Community Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "extensive-waterproof",
   "metadata": {},
   "source": [
    "# Community analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fiscal-economy",
   "metadata": {},
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "lovely-projection",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "processed-circulation",
   "metadata": {},
   "source": [
    "## Importing database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "hindu-raise",
   "metadata": {},
   "outputs": [],
   "source": [
    "crimes = pd.read_csv(\"/Users/oleoaraujo/Desktop/Crimes-in-Chicago/Crimes_-_2016_to_2020.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "irish-chile",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>Crime_Type</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Beat</th>\n",
       "      <th>District</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated On</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11646166</td>\n",
       "      <td>JC213529</td>\n",
       "      <td>2018-09-01 00:01:00</td>\n",
       "      <td>082XX S INGLESIDE AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>631</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019-04-06 16:04:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11645836</td>\n",
       "      <td>JC212333</td>\n",
       "      <td>2016-05-01 00:25:00</td>\n",
       "      <td>055XX S ROCKWELL ST</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>DECEPTIVE PRACTICE</td>\n",
       "      <td>FINANCIAL IDENTITY THEFT OVER $ 300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>824</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2019-04-06 16:04:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12014684</td>\n",
       "      <td>JD189901</td>\n",
       "      <td>2020-03-17 21:30:00</td>\n",
       "      <td>039XX N LECLAIRE AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1634</td>\n",
       "      <td>16.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-03-25 15:45:43</td>\n",
       "      <td>41.952052</td>\n",
       "      <td>-87.754660</td>\n",
       "      <td>(41.952051946, -87.754660372)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11645648</td>\n",
       "      <td>JC212959</td>\n",
       "      <td>2018-01-01 08:00:00</td>\n",
       "      <td>024XX N MONITOR AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>DECEPTIVE PRACTICE</td>\n",
       "      <td>FINANCIAL IDENTITY THEFT OVER $ 300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2515</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019-04-06 16:04:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11864018</td>\n",
       "      <td>JC476123</td>\n",
       "      <td>2019-09-24 08:00:00</td>\n",
       "      <td>022XX S MICHIGAN AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>DECEPTIVE PRACTICE</td>\n",
       "      <td>FINANCIAL IDENTITY THEFT $300 AND UNDER</td>\n",
       "      <td>COMMERCIAL / BUSINESS OFFICE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-20 15:56:02</td>\n",
       "      <td>41.852248</td>\n",
       "      <td>-87.623786</td>\n",
       "      <td>(41.852248185, -87.623786256)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11859805</td>\n",
       "      <td>JC471592</td>\n",
       "      <td>2019-10-13 20:30:00</td>\n",
       "      <td>024XX W CHICAGO AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>RETAIL THEFT</td>\n",
       "      <td>GROCERY FOOD STORE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1221</td>\n",
       "      <td>12.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-20 16:03:03</td>\n",
       "      <td>41.895732</td>\n",
       "      <td>-87.687784</td>\n",
       "      <td>(41.895732399, -87.687784384)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11645959</td>\n",
       "      <td>JC211511</td>\n",
       "      <td>2018-12-20 16:00:00</td>\n",
       "      <td>045XX N ALBANY AVE</td>\n",
       "      <td>INFRACTION</td>\n",
       "      <td>OTHER OFFENSE</td>\n",
       "      <td>TELEPHONE THREAT</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1724</td>\n",
       "      <td>17.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019-04-06 16:04:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11645557</td>\n",
       "      <td>JC212685</td>\n",
       "      <td>2018-04-01 00:01:00</td>\n",
       "      <td>080XX S VERNON AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>DECEPTIVE PRACTICE</td>\n",
       "      <td>FINANCIAL IDENTITY THEFT OVER $ 300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>631</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019-04-06 16:04:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11646293</td>\n",
       "      <td>JC213749</td>\n",
       "      <td>2018-12-20 15:00:00</td>\n",
       "      <td>023XX N LOCKWOOD AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>DECEPTIVE PRACTICE</td>\n",
       "      <td>FINANCIAL IDENTITY THEFT $300 AND UNDER</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2515</td>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019-04-06 16:04:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12012127</td>\n",
       "      <td>JD189186</td>\n",
       "      <td>2020-03-18 02:03:00</td>\n",
       "      <td>039XX W JACKSON BLVD</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>AUTOMOBILE</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1132</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-03-25 15:47:29</td>\n",
       "      <td>41.877110</td>\n",
       "      <td>-87.723990</td>\n",
       "      <td>(41.877110187, -87.723989719)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11863808</td>\n",
       "      <td>JC476236</td>\n",
       "      <td>2019-10-05 18:30:00</td>\n",
       "      <td>0000X N LOOMIS ST</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1224</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-20 15:56:02</td>\n",
       "      <td>41.882002</td>\n",
       "      <td>-87.662287</td>\n",
       "      <td>(41.88200224, -87.662286977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11227287</td>\n",
       "      <td>JB147188</td>\n",
       "      <td>2017-10-08 03:00:00</td>\n",
       "      <td>092XX S RACINE AVE</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>CRIMINAL SEXUAL ASSAULT</td>\n",
       "      <td>NON-AGGRAVATED</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2222</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2018-02-11 15:57:41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11227583</td>\n",
       "      <td>JB147595</td>\n",
       "      <td>2017-03-28 14:00:00</td>\n",
       "      <td>026XX W 79TH ST</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>UNLAWFUL ENTRY</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>835</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2018-02-11 15:57:41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11227293</td>\n",
       "      <td>JB147230</td>\n",
       "      <td>2017-09-09 20:17:00</td>\n",
       "      <td>060XX S EBERHART AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>313</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2018-02-11 15:57:41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11227634</td>\n",
       "      <td>JB147599</td>\n",
       "      <td>2017-08-26 10:00:00</td>\n",
       "      <td>001XX W RANDOLPH ST</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>CRIMINAL SEXUAL ASSAULT</td>\n",
       "      <td>NON-AGGRAVATED</td>\n",
       "      <td>HOTEL/MOTEL</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2018-02-11 15:57:41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11645568</td>\n",
       "      <td>JC212803</td>\n",
       "      <td>2018-12-18 11:00:00</td>\n",
       "      <td>032XX N WILTON AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>DECEPTIVE PRACTICE</td>\n",
       "      <td>FINANCIAL IDENTITY THEFT OVER $ 300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1924</td>\n",
       "      <td>19.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019-04-06 16:04:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11859727</td>\n",
       "      <td>JC471542</td>\n",
       "      <td>2019-10-13 19:00:00</td>\n",
       "      <td>016XX W ADDISON ST</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO VEHICLE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1922</td>\n",
       "      <td>19.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-20 16:03:03</td>\n",
       "      <td>41.946987</td>\n",
       "      <td>-87.669164</td>\n",
       "      <td>(41.946987144, -87.669163602)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11859656</td>\n",
       "      <td>JC471240</td>\n",
       "      <td>2019-10-13 14:10:00</td>\n",
       "      <td>051XX N BROADWAY</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>GAS STATION</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2033</td>\n",
       "      <td>20.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-20 16:03:03</td>\n",
       "      <td>41.975838</td>\n",
       "      <td>-87.659854</td>\n",
       "      <td>(41.975837637, -87.659853835)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11227508</td>\n",
       "      <td>JB146365</td>\n",
       "      <td>2017-01-01 00:01:00</td>\n",
       "      <td>027XX S WHIPPLE ST</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>OFFENSE INVOLVING CHILDREN</td>\n",
       "      <td>AGG SEX ASSLT OF CHILD FAM MBR</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1033</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2018-02-11 15:57:41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11022695</td>\n",
       "      <td>JA353568</td>\n",
       "      <td>2017-07-17 10:10:00</td>\n",
       "      <td>021XX W MC LEAN AVE</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1432</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-24 15:54:23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID Case Number                 Date                  Block  \\\n",
       "0   11646166    JC213529  2018-09-01 00:01:00  082XX S INGLESIDE AVE   \n",
       "1   11645836    JC212333  2016-05-01 00:25:00    055XX S ROCKWELL ST   \n",
       "2   12014684    JD189901  2020-03-17 21:30:00   039XX N LECLAIRE AVE   \n",
       "3   11645648    JC212959  2018-01-01 08:00:00    024XX N MONITOR AVE   \n",
       "4   11864018    JC476123  2019-09-24 08:00:00   022XX S MICHIGAN AVE   \n",
       "5   11859805    JC471592  2019-10-13 20:30:00    024XX W CHICAGO AVE   \n",
       "6   11645959    JC211511  2018-12-20 16:00:00     045XX N ALBANY AVE   \n",
       "7   11645557    JC212685  2018-04-01 00:01:00     080XX S VERNON AVE   \n",
       "8   11646293    JC213749  2018-12-20 15:00:00   023XX N LOCKWOOD AVE   \n",
       "9   12012127    JD189186  2020-03-18 02:03:00   039XX W JACKSON BLVD   \n",
       "10  11863808    JC476236  2019-10-05 18:30:00      0000X N LOOMIS ST   \n",
       "11  11227287    JB147188  2017-10-08 03:00:00     092XX S RACINE AVE   \n",
       "12  11227583    JB147595  2017-03-28 14:00:00        026XX W 79TH ST   \n",
       "13  11227293    JB147230  2017-09-09 20:17:00   060XX S EBERHART AVE   \n",
       "14  11227634    JB147599  2017-08-26 10:00:00    001XX W RANDOLPH ST   \n",
       "15  11645568    JC212803  2018-12-18 11:00:00     032XX N WILTON AVE   \n",
       "16  11859727    JC471542  2019-10-13 19:00:00     016XX W ADDISON ST   \n",
       "17  11859656    JC471240  2019-10-13 14:10:00       051XX N BROADWAY   \n",
       "18  11227508    JB146365  2017-01-01 00:01:00     027XX S WHIPPLE ST   \n",
       "19  11022695    JA353568  2017-07-17 10:10:00    021XX W MC LEAN AVE   \n",
       "\n",
       "     Crime_Type                Primary Type  \\\n",
       "0   MISDEMEANOR                       THEFT   \n",
       "1   MISDEMEANOR          DECEPTIVE PRACTICE   \n",
       "2   MISDEMEANOR                       THEFT   \n",
       "3   MISDEMEANOR          DECEPTIVE PRACTICE   \n",
       "4   MISDEMEANOR          DECEPTIVE PRACTICE   \n",
       "5   MISDEMEANOR                       THEFT   \n",
       "6    INFRACTION               OTHER OFFENSE   \n",
       "7   MISDEMEANOR          DECEPTIVE PRACTICE   \n",
       "8   MISDEMEANOR          DECEPTIVE PRACTICE   \n",
       "9   MISDEMEANOR         MOTOR VEHICLE THEFT   \n",
       "10  MISDEMEANOR                       THEFT   \n",
       "11       FELONY     CRIMINAL SEXUAL ASSAULT   \n",
       "12       FELONY                    BURGLARY   \n",
       "13  MISDEMEANOR                       THEFT   \n",
       "14       FELONY     CRIMINAL SEXUAL ASSAULT   \n",
       "15  MISDEMEANOR          DECEPTIVE PRACTICE   \n",
       "16       FELONY             CRIMINAL DAMAGE   \n",
       "17  MISDEMEANOR                     ASSAULT   \n",
       "18       FELONY  OFFENSE INVOLVING CHILDREN   \n",
       "19  MISDEMEANOR                       THEFT   \n",
       "\n",
       "                                Description          Location Description  \\\n",
       "0                                 OVER $500                     RESIDENCE   \n",
       "1       FINANCIAL IDENTITY THEFT OVER $ 300                           NaN   \n",
       "2                            $500 AND UNDER                        STREET   \n",
       "3       FINANCIAL IDENTITY THEFT OVER $ 300                     RESIDENCE   \n",
       "4   FINANCIAL IDENTITY THEFT $300 AND UNDER  COMMERCIAL / BUSINESS OFFICE   \n",
       "5                              RETAIL THEFT            GROCERY FOOD STORE   \n",
       "6                          TELEPHONE THREAT                     RESIDENCE   \n",
       "7       FINANCIAL IDENTITY THEFT OVER $ 300                     RESIDENCE   \n",
       "8   FINANCIAL IDENTITY THEFT $300 AND UNDER                     APARTMENT   \n",
       "9                                AUTOMOBILE                     APARTMENT   \n",
       "10                                OVER $500                     RESIDENCE   \n",
       "11                           NON-AGGRAVATED                     RESIDENCE   \n",
       "12                           UNLAWFUL ENTRY                         OTHER   \n",
       "13                                OVER $500                     RESIDENCE   \n",
       "14                           NON-AGGRAVATED                   HOTEL/MOTEL   \n",
       "15      FINANCIAL IDENTITY THEFT OVER $ 300                     RESIDENCE   \n",
       "16                               TO VEHICLE                        STREET   \n",
       "17                                   SIMPLE                   GAS STATION   \n",
       "18           AGG SEX ASSLT OF CHILD FAM MBR                     RESIDENCE   \n",
       "19                                OVER $500                     RESIDENCE   \n",
       "\n",
       "    Arrest  Domestic  Beat  District  Ward  Community Area  Year  \\\n",
       "0        0         1   631       6.0   8.0            44.0  2018   \n",
       "1        0         0   824       8.0  15.0            63.0  2016   \n",
       "2        0         0  1634      16.0  45.0            15.0  2020   \n",
       "3        0         0  2515      25.0  30.0            19.0  2018   \n",
       "4        0         0   132       1.0   3.0            33.0  2019   \n",
       "5        0         0  1221      12.0  26.0            24.0  2019   \n",
       "6        0         0  1724      17.0  33.0            14.0  2018   \n",
       "7        0         0   631       6.0   6.0            44.0  2018   \n",
       "8        0         0  2515      25.0  36.0            19.0  2018   \n",
       "9        0         1  1132      11.0  28.0            26.0  2020   \n",
       "10       0         0  1224      12.0  27.0            28.0  2019   \n",
       "11       0         0  2222      22.0  21.0            73.0  2017   \n",
       "12       0         0   835       8.0  18.0            70.0  2017   \n",
       "13       0         0   313       3.0  20.0            42.0  2017   \n",
       "14       0         0   122       1.0  42.0            32.0  2017   \n",
       "15       0         0  1924      19.0  44.0             6.0  2018   \n",
       "16       0         0  1922      19.0  47.0             6.0  2019   \n",
       "17       0         0  2033      20.0  47.0             3.0  2019   \n",
       "18       0         0  1033      10.0  12.0            30.0  2017   \n",
       "19       0         0  1432      14.0  32.0            22.0  2017   \n",
       "\n",
       "             Updated On   Latitude  Longitude                       Location  \n",
       "0   2019-04-06 16:04:43        NaN        NaN                            NaN  \n",
       "1   2019-04-06 16:04:43        NaN        NaN                            NaN  \n",
       "2   2020-03-25 15:45:43  41.952052 -87.754660  (41.952051946, -87.754660372)  \n",
       "3   2019-04-06 16:04:43        NaN        NaN                            NaN  \n",
       "4   2019-10-20 15:56:02  41.852248 -87.623786  (41.852248185, -87.623786256)  \n",
       "5   2019-10-20 16:03:03  41.895732 -87.687784  (41.895732399, -87.687784384)  \n",
       "6   2019-04-06 16:04:43        NaN        NaN                            NaN  \n",
       "7   2019-04-06 16:04:43        NaN        NaN                            NaN  \n",
       "8   2019-04-06 16:04:43        NaN        NaN                            NaN  \n",
       "9   2020-03-25 15:47:29  41.877110 -87.723990  (41.877110187, -87.723989719)  \n",
       "10  2019-10-20 15:56:02  41.882002 -87.662287   (41.88200224, -87.662286977)  \n",
       "11  2018-02-11 15:57:41        NaN        NaN                            NaN  \n",
       "12  2018-02-11 15:57:41        NaN        NaN                            NaN  \n",
       "13  2018-02-11 15:57:41        NaN        NaN                            NaN  \n",
       "14  2018-02-11 15:57:41        NaN        NaN                            NaN  \n",
       "15  2019-04-06 16:04:43        NaN        NaN                            NaN  \n",
       "16  2019-10-20 16:03:03  41.946987 -87.669164  (41.946987144, -87.669163602)  \n",
       "17  2019-10-20 16:03:03  41.975838 -87.659854  (41.975837637, -87.659853835)  \n",
       "18  2018-02-11 15:57:41        NaN        NaN                            NaN  \n",
       "19  2017-07-24 15:54:23        NaN        NaN                            NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "standing-davis",
   "metadata": {},
   "outputs": [],
   "source": [
    "crimes.set_index(crimes.ID, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "exterior-authorization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>1277232.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11302467.393457</td>\n",
       "      <td>783643.814895</td>\n",
       "      <td>22245.0</td>\n",
       "      <td>10882663.75</td>\n",
       "      <td>11333324.5</td>\n",
       "      <td>11784053.25</td>\n",
       "      <td>12305810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Case Number</th>\n",
       "      <td>1277232</td>\n",
       "      <td>1277072</td>\n",
       "      <td>HZ140230</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>1277232</td>\n",
       "      <td>589815</td>\n",
       "      <td>2016-01-01 00:01:00</td>\n",
       "      <td>168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Block</th>\n",
       "      <td>1277232</td>\n",
       "      <td>34700</td>\n",
       "      <td>001XX N STATE ST</td>\n",
       "      <td>4326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime_Type</th>\n",
       "      <td>1277094</td>\n",
       "      <td>4</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>626741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary Type</th>\n",
       "      <td>1277232</td>\n",
       "      <td>32</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>294745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <td>1277232</td>\n",
       "      <td>491</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>140645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location Description</th>\n",
       "      <td>1271428</td>\n",
       "      <td>190</td>\n",
       "      <td>STREET</td>\n",
       "      <td>286880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrest</th>\n",
       "      <td>1277232.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.194647</td>\n",
       "      <td>0.395929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domestic</th>\n",
       "      <td>1277232.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.166526</td>\n",
       "      <td>0.372552</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beat</th>\n",
       "      <td>1277232.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1143.700576</td>\n",
       "      <td>695.430329</td>\n",
       "      <td>111.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>2535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District</th>\n",
       "      <td>1277231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.20845</td>\n",
       "      <td>6.94722</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ward</th>\n",
       "      <td>1277203.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.222541</td>\n",
       "      <td>13.975698</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Area</th>\n",
       "      <td>1277231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.778095</td>\n",
       "      <td>21.4319</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>1277232.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.900504</td>\n",
       "      <td>1.380767</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Updated On</th>\n",
       "      <td>1277232</td>\n",
       "      <td>3128</td>\n",
       "      <td>2018-02-10 15:50:01</td>\n",
       "      <td>487694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>1263290.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.843492</td>\n",
       "      <td>0.086297</td>\n",
       "      <td>36.619446</td>\n",
       "      <td>41.768534</td>\n",
       "      <td>41.863197</td>\n",
       "      <td>41.905098</td>\n",
       "      <td>42.022671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>1263290.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-87.670533</td>\n",
       "      <td>0.059106</td>\n",
       "      <td>-91.686566</td>\n",
       "      <td>-87.713031</td>\n",
       "      <td>-87.664054</td>\n",
       "      <td>-87.627842</td>\n",
       "      <td>-87.524529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>1263290</td>\n",
       "      <td>333826</td>\n",
       "      <td>(41.883500187, -87.627876698)</td>\n",
       "      <td>2248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count   unique                            top  \\\n",
       "ID                    1277232.0      NaN                            NaN   \n",
       "Case Number             1277232  1277072                       HZ140230   \n",
       "Date                    1277232   589815            2016-01-01 00:01:00   \n",
       "Block                   1277232    34700               001XX N STATE ST   \n",
       "Crime_Type              1277094        4                    MISDEMEANOR   \n",
       "Primary Type            1277232       32                          THEFT   \n",
       "Description             1277232      491                         SIMPLE   \n",
       "Location Description    1271428      190                         STREET   \n",
       "Arrest                1277232.0      NaN                            NaN   \n",
       "Domestic              1277232.0      NaN                            NaN   \n",
       "Beat                  1277232.0      NaN                            NaN   \n",
       "District              1277231.0      NaN                            NaN   \n",
       "Ward                  1277203.0      NaN                            NaN   \n",
       "Community Area        1277231.0      NaN                            NaN   \n",
       "Year                  1277232.0      NaN                            NaN   \n",
       "Updated On              1277232     3128            2018-02-10 15:50:01   \n",
       "Latitude              1263290.0      NaN                            NaN   \n",
       "Longitude             1263290.0      NaN                            NaN   \n",
       "Location                1263290   333826  (41.883500187, -87.627876698)   \n",
       "\n",
       "                        freq             mean            std        min  \\\n",
       "ID                       NaN  11302467.393457  783643.814895    22245.0   \n",
       "Case Number                6              NaN            NaN        NaN   \n",
       "Date                     168              NaN            NaN        NaN   \n",
       "Block                   4326              NaN            NaN        NaN   \n",
       "Crime_Type            626741              NaN            NaN        NaN   \n",
       "Primary Type          294745              NaN            NaN        NaN   \n",
       "Description           140645              NaN            NaN        NaN   \n",
       "Location Description  286880              NaN            NaN        NaN   \n",
       "Arrest                   NaN         0.194647       0.395929        0.0   \n",
       "Domestic                 NaN         0.166526       0.372552        0.0   \n",
       "Beat                     NaN      1143.700576     695.430329      111.0   \n",
       "District                 NaN         11.20845        6.94722        1.0   \n",
       "Ward                     NaN        23.222541      13.975698        1.0   \n",
       "Community Area           NaN        36.778095        21.4319        1.0   \n",
       "Year                     NaN      2017.900504       1.380767     2016.0   \n",
       "Updated On            487694              NaN            NaN        NaN   \n",
       "Latitude                 NaN        41.843492       0.086297  36.619446   \n",
       "Longitude                NaN       -87.670533       0.059106 -91.686566   \n",
       "Location                2248              NaN            NaN        NaN   \n",
       "\n",
       "                              25%         50%          75%         max  \n",
       "ID                    10882663.75  11333324.5  11784053.25  12305810.0  \n",
       "Case Number                   NaN         NaN          NaN         NaN  \n",
       "Date                          NaN         NaN          NaN         NaN  \n",
       "Block                         NaN         NaN          NaN         NaN  \n",
       "Crime_Type                    NaN         NaN          NaN         NaN  \n",
       "Primary Type                  NaN         NaN          NaN         NaN  \n",
       "Description                   NaN         NaN          NaN         NaN  \n",
       "Location Description          NaN         NaN          NaN         NaN  \n",
       "Arrest                        0.0         0.0          0.0         1.0  \n",
       "Domestic                      0.0         0.0          0.0         1.0  \n",
       "Beat                        612.0      1024.0       1713.0      2535.0  \n",
       "District                      6.0        10.0         17.0        31.0  \n",
       "Ward                         10.0        24.0         34.0        50.0  \n",
       "Community Area               23.0        32.0         54.0        77.0  \n",
       "Year                       2017.0      2018.0       2019.0      2020.0  \n",
       "Updated On                    NaN         NaN          NaN         NaN  \n",
       "Latitude                41.768534   41.863197    41.905098   42.022671  \n",
       "Longitude              -87.713031  -87.664054   -87.627842  -87.524529  \n",
       "Location                      NaN         NaN          NaN         NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.describe(include='all').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "invalid-registrar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                        int64\n",
       "Case Number              object\n",
       "Date                     object\n",
       "Block                    object\n",
       "Crime_Type               object\n",
       "Primary Type             object\n",
       "Description              object\n",
       "Location Description     object\n",
       "Arrest                    int64\n",
       "Domestic                  int64\n",
       "Beat                      int64\n",
       "District                float64\n",
       "Ward                    float64\n",
       "Community Area          float64\n",
       "Year                      int64\n",
       "Updated On               object\n",
       "Latitude                float64\n",
       "Longitude               float64\n",
       "Location                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "local-elevation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                               int64\n",
       "Case Number                     object\n",
       "Date                    datetime64[ns]\n",
       "Block                           object\n",
       "Crime_Type                      object\n",
       "Primary Type                    object\n",
       "Description                     object\n",
       "Location Description            object\n",
       "Arrest                           int64\n",
       "Domestic                         int64\n",
       "Beat                             int64\n",
       "District                       float64\n",
       "Ward                           float64\n",
       "Community Area                 float64\n",
       "Year                             int64\n",
       "Updated On              datetime64[ns]\n",
       "Latitude                       float64\n",
       "Longitude                      float64\n",
       "Location                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define \"Date\" and \"Updated on\" columns as datetime type\n",
    "crimes.Date = pd.to_datetime(crimes.Date)\n",
    "crimes[\"Updated On\"] = pd.to_datetime(crimes[\"Updated On\"])\n",
    "\n",
    "crimes.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bigger-tuition",
   "metadata": {},
   "source": [
    "## Understanding the correlations between my numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "anticipated-accident",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6AAAAJDCAYAAAAVYnkpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAADkZklEQVR4nOzdd3hUxf7H8fdk00kPhIQiJQSVTmgWVDqI13JVvPZeQUWa2FCvFcGLXbH3cq/lZ5cixYKFEkAEld4TAul9k935/bFrSALBwmaTwOf1PHnInjPnZGY4Zed8Z+YYay0iIiIiIiIidS2gvjMgIiIiIiIihwc1QEVERERERMQv1AAVERERERERv1ADVERERERERPxCDVARERERERHxCzVARURERERExC/UABURERERETlEGWNeMsZkGmN+rmW9McY8boxZb4z5yRiTWmXdJcaYdd6fS3yRHzVARUREREREDl2vACMOsP5kIMX7czXwDIAxJg64C+gH9AXuMsbEHmxm1AAVERERERE5RFlrvwayD5DkdOA16/EDEGOMSQKGA3OttdnW2hxgLgduyP4paoCKiIiIiIgcvloC26p83u5dVtvygxJ4sDsQ3yjfs9HWdx4aqiVdJ9V3FhqssKCK+s5Cg+Zym/rOgjRCAUaX4wNxufXsujZdHulZ31lo0FaPT6vvLEgj1Xv7h43uhu7P7/bBzZKvwdN19nfPWWuf89ff/6vUABUREREREWmkvI3Ng2lw7gBaV/ncyrtsBzCgxvKFB/F3AHXBFREREREROZx9DFzsnQ33GCDPWpsOzAaGGWNivZMPDfMuOyiKgIqIiIiIiPiS21XfOahkjHkbTySzqTFmO56ZbYMArLUzgc+BkcB6oBi4zLsu2xhzL7DEu6t7rLUHmszoT1EDVERERERE5BBlrT3vD9ZbYEwt614CXvJlftQAFRERERER8SXrru8cNFgaAyoiIiIiIiJ+oQioiIiIiIiIL7kVAa2NIqAiIiIiIiLiF4qAioiIiIiI+JDVGNBaKQIqIiIiIiIifqEIqIiIiIiIiC9pDGitFAEVERERERERv1AEVERERERExJc0BrRWioCKiIiIiIiIX6gBKiIiIiIiIn6hLrgiIiIiIiK+5HbVdw4aLEVARURERERExC8UARUREREREfElTUJUK0VARURERERExC8UARUREREREfEltyKgtVEEVERERERERPxCEVCpdMcDM/h60WLiYmP48I2Z9Z0dv2l37+XEDE7FXeJk/U1PULRq0z5pmnRrT4dHrycgNJjceWlsmvISAG2mXEzssN5YZwWlWzJYf9OTuPKLMUGBJE+7hibdk8Ft2TTlJfK/X+3voh20yJN60uruqzCOALLemcuup9+vtt4EB9LmkXGEd02mIqeAzWOm49yeSeQJ3Wlxy8WYoEBseQU77n+Fwu9WAZD82l0EJcRCoIOixWvYdsezjeYpYdSAnhzx7yvBEcCet+eS8dQH1dab4EDaPXoT4d089bHxuodxbs8EIHHMWTQ9bwi43Gy983nyv1oBgCOqCW2mjyHsyCPAWjZPeJKitN8ASLjsFJpdcjK43OTNX8b2+1/1a3n/CtXNgUUN8JxLOALIenv/51LbR8cR1jUZV04Bm0ZPr6yf5mPOIv7coeBys+2u5yn4ajkAzS7/B03PHwYY9rw9h90vfgJA0rhziT9/GBVZeQDsfOgN8hcs819h/4Yj7rmC6EG9cJeUsWncExT/vHGfNOFd29PukRsJCA0mb/4ytt75IgCOmAiSn5lASOsEyrZlsuHah3HlFZF47RnEn3miZ2OHg7CUlizvdilB8VEkPzOxcr8hRzRnx8Nvs+uFT/1SVl9ZtG4n0z5bitta/tmrA5ef2HmfNLNXbeHZBT8Bho6JMUw9pz8A6blF/PvDH9iVV4wx8MRFA2kZG+HnEhw8XXcOzJ/1E3vKcbQYfy6hKa345R+TKP5pg7+L2+BYjQGtlRqgPmCMKbTWRhhj2gK/AL8CoUAB8LS19pV6zN6fdsbIoZx/1mncdu/D9Z0Vv4kZlEpo+ySWH3c9EakptJ96NatOuXWfdO2nXs2Gic9QmLaOo9+8nZhBPcmdv5zcr1ey5YE3wOWmze0X0uqGM9ly/xs0v2AIACsHjScoPoqj37qDn0ZMBmv9XcS/LyCA1vddw/oL7qI8PYsjP3mYvLmLKV23rTJJ/L+G4sorZM2J1xJz6gm0uPUSNo+ZTkV2Phsuv5+KXdmEdjyC5DfuZnXfywHYNHoa7sISANrNnEzMKceT+8k39VLEvyQggCPuu4a153vq4+jPppM7ZzGl67ZXJml67lAq8gr5uf91xJ7Wn1a3XczG0Q8TmtKKuNP7s3rQDQQ1j6Pj2/fw84mjwe2m9b+vIH9hGhuvmYYJCiQgLASAyOO6EDOsL2uG3YR1VhAYH11fJf9jqpsD855L67z1c+Sn+zmXzh1KRW4ha064ltjTTqDlbZewafR0QlNaE3vaCfwy+HqCmseR8vY9rD5xNKEprWh6/jB+/cdEbHkFHV6/m/x5SyjbnAFA5gsfk/nsh/VU4L8melAqIe1asKr/aJqkdqTNg9fwy6mT90nX5sFr2Xzz0xSlrSXl9SlED0wlb0EaSWPOJP/bVWQ89QGJY84kacyZbH/gdTJmfkjGzA89f2NobxKvOg1XbiGu3EJWDxvv2WlAAD2WvUDOFz/6scQHz+V28+AnS5h56SCaR4VzwcxZnHRUK5IT9p4LW7Lyeenr1bxy1TCiwkLILiytXHfH+99x5UldOLZDEsVl5Rhj6qMYB0fXnQPzc/2U/LaV9VdNpe1Do+urxNKIqAuu722w1va01h4NnAvcZIy5rL4z9Wf07tGV6KjI+s6GX8WN6MPud78CoDBtHYFRTQhKiKmWJighBkdkOIVp6wDY/e5XxI3oC0DeVyvB5XnCVZC2luAW8QCEdWxF3qKfASjPyqcir4iI7sn+KJLPhPdIoWxzBs6tu7DlFeR88g3Rw/pWSxM9rB9Z780HIPfzRUQe3w2AktWbqNiVDUDp2q0EhAZjgj3Pu35vfBLo8C5rHI3yJj1SKNucXlkf2R99S8ywftXSxAzrS9a7CwDI+ew7Ivt38y7vR/ZH32KdFTi3ZVK2OZ0mPVJwRIYT2a8ze97+EgBbXoErvwiAZhedTPpT72OdFQCV0ayGSHVzYE1qnksf73suxQzrR7b3XMr5bO+5FD2sLzkff1OlfjJo0iOF0A6tKFq+FlvqBJebwh9/JmbEsX4vmy/EDO9L1nueY6MobS2O6CaeXhJVBCXE4ogMoyhtLQBZ7y0gxnsdjhm+99jKencBMSOqH3sA8aefQNaH+z7oiurfldItGTh37PZpmeraz9uzaB0fSau4SIICHQzv2oaFv2yrluaDpev5V7+ORHkbCHERoQBsyMzD5bYc2yEJgPCQIMKCG188QtedA/N3/ZSu307Zxp1+LGEj4Hb776eRUQO0DllrNwLjgRvrOy+yf8GJcZTt3FP5uSw9i+Ck+OppkuJx7syqniYxbp99JZw7mJz5nq5xxWu2EDusNzgCCGmdQES3ZIJbNq2jUtSN4MR4nFXqxpmeRVDz6nUTlBhH+e9pXG5cBUU4Yqs/xIgZeRwlP2+svGkDJL9+N12Xv4arsITcz76ru0L4UHBSHM70KvWRkUVwUvXjIDixShqXG1d+MYGxkbVuG9y6ORXZebSdcSOdZs2gzfQxlU+TQ9u3ILJfJ476ZBpHvncf4d071H0h/ybVzYEF1TiXytOzCErc91xy7udcCkqM33uOAc70PQQlxlP621Yi+nbCEROJCQ0mamAvglrsvcY0u2QkR895jCMevgFHdJO6LeBB8lxr9l5jPfVT/fgJSozDmb43jTM9i2BvHQY1jaE8M8ezbWYOQU1jqm0bEBpM9ICe5Hz+/T5/O+70E8jeT8O0ocvMLyExOrzyc/PocDILSqql2bKngC1Z+Vzy/GwuenYWi9bt9C7PJzI0iPFvfc2/nvqcGbPScDXCL7C67hyYv+tH5K9QA7TupQFH1XcmpG61HHsW1uViz/tfA7Dr7Xk407PoPmsa7e65jIKlv1VGSg8noR1b0+LWi9l669PVlm+46G5+7n0pAcFBRB7ftZ5yV/9MYADhXZLZ/foXrBkxHndxKYljzvKscwTgiInk11NvZvt9r5L8zKR6zq1/qW4OrHT9dnY9/QEpb95NhzfupmTNpsprzO7Xv2B1/2v5ZfhNVGTm0HLK5fWcWz+rMdQhZlgfCpf+iiu3sNpyExRIzLA+ZH/aOB6C/VUut5utWQW8cPlQpp7Tn3s+/JH8Eicut2X5lt2MH9GTN68dwY6cQj5evu+Y28ORrjsHdqD6kf2wbv/9NDJqgNa9WgdWGGOuNsYsNcYsfeG1t/2Zp8Na4qUj6D73YbrPfZjyzBxCqkQNQpLiqz1lB++T9hbx1dNkZFd+bnbOQOKG9GLdmEf3buRys/muV1g5dCK/XvYQjqhwShpZ1xRnRhbBVeomOCme8l3V66Y8I3tv1MURgCOyCa6cAsAT9Wn33K1sGfcozi0Z++zflpWTN3cx0UP37S7XEDnTswlOqlIfifE407Orp8moksYRgCMqnIqcglq3daZn4UzPomi5p3t3zmffE961vXdfWeR+4YnYFK1Yh3VbAuOi6rKIf5vq5sDKa5xLQUnxlGfsey4F7+dcKs/IqhbZDE5qWrlt1n+/5NdTJrDu7Ntw5RVSuslzjanYk+fpkmUte96aQ5MeKXVcwr8u4ZKT6TxnBp3nzKB8V061a6ynfqofP+UZ2dV6pwQnxeP01kP5ntzKLrtBCbGU1+gaGXda//12v40emErxqo2e+mpkEqLCyMgrrvy8K6+YhMiwammaR4dz0lGtCHIE0DI2gjZNI9maVUDz6HCOTIqlVVwkgY4ABh7dil92Ztf8Ew2erjsH5u/6Efkr1ACtez3xTEy0D2vtc9ba3tba3ldefJ6fs3X4ynhlFiuHTmTl0Ilkf7GYZqNOAiAiNYWKgmLKM3OrpS/PzMVVUExEqudLXLNRJ5E9awkAMQN70HLM6fxy6VTcJc7KbQLCgiu7pUSf2A3rclOydjuNSfHKdYS0SyK4dQImKJDYU08gb+7iamny5i4m/uxBAMSMPJ6C734CPLPkJb8yhZ1TX6No6a+V6QPCQwn8fWyXI4CoQb0p3dA46qVo5TpCq9RH3On9ya1RH7lzFxM/aiAAsaccR8GiVZXL407vjwkOJLh1AqHtkihasY6K3bk4d+4hpH0LAKL6d6ucmCZ31o9EHueJDoe0a0FAcCAV2fn+Ku5foro5sKKV6whpW+VcOm3fcyl37mLivOdS7CnHU7DIcy7lzV1M7GknVNZPSFtP/QCVk6AEtWhKzIhjyfnQ0wMjsMr4yZgRx1Dy29Y6L+NflfnqF6weNp7Vw8aTM/tH4s/2HBtNUjviyi+u7FL7u/LMHFwFJTRJ7QhA/NkDyZ3tqcPcOUsqj634UXuXA54xa8d0rrbsd3Fn9G+U3W8BOreMZ2tWATtyCimvcDF71RZOOqpVtTQDj27N0k27AMgpKmXLngJaxUXQuWUcBSVOsos8kxIt3riL9gkNfEKd/dB158D8XT+yH26X/34aGWMb06ycDVSNWXA/tdZ28S5vC3wAPGGtfflA+yjfs7He/yMm3TWVJct/Ijc3n/i4GEZfcRFnnTq8vrPFkq51282l3QNXEjuwJ66SMtaPe4qilZ6pw7vPfZiVQz1T9TfpnkyK9zUsOfOXs+n2FwDo+d2TBAQHUeGN+hWkrWXj5OcIadWMTm9PwVqLMz2bDROepmy77ye5CAuq+ONEByFqYC9a3nWF5zUs/53HriffJXH8+RSvWk/+3MWYkCDaPDqO8M7tqcgtYPP1D+PcuovmN4yi+ZizKdu0N+q74cK7wUD7l6cQEBwEAYbC71ax/Z4X66x7ssvt25kdowf1ovXdl0OAg6z/fkn6E+/RYuJ5FK1cT97cJZiQINo9dhPhXdrjyi1gw+j/4Nzq+QKYdMPZxP9rCLhcbL37RfIXpAEQ1qkdbaePwQQHUrZlF5snPI4rrwgTFEjb/1xPeKd2uMsr2H7vKxR4X2XTEB1KdRNgfH85jhrYi1Z37z2XMp54l6QJ51P803ryvOdS20fHEeatn01jHq6sn8QbRhH/r8HYCjfb736B/IWe+un4/gM4YqKwFRXsuOelykZrm0dvIrxzO7BQtj2Trbc8TUWNBt3BcLl9/+z6iPuvJnpAT89rWMY/UfkKh85zZlTOWBveLXnva1gWpLH1jucBcMRG0mHmRIJbNqVs+27Pa1i83W3jzxlI9ICebBw9o9rfCwgLofuS5/np2GtxFRTjK10e6emzff2Rb9buYPrny3C7LaenJnPVgC48PW8lnVrEM+DoVlhr+c+sNL5bt5MAY7jypC6M6NYWgO/XpzNjVhrWWo5uGc+dp/UlKNBR53lePT7Np/s7lK47dcGf9RMzoh9H3HsVgXHRuPKLKF69iXUX/ttnZem9/cNGN1Vz2a9f+e27fchRJzWq+lED1Ad88RqWhtAAbajqugHamNV1A7Sx83UDVA4PddEAPZTURQP0UOHPBmhj5OsGqBw+GmUD9JcF/muAHj2wUdVP45t3uwGy1kZ4/90MhB04tYiIiIiIyOFJjzFFRERERETELxQBFRERERER8aVG+H5df1EEVERERERERPxCEVARERERERFfsoqA1kYRUBEREREREfELRUBFRERERER8SWNAa6UIqIiIiIiIiPiFIqAiIiIiIiI+ZK2rvrPQYCkCKiIiIiIiIn6hCKiIiIiIiIgvaRbcWikCKiIiIiIiIn6hCKiIiIiIiIgvaRbcWikCKiIiIiIiIn6hCKiIiIiIiIgvaQxorRQBFREREREREb9QBFRERERERMSX3HoPaG0UARURERERERG/UANURERERERE/EJdcEVERERERHxJkxDVShFQERERERER8QtFQEVERERERHzJrQhobRQBFREREREREb9QBLSBWNJ1Un1nocHqs2p6fWehwVrc5eb6zoLIIccYW99ZaNAiQpz1nYUGa+VNK+o7Cw1auVtfO2vj0HXn0KMxoLVSBFRERERERET8Qo+iREREREREfEljQGulCKiIiIiIiIj4hSKgIiIiIiIivqQIaK0UARURERERERG/UARURERERETEh6x11XcWGixFQEVERERERMQvFAEVERERERHxJY0BrZUioCIiIiIiIuIXioCKiIiIiIj4km1YEVBjzAjgMcABvGCtnVpj/SPAQO/HcCDBWhvjXecCVnnXbbXWnnYweVEDVERERERE5BBljHEATwFDge3AEmPMx9baNb+nsdaOq5L+BqBnlV2UWGt7+Co/6oIrIiIiIiJy6OoLrLfWbrTWOoF3gNMPkP484O26yowioCIiIiIiIr7UsCYhaglsq/J5O9BvfwmNMW2AdsD8KotDjTFLgQpgqrX2w4PJjBqgIiIiIiIijZQx5mrg6iqLnrPWPvc3d3cu8J6t/iLTNtbaHcaY9sB8Y8wqa+2Gv5tfNUBFRERERER8yY+TEHkbmwdqcO4AWlf53Mq7bH/OBcbU2P8O778bjTEL8YwP/dsNUI0BFREREREROXQtAVKMMe2MMcF4Gpkf10xkjDkKiAW+r7Is1hgT4v29KXA8sKbmtn+FIqAiIiIiIiK+1IDGgFprK4wx1wOz8byG5SVr7WpjzD3AUmvt743Rc4F3rLW2yuZHA88aY9x4gpdTq86e+3eoASoiIiIiInIIs9Z+DnxeY9mdNT7fvZ/tvgO6+jIvaoCKiIiIiIj4kh/HgDY2GgMqIiIiIiIifqEIqIiIiIiIiC81oDGgDY0ioCIiIiIiIuIXioAeJtrdezkxg1NxlzhZf9MTFK3atE+aJt3a0+HR6wkIDSZ3XhqbprwEQJspFxM7rDfWWUHplgzW3/QkrvxiTFAgydOuoUn3ZHBbNk15ifzvV/u7aH5zxwMz+HrRYuJiY/jwjZn1nR2/aXff5cQO7om7xMm6sU/WeuykPDaGgNBgcuYtZ9MdnmPniJvPJW5EH6zbTfmefNaPfRLnrhzCOrSgw6NjiOjani1T32bnM/vMBN4o1EXdOCLD6fjUjYS0bIoJdLDjmY/JfGeBv4vmE6qf2tXFNRkg/Og2JE+7BkdkONbt5qeTJ2PLyv1atoMVcVIqLe+8ChwBZP93Lrufea/aehMcSOsZ4wnrkowrt4At10+jfHsmYd1TaPXg9d5Ehl2PvkX+7B8ISmpK6xnjCGwaAxay3p5F1suf+L9gPtLm3iuIHZSKq6SMDeOepHjVxn3SNOnanuRHb/CcV/PT2DLlRQDi/nEsrSb8i7CUVvw8cjJFP+19jV/40W1o99C1OCLDwG1ZNfLmRnfs1NT+vsuJ816DfqvlGhTRrT0dvdeg7HnL2ei9Bv2u5bWn0v7uS/i+02VUZBf4K+t1QtcdP1MEtFaHdQTUGHOGMcZ633lTF/u/rS72+1fFDEoltH0Sy4+7ng2TnqH91Kv3m6791KvZMPEZlh93PaHtk4gZ1BOA3K9XsmLATawcPJ7SDTtpdcOZADS/YAgAKweNZ82//k3buy8BY/xTqHpwxsihzJxxX31nw69iB/ckrH0SacfewPqJM0l+aP/HTvJDV7F+wkzSjr2BsCrHzo6nP2LFoAmsHDKJnLnLaD1+FAAVuYVsuuMldjTShifUXd0kXTaC4rXbWTF4IqvOvIu2d12MCWp8zwpVP7Wrq2syjgBSnhzLhsnPsmLATaw+605suctfxfKNgABa3nMtmy69m7VDxxBz2omEdGhdLUncOcNw5RXy24Br2P3iRyTdcikApb9tZd2p41g3ciybLr6LVvePAUcAtsJF+n0vsXboGNb/cyJNLzpln302FjGDUglrl8SK48ew6eaZtH9w/8dOu6nXsHHSM6w4fgxh7ZKIGeg5dop/3craK6dR8EONNyg4Akh+YiybbnmWnwbexJqzpzS+Y6eG369BS4+9gXUTZ9KhlmtQh4euYt2EmSz1XoNivecZQHCLeGJP6k7p9t3+ynad0XVHGpLDugEKnAd86/23GmNM4IE+/0kNogEaN6IPu9/9CoDCtHUERjUhKCGmWpqghBgckeEUpq0DYPe7XxE3oi8AeV+tBJfnKU5B2lqCW8QDENaxFXmLfgagPCufirwiIron+6NI9aJ3j65ER0XWdzb8Km54HzL/txD4/dgJ3/+xE7H32Mn830LiR/QBwFVYUpkuIDwEi+e1UuV78ilcsQFb0XhvUnVVN9ZaHBGhADiahFKRW9go60n1U7u6uibHnNSD4l82U7xmCwAVOYWN7gl8eI8UnFvScW7bhS2vIPeTr4ka1q9amqhh/ch5fx4AeZ8vIuK47gDY0rLKejEhwfz+GruK3TmUrPZE+txFJZRu2EZQYry/iuRTscP7svu9hQAUpq3FEd2EoITYammCEmJxRIZRmLYWgN3vLSR2hKcOS9fvoHTDzn326zl2tlC8ZjPQOI+dmuKrXIMK/uAaVLCfaxBA8j2Xsune16HaKxEbJ1136oF1+++nkTlsG6DGmAigP3AFnpeuYowZYIz5xhjzMbBmP58dxpjpxpglxpifjDHXeLdLMsZ8bYxZYYz52RhzgjFmKhDmXfZmfZUTIDgxjrKdeyo/l6VnEZxU/eYbnBSPc2dW9TSJcfvsK+HcweTMXw5A8ZotxA7rDY4AQlonENEtmeCWTeuoFFIfgpPiKat2XGQTUuPYCUmKx5m+N40zPbva8XXELefRe9lMmp11Alun/bfuM+0ndVU3GS99QXhKK/qsfJ6eC/7DpikvN8ovP6qf2tXVNTksOQlr4ei3p9BtznRajD69jkpQd4Kax1NepW7K07MIah5fexqXG1dBEY7YKADCenSk45yn6Dj7CXbc8XTlF+bKbVslENYpmeIVv9VtQepIcGIczir149y573ERnBhX/bzaT5qaQtu3AGs56q0pdJ39MEmjz/BpvutDzWuQs5ZrUFl69evU7+di3PA+lKVnU+RtWDV2uu5IQ3LYNkCB04FZ1tq1QJYxppd3eSow1lrbcT+frwDyrLV9gD7AVcaYdsD5wGxrbQ+gO7DCWnsLUGKt7WGtvcB/xao7LceehXW52PP+1wDsensezvQsus+aRrt7LqNg6W/73OxFtk59m6W9rmX3+9+QdPmI+s5Og7K/uokZ2IOinzezpPtVrBg8ifYPXIEjIqyec1o/VD8HVvOabBwOovoexboxj/Lz6bcTf3I/ovv79N3hDV7JirWsHTaG9aeNJ+G6UZiQoMp1AeGhtHnmVnbe8zzuKhF2ARPoILLv0ay//lFWn3EbcSP6EXWYHTtVBYQF03rsmWw5hB6a+oquO3+B2+2/n0amcQ2c8a3zgMe8v7/j/fwpsNhaW3VUdtXPw4BuxpizvZ+jgRRgCfCSMSYI+NBau+LPZMAYczVwNcDNUT05PbzdQRSnusRLR1SO0SxcuZ6QFk35feh8zagDgDM9q7I7RWWajOzKz83OGUjckF6sPufuvRu53Gy+65XKj10+vp+Sjft27ZHGJfGyETS/YDAAhSs2ENIivsqxE1ftaTHs+xQ1OClun+MLYPcH39DpzdvYNv1/dZb3uuaPukk4dyA7nvgQgNLNGZRuzSQspSWFy9fXSZl8SfVTO39ck8vSs8j/YU3lRCk589No0rU9ed+uqptC1YHyXVkEtdjbkyYoKZ7yXVn7TVOekQWOAByRTXDl5FdLU7ZhO+7iEkI7tqFk1XoIdNBm5q3kfriQ/Nnf+6UsvtL80hEkXDAUgMIV6wmuUj/BLaofFwDOjOo9CfaXpiZn+h4Kqhw7ud5jJ78RHTvgGSOe6L0GFXivQb8LruUaVDUqGuK9BoW2SST0iARS5z/sXR5PzznTWHHyrZTvzq37gviIrjvSUB2WEVBjTBwwCHjBGLMZmAScAxigqEbyqp8NcIM3qtnDWtvOWjvHWvs1cCKwA3jFGHPxn8mHtfY5a21va21vXzY+ATJemcXKoRNZOXQi2V8sptmokwCISE2hoqCY8szcaunLM3NxFRQTkZoCQLNRJ5E9awngiTi0HHM6v1w6FXeJs3KbgLBgAsJCAIg+sRvW5aZk7XaflkP8L+PlWawcMomVQyaRPWsxCecMAP7g2Cnce+wknDOA7NmeYye0XWJluvgRfShZv8MvZagr/qibsh17iD7B8/Q4qGk0YcktKN2yq45L5huqn9r545qcu3AF4Ue3ISAsGBwBRB3TmZK12/xTQB8pXrmO4LYtCGrVHBMUSMypJ5I/d3G1NPlzfyT2LE8jI3rk8RR+9xMAQa2ag8PztSaoZTNCklvh3J4JQOuHbqR0/Tb2vPiRH0vjG7temcWqoRNYNXQCObMW0+zsAQBEpHbElV9MeWZOtfTlmTm4CkqISPV05Gp29gByZi+uudtq9jl2ju3UKO/n6S/PYvmQSSwfMomsKtegyNQUXAe4BkVWuQZlzV5C8a9b+bHLFSzpM5olfUZTlp7F8mE3N6rGJ+i6Iw2XsY1s7IwveCOPvay111RZ9hUwD+hrrf2Hd9kAYGKVz1cDI4FR1tpyY0xHPI3OpsB2a63LGHM90MFae5MxJgdIsNb+4VzU3yWdVaf/Ee0euJLYgT1xlZSxftxTFK30TMjQfe7DrBw6EYAm3ZNJ8U69nTN/OZtufwGAnt89SUBwEBU5nqdbBWlr2Tj5OUJaNaPT21Ow1uJMz2bDhKcpq4OZ4vqsmu7zff4dk+6aypLlP5Gbm098XAyjr7iIs04dXq95Wtzl5jr/G+0fvJKYgT1wl5Sx/qanKfz92PlyOiuHTAIgonsyHbzT2OfOX87G2zxT/h/5wkTCOrQAt6Vs+2423PwczoxsgprF0H32Q5XT/buKSll+4k3VJp5pDOqiboKbx9LhsesJbh4DxrDjif9j9/vf1FcRD0pjrR9j6v6+WBfXZICmZ53omZ3SWnLmpbHlvtd9nveIEOcfJzoIkQN60cL7Gpac/31J5lP/o/m4CyhZtY78LxdjQoI8r2Hp3B5XbiFbb5iGc9suYv45kITrzsZWVIDbsuvxd8if8wPhvTvR4b2HKPllU+V44Yxpr1GwcJnP817sDPrjRAep7QNXETOgJ27va1h+f5VK17n/YdXQCQA06ZZc+RqW3AVpbPYeO7Ej+tH2visJio+iIr+I4tWb+PX8ewFoeuaJtLjhTLCQO38ZW+vg2Cl3+zfukfzglcR6r0Frq1yDen45neVVrkG/v4YlZ/5yNnivQVX1WfI0y4dPrtPXsDh03Tmg49Lfb3SvWSj5aJrfGllhp9/cqOrncG2ALgAestbOqrLsRuA6YMMBGqABwH3AqXiiobuBM7w/k4ByoBC42Fq7yRjzEHAakPZH40DrugHamDWUBmhD5I8GqMjhxh8N0MasrhugjZk/GqCNmb8boI2JPxqgjZkaoAfW2Bqgh+UYUGvtwP0sexx4vMayhcDCKp/deF6tUvP1Kq96f2ruczIw+aAzLCIiIiIijUcjnBzIX/QoSkRERERERPzisIyAioiIiIiI1BmrCGhtFAEVERERERERv1AEVERERERExJc0BrRWioCKiIiIiIiIXygCKiIiIiIi4kuKgNZKEVARERERERHxC0VARUREREREfMna+s5Bg6UIqIiIiIiIiPiFIqAiIiIiIiK+pDGgtVIEVERERERERPxCEVARERERERFfUgS0VoqAioiIiIiIiF8oAioiIiIiIuJLVhHQ2igCKiIiIiIiIn6hBqiIiIiIiIj4hbrgioiIiIiI+JImIaqVIqAiIiIiIiLiF4qAioiIiIiI+JK19Z2DBksRUBEREREREfELRUBFRERERER8SWNAa6UGaAMRFlRR31losBZ3ubm+s9Bg9f15Wn1noUFb1HlyfWdBGqFy66jvLDRozSMK6zsLDZYrXx3LDmS3M6S+s9BgtQwsru8siPiNGqAiIiIiIiK+pAhorfSoTkRERERERPxCEVARERERERFfsoqA1kYRUBEREREREfELRUBFRERERER8yLr1HtDaKAIqIiIiIiIifqEIqIiIiIiIiC9pFtxaKQIqIiIiIiIifqEIqIiIiIiIiC9pFtxaKQIqIiIiIiIifqEGqIiIiIiIiPiFuuCKiIiIiIj4kl7DUitFQEVERERERMQvFAEVERERERHxJb2GpVaKgIqIiIiIiIhfKAIqIiIiIiLiS4qA1koRUBEREREREfELRUBFRERERER8yWoW3NooAioiIiIiIiJ+oQioiIiIiIiIL2kMaK0UARURERERERG/UARURERERETEl9waA1obNUAPA5En9aTV3VdhHAFkvTOXXU+/X229CQ6kzSPjCO+aTEVOAZvHTMe5PZPIE7rT4paLMUGB2PIKdtz/CoXfrQIg+bW7CEqIhUAHRYvXsO2OZxttV4N2911O7OCeuEucrBv7JEWrNu2Tpkm39qQ8NoaA0GBy5i1n0x0vAXDEzecSN6IP1u2mfE8+68c+iXNXDmEdWtDh0TFEdG3Plqlvs/OZj/1dLL+644EZfL1oMXGxMXz4xsz6zo7fdLj/MuIHp+IqKePXG5+icD/HTkS39hz1+BgcocFkzUtj/e0vA9B24iiSLhxCeVY+ABsfeIvsecsxgQ6OnHEtEd3aYxwB7Hr3K7Y+/qE/i+Uzqp/aHXn/pTQd3BNXSRmrb3yGgv3UTWS3dnR+fDSO0GD2zFvOb7e/AkBE5zYcPf0qHCFB2AoXv9zyIvnLN1RuF9UjmT6f3cuqax4j89Mf/VWkOhd2fG+a3nItxuEg//0vyH3xf9XWh/bqQtPJ1xLcsT27Jj1A0dxv6ymndcfX93MTGky7ZyYT0iYR63aT/+USdk59rZ5K51ud77uE5oN74CpxsmLsM+St2rxPmqNuOYdWo04kKKYJXyRfVrm8/TUjOeKCgdgKN2VZ+awc9ywl2/f4Mfe+FzXAc+zgCCDr7f0fO20fHUdY12RcOQVsGu05dhwxkbR/djLh3TuQ9e58tk95rnKblP/dR1BCHO7SMgDWX3A3FVl5fi2XNE6NsguuMcZljFlhjFltjFlpjJlgjPF7WYwxPYwxI6t8Ps0Yc4u/83FAAQG0vu8aNlzyb34ZfD2xp51AaErrakni/zUUV14ha068lswXPqbFrZcAUJGdz4bL7+fXYWPZMu4x2jw6rnKbTaOn8euIm/h1yA0ExkURc8rxfi2Wr8QO7klY+yTSjr2B9RNnkvzQ1ftNl/zQVayfMJO0Y28grH0SMYN6ArDj6Y9YMWgCK4dMImfuMlqPHwVARW4hm+54iR2HeMPzd2eMHMrMGffVdzb8Km5wT8LaJfHjMTewduKzdJx21X7TdZx2FWsnzOTHY24grF0ScYN6VK7b/uynLB08iaWDJ5E9bzkAzU47loCQIJYOmMCyYZNpcdFQQls380eRfEr1U7umg3sQ3i6RRceM5ZeJz3P0tCv2m+7oaVfyy4TnWHTMWMLbJRLvrZuOd17Axoff44fBk9kw7X+kTLlg70YBhpQp55O98Cc/lMSPAgJodscY0q+7g62nXUXEyIEEtT+iWpKK9N1k3vEfCj9fUE+ZrGN1dD/PfO5Dfhk0ht9OHkeT3kcRNSDVr8WqCwmDexDRPpH5x45j5cTn6frQ/s+xjDlpfHPyHfssz/t5M98Mv52vBk0m/dMfOXrK+XWd5brlPXbWX/xvfhl0PbGn7+fYOXcoFbmFrDnBc+y0vM1z7NgyJzsffpMd972y311vvnEGv44Yx68jxqnxWZN1++/nTzDGjDDG/GaMWb+/9oox5lJjzG5vG2uFMebKKusuMcas8/5ccrBV0ygboECJtbaHtbYzMBQ4GbirHvLRA6hsgFprP7bWTq2HfNQqvEcKZZszcG7dhS2vIOeTb4ge1rdamuhh/ch6bz4AuZ8vIvL4bgCUrN5Exa5sAErXbiUgNBgT7AmauwtLPBsHOrzLGmc3g7jhfcj830IACtPWERgVTlBCTLU0QQkxOCLCKUxbB0Dm/xYSP6IPAK7f6wEICA/BeuuhfE8+hSs2YCtcdV+IBqB3j65ER0XWdzb8qumIPux69ysA8petIzCqCcE1jp3ghBgCI8LIX+Y5dna9+xVNT+5bc1fVWUtAeAjGEUBAaDDu8goqCkoOvE0DpPqpXbMRfUh/92sA8v6gbvK8dZP+7tcknOy57lgLgZFhAARGhVO2K6dyuyOuPJldn/6Ic8+h9UUwpOuRlG/dScX2DKiooPCLhTQZdGy1NBU7d+FcuwnbSHvj/JG6uJ/bUieF33t6NtnyCop/3khQUrwfS1U3Eof3Ytv/vgEgN209QVHhhNQ4x35fV5aZu8/yrEVrcJU4AchZtp6wpLi6zG6da1Lz2Pl432MnZlg/sr3HTs5ne48dd0kZRUt+wV3m9Hu+xXeMMQ7gKTxtpk7AecaYTvtJ+l9vG6uHtfYF77ZxeNpZ/YC+wF3GmNiDyU9jbYBWstZmAlcD1xuPUGPMy8aYVcaY5caYgVDZqv/QGDPXGLPZGHO9MWa8N80P3srFGJNsjJlljFlmjPnGGHOUd/koY8zP3ojr18aYYOAe4F/epwT/8v6NJ73pmxtj/s+bfqUx5rj6qJ/gxHicO/d2G3GmZxHUvPrNJSgxjvLf07jcuAqKcMRWb0zEjDyOkp83Yp0VlcuSX7+brstfw1VYQu5n39VdIepQcFI8ZTuzKj+XpWcTUuPmG5IUjzN9bxpnejbBVdIccct59F42k2ZnncDWaf+t+0xLgxCSFEfZjqrHThYhNb6khCTFUVbl2CnbWT1Ny8tH0HvBwxz56HUERjcBYPcnP+AuLuPYn57n2LRn2PbMJ1TkFtZxaXxP9VO7kKRYSqvUTWl6FqE16iY0KY7S9Oy9aXZmE5Lkud+vnfIqKXdeyAlpT5Fy10Wsv/9tz34TY0k4uQ/bX5nrh1L4V2BCPBUZuys/V+zaQ2BC03rMkf/V5f0cwBHVhOghfShY1Pij56FJcZRWubeXpGfvc479WUecP4DM+St9lbV6EVTj2ClPzyIocd9jx/kHx87+tPnPDRw16xESx57j0zwfEtzWfz9/rC+w3lq70VrrBN4BTv+TJRkOzLXWZltrc4C5wIi/VSdejb4BCmCt3Qg4gARgjGeR7QqcB7xqjAn1Ju0CnAn0Ae4Hiq21PYHvgYu9aZ4DbrDW9gImAk97l98JDLfWdgdO8/7n3cneJwU1Wx6PA19506cCq31dbn8J7diaFrdezNZbn662fMNFd/Nz70sJCA4i8viu9ZS7+rd16tss7XUtu9//hqTLD+p8lMPIjlfn8EO/G1g6aBLOXbkk/9tzCYrs2QHrcvN996v5oc8YWl97KqFtEuo5t/6n+qldq0uHsvbOV/kmdQxr73yVTo9cC8CR917Kuvve0svPpVa13c9xBND2iQnsfvlTnFt31U/mGqCWZ/Unpnt7Njz9SX1npUHafOMMfhk6lrVn3UZE307EnTWwvrMktWsJbKvyebt3WU1nGWN+Msa8Z4z5vZ/2n932TzskGqA19AfeALDW/gpsATp61y2w1hZYa3cDecDvV5RVQFtjTARwHPCuMWYF8CyQ5E2zCHjFGHMVnsbuHxkEPOPNh8tau09/KGPM1caYpcaYpe8Xbv7LBf0znBlZBLfY+5Q4OCme8l1Z1dKUZ2QT9HsaRwCOyCa4cgoAz1Ozds/dypZxj+LckrHP/m1ZOXlzFxM9tF+d5L8uJF42gu5fTqf7l9Nx7sohpMXep4A1IzLgidxUjXgGJ8VVi4j+bvcH3xB/yjF1l3Gpdy0uG07vedPpPW86ZbtyCGlZ9diJp6xKxAr2jaiHtNibpnx3nmfiLmtJf+NLonp2AKD5mf3Jnr8CW+GifE8+eUt+JbJ7sh9Kd/BUP7Vrddkwjpn3EMfMe4iyXbmEVqmb0KT4atFOgNIaEZvQFnGUpXu62iadcxKZny0GYNfHPxDd01P+qB7t6TrzRvoveYKEU4/h6IeuoNnJveu6aH5RkZlFYOLesb6BzZtSkdm4J4X5q+ryfn7E1DGUbk5n94uNt6HV9rKhnPjlg5z45YOU7soltMq9PaxGj4I/o+kJXUgZewaLL3kYd41ocWNTXuPYCUqKpzxj32MnuJZjp/b9eurUXVRC9odfE94jxbcZlz+tapvC+7P/SU0O7BOgrbW2G54o56u+zeVeh0QD1BjTHnABmX+QtKzK7+4qn914ZgQOAHKr9H3uYa09GsBaey1wB9AaWGaMOehBEtba56y1va21vc+KaHuwu9uv4pXrCGmXRHDrBExQILGnnkDe3MXV0uTNXUz82YMAiBl5PAXfebrfOKKakPzKFHZOfY2ipb9Wpg8IDyUwwdv12xFA1KDelG7YXif5rwsZL89i5ZBJrBwyiexZi0k4ZwAAEakpVBQUU15jPEh5Zi6uwmIiUj0X1oRzBpA9ewkAoe0SK9PFj+hDyfodfimD1I+dL8+unBRnzxdLaD7qJACienmOHWeNY8eZmUtFYQlRvTzHTvNRJ7FnlufYqTrmr+nIvhT96nm4WLpjDzH9uwCeccVRqR0pbiTHleqndttfnsMPgyfzw+DJ7P5iCUmjTgQg+g/qJtpbN0mjTmS3t27KMnKIPc4zdCfuhC4Ub/Q0Jr7tc0PlT+YnP/DL5BfZ/cVSP5WwbpX9/BtBR7QksGVzCAwk4uQBFC34ob6z5Vd1cT8HSJp4AY7IcHbc/YJ/ClJHNr88l6+H3MrXQ24lY9ZSWp9zAgAxqR0oLyje71jP2kR1aUu36Vey5JKHce7Jr6Mc+0/RynWEtK1y7Jy277GTO3cxcd5jJ/aU4/+4K7YjYG8X3UAH0YP7UPrb1rrIfqNl3W7//VRpU3h/nquRnR142jC/a+Vdtje/1mZZa39vG70A9Pqz2/5Vjf41LMaYZsBM4ElrrTXGfANcAMw3xnQEjgB+w9MN9oCstfnGmE3GmFHW2neNMQboZq1daYxJttb+CPxojDkZz39EAVBbB/l5wHXAo96BvxH7i4LWOZeb7VOeI/n1uz3Ttv93HqVrt5E4/nyKV60nf+5isv47lzaPjqPT1zOpyC1g8/UPA9D0kpEEt00icey/SBz7LwA2XHg3GGj/4u0EBAdBgKHwu1XseWOW34vmCzlfphE7OJXUH57EXVLG+pv2dkvq/uV0Vg6ZBMDGW16gg/c1LLnzl5PjnZGzze0XEtahBbgtZdt3s+Fmz/ke1CyG7rMfwhEZBm5Li6tOYfmJN1WbtOhQMumuqSxZ/hO5ufkMPuNCRl9xEWedOry+s1Wnsr9MI35wT/r9+ASuEie/jX2qcl3vedNZOthz7Kyb/DxHPe45drLnraiczbX9nRcR0aUtWEvptt2snfgsADtfms2Rj42mz1czwBgy3llA0ZrGd1NX/dRuz5fLaTq4J8f/+BiuEidrxj5Tue6YeQ/xw+DJAPw6+UU6Pz6agNAg9sxbwZ55KwD4ZcKzHHnfpZhAB+4yJ2sm1vyecQhyudnzwFMkPfsAxhFA/v/NoXzDFmLHXEzZ6rUUL/yBkC4dSXz0TgKiImky4BjixlzMtjP+ThCggaqD+7kJCiTxxnMoXbeNIz+fAcCeVz8n653GPY4488vlJAzuwaAfHsVVUsaKm56tXHfilw/y9ZBbATh6yvm0/OdxOMKCGZL2JFvfWsDah9+n053nE9gklF7PjwWgZEcWSy55uF7K4hMuN9umPEeHN6ofO0kTzqf4p/XkzV1M1jtzafvoODp9MxNXbgGbxuwtb+fvnsMRGY4JCiRmeD/WX3A3zu2ZpLzhOYYICKDg25XseWtOPRZS/sASIMUY0w5P4/FcoNr0zsaYJGttuvfjacAv3t9nAw9UmXhoGHDrwWTG2EY4VsQY48LTbTYIqABeB2ZYa93e8Z7PAL2968ZbaxcYYy4Feltrr/fuY7P3856q67z/Mc/g6XobBLxjrb3HGPMBkAIYPI3Lm4BYPP8pQcCDQFiV/TTHM5709+jsddba72sr0/IjTm98/xF+UuwMqu8sNFh9f55W31lo0BZ1nlzfWZBGqPzQ6BxUZ9o3y63vLDRY+fmhf5zoMLbdGV7fWWiwWgYV13cWGrTUbR+Z+s7DX1V0/8V++27f5PbX/rB+vK+OfBTPUMKXrLX3G2PuAZZaaz82xjyIp+FZAWTjabv86t32cuA2767ut9a+fDD5bZQN0EORGqC1UwO0dmqAHpgaoPJ3qAF6YGqA1k4N0ANTA7R2aoAemBqgB/ZnGqANSaPvgisiIiIiItKg2EPzncS+oMe8IiIiIiIi4heKgIqIiIiIiPiSW6PraqMIqIiIiIiIiPiFIqAiIiIiIiK+5NYY0NooAioiIiIiIiJ+oQioiIiIiIiIL2kMaK0UARURERERERG/UARURERERETEl/Qe0FopAioiIiIiIiJ+oQioiIiIiIiIL2kMaK0UARURERERERG/UANURERERERE/EJdcEVERERERHzIujUJUW0UARURERERERG/UARURERERETElzQJUa0UARURERERERG/UARURERERETElxQBrZUioCIiIiIiIuIXioCKiIiIiIj4ktUsuLVRBFRERERERET8QhFQERERERERX9IY0FqpAdpAuNymvrMgjdCizpPrOwsN2vGrH6rvLEgjVDHv9frOQoN2w+RV9Z2FBusal6u+s9CgDXm4fX1nocG64vY19Z2FBu2t+s6A+JQaoCIiIiIiIj5kFQGtlcaAioiIiIiIiF8oAioiIiIiIuJLioDWShFQERERERER8QtFQEVERERERHzJrfeA1kYRUBEREREREfELNUBFRERERETEL9QFV0RERERExJc0CVGtFAEVERERERERv1AEVERERERExJcUAa2VIqAiIiIiIiLiF4qAioiIiIiI+JC1ioDWRhFQERERERER8QtFQEVERERERHxJY0BrpQioiIiIiIiI+IUioCIiIiIiIr6kCGitFAEVERERERERv1AEVERERERExIesIqC1UgRURERERERE/EIRUBEREREREV9SBLRWioCKiIiIiIiIXygCeoiKGtCTI/59JTgC2PP2XDKe+qDaehMcSLtHbyK8WzIVOQVsvO5hnNszAUgccxZNzxsCLjdb73ye/K9WAOCIakKb6WMIO/IIsJbNE56kKO03ABIuO4Vml5wMLjd585ex/f5X/Vreg9XuvsuJHdwTd4mTdWOfpGjVpn3SNOnWnpTHxhAQGkzOvOVsuuMlAI64+VziRvTBut2U78ln/dgnce7KwREZTsenbiSkZVNMoIMdz3xM5jsL/F20g9bh/suIH5yKq6SMX298isL91E1Et/Yc9fgYHKHBZM1LY/3tLwPQduIoki4cQnlWPgAbH3iL7HnLMYEOjpxxLRHd2mMcAex69yu2Pv6hP4vlV3c8MIOvFy0mLjaGD9+YWd/ZaVAO97pZtG4n0z5fhtta/pmazOUndt4nzeyft/DsglWAoWNiDFNHHQ9A6l1v06F5NABJ0U147IKT/Jl1vznvrsvpOrAnzhInL018kq2r970G/e765yfT7Ijm3DV8PACtjm7DRfdfTUh4KFnbd/P8TY9RWljir6z7zBH3XEH0oF64S8rYNO4Jin/euE+a8K7taffIjQSEBpM3fxlb73wRAEdMBMnPTCCkdQJl2zLZcO3DuPKKiPvniSSN/icYg7uohM23PkvJms0ANL/qVJqdNwRroeTXLWwa/wS2rNyfRT4oi9ZnMG32Cs951bMdlx9/1D5pZq/exrNfrwEMHZtHM/XMfuzMLWL8u9/jtpYKl+W8vsmM6pXs/wL4wcV3X0GPgb1wlpQxc+ITbN7PMXXHO/cSkxCLs9QJwNSL/k1+Vl7l+j4nH8O4mZO5/R8T2bRqg9/y3mi46zsDDZcaoPthjHEBnrs9uIDrrbXf/c193WatfcCX+ftDAQEccd81rD3/LsrTszj6s+nkzllM6brtlUmanjuUirxCfu5/HbGn9afVbRezcfTDhKa0Iu70/qwedANBzePo+PY9/HziaHC7af3vK8hfmMbGa6ZhggIJCAsBIPK4LsQM68uaYTdhnRUExkf7tbgHK3ZwT8LaJ5F27A1EpKaQ/NDV/DTy1n3SJT90FesnzKQwbR2d3rqdmEE9yZ2/nB1Pf8TWae8AkHTFSFqPH8WGyc+RdNkIitdu55eLpxIYH0Xqt4+x+/1vsOUV/i7i3xY3uCdh7ZL48ZgbiOqVQsdpV5F28m37pOs47SrWTphJ/rJ1dH3rNuIG9SB7/goAtj/7Kdue+aRa+manHUtASBBLB0wgICyYvl8/Qub/LaJ0225/FMvvzhg5lPPPOo3b7n24vrPS4BzOdeNyu3nw06XMvGQQzaPCuODZ2Zx0VCuSE/ZeQ7dk5fPS12t45cphRIUFk11YWrkuJMjB/0aPrI+s+03XAT1JaJfEbQNuoH3PFC68/2oeOGPf6zNA6vB+lBWXVlt2ydTrePeB11j74xqOHzWI4Vefzkcz3vFH1n0melAqIe1asKr/aJqkdqTNg9fwy6mT90nX5sFr2Xzz0xSlrSXl9SlED0wlb0EaSWPOJP/bVWQ89QGJY84kacyZbH/gdZzbdvHr2XfgyisiemAqbR+6jl9OnUxQYhzNLz+FVQNvxJY6SZ45kbjT+5P1v8bxANXltjw4azkzLziB5lHhXPDCPE7q2ILkZlGVabZkFfDSot945dKBnvOqyHPcNIsM47XLBhIc6KDYWcFZM+dwUscWJESG1Vdx6kSPgakktmvB+JNG06FnRy6/7xruPGPfYwrgqbGP7LdxGdoklBGX/YN13kCEyF+hLrj7V2Kt7WGt7Q7cCjx4EPva99t6HWvSI4Wyzek4t+7ClleQ/dG3xAzrVy1NzLC+ZL3ruZnkfPYdkf27eZf3I/ujb7HOCpzbMinbnE6THik4IsOJ7NeZPW9/CYAtr8CVXwRAs4tOJv2p97FOT8OqosrTscYgbngfMv+3EIDCtHUERoUTlBBTLU1QQgyOiHAK09YBkPm/hcSP6AOAq8rT9IDwECyePv/WWhwRoQA4moRSkVuIrXDVcWl8q+mIPux69ysA8petIzCqCcE16iY4IYbAiDDyl3nqZte7X9H05L4H3rG1BISHYBwBBIQG4y6voKKg8UUl/qzePboSHRVZ39lokA7nuvl5exat4yJoFRdBUKCD4V3bsPDX7dXSfLB0A//ql0JUWDAAcd5ryuGix7A+fP/BQgA2Ll9HeGQ40c1i9kkXEh7K0Cv/wadPvF9tefN2Saz9cQ0Aa75dSa+T++2zbUMXM7wvWe957tdFaWtxRDchKCG2WpqghFgckWEUpa0FIOu9BcSM6Lt3e+/9PuvdBcSM8NRB4dLfcOV57uOFab8RnBRfuT8T6CAgNBgcAQSEhVCekV23hfShn3dm0zo2glaxEQQ5AhjeuTULf9tZLc0Hyzfxrz7Je8+rJp7zKsgRQHCgAwBnhQtrD80xfL2G9uWb9z3HxPrlawmPakJMjWPqj4yacD6fzPw/yhtRZFwaDjVA/1gUkPP7B2PMJGPMEmPMT8aYf1dZ/qExZpkxZrUx5mrvsqlAmDFmhTHmTX9lODgpDmf6nsrPzowsgpPiqqdJrJLG5caVX0xgbGSt2wa3bk5Fdh5tZ9xIp1kzaDN9TGUENLR9CyL7deKoT6Zx5Hv3Ed69Q90X0oeCk+Ip25lV+bksPZuQKjdigJCkeJzpe9M407Or3ayPuOU8ei+bSbOzTmDrtP8CkPHSF4SntKLPyufpueA/bJryMjSym1lIUhxlO6rWTRYhNY6lkKQ4yqrUTdnO6mlaXj6C3gse5shHryMwugkAuz/5AXdxGcf+9DzHpj3Dtmc+oSK3sI5LI9KwZBaUkOg9JwCaR4WTmV9cLc2WrAK27CngkufncNFzs1m0bu8XaWeFi/NnzuKi52Yz/5dtfsu3P8U0jye7yvU5JyObmMT4fdKdMeFc5rzwCc7SsmrLd67bTo9hnoeFvUceS1xS07rNcB0ITozHWaUOytOzCEqsfh0OSoyrcY/KIthbT0FNYyjP9HyNKc/MIahpzD5/o9m5Q8hbkOZJk5FNxsyP6L74OXosfwlXfhH5X6/0dbHqTGZ+CYlReyOWzaPCyKzxgHNLViFbsgq45OUFXPTSfBatz6hcl5FXzKhn5zLisc+59LgjD7noJ0BsYvXzKjsji9jmcftNe83DN/DA5zP4542jKpe17dKe+BZNWTF/WZ3ntTGzbuu3n8ZGDdD9+73R+CvwAnAvgDFmGJAC9AV6AL2MMSd6t7ncWtsL6A3caIyJt9bewt5o6gV+L4UPmcAAwrsks/v1L1gzYjzu4lISx5zlWecIwBETya+n3sz2+14l+ZlJ9Zxb/9s69W2W9rqW3e9/Q9LlIwCIGdiDop83s6T7VawYPIn2D1yBI+LQu5EdyI5X5/BDvxtYOmgSzl25JP/7YgAie3bAutx83/1qfugzhtbXnkpom4R6zq1Iw+Nyu9maXcALlw9h6qjjueejxeSXeMZjfT7+dN66dgQPnn08079IY1t2QT3ntn607tSWZkc0Z/nsxfuse+Xmpxh44QimfPIQoRFhVDSiIRB1psaD0MjjutD0vCFse+B1ABzRTYgZ3pefjrmWlalXEBAeSvyZh9b4Ypd1szW7kBcuPomp/+zHPZ8tI987zjExOpx3rxnKx9eP4JOftpBVWPoHezt0PTX2EW4ZfhP3jLqNI/t04oQzB2CM4cI7LuON+16u7+xJI6YxoPtXYq3tAWCMORZ4zRjTBRjm/VnuTReBp0H6NZ5G5z+9y1t7l2dxAN5I6dUAt8Z058wmbX2SeU90bu9T3uDEeJzp1bvPODM8acrTs8ARgCMqnIqcglq3daZn4UzPomi5p5tlzmffkzjmTO++ssj94nsAilasw7otgXFRVGTn+6Q8dSHxshE0v2AwAIUrNhDSIp7fv7rVjOiBJ/JXNeLpiRTv+9+7+4Nv6PTmbWyb/j8Szh3Ijic+BKB0cwalWzMJS2lJ4fL1dVImX2lx2XBaXDgEgPwV6wlpubfcIUnxlNU4lmpGjENa7E1Tvntvd+z0N76k6xu3AND8zP5kz1+BrXBRviefvCW/Etk9mdItmXVWLpGGJiEyjAxvF0iAXfnFJESFV0vTPCqcLq3iCXIE0DI2gjbxkWzNLqBLy3iae9O2iougd9sEfk3PoXVc4+/OPPCiEZxwnuf6vHnlBuJa7L2+xCbGkZtR/dqbnNqRtt2Smfrt0wQ4HETFRzHpnX8z/dy7yNiwk0cuvhfwdMftNjDVfwU5CAmXnEyzC4YCULRiPcFV6iAoKX6fLrHlGdk17lHxOL31VL4nl6CEWE/0MyGW8irDZMKObkPb6WNYe9G9uHI8d8GoE7pTtnVX5T0854sfiOh9JFkffFU3hfWxhKgwMvL3Rjx35ZfsE8VsHhlOl5Zx3vOqCW3iItiaXUiXFnujgAmRYXRoFk3a1j0M7dTKb/mvK0MvPpmB53qOqY0/ra92XsUlxpOza99u1r8vKy0q5buPvia5RwpL5y6m9ZFHMOWd+wCIbhbDxBdv4+ErHtBERDU1wsikvygC+gestd8DTYFmeCYletAb0exhre1grX3RGDMAGAIc6x03uhz4w4E61trnrLW9rbW9fdX4BChauY7QdkkEt07ABAUSd3p/cudWfzKcO3cx8aMGAhB7ynEULFpVuTzu9P6Y4ECCWycQ2i6JohXrqNidi3PnHkLatwAgqn83Std5unzlzvqRyOO6AhDSrgUBwYENuvEJkPHyLFYOmcTKIZPInrWYhHMGABCRmkJFQTHlmbnV0pdn5uIqLCYiNQWAhHMGkD17CQCh7RIr08WP6EPJ+h0AlO3YQ/QJnnoJahpNWHILSrfsquOSHbydL89m6eBJLB08iT1fLKH5KM+T76henrpx1qgbZ2YuFYUlRPXy1E3zUSexZ5anbqqOF206si9Fv3qOmdIde4jp3wXwjJuNSu1IsbfeRA4XnVvGszW7gB05hZRXuJi9agsnHdWyWpqBR7di6WbPg5mcolK2ZBXQKjaC/BInTu+Y8pyiUlZs3U37Zo1rArjaLHh9FveMnMQ9IyexfM5ijj1zAADte6ZQUlBM3u7caukXvjGHif2u5pb+o3lo1B3s2pTO9HPvAiAy3jPxjDGGU64/m4VvzvVnUf62zFe/YPWw8aweNp6c2T8Sf7bnft0ktSOu/OLKLrW/K8/MwVVQQpPUjgDEnz2QXG9EOHfOksr7ffyovcuDWzSlw/OT2TT2Uco2VunavWM3EakdPWNA8dzvS9ZVH5vckHVuEcvW7EJ25BRR7nIze/U2TuqYVC3NwCNbsHSLZ9K7nOIytmQX0iqmCbvyiykt95xX+SVOlm/bQ9v4xv9QB2Dua19w28jx3DZyPEvn/MgJZ3mOiQ49O1JSUExujWMqwBFAZKyn7I5ABz0H92bbb1spKSjmmp6XMLb/NYztfw3rl69V41P+MkVA/4Ax5ijAgSeaORu41xjzprW20BjTEigHooEca22xN/0xVXZRbowJstb6b5S2y83WKc/T8c27IMBB1n+/pHTtNlpMPI+ilevJm7uEPe98SbvHbqLLt8/gyi1gw+j/AFC6dhs5nyyi8/wnweViyx3Pgdszj/TWKc/T/onxmOBAyrbsYvOExwHY8995tP3P9XT+8jHc5RVsuukxvxXVF3K+TCN2cCqpPzyJu6SM9Tc9Xbmu+5fTWTnE06V44y0v0MH7Gpbc+cvJmecJhLe5/ULCOrQAt6Vs+2423PwcANtnvEeHx66nx4L/gDFsue8NKhpZF7nsL9OIH9yTfj8+gavEyW9jn6pc13vedJYO9tTNusnPc9TjnrrJnreCbG/dtL/zIiK6tAVrKd22m7UTnwVg50uzOfKx0fT5agYYQ8Y7Cyhas9Xv5fOXSXdNZcnyn8jNzWfwGRcy+oqLOOvU4fWdrQbhcK6bQEcAt5zSm+teW4DbbTk9tT0dEmJ4et5PdGoZx4CjWnFchyS+X5/OmU98SoAxjBveg5jwEFZs3c19Hy8mwBjc1nL5CZ2rzZ57qFi1II2uA1N54KsncZaU8fKkvdfnOz+fzj0jDzzko+9p/Rl4kWdYxPLZP7Lo3fl1mt+6kDdvGdGDetF10TOe17CMf6JyXec5M1g9zPPKmS23Pbv3NSwL0sib7xnTmf7UB3SYOZFm5w323KOu9cw43WLcOQTGRtLmgWsAsBUu1oycRNHydWR/9j2dZv8HW+GmePVGdr85x8+l/vsCAwK4ZUQPrnvrG9zWcnr3tnRIiObphavplBTLgCNbcFxyc77fuIszn5ntOa8GdyMmPITvN+5ixtxFGMACFx/bkZTmh955tWL+MnoM7MUjXz9DWUkZz07ce0w98PkMbhs5nqDgIG55/S4cgQ4CHAH8/O1PzH+7cTzAaTD0GpZamUN1hq+DUeU1LOCJet5mrf3Mu24scKV3XSFwIbAd+BBoC/wGxAB3W2sXGmMeAk4D0g40DnRpqzP0H1GLsgo9J6lNuTX1nYUG7fjVD9V3FqQRqpj3en1noUG7YfKqP050mLqmvHHNdO5vXR7qVN9ZaLCuuH1NfWehQXtry/81ui88uf8a6Lfv9jH/XdCo6kff7PfDWus4wLrHgP2F+E6uJf1kYP8vVxIRERERkUNOY5yd1l80BlRERERERET8QhFQERERERERX9IY0FopAioiIiIiIiJ+oQioiIiIiIiID2kMaO0UARURERERERG/UANURERERETEl9x+/PkTjDEjjDG/GWPWG2Nu2c/68caYNcaYn4wx84wxbaqscxljVnh/Pv6rVVGTuuCKiIiIiIgcoowxDuApYCiwHVhijPnYWlv1BbTLgd7W2mJjzHXANOBf3nUl1toevsqPIqAiIiIiIiI+ZN3++/kT+gLrrbUbrbVO4B3g9Gr5tXaBtbbY+/EHoJUv66MqNUBFREREREQOXS2BbVU+b/cuq80VwBdVPocaY5YaY34wxpxxsJlRF1wREREREZFGyhhzNXB1lUXPWWuf+5v7uhDoDZxUZXEba+0OY0x7YL4xZpW1dsPfza8aoCIiIiIiIr70JycH8gVvY/NADc4dQOsqn1t5l1VjjBkC3A6cZK0tq7L/Hd5/NxpjFgI9gb/dAFUXXBERERERkUPXEiDFGNPOGBMMnAtUm83WGNMTeBY4zVqbWWV5rDEmxPt7U+B4oOrkRX+ZIqAiIiIiIiI+9CcnB/ILa22FMeZ6YDbgAF6y1q42xtwDLLXWfgxMByKAd40xAFuttacBRwPPGmPceIKXU2vMnvuXqQEqIiIiIiJyCLPWfg58XmPZnVV+H1LLdt8BXX2ZFzVARUREREREfKkBRUAbGo0BFREREREREb9QBFRERERERMSHGtIY0IZGEVARERERERHxC0VARUREREREfEgR0NopAioiIiIiIiJ+oQioiIiIiIiIDykCWjtFQEVERERERMQvFAEVEREREalHFlvfWRBfs6a+c9BgKQIqIiIiIiIifqEIqIiIiIiIiA9pDGjtFAEVERERERERv1ADVERERERERPxCXXBFRERERER8yLo1CVFtFAEVERERERERv1AEVERERERExIc0CVHtFAEVERERERERv1AEVERERERExIes1RjQ2igCKiIiIiIiIn6hCKiIiIiIiIgPaQxo7RQBFREREREREb9QBFRERERERMSH9B7Q2ikCKiIiIiIiIn6hCKiIiIiIiIgPWVvfOWi4FAEVERERERERv1AEVERERERExIc0BrR2ioCKiIiIiIiIXygCKiIiIiIi4kOKgNZOEVARERERERHxCzVARURERERExC/UBVdERERERMSH9BqW2qkBeoiKGtCTI/59JTgC2PP2XDKe+qDaehMcSLtHbyK8WzIVOQVsvO5hnNszAUgccxZNzxsCLjdb73ye/K9WAOCIakKb6WMIO/IIsJbNE56kKO03ABIuO4Vml5wMLjd585ex/f5X/Vreg9XuvsuJHdwTd4mTdWOfpGjVpn3SNOnWnpTHxhAQGkzOvOVsuuMlAI64+VziRvTBut2U78ln/dgnce7KwREZTsenbiSkZVNMoIMdz3xM5jsL/F20g9bh/suIH5yKq6SMX298isL91E1Et/Yc9fgYHKHBZM1LY/3tLwPQduIoki4cQnlWPgAbH3iL7HnLMYEOjpxxLRHd2mMcAex69yu2Pv6hP4vlV3c8MIOvFy0mLjaGD9+YWd/ZaVAO97pZtG4n0z5fhtta/pmazOUndt4nzeyft/DsglWAoWNiDFNHHQ9A6l1v06F5NABJ0U147IKT/Jl1vznvrsvpOrAnzhInL018kq2r970G/e765yfT7Ijm3DV8PACtjm7DRfdfTUh4KFnbd/P8TY9RWljir6z7zBH3XEH0oF64S8rYNO4Jin/euE+a8K7taffIjQSEBpM3fxlb73wRAEdMBMnPTCCkdQJl2zLZcO3DuPKKiPvniSSN/icYg7uohM23PkvJms0ANL/iHzQ9fyjGwO635rLrhU/9WdyDtmh9BtNmr/CcVz3bcfnxR+2TZvbqbTz79RrA0LF5NFPP7MfO3CLGv/s9bmupcFnO65vMqF7J/i+AH1xy95X0GNgLZ0kZz0x8nM37OaamvHMfMQmxOEudADx40d3kZ+VVru978rGMmzmZ2/8xgY2rNvgt79L4HfINUGOMC1gFBAEVwGvAI9ZatzGmN3CxtfbGWrZtCxxnrX2rlvUtgMettWcf4O/fBDxnrS0+qIL8FQEBHHHfNaw9/y7K07M4+rPp5M5ZTOm67ZVJmp47lIq8Qn7ufx2xp/Wn1W0Xs3H0w4SmtCLu9P6sHnQDQc3j6Pj2Pfx84mhwu2n97yvIX5jGxmumYYICCQgLASDyuC7EDOvLmmE3YZ0VBMZH+62ovhA7uCdh7ZNIO/YGIlJTSH7oan4aees+6ZIfuor1E2ZSmLaOTm/dTsygnuTOX86Opz9i67R3AEi6YiStx49iw+TnSLpsBMVrt/PLxVMJjI8i9dvH2P3+N9jyCn8X8W+LG9yTsHZJ/HjMDUT1SqHjtKtIO/m2fdJ1nHYVayfMJH/ZOrq+dRtxg3qQPX8FANuf/ZRtz3xSLX2z044lICSIpQMmEBAWTN+vHyHz/xZRum23P4rld2eMHMr5Z53Gbfc+XN9ZaXAO57pxud08+OlSZl4yiOZRYVzw7GxOOqoVyQl7r6FbsvJ56es1vHLlMKLCgskuLK1cFxLk4H+jR9ZH1v2m64CeJLRL4rYBN9C+ZwoX3n81D5yx7/UZIHV4P8qKS6stu2Tqdbz7wGus/XENx48axPCrT+ejGe/4I+s+Ez0olZB2LVjVfzRNUjvS5sFr+OXUyfuka/PgtWy++WmK0taS8voUogemkrcgjaQxZ5L/7SoynvqAxDFnkjTmTLY/8DrObbv49ew7cOUVET0wlbYPXccvp04m7MgjaHr+UH45ZRLu8go6vnknuV8upWxzRj2U/q9zuS0PzlrOzAtOoHlUOBe8MI+TOrYguVlUZZotWQW8tOg3Xrl0oOe8KvIcN80iw3jtsoEEBzoodlZw1sw5nNSxBQmRYfVVnDrRY2AvEtslMe6k6+jQsyNX3HctU864eb9pnxo7Y7+Ny9AmoYy47B+s8wYiZF+ahKh2h8MY0BJrbQ9rbWdgKHAycBeAtXZpbY1Pr7bA+ftbYYwJtNbuPFDj0+smIPwv5/ogNOmRQtnmdJxbd2HLK8j+6FtihvWrliZmWF+y3vVE43I++47I/t28y/uR/dG3WGcFzm2ZlG1Op0mPFByR4UT268yet78EwJZX4MovAqDZRSeT/tT7WKenYVVR5elYYxA3vA+Z/1sIQGHaOgKjwglKiKmWJighBkdEOIVp6wDI/N9C4kf0AcBV5Wl6QHgIFk+fC2stjohQABxNQqnILcRWuOq4NL7VdEQfdr37FQD5y9YRGNWE4Bp1E5wQQ2BEGPnLPHWz692vaHpy3wPv2FoCwkMwjgACQoNxl1dQUdD4ohJ/Vu8eXYmOiqzvbDRIh3Pd/Lw9i9ZxEbSKiyAo0MHwrm1Y+Ov2amk+WLqBf/VLISosGIA47zXlcNFjWB++/2AhABuXryM8MpzoZjH7pAsJD2Xolf/g0yfer7a8ebsk1v64BoA1366k18n99tm2oYsZ3pes9zz366K0tTiimxCUEFstTVBCLI7IMIrS1gKQ9d4CYkb03bu9936f9e4CYkZ46qBw6W+48jz38cK03whOigcgNKUVRcvX4i51gstNwQ+riT35mLovqI/8vDOb1rERtIqNIMgRwPDOrVn4285qaT5Yvol/9Unee1418ZxXQY4AggMdADgrXNhDtA9lr6F9+eb9hQCsX76W8KgmxNQ4pv7IORMu4JOZH1BeVl4HOZRD3eHQAK1krc0ErgauNx4DjDGfAhhjTjLGrPD+LDfGRAJTgRO8y8YZYy41xnxsjJkPzDPGtDXG/Ozd3mGMedgY87Mx5idjzA3GmBuBFsACY4zf+l4GJ8XhTN9T+dmZkUVwUlz1NIlV0rjcuPKLCYyNrHXb4NbNqcjOo+2MG+k0awZtpo+pjICGtm9BZL9OHPXJNI587z7Cu3eo+0L6UHBSPGU7syo/l6VnE+K9Ef8uJCkeZ/reNM707MqbNcARt5xH72UzaXbWCWyd9l8AMl76gvCUVvRZ+Tw9F/yHTVNebnQDAkKS4ijbUbVusgipcSyFJMVRVqVuynZWT9Py8hH0XvAwRz56HYHRTQDY/ckPuIvLOPan5zk27Rm2PfMJFbmFdVwakYYls6CERO85AdA8KpzM/OqdZbZkFbBlTwGXPD+Hi56bzaJ1e79IOytcnD9zFhc9N5v5v2zzW779KaZ5PNlVrs85GdnEJMbvk+6MCecy54VPcJaWVVu+c912egzzPCzsPfJY4pKa1m2G60BwYjzOKnVQnp5FUGL163BQYlyNe1QWwd56CmoaQ3lmjmfbzByCmsbs8zeanTuEvAVpAJT8upXIfp1wxEYSEBpMzKBeBLdoPPWWmV9CYtTeiGXzqDAyazzg3JJVyJasAi55eQEXvTSfRev3Rncz8ooZ9excRjz2OZced+QhF/0EiEuMI2vn3u962RlZxDWP22/aax6+kQc/f4R/3nhO5bK2XdoT16Ipy+cvq/O8NmbWGr/9NDaHVQMUwFq7EXAACTVWTQTGWGt7ACcAJcAtwDfeCOoj3nSpwNnW2pqDba7GEzHtYa3tBrxprX0c2AkMtNYOrIvy+IsJDCC8SzK7X/+CNSPG4y4uJXHMWZ51jgAcMZH8eurNbL/vVZKfmVTPufW/rVPfZmmva9n9/jckXT4CgJiBPSj6eTNLul/FisGTaP/AFTgiDr0b2YHseHUOP/S7gaWDJuHclUvyvy8GILJnB6zLzffdr+aHPmNofe2phLapeUqKiMvtZmt2AS9cPoSpo47nno8Wk1/iGY/1+fjTeevaETx49vFM/yKNbdkF9Zzb+tG6U1uaHdGc5bMX77PulZufYuCFI5jyyUOERoRR0YiGQNSZGg9CI4/rQtPzhrDtgdcBKF2/nfSnPuDIt+6i45t3Urx6E9btro+c1hmXdbM1u5AXLj6Jqf/sxz2fLSPfO84xMTqcd68ZysfXj+CTn7aQVVj6B3s7dD05dgaTh4/l36Nu5ag+nTjhzAEYY7jojst5476X6zt70ogd8mNA/4JFwAxjzJvAB9ba7cbs94nCXGtt9n6WDwFmWmsrAGpJU40x5mo8DVdujenOmU3a/t28V+OJzu19WhmcGI8zvXp2nBmeNOXpWeAIwBEVTkVOQa3bOtOzcKZnUbTc080y57PvSRxzpndfWeR+8T0ARSvWYd2WwLgoKrLzfVKeupB42QiaXzAYgMIVGwhpEc/vX91qRvTAE/mrGvH0RIqrpwHY/cE3dHrzNrZN/x8J5w5kxxMfAlC6OYPSrZmEpbSkcPn6OimTr7S4bDgtLhwCQP6K9YS03FvukKR4ymocSzUjxiEt9qYp3723O3b6G1/S9Y1bAGh+Zn+y56/AVrgo35NP3pJfieyeTOmWzDorl0hDkxAZRoa3CyTArvxiEqKqj9hoHhVOl1bxBDkCaBkbQZv4SLZmF9ClZTzNvWlbxUXQu20Cv6bn0Dqu8XdnHnjRCE44z3N93rxyA3Et9l5fYhPjyM2ofu1NTu1I227JTP32aQIcDqLio5j0zr+Zfu5dZGzYySMX3wt4uuN2G5jqv4IchIRLTqbZBUMBKFqxnuAqdRCUFE95RvXrcHlGdo17VDxObz2V78klKCHWE/1MiKW8yjCZsKPb0Hb6GNZedC+unL0PMPa8M48978wDoOUtF+z3ftdQJUSFkZG/N+K5K79knyhm88hwurSM855XTWgTF8HW7EK6tNgbBUyIDKNDs2jStu5haKdWfst/XRl68ckMOncYABt/Wkd8lah2XGI82bv2/dqa411WWlTKoo++JrlHCkvnLqb1kUdw5zv3ARDdLIaJL97Ow1fcr4mIarCH1nMbnzrsIqDGmPaAC6j2TddaOxW4EggDFhlj9p0yzaOoluV/mbX2OWttb2ttb181PgGKVq4jtF0Swa0TMEGBxJ3en9y51Z8M585dTPwoT1A29pTjKFi0qnJ53On9McGBBLdOILRdEkUr1lGxOxfnzj2EtG8BQFT/bpSu83T5yp31I5HHdQUgpF0LAoIDG3TjEyDj5VmsHDKJlUMmkT1rMQnnDAAgIjWFioJiyjNzq6Uvz8zFVVhMRGoKAAnnDCB79hIAQtslVqaLH9GHkvU7ACjbsYfoEzz1EtQ0mrDkFpRu2VXHJTt4O1+ezdLBk1g6eBJ7vlhC81GeYH9UL0/dOGvUjTMzl4rCEqJ6eeqm+aiT2DPLUzdVx4s2HdmXol89x0zpjj3E9O8CeMbNRqV2pNhbbyKHi84t49maXcCOnELKK1zMXrWFk45qWS3NwKNbsXSz53aVU1TKlqwCWsVGkF/ixOkdU55TVMqKrbtp36xxTQBXmwWvz+KekZO4Z+Qkls9ZzLFnDgCgfc8USgqKydudWy39wjfmMLHf1dzSfzQPjbqDXZvSmX7uXQBExnsmnjHGcMr1Z7Pwzbn+LMrflvnqF6weNp7Vw8aTM/tH4s/23K+bpHbElV9c2aX2d+WZObgKSmiS2hGA+LMHkuuNCOfOWVJ5v48ftXd5cIumdHh+MpvGPkrZxupjJH+fTDC4RVNiTz6G7P/7uu4K62OdW8SyNbuQHTlFlLvczF69jZM6JlVLM/DIFizd4pn0Lqe4jC3ZhbSKacKu/GJKyz3nVX6Jk+Xb9tA2vvE/1AGY+9oX3DpyHLeOHMfSOT9ywlkDAOjQsyPFBUXk1jimAhwBRMZ6yu4IdJA6uDfbf9tKSUExV/e8mBv7X82N/a9m/fK1anzKX3ZYRUCNMc2AmcCT1lpbNcJpjEm21q4CVhlj+gBHAduAP3vlmQtcY4xZYK2tMMbEeaOgBd597Dnw5j7kcrN1yvN0fPMuCHCQ9d8vKV27jRYTz6No5Xry5i5hzztf0u6xm+jy7TO4cgvYMPo/AJSu3UbOJ4voPP9JcLnYcsdz4O16s3XK87R/YjwmOJCyLbvYPOFxAPb8dx5t/3M9nb98DHd5BZtuesxvRfWFnC/TiB2cSuoPT+IuKWP9TU9Xruv+5XRWDvF0Kd54ywt08L6GJXf+cnLmLQegze0XEtahBbgtZdt3s+Hm5wDYPuM9Ojx2PT0W/AeMYct9b1DRyLrIZX+ZRvzgnvT78QlcJU5+G/tU5bre86azdLCnbtZNfp6jHvfUTfa8FWR766b9nRcR0aUtWEvptt2snfgsADtfms2Rj42mz1czwBgy3llA0Zqtfi+fv0y6aypLlv9Ebm4+g8+4kNFXXMRZpw6v72w1CIdz3QQ6ArjllN5c99oC3G7L6ant6ZAQw9PzfqJTyzgGHNWK4zok8f36dM584lMCjGHc8B7EhIewYutu7vt4MQHG4LaWy0/oXG323EPFqgVpdB2YygNfPYmzpIyXJ+29Pt/5+XTuGXngIR99T+vPwIs8wyKWz/6RRe/Or9P81oW8ecuIHtSLroue8byGZfwTles6z5nB6mGeV85sue3Zva9hWZBG3nzPmM70pz6gw8yJNDtvsOceda1nxukW484hMDaSNg9cA4CtcLHGW58dnr+ZwNhIbEUFW25/Dle+/ybyP1iBAQHcMqIH1731DW5rOb17WzokRPP0wtV0SoplwJEtOC65Od9v3MWZz8z2nFeDuxETHsL3G3cxY+4iDGCBi4/tSErzQ++8Wj5/GT0G9uLRr2dSVlLGsxMfr1z34OePcOvIcQQFB3HL63cTGOggwBHAqm9XMu/txvEAp6FwN8Kxmf5iDtUZvn63n9ewvA7M8L6GZQAw0Vr7D2PME8BAwA2sBi71/j4biAdeAXKA3tba6737bgt8aq3tYowJBKYBI4By4Hlr7ZPGmBuA64GdBxoHurTVGYf2f8RBKKs4rJ6T/CXlurgd0PGrH6rvLEgjVDHv9frOQoN2w+RV9Z2FBuua8sY107m/dXmoU31nocG6/PbV9Z2FBu3tLR82ui88a48e4bfv9h1/mdWo6ueQ/2ZvrXUcYN1CYKH39xtqSTaoxudXqmy/Geji/b0CGO/9qfo3ngCeQEREREREDguNcXZafznsxoCKiIiIiIhI/TjkI6AiIiIiIiL+ZN2KgNZGEVARERERERHxC0VARUREREREfOgQn+f1oCgCKiIiIiIiIn6hBqiIiIiIiIj4hbrgioiIiIiI+JAmIaqdIqAiIiIiIiLiF4qAioiIiIiI+JDbKgJaG0VARURERERExC8UARUREREREfEhqwhorRQBFREREREREb9QBFRERERERMSHrK3vHDRcioCKiIiIiIiIXygCKiIiIiIi4kOaBbd2ioCKiIiIiIiIXygCKiIiIiIi4kOaBbd2ioCKiIiIiIiIX6gBKiIiIiIi4kPW+u/nzzDGjDDG/GaMWW+MuWU/60OMMf/1rv/RGNO2yrpbvct/M8YMP9i6UQNURERERETkEGWMcQBPAScDnYDzjDGdaiS7Asix1nYAHgEe8m7bCTgX6AyMAJ727u9vUwNURERERETEh9zW+O3nT+gLrLfWbrTWOoF3gNNrpDkdeNX7+3vAYGOM8S5/x1pbZq3dBKz37u9vUwNURERERETk0NUS2Fbl83bvsv2msdZWAHlA/J/c9i/RLLgNRID5kx24D0NGdVOrcntQPSAOeRXzXq/vLEgjFDj4ovrOQoNWaifWdxaksQoKru8cNFgJJrS+syA+5s9ZcI0xVwNXV1n0nLX2Ob9l4C9SA1RERERERKSR8jY2D9Tg3AG0rvK5lXfZ/tJsN8YEAtFA1p/c9i9RF1wREREREZFD1xIgxRjTzhgTjGdSoY9rpPkYuMT7+9nAfGut9S4/1ztLbjsgBVh8MJlRBFRERERERMSH/uTkQH5hra0wxlwPzAYcwEvW2tXGmHuApdbaj4EXgdeNMeuBbDyNVLzp/gesASqAMdZa18HkRw1QERERERGRQ5i19nPg8xrL7qzyeykwqpZt7wfu91Ve1AAVERERERHxIU2hWTuNARURERERERG/UARURERERETEhxrSGNCGRhFQERERERER8QtFQEVERERERHzIKgJaK0VARURERERExC8UARUREREREfEhd31noAFTBFRERERERET8QhFQERERERERH7JoDGhtFAEVERERERERv1AEVERERERExIfctr5z0HApAioiIiIiIiJ+oQioiIiIiIiID7k1BrRWioCKiIiIiIiIX6gBKiIiIiIiIn6hLrgiIiIiIiI+pNew1E4RUBEREREREfELRUAPUVEDetLq7qvAEUDW23PZ9fT71dab4EDaPjqOsK7JuHIK2DR6Os7tmQA0H3MW8ecOBZebbXc9T8FXywFodvk/aHr+MMCw5+057H7xEwCSxp1L/PnDqMjKA2DnQ2+Qv2CZ/wp7kNrdezkxg1NxlzhZf9MTFK3atE+aJt3a0+HR6wkIDSZ3XhqbprwEQJspFxM7rDfWWUHplgzW3/QkrvxiAMKPbkPytGtwRIZj3W5+Onkytqzcr2XzhSPvv5Smg3viKilj9Y3PULCf+ons1o7Oj4/GERrMnnnL+e32VwCI6NyGo6dfhSMkCFvh4pdbXiR/+YbK7aJ6JNPns3tZdc1jZH76o7+K5HOL1u1k2ufLcFvLP1OTufzEzvukmf3zFp5dsAowdEyMYeqo4wFIvettOjSPBiApugmPXXCSP7PuF6qfv++OB2bw9aLFxMXG8OEbM+s7O/XioruvoMfAVMpKynhu4pNs/nnjPmluf+ceYhJicZY6AXjoonvIz8pj0AXDGHrxybhdbkqLS3nx1mfYuW67v4vwtxxxzxVED+qFu6SMTeOeoHg/5Q7v2p52j9xIQGgwefOXsfXOFwFwxESQ/MwEQlonULYtkw3XPowrr6jW/YZ1bkvbB6/FERGGdblJf+I9sj9eBEBk/660vuMSTEAArqJSNo17nLLNGf6riL/hYK456blF/PujH9mVV4wx8MSFA2gZG+HnEtS9M++6hE4De1JeUsabE59h++rNtaa98vmJND2iOVOHT6q2fOCVp3DGHRdxW8+rKMopqOMcNz7u+s5AA6YGKGCMeQTYYq191Pt5NrDNWnul9/N/gB3W2hl/cb9tgU+ttV18m+M/EBBA6/uuYd35d1GensWRnz5M3tzFlK7bVpkk/tyhVOQWsuaEa4k97QRa3nYJm0ZPJzSlNbGnncAvg68nqHkcKW/fw+oTRxOa0oqm5w/j139MxJZX0OH1u8mft6TyJpT5wsdkPvuhX4vpCzGDUgltn8Ty464nIjWF9lOvZtUpt+6Trv3Uq9kw8RkK09Zx9Ju3EzOoJ7nzl5P79Uq2PPAGuNy0uf1CWt1wJlvufwMcAaQ8OZZ1NzxG8ZotBMZGYMtd9VDCg9N0cA/C2yWy6JixRPdK4ehpV7D45Dv2SXf0tCv5ZcJz5C1bR8+3biF+UA+y5q+g450XsPHh98iav4Kmg3uQMuUClp15j2ejAEPKlPPJXviTn0vlWy63mwc/XcrMSwbRPCqMC56dzUlHtSI5IboyzZasfF76eg2vXDmMqLBgsgtLK9eFBDn43+iR9ZF1v1D9HJwzRg7l/LNO47Z7H67vrNSL7gNTSWyXxISTxpDcsyOX3nc1d59xy37TPj32UTat2lBt2fcffcP8N+cAkDqkDxfecRnTLrm3zvN9sKIHpRLSrgWr+o+mSWpH2jx4Db+cOnmfdG0evJbNNz9NUdpaUl6fQvTAVPIWpJE05kzyv11FxlMfkDjmTJLGnMn2B16vdb/uEicbxz5G2aZ0gprH0umLh8lbuBxXfjFtH7yWdZc9SOn67TS7ZAQtxo5i07gn6qFW/pyDvebc8cH3XHliZ47tkERxWTnGHHrdKDsN6EGzdkncN+Am2vTswKj7r+SRM/a9twN0G94HZ3HZPstjkuI58sRuZG/fXdfZlUOQuuB6LAKOAzDGBABNgaqPy44DvvujnRhjGkSDvkmPFMo2Z+DcugtbXkHOx98QPaxvtTQxw/qR/d58AHI+W0Tk8d0AiB7Wl5yPv8E6K3Buy6RscwZNeqQQ2qEVRcvXYkud4HJT+OPPxIw41u9l87W4EX3Y/e5XABSmrSMwqglBCTHV0gQlxOCIDKcwbR0Au9/9irgRnvrM+2oluDzPuArS1hLcIh6AmJN6UPzLZorXbAGgIqcQ3I3vWVizEX1If/drAPKWeeonuEb9BCfEEBgRRt4yT/2kv/s1CSf3AcBaCIwMAyAwKpyyXTmV2x1x5cns+vRHnHvy/FCSuvPz9ixax0XQKi6CoEAHw7u2YeGv1SMsHyzdwL/6pRAVFgxAXERofWS1Xqh+Dk7vHl2Jjoqs72zUm15D+/Lt+wsB2LB8LU2imhCTEPunty8pLKn8PSQ8BEvjeDN8zPC+ZL23AICitLU4opsQVKPcQQmxOCLDKEpbC0DWewuI8d6bYob3Jetdz/ZZ7y4gZkS/A+63bONOyjalA1C+K4eKrDwC4z0NNmstjt+v45HhOHdl12XRD9rBXHM2ZObhcluO7ZAEQHhIEGHBDeKrnU91GdabJR947u1blq8nLDKcqGYx+6QLDg9h4JWnMPuJD/ZZ988pF/Pxg282kjOqfliM334am0PvrPp7vgMe8f7eGfgZSDLGxALFwNHAMGPME0CYN/011lprjFkIrAD6A297P7/k3dccfxWgqqDEeJw791R+Lk/PIrxnxxpp4vamcblxFRThiI0kKDGe4rTfKtM50/cQlBhP6W9baXHzhThiInGXlhE1sBfFP62vTNfskpHEnzWQop/Ws+Pelyq7+jR0wYlxlFWpq7L0LIKT4inPzN2bJike586s6mkS4/bZV8K5g9nj7bIUlpyEtXD021MIio9iz4ffsvPpj+quIHUkJCmW0h17y16ankVoUhzOKvUTmhRHafreLySlO7MJSfJ8UVo75VV6vnMbHe+6EAICWPKPKZ79JsaScHIflp55D9GPJvunMHUks6CExOgmlZ+bR4Wzavueamm2ZHm6Jl3y/Bzc1nLtwK4cn9ICAGeFi/NnzsIRYLjshE4MOrq1/zLvB6ofORixiXFkVblGZ2dkEds8jtzMnH3SXv3w9bhdbpbM+oEPH3+3cvmQi0dw8pWnERgUyAPn3eWXfB+s4MTq953y9CyCEuMor1LuoMQ4nOl70zjTswhO9DwEDWoaU5m2PDOHoKYxf3q/TXqkYIKCKns4bZ74FB1fn4K7tAxXQQlr9hOJbUgO5pqzJSufyNAgxr/9NTtyiuiXnMjYod1xBBxa8ZqY5nHkVjkO8jKyiU6MI393brV0p0z4Fwte+Ixyb9f233UZ2ou8Xdns/GWrP7Irh6BD64z6m6y1O4EKY8wReKKd3wM/AscCvYFVwJPW2j7e7rRhwD+q7CLYWtvbWvsf4GXgBmttd78Woo6Vrt/Orqc/IOXNu+nwxt2UrNlUGfnb/foXrO5/Lb8Mv4mKzBxaTrm8nnPrfy3HnoV1udjzvueJonE4iOp7FOvGPMrPp99O/Mn9iO7ftZ5z6X+tLh3K2jtf5ZvUMay981U6PXItAEfeeynr7nvLEyI9DLjcbrZmF/DC5UOYOup47vloMfklnhv65+NP561rR/Dg2ccz/Ys0tmUffuNoVD9ysJ4e+yi3Dh/HvaNu58g+R9P/zAGV6758bRYTThzNO1Nf54wbzq6/TNanP3mtDUqIpd3jY9k0/onKbRKvOo21F93Lyt5Xsee/8znirsvqMqd+Uds1x+W2LN+ym/HDU3nzmuHsyCnk4+X7zntwOGjZqQ3xRyTw0+wl1ZYHhQYzdMw/+XzG/+opZ42H248/jY0ioHt9h6fxeRwwA2jp/T0PTxfdgcaYm4FwIA5YDXzi3fa/AMaYGCDGWvu1d/nrwMm1/UFjzNXA1QC3x3TjzIi2PilIeUYWwS2aVn4OSoqnPCOrRppsgls09Sx3BOCIbIIrp4DyjCyCqmwbnNS0ctus/35J1n+/BKDF5Asrn7xWVOlCueetOSS/sv9xBA1F4qUjaH7BEAAKV64npEVTfv9KG5IUX+2JMnifKnu71lamydgb8Wt2zkDihvRi9Tl3Vy4rS88i/4c1VHi/LOfMT6NJ1/bkfbuqbgrlQ60uG0arCwcDkLdiA6Et95Y9NCm+WrQToDQ9m9CkvRHh0BZxlKV7nqYnnXNS5YREuz7+gU4zrgEgqkd7us68EYCg+CiaDumJdbnY/cXSOitXXUmIDCOjSsR/V34xCVHh1dI0jwqnS6t4ghwBtIyNoE18JFuzC+jSMp7m3rSt4iLo3TaBX9NzaB136HS5VP3IXzXk4hEMPHcoABt/Wk98lXtSXGI8OfvpAvr7stKiUr776Bva9+jAtx8srJbmh4+/5bL7rq67jB+khEtOptkFnnIXrVhf7b7juY9XL3d5RjbBSXvTBCfF4/Ter8v35BKUEOuJfibEUu6dJNCZkVXrfgMiwkh57XZ2PPRmZbfewLgowjq1pWi5Z4hF9sff0vHNO31ddJ86mGtO86hwjkyMpVWcZ9KhgUe14qfte/gnjbunDkD/i4Zx7HmDANi6cgMxVY6D6MQ48mocX21TO3JEt/bc+e0TOBwBRMRHc/07d/L+XS8T36oZN38xDYCYxDgmffog/znjdgp2N+4hNeI/ioDu9fs40K54uuD+gCcC+vv4z6eBs621XYHngaqDlP5Wf1Nr7XPeyGlvXzU+AYpWriOkbRLBrRMwQYHEnnYCeXMXV0uTO3cxcWd7LkSxpxxPwSLPRDB5cxcTe9oJmOBAglsnENI2iaIVnhvP7+NBglo0JWbEseR86GlnB1YZlxIz4hhKfmvYXTIyXpnFyqETWTl0ItlfLKbZKM+smhGpKVQUFFfrfgtQnpmLq6CYiNQUAJqNOonsWZ4ngjEDe9ByzOn8culU3CV7u6jkLlxB+NFtCAgLBkcAUcd0pmTtNhqD7S/P4YfBk/lh8GR2f7GEpFEnAhDdy1M/zhr148zMpaKwhOhenvpJGnUiu731U5aRQ+xxnQCIO6ELxRs9Xbq+7XND5U/mJz/wy+QXG2XjE6Bzy3i2ZhewI6eQ8goXs1dt4aSjWlZLM/DoVizd7JllOqeolC1ZBbSKjSC/xImzwlW5fMXW3bRvFr3P32jMVD/yV3352ixuHzmB20dOYNmcxfQ/awAAyT07UlxQvE/32wBHABGxnocSjkAHPQf3Zrv3PtS8bVJluh6DepGxOd0/hfgbMl/9gtXDxrN62HhyZv9I/NkDAWiS2hFXfnG1brLg6VrrKiihSapniE382QPJne251+fOWUL8KM/28aNqLN/Pfk1QICkv3kLWewvJ+ez7yr9RkVeIIyqckPaeLvFRJ3anpIHPInww15zOLeMoKHWSXeSZlGjxpl2HzDXn29fnMH3kLUwfeQur5iylz5mee3ubnh0oLSjep/vtojfmcme/0dzT/wYeG3U3uzel8+S595D+2zbu6H0N9/S/gXv630BuRjbT/3GrGp/7oQho7RQB3es7YCKw0VrrArK9Ec3OwFXeNHuMMRHA2cB7NXdgrc01xuQaY/pba78FLvBP1mtwudk25Tk6vHE3xhFA1n/nUbp2G0kTzqf4p/XkzV1M1jtzafvoODp9MxNXbgGbxnhmWCxdu43cTxfRaf6T2Ao32+54tnLynPbPTcYRE4WtqGDbHc/iyve0u1vedgnhnduBhbLtmWy95el6KfbfkTMvjZjBqaR+/xSukjLWj3uqcl33uQ+zcuhEADbe+jwp3tew5MxfTu78NADa3X8lAcFBdH7H80S4IG0tGyc/hyuviJ3PfkK3L6aBteTMSyNnXpr/C3iQ9ny5nKaDe3L8j4/hKnGyZuwzleuOmfcQPwz2jAX6dfKLdH58NAGhQeyZt4I981YA8MuEZznyvksxgQ7cZU7WTHyuPopRpwIdAdxySm+ue20Bbrfl9NT2dEiI4el5P9GpZRwDjmrFcR2S+H59Omc+8SkBxjBueA9iwkNYsXU39328mABjcFvL5Sd0rjZT46FA9XNwJt01lSXLfyI3N5/BZ1zI6Csu4qxTh9d3tvxmxfxldB+Yyn++fhqn9zUsv7v/8/9w+8gJBAUHMfn1O3EEOghwBLD6259Y8Lant86wS06mc/9uuMpdFOUX8uz4hjt7a1V585YRPagXXRc943ldSpV8d54zg9XDxgOw5bZn976GZUEaed57U/pTH9Bh5kSanTeYsu272XDtwwfcb9ypxxPRrxOBsZE0PcfzcHrjuMcpWb2ZzZOepsNzN4N1U5FbxOYJT9KQHcw1B2Dc8J5c88p8rLUc3SKOs3o1/uhnTWsWLKfTwB5M+eoxnCVlvDVp7yueJn0+lekj9z/TtIivGHuYjMH6I8YYB5ADPG6tvcO77BXgWGvtkcaY+4DzgAxgLZ7XttztnXRoorV2qXebXngmIbJ4JiEa+Wdew5LW+nT9R9SitELPSWpT5FbdHEj/xzvVdxakEQocfFF9Z6FBu6zXxPrOQoM1tkK38gPpMqNHfWehwbpl8i/1nYUG7bHN7zS6qV4/a36e3y4Ip+x6u1HVj769enmjnlE1ll1a5fc7gH0GN1prB9T4vAyoOgHRzb7Mp4iIiIiISGOlBqiIiIiIiIgPuRtVTNK/NAmRiIiIiIiI+IUioCIiIiIiIj7kRiHQ2igCKiIiIiIiIn6hBqiIiIiIiIj4hbrgioiIiIiI+JBeylQ7RUBFRERERETELxQBFRERERER8SF3fWegAVMEVERERERERPzi/9u77/goqr2P45+TngAhhZLQi6AgIE18VFC6gA0LdsGKCipSLIgFsaGicq+9e+1ey7WLgIgIiqh0UOlNQkshIT275/ljJmETsqCw2STwffPaV3ZnzsycOczO7JnfOWcUARUREREREQkgr9FjWPxRBFRERERERESCQhFQERERERGRANIouP4pAioiIiIiIiJBoQioiIiIiIhIAGkUXP8UARUREREREZGgUARUREREREQkgLwaBNcvRUBFREREREQkKBQBFRERERERCSAvCoH6owioiIiIiIiIBIUioCIiIiIiIgGk54D6pwioiIiIiIiIBIUqoCIiIiIiIhIUaoJbRXi8uhfgT83IgsrOQpVVv+aeys5ClXbT7csqOwtSDeXZcZWdhSrttd+mVHYWqqwlHcdUdhaqtONv/rqys1Blze4YWdlZkADTY1j8U61HREREREREgkIRUBERERERkQDyVnYGqjBFQEVERERERCQoFAEVEREREREJID2GxT9FQEVERERERCQoFAEVEREREREJII2C658ioCIiIiIiIhIUioCKiIiIiIgEkEbB9U8RUBEREREREQkKVUBFREREREQCyBvE16EwxiQYY2YYY1a7f+PLSdPRGPOTMWaFMWapMeZCn3mvG2PWG2MWu6+OB9qmKqAiIiIiIiJHpjuAb621rYBv3c9l5QBDrbXHAgOAqcaYOJ/5t1prO7qvxQfaoPqAioiIiIiIBJCtPqPgng30dN//B5gN3O6bwFq7yuf9VmPMDqAukHEwG1QEVERERERE5MhU31qb4r7fBtTfX2JjTDcgAljrM/lBt2nuk8aYyANtUBFQERERERGRAArmKLjGmOHAcJ9JL1prX/SZPxNIKmfRCb4frLXWGGP3s51k4E1gmLW2eBfH41RcI4AXcaKnk/aXX1VARUREREREqim3svnifub39TfPGLPdGJNsrU1xK5g7/KSLBb4EJlhr5/usuzh6mm+MeQ0Yd6D8qgmuiIiIiIjIkekzYJj7fhjwadkExpgI4H/AG9baD8vMS3b/GmAwsPxAG1QFVEREREREJICqy2NYgMlAP2PMaqCv+xljTFdjzMtumguAU4ArynncytvGmGXAMqAO8MCBNqgmuCIiIiIiIkcga20q0Kec6b8C17jv3wLe8rN873+6TVVARUREREREAsjvSD6iJrgiIiIiIiISHIqAioiIiIiIBJDXVHYOqi5FQEVERERERCQoFAE9jDWZdDW1e3fBm5vP+tFPkbN83T5pYtq3oPmTNxMSFcHuWb+x6Z5XAAiNq0nL58YS2bge+Zt3sPb6KXh2Z5N0/WASzz3FWTg0lOhWDVnU4QrCE2Np+dzex/5ENqnPX1PeZfvLXwRlXw9WzVM70/CeayE0hLT3Z7DzuVIjS2Miwmj8xBii27XEk5HFxhsfpXDLDqKPa0Wjh290Exm2T32HzG/mE55ch8ZPjCasThxYSH13GqmvfR78Hatg0Sd3pc4d12NCQ8n86GsyXvlvqflRXdpR5/briWjdgu23PkT2jLmVlNPgufjeq2jfqxMFuQW8Ou5pNq1Y7zftjS/dTt0m9bn3tDEANGrTlMsfHE5kTBSpW3by0i3/Im9PbrCyHhQqH/8un3g1HXt1Jj83nxfHPc2Gcs7VE96bRFy9eAryCgB45PJJZKbupvel/ek3dCBej5e8nDxeGf8cW1dvCfYuVIq7HnqCOfMWkBAfxydvPV/Z2akwsT070eS+ayA0hF3vzmDbMx+Xmm8iwmg+9RZiOrSkKD2LdTdMoWCL8xi/pJHnUefivuDxsumel8j8fjEA7X96EU92Lni82CIPv5/uXL+j2zan6eTrCYmMwBZ52DThBbIXrw7q/gbK+AfH0KPPieTl5jPh5vv5fdmfpebH1Ijhjc/2Hjf1k+vxxUfTeOTuqdw2aRTdTu4CQFR0FAl14jmpdb+g5r8ihXftRo3rb8KEhpD39Zfk/vedUvOjzr2AqAGng8eDd3cGe554BO+O7YTUq0/sPQ9AiIGwMPI+/Zi8Lz+rpL2o+gIwOu1h64AVUGNMEjAVOB7IALYDt1hrV1Vozg6SMaYB8G9r7fnu8MANrLVfHcR6PgGSrLX/F+AsBkXt3p2JbN6AZd1HUKNza5o+fB2/n3n7PumaPnw9G257luyFq2j15t3U7tWZ3d8tJHnkuWTOXca2Zz4maeS5JI88ly0Pvcm25z9h2/OfONvo15Wka8/Ck7EHT8YeVvR3fiwSEkLH314m/eufg7jHByEkhIaTrmf9ZXdTuC2Voz57gswZP5O/ZnNJkoQL+uPZvYc/e15H7TN7kHzHFWy68VHy/tzE6jNHg8dLWN14Wn/9b1bOXIAt8pDywKvkrlhLSI1oWn3+JHt+WFxqndVeSAh17xrJ1mvHU7RtF43ef4rs7+ZTuG5TSZKilJ3suOtx4q44vxIzGjzte3aiXvNk7ux5Ey06teKyB4fz0ODx5abtfNoJ5OfklZo2bPINfPDQG6z6eSUnD+nNacPP5tMn3gtG1oNC5ePfcb06k9Q8mbGnjqRlp9Zc8cBwJg6+o9y0z46ayvpla0tN++nTH5j19nQAOvc9nsvuupJHh91f4fmuCgYP6scl553FnfdPqeysVJyQEJo8cB2rLrmXwpRU2nz5GBnTF5Dnc5OhzkX9KNq9h+XdbyD+rO40unMo60ZMIapVIxLO7s6K3jcRXj+B1u9OYvkpI8Dr/CxeNeQuitKzSm2u0YRhbH3yfTK/W0jt3l1oNGEYfw65K6i7HAg9+pxIk+aNGfR/Q+jQ5VjufvQ2Lhl4dak0Odk5nN9naMnn96e/zswvZwPw6D3/Kpl+ydVDaNO+dVDyHRQhIdQceQu7x4/Fu2sncU+9QMH8eXg2bSxJ4lm7moybhkN+PlFnnE2Na64n66H78KalkjF6BBQWQlQ08S+8RsFP8/CmpVbiDkl1tN8muO4DRf8HzLbWtrTWdgHGA/WDkbmDYa3daq0t/tXbERj0T9dhjIkDugC1jTEt/KSp0tHjuNO6kfrhdwBkL1xFaO0ahNeLL5UmvF48obWiyV7o3EtI/fA74gZ027v8B87yqR98R9yAE/bZRuLZPUj95Id9psd2b0/exm0U/LUzoPsUaDEdW1GwMYWCzduxhUVkfD6H2P6l9zO2/wmkf/QtALu/mkfNk44DwOblg8e5iJvICKx1xjor2plO7grnB6I3O5e8tZsJT0oM1i4FRWT7oynctJWiLdugqIg9X8+mRu8TS6Up2rqdglXrsd4j4/5fx/7H89PHswFYt2g1MbViqF03bp90kTFR9LvmDL546qNS0+s3T2bVzysBWDl3CV0G7vt9q85UPv516deNuR/NBmDtolXUiK1BXJlz9f7k+kSCI2MisUfQuItdO7andmytys5GharRsRX5G1Io2ORcp9I+nUtcmetUXP+91+v0L3+kVvcO7vQTSPt0LragiILNO8jfkEKNjq32v0FrCa0ZDUBorRgKtqcFfqeCoNeAU/jsAyf2sPS3FdSKrUmdev6vxU1bNCaxTjy/zV+8z7xB5/Tjq49nVFRWgy7s6DZ4tv6Fd1sKFBWRP3sWESd2L5WmcMkiyM933v++kpA6dZ0ZRUVO5RMw4eEQop58+1ONngMadAc6cnoBhdbakjYK1tol1tofjOMxY8xyY8wyY8yFAMaYnsaY740xnxpj1hljJhtjLjXGLHDTtXTTvW6Mec4YM99N19MY86ox5ndjzOvF2zPG7PF5f37xPHf5fxtjfnSXP9+d3szNUwQwCbjQfVjqhcaY1caYum66EGPMmuLPZZwLfA68B1zks/3XjTHPG2N+Bh41xrQ0xkwzxvxmjPnBGHOMm+5MY8zPxphFxpiZxpigV9gjkhIp2Lr3jlRhSirhSQml0oQnJVCQsjdNQUoqEW5lKbxOHIU70p1ld6QTXieu1LIhURHU7tmJ9K9+2mfbCWf3IK2cimlVE14/kcKtu0o+F6akEl4/0X8ajxdPVjah8bEARHdsTevpz9D6m6f4665nSyqkJcs2qkd025bkLC7d7Ke6C6uXSNG2vTcXirbvIqxenUrMUeWLq59Ims/3LX1bGnHl3HgYPPYipr/8OQV5+aWmb129hY79jweg66ATSUg+vMpT5eNffFICqT7nobRtqcTXTyg37fApN/LgV48z+OYhpab3HTqAx+c8y0Xjh/LGva9UaH4luCKSEyhI2Xt8FGxLJSK59PERkeSTxuPFk5lDWHyt/S9rLa3emUibrx6nzqX9S9JsnvgKje66gg4LXqbR3Vfw18NvVtzOVaD6yXXZ9teOks/bU3ZQP7m8n3uOgYP7Me3TmftMT26URMMmDfh57q8Vks/KEJJYB+/OvWXj3bWTkDr+z6lRAwZR8MveFm0hdesS99yrJLz1Abn/fUfRTzkoB6qAtgN+8zPvXJwI43FAX+AxY0yyO+844HqgDXA50Npa2w14GbjJZx3xwInAaOAz4EngWKC923z2QJKB7sAZwGTfGdbaAuAe4H1rbUdr7fs4D1C91E3SF1hirS0vTHcx8K77urjMvEbASdbaMcCLwE1uZHgc8KybZi7wf9baTjiV2Nv+xr5Ubbb0XfW4/sez59c/8GTsKTXdhIcR1/940r74MZi5qxS5i1exqv9I1pw1hno3DMFEhpfMC4mJoulz49k66SW8h1FfNTl4jds2o26T+iz6ZsE+816/7Rl6XTaAuz9/hKia0RQVFlVCDiuXymf/nh01lfGnjeb+IRM4+vg2dD+3Z8m8mW9MY+wpI3hv8psMvunIaPYuh+aPc8fz+8CxrL58EvWGDaTmCW0BqDt0AJvve5Wl3a5h88RXaTblxkrOaXAMHNyPr/43vdzp07/4Du8R0pqnrMje/QhrdTS5H+7t8uDduZOMG64i/cpLiOw3ABP391tsHGlsEF/VzaE0I+0OvGut9QDbjTHf4/QTzQR+sdamABhj1gLF3+plOFHVYp9ba60xZhmw3Vq7zF1mBdAMWHyAPHxirfUCK/9mlPFV4FOcPq1XAa+VTeCupxUw181boTGmnbV2uZvkA2utxxhTEzgJ+MBpqQxApPu3EfC+WyGPAModbcMYMxwYDjC+dkfOqdHsb+yCf/WGDaTupU4n+ezFa4hosDfCEJ6cSOG20k1pCrelEZG8N01EciIF25w7WYW7MgivF+9EP+vFU5i6u9SyCWd1L7f5be1enclZto6iXbv3mVfVFG5PJbzB3rt+4cmJFG5PLTdN4bZUCA0htFYNPOmZpdLkr92CNyeXqNZNyV22BsJCafr8eDI+mU3mN/tGiKu7oh2phCXtvZMcVr8ORTt27WeJw1OvywfQ4+I+AGxYspYEn+9bfFICGdtKH0stO7emWYeWTJ77LCGhocQmxnLre/fx2EX3sm3tVp4c6vTbq988mQ69OgdvRyqIyse/vkMH0Osi51y9bukaEn3OQwlJiaSX0+yxeFpedh4/fvoDLToexVy3WXOx+Z/N5coHhldcxiXoClLSiPCJ+EckJVKQUvr4KNjmpClMca9TsTEUpWftd9ni3wNFqbvJmPYzNTq2Ys/PK0k8vxeb73kZgPQv5tHssZEVvYsBc9GV53H+ZWcDsHzx7yQ1rFcyr35yPbanlN8t6Oi2RxEaFsrKpfu2Vho4uC8P3nF49TH2pu4ipO7esgmpUxfvrn2v4eGduhB98eXsHndzSbPbUutJS8WzYT3h7TpQMPf7Cs2zHH4OFAFdgdMX8p/ybT/l9fnspXSlN7+cNGXT+Vbso/aznQM+bcdauxmnstwb6AZ8XU6yC3Ais+uNMRtwKsK+UdBs928IkOFGV4tfbdx5TwFPW2vbA9eVk+/i/Lxore1qre16qJVPgB3/+ZoV/cewov8Y0r/5mcTznbp+jc6t8WTmlDSpLVa4Ix1PVi41Ojud6xPP70WGG33ImP4LiUOc5ROH7J0OTr+QWv93bKlpxRIGd68WzW8BcpasJqJZA8Ib1Xcit2eeQuaM0vuUOeNn4s9zfkTXHnQye35cCkB4o/oQ6nx9whvWJbJlo5JRBxs/cjN5azaz65VPg7g3wZO//E/CmzQkrGF9CAuj5sCeZH83v7KzFXTfvTmNSYNuZdKgW1k0fQEnuhGpFp1akZuVw+6dGaXSz35rOuNOGM4d3UfwyJC72L4+hccuuheAWolOs25jDKffeD6z367+/Y1UPv7NfGMaEwaNZcKgsfw2fQHdz+sJQMtOrcnJyiGjzLk6JDSEmvFOX8fQsFA69enKlj+dQb/qN0suSdexdxe2bUgJzk5IUGQvWU1U82QiGtfDhIeRcHZ3MspcpzJmLCi5XseffhJZ85aVTE84uzsmIoyIxvWIap5M9uLVhERHElLD+VkSEh1J7CkdyXWPp8LtadQ6sR0AtU7uQN766nM8vffaR5zfZyjn9xnKrK+/56whzhAgHbocy56sPezaUX5T0YHn9ufrcqKfzY9qSmztWBb/uqxC8x1sRX/+QWjDRoTUT4KwMCJ79qZg/rxSaUJbtqLmzWPJvHc8dndGyfSQOnUhIgIAU7Mm4ce2x7PlMBpkMcC8Jniv6uZAEdBZwEPGmOHW2hcBjDEdgNrAD8B1xpj/AAnAKcCtwDEBzuN2Y0wb4E/gHCDrAOl9ZQFlRyh4Gacp7ptu9Lasi4EB1tqfAIwxzYGZwATfRNbaTGPMemPMEGvtB+6ATR2stUtwyucvN+mwf5DfgNn97W/U7t2F9vOecx7DMuapknnHTn+iZMTajXe+sPcxLN8tZPeshQCkPPMxRz0/jroX9yF/y07WXr/3DmDcwBPYPWcx3tzS/bRCoiOpfUpHNt5eTYbD93jZes/ztHjjPggNIf2/M8lfvYn6oy8ld9lqMmcuIO2/M2j8xBiOnv0Cnow9bLrpUQBqHN+Wejecjy0qAq/lr7ufx5OeSUzXtsSf15vc39fT6itnFL1tj75B1mx/LdmrIY+XXQ89Q/ILD2FCQ8j833QK124kfuRQ8lesImf2fCLbtSZp6j2ExNaiRs//I2HkUDYPPnwjM8u+W0j7Xp156PunKcjN57Vbny2Zd89XjzFp0K37Xb7bWd3pdfkAABZ98zPzPphVofkNNpWPf4tn/cZxvTrz+JxnKXAfw1Lswa8eZ8KgsYRHhHP7m/cQGhZKSGgIK+Yu5bt3nf5q/YcN5NjuHfAUesjO3MMLPuf6w92t907ml0VLycjIpM/gyxhx9eWcd+ZplZ2twPJ42XT3S7R++14ICSX1/ZnkrdpMg3EXk71kDbtn/MKu92bS/F+30G7uc3gyslg74nEA8lZtJv3zeRw762nweNh414vg9RJWN46jXnZGWjahoaR9MofM2YsA2HjbszS+7xpMWAje/EI23v6s36xVZXNm/kiPPifx9c8fkpubx92jHiiZ9+G3b5Qa/fa0s/ow4pIx+6xj4OB+fP1p9b7ZVS6vhz3PTKX2Q1MgJIS86V/h2biBmKFXUbTqDwrm/0iNa6/HREcTe9d9AHh27CBr4p2ENmlK7LUjcGJDhpwP38ezYd/HRokciLF2/y2H3ceaTMWJhOYBG4BbgDXAo8BAnCPxAWvt+8aYnsA4a+0Z7vKz3c+/+s5zBxP6wlr7oTGmmfu+nbuM77zzgUeAncCvQE1r7RW+adxl9lhra/quyxiTAHwDhAMPu/kLB1KBbtbaP8rsazNgHtDI+hSMMWYhcIP78t1mc+A5nL6o4cB71tpJxpizcfqzpuNU4o+31vbcXzn/0vCc6tiEOygiw4+8/l5/V42aBZWdhSrt4T2H9wiZUjHy7JHZ3+vveu23w6tJYiAt6bhvRUb2uqJwW2Vnocqa3THywImOYHW++b7axfkmN70saL/t79j4VrUqnwNWQA83xpiuwJPW2h6VnRdfqoD6pwqof6qA7p8qoHIwVAHdP1VA/VMFdP9UAfVPFdD9UwV0/6pbBbRKP8sy0Iwxd+BEMS89UFoREREREREJrCOqAmqtnUyZx7WIiIiIiIgEkpo2+negUXBFREREREREAuKIioCKiIiIiIhUNK9ioH4pAioiIiIiIiJBoQioiIiIiIhIAGk8df8UARUREREREZGgUARUREREREQkgNQD1D9FQEVERERERCQoFAEVEREREREJIPUB9U8RUBEREREREQkKRUBFREREREQCyGsqOwdVlyKgIiIiIiIiEhSKgIqIiIiIiASQV+Pg+qUIqIiIiIiIiASFIqAiIiIiIiIBpPinf4qAioiIiIiISFCoAioiIiIiIiJBoSa4IiIiIiIiAeSt7AxUYYqAioiIiIiISFAoAioiIiIiIhJAegyLf6qAVhHtnuxU2Vmospbcsriys1BleTLViGF/rvN4KjsLIoedJR3HVHYWqqzjFj9R2Vmo0l7pMK6ys1BlbVqp69X+1KnsDEhAqQIqIiIiIiISQIp/+qfwiYiIiIiIiASFIqAiIiIiIiIBpFFw/VMEVERERERERIJCEVAREREREZEA0ii4/ikCKiIiIiIiIkGhCKiIiIiIiEgAKf7pnyKgIiIiIiIiEhSKgIqIiIiIiASQRsH1TxFQERERERERCQpFQEVERERERALIqheoX4qAioiIiIiISFCoAioiIiIiIiJBoSa4IiIiIiIiAaRBiPxTBFRERERERESCQhFQERERERGRAPJqECK/FAEVERERERGRoFAEVEREREREJIAU//RPEVAREREREREJCkVARUREREREAkh9QP1TBFRERERERESCQhFQERERERGRANJzQP1TBfQIM2/1Vh798le81nJOl6O46pRj90nzzbKNvPDdUsDQOimOyRd0ByAlI5v7PpnP9t05GANPXd6LhvE1g7wHgdf0/quJ790ZT24+a0c/Tc6ydfukqdG+BS2n3kRIVATpsxay8e5XAEg440Qajb2Q6FaNWD7odrKXri1ZJqZNU5o/cj2htaLBa1k26DZsfmHQ9isQap3aiUYTr8WEhpD63gy2P/tRqfkmIoymT44mpn1LitKz2DDyMQq27KBWj+NocMdQTHgYtrCIvx58nT0/LsNERdD8uduJbJqE9XrJnPkLWye/UUl7d3CaTLqa2r274M3NZ/3op8hZvu/xEtO+Bc2fvJmQqAh2z/qNTfc4x0toXE1aPjeWyMb1yN+8g7XXT8GzO5uEc04hecQ5YAze7Fw2jH+B3JUbAKh/7ZnUvbgv1kLuHxtZP+apKn0cBb18rj6DOpf0wxjY+c4Mtr/8RTB394Aqojz8rTf62GY0e/h6QmtGYz1eUp76kLTP5gFQq3t7Gt81DBMSgic7j/Wj/03+hm3BK4j9iO3ZiSb3XQOhIex6dwbbnvm41HwTEUbzqbcQ08E5z6y7YQoFW3YAkDTyPOpc3Bc8Xjbd8xKZ3y8GoP1PL+LJzgWPF1vk4ffTxwEQ3bY5TSdfT0hkBLbIw6YJL5C9eHVQ9zcY7nroCebMW0BCfByfvPV8ZWen0jS9/2rienfGu5/re4zP9T2jzPW9oXt9X1Hm+l5dxPZ0ruGEhpD6bvnX8GZTRxPdviWe9CzWj3is5LtVf+R5JF7UDzxeNt/7ElnfLyKyRUOaPzuuZPnIJklsffwddr7yOdFtmtH44RsIrRFFweYdrL/5Cbx7coO6v1J9qAmuH8Yx1xgz0GfaEGPMtMrM16HweL08/PkvPDO0Fx/fdAbTlm5g7Y7dpdJsTM3k1TkreP3a/nx88xncNqhryby7PvqRYd3b8r9RZ/LWdQNIqBEV7F0IuLjenYlunszik0ey/rbnafHw8HLTNZ98HetufY7FJ48kunkycb06AZDzxyZWXfMoWfNXll4gNISWT41i/R0vsLTXLaw8/25soaeidyewQkJo/MB1rB12H7/3uZH4s3oQ1apxqSSJF/bDs3sPK0+5nh0vf0aD8cMAKErLZO1VD/JH/1FsHP0vmk4dXbLMjhc/4ffeI/lz4GhqdD2G2J6dg7pbh6J2785ENm/Asu4j2HD7czR9+Lpy0zV9+Ho23PYsy7qPILJ5A2r3cvYxeeS5ZM5dxrLuI8mcu4zkkecCULB5O3+cfxcr+t7C1qkf0OyRGwAIT0qg/lWns2LQrazoMwoTGkLC2d2Ds7MHIdjlE310E+pc0o/fT7+V5f1GU7tvVyKbJQVnZ/+GiioPf+v15hawbtS/WN57FKsum0TjiVcRGhsDQLOHr2fdjVNZ0X8MqZ/MocGoIUEogb8hJIQmD1zHqssnsaLXTSSc3YOoVo1KJalzUT+Kdu9hefcb2P7SZzS6cygAUa0akXB2d1b0volVl91Hkwevh5C9P2tWDbmLlaeNLql8AjSaMIytT77PytNGs/Xxd2k0YVhw9jPIBg/qx/NPPFDZ2ahUtXt3Jqp5Mkvc63vz/Vzf19/6HEtOHklU82Rq+1zfV5d3fa8u3Gv4mqH38XvvG4k/u5xr+EX9KMrYw8oezjW84Z3O9yGqVWPiz+rB731uZM3lE2ny4HUQEkL+ur/4Y8Bo5zVoLN7cfHZPmw9Ak8duZOvkN/i93ygyvplP/evPCfouVzU2iP8OhTEmwRgzwxiz2v0b7yedxxiz2H195jO9uTHmZ2PMGmPM+8aYiANtUxVQP6y1FrgeeMIYE2WMqQk8BIw8mPUZYyo92rx8SyqNE2vRKKEW4WGhnNa+KbN/31wqzce/ruHCE1oTGx0JQEJNp5K5dsduPF7LiUclAxATGU50RKXv0iGLP60bOz+cDcCehasIrV2D8Hqlv3fh9eIJrRXNnoWrANj54WziB5wAQN6av8hbu3Wf9cad2pGc3zeS40ZpitL3gLd6NcaI6diK/A3bKNi0HVtYRPrnP1C7f7dSaWr3P4HUD2cBkPHVPGqd3AGA3BXrKdqeBkDeqk2EREVgIsKweQXs+WkZALawiJzl6whPTgziXh2auNO6kfrhdwBkH+B4yXaPl9QPvyNuQLe9y3/gLJ/6wXfEucfRnl//LIls7Vn4JxE+ZWLCQgmJioDQEEKiIyncllaxO3kIgl0+Ua0akb1oFd68AvB4yZq/gviB/1fxO/o3VVR5+Ftv/rqt5K9PAaBwezpFqbsJS6wNgLXWaY0BhNWKoWB71TiOanRsRf6GlJLzTNqnc4nrf0KpNHH995ZD+pc/Uqt7B3f6CaR9OhdbUETB5h3kb0ihRsdW+9+gtYTWdMohtAqVQ6B17die2rG1KjsblSr+tG7s+ofX911/4/peXdQoew3/bN9reFz/E0hzr+HpX+69htfu3430z37w+W5t2+e7Vat7B/I3bqPgr50ARDVvwJ75KwDInLOEuIEnVfQuSuDcAXxrrW0FfOt+Lk+utbaj+zrLZ/ojwJPW2qOAdODqA21QFdD9sNYuBz4HbgfuAd4CJhhjFhhjFhljzgYwxjQzxvxgjFnovk5yp/d0p38GVPottB2ZuSTVjin5XL92DDuySjeP2Lgri42pmQx76Rsuf2Ea81ZvdadnUisqnDHvzOHCZ77iiWkL8VSzClV5IpISKNi6q+RzwdZUIpIS9k2TkrrfNGVFtWgA1nLMO3fT/pspJI8YHNB8B0NEUmLpsklJJbx+6cpieFIChcVpPF48WdmExpf+0RM36CRyl6/DFhSVmh4aW4PafY8na97SitmBCuCUyd5joTAllfAyx0J42eMlJZWIJKfcwuvEUbgj3Vl2RzrhdeL22Ubdi/qy+7uFTpptaWx7/lOOW/AiHRe9iiczm8w5SwK9WwET7PLJ/WMTtU5oS2h8LUKiIojr3YWIBnUCvVsHraLK4++st0bHVpjw8JJmthvGPUPrN+/muF9fIvG8nqQ8XbqZa2WJSE6gIMXnPLMtlYjk8s7BPueZzBzC4mvtf1lrafXORNp89Th1Lu1fkmbzxFdodNcVdFjwMo3uvoK/Hn6z4nZOKlVEUgL5FXB9ry7Cy1zDnfPEvtfwgnKu4eFJiXuv7UBByq59lo0/qwfpn84p+Zy7ajO1T3Mq7/FnnFSlzsWVxRvE1yE6G/iP+/4/wOC/u6AxxgC9gQ//yfKqgB7YfcAlwEAgCphlre0G9AIeM8bUAHYA/ay1nYELgX/7LN8ZGGWtbR3cbB8cj9fLptQsXr6qH5Mv6M6kT34mM7cAj9eyaONOxgzoxNvXD+Cv9D18tmjfvhTiMGGh1OrWhjU3TmXF4DtJGHACsd3bV3a2gi6qdWMajB/KpvHPlp4RGkKzp8ay87UvKNi0vXIyVxXY0s1map3UjjoX92XzQ86P4tDaNYg7rRtL/+96lnS+mpCYKBLPPbUyclo5DlA+eWu2kPLMxxz9zr20fvseclasxx4GN8b8sn+vmVV4vXia/3sU68c8VbJM0rVnsery+1nS9Vp2vT+LJvdeWZE5rXR/nDue3weOZfXlk6g3bCA1T2gLQN2hA9h836ss7XYNmye+SrMpN1ZyTkWqHxMeRly/bqR/Oa9k2sZx/6bu0IEc8+XjhNSIxhZW3bEKZB/1rbUp7vttQH0/6aKMMb8aY+YbYwa70xKBDGttcZRhC9DwQBus/m0oK5i1NtsY8z6wB7gAONMYU9yhJApoAmwFnjbGdAQ8gG9lc4G1dn156zbGDAeGAzx17Zlc3bdreckCpl5sNNt255R83r47h3puk6xi9WvH0K5RHcJDQ2gYX5OmdWqxKTWL+rVjODo5nkYJTnSrV5tGLN28i3O6VGiWK0T9KwZQ79J+AOxZvKbUXbqIBokUlGniWLAtrVSTyPLSlFWQsous+SspSssCIGPWQmq0b0Hm3GWB2o0KV7AttXTZJCdSuD21VJrCbWmEN6hD4bZUCA0htFYNPOnOPocnJdL8xfFsHD2Vgo2lBztpMnkkeRtS2PnK5xW/I4eo3rCB1HWPl+zFa4hosPdYCE9O3KdJbGHZ4yU5kYJtTrkV7sogvF68E82qF09h6t4+2NFtmtLssZGsuvz+kjKM7XEc+Zu2U5SWCUD61/Op2fVoUj/+vmJ29iBUZvkA7HrvW3a99y0ADe+4tFQ0ozIEozyc72b56w2pGU2rNybw1yNvlzTrDUuIJbptM7IXOYPtpH02l9Zv3xPoXT8oBSlpRCT7nGeSEilIKe8cXIfCFPc8ExtDUXrWfpctLo+i1N1kTPuZGh1bsefnlSSe34vN97wMQPoX82j22EH1qpEqqv4VA0p9/yIb1GGPOy9Q1/fqorDMNdw5T+x7DY8o5xpeuC2V8FLX/zqllo3t1Zmc5Wsp2rX3HJ2/9i/WXDoRwOnL3qdif9NWB4faN/Of8K1TuF601r7oM38mUN4gCRN8P1hrrTHGX8abWmv/Msa0AGYZY5YBu/2k3S9FQP+e4gi3Ac7zaf/cxFr7OzAa2A4cB3QFfDvfZvtbqbX2RWttV2tt14qufAIc2zCRTalZ/JW+h8IiD98s28ipx5Qe7KFXm8b8ut6JSKVn57FxVxaNEmpybMMEsnILSMvOA2DBuu20qFe7wvNcEba/Po1l/cayrN9Y0qctoO75PQGo2bk1nsyckiZvxQp3pOPJyqVmZ+e+Qt3ze5L+zYL9biNj9mJi2jQlJNrpuxd7YltyV22pkP2pKDlLVhPZPJmIxvUw4WHEn9mD3TNK7/fuGQtIPL83AHGDTibrR6c5bWhsDVq+fjdbJ79B9q9/lFomedylhNaK4a+JLwdnRw7Rjv98zYr+Y1jRfwzp3/xM4vm9AKhxgOOlhnu8JJ7fiwz3eMmY/guJQ5zlE4fsnR7RoA5HvXQ760dNJX/d3j5HBX/tpGbn1k4fUCC2ewdyV1et46gyywco6eMY0aAO8QP/j7T/zaEyBaM8Mqb/Uu56TXgYrV65g9QPZ5P+5U8l2yjavYfQ2BgiWzQAIPaU46rMcZS9ZDVRPueZhLO7k1HmPJMxY0FJOcSffhJZ85aVTE84uzsmIoyIxvWIap5M9uLVhERHEuIOkhcSHUnsKR3J/XMTAIXb06h1YjsAap3cgbz1KcjhY/vr01jebyzL3et7nX94fa/zN67v1UX2ktVENvO5hp+17zU8Y8YCEtxrePzpJ5d0idk9YwHxZ/Uo+W5FNksuNVp0/NmnkPbpD6XWVXwuxhiSbr6AXW9V2zE7qyXfOoX7erHM/L7W2nblvD4FthtjkgHcvzv8bOMv9+86YDbQCUgF4nzGumkE/HWg/Br7N5v0HMmMMRNxIqAJQCxwk3uHoJO1dpEx5klgi7X2cWPMlcCrzmzTExhnrT3jQNvI/e+koPxH/LDqLx776je8XsvZnVtybc92PPvtEto2SKRnm0ZYa3l82kJ+XL2VEGO45tR2DOjQDICf1qTwxLSFWGtp0zCRe87qRnhYaIXnecktiyt0/c0eupa4np1KhmkvHmq9/YzHWdZvLAA1OrTcO0z7dwvZMMGpPMUPOIFmD1xDeGIsRZnZ5KxYzx+X3A9AnXNPocFN54KFjFm/semBwPc1igyr2JF1Y3t1oeG9VzuPYXn/W7Y//QFJYy4hZ9kaMmcswESG03TqaGKObUFRRhYbbpxCwabt1L9pCPVHnk/++r2VhbWXTcSEh9Fuwavkrd6Mt8BpnrPrP1+R+t6MCsl/kSfw99iaPDic2u7xsn7MU+S4x8ux059gRf8xAMR0aLn3sRrfLWTTXS8BEBpfi6OeH0dEwzrkb9npPFYjYw/NHhtB/KATSwZzsEUeVg66FYAGYy8i4ayTsUVeclasY8O4Z/bpT1uVBLt8jvn4QcLia2GLith032tkVbFWBhVRHv7Wm3juqTR74kbyVu0dXG7d6H+Tu2IDcQNOoOG4i8F6KcrIZsPYp8k/yObv/m+OH5zavbvQeOJVEBJK6vszSXnqQxqMu5jsJWvYPeMXTGQ4zf91CzHtWuDJyGLtiMdLmu4n33Q+iRf2BY+HTRNfIfO7hUQ0qc9RLztjaJjQUNI+mUPKU073pJrHt6HxfddgwkLw5hey6c4XyFkWuMdrHLf4iYCt61Dceu9kflm0lIyMTBIT4hhx9eWcd+ZplZ0tFnYYd+BEAdTsoWtLvifrfK7v7WY8znKf63sLn+v7xjLX97DEWDyZ2WSvWM+f7vW9IoSHBv56HturC40m7r2Gb3vqA5LHXkLO0jXsdq/hzaaOJtr9bq0fOaXku5V00xASL+yDLfKyZeLLZM52+t6HREfS7ueXWX7ydXiz9raqq3vVGdQdNgiAjK/nB/wRa503f2oCusIguLLZeUGrZL224aODLh9jzGNAqrV2sjHmDiDBWntbmTTxQI61Nt8YUwf4CTjbWrvSGPMB8JG19j1jzPPAUmvts/tsyHd9qoAemE8F9BlgKnASTvR4vbX2DGNMK+AjwALTgJHW2ppVsQJaHVV0BbQ6q+gKaHVXERVQkSNdoCugh5OqUgGtqoJdAa1OKqICejipjhXQYUGsgP7n0CqgicB/cboVbgQusNamGWO6Atdba69xB1h9AadFaAgw1Vr7irt8C+A9nEDdIuAya23+/rapPqB/g7V2os/HfR7kZq1dDXTwmXS7O302TohaRERERESkSrHWpgJ9ypn+K3CN+/5HoNzRNN0mud3Km+ePKqAiIiIiIiIB5FUrU7/UPk1ERERERESCQhFQERERERGRAFL80z9FQEVERERERCQoFAEVEREREREJIK9ioH4pAioiIiIiIiJBoQioiIiIiIhIAFlFQP1SBFRERERERESCQhFQERERERGRAPJWdgaqMEVARUREREREJCgUARUREREREQkgjYLrnyKgIiIiIiIiEhSKgIqIiIiIiASQRsH1TxFQERERERERCQpVQEVERERERCQo1ARXREREREQkgPQYFv8UARUREREREZGgUARUREREREQkgKzVIET+KAIqIiIiIiIiQaEIqIiIiIiISAB59RgWvxQBFRERERERkaBQBFRERERERCSANAquf6qAVhErxiys7CxUWYVeHab+7CyIrOwsVGl9p7So7CxIdRQeUdk5qNKOv/nrys5ClfVKh3GVnYUqrfPSKZWdhSrryi46dvbnrcrOgASUftmLiIiIiIgEkFUfUL/UB1RERERERESCQhFQERERERGRANIouP4pAioiIiIiIiJBoQioiIiIiIhIAFmrCKg/ioCKiIiIiIhIUCgCKiIiIiIiEkB6Dqh/ioCKiIiIiIhIUCgCKiIiIiIiEkB6Dqh/ioCKiIiIiIhIUKgCKiIiIiIiIkGhJrgiIiIiIiIB5FUTXL8UARUREREREZGgUARUREREREQkgKxVBNQfRUBFREREREQkKBQBFRERERERCSD1AfVPEVAREREREREJCkVARUREREREAsgqAuqXIqAiIiIiIiISFIqAioiIiIiIBJBXo+D6pQioiIiIiIiIBIUioIep2J6daHLfNRAawq53Z7DtmY9LzTcRYTSfegsxHVpSlJ7FuhumULBlBwBJI8+jzsV9weNl0z0vkfn9YgBCY2vQ9LGRRB/dBKxlw9inyV74JwD1rjydusMGgsfL7lm/seXB/wR1fwOpxQNXkdCnE97cAv4c9TTZy9bvk6Zmhxa0/tdIQqIiSPt2EevuerXU/IbXn0mLicP4qe2VFKVlBSvrFe7YB4ZRv09HPLkFLB71HLuXbdgnzTF3XECjIacQHleDr1teWTK9xXWDaHJpL2yRl/zUTJaMfoHcLbuCmPuKM2/NNh79ZjFeazmnU3OuOvmYfdJ8s2IzL8xZCRha16/N5HNPYGtGNmM++AmvtRR5LBd3a8mQLi2DvwMVTOXj37zVW3n0q9+csunckqtOOXafNN8s38gL3y0DDK2T4pg85GQAUjKyue/Tn9m+Owdj4KnLetIwvmaQ96DijX9wDD36nEhebj4Tbr6f35f9WWp+TI0Y3vjs+ZLP9ZPr8cVH03jk7qncNmkU3U7uAkBUdBQJdeI5qXW/oOa/IjW9/2rienfGm5vP2tFPk7Ns3T5pYtq3oOXUmwiJiiBj1kI23v0KAAlnnEjDsRcS3aoRKwbdTvbStcHOfqW566EnmDNvAQnxcXzy1vMHXuAwdPnEq+nYqzP5ufm8OO5pNizf99iZ8N4k4urFU5BXAMAjl08iM3U3vS/tT7+hA/F6vOTl5PHK+OfYunpLsHehylP807/DqgJqjNljrf1bV19jTE+gwFr7o/v5eiDHWvuGMeYKYLq1dus/3P4GoKu1tnJ/VYeE0OSB61h1yb0UpqTS5svHyJi+gDyfk0Odi/pRtHsPy7vfQPxZ3Wl051DWjZhCVKtGJJzdnRW9byK8fgKt353E8lNGgNdL4/uuJnP2QtZd9ygmPIyQ6EgAap3Ujrj+3VjZ/xZsQRFhibUra88PWXyfTkS3SObXE2+iVudWHPXIcJYMGr9PuqMeuZbVY58na+Fqjn1nAvG9O5E+axEAEQ0SiT/1OPK27Ax29itUvT4dqdkiiVknjiau81G0f+Rq5g66e59026YvZP2r0+n905Olpu9evoEfTpuAJ7eApsP60ubuS1h43b+Dlf0K4/FaHp62iOcv7UH92BgufflbTm3dgJZ1Y0vSbEzN4tV5f/L6Fb2IjY4gLTsPgLq1onnjyl5EhIWSU1DEec9P59TWDahXK7qydifgVD7+ebxeHv7iV54f1pv6sdFc+sI3nHpMI1rW23sO3ZiayatzVvL6Nf2dstmTVzLvro9/4ppTjuXEo5LJyS/EGFMZu1GhevQ5kSbNGzPo/4bQocux3P3obVwy8OpSaXKyczi/z9CSz+9Pf52ZX84G4NF7/lUy/ZKrh9Cmfeug5DsYavfuTFTzZJacPJKanVvT/OHhrDjjjn3SNZ98HetvfY49C1dx9Ft3UbtXJ3Z/t4icPzax+ppHaf7I9ZWQ+8o1eFA/LjnvLO68f0plZ6VSHNerM0nNkxl76khadmrNFQ8MZ+LgfY8dgGdHTWX9stI3J3769AdmvT0dgM59j+eyu67k0WH3V3i+5fBxJDfB7QmcVPzBWvu8tfYN9+MVQINKyFNA1OjYivwNKRRs2o4tLCLt07nE9T+hVJq4/t1I/eA7ANK//JFa3Tu4008g7dO52IIiCjbvIH9DCjU6tiK0Vgy1TjiWXe/OBMAWFuHJzAag7uUDSXnmI2xBEQBFqbuDtasBl3ja8ez472wAshauJiw2hvB6caXShNeLI7RmDFkLVwOw47+zSRxwfMn8lpOuYP39b8Jh1vY/6bQubP7vDwBkLFxDeGwMkWXKpnhe/o6MfaanzluJJ9e5i5r+2xqikxMqMrtBs3xrGo3ja9IovibhoSGcdmxjZv9Z+t7Vx4vWc+HxLYmNjgAgoUYUAOGhIUSEhQJQUOTBHmbHDKh89mf5llQaJ9SkUUJNwsNCOa19U2b/UTqK8PGva7nwhFZ7y6amUzZrd+zG47WceFQyADGR4URHHFb3lAHoNeAUPvvgKwCW/raCWrE1qVMv0W/6pi0ak1gnnt/mL95n3qBz+vHVxzMqKqtBF39aN3Z9OBuAPQtXEVq7BuH14kulCa8XT2itaPYsXAXArg9nEz/A+T2Qt+Yv8tb+o/vsh42uHdtTO7ZWZWej0nTp1425H80GYO2iVdSIrUFcmWNnf3L35Ja8j4yJ1GivfnixQXtVN4ff1aoMY8yZwF1ABJAKXApEA9cDHmPMZcBNQB9gD7AB6Aq8bYzJBU4EfseNbBpjugJTrLU9jTGJwLtAQ+AnwPhs9zLgZne7PwMjrLWeit9jiEhOoCBlbxC2YFsqNTu1Kp0mySeNx4snM4ew+FpEJCeUXKiKl41ITsCbV0BR2m6aPXEzMW2bkb1sLZvveRlvbj5RLRpQ64S2NLz9Mmx+AZvvf52cJWuCsasBF5GcSP7W1JLPBSlpRCYnUuhToYpMTiQ/ZW+a/JQ0IpKdH0QJpx1Pfkoa2Ss3Bi3PwRKVnECeT9nkpqQRlZxQbmXzQJpc0pMds5YEMHeVZ0dmLkmxeyNy9WOjWfZXWqk0G1P3ADDste/wWsv1p7Tl5KOSANi2O4eb3pvH5rQ93NK3/WET3Sum8vFvR1YuSbVrlHyuHxvDsjLN0jemOk34h7003SmbXu05uVUDNqZmUisqnDHvzuGv9GxOaJnEqH7HERpyeN1Xrp9cl21/7Sj5vD1lB/WT67JrR2q56QcO7se0T2fuMz25URINmzTg57m/Vlhegy0iKYH8rT7X+q2pRCQlULgjvVSaAp/rVXEaObLFJyWQ6nPspG1LJb5+Ahk+x06x4VNuxOvx8su0+Xzy7w9KpvcdOoCB15xFWHgYD118b1DyLYePw+tKVb65wP9ZazsB7wG3WWs3AM8DT1prO1prfyhObK39EPgVuNSdl1veSl33AnOttccC/wOaABhj2gAXAidbazsCHpyKb7VlwkKIadeSnW9+zcoBY/Dm5JE08jxnXmgIoXG1+OPM29jywH9o+dytlZzbyhESHUHjUeey8dH3KzsrVVrD87oTd1wL1j77eWVnJWg81sumtD28PPRUJp9zApO+/I1Mt09NUu0YPriuH5/dOIDPl24k1aeJ5ZFC5eOfx+tlU1oWL1/Vl8lDTmbSpwvIzC3A47Us2riTMad15u3rTuOv9D18tmjf/upHmoGD+/HV/6aXO336F9/h9XorIVci1dOzo6Yy/rTR3D9kAkcf34bu5/YsmTfzjWmMPWUE701+k8E3nV95mazCFAH177CPgAKNgPeNMck40chAXqFPAc4FsNZ+aYwpvnXUB+gC/OL2yYkGdpRd2BgzHBgOMD7uOM6t0SwgmSpISSMiuU7J54ikRApSSkccCrY5aQpTUiE0hNDYGIrSs/wuW5CSSkFKKtmLnGan6V/+RNLIc911pZLx9U8AZC9ejfVawhJiKUrLDMj+VLTkKweQdGkfALIWryWywd7mXRHJCaWinQD5KalEJu9NE5ns3GGOappEVJN6dJ41xZ2eSKfpj7J44HgKd2ZU/I5UgGZX9qPJpb0ByFi8jiifsolOTiCvzHF1IHV6tKPVqMH8eO4kvG6T7equXmw02zL33qfanpm7T5Sufq0Y2jVMIDw0hIbxNWiaUJNNaXto12BvJKJerWiOqlubhZt20a9to6Dlv6KpfPyrVyuabbuzSz5vz8yhXmxMqTT1Y2No1yjRLZuaNE2sxaa0LOrHxnB0UjyNEpxhD3od04ilW3ZxDtV/kKaLrjyP8y87G4Dli38nqWG9knn1k+uxPaX8/vVHtz2K0LBQVi79c595Awf35cE7qn9/v/pXDKDupc4gStmL1xDZoA573HkRDRIp2Fbetd7nmlZOGjky9B06gF4XOcfOuqVrSGyw97deQlIi6dv3PS6Kp+Vl5/Hjpz/QouNRzP14dqk08z+by5UPDK+4jMth6UiIgD4FPG2tbQ9cB0QdxDqK2FtWf2d5A/zHjaB2tNYeba2dWDaRtfZFa21Xa23XQFU+AbKXrCaqeTIRjethwsNIOLs7GTMWlEqTMWMBiUN6ARB/+klkzVtWMj3h7O6YiDAiGtcjqnky2YtXU7Qzg4Ktu4hs4XSNje3egbzVm51lpv1MrZPaAxDZvAEhEWHVpvIJkPLaNBb1vZVFfW8lddoC6l3QE4BanVvhycop1fwWoHBHBp49OdTq7DRrrndBT1K/+YWcPzbxc7ur+eX4Efxy/AjyU1JZ1P+2alv5BNjw2gzm9B3PnL7j2TbtVxpf0AOAuM5HUZiV84+a38a2a0aHx67hl2FTKNhVfY6PAzm2QTyb0vbwV3o2hR4v36zYzKmtk0ul6XV0A37d6PxoTs/JZ2PaHhrF1WB7Zg55hU7L/MzcAhZt3kWzxMOrX5LKx79jGyayKS2Lv9L3UFjk4ZtlGzn1mIal0vRq04hfNzj3L9Oz89iYmkWj+Joc2zCBrLyCkgGbFqzfTou61XcAOF/vvfYR5/cZyvl9hjLr6+85a8ggADp0OZY9WXv8N789tz9flxP9bH5UU2Jrx7L412UVmu9g2P76NJb3G8vyfmNJn7aAOuf3BKBm59Z4MnNKNb8FKNyRjicrl5qdncGX6pzfk/RvFpRdrRwBZr4xjQmDxjJh0Fh+m76A7uf1BKBlp9bkZOXs0/w2JDSEmvHO+TY0LJROfbqy5c9NANRvtvcc3rF3F7ZtSAnOTshh40iIgNYG/nLfD/OZngXE7pu8ZJ7vr5wNOBHNr4HzfKbPAS4BHjDGDASKe3B/C3xqjHnSWrvDGJMA1LLWBqdjoMfLprtfovXb90JIKKnvzyRv1WYajLuY7CVr2D3jF3a9N5Pm/7qFdnOfw5ORxdoRjwOQt2oz6Z/P49hZT4PHw8a7XgS3ydKmu1+ixVNjMBFh5G/czoaxzgimu97/lmaP38ixM/+Ft7CI9bf8y2/Wqrr0mQtJ6NOZrvOfxpubz6pbni2Z12nmYyzq6zQvXnPHyyWPYUmftYj0bxdVVpaDZsfMRdTr05He86fiyc1n8S0vlMw7ZebDzOnrjBbc5u5LaHjOSYRGR9B34dNseuc7Vk35iLb3XEJYjSi6vDQKgNy/UvllWPWPSISFhHDHgI7c8M4PeK3l7OOacVS92jw7ewVtk+PpeXQDTmpZn5/Wbefc574hxBhG9+lAXEwkP63bzhMz5mFwhmsfemJrWtU/PCoRxVQ+/oWFhnDH6V254Y3v8HotZ3duwVH14nj226W0bZhAz2MacdJRyfy0JoVzn/rCKZvTOhIX44xAPvq0Tlz3+iystbRpkMB5h9kjagDmzPyRHn1O4uufPyQ3N4+7Rz1QMu/Db98oNfrtaWf1YcQlY/ZZx8DB/fj608Nn8KFiGd/+Rlyfzhz347N4c/NZN/rpknntZjzO8n5jAdgw/kVaFD+G5buF7J61EID4ASfQ7IFrCEuM5eg3J5C9Yj1/XnJkjGR6672T+WXRUjIyMukz+DJGXH055515WmVnK2gWz/qN43p15vE5z1LgPoal2INfPc6EQWMJjwjn9jfvITQslJDQEFbMXcp37kCU/YcN5NjuHfAUesjO3MMLY56qrF2p0g63gfMCyRxOhWOM8QK+Q7o9AawFngTSgVnA8e4AQq2BDwEvPoMQWWunGGPOAx4Cigch6gq8AmQCs3EGJCo7CNGPQH+giztY0YXAeJzIaSEw0lo731/ef200+PD5jwiw3KIj4T7JwckgvLKzUKX1ndKisrMg1VF4RGXnoEo7/uavKzsLVdYrodV2AP2g6Ly0+t90rChXdhlX2Vmo0t7a+HG1e87U/zXoGbTf9vO3zq5W5XNY/bK31vprUvxpOWlXAR18JvkORPQR8FGZefs8PMxam4pT6SwvL+8DGo1GREREROQIUx0HBwqWI6EPqIiIiIiIiFQBh1UEVEREREREpLJZRUD9UgRUREREREREgkIRUBERERERkQA6nAZ6DTRFQEVERERERCQoVAEVEREREREJIC82aK9DYYxJMMbMMMasdv/Gl5OmlzFmsc8rzxgz2J33ujFmvc+8jgfapiqgIiIiIiIiR6Y7gG+tta2Ab93PpVhrv7PWdrTWdgR6AznAdJ8ktxbPt9YuPtAG1QdUREREREQkgKpRH9CzgZ7u+/8As4Hb95P+fOBra23OwW5QEVAREREREZEjU31rbYr7fhtQ/wDpLwLeLTPtQWPMUmPMk8aYyANtUBFQERERERGRADrUvpn/hDFmODDcZ9KL1toXfebPBJLKWXSC7wdrrTXG+M24MSYZaA984zN5PE7FNQJ4ESd6Oml/+VUFVEREREREpJpyK5sv7md+X3/zjDHbjTHJ1toUt4K5Yz+bugD4n7W20GfdxdHTfGPMa8C4A+VXTXBFREREREQCyAbx3yH6DBjmvh8GfLqftBdTpvmtW2nFGGOAwcDyA21QFVAREREREZEj02SgnzFmNdDX/Ywxpqsx5uXiRMaYZkBj4Psyy79tjFkGLAPqAA8caINqgisiIiIiInIEstamAn3Kmf4rcI3P5w1Aw3LS9f6n21QFVEREREREJIC81ecxLEGnJrgiIiIiIiISFIqAioiIiIiIBFAABgc6bCkCKiIiIiIiIkGhCKiIiIiIiEgAqQ+of4qAioiIiIiISFAoAioiIiIiIhJA6gPqnyKgIiIiIiIiEhSKgEqVF2p0B8mfhmE5lZ2FKu3qCSsrOwtVlu7M+lfPRFV2Fqq02R0jKzsLVdamlZ7KzkKVdmWXcZWdhSrrtd+mVHYWJMDUB9Q/RUBFREREREQkKBQBFRERERERCSC1NPJPEVAREREREREJCkVARUREREREAkh9QP1TBFRERERERESCQhFQERERERGRAFIfUP8UARUREREREZGgUAVUREREREREgkJNcEVERERERALIWm9lZ6HKUgRUREREREREgkIRUBERERERkQDyahAivxQBFRERERERkaBQBFRERERERCSArFUE1B9FQEVERERERCQoFAEVEREREREJIPUB9U8RUBEREREREQkKRUBFREREREQCSH1A/VMEVERERERERIJCEVAREREREZEA8ioC6pcioCIiIiIiIhIUioCKiIiIiIgEkNUouH4pAioiIiIiIiJBoQjoYSq2Zyea3HcNhIaw690ZbHvm41LzTUQYzafeQkyHlhSlZ7HuhikUbNkBQNLI86hzcV/weNl0z0tkfr8YgNDYGjR9bCTRRzcBa9kw9mmyF/5J/Okn0WDMRUS1asTvZ9xKztK1wd7dQ9L8/quI69MZb24Ba255iuxl6/dJU6NDC46aeiMhURFkfLuQ9Xe/CkDTu4cS378rtqCIvI3bWHPL03gycwCIadOUlo9eR2itGKzXy9KBt2PzC4O6b4cqtmcnGk28FkJDSH13Btuf/ajUfBMRRrOpo4lu3xJPehbrRzxGwZYdhMbVosULtxNz3FGkfjCLLXe/WLJMq/8+QHi9BLx5+QCsuXQiRam7g7pfFWHoxKvp2KsLBbn5PD/uKTYsX7dPmrveu5+4evEU5BUAMPny+8j02ffjB/4fo5+/nQlnjGP9sur1PTqQYROvKSmf58b9u9zyufu9B0qVz8OXTyxVPt0GnuiWz1jWHUblc+69w2jbqxOFufm8Pe45tqzY4DftNS+No06T+kw+7dZS03tdczqD77qcOztdS3Z6VgXnOHjCu3ajxvU3YUJDyPv6S3L/+06p+VHnXkDUgNPB48G7O4M9TzyCd8d2QurVJ/aeByDEQFgYeZ9+TN6Xn1XSXhyagz0PA9QfeR6JF/UDj5fN975E1veLiGzRkObPjitZPrJJElsff4edr3xOdJtmNH74BkJrRFGweQfrb34C757coO5vIF0+8Wo69upMfm4+L457utzzzoT3JpU67zxy+SQyU3fT+9L+9Bs6EK/HS15OHq+Mf46tq7cEexeC7q6HnmDOvAUkxMfxyVvPV3Z2qjWNgutftamAGmP2WGtrVuD6vwIucT9eYq199h8u3xMYZ609I8BZ++dCQmjywHWsuuReClNSafPlY2RMX0Cez4mzzkX9KNq9h+XdbyD+rO40unMo60ZMIapVIxLO7s6K3jcRXj+B1u9OYvkpI8DrpfF9V5M5eyHrrnsUEx5GSHQkALl/bmLNtZNp9siIytrjgxbXuzNRLZJZdNKN1OzcihaTh7Ps9PH7pGsxeThrxz3HnoWrafP2BOJ6dyJj1iIy5ixh40NvgcdL0wmX0eimc9n44FsQGkKrp0ex+qZ/kbNyI2HxNbGFnkrYw0MQEkLjB65jtXscHf3FFHbPWEDe6s0lSRIv6kdRxh5W9rie+LN60PDOYawf8Rg2v4CtU94m+uimRB3dZJ9Vb7j5CXKWrgnm3lSojr06k9S8AWNOHcFRnVpz1QPXcc/g28tN+8yoJ8utXEbViGLAlWeweuGfFZ3doOvYqwtJzZMZfeoNHNWpNVc/cD13D76t3LTPjHqi3Mrl4Vo+bXt2pG7zZB7oeQtNOx3FkAev4cnBd5WbtsNpx1OQk7/P9LjkRI4+pQNpW3ZWdHaDKySEmiNvYff4sXh37STuqRcomD8Pz6aNJUk8a1eTcdNwyM8n6oyzqXHN9WQ9dB/etFQyRo+AwkKIiib+hdco+Gke3rTUStyhg3AI5+GoVo2JP6sHv/e5kfD6CbR6dxIrThlB/rq/+GPA6JL1t//lVXZPmw9Ak8du5K8HXmPP/BUkXtiH+tefQ8qUd8rLWZV3XK/OJDVPZuypI2nZqTVXPDCciYPvKDfts6Om7nNe/unTH5j19nQAOvc9nsvuupJHh91f4fmubIMH9eOS887izvunVHZW5DCmJrgua+0ga20GEAdUv5qUjxodW5G/IYWCTduxhUWkfTqXuP4nlEoT178bqR98B0D6lz9Sq3sHd/oJpH06F1tQRMHmHeRvSKFGx1aE1oqh1gnHsuvdmQDYwiI8mdkA5K3ZQv66rUHcw8BJGHA8Oz/4HoA9C1cTFluD8HpxpdKE14sjtFYMexauBmDnB9+TMKAbALu/XwIeLwBZC1cR0SARgLhTO5Lz+wZyVjo/lIrS94DXG4xdChjnONpWchylf/YDtft3K5Umrv8JpH04C4D0L+dR62TnOPLm5pP9y+948wuCnu/K0KVfN374yPk+rVm0ipjYGsTVi/9H6xgy9hI+f/5/FFazKPnf4ZTPbODgy+eCsZfy+fMfH3bl065/V375eA4AGxetIbpWDLF14/ZJFxETSa9rTuebpz7eZ945dw/ls4ffPux6G4Ud3QbP1r/wbkuBoiLyZ88i4sTupdIULlkE+U6lvPD3lYTUqevMKCpyKp+ACQ+HkOr5c+dQzsO1+3cj/bMffK7n26jRsVWpZWt170D+xm0U/OXcvIhq3oA981cAkDlnCXEDT6roXawwXfp1Y6573lm7aBU1/uF5J9cn8hsZE3nE9Ofr2rE9tWNrVXY25DBXPc/ILmNMR2PMfGPMUmPM/4wx8e702caYR4wxC4wxq4wxPdzpMcaY/xpjVrrpfzbGdHXnbTDG1AEmAy2NMYuNMY8ZY3oaY77w2ebTxpgr3PcDjDF/GGMWAuf6pKlhjHnV3f4iY8zZwSsViEhOoCBlV8nngm2pRCQnlE6T5JPG48WTmUNYfC2/y0Y0rk9R2m6aPXEzbac9QdPHRpZEQKuziKQE8rfu3d/8lFQikhNLp0lOpGBrauk0SaXLE6DeRX1In7UIgOiWyVgLbd69mw7TH6PBiKAeAgERnpRIgU/ZFKakEp6UWCZNwt40Hi+erGxC4w984Wr6+E0cM+1JkkZdENA8V5b4pETSfI6RtG2pxNff9xgBuG7KTTz01ROcc/OQkmnN2rUgsUEdFs/6rcLzWhkSkhJI9TmW0ralkuC3fG7m4a+e5Jyb9x4bzdq1IKFBHRYdhuUTVz+BDJ9jZ/e2NGqXc345feyFfPfylxTmlb6p065fF3ZvT2Pr75sqPK/BFpJYB+/OHSWfvbt2ElKnjt/0UQMGUfDLz3uXr1uXuOdeJeGtD8j97zvVL/rJoZ2Hw5MSKfRZtiBl1z7Lxp/Vg/RP55R8zl21mdqnOTes4884iYgG/su7qosv57zj77w8fMqNPPjV4wz2OS8D9B06gMfnPMtF44fyxr2vVGh+5fDjxQbtVd1U6woo8AZwu7W2A7AMuNdnXpi1thtwi8/0EUC6tbYtcDfQpZx13gGstdZ2tNbeWs58AIwxUcBLwJnuepJ8Zk8AZrnb7wU8ZoypcRD7V2WYsBBi2rVk55tfs3LAGLw5eSSNPK+ys1VlNBx1HtbjYddHzoXchIYS2+0YVo+cyvKzJ5A48ARqd29fybmsGjbc/AS/9xvFqvPupGa3tiSc16uysxQ0z4x6kjtOu4VJQ+7k6OPb0uPcnhhjuOyuK3nrgdcqO3uV7ulRT3D7aaO4b8h4jvEpn8vvuuqILp+GbZuS2KQeS7/5pdT08KgI+o08h6+e+G8l5azqiOzdj7BWR5P74Xsl07w7d5Jxw1WkX3kJkf0GYOL+WdT9cGfCw4jr1430L+eVTNs47t/UHTqQY758nJAa0djCw6vFQXmeHTWV8aeN5v4hEzj6+DZ0P7dnybyZb0xj7CkjeG/ymwy+6fzKy6TIYaba9AEtyxhTG4iz1n7vTvoP8IFPkuJ2Sr8Bzdz33YF/AVhrlxtjlh5CFo4B1ltrV7v5eQsY7s7rD5xljCnu5R8FNAF+L7MPw4uXGR93HOfWaEYgFKSkEZG8965lRFIiBSlppdNsc9IUpqRCaAihsTEUpWf5XbYgJZWClFSyFznNUNO//ImkkedSHSVdMYD6l/YFYM+SNUQ2qEPxkB2RyYkUpJS+S16QklrStLYkzba95Vn3gl4k9O3CigsmlkzLT0klc/5KitKcNafPWkiN9i3YPXdZxexUBSjcllrq7nd4ciKF21LLpEkjokEdZ3poCKG1auA5wAAohW7ZebNzSftkDjEdW5HmNl+tTvoNHUivi/oBsG7pGhJ8jpGEpETSt6fts0zxtLzsPH78dA4tO7bi1xkLaHx0E+5+7wEAateNY9wrdzLl6oeq9UBE/YYOpPdF/QFYt3Q1iT7HUkJSImkHKJ95ZcrnnlLlM4EpVz9YbQci6n55f068uDcAm5asJc7n2KmdlMDubaXLplnn1jTp0IJ75j5FaGgINRNrc+N79/DRva+R2Kgut339KABxSQnc+sXDPD54Alk7q//AXt7UXYTUrVfyOaROXby7du2TLrxTF6Ivvpzd424uaXZbaj1pqXg2rCe8XQcK5n6/z/yq7FDOw4XbUgn3WTYiuU6pZWN7dSZn+VqKdu09VvLX/sWaSycCENm8AbX7dK2gPasYfYcOKHVeLnveOfB5+QdadDyKuR/PLpVm/mdzufKB4fssK7I/GoTIv+oeAd2f4pEaPBxaRbuI0uUU9TeWMcB5bhS1o7W2ibX297KJrLUvWmu7Wmu7BqryCZC9ZDVRzZOJaFwPEx5GwtndyZixoFSajBkLSBziRJ7iTz+JrHnLSqYnnN0dExFGRON6RDVPJnvxaop2ZlCwdReRLRoAENu9Q6lBEKqTba9PY0m/cSzpN460rxdQd8ipANTs3IqirBwKd2SUSl+4IwNPVg41Ozt9Z+oOOZW0aU4kIq5XRxqOPJvfr5iMN3dv07iM2YuJadOUkOgICA0h9v+OJXdV9Sqv7CWriWy29ziKP6sHu8s5jhLOd35Ix59+MlnzDnBPJzRkbxPdsFBq9zmevD+rZ9PBGW98zZ2DxnDnoDH8Ov1neriR3KM6tSY3K4eMHeml0oeEhlDL3ffQsFA69enK5j83kZuVw3WdhjGq+3WM6n4daxatqvaVT3DKZ/yg0YwfNNotn56AUz45WdkHLJ/OfbqyxS2f4Z2GcnP34dzcfbhbPtW38gkw983pPDboDh4bdAfLpv/K8eeeAkDTTkeRl5VD5s6MUunnvTWDe04YwaTuN/GvIRPZuT6Fpy+aRMqfm7mr63VM6n4Tk7rfRMa2NB47Y/xhUfkEKPrzD0IbNiKkfhKEhRHZszcF8+eVShPashU1bx5L5r3jsbszSqaH1KkLEREAmJo1CT+2PZ4t1escDId2Ht49YwHxZ/UouZ5HNnOu58Xizz6FtE9/KLWusMTazhtjSLr5Ana9Na0C9y7wZr4xjQmDxjJh0Fh+m76A7u55p2Wn1uT4OS/XLHNe3uJek+o3Sy5J17F3F7ZtSAnOTogcAaptBNRau9sYk26M6WGt/QG4HDjQrc15wAXAd8aYtkB5bSKzAN9ObBuBtsaYSCAa6APMBf4AmhljWlpr1wIX+yzzDXCTMeYma601xnSy1i46mP08KB4vm+5+idZv3wshoaS+P5O8VZtpMO5ispesYfeMX9j13kya/+sW2s19Dk9GFmtHPA5A3qrNpH8+j2NnPQ0eDxvverFk8JxNd79Ei6fGYCLCyN+4nQ1j/w1A3IATaHL/tYQl1KbVf+4mZ8V6Vl92X9B291Ckf7uQuD6d6fzTM3hy81kz+pmSecfNmMKSfk4Qe934l2jlPoYlfdYiMmYtBKD5g9cQEhHOse/dAzgDEa27/UU8u7PZ+sLndPj6UbCW9G8Xkv7twuDv4KHweNl894sc9dZETGgIqe9/S96qzSSPvYScpWvYPWMBqe/NoNnU0bT94Xk8GVmsH7l31Lxjf3yR0FoxTjOv005gzaUTKdiyg1ZvTcSEh0FICFlzl7DrnemVuJOBsXjWb3Ts1YUn5zxHfm4+L4x7qmTeQ189wZ2DxhAeEc4db95LaFgoIaEhLJ+7lFnvzqjEXAfPIrd8ps553i2ff5fMe/irJxk/aLRbPhMJc8tn2dwlfHsElM/K7xbRtldH7v7+XxTk5vPOrXsfe3DrV5N5bFD5o3YeEbwe9jwzldoPTYGQEPKmf4Vn4wZihl5F0ao/KJj/IzWuvR4THU3sXc41x7NjB1kT7yS0SVNirx0BWMCQ8+H7eDbs+wiOKu8QzsN5qzaT8cU82s56GlvkZfNdL5Rcz0OiI4ntcRyb7ig94H/82T2oO2wQABlfzyf1/W+Du78BtHjWbxzXqzOPz3mWAvcxLMUe/OpxJgwaS3hEOLe/eU/JeXnF3KV85w622H/YQI7t3gFPoYfszD28MOYpf5s6rNx672R+WbSUjIxM+gy+jBFXX855Z55W2dmqlryKgPplqkt42BjjBXyHWn0CmAU8D8QA64ArrbXpxpjZOI9E+dUdWOhXa20ztx/mf4C2OBXIFsAQa+1qY8wGoKu1dpcx5h2gA/C1tfZWY8yjwDnAemAP8Jm19nVjzABgKpAD/AC0tNaeYYyJdqefhBM9XX+gx7P82mhw9fiPqAQFntDKzkKVFRVWVNlZqNKmVNORL4PhSBnR8WDUM3+nocuR6+6jFQnyZ9PKuMrOQpX2RIiu5/689psee7I/4XVamMrOwz+VUKtV0C60aVmrq1X5VJsIqLXW3y/J/ysnbU+f97vY2wc0D7jMWptnjGkJzMSJcGKtbeazTPHzQIs/3wbs89A6a+00nL6gZafnAtftb39EREREROTwVF2CfJWh2lRAAyQGp/ltOE4/zRHW2iPjQYUiIiIiIiKV7IiqgFprs4DqNaSbiIiIiIhUK9Xx+ZzBog5SIiIiIiIiEhRHVARURERERESkoqkPqH+KgIqIiIiIiEhQKAIqIiIiIiISQHoOqH+KgIqIiIiIiEhQKAIqIiIiIiISQFaj4PqlCKiIiIiIiIgEhSqgIiIiIiIiEhRqgisiIiIiIhJAGoTIP0VARUREREREJCgUARUREREREQkgqwioX4qAioiIiIiISFAoAioiIiIiIhJAegyLf4qAioiIiIiISFAoAioiIiIiIhJA6gPqnyKgIiIiIiIiEhSqgIqIiIiIiASQtTZor0NhjBlijFlhjPEaY7ruJ90AY8yfxpg1xpg7fKY3N8b87E5/3xgTcaBtqgIqIiIiIiJyZFoOnAvM8ZfAGBMKPAMMBNoCFxtj2rqzHwGetNYeBaQDVx9og6qAioiIiIiIBJAN4uuQ8mnt79baPw+QrBuwxlq7zlpbALwHnG2MMUBv4EM33X+AwQfapiqgIiIiIiIi4k9DYLPP5y3utEQgw1pbVGb6fmkU3Cqi65ZPTGXnwZcxZri19sXKzkdVpLLxr6qVzTuVnYEyqlr5VCUqm/1T+fhXlcqmTmVnoBxVqXzequwMlFGVyqaqUdkcuqKCv4L2294YMxwY7jPpRd//P2PMTCCpnEUnWGs/rej8laUIqPgz/MBJjlgqG/9UNvun8vFPZbN/Kh//VDb7p/LxT2Xjn8qmGrHWvmit7erzerHM/L7W2nblvP5u5fMvoLHP50butFQgzhgTVmb6fqkCKiIiIiIiIv78ArRyR7yNAC4CPrPOELzfAee76YYBB6zUqgIqIiIiIiJyBDLGnGOM2QKcCHxpjPnGnd7AGPMVgNvH80bgG+B34L/W2hXuKm4Hxhhj1uD0CX3lQNtUH1DxR+3+/VPZ+Key2T+Vj38qm/1T+finstk/lY9/Khv/VDZHCGvt/4D/lTN9KzDI5/NXwFflpFuHM0ru32YO9eGlIiIiIiIiIn+HmuCKiIiIiIhIUKgCKhhj9rh/mxljco0xi4wxvxtjFhhjrqjk7FUoY8xgY4w1xhxTQeu/syLWe6iMMR5jzGJjzApjzBJjzFhjTNDPB8aYjsaYQT6fzzLG3BHsfBwKn7JcYoxZaIw56RDWVSWPlwPZ3/FkjOlqjPn3fpZtZoy5ZD/zGxhjPvQ3301zizEm5uD3ILiMMU8aY27x+fyNMeZln8+PG2PGHMR6mxljlgcom8XrTDLGvGeMWWuM+c0Y85UxpnUgtxFIvsdL2fPLP1zPJ8aY+YHNXeAYx1xjzECfaUOMMdMqM18Vrfj3yt9M29P3fGyMud4YM9R9f4UxpsFBbH+DMaZKPG3nn5TFQa7/K2NMnPsacRDL9zTGfFEReZPqTxVQKWuttbaTtbYNzghXtxhjrqzsTFWgi4G57t9SfIaULvfz31RVKxS51tqO1tpjgX7AQODeSshHR0r3L/jMWju5EvJxKIrL8jhgPPDwIayrqh4vB+L3eLLW/mqtvXk/yzYDyq2AGmPCrLVbrbXnlzffxy1AtamAAvOAkwDcinod4Fif+ScBPx5oJQd5TvrbjDEGp1/QbGttS2ttF5xjvH5FbvdQlDleOuJzfvm7jDFxQBegtjGmhZ80lTqGhjvy5PXAE8aYKGNMTeAhYOTBrK+y96eC9MT9ngFYa5+31r7hfrwC+McV0COJtXaQtTYDiAP+cQVUZH9UARW/3E7FY4D9/XisttwLdnfgapzKdvEdux+MMZ8BK8v5HGqMecwY84sxZqkx5jp3uWRjzBw3CrTcGNPDGDMZiHanvV1Z+3kg1todOM/7utG9qx5ljHnNGLPMjYb3gpI7xp8YY2a4d4FvNMaMcdPMN8YkuOlaGmOmudGSH4wbXXbvzi93I2RzjDOM9yTgQreMLnS38bSbvr4x5n9u+iXmECKLQRQLpBd/MMbc6nOs3Ocz/RO3fFYY5+HRVJfj5UDKOZ5K7oIbY05192+xe9zUAiYDPdxpo91j4DNjzCzgW+MT1XO/f1Pc42ipMeYmY8zNOD8kvzPGfFdJu/1P/Ygz2iA4Fc/lQJYxJt4YEwm0Afq7x85yY8yLbmUQY8xsY8xUY8yvwChjTJfi7wgHWfnYj15AobX2+eIJ1tol1tof3P/bx9z8LTPGXOjmr6cx5ntjzKfGmHXGmMnGmEuN06JmmTGmpZvudWPMc+65Y5273KvGaX3zevH2jE+UxxhzfvE8d/l/G2N+dJc/353ezM1TeeeX1caYum66EGPMmuLPZZwLfA68h3tt8Nnm88aYn4FH93OuO9MY87N7jM80xlRIhd1au9zN5+3APcBbwAS3rBcZY872KZMfjNNCo6SVhilzfauIPAZDeeVtjGmGU0Ef7f7/9zDGTDTGjHOPla7A2+68aOMT2TROq43Z7vtEY8x045yrXwaMz3Yvc8t6sTHmBWNMaNB3vgzjRP3nu+fH/xlj4t3ps40xj7j5XWWM6eFOjzHG/NcYs9JN/7Mxpqs7r7hMJgMt3f18zJSJbBpjnjZuazljzABjzB/GmIU436PiNDXc73epY1OOYNZavY7wF7DH/dsMWF5mXhxOdKPS81kB+30p8Ir7/kecO949gWyguTu97OfhwF3u+0jgV6A5MBaY4E4PBWr5lm1Ve5WXLyADJ7IxFnjVnXYMsAmIwrljvAaoBdQFdgPXu+meBG5x338LtHLfnwDMct8vAxoWH1fu3yuAp33yUPIZeN9nnaFA7couNz9l6QEWA3+4ZdLFnd4fZxRBg3Oz7wvgFHdegvs3GqfykViVj5dDPJ56Al+40z4HTnbf18QZib1kvs8xsMWnjJrhnpeAG4APgbAy5bgBqFPZ5fAPy2w90AS4DueH8v040bqTgR+K981N+yZwpvt+NvCsz7ylPsfVY5Q5hx9iHm8GnvQz7zxghvvdrO+eJ5Ld/88M930kzgPJ73OXGQVMdd+/jlPBM8DZQCbQ3v2u/AZ0LHts4Txn7nWf5T9w07cF1pRzvFxB6fPLvew9p/QHPvKzbzOAHkBrYJnP9Ndxvseh7md/57p49g7yeA3weAUeRzWAP3HOrw8Dl7nT44BV7vwYIMqd3gr41X3fE5/rW3V4Uf65ptzyBiYC43zSlXx2v0ddfeZtwD2H4FROZ7vv/w3c474/HbA4LRba4JzPwt15zwJDq0BZLAVOdd9P8vm+zfYpl0HATPf9OOAF9307oKi4XIrLhDK/Ddn3nP20+12LAja7x5gB/svec/9D5R2blX086VV5r8OxyYUEljlwkmrrYuBf7vv33M9fAAustet90vl+7g90KL7bDtTGOdn+ArxqjAkHPrHWLq7ozFeg7sBTANbaP4wxG3F+iAF8Z63NwonW7Ma5AIPz46eDcaLKJwEfGFNy6ES6f+cBrxtj/gt8/Dfy0RsY6ubDg1O5q4pyrbUdAYwxJwJvGGPa4Rwr/YFFbrqaOMfKHOBmY8w57vTG7vTUYGa6kszDaTL4NvCxtXaLz3Hia4a1Nq2c6X2B563zPDL8pKkufsT5rpwEPAE0dN/vximnXsaY23AqDwnACvZ+396HkqaicdbaOe70N3GaPwdDd+Bd97u53RjzPXA8TkXyF2ttipvHtcB0d5llOFHVYp9ba60xZhmw3Vq7zF1mBc6P3sUHyMMn1lovTuuUvxNlfBXnAelTgauA18omcNfTCpjr5q3QGNPOOtFGgA+stZ4DnOsaAe8bY5KBCJybDRXCWpttjHkf2ANcAJxpjBnnzo7CucmxFXjaGNMR54aZbx/este76qgiy/sU3EietfZLY0xxC5c+ODetf3H//6OBHQHc7j9mjKmNcz743p30H5ybNMWKr7u/4Xy/wPke/wuciLoxZukhZOEYYL21drWbn7dwbtqDcy08q5xj8/dD2J5UY6qAyoF04jA8QRinuWhvoL0xxuLcxbfAlzh3hH35fjbATdbab8pZ5yk4d0hfN8Y8Yff2NanyjNPPycOBL6D5Pu+9Pp+9OOeTECCjuELmy1p7vTHmBJwy+s0Y0+VQ813VWGt/cpss1cU5Vh621r7gm8YY0xOnInWitTbHbeoVFeSsVqgyx1Ob4unW2snGmC9x7sDPM8ac5mcVZb+Dh6PifqDtcaLgm3FaH2TiVIxewolEbDbGTKT0MRKs8lmBE3X8pw50niibzlvOMsXpfJ8VV/Z74rvMAW+WumW53RjTG+eZdZeWk+wCnIjaerdiEYtzc3KCO7+47P2e63Bu4D1hrf3M/b5PPFDeDpHXfRngPGvtn74z3eNnO3AcTr7zfGYfDt+1QJR3EXu7pf2d87EB/mOtHX8Q26osxd8XD4f2+9+3rODvl9c+x6YcudQHVPxy+1BMwY2GHWbOB9601ja11jaz1jbGuWva4wDLfQPc4EY6Mca0dvs2NMW5g/8S8DLQ2U1fWJy2qjJOH6jncZqqWZzmf5e681rj3KX8WxcNa20mzg+3Ie7yxhhznPu+pbX2Z2vtPcBOnMhfFk6T3vJ8i9PksrjvX+2D3MWgMU4fsFCcaOY3wFVupARjTENjTD2cqHm6W/k8Bvg/n1VU+ePlQMo5nnzntbTWLrPWPoLTauAY9n8MlDUDuM64A6a4N5L4h+uoKn4EzgDSrLUeN5obh9M3tHgAol3u8VNuJdA6A4RkGGO6u5PKq1AdillApHH7KQMYYzq4/cd+wOlfGer+n58CLAjw9sGJrrYxzmBN5xwwdWnlHRcv4/SV/MCN3pZ1MTDAvS40w4lyXVQ20f7OdTjf8b/c98P+YZ4PxTfATcaU9Bfu5JOfFDdafDnOOepw4q+893deKDtvA87/NTjNy4vNwR0kzTgjDse7078FznfP6RhjEtzfAZXGWrsbSHe/n+D8X3+/n0XAuRF2AYAxpi3ODbGyypbVRqCtMSbSbYXRx53+B9DMuP28KT24o79jU45QqoBKWS3dDuK/47Tf/7e1dp9mSoeBi3FGd/T1EeWMhlvGyziDNSw0zsAoL7C3H9sSY8wi4EL2Nu19EVhqqt6gMsWD3awAZuI0kSseJOdZIMRtFvc+cIW1Nt/PespzKXC1cQZFWYHTvwvgMeMMQrIc5wf2EuA7nAvZYuMOYuJjFE4zxGU4TYba/vPdDIrislyMU17D3ArFdOAd4Cd3Hz7EuYhPA8Lc79hkwPdRD1X1eDmQ/R1Pvm4x7gBCQCHwNU6fJY9xBtIZfYDtvIzT13Cpe3wVj577IjDNVJ9BiMBpjlqH0v//y4Dd1tpdOBHQ5Tg/3H7Zz3quBJ5xj7+AdplwbyCcA/Q1zmNYVuD0M9yGc/5civM9ngXcZq3dFsjtu+7A6RrxI5DyD5ct7/zyGU5z+PKa3zYDmuLzf+I2T93ttt4oy9+5biJO09zfgF3/MM+H4n4gHOf7scL9DM45fZibz2Oo3lHPGGPMFp/XGPyX9+fAOe7/f9mby68Dz7vzonHOV/8yzuBevjcm7gNOccvzXJzzD9balcBdwHT3fDYDp99zMJVXFsNwrrVLcUaBnnSAdTwL1DXGrAQewDmOS3V3sdam4rRYWW6Mecxauxnn9+Fy9+8iN10eTpPbL40zCJFviyp/x6YcoUyZG9QiIiIihyXjjPD5pLX2QK1dRA57xhm5N9xam+dGLmcCR1trCyo5a3KYUx9QEREROewZY+7AadYf6KbKItVVDM4jrMJxWlCMUOVTgkERUBEREREREQkK9QEVERERERGRoFAFVERERERERIJCFVAREREREREJClVARUREREREJChUARUREREREZGgUAVUREREREREguL/AWGa/Tm6o4b0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,10))\n",
    "sns.heatmap(crimes.select_dtypes(include=np.number).corr(), vmin=-1.0, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "changed-nightlife",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(crimes.select_dtypes(exclude=\"object\").corr(), vmin=-1.0, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "veterinary-membrane",
   "metadata": {},
   "source": [
    "## Arrests and Domestic variable per community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "private-basketball",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Domestic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.134738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.136960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.107467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.093178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.059511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73.0</th>\n",
       "      <td>0.208531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.0</th>\n",
       "      <td>0.169510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>0.191537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.0</th>\n",
       "      <td>0.053750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77.0</th>\n",
       "      <td>0.100443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Domestic\n",
       "Community Area          \n",
       "1.0             0.134738\n",
       "2.0             0.136960\n",
       "3.0             0.107467\n",
       "4.0             0.093178\n",
       "5.0             0.059511\n",
       "...                  ...\n",
       "73.0            0.208531\n",
       "74.0            0.169510\n",
       "75.0            0.191537\n",
       "76.0            0.053750\n",
       "77.0            0.100443\n",
       "\n",
       "[77 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of domestic crimes by community\n",
    "domestic_rates_per_community = (crimes.pivot_table(values=\"Domestic\",index=\"Community Area\", aggfunc='sum')/\n",
    "                        crimes.pivot_table(values=\"Domestic\",index=\"Community Area\", aggfunc='count'))\n",
    "domestic_rates_per_community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "greek-revelation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Arrest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.134738</td>\n",
       "      <td>0.158704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.136960</td>\n",
       "      <td>0.120415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.107467</td>\n",
       "      <td>0.171420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.093178</td>\n",
       "      <td>0.124688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.059511</td>\n",
       "      <td>0.102930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73.0</th>\n",
       "      <td>0.208531</td>\n",
       "      <td>0.183730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.0</th>\n",
       "      <td>0.169510</td>\n",
       "      <td>0.124501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>0.191537</td>\n",
       "      <td>0.178720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.0</th>\n",
       "      <td>0.053750</td>\n",
       "      <td>0.254750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77.0</th>\n",
       "      <td>0.100443</td>\n",
       "      <td>0.129389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Domestic    Arrest\n",
       "Community Area                    \n",
       "1.0             0.134738  0.158704\n",
       "2.0             0.136960  0.120415\n",
       "3.0             0.107467  0.171420\n",
       "4.0             0.093178  0.124688\n",
       "5.0             0.059511  0.102930\n",
       "...                  ...       ...\n",
       "73.0            0.208531  0.183730\n",
       "74.0            0.169510  0.124501\n",
       "75.0            0.191537  0.178720\n",
       "76.0            0.053750  0.254750\n",
       "77.0            0.100443  0.129389\n",
       "\n",
       "[77 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates_per_community = pd.concat([domestic_rates_per_community,(\n",
    "                        crimes.pivot_table(values=\"Arrest\",index=\"Community Area\", aggfunc='sum')/\n",
    "                        crimes.pivot_table(values=\"Arrest\",index=\"Community Area\", aggfunc='count'))],axis=1)\n",
    "rates_per_community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "widespread-automation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Arrest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54.0</th>\n",
       "      <td>0.273601</td>\n",
       "      <td>0.205395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
       "      <td>0.252301</td>\n",
       "      <td>0.185314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68.0</th>\n",
       "      <td>0.248053</td>\n",
       "      <td>0.249098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.0</th>\n",
       "      <td>0.246696</td>\n",
       "      <td>0.228286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53.0</th>\n",
       "      <td>0.246694</td>\n",
       "      <td>0.203570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.0</th>\n",
       "      <td>0.053750</td>\n",
       "      <td>0.254750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.052037</td>\n",
       "      <td>0.137221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.045869</td>\n",
       "      <td>0.165863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.036429</td>\n",
       "      <td>0.075060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>0.028296</td>\n",
       "      <td>0.167593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Domestic    Arrest\n",
       "Community Area                    \n",
       "54.0            0.273601  0.205395\n",
       "43.0            0.252301  0.185314\n",
       "68.0            0.248053  0.249098\n",
       "71.0            0.246696  0.228286\n",
       "53.0            0.246694  0.203570\n",
       "...                  ...       ...\n",
       "76.0            0.053750  0.254750\n",
       "6.0             0.052037  0.137221\n",
       "8.0             0.045869  0.165863\n",
       "7.0             0.036429  0.075060\n",
       "32.0            0.028296  0.167593\n",
       "\n",
       "[77 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates_per_community.sort_values(by=\"Domestic\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "foreign-malpractice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Arrest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.163964</td>\n",
       "      <td>0.171577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.059347</td>\n",
       "      <td>0.062153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.028296</td>\n",
       "      <td>0.075060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.123730</td>\n",
       "      <td>0.124688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.177296</td>\n",
       "      <td>0.167516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.208765</td>\n",
       "      <td>0.203570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.273601</td>\n",
       "      <td>0.397570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Domestic     Arrest\n",
       "count  77.000000  77.000000\n",
       "mean    0.163964   0.171577\n",
       "std     0.059347   0.062153\n",
       "min     0.028296   0.075060\n",
       "25%     0.123730   0.124688\n",
       "50%     0.177296   0.167516\n",
       "75%     0.208765   0.203570\n",
       "max     0.273601   0.397570"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates_per_community.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "lesser-whale",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Domestic</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.155293</td>\n",
       "      <td>0.180610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.109913</td>\n",
       "      <td>0.186586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.168635</td>\n",
       "      <td>0.194550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.117775</td>\n",
       "      <td>0.191964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.098144</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73.0</th>\n",
       "      <td>0.192109</td>\n",
       "      <td>0.151930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.0</th>\n",
       "      <td>0.122378</td>\n",
       "      <td>0.134904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>0.187343</td>\n",
       "      <td>0.142326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.0</th>\n",
       "      <td>0.256539</td>\n",
       "      <td>0.223256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77.0</th>\n",
       "      <td>0.123703</td>\n",
       "      <td>0.180315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Domestic               0         1\n",
       "Community Area                    \n",
       "1.0             0.155293  0.180610\n",
       "2.0             0.109913  0.186586\n",
       "3.0             0.168635  0.194550\n",
       "4.0             0.117775  0.191964\n",
       "5.0             0.098144  0.178571\n",
       "...                  ...       ...\n",
       "73.0            0.192109  0.151930\n",
       "74.0            0.122378  0.134904\n",
       "75.0            0.187343  0.142326\n",
       "76.0            0.256539  0.223256\n",
       "77.0            0.123703  0.180315\n",
       "\n",
       "[77 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arrests for non-domestic and domestic crimes by community area\n",
    "domestic_community = (crimes.pivot_table(values=\"Arrest\",index=\"Community Area\", columns=[\"Domestic\"],aggfunc='sum')\n",
    "                     /crimes.pivot_table(values=\"Arrest\",index=\"Community Area\", columns=[\"Domestic\"],aggfunc='count'))\n",
    "domestic_community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "decimal-plaintiff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Domestic</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.116240</td>\n",
       "      <td>0.211538</td>\n",
       "      <td>-0.095298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>0.110840</td>\n",
       "      <td>0.205128</td>\n",
       "      <td>-0.094288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.098144</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>-0.080428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>0.098989</td>\n",
       "      <td>0.179032</td>\n",
       "      <td>-0.080043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.127494</td>\n",
       "      <td>0.207117</td>\n",
       "      <td>-0.079623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.312815</td>\n",
       "      <td>0.138566</td>\n",
       "      <td>0.174249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>0.320724</td>\n",
       "      <td>0.130812</td>\n",
       "      <td>0.189912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>0.351072</td>\n",
       "      <td>0.155684</td>\n",
       "      <td>0.195388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>0.397225</td>\n",
       "      <td>0.160860</td>\n",
       "      <td>0.236365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>0.455073</td>\n",
       "      <td>0.131098</td>\n",
       "      <td>0.323975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Domestic               0         1      Diff\n",
       "Community Area                              \n",
       "21.0            0.116240  0.211538 -0.095298\n",
       "17.0            0.110840  0.205128 -0.094288\n",
       "5.0             0.098144  0.178571 -0.080428\n",
       "18.0            0.098989  0.179032 -0.080043\n",
       "22.0            0.127494  0.207117 -0.079623\n",
       "...                  ...       ...       ...\n",
       "50.0            0.312815  0.138566  0.174249\n",
       "27.0            0.320724  0.130812  0.189912\n",
       "23.0            0.351072  0.155684  0.195388\n",
       "29.0            0.397225  0.160860  0.236365\n",
       "26.0            0.455073  0.131098  0.323975\n",
       "\n",
       "[77 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rate of arrests by domestic and non-domestic crimes per community ID\n",
    "domestic_community['Diff'] = domestic_community[0].sub(domestic_community[1], axis = 0) \n",
    "pd.set_option(\"display.max_rows\",15)\n",
    "domestic_community.sort_values(\"Diff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "reflected-possibility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Domestic</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.174936</td>\n",
       "      <td>0.166274</td>\n",
       "      <td>0.008663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.075294</td>\n",
       "      <td>0.027178</td>\n",
       "      <td>0.079445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.068557</td>\n",
       "      <td>0.109848</td>\n",
       "      <td>-0.095298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.116240</td>\n",
       "      <td>0.147846</td>\n",
       "      <td>-0.046623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.168036</td>\n",
       "      <td>0.163069</td>\n",
       "      <td>-0.019090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.216296</td>\n",
       "      <td>0.183400</td>\n",
       "      <td>0.045017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.455073</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.323975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Domestic          0          1       Diff\n",
       "count     77.000000  77.000000  77.000000\n",
       "mean       0.174936   0.166274   0.008663\n",
       "std        0.075294   0.027178   0.079445\n",
       "min        0.068557   0.109848  -0.095298\n",
       "25%        0.116240   0.147846  -0.046623\n",
       "50%        0.168036   0.163069  -0.019090\n",
       "75%        0.216296   0.183400   0.045017\n",
       "max        0.455073   0.235294   0.323975"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domestic_community.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "typical-consideration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAMiElEQVR4nO3df4ykB1nA8e9jzwrt4gmUrORKWEkISeX81fFnE9ilTUQOi380sQqEGs3GH0ij9Y81aEhMiKeJJo1p1AuaYGhcwkFC04tVWjoqf1DYLYXzeiIFV+lZWtBwZc/GevHxj50rw3V233cu8+773O73k2y6s/PO7LNPb783fXenE5mJJKmub+t7AEnSzgy1JBVnqCWpOEMtScUZakkq7kAXd3rNNdfkwsJCq2PPnTvH1Vdf3cUYe4Y7auaOduZ+mvW9o/X19a9l5ssmXddJqBcWFlhbW2t17HA4ZHFxsYsx9gx31Mwd7cz9NOt7RxHxb9td56kPSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKq6TZybuZQsrJ2Z+nxtHj8z8PiXtHT6ilqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpuFahjojfiIhTEfFPEfHXEfGCrgeTJG1pDHVEHALeBQwy87XAFcCtXQ8mSdrS9tTHAeCFEXEAuAr4j+5GkiSNi8xsPijiduC9wDPA32XmWyccswwsA8zPz1+/urraaoDNzU3m5uammRmAk2fOtjru8KGDU9/3LD7vNJpmvNQdNelrh13oakd7hftp1veOlpaW1jNzMOm6xlBHxIuBDwM/C3wd+BBwPDM/sN1tBoNBrq2ttRpuOByyuLjY6thxCysnWh23cfTI1Pc9i887jaYZL3VHTfraYRe62tFe4X6a9b2jiNg21G1OfdwE/GtmfjUz/xf4CPATsxxQkrS9NqH+d+DHIuKqiAjgRuB0t2NJki5oDHVmPgQcBx4GTo5uc6zjuSRJIwfaHJSZ7wHe0/EskqQJfGaiJBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQV1+oVXvaDLl5dXJJmwUfUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQV1yrUEfFdEXE8Iv45Ik5HxI93PZgkaUvbF7e9E7gvM2+JiCuBqzqcSZI0pjHUEXEQeB1wG0BmPgs82+1YkqQLIjN3PiDiB4BjwKPA9wPrwO2Zee6i45aBZYD5+fnrV1dXWw2wubnJ3Nzc1IOfPHN26ttUdfjQwR2vv9QdNWm7w6b5KuhqR3uF+2nW946WlpbWM3Mw6bo2oR4AnwRuyMyHIuJO4OnM/N3tbjMYDHJtba3VcMPhkMXFxVbHjltYOTH1baraOHpkx+svdUdN2u6wab4KutrRXuF+mvW9o4jYNtRtfpj4OPB4Zj40unwc+KFZDSdJ2lljqDPzK8CXI+I1ow/dyNZpEEnSLmj7Wx+/Dtw9+o2PLwG/0N1IkqRxrUKdmY8AE8+dSJK65TMTJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpuLYvxaV9rItXfL8cXtlcqsJH1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFdc61BFxRUR8JiLu7XIgSdK3muYR9e3A6a4GkSRN1irUEXEtcAR4X7fjSJIuFpnZfFDEceD3gRcBv5WZb55wzDKwDDA/P3/96upqqwE2NzeZm5ubZmYATp45O/Vtqjp86OCO11/qjpr0ucOmr3laXe1or3A/zfre0dLS0npmDiZdd6DpxhHxZuCpzFyPiMXtjsvMY8AxgMFgkIuL2x76LYbDIW2PHXfbyompb1PVxlsXd7z+UnfUpM8dNn3N0+pqR3uF+2lWeUdtTn3cANwcERvAKvCGiPhAp1NJkp7TGOrM/O3MvDYzF4BbgY9n5ts6n0ySBPh71JJUXuM56nGZOQSGnUwiSZrIR9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklTcVK/wom4sNLwa+B2Hz3Pbygk2jh6Zyf1Jurz4iFqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIaQx0Rr4iIByPi0Yg4FRG378ZgkqQtbV7c9jxwR2Y+HBEvAtYj4mOZ+WjHs0mSaPGIOjOfyMyHR+9/AzgNHOp6MEnSlsjM9gdHLAD/ALw2M5++6LplYBlgfn7++tXV1Vb3ubm5ydzc3HOXT54523qe/WL+hfDkM31PMVuHDx2c6f099V9nW+1o1p+3T22/Vw4fOvi87zM9X987WlpaWs/MwaTrWoc6IuaAvwfem5kf2enYwWCQa2trre53OByyuLj43OWFlROtbref3HH4PH90ss1ZqsvHxtEjM72/P7n7o612NOvP26e23ysbR4887/tMz9f3jiJi21C3+q2PiPh24MPA3U2RliTNVpvf+gjgL4DTmfnH3Y8kSRrX5hH1DcDbgTdExCOjtzd1PJckaaTxpF5mfgKIXZhFkjSBz0yUpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTi9tZLW+uyMc0raPfxedvq4lXNZz3jrPX1765PfX/NPqKWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJam4VqGOiDdGxOcj4rGIWOl6KEnSNzWGOiKuAO4Cfgq4Dvi5iLiu68EkSVvaPKL+EeCxzPxSZj4LrAJv6XYsSdIFkZk7HxBxC/DGzPyl0eW3Az+ame+86LhlYHl08TXA51vOcA3wtWmG3ofcUTN3tDP306zvHb0yM1826YoDs/oMmXkMODbt7SJiLTMHs5pjL3JHzdzRztxPs8o7anPq4wzwirHL144+JknaBW1C/Wng1RHxPRFxJXArcE+3Y0mSLmg89ZGZ5yPincDfAlcAf5mZp2Y4w9SnS/Yhd9TMHe3M/TQru6PGHyZKkvrlMxMlqThDLUnF7XqoI+IlEfGxiPjC6J8v3ua4+yLi6xFx727P2Jemp+pHxHdExAdH1z8UEQs9jNmbFvt5XUQ8HBHnR7//v++02NFvRsSjEfG5iHggIl7Zx5x9arGjX46IkxHxSER8osQzsTNzV9+APwRWRu+vAH+wzXE3Aj8N3LvbM/bxxtYPar8IvAq4EvgscN1Fx/wq8Gej928FPtj33MX2swB8H/BXwC19z1x0R0vAVaP3f2U//RmaYkffOfb+zcB9fc/dx6mPtwDvH73/fuBnJh2UmQ8A39ilmSpo81T98d0dB26MiNjFGfvUuJ/M3MjMzwH/18eABbTZ0YOZ+d+ji59k63kR+0mbHT09dvFqoPffuOgj1POZ+cTo/a8A8z3MUNEh4Mtjlx8ffWziMZl5HjgLvHRXputfm/3sd9Pu6BeBv+l0onpa7Sgifi0ivsjWGYB37dJs25rZU8jHRcT9wHdPuOrd4xcyMyOi97+tpP0mIt4GDIDX9z1LRZl5F3BXRPw88DvAO/qcp5NQZ+ZN210XEU9GxMsz84mIeDnwVBczXIbaPFX/wjGPR8QB4CDwn7szXu/8Xxk0a7WjiLiJrQdNr8/M/9ml2aqY9s/RKvCnnU7UQh+nPu7hm387vQP4aA8zVNTmqfrju7sF+HiOfuKxD/i/MmjWuKOI+EHgz4GbM3M/Pkhqs6NXj108AnxhF+ebrIefur4UeICtL/5+4CWjjw+A940d94/AV4Fn2DqP9JN9/+R1F3bzJuBf2Pqp9LtHH/s9tr6pAF4AfAh4DPgU8Kq+Zy62nx8e/Vk5x9Z/aZzqe+aCO7ofeBJ4ZPR2T98zF9zRncCp0X4eBL6375l9CrkkFeczEyWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTi/h90zIDEPkL6BQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "domestic_community[\"Diff\"].hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "sublime-british",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arrest Rate for Domestic Crimes')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig = plt.figure(figsize=(10,7))\n",
    "sns.scatterplot(domestic_community[0],domestic_community[1],hue=domestic_community[\"Diff\"])\n",
    "#plt.lineplot()\n",
    "plt.xlabel(\"Arrest Rate for Non-Domestic Crimes\")\n",
    "plt.ylabel(\"Arrest Rate for Domestic Crimes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sudden-level",
   "metadata": {},
   "source": [
    "### Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "relevant-basics",
   "metadata": {},
   "source": [
    "Let's test first if the difference is statistically different across both columns for all communities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dried-campus",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "younger-shame",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.9496178912788671, pvalue=0.3438138714962272)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(domestic_community[0], domestic_community[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "artistic-pharmacy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=0.9568398618288926, pvalue=0.3416819734127905)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way to verify the same thing:\n",
    "from scipy.stats import ttest_1samp\n",
    "ttest_1samp(domestic_community[\"Diff\"],0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "illegal-province",
   "metadata": {},
   "source": [
    "**We can verify that it is not statistically different, since pvalue is 34% (which is higher than 5% confidence level).**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mechanical-broadcasting",
   "metadata": {},
   "source": [
    "Also as seen previously:\n",
    "- **Community 21** is the one with lower difference between rate of arrests of NON-domestic crimes compared to domestic crimes --> -9,5%\n",
    "- **Community 26**, on the contrary, is the one with higher difference between rate of arrests of NON-domestic crimes compared to domestic crimes. --> 45,5%\n",
    "\n",
    "Let's isolate these two and conduct another test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elder-southwest",
   "metadata": {},
   "source": [
    "**Community 21**: Let's test first if the difference is statistically different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cubic-model",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Domestic</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.155293</td>\n",
       "      <td>0.180610</td>\n",
       "      <td>-0.025317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.109913</td>\n",
       "      <td>0.186586</td>\n",
       "      <td>-0.076673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.168635</td>\n",
       "      <td>0.194550</td>\n",
       "      <td>-0.025915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.117775</td>\n",
       "      <td>0.191964</td>\n",
       "      <td>-0.074189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.098144</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>-0.080428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.134526</td>\n",
       "      <td>0.186301</td>\n",
       "      <td>-0.051775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.073128</td>\n",
       "      <td>0.126150</td>\n",
       "      <td>-0.053021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.165969</td>\n",
       "      <td>0.163672</td>\n",
       "      <td>0.002297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.081013</td>\n",
       "      <td>0.156757</td>\n",
       "      <td>-0.075744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.099686</td>\n",
       "      <td>0.153179</td>\n",
       "      <td>-0.053493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.113284</td>\n",
       "      <td>0.183400</td>\n",
       "      <td>-0.070116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.068557</td>\n",
       "      <td>0.145522</td>\n",
       "      <td>-0.076965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.110973</td>\n",
       "      <td>0.135802</td>\n",
       "      <td>-0.024830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.110499</td>\n",
       "      <td>0.156091</td>\n",
       "      <td>-0.045592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.130615</td>\n",
       "      <td>0.186869</td>\n",
       "      <td>-0.056254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.091955</td>\n",
       "      <td>0.157291</td>\n",
       "      <td>-0.065336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.110840</td>\n",
       "      <td>0.205128</td>\n",
       "      <td>-0.094288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.098989</td>\n",
       "      <td>0.179032</td>\n",
       "      <td>-0.080043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.171522</td>\n",
       "      <td>0.190823</td>\n",
       "      <td>-0.019301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.173735</td>\n",
       "      <td>0.192825</td>\n",
       "      <td>-0.019090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.116240</td>\n",
       "      <td>0.211538</td>\n",
       "      <td>-0.095298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.127494</td>\n",
       "      <td>0.207117</td>\n",
       "      <td>-0.079623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.351072</td>\n",
       "      <td>0.155684</td>\n",
       "      <td>0.195388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.101480</td>\n",
       "      <td>0.155354</td>\n",
       "      <td>-0.053875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.270423</td>\n",
       "      <td>0.163069</td>\n",
       "      <td>0.107355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.455073</td>\n",
       "      <td>0.131098</td>\n",
       "      <td>0.323975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.320724</td>\n",
       "      <td>0.130812</td>\n",
       "      <td>0.189912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.152290</td>\n",
       "      <td>0.137193</td>\n",
       "      <td>0.015097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.397225</td>\n",
       "      <td>0.160860</td>\n",
       "      <td>0.236365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.218853</td>\n",
       "      <td>0.224464</td>\n",
       "      <td>-0.005611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0.176984</td>\n",
       "      <td>0.200501</td>\n",
       "      <td>-0.023517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.167843</td>\n",
       "      <td>0.159021</td>\n",
       "      <td>0.008822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.152389</td>\n",
       "      <td>0.130332</td>\n",
       "      <td>0.022057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.146673</td>\n",
       "      <td>0.166333</td>\n",
       "      <td>-0.019660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.185013</td>\n",
       "      <td>0.139619</td>\n",
       "      <td>0.045394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36.0</td>\n",
       "      <td>0.072361</td>\n",
       "      <td>0.109848</td>\n",
       "      <td>-0.037488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.246549</td>\n",
       "      <td>0.138386</td>\n",
       "      <td>0.108163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.172212</td>\n",
       "      <td>0.140657</td>\n",
       "      <td>0.031555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0.082361</td>\n",
       "      <td>0.128984</td>\n",
       "      <td>-0.046623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.177055</td>\n",
       "      <td>0.138277</td>\n",
       "      <td>0.038778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.087570</td>\n",
       "      <td>0.111849</td>\n",
       "      <td>-0.024280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.203998</td>\n",
       "      <td>0.160822</td>\n",
       "      <td>0.043177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43.0</td>\n",
       "      <td>0.192730</td>\n",
       "      <td>0.163336</td>\n",
       "      <td>0.029393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.231219</td>\n",
       "      <td>0.178076</td>\n",
       "      <td>0.053143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.202261</td>\n",
       "      <td>0.155102</td>\n",
       "      <td>0.047159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46.0</td>\n",
       "      <td>0.230301</td>\n",
       "      <td>0.197919</td>\n",
       "      <td>0.032382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47.0</td>\n",
       "      <td>0.211789</td>\n",
       "      <td>0.148325</td>\n",
       "      <td>0.063464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48.0</td>\n",
       "      <td>0.159415</td>\n",
       "      <td>0.173462</td>\n",
       "      <td>-0.014047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0.245119</td>\n",
       "      <td>0.177033</td>\n",
       "      <td>0.068086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.312815</td>\n",
       "      <td>0.138566</td>\n",
       "      <td>0.174249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0.239745</td>\n",
       "      <td>0.177285</td>\n",
       "      <td>0.062460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0.184524</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>-0.050770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.216296</td>\n",
       "      <td>0.164708</td>\n",
       "      <td>0.051588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0.226419</td>\n",
       "      <td>0.149575</td>\n",
       "      <td>0.076844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.117910</td>\n",
       "      <td>0.162500</td>\n",
       "      <td>-0.044590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.168036</td>\n",
       "      <td>0.176626</td>\n",
       "      <td>-0.008589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.129544</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>-0.042870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.179233</td>\n",
       "      <td>0.210222</td>\n",
       "      <td>-0.030989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59.0</td>\n",
       "      <td>0.187625</td>\n",
       "      <td>0.148862</td>\n",
       "      <td>0.038763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60.0</td>\n",
       "      <td>0.140015</td>\n",
       "      <td>0.169697</td>\n",
       "      <td>-0.029682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.238621</td>\n",
       "      <td>0.203717</td>\n",
       "      <td>0.034905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.143385</td>\n",
       "      <td>0.169297</td>\n",
       "      <td>-0.025912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0.186651</td>\n",
       "      <td>0.214545</td>\n",
       "      <td>-0.027894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.108065</td>\n",
       "      <td>0.163697</td>\n",
       "      <td>-0.055633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0.159795</td>\n",
       "      <td>0.192422</td>\n",
       "      <td>-0.032627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66.0</td>\n",
       "      <td>0.182039</td>\n",
       "      <td>0.159073</td>\n",
       "      <td>0.022966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0.274731</td>\n",
       "      <td>0.159303</td>\n",
       "      <td>0.115428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68.0</td>\n",
       "      <td>0.282500</td>\n",
       "      <td>0.147846</td>\n",
       "      <td>0.134653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69.0</td>\n",
       "      <td>0.228139</td>\n",
       "      <td>0.161899</td>\n",
       "      <td>0.066240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.104314</td>\n",
       "      <td>0.139840</td>\n",
       "      <td>-0.035526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71.0</td>\n",
       "      <td>0.244063</td>\n",
       "      <td>0.180107</td>\n",
       "      <td>0.063957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.093805</td>\n",
       "      <td>0.126645</td>\n",
       "      <td>-0.032840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.192109</td>\n",
       "      <td>0.151930</td>\n",
       "      <td>0.040179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74.0</td>\n",
       "      <td>0.122378</td>\n",
       "      <td>0.134904</td>\n",
       "      <td>-0.012526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.187343</td>\n",
       "      <td>0.142326</td>\n",
       "      <td>0.045017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76.0</td>\n",
       "      <td>0.256539</td>\n",
       "      <td>0.223256</td>\n",
       "      <td>0.033283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77.0</td>\n",
       "      <td>0.123703</td>\n",
       "      <td>0.180315</td>\n",
       "      <td>-0.056612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Domestic  Community Area         0         1      Diff\n",
       "0                    1.0  0.155293  0.180610 -0.025317\n",
       "1                    2.0  0.109913  0.186586 -0.076673\n",
       "2                    3.0  0.168635  0.194550 -0.025915\n",
       "3                    4.0  0.117775  0.191964 -0.074189\n",
       "4                    5.0  0.098144  0.178571 -0.080428\n",
       "5                    6.0  0.134526  0.186301 -0.051775\n",
       "6                    7.0  0.073128  0.126150 -0.053021\n",
       "7                    8.0  0.165969  0.163672  0.002297\n",
       "8                    9.0  0.081013  0.156757 -0.075744\n",
       "9                   10.0  0.099686  0.153179 -0.053493\n",
       "10                  11.0  0.113284  0.183400 -0.070116\n",
       "11                  12.0  0.068557  0.145522 -0.076965\n",
       "12                  13.0  0.110973  0.135802 -0.024830\n",
       "13                  14.0  0.110499  0.156091 -0.045592\n",
       "14                  15.0  0.130615  0.186869 -0.056254\n",
       "15                  16.0  0.091955  0.157291 -0.065336\n",
       "16                  17.0  0.110840  0.205128 -0.094288\n",
       "17                  18.0  0.098989  0.179032 -0.080043\n",
       "18                  19.0  0.171522  0.190823 -0.019301\n",
       "19                  20.0  0.173735  0.192825 -0.019090\n",
       "20                  21.0  0.116240  0.211538 -0.095298\n",
       "21                  22.0  0.127494  0.207117 -0.079623\n",
       "22                  23.0  0.351072  0.155684  0.195388\n",
       "23                  24.0  0.101480  0.155354 -0.053875\n",
       "24                  25.0  0.270423  0.163069  0.107355\n",
       "25                  26.0  0.455073  0.131098  0.323975\n",
       "26                  27.0  0.320724  0.130812  0.189912\n",
       "27                  28.0  0.152290  0.137193  0.015097\n",
       "28                  29.0  0.397225  0.160860  0.236365\n",
       "29                  30.0  0.218853  0.224464 -0.005611\n",
       "30                  31.0  0.176984  0.200501 -0.023517\n",
       "31                  32.0  0.167843  0.159021  0.008822\n",
       "32                  33.0  0.152389  0.130332  0.022057\n",
       "33                  34.0  0.146673  0.166333 -0.019660\n",
       "34                  35.0  0.185013  0.139619  0.045394\n",
       "35                  36.0  0.072361  0.109848 -0.037488\n",
       "36                  37.0  0.246549  0.138386  0.108163\n",
       "37                  38.0  0.172212  0.140657  0.031555\n",
       "38                  39.0  0.082361  0.128984 -0.046623\n",
       "39                  40.0  0.177055  0.138277  0.038778\n",
       "40                  41.0  0.087570  0.111849 -0.024280\n",
       "41                  42.0  0.203998  0.160822  0.043177\n",
       "42                  43.0  0.192730  0.163336  0.029393\n",
       "43                  44.0  0.231219  0.178076  0.053143\n",
       "44                  45.0  0.202261  0.155102  0.047159\n",
       "45                  46.0  0.230301  0.197919  0.032382\n",
       "46                  47.0  0.211789  0.148325  0.063464\n",
       "47                  48.0  0.159415  0.173462 -0.014047\n",
       "48                  49.0  0.245119  0.177033  0.068086\n",
       "49                  50.0  0.312815  0.138566  0.174249\n",
       "50                  51.0  0.239745  0.177285  0.062460\n",
       "51                  52.0  0.184524  0.235294 -0.050770\n",
       "52                  53.0  0.216296  0.164708  0.051588\n",
       "53                  54.0  0.226419  0.149575  0.076844\n",
       "54                  55.0  0.117910  0.162500 -0.044590\n",
       "55                  56.0  0.168036  0.176626 -0.008589\n",
       "56                  57.0  0.129544  0.172414 -0.042870\n",
       "57                  58.0  0.179233  0.210222 -0.030989\n",
       "58                  59.0  0.187625  0.148862  0.038763\n",
       "59                  60.0  0.140015  0.169697 -0.029682\n",
       "60                  61.0  0.238621  0.203717  0.034905\n",
       "61                  62.0  0.143385  0.169297 -0.025912\n",
       "62                  63.0  0.186651  0.214545 -0.027894\n",
       "63                  64.0  0.108065  0.163697 -0.055633\n",
       "64                  65.0  0.159795  0.192422 -0.032627\n",
       "65                  66.0  0.182039  0.159073  0.022966\n",
       "66                  67.0  0.274731  0.159303  0.115428\n",
       "67                  68.0  0.282500  0.147846  0.134653\n",
       "68                  69.0  0.228139  0.161899  0.066240\n",
       "69                  70.0  0.104314  0.139840 -0.035526\n",
       "70                  71.0  0.244063  0.180107  0.063957\n",
       "71                  72.0  0.093805  0.126645 -0.032840\n",
       "72                  73.0  0.192109  0.151930  0.040179\n",
       "73                  74.0  0.122378  0.134904 -0.012526\n",
       "74                  75.0  0.187343  0.142326  0.045017\n",
       "75                  76.0  0.256539  0.223256  0.033283\n",
       "76                  77.0  0.123703  0.180315 -0.056612"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communities_selection = domestic_community.reset_index()\n",
    "communities_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "atmospheric-ivory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Domestic</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.155293</td>\n",
       "      <td>0.180610</td>\n",
       "      <td>-0.025317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.109913</td>\n",
       "      <td>0.186586</td>\n",
       "      <td>-0.076673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.168635</td>\n",
       "      <td>0.194550</td>\n",
       "      <td>-0.025915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.117775</td>\n",
       "      <td>0.191964</td>\n",
       "      <td>-0.074189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.098144</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>-0.080428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.134526</td>\n",
       "      <td>0.186301</td>\n",
       "      <td>-0.051775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.073128</td>\n",
       "      <td>0.126150</td>\n",
       "      <td>-0.053021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.165969</td>\n",
       "      <td>0.163672</td>\n",
       "      <td>0.002297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.081013</td>\n",
       "      <td>0.156757</td>\n",
       "      <td>-0.075744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.099686</td>\n",
       "      <td>0.153179</td>\n",
       "      <td>-0.053493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.113284</td>\n",
       "      <td>0.183400</td>\n",
       "      <td>-0.070116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.068557</td>\n",
       "      <td>0.145522</td>\n",
       "      <td>-0.076965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.110973</td>\n",
       "      <td>0.135802</td>\n",
       "      <td>-0.024830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.110499</td>\n",
       "      <td>0.156091</td>\n",
       "      <td>-0.045592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.130615</td>\n",
       "      <td>0.186869</td>\n",
       "      <td>-0.056254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.091955</td>\n",
       "      <td>0.157291</td>\n",
       "      <td>-0.065336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.110840</td>\n",
       "      <td>0.205128</td>\n",
       "      <td>-0.094288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.098989</td>\n",
       "      <td>0.179032</td>\n",
       "      <td>-0.080043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.171522</td>\n",
       "      <td>0.190823</td>\n",
       "      <td>-0.019301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.173735</td>\n",
       "      <td>0.192825</td>\n",
       "      <td>-0.019090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.127494</td>\n",
       "      <td>0.207117</td>\n",
       "      <td>-0.079623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.351072</td>\n",
       "      <td>0.155684</td>\n",
       "      <td>0.195388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.101480</td>\n",
       "      <td>0.155354</td>\n",
       "      <td>-0.053875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.270423</td>\n",
       "      <td>0.163069</td>\n",
       "      <td>0.107355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.320724</td>\n",
       "      <td>0.130812</td>\n",
       "      <td>0.189912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.152290</td>\n",
       "      <td>0.137193</td>\n",
       "      <td>0.015097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.397225</td>\n",
       "      <td>0.160860</td>\n",
       "      <td>0.236365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.218853</td>\n",
       "      <td>0.224464</td>\n",
       "      <td>-0.005611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0.176984</td>\n",
       "      <td>0.200501</td>\n",
       "      <td>-0.023517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.167843</td>\n",
       "      <td>0.159021</td>\n",
       "      <td>0.008822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.152389</td>\n",
       "      <td>0.130332</td>\n",
       "      <td>0.022057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.146673</td>\n",
       "      <td>0.166333</td>\n",
       "      <td>-0.019660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.185013</td>\n",
       "      <td>0.139619</td>\n",
       "      <td>0.045394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36.0</td>\n",
       "      <td>0.072361</td>\n",
       "      <td>0.109848</td>\n",
       "      <td>-0.037488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.246549</td>\n",
       "      <td>0.138386</td>\n",
       "      <td>0.108163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.172212</td>\n",
       "      <td>0.140657</td>\n",
       "      <td>0.031555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0.082361</td>\n",
       "      <td>0.128984</td>\n",
       "      <td>-0.046623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.177055</td>\n",
       "      <td>0.138277</td>\n",
       "      <td>0.038778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.087570</td>\n",
       "      <td>0.111849</td>\n",
       "      <td>-0.024280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.203998</td>\n",
       "      <td>0.160822</td>\n",
       "      <td>0.043177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43.0</td>\n",
       "      <td>0.192730</td>\n",
       "      <td>0.163336</td>\n",
       "      <td>0.029393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.231219</td>\n",
       "      <td>0.178076</td>\n",
       "      <td>0.053143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.202261</td>\n",
       "      <td>0.155102</td>\n",
       "      <td>0.047159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46.0</td>\n",
       "      <td>0.230301</td>\n",
       "      <td>0.197919</td>\n",
       "      <td>0.032382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47.0</td>\n",
       "      <td>0.211789</td>\n",
       "      <td>0.148325</td>\n",
       "      <td>0.063464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48.0</td>\n",
       "      <td>0.159415</td>\n",
       "      <td>0.173462</td>\n",
       "      <td>-0.014047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0.245119</td>\n",
       "      <td>0.177033</td>\n",
       "      <td>0.068086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.312815</td>\n",
       "      <td>0.138566</td>\n",
       "      <td>0.174249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0.239745</td>\n",
       "      <td>0.177285</td>\n",
       "      <td>0.062460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0.184524</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>-0.050770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.216296</td>\n",
       "      <td>0.164708</td>\n",
       "      <td>0.051588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0.226419</td>\n",
       "      <td>0.149575</td>\n",
       "      <td>0.076844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.117910</td>\n",
       "      <td>0.162500</td>\n",
       "      <td>-0.044590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.168036</td>\n",
       "      <td>0.176626</td>\n",
       "      <td>-0.008589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.129544</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>-0.042870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.179233</td>\n",
       "      <td>0.210222</td>\n",
       "      <td>-0.030989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59.0</td>\n",
       "      <td>0.187625</td>\n",
       "      <td>0.148862</td>\n",
       "      <td>0.038763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60.0</td>\n",
       "      <td>0.140015</td>\n",
       "      <td>0.169697</td>\n",
       "      <td>-0.029682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.238621</td>\n",
       "      <td>0.203717</td>\n",
       "      <td>0.034905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.143385</td>\n",
       "      <td>0.169297</td>\n",
       "      <td>-0.025912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0.186651</td>\n",
       "      <td>0.214545</td>\n",
       "      <td>-0.027894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.108065</td>\n",
       "      <td>0.163697</td>\n",
       "      <td>-0.055633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0.159795</td>\n",
       "      <td>0.192422</td>\n",
       "      <td>-0.032627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66.0</td>\n",
       "      <td>0.182039</td>\n",
       "      <td>0.159073</td>\n",
       "      <td>0.022966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0.274731</td>\n",
       "      <td>0.159303</td>\n",
       "      <td>0.115428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68.0</td>\n",
       "      <td>0.282500</td>\n",
       "      <td>0.147846</td>\n",
       "      <td>0.134653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69.0</td>\n",
       "      <td>0.228139</td>\n",
       "      <td>0.161899</td>\n",
       "      <td>0.066240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.104314</td>\n",
       "      <td>0.139840</td>\n",
       "      <td>-0.035526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71.0</td>\n",
       "      <td>0.244063</td>\n",
       "      <td>0.180107</td>\n",
       "      <td>0.063957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.093805</td>\n",
       "      <td>0.126645</td>\n",
       "      <td>-0.032840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.192109</td>\n",
       "      <td>0.151930</td>\n",
       "      <td>0.040179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74.0</td>\n",
       "      <td>0.122378</td>\n",
       "      <td>0.134904</td>\n",
       "      <td>-0.012526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.187343</td>\n",
       "      <td>0.142326</td>\n",
       "      <td>0.045017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76.0</td>\n",
       "      <td>0.256539</td>\n",
       "      <td>0.223256</td>\n",
       "      <td>0.033283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77.0</td>\n",
       "      <td>0.123703</td>\n",
       "      <td>0.180315</td>\n",
       "      <td>-0.056612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Domestic  Community Area         0         1      Diff\n",
       "0                    1.0  0.155293  0.180610 -0.025317\n",
       "1                    2.0  0.109913  0.186586 -0.076673\n",
       "2                    3.0  0.168635  0.194550 -0.025915\n",
       "3                    4.0  0.117775  0.191964 -0.074189\n",
       "4                    5.0  0.098144  0.178571 -0.080428\n",
       "5                    6.0  0.134526  0.186301 -0.051775\n",
       "6                    7.0  0.073128  0.126150 -0.053021\n",
       "7                    8.0  0.165969  0.163672  0.002297\n",
       "8                    9.0  0.081013  0.156757 -0.075744\n",
       "9                   10.0  0.099686  0.153179 -0.053493\n",
       "10                  11.0  0.113284  0.183400 -0.070116\n",
       "11                  12.0  0.068557  0.145522 -0.076965\n",
       "12                  13.0  0.110973  0.135802 -0.024830\n",
       "13                  14.0  0.110499  0.156091 -0.045592\n",
       "14                  15.0  0.130615  0.186869 -0.056254\n",
       "15                  16.0  0.091955  0.157291 -0.065336\n",
       "16                  17.0  0.110840  0.205128 -0.094288\n",
       "17                  18.0  0.098989  0.179032 -0.080043\n",
       "18                  19.0  0.171522  0.190823 -0.019301\n",
       "19                  20.0  0.173735  0.192825 -0.019090\n",
       "21                  22.0  0.127494  0.207117 -0.079623\n",
       "22                  23.0  0.351072  0.155684  0.195388\n",
       "23                  24.0  0.101480  0.155354 -0.053875\n",
       "24                  25.0  0.270423  0.163069  0.107355\n",
       "26                  27.0  0.320724  0.130812  0.189912\n",
       "27                  28.0  0.152290  0.137193  0.015097\n",
       "28                  29.0  0.397225  0.160860  0.236365\n",
       "29                  30.0  0.218853  0.224464 -0.005611\n",
       "30                  31.0  0.176984  0.200501 -0.023517\n",
       "31                  32.0  0.167843  0.159021  0.008822\n",
       "32                  33.0  0.152389  0.130332  0.022057\n",
       "33                  34.0  0.146673  0.166333 -0.019660\n",
       "34                  35.0  0.185013  0.139619  0.045394\n",
       "35                  36.0  0.072361  0.109848 -0.037488\n",
       "36                  37.0  0.246549  0.138386  0.108163\n",
       "37                  38.0  0.172212  0.140657  0.031555\n",
       "38                  39.0  0.082361  0.128984 -0.046623\n",
       "39                  40.0  0.177055  0.138277  0.038778\n",
       "40                  41.0  0.087570  0.111849 -0.024280\n",
       "41                  42.0  0.203998  0.160822  0.043177\n",
       "42                  43.0  0.192730  0.163336  0.029393\n",
       "43                  44.0  0.231219  0.178076  0.053143\n",
       "44                  45.0  0.202261  0.155102  0.047159\n",
       "45                  46.0  0.230301  0.197919  0.032382\n",
       "46                  47.0  0.211789  0.148325  0.063464\n",
       "47                  48.0  0.159415  0.173462 -0.014047\n",
       "48                  49.0  0.245119  0.177033  0.068086\n",
       "49                  50.0  0.312815  0.138566  0.174249\n",
       "50                  51.0  0.239745  0.177285  0.062460\n",
       "51                  52.0  0.184524  0.235294 -0.050770\n",
       "52                  53.0  0.216296  0.164708  0.051588\n",
       "53                  54.0  0.226419  0.149575  0.076844\n",
       "54                  55.0  0.117910  0.162500 -0.044590\n",
       "55                  56.0  0.168036  0.176626 -0.008589\n",
       "56                  57.0  0.129544  0.172414 -0.042870\n",
       "57                  58.0  0.179233  0.210222 -0.030989\n",
       "58                  59.0  0.187625  0.148862  0.038763\n",
       "59                  60.0  0.140015  0.169697 -0.029682\n",
       "60                  61.0  0.238621  0.203717  0.034905\n",
       "61                  62.0  0.143385  0.169297 -0.025912\n",
       "62                  63.0  0.186651  0.214545 -0.027894\n",
       "63                  64.0  0.108065  0.163697 -0.055633\n",
       "64                  65.0  0.159795  0.192422 -0.032627\n",
       "65                  66.0  0.182039  0.159073  0.022966\n",
       "66                  67.0  0.274731  0.159303  0.115428\n",
       "67                  68.0  0.282500  0.147846  0.134653\n",
       "68                  69.0  0.228139  0.161899  0.066240\n",
       "69                  70.0  0.104314  0.139840 -0.035526\n",
       "70                  71.0  0.244063  0.180107  0.063957\n",
       "71                  72.0  0.093805  0.126645 -0.032840\n",
       "72                  73.0  0.192109  0.151930  0.040179\n",
       "73                  74.0  0.122378  0.134904 -0.012526\n",
       "74                  75.0  0.187343  0.142326  0.045017\n",
       "75                  76.0  0.256539  0.223256  0.033283\n",
       "76                  77.0  0.123703  0.180315 -0.056612"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communities_selection = communities_selection[(communities_selection[\"Community Area\"]!=21.0)&(communities_selection[\"Community Area\"]!=26.0)]\n",
    "communities_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "satisfactory-myrtle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.6874997617626142, pvalue=0.492843833109873)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(communities_selection[0], communities_selection[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "maritime-hometown",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    75.000000\n",
       "mean      0.171984\n",
       "std       0.068604\n",
       "min       0.068557\n",
       "25%       0.115530\n",
       "50%       0.168036\n",
       "75%       0.214043\n",
       "max       0.397225\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communities_selection[0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "configured-regulation",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "internal-profit",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "provincial-chapter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Arrest</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domestic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666595</td>\n",
       "      <td>0.166879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.138757</td>\n",
       "      <td>0.027769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Arrest           0         1\n",
       "Domestic                    \n",
       "0         0.666595  0.166879\n",
       "1         0.138757  0.027769"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domestic_arrest = pd.crosstab(crimes[\"Domestic\"],crimes[\"Arrest\"])/crimes.shape[0]\n",
    "domestic_arrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "choice-roman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11.277800829139046,\n",
       " 0.0007843942186533864,\n",
       " 1,\n",
       " array([[0.67124059, 0.16223367],\n",
       "        [0.13411192, 0.03241382]]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "chi2_contingency(domestic_arrest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fluid-postcard",
   "metadata": {},
   "source": [
    "## Analyzing Beats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compound-deadline",
   "metadata": {},
   "source": [
    "- **Beat** - Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. Three to five beats make up a police sector, and three sectors make up a police district. The Chicago Police Department has 22 police districts. See the beats at https://data.cityofchicago.org/d/aerh-rz74."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "wound-awareness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834    14239\n",
       "421     10375\n",
       "111      9992\n",
       "511      9868\n",
       "1831     9841\n",
       "        ...  \n",
       "1125     1710\n",
       "1621     1556\n",
       "1653      938\n",
       "1655      379\n",
       "1652      289\n",
       "Name: Beat, Length: 274, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.Beat.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "phantom-queensland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes.Beat.value_counts().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "reliable-hollywood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.277232e+06\n",
       "mean     1.143701e+03\n",
       "std      6.954303e+02\n",
       "min      1.110000e+02\n",
       "25%      6.120000e+02\n",
       "50%      1.024000e+03\n",
       "75%      1.713000e+03\n",
       "max      2.535000e+03\n",
       "Name: Beat, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.Beat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "consecutive-extreme",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1116.753831\n",
       "mean        1.000000\n",
       "std         0.608053\n",
       "min         0.097053\n",
       "25%         0.535105\n",
       "50%         0.895339\n",
       "75%         1.497770\n",
       "max         2.216489\n",
       "Name: Beat, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.Beat.describe()/crimes.Beat.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "quantitative-audio",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beat</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beat</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.037528</td>\n",
       "      <td>-0.057886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrest</th>\n",
       "      <td>-0.037528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domestic</th>\n",
       "      <td>-0.057886</td>\n",
       "      <td>-0.031492</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Beat    Arrest  Domestic\n",
       "Beat      1.000000 -0.037528 -0.057886\n",
       "Arrest   -0.037528  1.000000 -0.031492\n",
       "Domestic -0.057886 -0.031492  1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes[[\"Beat\",\"Arrest\", \"Domestic\"]].corr()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}