\" >32868</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow3_col4\" class=\"data row3 col4\" >17098</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8clevel0_row4\" class=\"row_heading level0 row4\" >North Inland Region</th> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow4_col0\" class=\"data row4 col0\" >173814</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow4_col1\" class=\"data row4 col1\" >318043</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow4_col2\" class=\"data row4 col2\" >11827</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow4_col3\" class=\"data row4 col3\" >69144</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow4_col4\" class=\"data row4 col4\" >23809</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8clevel0_row5\" class=\"row_heading level0 row5\" >South Region</th> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow5_col0\" class=\"data row5 col0\" >301279</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow5_col1\" class=\"data row5 col1\" >96974</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow5_col2\" class=\"data row5 col2\" >19802</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow5_col3\" class=\"data row5 col3\" >66451</td> \n",
       "        <td id=\"T_a580efe2_8d48_11e9_97f5_4a53f6020b8crow5_col4\" class=\"data row5 col4\" >13817</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe5f0d69c50>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raceregdf = pd.read_excel('RaceByRegion.xlsx')\n",
    "raceregdf.columns = ['Geography','Hispanic(#)','Hispanic(%)','White(#)','White(%)','Black(#)','Black(%)','API(#)','API(%)','Other(#)','Other(%)']\n",
    "raceregdf = raceregdf.drop(['Hispanic(%)','White(%)','Black(%)','API(%)','Other(%)'], axis=1)\n",
    "raceregdf.set_index('Geography', inplace=True)\n",
    "graph11 = raceregdf.plot(kind = 'bar', title = \"Graph 11: Ethnicity Breakdown by Region\", stacked = True,figsize=(7,7)).legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "plot.ylabel('Population Count',fontsize=12)\n",
    "raceregdf.style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning For Population 2000 Sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This data was provided by the Census Bureau. This data had a little more than we needed, so more cleaning was done. We transposed the data, as the Bureau organized it differently. We then deleted some unwanted rows. We also noticed that values were given in percentages and whole numbers, luckily they were separated in an 'every other' fashion, so we deleted every other row to save the whole number values. We then deleted any values below 10,000 as we felt that was not going to play a large role in the majority. Lastly, we made some cosmetic changes with some dropped rows and renaming for better readability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8c\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Percent/Total Number</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Race</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8clevel0_row0\" class=\"row_heading level0 row0\" >White</th> \n",
       "        <td id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8crow0_col0\" class=\"data row0 col0\" >1871839</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8clevel0_row1\" class=\"row_heading level0 row1\" >African American/Black</th> \n",
       "        <td id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8crow1_col0\" class=\"data row1 col0\" >161480</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8clevel0_row2\" class=\"row_heading level0 row2\" >American Indian</th> \n",
       "        <td id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8crow2_col0\" class=\"data row2 col0\" >24337</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8clevel0_row3\" class=\"row_heading level0 row3\" >Asian</th> \n",
       "        <td id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8crow3_col0\" class=\"data row3 col0\" >249802</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8clevel0_row4\" class=\"row_heading level0 row4\" >Native Hawaiian</th> \n",
       "        <td id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8crow4_col0\" class=\"data row4 col0\" >13561</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8clevel0_row5\" class=\"row_heading level0 row5\" >Hispanic/Latino</th> \n",
       "        <td id=\"T_7d143ad2_8d48_11e9_97f5_4a53f6020b8crow5_col0\" class=\"data row5 col0\" >750965</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe5f0ae6128>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_2000df = pd.read_excel('populationtotals2000.xlsm')\n",
    "race_2000df = race_2000df.T\n",
    "race_2000df.columns = ['Race', 'Percent/Total Number']\n",
    "race_2000df = race_2000df.iloc[3:] # deleted first 3 rows\n",
    "race_2000df = race_2000df.iloc[::2] # deleted all percentage values\n",
    "race_2000df = race_2000df.iloc[2:] # delete total population row\n",
    "race_2000df['Percent/Total Number'] = race_2000df['Percent/Total Number'].astype(int)\n",
    "\n",
    "\n",
    "race_2000df2 = race_2000df[race_2000df['Percent/Total Number'] > 10000] \n",
    "race_2000df2 = race_2000df2.drop(['HC01_VC07','HC01_VC12','HC01_VC13','HC01_VC14','HC01_VC15','HC01_VC16','HC01_VC18','HC01_VC17','HC01_VC35','HC01_VC38','HC01_VC39','HC01_VC41','HC01_VC42','HC01_VC43','HC01_VC44','HC01_VC45','HC01_VC46','HC01_VC47' ,'HC01_VC33','HC01_VC36'])\n",
    "race_2000df3 = race_2000df2.replace(['Number; RACE - Total population - One race - White','Number; RACE - Total population - One race - Black or African American','Number; RACE - Total population - One race - American Indian and Alaska Native','Number; RACE - Total population - One race - Asian','Number; HISPANIC OR LATINO - Total population','Number; RACE - Total population - One race - Native Hawaiian and Other Pacific Islander','Number; HISPANIC OR LATINO - Total population - Hispanic or Latino (of any race)'],['White','African American/Black','American Indian','Asian', 'Hispanis/Latino','Native Hawaiian','Hispanic/Latino']) \n",
    "race_2000df3.set_index('Race', inplace=True)\n",
    "\n",
    "race_2000df3.style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning For Population 2010 Sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This data was provided by the Census Bureau. This data had a little more than we needed, so more cleaning was done. We transposed the data like the one above. We then deleted some unwanted rows. Just like the sheet below, rows we wanted were separated in an 'every other' fashion, so we deleted every other row to save the whole number values. We then deleted any values below 10,000 as we felt that was not going to play a large role in the majority. We used truncate, as this was a bigger dataset and values that were needed were inbetween unneccessary data. Lastly, we made some cosmetic changes with some dropped rows and renaming for better readability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8c\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Percent/Total Number</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Race</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8clevel0_row0\" class=\"row_heading level0 row0\" >White</th> \n",
       "        <td id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8crow0_col0\" class=\"data row0 col0\" >1981442</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8clevel0_row1\" class=\"row_heading level0 row1\" >African American/Black</th> \n",
       "        <td id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8crow1_col0\" class=\"data row1 col0\" >158213</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8clevel0_row2\" class=\"row_heading level0 row2\" >American Indian</th> \n",
       "        <td id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8crow2_col0\" class=\"data row2 col0\" >26340</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8clevel0_row3\" class=\"row_heading level0 row3\" >Asian</th> \n",
       "        <td id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8crow3_col0\" class=\"data row3 col0\" >336091</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8clevel0_row4\" class=\"row_heading level0 row4\" >Native Hawaiian</th> \n",
       "        <td id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8crow4_col0\" class=\"data row4 col0\" >15337</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8clevel0_row5\" class=\"row_heading level0 row5\" >Hispanic/Latino</th> \n",
       "        <td id=\"T_7d22d89e_8d48_11e9_97f5_4a53f6020b8crow5_col0\" class=\"data row5 col0\" >991348</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe64eb78470>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_2010df = pd.read_csv('2010Population.csv')\n",
    "\n",
    "race_2010df = race_2010df.T\n",
    "\n",
    "race_2010df.columns =['Race', 'Percent/Total Number']\n",
    "race_2010df = race_2010df.iloc[3:]\n",
    "\n",
    "race_2010df = race_2010df.iloc[::2]\n",
    "race_2010df['Percent/Total Number'] = race_2010df['Percent/Total Number'].astype(int)\n",
    "race_2010df2 = race_2010df[race_2010df['Percent/Total Number'] > 10000] \n",
    "race_2010df2 = race_2010df2.drop(['HD01_S01','HD01_S02','HD01_S06','HD01_S23','HD01_S24','HD01_S25','HD01_S26','HD01_S29'])\n",
    "race_2010df2 = race_2010df2.truncate(before = 'HD01_S03', after = 'HD01_S30') \n",
    "race_2010df3 = race_2010df2.replace(['Number; RACE - Total population - One race - White','Number; RACE - Total population - One race - Black or African American','Number; RACE - Total population - One race - American Indian and Alaska Native','Number; RACE - Total population - One race - Asian','Number; HISPANIC OR LATINO - Total population - Hispanic or Latino (of any race)','Number; RACE - Total population - One race - Native Hawaiian and Other Pacific Islander'],['White','African American/Black','American Indian','Asian', 'Hispanic/Latino','Native Hawaiian']) \n",
    "race_2010df3.set_index('Race', inplace=True)\n",
    "race_2010df3.style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning For Population 2017 Sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This data was provided by the Census Bureau. This data had a little more than we needed, so more cleaning was done. We transposed the data like the one above. We then deleted some unwanted rows. Just like the sheet below, rows we wanted were separated in an 'every other' fashion, so we deleted every other row to save the whole number values. We then deleted any values below 10,000 as we felt that was not going to play a large role in the majority. Lastly, we made some cosmetic changes with some dropped rows and renaming for better readability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8c\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Percent/Total Number</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Race</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8clevel0_row0\" class=\"row_heading level0 row0\" >White</th> \n",
       "        <td id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8crow0_col0\" class=\"data row0 col0\" >1512229</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8clevel0_row1\" class=\"row_heading level0 row1\" >Black/African American</th> \n",
       "        <td id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8crow1_col0\" class=\"data row1 col0\" >153740</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8clevel0_row2\" class=\"row_heading level0 row2\" >American Indian</th> \n",
       "        <td id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8crow2_col0\" class=\"data row2 col0\" >12699</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8clevel0_row3\" class=\"row_heading level0 row3\" >Asian</th> \n",
       "        <td id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8crow3_col0\" class=\"data row3 col0\" >394340</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8clevel0_row4\" class=\"row_heading level0 row4\" >Native Hawaiian</th> \n",
       "        <td id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8crow4_col0\" class=\"data row4 col0\" >12808</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8clevel0_row5\" class=\"row_heading level0 row5\" >Hispanic/Latino</th> \n",
       "        <td id=\"T_7d308318_8d48_11e9_97f5_4a53f6020b8crow5_col0\" class=\"data row5 col0\" >1129970</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe5f0c75f98>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_2017df = pd.read_csv('2017Population.csv')\n",
    "race_2017df = race_2017df.T\n",
    "race_2017df.columns =['Race', 'Percent/Total Number']\n",
    "race_2017df = race_2017df.iloc[5:]\n",
    "race_2017df = race_2017df.iloc[::2]\n",
    "\n",
    "race_2017df['Percent/Total Number'] = race_2017df['Percent/Total Number'].astype(int)\n",
    "race_2017df2 = race_2017df[race_2017df['Percent/Total Number'] > 10000] \n",
    "race_2017df2 = race_2017df2.drop(['HD01_VD02','HD01_VD09','HD01_VD11'])\n",
    "race_2017df3 = race_2017df2.replace(['Estimate; Not Hispanic or Latino: - White alone','Estimate; Not Hispanic or Latino: - Black or African American alone','Estimate; Not Hispanic or Latino: - American Indian and Alaska Native alone','Estimate; Not Hispanic or Latino: - Asian alone','Estimate; Not Hispanic or Latino: - Native Hawaiian and Other Pacific Islander alone','Estimate; Hispanic or Latino'],['White','Black/African American','American Indian','Asian','Native Hawaiian','Hispanic/Latino'])\n",
    "race_2017df3.set_index('Race', inplace=True)\n",
    "\n",
    "race_2017df3.style\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Change in Population from 2000 to 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QDgPQVtlEDisfwFYAXwHYnJOYUKRwHiIiugEsUIlakOUyToMAjAcwDkBXZRM5pSoA2wFsBLAxJzEhR9k4RETUVCxQiW4yrd6gBjAU5qL0XgCdlE3U6qTCXKx+mZOYcEDpMERE1DAWqEQ3gaVTOhzAJABjAbRXNhFZHAaQBGB5TmJCrtJhiIiobixQiZqR5TJQjwB4FMAtyqahephgPrlqKYC1vIwVEZF9YYFKdIO0eoMLgDEAHgPwOwAqZRNRE5UAWAvg45zEhB1KhyEiIhaoRNdNqzf0ADAdwFQAHRSOQ83jAID/AFiRk5hQqXQYIqLWigUqURNp9YZBAOYAuAfsljqrfAAfAXiPY1WJiFoeC1SiRtDqDSqYz8CfDWCgwnGo5VQD+BLAv3MSE35UOgwRUWvBApWoHpbbjT4K4GkA3RWOQ8raDuDlnMSEbUoHISJydixQieqg1Rs8APwFwDMA/BWOQ/ZlB8yF6v8pHYSIyFmxQCWyodUbXAE8AeAFAEEKxyH7thPmQvU7pYMQETkbFqhEqLnb01QAfwegVTYNOZhdAObx0D8RUfNhgUqtmuWOTxMAzAcQrnAccmxfAZidk5hwROkgRESOjgUqtVpavaEfgEUABiidhZxGFYB3YT70X6h0GCIiR8UClVodrd4QAOAfAP4AXseUbo4LMA8X+SAnMaFa6TBERI6GBSq1GpZxptMBvALAV+E41DqkA5iVk5jwjdJBiIgcCQtUahW0esNtMB/O7610FmqVVgKYmZOYkK90ECIiR8AClZyaVm/wAfAWgEcUjkJ0AcDTOYkJSUoHISKydyxQyWlp9Ybfw3w/9U5KZyGy8Q2AaTmJCSeUDkJEZK9YoJLT0eoN7WDumj6qdBaiayiB+WYQi3ISE0xKhyEisjcsUMmpaPWG3wH4GECI0lmIGuFHAA/lJCYcVToIEZE9YYFKTkGrN3gDWAjgcaWzEDVRMYAZOYkJy5QOQkRkL1igksPT6g1xMJ8l3U3pLEQ3YBnMhWqx0kGIiJTGApUcluU2pbMBvA7AReE4RM3hKIDJOYkJe5UOQkSkJBao5JC0eoMfgKUAEpTOQtTMqmA+gWpBTmIC/wdNRK0SC1RyOFq9oT+ANQC6KJ2F6Cb6BsCUnMSEC0oHISJqabwPOTkUrd4wA8BOsDgl5/c7AL9o9YYYpYMQEbU0dlDJIWj1BhcAH4DXNqXWpwzAYzmJCSuVDkJE1FJYoJLd0+oN/gC+BDBM6SxECnoDwPO8sD8RtQYsUMmuafWGUAAGAD2UzkJkBzbCPC61ROkgREQ3EwtUsltavWE4zJ1TX6WzENmRVAB35SQm/KZ0ECKim4UnSZFd0uoNj8F8FjOLU6IrRQP4Uas3RCkdhIjoZmEHleyK5eL7iQCeUToLkZ0rAHB3TmLCLqWDEBE1NxaoZDe0eoMKwIcAHlM6C5GDKAdwX05iwialgxARNSce4ie7YLmM1OdgcUrUFB4A1mn1hkeUDkJE1JxYoJLitHqDO4B1AO5TOguRA9IA+FSrN8xVOggRUXPhIX5SlFZv8ALwFYDbFY5C5AzeyElM0CsdgojoRrGDSorR6g2+AL4Di1Oi5vKsVm94XekQREQ3igUqKUKrN/gB+B7AAKWzEDmZ57R6w8tKhyAiuhEsUKnFafUGbwBbAMQonYXISc3T6g0vKh2CiOh6sUClFqXVGzwAbALQX+ksRE7uFa3ewPGoROSQWKBSi9HqDa4A1gIYqnQWolbiH1q9YbbSIYiImooFKrUIrd6gBrAcQLzSWYhamQVaveHPSocgImoKXmaKbjrL7Us/AfCIwlGIWisTgPE5iQnrlQ5CRNQY7KBSS3gLLE6JlKQCsEKrN9yqdBAiosZgB5VuKq3e8CSA/yidg4gAAOcBDMxJTDiqdBAiovqwQKWbRqs3xMN8xr5a6SxEVOMogEE5iQn5SgchIroWFqh0U2j1hkgAuwG0VToLEV3lJwAjchITypUOQkRUF45BpWan1Rs6wNw5ZXFKZJ9uhXlMKj8DiMgu8X9O1Ky0eoM7gPUAtApHIaL6jQXwd6VDEBHVhQUqNbdPAAxUOgQRNcrftHrDXUqHICKqjQUqNRvLHWsmK52DiBpNAEjS6g3dlQ5CRGSLJ0lRs9DqDQMBbAegUToLETVZKoBbcxITypQOQkQEsINKzUCrN/gDWAUWp0SOKhrAR0qHICKyYoFKN8RyG9MkAJ2VzkJEN+QBrd4wU+kQREQAC1S6cc8CGK10CCJqFgt4O1Qisgccg0rXTas3DAWwDbxTFJEzOQagd05iQonSQYio9WIHla6LVm8IAPA5WJwSOZvuAN5SOgQRtW4sUOl6vQ+gk9IhiOimeFyrN9yjdAgiar14iJ+aTKs3TIK5e0pEzisfQHROYsJZpYMQUevDDio1iVZvCASwSOkcRHTTtQfwX6VDEFHrxAKVmmoxAH+lQxBRi0jQ6g3TlA5BRK0PD/FTo2n1hgdhvuYpEbUepTAf6j+udBAiaj3YQaVG0eoNQQDeUToHEbW4NuCwHiJqYSxQqbE+AOCrdAgiUsTvtXrDBKVDEFHrwUP81CDL5WbWK52DiBSVCyAiJzGhWOkgROT82EGlemn1BncAbyudg4gUFwzgVaVDEFHrwAKVGqIHoFU6BBHZhT9r9YZYpUMQkfPjIX66Jq3e0A1AOgB3pbMQkd3YC2BATmKCSekgROS82EGl+rwFFqdEdKV+AP6kdAgicm7soFKdtHpDPIDNSucgIruUD6A7T5giopuFHVS6ilZvcAXwb6VzEJHdag/gWaVDEJHzYoFKdfkTgFClQxCRXXtaqzd0UjoEETknFqh0Ba3e4A3gBaVzEJHd8wTwstIhiMg5sUCl2ubAfPiOiKghj2r1hgilQxCR82GBSjW0ekN7ALOUzkFEDkMNIFHpEETkfFigki09AC+lQxCRQxmj1RuGKB2CiJwLC1QCAGj1hiDw2oZEdH1eUToAETkXFqhk9RwAD6VDEJFDGqbVGwYrHYKInAcLVLJ2T/+odA4icmh/UzoAETkPFqgEADMBuCkdgogc2iit3tBf6RBE5BxYoLZyWr3BC8A0pXMQkVPQKx2AiJwDC1R6DICv0iGIyCmM1eoNvAsdEd0wFqitmFZvUAN4SukcROQ0VADmKh2CiBwfC9TWbTwArdIhiMipTNXqDYFKhyAix8YCtXWbrXQAInI6bgAeVzoEETk2IaVUOgMpQKs3DAWQrHQOInJKvwHolpOYYFI6CBE5JnZQW6+/KB2AiJxWFwC/VzoEETkuFqitkFZvaA9gjNI5iMip8dbJRHTdWKC2TlMBuCodgoicWrxWb9AqHYKIHBML1NbpMaUDEJHTU4E3ASGi68STpFoZrd5wK4Aflc5BRK3COQCdcxITLisdhIgcCzuorQ+7p0TUUjoAuEfpEETkeFigtiJavcELwCSlcxBRq/KA0gGIyPGwQG1d7gPgpXQIImpVRmv1hrZKhyAix8ICtXWZonQAImp13ADcq3QIInIsLFBbCa3eEABgmNI5iKhVmqx0ACJyLCxQW497AKiVDkFErdJIy5dkIqJGYYHaekxQOgARtVoa8P9BRNQELFBbAa3e4ANgpNI5iKhV4xVEiKjRNEoHoBZxNwAXpUMQUas2RKs3dMxJTMhTOsiN2rdvXweNRvMxgCiw0UN0PUwADldXVz8eGxt7rq4ZWKC2DuOVDkBErZ4KwO8AfKZ0kBul0Wg+7tixY0T79u0LVCoVb8dI1EQmk0nk5+fr8vLyPgYwpq55+M3PyVkuzv87pXMQEQEYrXSAZhLVvn37SyxOia6PSqWS7du3L4L5KETd87RgHlLGSADuSocgIgIwSqs3OMPVRFQsTolujOXf0DXrUBaozu8OpQMQEVn4AhigdAhH99hjj3WeP39+B+vjwYMH97z//vu7Wh8/8cQTIS+99FLg8OHDe9S1/P33399137597gCg1+s73vzERE3HMajOjwUqEdmTeAC7lQ7RnLR6Q2xzri8nMWFffdNvu+22kjVr1vgCOGc0GlFQUKApKSmp6Uz/8ssvXh06dCi81vKrVq06Yf39nXfeCUpMTHT4E9fI+bCD6sS0ekMwgHClcxAR2XCWcaiKGTFiRMm+ffu8AGDfvn0eYWFh5W3atDHm5+ery8vLxbFjx9xjY2PLSktL1fHx8bd069YtcsyYMd1MJhMAIC4uLmz79u2eM2bMCK6srFSFh4frxowZ0w0A3nvvPb/o6OiI8PBw3QMPPNC1urpawS2l1owFqnNj95SI7E2sVm/o0PBsdC1arbZKo9HII0eOuCYnJ7e59dZbS/v161e6bds2rx07dniGhYWVu7q6yoyMDI9333335NGjR9N+++03t61bt3rZrue9997LdXNzM2VmZqZv3Ljx+P79+93XrFnjt3fv3szMzMx0lUolFy9e7K/UdlLrxkP8zo0X5ycieyMA3AlgudJBHFlsbGzJ999/3+bHH3/0mjt37tnffvvNddeuXW3atWtnjIuLKwGA6Ojo0u7du1cBQGRkZNmxY8dc61vnli1bvA8fPuwZExMTAQAVFRWqDh06sIVKimCB6txYoBKRPboNLFBvyMCBA0t2797tlZmZ6dG/f//yW2655fLbb78d6OXlZXz00UfPA4Cbm1vNlQbUajWqq6tFfeuUUoqJEydeePfdd3Nvdn6ihvAQv5PS6g06AJ2UzkFEVIdBSgdwdMOGDSv57rvvfHx8fIwajQaBgYHGS5cuqQ8cOOA1fPjw0sauR6PRyMrKSgEA8fHxlzZt2uSbm5urAYCzZ8+qs7Oz6+26Et0sLFCd11ClAxARXUOUVm/wVjqEI4uLiysvLCzU9OvXr8T6XHh4eLmXl5cxKCio0Yflp0yZkh8REaEbM2ZMt9jY2IoXX3wxd+TIkaGhoaG6ESNGhJ48eZK3ySZFCCl5rWFnpNUbPgHwqNI5iIiu4c6cxITvlA5xPVJSUnJiYmLOK52DyNGlpKQExMTEaOuaxg6q8+qndAAionrwMD8RXRMLVCek1Rs8AeiUzkFEVI+BSgcgIvvFAtU59QXgDPe7JiLndatWb6j3rHIiar1YoDonHt4nInvnAyBC6RBEZJ9YoDqn/koHICJqhF5KByAi+8QC1TmxQCUiRxCpdAAisk8sUJ2M5dqCPZTOQUTUCCxQr5NarY4NDw/XhYWF6XQ6XcTWrVvbAEBWVpZrz549r2u/xsXFhW3fvt2zeZP+T1hYmO7uu+/uZvvcgQMH3MPDw3URERG6tLQ0t9rLDBs2rMf58+dv6jkVu3bt8hBCxH755Zdtb9Zr5OTkuMTHx99ys9bvjHirU+cTBvO9romI7J1zFKgvtYtt3vUV7WtoFjc3N1NmZmY6AHz55Zdtn3/++ZA777wzq1lzXKfq6mpoNFeWF/v373eXUmLPnj3ely5dUrVt29YEAKtXr/YZPXp04VtvvXXadn6TyQQpJZKTk4/e7LxJSUn+ffv2LVmxYoXf+PHjLzX3+quqqqDVaqu2bNnya3Ov25mxg+p8QpUOQETUSN21esNVXTNqmqKiInW7du2uuntUVlaWa2xsbJhOp4uw7bICwIsvvhgYGhqqCwsL082YMSPYdjmj0Yhx48ZpZ86cedXtsjds2OAdERGhCw0N1U2cOFFbXl4uACA4ODh6zpw5QbGxsWGffPKJb+3lPvvsM7/77rvvwtChQy99/vnnPgCwatWqdh9++GHg8uXLAwYMGBCalZXlesstt0Q++OCDXSIjI3XHjh1zDQ4Ojj5z5owGABYtWuRvzTx27NhuALBixYp2vXr1Co+IiNANGjQo9OTJkxoAmDVrVqeJEydq4+LiwkJCQqJfffXVDnXtO5PJhE2bNvkuXbo0Z8eOHW3LysqEdd9169Yt8v777+/as2fPyDFjxnRbv369d9++fcO7du0a9f3333sCwKVLl1QTJ07URkVFRUREROiWLVvmAwDvvPOO/+jRo28ZMWJEjyFDhoTadrarq6vxxz/+MSQ0NFQXGhqqe+211zoAwJw5c4KioqIievbsGTl58uSuJpMJgLmz/ac//Sk4Ojo6QqvVRm3ZssWrgT8Jp8AOqvMJUzoAEVEjqQGEA0hROoijqaywvgdWAAAgAElEQVSsVIWHh+sqKyvF+fPnXb7++uvs2vN06tSpeseOHdmenp4yNTXVbfLkybccPnw444svvmhrMBh89+3bl+nt7W06e/ZszSH0qqoqMXbs2G46na78jTfeyLNdX1lZmZg2bVq3b7/9NqtXr16V9957r/Zf//pX+3nz5p0DAHd3d9O+ffvq7OJu2LDB79tvv80+fPhw+aJFizpMmzbt4v3331+0Z8+efC8vL+P8+fPPZmVluebk5Lh/9NFHOcuWLfvNdvm9e/e6L1iwIOjHH3/MDAoKqrZmvvPOO0smTZqUqVKpsHDhwoD58+d3/Oijj04BwNGjR913796dVVhYqI6IiIiaO3duvpub2xW3z9y6datX586dKyMjIysHDBhQvHr16nYPP/xwIQCcPHnSfdWqVb/Gxsae6NWrV8Ty5cv99+7dm7lixQqf1157LWj48OHHnn/++aDhw4dfWr16dc758+fV/fr1ixgzZswlANi/f7/XoUOH0gIDA41ZWVmu1td888032584ccItLS0t3cXFBdZtmTt37rkFCxacAYCxY8d2W7lyZbsHHnigCACqq6tFampqxqpVq9rNnz+/U3x8/FXvt7NhB9X5sINKRI7EOQ7ztzDrIf7jx4+nrVu37sijjz7azdpxs7p8+bJ44IEHtJZuZ/djx465A8DWrVvbPvjgg+e9vb1NABAYGGi0LjNjxoyudRWnAJCSkuIeEhJS2atXr0oAeOSRRy7s3LnT2zr9oYceKqgra3Jysqefn191aGjo5TFjxlxKS0vzzM/Pr3NcaVBQ0OWRI0eW1n7+m2++aXv33XcXBAUFVdtmPn78uOuQIUN6hoaG6t55552OmZmZHtZlRo0aVejh4SGDgoKq/fz8qk6dOnVVU27ZsmV+EyZMuAgAkyZNurhy5Uo/67Tg4ODKuLi4crVajdDQ0PIRI0ZcUqlU6Nu3b9mpU6fcAOCHH35o+9ZbbwWFh4frBg8eHFZZWSmOHj3qCgBDhgy5ZLtvrbZt29Z2+vTp+S4uLrDdls2bN3v36tUrPDQ0VLd7927vw4cP12zLxIkTCwBg0KBBpadOnXKtvU5nxA6q82GBSkSOhAXqDbrjjjtKCwoKNNZD4VavvfZaYIcOHaq+/PLL4yaTCR4eHrEAIKWEEHWfqtCvX78Sy6Hus56enld0G6WUdS5jZS14a0tKSvL79ddf3YODg6MBoLS0VJ2UlOQ7a9as87Xn9fT0rHMdlsxXBXjyySe7/PWvf82bMmVK0aZNm7znz59fMyzBtluqVqtRXV19xUZXV1dj8+bNvlu3bvVZuHBhkJQShYWFmoKCAhUAuLq61iyvUqng7u4uresyGo3CmmvNmjVHY2JiKm3XvXPnzjZN2ZaysjIxe/bsrnv27Env0aNH1axZszpVVFTUNBGtr63RaGpe29mxg+p8eiodgIioCXjVkRt04MABd5PJhMDAwCvGoRYVFamDgoKq1Go13nvvPX+j0dzMi4+Pv5SUlBRQXFysAgDbQ/zTpk07P2rUqKK77rqre1VV1RWv07t374rc3FzXw4cPuwHA0qVL/YcMGVJcXzaj0YhNmzb5HThwIC03Nzc1Nzc39fPPPz+6evVqv/qWqy0+Pv7Sxo0b/fLy8tS2mYuLi9VdunSpAoAlS5b4N2WdGzZsaBseHl6Wl5d3KDc3N/X06dOp8fHxBStWrPBp7DqGDx9+6c033wy0dq937drl0cAiuOOOOy4tXry4vXX/nj17Vl1WVqYCgI4dO1YXFRWpvvrqq6vG8bY2LFCdiFZv6ATAu8EZiYjsR4jSARyRdQxqeHi4btKkSbe8//77ObXPnH/qqafOff755/4xMTHh2dnZ7h4eHiYAmDBhwqXRo0cX9u7dOyI8PFz3yiuvdLRd7qWXXjobExNTNm7cuG7WohYAPD095eLFi3MmTpzYPTQ0VKdSqTBnzpz8+nJu3rzZOzAw8HK3bt1qqt3Ro0cXHz161P3EiRMujd3efv36VcyePfvMkCFDwi0ndnUGgBdeeOH05MmTu8fGxob5+/tfdaJYfVasWOE3ZsyYQtvnxo8fX7Bq1apGF7qJiYmnq6urRXh4uK5nz56RL774YnBDyzz99NP5ISEhl8PDwyPDwsJ0//3vf/0CAgKMU6ZMydfpdJGjR4/uERMTc9Uwh9ZGNNSyJ8eh1RuGAfhB6RxERE1wMicxoYvSIZoiJSUlJyYm5qrD00TUNCkpKQExMTHauqaxg+pcOisdgIioiYK0egM/i4joCvyfgnMJVDoAEVETaQAEKR2CiOwLC1Tn0rHhWYiI7A7HoRLRFVigOhcWqETkiDg8iYiuwALVubBAJSJHxAKViK7AAtW5cAwqETkijkEloiuwQHUu7KASkSNq9IXR6X+WLl3qI4SIPXDggPvNeo3t27d7PvLIIzetw71p0ybv4cOHN+lmDbNmzeo0b968QAB46qmnOq1fv17x639nZWW59uzZ85p3RXv55Zc7uLm59b1w4ULNTRGuZ9sb81oNEULEPvHEEzXjvufNmxc4a9asTvUts2nTJu+tW7e2sT7+5z//2X7RokVNujFCU/FWp05CqzdoANzUPxYiopukrdIBbkT0Z9Gxzbm+1IdT9zVmvpUrV/r17du3JCkpya9Pnz6nmzMDAFRVVWHo0KFlQ4cOLWvudTeXt99+u9m3+2ZYs2aNf1RUVOny5ct9Zs6ceUHJLK6urvLrr7/2PXPmTF5QUFCjbm6wbds2by8vL+Odd95ZCgDPPPNMvTdoaA7soDoPX/D9JCLH5NAFqhKKiopUe/fu9fr0009z1q1bV3NbzE2bNnn3798/7Pe///0tWq02asaMGcHvv/++X3R0dERoaKguLS3NDQBOnz6t+d3vftc9KioqIioqKuLbb79tA5i7k5MnT+5622239Rw3blw32y5fUVGRasKECdrQ0FBdaGiobsmSJT4AMGXKlC5RUVERPXr0iHz66adrOnHBwcHRTz/9dCedThcRGhqqa6jTO2vWrE4TJ07UxsXFhYWEhES/+uqrHazTnn322Y5arTZq0KBBoUeOHHGzPj9+/Hjtp59+6gsAc+bMCYqKioro2bNn5OTJk7tabz8aFxcX9qc//Sk4Ojo6QqvVRm3ZssWrrv05cODAUGvWZcuW+QDmbuUtt9wSOWnSpK49evSIvO2223qWlJQIANixY4dnWFiYrnfv3uELFy7sUHudVmlpaW5lZWWq+fPn537xxRd13uL1+++/9+zTp094RESErk+fPuEpKSluALB371736OjoiPDwcF1oaKguNTXVzXa59PR014iICF1ycrJnVlaWa2xsbJhOp4vQ6XQRth1PW2q1Wj700EP5r7/++lXDAlesWNGuV69e4REREbpBgwaFnjx5UpOVleW6dOnS9osXLw4MDw/Xbdmyxcvaxd6/f797dHR0hHX5rKws19DQUJ11//Tv3z8sMjIyYvDgwT2bcucwgAWNM1H8EAcR0XVigdpEy5cv97n99tuLevXqVenj42PcuXOnp3VaZmamx/vvv38yIyMjbc2aNf7Z2dnuqampGVOnTj3/5ptvdgCAadOmdZ41a9bZw4cPZ6xbt+7Y9OnTtdblDx065PnNN98c/eqrr47bvqZerw9q27atMTs7Oz07Ozs9ISGhGAAWLlyYe/jw4YzMzMy0Xbt2ee/Zs6fmfvQBAQHV6enpGX/4wx/yExMTGzxP4ujRo+7JycnZv/zyS8aCBQs6VVZWih07dniuW7fOLzU1NX3Tpk1HU1JS6iy85s6de+7w4cMZR44cSSsvL1etXLmynXVadXW1SE1NzXjjjTdOzp8//6rD2Z6eniaDwXA0PT09Izk5Ofv5558PsRa4v/32m/vMmTPPHT16NK1du3bGpUuX+gLAY489pl24cOFvBw8ezKxvmz777DO/cePGXYyPjy85fvy4e25u7lVHr2NiYip+/vnnzIyMjPS///3vuc8880wIAPznP/9pP2PGjLOZmZnphw4dyujWrdtl6zIpKSlu48eP7/Hf//73+LBhw8o6depUvWPHjuz09PSMVatW/fr0009f8w5tc+fOPbd27Vo/2yEHAHDnnXeWHDx4MDMjIyN9woQJF+fPn98xLCzs8kMPPZQ/ffr0s5mZmenx8fEl1vn79u1bUVVVJdLT010BYOnSpX5jx44tqKysFDNnzuyyYcOGY2lpaRkPP/zw+Tlz5jR4G1hbPMTvPK76RkhE5CBYoDbRF1984ffXv/71HACMHz/+YlJSkt/gwYPLACA6Orq0a9euVQDQpUuXytGjRxcBQExMTHlycrI3AOzatavtkSNHagrJkpISdUFBgQoA4uPjC728vK66D/r27dvbrly58lfr4/bt2xsBcwG2ZMmSgOrqapGfn++SkpLiPmDAgHIAeOCBBwoAIC4urmzjxo2+tddZ26hRowo9PDykh4dHtZ+fX9WpU6c033//vdfvf//7Qm9vb5N1nrqW3bx5s/fChQs7VlRUqAoLCzU6na4cQBEATJw4sQAABg0aVDp37lzX2suaTCbx1FNPhfz0009eKpUK586dcz116pQGAIKDgysHDRpUDgB9+vQpy8nJcbtw4YK6uLhYnZCQUAIAf/jDHy5s27atXe31AsC6dev81q5de1StVmP06NEFS5cu9X3uueeuOER+8eJF9f33398tJyfHXQghq6qqBAAMHDiwdMGCBUGnTp1ynTRpUkF0dHSlZX7N2LFje6xevfpYv379KgDg8uXL4rHHHuuanp7uoVKpcOLECber05j5+fmZJk6ceCExMbGDh4eHyfr88ePHXceOHRuSn5/vcvnyZVXnzp0rr7UOq7Fjx15ctmyZ3+uvv563bt0631WrVv166NAhtyNHjniMGDEi1LJ/0b59+6qG1mWLHVTnwQKViBxVnR/sVLe8vDz1Tz/91PbPf/5z1+Dg4OhFixZ13Lhxo6+14+fm5lZTXKpUKri7u0vr70ajUQCAlBJ79+7NyMzMTM/MzEw/d+7cIV9fXxMAtGnTxlTHy0JKCSHEFc9lZma6Llq0KDA5OTk7Ozs7fcSIEUUVFRU1tYX1tTUajayurhZogG12tVoN6zK1X7e2srIyMXv27K5r1649lp2dnf7ggw+ev0aOmn1g64MPPvC7cOGCJjU1NSMzMzPd39+/qry8XAWYx2zaZJLV1dWirn1Rlz179nicOHHCLT4+PjQ4ODh648aNfmvWrLnqMP+zzz4bPGzYsOIjR46kffXVV0cvX76sAoDp06df3LBhw1EPDw/T6NGjQzdu3OgNAN7e3sagoKDLP/zwQ81n/2uvvRbYoUOHqoyMjPTU1NT0qqqqemu855577uyKFSsCSktLa+Z78sknu8yYMeNcdnZ2+qJFi05UVlY2WCdOnTq1YP369b6HDh1yE0IgOjq6UkopevToUW79+8rOzk7ftWvXkQZ3mA0WqM7Ds+FZiIjsEjuoTZCUlOQ7bty4C6dPn07Nzc1NzcvLOxQSEnL522+/bXSjYvDgwZfeeOONmnGTu3fv9qhvfgC4/fbbL9mOtczPz1cXFBSoPTw8TH5+fsaTJ09qfvjhh2b/sjFixIgSg8HgU1JSIgoKClRbt2696qoPZWVlKgDo2LFjdVFRkeqrr75qsFtrq6ioSB0QEFDl5uYmv/rqK+/Tp09f1WW1FRAQYPTy8jJ+8803XgCwZMmSOseWLl261G/27Nmnc3NzU3Nzc1PPnTt3KC8vzzU7O/uK9V+6dEkdEhJyGQA++OCDAOvzljGmlS+++OK5UaNGFR48eNADAFxcXOSWLVuOff755/6LFy/2s25DUFBQlVqtxnvvvedvNBrr3ebAwEDj3XffXbBixYqa1ysuLlZ36dKlyrJNNSdee3t7G4uLi9V1rScyMrJSpVJh3rx5ne69996LANCrV6+Kixcvar777rs2AFBZWSn27t3bpKtNsEB1HjftMiNERDcZx9A3werVq/3HjRtXYPvcPffcU5CUlFRnkVSXDz/88OT+/fvbhIaG6rp37x65aNGi9g0t849//ONMYWGhumfPnpFhYWG6r7/+2nvgwIHlUVFRZT179oycOnWqNjY2tqSh9TTV4MGDy+69996LUVFRkXfddVf3uLi4q14jICDAOGXKlHydThc5evToHjExMaVNeY3HH3/8YkpKSpuoqKiIZcuW+XXr1q2ioWX++9//5sycObNL7969wz08PK4aEgEA69ev97vvvvuuGJIwevTogs8+++yK9+rZZ5/Ne+mll0L69u0bbltYJiUl+YWGhkaGh4frjhw54j5t2rSaKwC0bdvW9M033xxdtGhR4LJly3yeeuqpc59//rl/TExMeHZ2trvtoftreeGFF/IKCws1No9PT548uXtsbGyYv79/zRn+48ePLzQYDD7Wk6Rqr2fcuHEXN2zY4Dd16tQCwNyxXrly5TG9Xh8SFhami4yM1CUnJzfpSK+Qss59Sg5GqzeMB7BG6RxERNdJk5OYUH/Lx06kpKTkxMTEnFc6B5GjS0lJCYiJidHWNY0dVOdxzcHQREQOgJ9HRFSD/0NwHvWOlyEisnN1jm8jotaJBarzaHCsCRGRHePnERHV4HVQnUeTri9G1BhuuFzxEL7ec0vqrxdzvTzilc5Dzst8wZ4EhVMQkb1ggeo8WKBSswlG/pmXXZZkRZrSOlww+HkeCgwxCqDBy9AQ3QAeBSKiGixQnQcLVLphw1QHU1/SLC3Rirz+mUa3c/kb/aVLleh/sY37bqWzkdNjgUpENTjmx3lUNzwL0dVcUVX5pHrdrjS3RzM+c/1ndDdV3sBvq7x2Vq/1a+tSJSJNQlVVqRFdlc5JTs8hLjFlT5YuXeojhIg9cOBAvdfBHjZsWI/z58/zJDRyKOygOg92UKlJOuLi2ZdclmSOUu3TqYS8DQCMgPG/5f7f3rbRra9KIhAACtu0z4JAlKJhydmZZq/a5LAFakZ4RGxzri8iM2NfY+ZbuXKlX9++fUuSkpL8+vTpc/pa8yUnJx9tvnRELYMdVOfBApUa5TbV4bTvXOfs/tHtSb949d5hKiHbA8AllSj656XAbwdvcBtkLU4B4JyP9xnl0lIrUax0AEdTVFSk2rt3r9enn36as27dOl8AOHHihEu/fv3CwsPDdT179oy03vEnODg4+syZMxoAuOOOO7pHRkZG9OjRI3LBggU1t7j09PTs85e//CU4LCxMFxMTE37y5Ek2sEhRLFCdBwtUuiYXVF+ert64O9XtsbTlrq9H9lCdHiQEXKzTj7loct7JC9pzz9fq4QK44l7a573cODaQbrZLSgdwNMuXL/e5/fbbi3r16lXp4+Nj3Llzp+cnn3ziN3LkyKLMzMz0jIyMtAEDBpTVsVxOWlpaxsGDB9M/+OCDwLy8PDUAlJeXqwYOHFiSlZWVPnDgwJL//Oc/Dd76lOhm4jck59Gk+w5T69ABBfnzXJLSR6v2RKiFHFTXPFvaeO5LO+xTOG4PRopaF0uXgCxxg2/LpKVWjAVqE33xxRd+f/3rX88BwPjx4y8mJSX5jR07tnDatGnaqqoq1YQJEwoGDRpUXnu5N954I9BgMPgAQF5enktaWpp7x44dS11cXOSkSZOKACA2Nrb0u+++a9uyW0R0JRaozuOC0gHIfsSJjPRXXD4tCBWn+guBYdea7x9+vskdvvc0/T5DjqxremmbTsdNqLjl5iUlAgAUKR3AkeTl5al/+umnttnZ2R5PPvkkjEajEELI999//9T27duzvvzyy3aPPPJIt5kzZ5598sknaz4bNm3a5J2cnOy9d+/eTG9vb1NcXFxYeXm5CgA0Go1UqcwHVTUaDaqrq4VCm0cEgAWqM2GB2sqpYax+RP3NL3/VrPVuK8rqPampCqh6uGPg7glr1Ig4JYdfa75zPsEngIvXnE7UTNhBbYKkpCTfcePGXVixYsUJ63P9+/cP27x5s9eoUaNKZs+efb60tFS1f/9+T9h8NhQWFqrbtWtn9Pb2Nh04cMA9JSWljSIbQNQILFCdRE5iQqlWb6gE4KZ0FmpZ/ig6/zeXpLS7VT+FqoVpYEPzX1Cpzk/s2PHEC5/CtVMB6p3/vLcLh45QS2CB2gSrV6/2f+aZZ644efGee+4p+OMf/9jN09PTpNFopKenp3H58uXHbecZP3580Ycfftg+NDRU171794qYmBj++ya7JaSUSmegZqLVG3IBdFI6B7WMPuJI1qsun+TrxIl+QqDe6yBaHXZ1PTLNr4N880NTSbsy9G1o/q0xA7dW4dydN56WqF4fz1616QmlQzRWSkpKTkxMzHmlcxA5upSUlICYmBhtXdPYQXUuF8AC1ampYDI+qN768yzNmjY+orQXgLDGLrvGq82ed9x9Oy5611TmXtVwcVrh5numShQHgN9h6ebjGFQiugILVOfCcahOyheXLj6vWZF6r3pnT00jDuPXpm/v/8P+Cg/tu++ZoDEhojHLnAvodRzyFC/QTy2B19oloiuwQHUuLFCdTC9x7MirLp/kRYvj/eo7G/9aKoQov69TxwOafE3QW0lGT5VEh8Yue76tbxFwipeaoZZwUukARGRfWKA6l3ylA9CNEzCZJqu3/TJHs9rNTxT3BtDzetaTp1bn3RMSdFF3FJ5zvzQGCaBJxWaRW7Xk4X1qIaeUDkBE9oUFqnM50fAsZK/aoqToOc3nByeot3d3EcYBN7Kun93d0p7o2CHgjv3y0mPfmvqIJl7doUrtUVShKmoDh707OjkYdlCJ6AosUJ3L8YZnIXujEznHXnP55HRvcbTv9RzGr+2Tdt673vL1iZ3yg+nnMT/JweI6bml8wT/qiDSeD2h4TqIbZgRwWukQRGRfmvzBRXaNBaqDEDCZJqp/+GWv2/T9X7s9372P6ugQIXBDF802AaYZge2T3/Lzve3p9aaf7vlJDr2e4hQAzrWPLgYu97iRPESNlDd71Sb26ptICBH7xBNPhFgfz5s3L3DWrFn1XsVl06ZN3lu3bq35/8w///nP9osWLfK/0SyzZs3qNG/evEDb54KDg6PPnDlzU5pgTz31VKf169d7A8D999/fdd++fY26zB45FnZQncuvSgeg+nmjtOgZzaqDk9Tfa12EsX9zrbdEiOJ7Q4IyzqrVQ179rHp76GncfiPrK3JXVaOaN32gFuHwh/ffnb4ttjnX9+fFI/Y1NI+rq6v8+uuvfc+cOZMXFBRU3Zj1btu2zdvLy8t45513lgLAM88845DnLbz99ts1HfdVq1ZxaJuTYgfVieQkJpwHUKJ0DrpamPjt+GrXl7cfcntCPVXz3TAXYezaXOs+odGcvL1LcN55qGP+vdj4c+hpDL2R9ZmE+nK5qohfXqml8ASp66BWq+VDDz2U//rrrwfWnrZixYp2vXr1Co+IiNANGjQo9OTJk5qsrCzXpUuXtl+8eHFgeHi4bsuWLV7Wzuf+/fvdo6Ojay4/l5WV5RoaGqoDgB07dnj2798/LDIyMmLw4ME9T5w44dLUrHfccUf3yMjIiB49ekQuWLAgAAA+/vhj38cffzwEAF555ZUOISEh0QCQlpbmFhsbGwYAc+bMCYqKioro2bNn5OTJk7uaTCYAwPjx47WffvqpLwDExcWFbd++3RMApkyZ0iUqKiqiR48ekU8//XRNNzk4ODj66aef7qTT6SJCQ0N1Bw4cYMfVAbBAdT48zG83pLxXtWPvHrcZ+7a46rX9VVlDhYBXc77CD54eB+8OCfJUXRaBixcZMzoW4tYbXWeBT2iW0XiKBSq1lGNKB3BUc+fOPbd27Vq/CxcuqG2fv/POO0sOHjyYmZGRkT5hwoSL8+fP7xgWFnb5oYceyp8+ffrZzMzM9Pj4+JpmRt++fSuqqqpEenq6KwAsXbrUb+zYsQWVlZVi5syZXTZs2HAsLS0t4+GHHz4/Z86c4LqyWAtf68+5c+dqCtnly5fnpKWlZRw8eDD9gw8+CMzLy1OPGjWq+KeffvIGgF27dnn5+PhUHz9+3GXbtm1et956a4l1+w4fPpxx5MiRtPLyctXKlSvb1bc/Fi5cmHv48OGMzMzMtF27dnnv2bPHwzotICCgOj09PeMPf/hDfmJi4lVFPdkffgg5n+MAopUO0Zq1QXnxbM3qAw+qv+vsKqr73azXecu33fZP2rUd6FuCgn9/YDzvXoXezbHecx1iL0jjL7wjGbWUNKUDOCo/Pz/TxIkTLyQmJnbw8PAwWZ8/fvy469ixY0Py8/NdLl++rOrcuXNlQ+saO3bsxWXLlvm9/vrreevWrfNdtWrVr4cOHXI7cuSIx4gRI0IBwGQyoX379lV1LT99+vSz8+fPP2t9HBwcXPM59MYbbwQaDAYfAMjLy3NJS0tzHzlyZGlZWZmqoKBAdfr0adeJEyde+Pbbb7137tzpNW7cuEIA2Lx5s/fChQs7VlRUqAoLCzU6na4c9dx17LPPPvNbsmRJQHV1tcjPz3dJSUlxHzBgQDkAPPDAAwUAEBcXV7Zx40bfhvYHKY8dVOfDcagK6S5yT6x0fWX7YbfH5B80W4a6iupuN+N1qoHqh4I6bP/Ep93Q4As4/e57xgr3qsbf8rQhF9tpXYDq7s21PqIGsEC9Ac8999zZFStWBJSWltZ8nj/55JNdZsyYcS47Ozt90aJFJyorKxv8rJ86dWrB+vXrfQ8dOuQmhEB0dHSllFL06NGjPDMzMz0zMzM9Ozs7fdeuXUeakm/Tpk3eycnJ3nv37s3MyspKj4iIKC8vL1cBQGxsbOm7774b0L1794rhw4eX7Nixw2vfvn1ed9xxR0lZWZmYPXt217Vr1x7Lzs5Of/DBB89XVFRcczsyMzNdFy1aFJicnJydnZ2dPmLEiCLb+d3d3SUAaDQaWV1dLZqyDaQMFqjOJ13pAK3NXaof9//o9uQv37nO7XyrKmOoEE27IH5TFKlUhat5mwcAACAASURBVCO7BB864O4+NOykzHjzY6OnxoQuzbV+CchydbEagLrBmYlunAlAhtIhHFlgYKDx7rvvLlixYkXNZeGKi4vVXbp0qQKAJUuW1Jyl7+3tbSwuLq7z33ZkZGSlSqXCvHnzOt17770XAaBXr14VFy9e1Hz33XdtAKCyslLs3bu3SeM3CwsL1e3atTN6e3ubDhw44J6SklJzFYEhQ4YUv/vuu4FDhgwpGTRoUNnu3bu9XV1dTf7+/saysjIVAHTs2LG6qKhI9dVXX9Xb9SwoKFB7eHiY/Pz8jCdPntT88MMP9Q4HIPvHAtX5HFQ6QGvgiYrS5zTLt2e5PXxsket/+gaJi/2FuLn/nrJcXH69vUtw0UW1uu+ATNP++cuMISqJ9s35GsVenY8ajScbPBxI1EyOz161qVzpEI7uhRdeyCssLNTYPD49efLk7rGxsWH+/v41Z/iPHz++0GAw+FhPkqq9nnHjxl3csGGD39SpUwsAc9dx5cqVx/R6fUhYWJguMjJSl5yc3KRx9OPHjy+qrq4WoaGhuueff75TTExMqXXayJEjS/Ly8lzvuOOOYo1Gg6CgoMtxcXElABAQEGCcMmVKvk6nixw9enQP2+XqMnDgwPKoqKiynj17Rk6dOlUbGxvLE4YdnJCS9zJ0Jlq9wR3mM/nZAbsJtOLMyVc0n/46WHW4txBosW/om9p47n2uvX8ohGg7+hfTj498Z4oVgGtzv87RW8buONIuVyVNF29r7nUT1WHj7FWb7lE6RFOlpKTkxMTEnFc6B5GjS0lJCYiJidHWNY0nSTmZnMSECq3ekA0gosGZqdF+p/r5wN9cllUF43w/IdC5JV/7JX+/5C+92wyBEKqHvzMm//4XOeR6L8DfkPP+USp5Oa3Os3SJbgIOSSKiOrFAdU4HwQL1hrmjsvwvmvV7H1N/3dFdVPVp6de/DFRO7tTxl2w312EAMGeNMTnuiLzhW6HWp8zNyx+XTc12jVaiBvAEKSKqEwtU53QQwGSlQziqzuJc7nzNp0eGqVJiVAJDlMiQr1bljwnulFeiVg1WmaTxtc+Mu7vn4aYWp+Xu/rlG0+lSADzDlVrKIaUDEJF9YoHqnHii1HUYqdqXMk+TVN5FnOsvBBQ7zH3QzTXzkaDAtkYhol2qZcVbHxpTOhTd/EL5XPs+J4xVv16+2a9DZFEMdlCJ6BpYoDonFqiN5IbLFX/SbNz7R7WhvaeojFE6zwpvrx//4e8bAyE825TLoncWG3O8KzCgJV47P6B3taz+P4+G56T/b+/O46Mqzz2A/56ZbCQhQCBsCRBly7AIAURQEazWK0WpVK/UXW9tbXut23ipXm8tLm2trRvibi2lbqO1pTLWtcWwiWyCESfKFmRNwpaFJGRmznP/OGfoELKSZM5k5vf9fPLJmbP+ZkR45j3veV9qF6vdHm/Q7hBEFJ1YoMag4odmlObe9c4OAOxL2IhslO29L/FPX3/L8dkoh+jZdudRQN29exV8mNplKkQks0JLnnguWJEcQMSK5qr07P5aUZnW/J5E7WKl3QGIKHpxHNTYtdTuANHoHMfGwn8l3fHJ8uRbs853rp/qEO3Z/FEdq0akekZOv1UfpqVOg4jklOn2+c8E/ckBDI1UBn9C6uEgatMA7Repa1Lc+8TuAJ1ZamrqcQ9uzps3r+e11147EAAefvjhrPnz50fk77bbbrut/6JFi7o2t9/IkSNdtbW1kp2dPXrv3r0tahxbuXJlF4/Hc2w4v1deeaXb//7v//ZtS17qPNiCGruWArjG7hDRIAn+oz9yetf+JGFxZprUjm7+iMjZneDcMyu7X0WNwzEZAEbs0C/vfTXYxwFEtHDe3+u0r4OBHQDAApUiQRFDBeojsy8a357nc3u869py/Jw5c8raK0tzHn/88T3N7fPVV18l9enTxx+abrSl1q5dm7p27dq02bNnlwPAVVddVQ6g/CSjUifDFtTYVWB3ALv1xcGSZxIfKyhKvr7izsQ3z0qT2qgaemtFl5TC6Tn9E2scjjwAOPNLY90vXw0OjHRxCgAlWeNqDP/26khfl+KWz+3xHrY7RKy64447+t977719AODBBx/sPXjw4JHDhg0bcdFFF50a2n7JJZecMmnSpGGDBg0a9cgjj/QCgPLycsfkyZOHjRgxwjVs2LARL7/8cnfALDBPPfXUkd///vcHDRkyZORZZ501tKqqSgDg0ksvzf3jH//YAwAKCgpS8/Pz84YPHz5i9OjRrkOHDjkAYNGiRd0uuOCCRgvLJUuWpObn5+e5XK4R+fn5eRs3bkyura2V3/zmN/0XL17cIy8vb8QLL7zQI7yV+NJLL829/vrrB+Tn5+fl5OSMDmUwDAM33XRTztChQ0cOGzZsxAsvvNDkFKkUvdiCGqOKH5qxOfeud/YiDlvEznR8sen+hAXlg2XP6SIdOzTTyXq2e8byp7p3mwiRJAC4eJWx4uolxkQBEu3IU5FxSqZxZDmnOKVIYf/TNjp69KgjLy9vROh1eXm589vf/vYJReC8efP67tixo7BLly66f//+YzMM+ny+LuvWrfNVVlY68/PzR1x66aXl2dnZ/nfeeWdLZmamsXfv3oQzzjgj78orrzwMAN98803Kyy+/vO3MM8/c8Z3vfOfUhQsX9vjpT396MHS+2tpaueqqqwa/8sorW6dOnVp98OBBR3p6ugEAH3zwQcaTTz65s7H3MmbMmNrVq1cXJSYmYtGiRV3nzJmT8/7772+9++6796xduzZt4cKF31jv5bgv7yUlJYlr164t2rBhQ8qsWbOG3HDDDYcWLlzYvbCwsIvP59u0d+/ehIkTJ7ouuOCCqkGDBvnb8nlT5LFAjW0FAL5vd4hISEDA/wPnu2tuTljUravUjLQ7T2MMwLipb9ayVV26HCucb/ggWHDhOj1HbBp/NOhIqA0kdBkGPdLkXNdE7YgFahslJycbRUVFx2bimjdvXs+1a9ee8JDj8OHDa2bNmnXKzJkzD1911VXHWq2nT59+OD09XdPT0wOTJ0+uWLZsWdrll19eftttt+WsWrUq3eFwoLS0NGnXrl0JAJCdnX30zDPPrAGA/Pz86uLi4uTw63z++ecpvXv39k+dOrUaADIzMw3ALFz37duXNGLEiEaHsDt48KBz9uzZpxQXF6eIiPr9/hb9XThz5szDTqcT48ePrz1w4EAiACxbtqzr5ZdffjAhIQEDBgwInHHGGVXLly9PHTRoELsGdDK8xR/bYv5BqV44XPZk4ryPv0q+7uDdia+dGc3FaYVDys8f0H99eHF61xvBgunrdKpdxSkAHOqR95VhHCoBkGlXBoo7H9gdIF4sWbJk83//93+XrVu3Lm3MmDEj/H6zIVHk+L9yRATPPfdc5oEDBxIKCwt9RUVFX/bs2dNfU1PjAICkpKRj/UedTqcGAoHjTqCqEJET+pi+//776RMnTqxqKuPPf/7z7KlTp1Zu3rx50+LFi7fU1dW1qDYJ79Oqqsf9ps6PBWpsi9l+qKdLke+9pJ+vWJP8024XO1dNc4r2sTtTU7YlJuw4d2DOgbKEhAkA4DA0+NuXAsvHbe3YqUtboiRr/GHDv32X3TkobhS6Pd7ddoeIB8FgEFu3bk26+OKLK59++uldlZWVzvLycicAvPvuu92rq6tl3759zlWrVnU9++yzj5SXlzt79erlT05O1sWLF3fds2dPUkuvNWbMmNqSkpKkgoKCVAA4dOiQw+/34x//+Ee3GTNmNNl6WVFR4czJyakDgOeee65XaH1GRkawqqqqVXXK1KlTK//yl79kBgIB7NmzJ2H16tXpU6ZM4d2hToi3+GNY8UMzvsy96519AGJiWA4ngoHrnB+svi3hrYwMqR5ld56W+iC1y/o7e/c6VUW6A0CSX2sefz5Y2KsCto+/CgCHegxLM+o+4l/gFCnv2h0gXgQCAbnyyitPqaysdKqq3HTTTSW9evUKAkB+fv6R8847b+iePXuS7rzzzr25ubn+G2+88eD06dOHjBo1yjVy5MjqU045pbal10pJSdFXXnll6y233DKwtrbWkZKSYixduvTrFStWdH3kkUeO+0IyZsyYEaEW3Isvvvjgz3/+83033njjKfPmzes7ZcqUitB+06dPr/z973/fLy8vb4Tb7d7bkhzXXHPN4ZUrV6a7XK6RIqL33XffroEDBwZa+j4oegibw2Nb7l3vvADgRrtztEUmyg/8IvHlL2Y6PhnmFKNTPfT128zuBS9ndD0LIgkAkF6jh+c9E/wm/ShOszsbACjEWDL1yara8meKobVRkYli3rluj/dju0O0xcaNG4vHjBmz3+4cJ+uOO+7on56eHrz//vtLOvI6W7duTbzhhhtyly5durkjr0Od18aNG3uNGTMmt6FtbEGNfW+jkxao+bL5qwcTXyobITsmROvT+I3xA/5r+/dZ9UVy8rHcPct17xPPB48kBaKjOAWAiozczQodAq0dbHcWiguVAFbYHYIiY/DgwX4Wp3SyWKDGvo8AVANItTtISzhgBK92frTm9oQ3U3vIkdMADLc7U2sddDgOzMzpt7Pc6ZwSWjewVLf99qVgslMxxM5s9ZX0HleiwTIHELlZqyiufeT2eDncj80effTRZgfXJ7IbH5KKccUPzaiBWaRGte6oPPRwwrMff5187b77ExdMsorTTmdTUtLm8wZmV5c7nWND60YVG5se/kOwu1ORbWe2hhzIHJlo+Ld16G0+ojDsf0pELcIW1PjwNoCZdodoyGmydfODiS/tGy3bJ4hgmt152uKv6Wmrf9kr0wWRY/NST/nCWHvzYmOERGkLdk2XrFOClf/62u4cFDfesztAOzEMwxCHw8GHOIhOkmEYAsBobDsL1PiwGOYfgqhoMRcYxvedS9bemfBGYk+pzEcM3F6+p1dmwdvpaVMgcuwzvmSlseKKAuMMidL/z6q79N4JcQzQ4P6Dze9N1Gafuj3eRmcT6mS+KCsrG5GVlVXOIpWo9QzDkLKysm4Avmhsn6j8h5PaV/FDM0pz73pnNYBJdubIQFX5XQmvb/hPZ8GpiRKcaGeW9nJUUHt5/37rtiUlHvcQ1w/fDRacv8G+2aFaojQrf4dqsA/gj6p+sRSzXrc7QHsJBAI37tu378V9+/aNQpR88SfqZAwAXwQCgUYf4maBGj/+BpsKVJfs2PqrxD/szpct4zvb0/hN2ed07rskp9/BIw7HWcdWquo9HmPpmO32D8DfnLJeY1QDe7YAGNHszkRtYwDw2B2ivYwfP74UUdptiihWsECNH68C+A0i9G1fYBiXOZeum5PgcWZJ+TgAMTWM0ZqU5C9v7Ns70xA5Vtw5DA389qXgqkFlnaMIr0rvnx2sXckZpCgSlro93hYNtE5EBLBAjRvFD83YlXvXO0sAnNeR10lHdcX/JHg2XOn816BECZ7ekdeyy4KMrisfyew+DiIpoXVJfq1+4tngpp5V0TE7VHPqEtMPqCPxVCPwDYeboUh4ze4ARNS5sECNLwvRQQXqMNm5/VeJL+2cIF+NE8E5HXENuymgt/TuVfBxWuq08PXp1XroyWeDu9OOotMU5GW9xmwB0FODB2NiGlyKan4Ab9kdgog6Fxao8eWvAJ4GkNY+p1O9xLFi3d2Jr6I3Do8XwSntc97oc0SkalZ2v017ExOmha/POqx7Hns+WJMUxCibop2U0qxxtap1VUDwVLuzUMz7yO3xHrA7BBF1Lnz6MI4UPzSjCubDUm2ShpqqXyT8ueDr5OuKH096ekIfOTxBJHqfVm+rbxISdk0dmL13b2LCGeHrc/fp1nnPBiUp2Pn611ZkDOpl+HdtBf8OoI7H2/tE1GpsQY0/CwFcfTIHnip7dvwq4aUdkxxfjo2lp/GbUtAlZePP+mTlqEhO+PrTthmF93iMAQJ0tyvbyQo6kqqDzpRhxtGtK+3OQjHvEIC/2B2CiDofFqjx558AdgMtn3ZzhmPV+nsSXw70w8EJIhjUcdGiy7we3Za90C1jEkQSw9dP+9xY/ZN3jNECdLErW1scyHR9DZGxRmBnkt1ZKOb9ye3x1tgdgog6Hxaocab4oRlG7l3vLARwd1P7paL2yK0Jf11/vfP9/sniHxeheFEhCAR/0Lf38nVdUk5oJb50ubH88mXGpGidHaolSnuPPwwAalTkNLcvURs9a3cAIuqcOu0/stQmzwGYA8BZf8Mg2bfrwYQ/bj3bUThWBFMiH81e5Q7H4Zk5/bYddDpPKE5/4g1+fG6hTrMhVrs61H1ohhrVBwBjgN1ZKKYtcXu8X9kdgog6Jz4gEYeKH5qxA4A3fN1/ONZsWJ58y+qPk+7oP8VZOFUE3WyKZ5uvExO3nzsw+/BBp/P4FmNV/cWrwYJYKE4VEvQndh1m+HdstzsLxbxn7A5ARJ0XW1Dj1/wUHL3g5oRFa290/qNvivjH2h3ITu+kpa69K6vnUIgcV5g7g+p/+KXg6gH7Y+OhsPJug7+GiCvo31ZldxaKafsALLI7BBF1XixQ41TxQzM+8v8yc2OiBOPuNn59D/TsUfBG1/SzIXJcl4fkOj0y79mgr8cRnGVXtvZW0ntcKQCXEdiTancWimkvuj1ev90hiKjzYoEaxxIl+EcAk+zOYZc6oO6q/n1XFyUnndA6mnFED8x7NrgvtQ4T7MjWUQ5kjkgGAGhVrr1JKIYFADxvdwgi6tzYBzW+LQSw3+4QdtjvdJRNG5hTVJScdHb9bX0O6a5nngpWpNZhpB3ZOlJtSs/BGqzYC2hvu7NQzHrD7fHutDsEEXVuLFDj2dzyWsThgwwbk5O+On9Adl2l03Fa/W2D9+rmx58LJiQGY2/a1qrUfsUQR1YwsH2H3Vkopj1sdwAi6vxYoNJTAI7aHSJSPF3TV13dr09OUOSEiQrGbjU+//WCYG+noq8d2Tpaae/8XQBg+LfX2p2FYtZ7bo93o90hiKjzY4Ea7+aWlwBYYHeMjqaA3pnVs+DBnj3OgEha/e3f2mB8evcbxjBB7A6vtb+n2WBsBPZl2ByFYtdv7Q5ARLGBBSoBwIOI4VbUGpHqi3L6rXo/PW0qRKT+9suXBpfd9K4xQYAUO/JFypG0fgNUVaHVg+3OQjFpudvj/djuEEQUG1igEjC3fBdidErCPQnOvVMHZu/4JjFxckPbb347WHDZCp0iDcyqFUuOJmWUqSNhkBoHdyCGW4nJVg/aHYCIYgcLVAr5NYAjdodoT6tSkr+YntPfWeNwuE7YqKpzXw4UnLNJY2IA/uaU9Rq7FQAM/7bddmehmLTG7fG+b3cIIoodLFDJNLe8FMCTdsdoL893y1j+w769hxoiJwynlBDUusefD34yYmdszA7VEqVZ+UcBwPBvD9qdhWLSvXYHIKLYwgKVwj0MoNzuEG1hAMZNfbIKnszsfjZEkutvT6nTqqfnB7/ofxBn2pHPLpVdB/YBACNY1sPuLBRz/uX2eN+zOwQRxRYWqPRvc8sPAXjU7hgnq1Kk4oIB/detTO3SYMtotyO6/7l5wZ3dqzEu0tnsFHAmVwadyUNVjQD06BC781BMUQBz7A5BRLGHBSrV9xg64exS2xMTdkwbmFNWkpBwekPb+x7UnU/PD1Z18ePE/qgx7kDmyM0QcWqwZCuALnbnoZjyutvjXWd3CCKKPSxQ6XhzyyvRyWaC+Si1y2ffze6XUeeQBodPGrJbv3r8+WBSooHcCEeLCqW9x1cAQNC/rdTuLBRT6gDcY3cIIopNLFCpIfMB7LE7REv8LrP70tt79xqtIg32rRy/2djwq4XBvg5Fn0hnixaHuw3pDgCGnzOcUrt62u3xbrc7BBHFJhaodKK55TUA7rI7RlMCQOCqfn2WLuyWcQ5EEhra59vrjVVz/mK4Ynl2qOYY4vD7E9OGAYAGD2TZnYdixmEAD9gdgohiFwtUatjc8j8DWG53jIYccjgOnjsw+4vPU5LPaWyfK5cEl974vjFRgBOe5I8nh7sN+RoiqaqBWsDPB6SovfzG7fEetDsEEcUuFqjUlJsBRNW4mV8mJW751sDsqsNO59jG9rl1UfDjS1bpOcI/3yjtPX4/ABiB3VsANNjSTNRKX8J8mJKIqMPE/T/g1IS55RsRRVOg/i09bfXs/n37BEQGNrRdVI0HFgaWnuXTaRGOFrUOZrpSAMDwbztgdxaKCQrgx26P1293ECKKbWxRoeb8H4D/BHDCjEyR9ItemR8vSk87ByINfqlKCOjRR18Iru97GI3e9o83Cmhtco8hAGD4v+H/69QeFrg93mV2hyCi2McWVGra3PLDAG636/J1wNFLsvuuWNQ1fVpjxWmXWq14dn7wy76HMTnS+aJZVVr2NoijJwCocbif3Xmo09sP4H/sDkFE8YEFKjVvbvmrAD6I9GVLnM6ScwblbNmalHRWY/t0r9Ky5+YH92TUID+S2TqD0t7j9wCA6tEKIHiK3Xmo0/sft8fLriJEFBEsUKmlfgKgJlIXW5ec7LtgQH/jiMMxsrF9+h/QHU8/FaxO8SMvUrk6k/09RzsAwPB/sw2A2ByHOrcCt8e7wO4QRBQ/WlSgisgsEVERyau3/ncisklEftfAMTNFpMPH0hSRv4vIJx18jftF5PwOOO8VInKPiFwvImUissH6PP8iIqnWPnNF5M6TOHeuiHzRbmHnlm8DcH+7na8Jf87ouvL6fr1zDZFGb0sP26VFj74QTE0wMCgSmTqj6tQ+gwDA8G87bHcW6tTqYH5BJSKKmJa2oF4Bc0zM79dbfxOAcap6XL8kEUlQ1bdV9aF2yNgoEekOYByA7iLSIbcwRcSpqveq6kcdcPoLAbxnLXtUdayqjoT5D8LsDrheW/0ewKqOOrkCekvvXgUP9+xxJkQanTN+4lfGZw/8OZjtUHDg+UbUJvfYqw5nDgAYgV0pduehTu0Xbo/XZ3cIIoovzRaoIpIO4CwAP0BYgSoibwNIA/CpiMwWkQUi8qiILAHwW6tVcL61bx8R+ZuIbLR+zrTWLxKRdVar4Y/Czl0lIr+y9l0lIo1NU3kpgMUAXq+XbYGIPCMiS0Rkm4hMFZGXRMQnIgvC9rtARD4RkfUi8qb1XiEixSJyr4gsB/Cf1vkus7adLiIrrWyrRaSr1Vq5zDrP+rD3N01EPrZaRItE5BUREWubABgLYH29zzvB+lwPNfDf4ocissa69lthrawNfr5hx50qIp+JyOmNfI4tM7c8AOAqAJVtOk8DqkWOTM/p/+mStNSpTe134VrjE/dfjRECdG3vDLGkNGvssSko1agYYGcW6tSWwvxiSkQUUS1pQb0EwHuq+jWAgyIyDgBUdSaAGqvVz2PtOwzA+arqrneOeQAKVHUMzBbPTdb6/1LV8QAmALhFRHpa69MArLL2Xwrgh41kuwLAa9bPFfW29QDwLZhPoC+GObD0SACjRWSsiPSCOYTS+ao6DsBaAHeEHV+rqmer6uuhFSKSBMAD4FYr2/kw+2WWAvi2dZ7Z1vsNyQdwG4ARAE6FWeyH1m9UVbVezxaRDQB2A8i0Mtf3V1U93bq2D+aXBqDxzxciMhzAWwBuUNU1DZyzdcxb/T9r83nC7ExI2DV1YPbu3YkJk5ra75p/Bpfe8KFxRrzPDtUSZb3GBgBAjSNlgGbbnYc6pXIA17o9XsPuIEQUf1pSoF4Bs4US1u/6hWC4N1W1oZmHvgXgGQBQ1aCqllvrbxGRjTBvGw8AMNRaXwfAay2vA5Bb/4RWq+oQAMut4jkgIqPCdllsFX+FAEpUtVBVDZjFWy6ASTCLxhVWYXgdcFx/Rg9ONBzA3lChp6oVqhoAkAjgBREpBPCmdd6Q1aq6y7r2hrD3ciGAd8Ovp6pjAfS1Mjc0nMsoq6W2EGZLZugBosY+3ywAfwdwtapuaOB8J2du+Z/Q8OfTaku7pGyckdMvpdbhGNbUfu63gh9fvJqzQ7VUZdcBfQDA8BcX2xyFOq+b3R7vDrtDEFF8anLwbqtF81swCyMF4ASgIjInrOUv3JGWXlhEpsFsgZysqtUi8jGAUF85f9j5g43knA2zlXS7ddc8A+Zt/v+zth+1fhthy6HXCdZ5P1TVxgruht6LwJxJpb7bAZQAGAOzgKoN2xZ+7fD3cgHMLgrHUVUVkcUwWynr9+FdAOASVd0oItcDmNZI9pByADthttpuambf1voxgMkAGpzVqSXmd++27LnuGWfAbJlukMPQ4IMLgyuH7G32vZLF7+xSbjiShgJA0L+txf9PEoV5w+3xvmx3CCKKX821Rl0GYKGqDlLVXFUdAGA7gLNbeZ1/wnoKVEScIpIBoBuAQ1ZxmgezRbM1rgBwoZUrF8B4nPgQV1NWAThLRIZYuVJFpMlWPABFAPqH+nJa/U8TrPey12olvQZmId8oEekGIEFVGxtT8GwAWxtY3xXAXhFJhNmCGtLQ5wuYLdGXALhWRK5s5r21jjmA/zUwC/5WCQLB/+rbu+C5Ht2mNFWcJgT06BPPBtcM2YspbYkabw70GrU5NKmBEdibZnce6nR2w/wCSkRkm+YK1CsA/K3eurcAtLbYuRXAudat6XUwb02/ByBBRD4H8ABa8XS4iOTCbLk7doyqbgdQISJntOQcqloG4HoAr1kZVgFNj6epqqGn65+0uiZ8CLPV92kA14nIKpj9cJtrtfo2gPqjAswWc5ipz2H2T32ggeN+AeBT67pFYesb+nxDmY8AuAjA7SLy3WZytc7c8qU4sZW3SeUOKT9/QPaGNV1SmnwYKrVWy5+bH/T1KW/1F5e4V5I17t9//rSKA/RTayiA690e7wkPaRIRRZI0fKeeOpKIvAjgRVXtsCGbImZutwQAKwE0O0LAlsTE7Zdn94W/mSHBMiu05InnguXJATTXok0NWHrWwxsDiWljjODhXXUVL+XYnYc6lbluj/c+u0MQEfGBExuo6o0xUZwCoaGnrkQzQ0+9m5a6blZ238zmitOcsS45VwAAF+FJREFUMi2e/0ywjsXpyTEk4WggITUPAAz/9p1256FOxYsITcZBRNQcFqjUdnPLtwC4Fg0/QIZf9eyxdE5Wz7Ew+942yvWNfvn7F4PpCQY4budJOtR96FcQSQYAw7/9aHP7E1m2ArjG7fHylhoRRQUWqNQ+5pYvQr1+s3VA3eX9+y57PaPrORBp8sGxST5j/dxXggMdQK8OzRnjSnpPONZ30AiWNPmFgMhSDWCW2+PllLhEFDWaHGaKqJXmwpwda+YBh2P/zJx+eyqczmafwJ+x2lh57T+NCQI0+kQ/tcyhHsNTAXO4MmjNYLvzUKfwQ7fHW2h3CCKicGxBpfYzt1wBXL0uOfm98wZm11Y4nac1d8gNHwQLrv2nMZnFadspoEeTuw8FAA2WbYc5NjBRU+a5Pd5X7Q5BRFQfC1RqX3PLK6/v3+dnQZHU5nb9+ZvBgunrdKqYEyBQG1V2HbgFIt0BwPBv32N3Hop6SwDcaXcIIqKGsECldld4XeEWmJM8+Bva7jA0+NBLgWXjt2iTY6FS65RmjdsbWg4Gihuacpgo5AuY/U4b/H+UiMhuLFCpQxReV7gEwI/qr0/ya838Z4LrTi3h7FDtbX/P0cf6lGugjA+bUWN2A/iO2+MttzsIEVFjWKBShym8rnABwp7sT6/Rw8/OD27uVYGJ9qWKXdWpWbkAoBr0A3VDbI5D0akSwAy3x8sxcokoqrFApQ5VeF3hvQBe6lmue5+ZHyxLr0WzD05R69Wk9NoNcfYHAA3u2wIg2eZIFH0CAC51e7wb7Q5CRNQcFqgUCTf97g/Bj5IDGGp3kFhVmjW2OLQcrNtWZmMUil4/dHu8H9odgoioJVigUocrvK4wkH4UPwJQYHeWWFWWNdYILRuBHRwVger7pdvjXWB3CCKilmKBShHhKvLVArgYwDq7s8SiqrTsfqFlDR7sY2cWijoPuT3e++0OQUTUGnFZoIrILBFREcnrwGtMEJF5HXj+aSLibeUxc0XkTmv5fhE5v2PSNcxV5KsEcCGAokheN9b5E9IOGY7EwQCg6q8GApxBikIecXu8d9sdgoioteKyQAVwBYDlAL7fEScXkQRVXauqt3TE+duDqt6rqh9F+rquIt9+AN8GsC3S145VZb1Gb4GIAIAR2LUFgNPmSBQdnnB7vByIn4g6pbgrUEUkHcBZAH6AsALVapEsEJE3RORrEXlIRK4SkdUiUigig639skTkLRFZY/2cZa2fKyLPi8gHABaGt3CKSLqI/NE6z+cicqm1/hkRWSsim0TkvrAsxSJyn4ist45psqXXuvZLIvKxiGwTkVvCtt0jIl+JyEcAhoetXyAil1nL91rv5QvrPYi1/mMR+a31GXwtIu0ydqmryLcLwBQAvvY4X7wrzRp3JLRs+LcdsjMLRY2n3B7vbXaHICI6WXFXoAK4BMB7qvo1gIMiMi5s2xgAtwIYDeAaAMNUdSKAFwH8zNrnCQCPqerpAC61toWMB/BdVb2y3jV/AaBcVUer6mkA/mWtv0dVJwA4DcBUEQkfgmm/qo4D8AxaNh1hHoD/ADARwC9FJFFExsMswvMBfA/A6Y0cO19VT1fVUQC6ALgobFuC9RncBuCXLcjRIq4i3x4A5wD4rL3OGa8qup1ybFB+w78zoal9KS48h3//fUVE1CnFY4F6BYDXreXXrdcha1R1r6oeBbAVwAfW+kIAudby+QDmi8gGAG8DyBCRrta2t1W1poFrng/gqdALVQ21cl0uIuthFmkjAYwIO+av1u91YdduyjuqelRV9wMoBdAHZivl31S1WlUrrLwNOVdEPhWRQgDfsrKcbI4Ws273nwtgZXueN54EHQm1AWeXYaHXahzOtjMP2e5FAD9xe7xqdxAioraIq9YWEekJswAbJSIKs6+eisgca5ejYbsbYa8N/PuzcgCYXL8Qte6KH0HDBMBx/2CIyCkwW0ZPV9VDIrIAQErYLqFrB9Gy/07h2cOPafIfKhFJAfA0gAmqulNE5rYxR6u4inzlvjzXBQAWwSzkqRUO9XB9BZExAKBGzWHAGGR3JrLN79we75zmdyMiin7x1oJ6GYCFqjpIVXNVdQCA7QDObsU5PgBwc+iFiIw9iWN6AMiAWdCWi0gfANNbkaGllgKYJSJdrFbeixvYJ1SM7rf6517WATma5CryHYHZraCxFl5qREnvccf6nBqBb7bC/DJE8UUB3MnilIhiSbwVqFcA+Fu9dW8BqN9ntCm3AJhgPez0JYAft+CYBwH0sB5C2gjgXFXdCPPW/iYALwFY0YoMLaKq6wF4AGyA+T6XNbDPYQAvwOzGsAjAmvbO0RKuIt9RmH16X7Xj+p3Voe7DQ91LEPRvq7QzC9kiAOA6t8f7iN1BiIjak6iyqxJFD1+eywHzwbAf2Z0l2inEWDL1ySqIZADA0fIXP1Wj4gy7c1HEVAP4T7fH+w+7gxARtbd4a0GlKOcq8hmuIt9NAH5td5ZoV5GRuzlUnAKAGpUD7cxDEXUQwPksTokoVrFApajkKvLdA7PrRUOjIhCAkt7jSkLLalSWANqvqf0pZmwHMMXt8X5idxAioo7CApWilqvI9xrMB9h22p0lGh3IHJUYWg76i3fYmYUiZimAiW6P90u7gxARdSQWqBTVXEW+9QAmwJyalsLUdOl1SmjZ8G+vtjMLRcSLMG/r77c7CBFRR4urcVCpc3IV+Up9ea7zAMwH8EO780SD6i69v4E4jvU5NQJ70+3MQx3KD+AOt8c73+4gRESRwhZU6hRcRb46V5HvRzDHkw3YncdupVnjju/2oEdOtSkKdaxSAOexOCWieMMClToVV5HvKZgzTsX1bc6yrNOM0LIRPPgNgEwb41DH+BTAeLfHe8L4xUREsY4FKnU6riJfAYDTAay3O4tdqtL654SWDf/2XXZmoXanAB6G+aQ+/9sSUVxiH1TqlFxFvmJfnmsSgPsAzAHgtDlSxNQldt2vjsTwB6T8duahdrUPwLVuj/dDu4MQEdmJLajUabmKfH5Xke9/AUwFsM3uPJFS1mvM1vDXRrC0h11ZqF39A8BpLE6JiFigUgxwFflWABgD4A92Z4mE0qz82tCyqhGE1g62Mw+1WR2A2wFc5PZ4y+wOQ0QUDVigUkxwFfmqXEW+GwF8F+aTzzGrIiM3K7SswbJtANJsjENt4wMwye3xPu72eLW9TioiVfVeXy8i863lH4vIte11rWZy3C8i57dgv3UikiQixSLSq4XnHisi3wl7PVNE7mpLXiKKHixQKaa4inxvAxgNYLHdWTpC0JFUHXQmDwu9NvzbSpran6KWH8ADAPLdHu9nkbywqj6rqgsjdK17VfWjpvYRkVwAu1W1rpWnHwvgWIGqqm+r6kOtDklEUYkFKsUcV5Gv1FXkmwlzUP+q5vbvTA5kur6CyLGHG4P+YqOp/SkqrYY5fNS9bo/3aKQvLiJzReROa/kWEflSRD4XkdfDtv9ZRP4lIptF5IfW+nQR+aeIrBeRQhH5rrU+V0R8IvKCiGwSkQ9EpIu1bYGIXGYtny4iK0Vko4isFpGuVqTpAN5rIu9E67jPrN/DRSQJwP0AZovIBhGZXa+VeIGIzLP23xaWQUTkdyLyhfUeZnfEZ0xEbccClWKWq8j3IoDTALxjd5b2Utp7fEX4aw3ub9HtUIoK1QDuADDZ7fEWdvC1uliF2wYR2QCzmGvIXQDyVfU0AD8OW38agBkAJgO4V0T6A6gFMEtVxwE4F8AjIiLW/kMBPKWqIwEcBnBp+EWsgtID4FZVHQNzLOMaa/OFaKJABVAE4BxVzQdwL4BfW62t9wLwqOpYVfU0cFw/AGcDuAhAqGX1ezBbXkMZfici/Zq4NhHZhMNMUUxzFfm2A7jIl+e6BMDjAAbZHKlNDnUfGmp1gmqwDvAPsTMPtdiHAG5ye7zbI3S9GlUdG3ohItcDmNDAfp8DeEVEFgFYFLb+76paA6BGRJYAmAjzi96vReQcAAaAbAB9rP23q+oGa3kdgNx61xkOYK+qrgEAVa2wciUByFHVpkbh6AbgTyIyFOYYsYlNvfEwi1TVAPCliIRyng3gNVUNAigRkdCYym+38JxEFCFsQaW44CryLQIwAsBvYD413ekY4gj4E7se63+qgT1bACTZGImatwvA1W6P94IIFqetMQPAUwDGA1gn/+4+Uv+BLQVwFYAsAOOt4rcEQIq1PbyrQhAnNn5IA+cEgCkAljeT8QEAS1R1FICLw67ZnPBMUu83EUU5FqgUN1xFvmpr3NTR6IS3/cszTv0aIumh10H/trie7jXKVQOYC2C42+N9xeYsDRIRB4ABqroE5mQX3QGE/nx9V0RSRKQngGkA1sBsySxVVb+InIvW3Y0oAtBfRE63rt3VKoYvBPBuM8d2A7DbWr4+bH0lgK4n7N20pTD7rTpFJAvAOTD7BBNRlOEtfoo7riLf1zBv+/8HgMcAuGyO1CKlvccfN0amEfiGXzCjjwJ4GcDdbo93d3M728wJ4GUR6QazZfExVT1sdStdDfNL3EAAD6jqHhF5BcBiEVkLYAPMorNFVLXOeiDpSesBqhqYfUCnwexLGu5zEQk9/PcGzGlf/yQidwD4V9h+SwDcZfWx/U0Lo/wNZr/ajTD/W81R1X0tfR9EFDmi2m5D7xF1Or48VwKAn8Bs7cq0N03TVp5x36raLr0mhV7XHnpiCxBkH9TosQLA7W6Pd43dQdpCROYCqFLV33fwdXIAvKCq0zvyOkTUObEFleKaq8gXAPCkL8+1EMCtAG4DEJVTh9amZB6bMUq1rgoInmpnHjrGB+CXbo/3TbuDdCaqugvmEFNERCdgCypRGF+eqyuAn8EcDqinzXGOqUrtV7x64v/lhl4H67Zt9B9ZNMbGSARsgvkAz5tuj5fj0RIRtSO2oBKFcRX5KgH82pfnmgfgpwDcAHrbmwoo7Z2/E2FD9xj+rYftSxP3vsC/C1N+wyci6gAsUIka4CryVQF42Jfnmg9zAPP/AdDXrjz7e445bngcI7CTw0tFXiHMAe/fYmFKRNSxWKASNcFV5KsG8Kgvz/U0gB/BHI4nO9I5jqT1HRD+Wo2KnEhniGP/BDAPwGIWpkREkcEClagFXEW+WgDzfHmuZ2FO43gTgKmRuPbRpG6l6kg4NuakGtUHAGNAU8dQm1UD+DOAJ90e7ya7wxARxRsWqBRRIjILwF8BuFS10XEUReQfAK5U1ajqa+kq8tUBeA3Aa748Vx7MVtXr0IFDVJVljdmKsH6whn/HdkTRA1wxphjmzEp/cHu8h2zOQkQUt1igUqRdAXNqw+/DHHu0Qar6nUgFOlmuIl8RgDt8ea67AVwGs1V1SntfpzQr3x/+OujfVtXe14hzAQAfAHgBwNt8Ip+IyH4sUClixJym8ywA5wJ4G8BcEekHwAMgA+afx5+o6jIRKQYwQVX3i8giAANgzsH9hKo+b52vCsATAC6COTPNd1W1JMJvC64i31EArwB4xZfnGgGzVfVatNN4qpVdB2WFvzYCe1Lb47yEzwAsBPCa2+ON+J8bIiJqHMdBpYgRkasBnKuqPxCRlQBuhlmspqjqr0TECSBVVSvrFaiZqnrQmiJxDYCpqnpARBTATFVdLCIPA6hQ1QdtenvH8eW5ugCYBbO/6oUATqqoDDiTK5ee/UgqzM8GAFB76LFSQG0f+qqT2g3zy8RC9i0lIopebEGlSLoCwOPW8uvW68UAXhKRRACLVHVDA8fdYvVdBcyW1KEADgCoA+C11q8D8O2OCt5ariJfDYBXAbxqFasXwCxYL0Yr+qseyBz1NUTGh15rsGIvoP3aO2+M2w3zz8lfAPyLt/CJiKIfC1SKCBHpCeBbAEZZLZ9OAApz2KZzAMwA8GcR+Z2qLgw7bhqA8wFMVtVqEfkY5q1+APDrv28BBBGlf56tYvXvAP7uy3MlwHz6fxaAS9DMkFUlvccd1980GNi+AwAL1OZtgNmN5G0A6zk8FBFR5xKV/6BTTLoMwEJVvSm0QkQKYBanK1T1BRFJAzAOZr/AkG4ADlnFaR6ASZEM3d5cRb4AzHE1/+nLc/0MwOkwi9WZAEbU37+825Bu4a8N//baSOTshGoBFMAsSBe7Pd6dNuchIqI2YIFKkXIFgIfqrXsLwAIAR0TED6AK5sNF4d4D8GMR+RzAVwBWdXDOiHEV+RTAauvnbl+eqzeAaaEfQxyD/Ylpw8OPMQL7MiKdM0rVAfgUwBLr5xO3x3vU3khERNRe+JAUUZRaPvl7WRvH3DwFwJkwuzjkHz38WB3MVuV4cxBmQboM5jBla9weL1uTiYhiFAtUok5i3g/+5PRXvTkKwPiwn9E4yRECopQC2ApgI8x+pBsBbOAteyKi+MIClagTe2T2RQLzQathAIZbv0M/p8B8GC0aHQKwPexnK4DPAXzu9ng5EQERUZxjgUoUox6ZfVEizCK1n/XTt5GfDJgjI0gbL+mHeSv+QNjv0PJehBWkbo+3vI3XIiKiGMYClYhCLbFdrJ9U6ye0nARzOtAAzCI0/Hdo+bDb462MfHIiIopFLFCJiIiIKKo47A5ARERERBSOBSoRERERRRUWqEREREQUVVigEhEREVFUYYFKRERERFGFBSp1CBFREXkk7PWdIjK3mWOmiciZYa9/LCLXtkOWuSJyZ711xSLSq63nbuR694vI+dbyiyIyoiOuQ0REFKsS7A5AMesogO+JyG9UdX8Lj5kGoArASgBQ1Wc7KFuHUtV7w5ZvtDMLERFRZ8QWVOooAQDPA7i9/gYRuVhEPhWRz0TkIxHpIyK5AH4M4HYR2SAiU0ItnyLiEpHVYcfnisjn1vJ4ESkQkXUi8r6I9GttUBFZZB2/SUR+ZK27XEQetZZvFZFt1vJgEVluLd8rImtE5AsReV5ExFq/QEQus5Y/FpEJ1vIzIrLWus59YdcvFpH7RGS9iBSKSF5r3wMREVEsYYFKHekpAFeJSLd665cDmKSq+QBeBzBHVYsBPAvgMVUdq6rLQjurqg9Akoicaq2aDeANEUkE8CSAy1R1PICXAPyqkSyhwneDiGwA0D9s239Zx08AcIuI9ASwFMAUa/sUAAdEJBvA2QBC2ear6umqOgrmrEsXNfN53KOqEwCcBmCqiJwWtm2/qo4D8AyAOxs8moiIKE7wFj91GFWtEJGFAG4BUBO2KQeAx2rtTII5P3tz3gBwOYCHYBaoswEMBzAKwIdW46UT5pzvDXlMVX8feiEixWHbbhGRWdbyAABDVXWViKSLSFdr3asAzoFZrP7V2vdcEZkDczrQTACbACxu4j1cbrXQJgDoB2AEgM+tbaFzrgPwvSbOQUREFPPYgkod7XEAPwCQFrbuSZitj6MB3AQgpQXn8cAs8IYBUFXdDEAAbLJaXMeq6mhVvaA14URkGoDzAUxW1TEAPgvL8wmAGwB8BbPVdAqAyQBWiEgKgKdhtt6OBvBCU+9DRE6B2TJ6nqqeBuCdevsftX4HwS+OREQU51igUodS1YMwWz9/ELa6G4Dd1vJ1YesrAXRt5DxbYRZvv4BZrAJm4ZglIpMBQEQSRWRkKyN2A3BIVautvp+TwrYthVlULoVZuJ4L4KiqluPfxeV+EUkHcFkz18kAcARAuYj0ATC9lTmJiIjiBgtUioRHAIQP6TQXwJsisgxA+BP+iwHMCj0k1cB5PACuhlnwQlXrYBaGvxWRjQA2ADizgeOa8h6ABOuhqwcArArbtgzm7f2lqhoEsBNm/1mo6mGYraaFABYBWNPURVR1I8widxPMvrIrWpmTiIgoboiq2p2BiIiIiOgYtqASERERUVRhgUpEREREUYUFKhERERFFFRaoRERERBRVWKASERERUVRhgUpEREREUYUFKhERERFFFRaoRERERBRVWKASERERUVRhgUpEREREUYUFKhERERFFFRaoRERERBRVWKASERERUVRhgUpEREREUYUFKhERERFFFRaoRERERBRVWKASERERUVRhgUpEREREUYUFKhERERFFFRaoRERERBRVWKASERERUVRhgUpEREREUYUFKhERERFFFRaoRERERBRVWKASERERUVRhgUpEREREUYUFKhERERFFFRaoRERERBRVWKASERERUVRhgUpEREREUYUFKhERERFFFRaoRERERBRVWKASERERUVT5f1SeJl3mvnvvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph3 = race_2000df3.plot.pie(y = \"Percent/Total Number\", figsize=(7, 7),title='Graph 8: 2000 Population Breakdown')\n",
    "graph3.legend([\"White\",\"Black or African American\", \"American Indian and Alaska Native\", \"Asian\",\"Native Hawaiian\", \"Hispanic/Latino\"],loc='center left',bbox_to_anchor=(1.0, 0.5))\n",
    "graph3.set_ylabel(\"\")\n",
    "\n",
    "graph4 = race_2010df3.plot.pie(y = \"Percent/Total Number\", figsize=(7, 7),title='Graph 9: 2010 population Breakdown')\n",
    "graph4.legend([\"White\",\"Black or African American\", \"American Indian and Alaska Native\", \"Asian\",\"Native Hawaiian\" ,\"Hispanic/Latino\"],loc='center left',bbox_to_anchor=(1.0, 0.5))\n",
    "graph4.set_ylabel(\"\")\n",
    "\n",
    "graph5 = race_2017df3.plot.pie(y = \"Percent/Total Number\", figsize=(7, 7),title='Graph 10: 2017 population Breakdown')\n",
    "graph5.legend([\"White\",\"Black or African American\", \"American Indian and Alaska Native\", \"Asian\", \"Native Hawaiian\",\"Hispanic/Latino\"],loc='center left',bbox_to_anchor=(1.0, 0.5))\n",
    "graph5.set_ylabel(\"\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### These three pie charts are a product of the cleaning described above. These piecharts show a breakdown of San Diego in a racial/ethnicity context. From start to finish we see differences over the years. For example, the white population in 2000 had the majority. However as time went on, 2010 and 2017 showed great decrease in white percentages, and showed an increase of minorites, specifically those of Asian and Hispanic Origin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As seen above, our team took a very graphical approach with our project. We knew there would be data out there to suggest or give us an answer to our question, in how people are dispersed in San Diego. To us, the best way to do achieve that was to plot these graphs and determine if there was a correlation or not, below are a few EDA plot routes we took to investigate the correlation between some variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe5f118de80>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plot.subplots(figsize=(10, 8))\n",
    "corr = jobregdf.corr()\n",
    "sns.heatmap(corr,\n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)\n",
    "\n",
    "\n",
    "\n",
    "f, ax = plot.subplots(figsize=(10, 8))\n",
    "corr = df3.corr()\n",
    "sns.heatmap(corr,\n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Above are 2 correlation matrices taken for 2 of the dataframes we used for ages jobs by region. These matrices help us determine correlation coefficients which are used in statistics to measure how strong a relationship is between two variables. The diagonal elements of the matrix value are always 1, the correlation between the same columns so all the numerical attributes are important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fe5f0b8ac88>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(populationdf2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This analysis is a representation of a bivariate distribution plot, where we can study the relationship between two variables by analyzing the scatter plot. Obviously there is a clear connection with  year and population, a positive correlation. As year increases, so does the population. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of Train data is 4643.5478\n",
      "MSE of test data is 10807.6145\n",
      "R2 of Train data is 0.9664\n",
      "R2 of Test data is 0.5725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:29: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:30: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:31: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:32: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-212.42322092,  -61.02667511,   -0.        ,  -22.78838198,\n",
       "         10.97793775,    0.        ,   62.63040147,    0.        ,\n",
       "        -43.50073983,    0.        ,   -0.        ,  -46.94127232,\n",
       "         -0.        ,   -0.        ,   -0.        ,    0.        ,\n",
       "         -0.        ,    0.        ,    2.66103642,  -59.86609979])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import pandas as pd\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import linear_model\n",
    "from scipy import stats\n",
    "from functools import reduce\n",
    "import seaborn as sn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "df_Age = pd.read_excel('Age.xlsx', index_col = 0)\n",
    "df_Job = pd.read_excel('Job.xlsx', index_col = 0)\n",
    "df_Income = pd.read_excel('Income.xlsx', index_col = 0)\n",
    "df_Population = pd.read_excel('Population.xlsx', index_col = 0)\n",
    "df_Price = pd.read_excel('Price.xls', index_col = 0)\n",
    "\n",
    "df_Age.index = df_Age.index.str.lower()\n",
    "df_Job.index = df_Job.index.str.lower()\n",
    "df_Income.index = df_Income.index.str.lower()\n",
    "df_Population.index = df_Population.index.str.lower()\n",
    "df_Price.index = df_Price.index.str.lower()\n",
    "\n",
    "cities = ['carlsbad', 'chula vista', 'coronado', 'del mar', 'el cajon', 'encinitas', 'escondido', 'imperial beach',\n",
    "          'la mesa', 'lemon grove', 'national city', 'oceanside', 'poway', 'san dieog', 'san marcos', 'santee', 'solana beach', 'vista']\n",
    "\n",
    "df_Age = df_Age.loc[cities]\n",
    "df_Job = df_Job.loc[cities]\n",
    "df_Income = df_Income.loc[cities]\n",
    "df_Population = df_Population.loc[cities]\n",
    "df_Price = df_Price.loc[cities]\n",
    "\n",
    "dfs = [df_Age, df_Job, df_Income, df_Population]\n",
    "df_X =  reduce(lambda left,right: pd.merge(left, right, left_index = True, right_index = True), dfs)\n",
    "\n",
    "df_X = df_X.apply(lambda x: x.fillna(x.mean()),axis = 0) # Filling NaN values with the mean\n",
    "df_Price = df_Price.apply(lambda x: x.fillna(x.mean()),axis = 0)\n",
    "\n",
    "ss = StandardScaler()\n",
    "df_X= ss.fit_transform(df_X)\n",
    "df_X = pd.DataFrame(df_X)\n",
    "\n",
    "number_train = int (0.7 * len(df_X))\n",
    "\n",
    "train_x, train_y = df_X.iloc[:number_train], df_Price.iloc[:number_train]\n",
    "test_x, test_y = df_X.iloc[number_train:], df_Price.iloc[number_train:]\n",
    "\n",
    "\n",
    "regr = linear_model.Lasso(alpha = 9, normalize= False)\n",
    "regr.fit(train_x,train_y)\n",
    "\n",
    "train_perf = r2_score(train_y, regr.predict(train_x))\n",
    "test_perf = r2_score(test_y, regr.predict(test_x))\n",
    "\n",
    "train_MSE = mean_squared_error(train_y, regr.predict(train_x))\n",
    "test_MSE = mean_squared_error(test_y, regr.predict(test_x))\n",
    "\n",
    "print(\"MSE of Train data is %.4f\" %(train_MSE))\n",
    "print(\"MSE of test data is %.4f\" %(test_MSE))\n",
    "print(\"R2 of Train data is %.4f\" %(train_perf))\n",
    "print(\"R2 of Test data is %.4f\" %(test_perf))\n",
    "\n",
    "regr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We performed a Lasso Regression using our data separated by cities. We sorted the data by the list called 'cities' first, then we merged them into one DataFrame. And since there were some cities with NaN values in some columns, we filled them up with there mean value. Later we scaled the data using StandardScaler. Finally, we tried to find the optinal alpha value for Lasso Regression and it turned out to be alpha = 9. As a result, the resulted coefficient shows that 'The number of population between 0 and 4' is the most important factor. And it's followed by 'The portion of Agricultural career' and 'The number of population between 5 and 14'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From our data, especially graph 1, we can see that from the 10-year period from 2006-2016, there has been an overall rise in income. Although there is a huge dip in income starting from 2008 due to the housing contract, we see a gradual income increase since 2012. From this data, we can assume that the areas around San Diego definitely attract people of higher income. The data is accurate since San Diego is close to the beach. The higher income reflects the location and property value. From Graph 6, we can easily the distribution of wealth in different parts of the San Diego County. In the graph, we can see that the North Central region and North Inland area has the wealthiest people. North Central Area have families with a median income of 87,963 while North Inland Area have families with a median income of 79,116. The redevelopment of the two areas mentioned earlier led to the increase of wealthier families. The lowest income area is the Central Region which had a median income of 53,523.\n",
    "\n",
    "#### Graph 2 looks more closely at the differences of population. From the data we collected, we can see that the three highest populated cities in San Diego county includes San Diego, Chula Vista, and Oceanside. San Diego has the biggest population of over 1.2 million people. This beats the second highest populated city, Chula Vista, by great margins of almost 1 million individuals.\n",
    "\n",
    "#### Graph 3 consists of the age of the San Diego County. From this graph, we are able to see that the highest population is the age group between 25-44 for all the cities. This is because of the high amount of families/parents that are residing in this region. Lastly, we want to examine the graphs that look at ethnicity/race around the San Diego County. North Central region has the highest amount of Caucasians and Asians compared to the rest of the regions. We also see that there is a high number of Hispanics in Central and South Regions, while there isn’t very many Hispanics in North Central. So clearly we see there is a disparity and a preference where people choose to live and who they choose to live next to. \n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Ethics and Privacy"
   ]
  },
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   "source": [
    "#### The main use of our data is to understand how people look for ideal housing around the San Diego County. Although not perfect, we strive to create a projection that can show where a certain demographic or population may live in. To do this, we take in consideration of race, age, and income. The target group we aim for are usually students or new families trying to find residency around San Diego. A side effect of this categorization of demographics can lead to stigmatization. Stigmatization is the act of categorizing and giving a title to a person or group that leads to stereotyping or other connotations. Some people may not want to be categorized by race or income or even be labeled so this can be a potential ethical issue. One way to reduce this effect is to make sure we clarify that not everyone living in the area is the specific income, race, or age. The projection is based off of the average or the majority of the people living there.\n",
    "#### Another ethical issue that can be brought up is that some people are protective of how much they are paying for their living space. Many people consider their living area or house to be a private area or space, and would not like other people to see their information. Furthermore, observations toward these details might not be warranted. This again can be solved with the emphasis on the average of the income. The projection does not show the exact rent/payment for each household so there is privacy for the individual households.\n",
    "#### As we talked about above, stigmatization is unfortunate bi-product of this type of study. However the question asked is one that many people think in reality when moving into a city and data like this can allow people to understand the area they live in and who else lives around them. Most of the data itself was provided by the Census Bureau, a government funded group that is tasked with collecting data on the American People and the US economy. We would think this data is unbiased and nonpartisan as this is just a direct survey and observation of the people and institutions of the US. Another dataset was provided by SANDAG, a research and city-planning committee specifically for San Diego. If anything there could be some bias as they are collecting data on themselves, but something that would relatively rare as they are trying to help, and any data, bad or good, would be able to help. After reviewing the data we used and reviewing the analysis, we could say there is a possibility of bias as we were looking for data to help us with our hypothesis and not necessarily taking the neutral route and looking for data on both sides of the argument. \n",
    "\n"
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   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ultimately, we decided to explore the region of the greater San Diego and visualize how the characterization of the area has changed in terms of the socio-economic values of people. To observe the changes of the socio-economic environment of the San Diego county, we selected factors that may display this data. Our factors were age, income, and race. We inferred that these factors would play a significant role in the movement of the population in San Diego. We hypothesized that there would be correlations between the factors and the locations of the population. To better analyze the data and visualize the population movement , we separated the area around San Diego County to multiple regions. Basing our research on multiple censuses and SANDAG, pertaining to factors, we concluded that there has been a rise in the overall income in San Diego from 2006 to 2016. There was a drop in income in 2008; however, this did not prevent the gradual rise of income since 2012. We inferred from this data that the areas around San Diego has a growing population of people of higher income. We assumed this as San Diego is near the beach and the higher income is reflective of the location and the property value. The wealthiest people were congreated in the North Central and North inland region which are areas that have been redeveloped. The median income of the region was 87,963 and 79,116, respectively while the lowest median income at 53,523 was the Central Region. Seeing the movement of the population, we found that the largest population resided in San Diego, Chula Vista, and Oceanside. The largest age group in San Diego is those from ages 25-44. We credit this as a result of the high number of families occupying the region. From our research and graphs, we extracted the racial diversity around the San Diego County and deduced that the North Central region had the highest number of Caucasians and Asians compared to the other regions while a high number of Hispanics lived in Central and South regions. In summary, our data displays that there is a clear disparity and a preference where people choose to live and that many factors play into this decision.\n",
    "\n",
    "#### In our project, there are possible improvements and different approaches that we may have taken if we addressed this question again. One mishap was the initial bias that we had entering the research. Living as college students, we had an initial idea and bias of how the population around San Diego is congregated so while searching for data, our research may have segregated itself to find articles and data that proved our hypothesis and possibly inflated our data to adjust to our preferred conclusion. We could have improved on approaching our research from a more neutral ground. We would have asked more neutral questions in our search for data. This would be an improvement as it could potentially lead to a more neutral conclusion. Another change that would exist if we took on this question and research again would be our understanding of GitHub. While utilizing github, we encountered many issues within github that forced us to restart the data and the analysis. This improvement would pave a way for a cleaner data where we would have had many more datasets that could have potentially improved our research. Understanding github at a better level would make our process simpler and easier. \n"
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