complete prediction of 911 calls with visualization .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflow: Predict number of 911 calls in each US block group.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Background**: The 911 has been designated as the \"<font color=green>Universal Emergency Number</font>\" for citizens throughout the United States to request emergency assistance In 1968, 911 became the national emergency number for the United States The calls are placed by a Voice over Internet Protocol (”VoIP”) End-User to Carrier’s Internet Protocol (”IP”) services"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importance**:\n",
    "\n",
    "- A good understanding of expected 911 calls might help with resource allocation for 911 call centers\n",
    "- Reduce the stress of the 911 operators, who suﬀer from signiﬁcant employee turnover\n",
    "- Help strategy makers anticipate the occurrences of emergencies and enable them to effectively handle the emergency by appropriate allocation of resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Technology**:\n",
    "\n",
    "1. Programming Languages: Python\n",
    "2. Data Visualization: Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workflow using Python ArcGIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to your ArcGIS online organization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gis = GIS(\"https://deldev.maps.arcgis.com\", \"demo_deldev\", \"P@ssword123\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's search **911 calls in Montgomery county** layer using the `search()` method from the content property of your gis object.you can pass a parameter **outside_org=True** to access the contents outside your organiation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "montgomery_calls = gis.content.search('title: \"Montgomery 911 calls\"', 'Feature layer', outside_org=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import **dislay** module to see the list of searched items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=67497a184b8f4bb69109f77d576440f7' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=67497a184b8f4bb69109f77d576440f7' target='_blank'><b>Join Features to Montgomery 911 calls</b>\n",
       "                        </a>\n",
       "                        <br/>Feature layer generated from Join Features<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by arcgis_python\n",
       "                        <br/>Last Modified: June 05, 2018\n",
       "                        <br/>0 comments, 2 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Join Features to Montgomery 911 calls\" type:Feature Layer Collection owner:arcgis_python>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=3b723ebf35e14e4195e5dcf35685678e' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=3b723ebf35e14e4195e5dcf35685678e' target='_blank'><b>Montgomery 911 calls</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: May 30, 2018\n",
       "                        <br/>0 comments, 1 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Montgomery 911 calls\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=7f7c68e762134efab7319437d69c2a33' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=7f7c68e762134efab7319437d69c2a33' target='_blank'><b>Aggregation of Montgomery 911 calls to USA Census BlockGroup Areas</b>\n",
       "                        </a>\n",
       "                        <br/>Feature layer generated from Aggregate Points<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: June 01, 2018\n",
       "                        <br/>0 comments, 2 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Aggregation of Montgomery 911 calls to USA Census BlockGroup Areas\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=cdd6975c9d134e84b209054f89a2e5b6' target='_blank'>\n",
       "                        <img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAACFCAYAAAAenrcsAAB+qUlEQVR42uy995tUVbY/7F/wPu/3/eF+b5o8zjjBGZ1xktecAwZEVDChYkSUnKGhEznnHAVEBYEOVZ1zzjnnnHM33Q3rXZ+1zz51qrq6aRAcnWs/z356VZ19du209spr37LmsJ1WH7GT96Eg8jwYRDs+CyQvhlEE5u/wbDXXW2PU8z0cRJuP2QRG2XLcJvUBrzuCugZ81C5Fv7PxmN1s2/tAID06358e4fLC4nOk/4aHhmj7mUh5R+rtD6CH5vrTw/P8ac5Gf/I22kZf0KfR+u1p9Nv7oJ2eW+ovv3XfHH+asd6PHpmn2vtonZ+0jWfvrw8junLZ7MfLK85L355Y4E9veqn6KK+t8qcnF6p+v8XfP7VQvf/MYh6H8Tto84O1flIfv/U+w3qu0G9fS7/PBNgpMjWMIlLCKCoxgoaH+6m/v4u6u9upq6tN4MHBXqqqKqeBgW4pjY0N9MPft/N3yxrLxneLIIeuH0HWu0EQbxcEeXScCPLItSLIIWcEeYx/535GkI+BIPNHIsgHoyAIkAEI8oix2V83EATvvOXtT08b8LNLRkGQ+QpBfH5AkO8ngqzlBVtnWbRdJwN5Me2yoDsFDpJna44EmfWAVEAKX66Hsvt0MG05HcklgnadCaftn0cIvJvh9Udt5jubjtuNtnlzHwyUjYvymsfXdOnSJWrv7KHW9k7acjKCPA/YaeV+rssI8swStQEXbwugrcbvbD8dzsgX5Kbfdqd+4/OLy3kjL1Ib/JONfvTUIrXJZ27wo8cXqGcfbQil5rYuamrroZ6+AXpt5QWp9yxThre9VX2UaV6qL3j2ri/3f4l6f9Iyf3pphYKf4HofM/KhPurNYNj3SLD0B3O48XgQbeCy9lgQnbUHU2xGFMWkR1FscjTPQ7cgR0dHC7W3N1NPT7sgSUVFKcMdUurqammID5LBwUsybygOeEBglIEBB4zyw991IAhOeJz0ONl8eAGx0fTpDFhTDtTbYNTDIm87YVOn4SEbZeWX0qXBQV6QS06FLg+S594L5jtAKt32dn4fyInP2/nEX3c0mNYdC6bNJ4Jo6iqFOBN4cy7f6a/q8TtHzoXzIT8sbXd2ddMORpLR+u1r9Hs19++Vlao9bNjZm/xkgwPpZjGyTFikNvwb/JsvrwygicsD6BWPQHpvtar3wjKFCM8Y70z3Ud/h2QerGTGWq7Zf8vCnqR6Ofn/CyKcReyZTLRww6A/mOig2hMKTQrmEUEZ2KnUzpejqaqXy8hJBgM7OFmpra6LW1kaGW+U7PEMdUJWyshLq7e0QuL1d1QWM92prKxnB2uRzTU2FIBg+o97Q0PAPO/56WKxvIoOsOmCjvOLKUX9g+e4LbmUQIItm0baecMBreQNNXOZgVRbt8FfsFvfj8LkIs12cjltPRdwQGQRs0ORlamOj3tNCKfzGJYM8PU4ZxMpiBcWFCkuFkpWbTpcGeoR1AhsFdupqLFZlZRlTih551tOjEANwX18nNTRUC4xSX18t9XV7oDo//F0jgmDja15+1UEHUngddN5ovpZ6giDY4Pxs1X4blVTUjPoDczd9SUt2B9CyPf7Cbum2TQQ5CASx05Jd/lJv5T612R7izXz/LH9auM2BIEfPR1pavkw7z0TQGmazfJh92fGZzW2/PRlBntcyyGw+zRkpHjU2LtighyGDMPwSU4JnmJo8Nk/9B4Lg+yeBIJ6q/qOGDII6D3Jb0zz9BEZfn+J6zy1y9BsU6IHZqt67vn7ksT+IPPYphA2ICaLQxGAKSQyizNw0QQBs+OrqCpFBsKlBNbCpAQ8N9fOzcqmDUlVVJu+oegqRAANBGhtrpA4+A0EGB3uEbUN7ly9f/mHHXyuCgFXaxpsLG3YTU4X9pwNk86PsM2A8wym//TP1PeDdzMboZztPBND6IwG0gcuWowG0icsGLt57z1M/L0xvN59uPd104nwEs1CqDby/xfidzUcu0vClPqnX2dHGrFQYyxF22sFl/+eO39nOvzN52UV61eMin94X6W0fP3qDN+krHn6064T7fm8+FkgfrPGjt7ju61x36XZ+19uPpnFZwvCbXurZDD7lsakBY0PP3XRR4HcYBluG+tP486Kt/rT7lE36tu80j/2U6ucu/m6X8T3KgTOBDpjHEJMSQpGJIRSRECysT1NjHbU0NVBRUb5s5Lq6aiooyGMKUMtwlSALkAJwQ0MNP8uVeij5+bnGO1XSFhAGMNir4uJCgVGKiwvMeqhTW1sl7Stk+0EmGT8FMVinVQcsrIobCqJZLLALYLG05mrLMeP0Bot0RFEYfL901wX+iSvmjx36OsbUTm0xWDTAvvsvOrRYw9BiRZms03YLm7fpaCDdM0uxN6AykB/A0uC7zUed+73KeMeLT2zIAXjnvtmKxQLr85DBYj1oaMheXOYQ5DWLhXpgsd7wUvXxGfLI6iOq35gDH1PLx+zT4aAR/Ua9rYyoMRnhwlJB9mjvaDZZJyCCZp2wcYeG+gzWSckOgPEdnqEOqIEzi9UucsrAgGaxaky2CsihqQnYMEWRupjitHDd/h92/7UiyFgyiO841bwQqL0P2hjZ7LR4x3mnH3NCEIsM4mNBEAjh2z6PVFosLluPB0hbKKBQgiDzFIIIS8Qb/17+bgsjiI+xQdFvj/12aQMs4HMWFutjiwwyw6LmtcogE0S1qxAEWq43PP1NWwwQBJox9Gfj0UDzMFgtQniQOb7tJ9Th4mmwk9HpjBzJoRSW6IogDtkCcoZGEGxmK4KMLoMoBHHIIM4IouEO/k3dHpClt/cHBBkXgmw5oVgmsFcbWIjey+wJYBTAEKwBW+uBfYGmSOoZ7JJ+Z98XoZSRV0oZ+WWUmV9KX9nj6VxoCp0PS6G9nwdJnY3HDBbNeGfnZwF0ITxV6p0LSaKU7GJ5HyW7ULdVThHx6bR0J8spexlx9ylNEzby1JV+5Lnbj2ZuZATY4E8Hvoig7KJKeS8+vYg+XBdg1luy7aKwZSiLt15kmUK18RGzYdN9FfwWlw1HAuV3VvHvrD0UQB571eftJ0Moq0D1LSuvmNYd8pcxbLXMD8Z37KyNxxJMZ7mcs9movKqEiksLqKA4T1ghbOSmpjphsRoba4X1KSrK4+/q5RnYIUe9eioszJN6KGDFGplFwzPFZql6QIjS0iKBUSorKxgx2qm9vY1aW6HtahW4paX5B3nkWimIl5XFMk6+HcYp6GWhIF5uWCzAut7uL6OYDDjYqmXMZqE9vAME1G1vtrBYQD4tYON0bm7vctvZyqpaWrbXZvbn2aXqVAcFmWlQgwfm+NH+s0nmO+1dffTCCpvUA/XRmiuwVh8y/MAcxT5NWqpsFqj3GD/bftLuYO0s49t/NtqhJhi6xGykn3yvWSwvgxIf/RoGQGarUsPIHmXjObkkpzkEagjMSvjuExZLC99KSO8bIaTjO2s9q5De29su6l9NKYAYYMPwGQjxw9+NVPMeGKea95ALi2VBFiDIFcvp5KTmPW53y2KJmlezckCQtk63na2orKXle21mf7T6VrRThmzx4FxnBGnr7KVJHjal5p3t3pLuquZ9fJ5CEC1vbbKM74AFQYYGBwRBRrBYPJZjXzss5ECQK5cHDPamVTa+YrF6RrBY16fmbXWr5gXV+OHvm1rSjyojHhYYbhw7P1N8ta+GjcWHbLHeqAdEwanve0Q9wwZHfcB7vojkTdnNJ3cv9fb1iWEM1m9Y1/d+EW5YwiNpz+chcuqiDWjHYDVfxcXrgI0KyhuoobVHSku7+t/Y1kvZzNZAFazrveyhLN0QpOfAMLcYArYfyzrOCPKqp03qPc6bf/EOO031DpEyb0ug+FLhvddX+jML5i9Wd6h8d5xU48Om32wZ34GvIqmzu1fG19bWxqyXn3wPQ+rmz5SFfD2Xzy7aKBK2jmRGkEgbI1OfbHpYx7XAjA1dWemwkAPu73e2pCtk6jYt6b29nWI0xLt4hrba25sY7hBKApZLU5/xIgjsI1COoAwODjrBpmzIXIH+HgVaMHf1/vUs6YaVfPVh5V6irefYELsMZFltuEisN+pBUwXNjGbPtukNdFhZ22G9hqC+bBcL6VeGRPBGuWz8h89TUVk1+RxUbew+baMnF/iJFunF5X68mf3480UWrC/SR2sv8sZW5ZO1F0XYVq4dfqJ5wuaGID17I7+/GEY8RpBzFhbLQBDUu3/WeebXa8WGAu3ayYBUeoLrg3K84+MvFG7tUfiQOca3xvAAWG2Mb7O42KjxQXO29kiQMSd2sseEMFKECmKERAeJUA6tFRAExkBQBFjHsfEBwxqOja8F8oqKEkEAZSFvNi3kGilQB0J5WVmxaf8AcqBNbUmH2ldZ0lvHhSDY+M3N9dIeClg5vI8COUj/AQnwG/ge/dZ9wOeamqoftFi+Y7FYozgrLhM1r3thsKi8RqzwqAdbwsNzHE5/Ty0aaZFGmckw2CltuX5mkYXFWnd1FuuvM87zotebz477p4nMAhbrtVUBJotkHZ+VxUKB0kJrrlDH95CDNbTFGBZyZq1CYoLlv4PFuuSGxXKnxep1o8W6FhbLocUaL4IAEXXbEPTxLtTBUAhYEQSIimf4bfRBvQNtWc2/sAzCp+FaLr4HmG1h1mUPU5D1vAlQ9p4KFKqxQTRZfHoeUzA2yU5mQdYbnzcc9qOlOy8IQnjvuyhOifjec+/5URGkpKKaFm//Wt5Zf/g8vcrs0qsr/egdbz9609OPXmN4ygo/+nT9Bbp3xld038df0fve52giU5fJy9TzqR4Kfm4xNFLq85QVF5m9SXQs7KV+RjKb1H9u0XlqanAgyNeh6fTy8ovyuzPX+TMr5X58OCj0nIBarj5ol/mCann1QTV3a2AhjwymsIQQCksMoaBIu/zHZ1tEoMHyNIm2CtorwKASJSWFskFRAIO1wjPUwQYFjO9KSgrMejAA4l1dD20CbmlpEESCSrejo4nfr+eNDXZoUDY4/mtYf4ZfG5AK73R2NgubB4qE9tCWcoAcZETs4/Ybnfrdw2xdd1c71VRXaecGZfa6YsD/AoqyW6KYJUCJTguluIwwOvilTRwD14jTn81kH9YbzoprLc6Ka8GKHQykgpIKfR5Ra3sHf6+ebeEN58UIs2KPH3nsuUg++y+w0O4n5fiFKPMdGu6hh2adE6H7FQ8lB8A9BFRiz5fxZmdramq4P4HCCm05ESSOjYA3Hg+mhVsV9UH5ZO0FumfmWXpw1ll6ct45llX8pG2wZu/7nqMHPj1L931yltm38/Ib8Mh91zdAkMF1fIDBbmE+8HnvGTvFpKn5iogPpmhj7qJSQig8QTkhRiSFUTBYrCTFYgFZBvqU/QGbGJtcySGKrVJOiNpZUbFK2KTYkIDByjicFVuFvcG7yqkR9eoFxiZvaWkxPXxhncemx/dwQUE9wBDkGxurBVbvt5iI09zcJO/gd9A31a5CRNTV/c7ISqWgOBsFx9vlALDHcYkNJFt0AFPSAIEDY/ypjFnI7zWCaD4ZrACMWfu+cFjFx2NJd3VWbGrtFNniagFTpwIcG5+G++gfH50bwWJBFbvtlKNeRZVDi2WNB0F7czY5HApnGupbzYqhzUctlnSrs+KDhhbLymJZx+fKYu36PMjUToXGMUKkhJoW8vDEq7NY2Hhjs1juLemaxRrNkq5ZrI4OhwawqanBZLf0xgZLpOUb7b/V1eV4p6MDv9kpz2CHgYJAs4ba9R6/nZ2XwWM35gFjTQtzzEOyMSfJIVRZXfH9RhBhAxLVyReZGioIIipXi+OizyGHoO5zWMkYkDsAQ5uUb0EQqGghxOLZBoNF0e+oeBDV3umAOIvfYR/dO/OceMlqV3JolyCQ7/g83skOsnRPgLiPQBGwxlAmoP35W5SwDo3Wpxv8zGCniUtVm4CBFHBDR7wG6kLlK2pdwyER8SWu45N+W+StXaftIohDOxUSG6w2Q7IFQZLVRoGQHmHRYg0P9Ytwq1iZVoG1PIFNqjRa4Ot7DFf2VqmH7zXPr+tBsMdG1fXQJr6HTaSdKTjkChSwWOr7ThHm1e92Gq70DQJj03d0dDhktjaFCHgPAj/qKMRzKBbQ78zcdApLCpHxC7VMUQet3ksYN55XVn3PEaS8sphQqqrLqLa+kuwRYXQ+PIQuoATbBUY5GxwsGwWs1sajdvGeXS+xJDY6cT6STgUmcUmkk35xYknGsy3iMKjYs40G/77eiD/ZxuyRx6E4KdtOxdBLyy4KewXXcriUA0YA0rKddq4TTysOxtHeL2KprKaRCsvrhGrtOBUibB+0Tku3+9HLXB9lwWaWTZarNuAmgjYBI6hpwRY/UQ+jbZ99/sz+2chjr00CuCBnjRyfnb60OebhYliQkjO4YGOExSs4lNmt0DglgwBRRAYx6tkjbHJ6g22CXNHcXGdqs2D51rJFaamWQRpFswQreE8PTvA+ZrFKnWQVJYOgXoNZDwWap85OJbgDqfQ72nIPWDs9AgbLB7irS8WQQIuFdvEM7+t2u7u7pADu7e2mZmbL6rm9+qZ6KuL+VNeVyx4qrSiiiMQwNScJQVRR+T1HEKu2BK7W0UlRJpsQbGETguLCTJZmhLOiky+W+5h0HyuLBQq1P9AMY4X37GNGfLqmHprFghZLOwou2RVmdhzCpY4H0SzWQxYW636DxYIK95nFziyW0nb5k9feACcnS91v6/jw3C882IzfwMJrKnFNLNaV0VisilFYrA4n+wIEcXcsFk54az0g4NjOit1OLBbeB5KptntEI6W0WN1OWqyx/pqbm00DJ8YXnRap5upfgcVSvK6auMuXBxSC8MJGGggi7hImggSZm33TKK4m2pvXy4Igmpe3upp4MYIgFgOqXLiUP2psbmzmJwx2CHLE+2uUnICyeKcDQfoHBmgLI4iWDWZv8jfrIc4DyIA2IJg/Y5FBPjJdUvzJkxHEXb8VgthNq7h/RIja7KkGghhIAASJ0AiiZTmZu3BBEO1qEhRlH8XVpHeEqwkQRG3cDpeNXyfI4epq0tfXNaKeVWVrRRCsNWBthNQyCKiI7oMzgtSNG0EwFjW+VopJjzTn5HuPINB0gCwrlWIjRcZFUGRiuJSQqGCK0nBsGO39PJh2fx4iVnAVD2KXAtXwJgPeLk57DniHxJDYhdWC6hSw1D0aICpdlIXM9ryL2AsuHzHFeH+1H01n+G1vP2Gd8B9l3eFQJwqy/4swsz3vPXjPX4rHTj96z8efPmT447VACjtNX2OnN72DaNUuVe89X3/adCTAbb9BEfedCaYdp0Np+6lQpgBhFJUSISU8nhEjVrFTwbzxQwxY5JG4UJmrqCTMXZA5j0ERdmpva5I5xlxj4wKGEyKcFcH6oBQW5stmxTNsVMU6gVL0S5iteqdOYkPAgql6tSJDoB5KWVmR6dQIVkyriyFPwMquYVAr5SRZIzKN7gNYPv2+0qr1mGU0B0c4PyqNGI+xqZZi02JkriKSw6mqpvL7jSBaGBThC6eYqS3pMfTgBimHpZVZsUE+xehKL60+cNFIjODwq/I5pBI7SJy4kdUERQv5Gw0hXeoeCBThGAUI8aQBT1zmoCI49XHig/1CWbo71ImCbDsdoRQFB4OUStpwfQHsY8DbTofRkCVhwYEvQs16Oz9z029ub9luPxoa6HU7YW3tbRINqFisYFNjE8rCKdSoA/3Oc2ca9gZ7DCG9RU5ZLeyinha+QRnwjkP4bhkhzEPD5CqkgxoABqsM5FOCeYcghNZuQUhHXcA63h0wqAoQQ7XdJQiMd5WQXi6wVi4gzHm0PyAPyhWLo+q/hKHweiy2V4Z7yfeA3yjxINeQF8sigzxupAB6bgxL+mgyyFgx6Zs/C6deS3DQvjOhTmps7Slg7ffS3f400NczCoK0UpgVQQwWC9qbZkaQkZZm5UcFj9xv25LuHA/SYranZRCNZKBGIy3pzmpesIbImPK/zllRh2qCpIK8FxbmmuQWoZ1gA/AMrBjiDgC3t9XTOqYgsMKjbDsWoNSuXBD1t1XCdxXbAkdEwBKa+5nSFElh9uY1Dz8p8zexXPDpl/TIrC9pwvwv6Yk5CsZ3b69S/1EWbg3UhhMaHOijfWCxJP4C4a+OtvedCjTjWPZ8Hsqneq+8g7/DXwUbsSvWetCqBTILp8awcvdF6unqouGhYckEImVwSP6D3w6OsYv8AU0V/qMExwQJ69Le2kSd7S0m6wQ+Pj8/R4x+isWqNFkdbEwVA6JZrDyTxbqedQFyKBZJsVXQiuk+OFisOkFKxWLVCSsHBAaMugjZVSwW4HyzLVAWsHpWF5W+vj4p/f0DjGh9xud+o/TJd997BAHpdORfUrp4TVahWtRkWAl2TYaOvIs3RCBFp4ZKCYdFOY35dIaR0kZ76ULdCzWs9orddMxht4DKF3YHOAeuPeg3rs7C0XDh9gvksZcF7P3+Ku2QYdxz9UIW9s/wQvbiungHlEHX83Gpt/1UkIwlSsYEzVMghcTblaVY4CD5HBRjM41hkEU0jEhBqZOgSnxqrMnqlJcXi4EOc6fsES2mrQFslZ5v5c3bZcz3ta8LTnnFIinHQyAO6gMGS4W6gIGsQDr1TqsggG4b74MqoQ0gknZiRH+sFKS+vs6gcu2mdzF+H9Z2xc4px8rGxsb/fSzWIH+HDaRVnyFxDpVmYEyIBD1djcWyxoNYY9LH+hvNku4ujsXTEsdixtwfVEjhzhlz68kgZQCE9oX/Kw2Voc6ND7lmNW9ieoIpg3wXWCxtSb9RLBY2vvt+t5njQx+bm7/nCNLJA+/AiSbCYI/LSVVinlQ6mRlUlP1MQXCqimqTi7YoowRGByt3+UMOSzqQQyjIcSUE+xiJDPSJD4FfyDaEPJdy2fiPKMXyyhry2KcoAITqtUccnrXY+N6CmHYDVm2jDmJetApXu/D7HLJQEFA0RpAIYwyOMamNL8gCODlMDIKKaoQ5jRvIEZ4QalrWE1LjXShIj0FBmk0Kgk1knW9QEJ3KR/tWqXoKya62LspF3UE1FNwpz3CyKwrSYfh5NQiMje+gIJ3yDt7FZ0VBVKxKh8TR95mCeH19vdt+K08BB4Vsarp2BEH7Wuj/Z4cG35I792Oq3ONFtbs9qGH7YirOTKUW8eZESpoC01qrVIrVMrE4ceCE1tjUQA1IXVNSQNW15VIKmKeGtdkvIoQCI4Np41Gb6Q0MeUSQ5qhKD7pRe8ueCKCTgYl0wj+BTgUk0EkugFHO2uNM+LRfJG94JXNsNuLhAa9h2SE2KYtKqxqojEtwTKrIE1oO0XIKqNkeljs2GI6XewxvZTxDTIrDKm5YyWEJZzgkxmE9Bxwa77CkaxgCu7ako8QlxZgbEepbIAbmUVm0lfyBTQtVLOqgwGMX7IlW4aIu3tEevKgz1rpALgHymGtUVmL2AbIKjI0KVvKNekfJJKoPjYLMeBefoTLWfcNvKDZNZXcsLS120281Pi1fKc+Ba88jDKQCVcRvtbW1/FOR5Ja8e+6ixsVTqHX+ROr45DGqzE4Xde/VSLnVf8fqFNfYXCsaHbAasczP+xy0X1NmRWtyZy+dPsd4pjOFuLJYK/bBYbLc7E9SZqFkNflmLFaw6Us1OosVPDqLlfbNWSwdcnttLFa1yDs3Wovl6qxo1dJZ+62Cqr4Zi9XQUPedyQh5S/4/7qCmRS9R+9xnqevjh6kqN4MGebA6DhoDx0D7+hQJV4ve5YQgmASdHbC5pU6c1LBJYtNCyeuA3dzUEiNiSdqgExwgfNfTSNqw2mB/vIwNvc145ilwIK08oOupxNmAIZfklzgstslZhbRiv0JGSbB9WNXzEFnF0R60bKt0pkdBkDBDlgoVpDC9dhMUHGmxpAM2ESfV1ZIeRkksg8AqjrhzbKYrVwZkfrDg2JSAgTTQLmHuUABfvtxv2kKwIQHjO2u90dZFDig+vbVVHKe5XhdtewGs2LcW8x2c9Lpth0tLj1Aa3RZ+QztOaoR112/Nnql6feI+f61/0JypfqvkFf9UBEmYM5My1y+lnDWLKNd7LqWH2agkM4VKM5IpIzVJSGlJSZGQdbAKgKH+6+7udgQ/8Xe65OVlU3hsKEXGh1NUQjgdOhNAh74MpMNcDjK8m09tlIOfB4g1HuUQw3sMGEFaKPKM6x0+42/WO8z1IEMAhor26LkwOnYunA6fDaOYpAwqq6ym8qoaCotJlmyPqLfH0h7ePfy5tT1/1R/0gfsXnRhJUUlR8t/O8wA1Loo93CbuIgJHOL63hQea36OEx6hxo8TER5lzl56eKqwPYMwh2CXAiOvIzMww5w4wvtPzDVWxqlfCz9IFhho3IyPd7brgWQ5TrtL8DCnZqQlUZsBFOWlSV94pLqS8dF0vneFEqYeSm5ls9ic7O9OE8Ru6P/jtjIw0+b2R/c43fwfPkYle//X29ors0tDQIGzU8LD7ZNq1tTVCvaCFg1ZttHrfCoLk/P1Oyv7Hnyjv9lsp/+f/RQXes6lt1lPUNvNxKg+7aDqhaZWgzuDnTEEazFQzKilZh2i6+nq7KDgukKLSwkQNvPPzoBG5eb0OKhZLG++UC7t9ZIbCg4rFWmXUW3cshLotyc9O+cfSir2BzG4F0rbjAc5aLKO9lQdGzz0s6Yosf7B1jEeLZX7PbGVrW4N5cjpb0kvNez+0+tPqrKjn7loyK4Iyua5L79AgNX/tRUOBK2mQS2PARhoO9JDP3eE7eL5UKG17Xz91f71Uvr8U6EntAesY9pC6TVEnqP9Sr8li6bBa/IYoaAzWSVv9Xfut4+b1+KwUBO731jiW78OVDLfkMHJk/89dlPfHX1P+L39EBavnU9u856htzgQqD/cbQwZxH5hj5XUxofYYpQ6OYrZj1+djyCAHx5ZBvFxkEFyV0N7lsHYjvsTz4PWreV3TFZkIMk4Z5GZY0q9VBunlDdd0YTUN2b1pkEtj4BaGvQTujtgtCCIySF8fdV7wkO8H7L7UZtso9VAao09Sv5Ft/kbLINgnjn53fD8QJPHdaZT43tuUPO1VSp46mVK3+1LRurlUtGY2ZYb4UyUPHgsMEgqSCRihmJWVVRKqiQKSigmrqCgzWQfAqBsQ5k9B0XYK5gI2ZhuzPij7T/nLhkdBwmzt2IisKtjEgKH1Avu1zah3gOGtxxW8+6SNF7BOIuD6erroC/9Isw2wb2Z73BbaBAx5Bs8Am/UMGOxae1uLtIck2oHcb2isYCGHsyHGgCIsVpQBg/XS30faqLAoT8aMuQB7gv8KzhBKgWeYQzU/pbKxcnOzzHqAq8z5LhFWRtWr4DayLO1lGvPtvC7l1dVU6LeLqu2qFATspxr7ToHLQ49RWXUVVdZWU2ldPZVe2CLfV3EpDtjH8E6pmx/+BVUyW1RZV025+blUWV3Je6CSSstVn9yNz7nfxeb48MzKYsG4qDNGonwfEmgLBclhCpIPCnLrj6nQd56iILMNCmKyWB3miQYKot2iAUOFqNiELqckyVrnf2mgV6zv7W2NctIMMzlGkBaEYbBfUYnBkhfL24XFUgnYkNc2XOqFxQYLDCEYAvHqI8inFUjL99kkC6Q71snpegdXFktTJC6IN/c6ECjtwVoPVxb919oKK3KvFAi0sAMBhjApJ6XYOJTjoOOELbdoscosV6u1mazKyOTVFeNgsTR7M3Jd+ukylS96jVrnPkut856nCq8PmRN4Rj7XL32dmuZPolZe17olr1Edf26d+xw1L5hENSveoTaug7oVnh9S65xnpV7JivdoOMSbhmyrqCfAm3raatyOz9pvK4ulKEgTfZ//bsm528Ji3apZrOdlssZisUa7qGW86sTS8kLTEzYygRFkn3sWy5qh0Br7DNeO9ceCruqs6I7F8nbDYq0bw1kR9gHdb+j9MRad5lPD2it6NBZLb6CbmlmRhql8yRvUOv95Li9Quc8MOezwuX7Zm9S0+GVR59evmEZ1y98QuHnhZKpeOV2tOdct9/mYWhe8IO+UrvqIhkJXCyvWE7iaelprRmWxHAFT/2KW9Oz7/kZZD/yDcu+6nfJ++wvKX7NATpVmnrBSFtL7oG5EBg3xDXJYbKECHMtii2faSKWvC7NabJHIWdTB4OcZQTz3K1vJOuOaN23tPqZz3GoESVVyARBk4wnltu5zKFhc3NU9gAp2a0k/qCzp2pqP+BTURxsbjgaZlvnle/ypt6dLtCcoMMBpqzEMazo2G5RTC686HgKwihsvuQ5LepmLJd0R5KTrQa2r29b1QJmBKD1DA1S67C3e9C+K6r7M9xO1lvy5dsXbVL9kqsA1Hu8w1VD1Ghe/QpWr3lP1uOAdvItnxV4zaSB0LfWznNIZuIa6mqtMC7l1fNZ+o89WSzoMhTD0YR7Ha/BDvSGuP2TM/2h/w8PO9YYs5YYhSMpLEynllRco9fmnKO2pRyh+/mxKf/FpSp/0FKWuXUGVG+ZS+cZ5VLbHk6qqK0wrLKyn9fUjL2qBxgZxBBpG0e9go+Ad1M3OyaCQ2CDlDRttF4v7FvH+DRRZYYtx0c45WxQ1NSMQB1bnYqqpraBqbr+C262oqqHq2gaq4lJT1yBwTV0jhcel0cZjgWZGerkgyMi8ri/X2cTPg6OSqba+Sd7LLSyjLcfVOxuPBFJsUgTFpERTdHIUFRTmGv2ukjFgLIAxbu2dqzxky4wLb6rFg1fFfldRXl6O6aGLetqzFvXgwavbRiAU0vPoC3RwMjtfoFNlXqCDdyWze0Uh1QTtpcbgXVQfvp9y1y1gKvAhlfvOoJQFH/M68lpOnkAp77xOqa9OEjh5+huUMm2KwGlTJlLye2/ymk+QuikLZ/K7H1G594eUvXEpNQZtE2G/JmAb1ZQXuB2fc7+t46sR72AcMCh1deNLMJeQnk97v4yg/Wcj6Ywtga5ccY9YF8OTaf9XkVKOnougA2cj1OcvQm4Oi1Xw6x9TyvQ3ReWb9/tfUs7iD8SAKKTY+13qNEjnt8linQtOdIl97jF53dGEvKTMAtOSvmYUFgv3e0Qn5zg0LEhXdMi4d5GpWVSS0lxJZo7q0nGxWHCN+NZZLGZ7emxrhA26xKd9mce7isVaOImypr1E+byOeX/4FWU9ej/l/P0OBT/+IGU99D8C5/7lD5T51KNKzc91s955Rd69ESzWyOurxxfCGxybIXe7uNMuWv+OX4w12WVrxkufA343DkFy//J7yv3rHyn/tz+jwp/9O6W89Rojyk+o4Fc/ptwFjBSznqR2CHqeb1GPJAhQFk74EylLao+cHspq3GtaXAHrqDi9qcFyQUhH3bKKYsmbJJZnFtKX71UbWA8SMOwW54KTLLJAq1hs0TbYNCROdveXnFXEgruy2vsarJanWNLVXYZoGwgUk5LnaLujR124cxCuK0GMPMp6DiSGjxn6jL4rhO+WPoDdgjJC9UcljAYMgVzbBrSKluiSPNOsB2BlIa806vUJDIs7nmn2BTC+s9ZD2/gN2ZQdddQf6E2X7avocqgvVSx/mzrmTKCO+c9R9usv8Dr+iPJ5PbMfuJty//RbKvjNTyn7oXso+96/CZx3x22U9diDVHCbWvOcaS/Ku2ijbOX7RGG+dDnIk/ptPvxbyNTIks7lAVMwd+23jkYc2e9esZeNC0FiMsTrAWu264vREeQYI8iqg8GyZuuPOWxnCG+4YQgSuXI5Ra5aQZErllLU8sUUvsZX4MjlSyjm0HZKO72TUk/votQvD1BqajKlp6fI/+TkRIHT0lJMGCUlJYm/Szbh3NwcJsd5zGbkmXVQEhLjKDqOWZj4aIqMiaAvLgTQVxedy5dcgsLCTWsxLLb6ffwGWI2Cgnx5lp+fb8AFFBoZS4fP+NHRL/zpCBf9/zCX418q+MgZfzofGEbRiakUlZBC4TEJ8nv6d20hgVxsFMj/0Vf9u8nJSSaclJRgwpgTPT8yvoR4FzjVbT1rGwpONedVzyO+s9ZLTHS0nZqSSKlRgZTOJS0umBL2b6bkfRsoef8GitmxgeI2eEmJ3sTrirXmtY3w9aRIj2VqnfGdp7H+eLbel+I2eVPcRi+K2r6e0j7ntT+1i1JO76a00IuUHhkoJTEqZJR+J0vf3fU7LS1V1gfrZC3YG52dDrtaTGKauX5f+YeNKrtcDI5Wa8nlmF5XLgdOfn0DKchf/0C5f7uD8n/3Cyr8+X9QytuvUz6fLPl8mhRvX8ro7EnDQV40ELGFuns6TF8s8NTqBlXl8zM0pKiJ9vkBrDL5OdggsFw4WVAXPj+wtg/B2sr8aVhSsNJWJavs6OL7lBaufJost8C6+vyoC2Yct8CibnRShvLFslAQL4Mibf/M2bcLlARXvW06YRdjZmSqJWOJQUGqaspNaqlZRkU560yWQ6tl8b22pOs5GemL1TlOX6wO0xcLY9dzp9MDKV+sTrUuyHKCmJlFrzJb/IxopCq9P6KOuROofd6zVDf3Fcr50++YU/g5ZT98L2Xf93eB8+78DWU+/hCv+c+EiqS98Iyw1/ic/OxT1LHgeeEgGhdOpv7zK2jYtorI7kE18edYGO5344ulFAmu/db1rFlbtAIC89DU1OTEYonP3aGrUxC9lhuOOiiI942kIKPKIL/9BRXtWCbIAR60L3wrdVrsIOORQaDN0JdFumYRhwYIkXLiYMfIohMh6Kx87rxix8Prom50Urps+mtR847mzXstMsh3Ss27wFnNWzN/KmUzKy0yyGOjyyBpk58XpMGzZEaW9sUvigzasGQK9V5YJXth2O5J1XHnaMBwSRmvDIKxOmRI5/FBvvxOyiAJ779DCe9Pp8R33qCkN6ZQrMcSSnnvTUp59w1KO7GLKsOOUgWssJGnRSOhLbfQzMABD59zc7MFRoFmBwuoLcdVVZXioAYrKlglXQ/v412B+f2IODj5RZj/dYlLjBWVIurl5GSZ1lrEUaBoqy0szbrtqLhEOnIulI59jRJCB7+w0f4zSOVjE+dJxKKgwDKv4YNfsNCeFCl5wfA/PCbM7E92TqbZNjQ2GD9gsHjaMo6+YOx6TLA063eysjJEm6fqFTj1G2PS9cY7Pt22rAW3V5yTRmX5mVRWkkcZm5ZRwYYFVLBxIWXu8GJ4vnzOXr+EEt57ixKZQ4j/ZAbFf/iuwInvvkVxn3xESQwnsfwZO3+22gv8OXreHCrctEjez928lEovbKeKgJ1UGbCDciIvUDms99fQ79HqqX1TLPuksaGBwmKSeP0i6OjXESyDJjhlSiktr6QyLuWVVXTyQpi5fshJsMuAd57wo6rqaqmHot9BqW9oukYWi9mr3L/fKadHAbNYhavnCklun/00VYT70aBxsb2+2MXdXXigJvqUB4nVumvoqWEX0QnKEOxjdcV2uFVXqu+NxGqaddInkH4HJNpxh5+Vxepzm4AN7XV2tTBlUNb3mMwIUSHr5HXbPwu03K2Iq9WuWPyG6sxTGipK3bZOl4Oxg03UsRPq0po2S7/LzHdwYIDdUPUUi6XvKMSY3I9vrDsKyx13FLbXUZ/Nl4b4VB8KXUPly6crTwhY0pnF0uxWLVisP/9eWOnsR+5jof3vAuex4J755EPCboG1TnvxWaYet8rn5Oeepo5FL8j7jYtepr4LK8Vf6zIoCLNYlww271rXxXV8mBscICoxXveYUYhrDvrxmtklPzNMAzpMYoORXkpfz+0ta6xCIlYfsgsMzeXZoIR/riXdGuCCuymAFO7UvGCxHLmYrE5xLWM6xV0rKceVy0hgBnYpJj1cfLTG46wIGUuPFRq7m6Xmtfb7+tW8q0eqecdisbSal9f8u8BiQa7CHDnUwaNru9Ye8nd/sdFxl4uN3F3P7WI2GJ8l/Z67KOe+v4oKMP+2n1LB2gXUulBNSlmEv3n644SEfKHVj6MlKMOgkQoTZBH/kUxNW32RtFkjhbbE6yziOnmaNYs46ukkac6J1Tplo1mDgByJ1bqknhYE29qbFIIkh4kfF9S8PkZWkx2W2PU9jCDDjNg65hqbX49PIwuKvq5M+aNVy8EBWMeQ6z5Y+w0WQqumla+aI3FcdXWZ+Km5Sxyns7G7jg++XfrQ6W6poS7bGrpk96GB0HWimm1Z+AK1LHqRyn1nMqJMlM/VC15TYQ28+bOeeJgyWVDPZTiHOYiMCY8xstwmSJH60kTK/fPvRP2bNImRa/FL1MzIVrdkKvVc8KQB/p1BpiJVjCDw+r3edbEmLMTc6Ewtal7r5aLkYVjgLQV/6w77m3kErLecIaRbB9vpK/N8DLva2iOOhIVg2cApjDfe/ZbU11+iIt9ZVOo9g0+f6VQSF0mtLC+01tVQa1OTpMN3LbhrG/yj9sxEkIyyllcZHpyOFJeQH/A9YPCbgLX1XXl1VknOJ21t1/KLslAriy2eoShrsgqmwcmEE0lZtWtEHnCuVy3PsFFDY0PMFKG2cLvkHEaxwogvj0yMoKgkpBcN5Xdr5ZJO57G3iXeq6l+VyGQYF2AsPoprv/EMQWTq7nPd7wqj39XSV3fj07mr3I1PwWp81eUFVBO4neoDt3DZRiV+u6jefzOXLQI3CLyZKs9tpmKfT2SdizYvpMSpL1PqpGfFiyJx+psMP0OpL0yg+I8/oNTJz8nnuJkfUurLExlpnqPk116m6q/Wy+80BG6mgqjzVFdfc93rYh1fU2MdBSMbfnyIzP2X/sG050wY7f0ijA58GUr7vwgVGHnQdp8MMD0kIHPIDQICq+8AIwBOe2NsN7zCtWfGqQswEEeqdY4NG0dM+r13UYMRk97+yePUmJMxzrDI2m9kSbdqsb4tFisyDTc/OTK1W7PXy10XxkVCwQl26ul2n1kRUXG6f98dS7rBYtl9qYk376CbeJDOr72oceFLss6NPtMp/Z6/M9UYW4sFapL3p98JK5bF1Kf7i+U3hcXCFdkIh9ChzMfPO8K0rU6kVqpx1bsjR2GxrM6viPm5upB+1+3UOP9FFsonUMeHD1JT7vgQBJtdxzsrO0ifaT3XZFNlptCp8bvN2GfUVUJ+j3xWsc+9hu2k3RL73Gfq/N3FPo+lb4eAqGOxJX0P3FpSQ8nOCyGXvXAJ0he/IAkc1MzJxtUFiXbuh3sEQXZBILa2pOuNroVOZzuI6rfVDqKEWGc7yNDQ1WPSrXYQbaUXOwiE9ABPuhy4goYCV1FzwHqBJTowYANdCVwun7vPelDzXD4EZz1NTaumUcbf7xKhHKxU5mMPMnutLOnpTEUgvIPdToVN5He/pLxf/5Sy//R76j29SNolbrMm7qtR7CDjWxfr+IiG5IoIXJcQnhIiCLLSUKSsMdhhjTCbLDcJ4FpyWNxRkAbK07B1bThqM20iklrKiCb12B9ER87Z5TfwW8jtdlUEKTy7g4rO76KSr3dS2bmtlJcWTxUSCFMqFnAdmAP/f2uafagawSah5ORkmmpOsBZgudT3WSYMazdIrIrHLpRn+v2srHRRl6p6eeY7qIdnum1Vr9BSL09gvItn+h0EKOl38Lu5zOKgX3gnLT2FChhGXwTm/3iWnpFqxpv7h/hRT68DQRCwpOag1BzDyPFdrd9qfAUFeZa4dPQ1XVS/ruNDW2jT3fisbRdxncIcfpafyf/TKDczReDivAzKyUimEsBc8lNjqZjXuJRLge0QxXz0AcXPmklxcz6l2HmzKf7Tj6VEL13E3ys4avlSytuwhLI3LKPMzSupIC1O2kWbWWlJ32hdnMeH/ZBNJQgSY1nkYlCUmU9g/2mVU0ByCzC8H/kL8Jmfn7fZKCDULsU/2AEHhILlCjDzEVwMcjyLiI2k0rJSKkZMP/+e/hsaGhSbW0xiNEUnRHEd9eyWAftq6ucCgyDZ+GQoyjJP87HuodBqUKXmdc6eoS3A6pL7ZpMd0PdQqOwZNU7ZM+AODhWfvhfcquYdtFjShw2180hLeoXR9sh7NqwnsWZPXLODWLOxBCc6s1gtLU1u1Lw9wmZqWKf+xBhc+63UvAOmOthxP0ifccL2jBifs6XZeXzWdRGBV2c1MS69kXVxyWrS2VRB/YFezB550UDcdkr7850ONe8TD4kvHqhGGssl+bc7q3nbRc07mXoaKqXdQYNC3oh10fOAyFT9FxKbaWavMe9tOWhhqwzq4h8R7DbbTBRTBzic6rtebEgiYtRLzU5xSykQ/huXEWMainOLlCPrLX22NdRreINetq2g6qJMt7yuu5uM3MsgzeOWQZzVvF03VAbR/R5LnWgdnzWfl1UGgUZrvAFT1yqDqM11IyzpLaOo36sc69JQRn2BSjbpi2UE+etdDjXv048q28cINe+z1L7EUPMufoW668tvyroAeZqaXCzpV5FBvAwEcZcrAJ7YiC/S75j313NJyUoeBUEGXRAkVyFIY/J5aki+QC0p56k95RyT4yxRx6LA8o1NrbOKWxEE2in1fa1op1Afn7V2RmXsU9oadZd3jUwQYNTFO7pdlTVQ/Q6oCYL7cWojqx5i3HV/dBZC1NP37AHGu2hD17O2p2MW1N3kjWbWddfxoW8ZzD5kF2ZRJtLfFORQSUUxFZcVSeyJbhunN7yX8Q5iQ/SlNui37o/K2p5vzglYKtiDdH+wiXW/EXfe1tYsYb2I+XYeX5Xb8Tn6rS7aUXOm5hgqZT2v5Zmx1JIXTc1capNtVHdsJTUe86CaL9dR1vQplD/tBcqb/jJlf/o2wxOp4M2JlL5gBuW/NUmepcyfQY37F1Ld7gVUtXcJ1VcUmlkh9fiud13UHNTLveu4UzETbDrPdymX6KRU8o/KoAAufuFJdIxlkqPng7jY6eQFOx1h+AgL27EpibJeKGnZqZSem8JrmEopGQl0ISyZLoRzCUtkqpEudbIKM5mFKxkl+GqY8kvyzfbymZVGjgKX5NWjXzd8Y+/CGz2Dn4rzGCtJ8sjYZ2u/HdqS3hGx3RAQNQVxHR/6bI1e01lNRvPFUqxlrUlB1L3lzhTEObNi/4g7CtEHq1HMetff9V4DbSavHmA2NvozYamgyTK1WPOeN0NuYRBuQYThSsP6PvdZqvCeIRQDzxAPMqzjQWy+1NNa63Z817cuzlfMQcWuT+/8IkcYQldPl2J9U4yEfnGOOJ16S3wJtIuu6zJgzM9YF/+MrqVVe/WW8ZLy8bNYLdfJYnWbG2gsBLmRFltXBLFm8LsagtwIFsvVamxFkG/KYvWxTAcEcafmHXdMuueNDZiyrotr2h8rguSNgiCuTqR1DXVXVb+rw7r/myBIm5GNo9PJ9cH5pOo0LKOXTMcxsBn9A+pKMKWm7TZSkjbLoqmsJuruCpVsrkPI66VL6uTUdhA8UwnKVFIzWJmtCAK/HLzjSFDWayQyazNOB1dv3m5L0rYui4+UOtFghXaMTy2YqjcSQRALry6iLDP63SXyCMaCd5S1vcPsNzaq6o+eOzUnOi+Wa7+VoF8/ImLS0e/R1sW5346Efh2O3LxoGxQkyIeGuHR+7SP+VEjmUO/xFtWteNOBIJ7vURuoBiNFuS8QZJI8K/GaQUNha+R9RC0iq8nAQKfL+K59XfSBa40IjUyKMOWEvGILgnR3in3KzEsQF2KuS71l7np7e5wQBHPhQJCBUbPIXxVBanlzw8ocHGenoFgb5eZlCb/cxvx6fFKMfC8lFpfHqP/BXA96a/1OfmGOvKMzh+tbkXS8tuZPoQfXGcpVFvJ6Izt4oZHgod684FJlB2+QNKe6bdSD8yPq6SzigNvbHVnSUddaD4iMuqpek/Dvuh54Ykf28yqpK+8wn427GuUuP36GDaD7oOUowJDDwFsDxm9AHsD7aFO1rd7Bb+K31fgc/XZkd6+XYu33aOvi3O96l+zudaKKlqzrPH8VjEit7a3UwqWON1Mj89QtXe1U29FJjX5rqT14M7XaN1JtZYF839rdQeXVldTS2S6fS/F+B97HnegNZr9dxzeedUEBYuOkR9paeGPodcbclBQXykWn7UYme9W2Gl9XVzf19fZRb49Kk2RdFz13OHARdIW2ceEo/utidSmpq6sz34GaWWfTVwdLv5PQjndvsVqaQcKQbUSrX5PTE0ysjtDJmV1IHTAZ8eXfVUv6eFksa7+dWBXJzFH7rceDjLYu48nuflXW15pZMdCTWqoLDAp+c9fFGpMO9sohq4yf9bU6kVrlYIxvPL5VVpbdui6j5Ti4pa+vjWLSwik6PYyi0kOpvKKIaQ8waYDSspLke3mWqq4YC00KppDEIApPCFHfpzGC8DvOQrpGkCY5WTSCKDtIt2wMnKSKzegWd3fdaW1HUSTaIQyinorF7rPYE9pN1kmpELtNvbyyEziEXcAQlDVroJOfaXuC9R5xbBDtzq/c3evMPig7SIfp7o6F0v2xIggmX7MgWkh3vie9y7Qu67YBI3b9ymXc9ee8LiXmwaXac87N2zai32gfCKJYMZd70vv7qevsYhoOWE6X/JZRa3X+t7Au3aK1sp7Qur2x1kWFT1x2UQ5pL2vHumDux3PDLlh2PSZwMTolrIoGHRyJIJvlMhr41tvFdG+9YAZWyA1H1DM4j7W1tQvZGbzUT5uO+Eud9YdtlJ6dI4gAsohN09kJI1C3FEXyMMFdxiUsDVIXndMsAyyujvfVBTOAQaJBbvUzWFyBdICVqlXXazLYNNW2tT3UQV3AHR1NRnuqHpwN8Rt4plSlteY7UOcCBtVTZL1RCr5XbEaDbFTNCuIdODS6jhu8cVVVldt+oz1cUqPbTs/KojWHAo0Lghzrgu8ycgoNPtvRNgrmWo/P2m/0EeyWngfr+ICI2GhgU5qbagURvp11KbzOdakz1eSgAGoOQOGqr7ou+A39O6pehcs8KhjJ2PU7qFdTo1IU3eIUkmq9688lhefWU5FOGAa/fC8jfU5WfqFJQVzVpdY/rfJFXZ261KrmtZ5U7tSJo2URd03NOZaaV18X5tq2YkHaTfd+dZI6UxDFYjWOVCcaFuTRrklubm4xhFg9Ptfs7sqSnl9UKGlUXUOFcUFQfnHFqBZgRSFH9lulhB05PsBt7U3mO80tDd+LdQGFtCZ3wFpoKj3aumjKrtsGoo9mKNRaVaU8UdrFW6x5o9yl8DRjtk9HOvFo64zAFU9BkIKr3ggE8gej0LcdMPVtySBjqROBILo/WgbpM68RcPQ7r7BQkMEVQTxwg1ZRudu2gZRd15gr4HrU79+VdbFeu3GtHg7qBq3REQSaNVf1+y3rDXZKp92EkxiCTOBFCX/71Ub6zp2fRzghSFhCDgUn5FFQfB5V1QJjwU5g4J1uhSUgCAyFylDWKpOFScdnnB5KyG2RgWLxtCwDizueoUBr5Li4UpFWvXBgDXQ9wNZ6kBv0vSVgG3Q9tK3VqdBG6fbAGmBzqMltNlOMouB0whgx3tZW/HanMW5nwxfId1+fKojL1/3B+GLT8igqJZeiU/MpNDaZ5bs8hvMoMCKBvA+qtbCuC76zRSRTfGYJxWaUUHkVZJ8e6UNnZ7uwBa79xmdQJ91vPT7AoIjwUsD7GIvSFH0/1sVKQSDT6HfgdaCC7Vpl3PoyUc0u6n6rAK4eWRfMgRVBHGlT0Ual1LluCnI9N5eOltXkxpxUZZaTquyaTyrYbPRJhZNEW/qtIbc3QlsCCrL1VLgZP73dcj/jduO6OVcK4inZWBwu3NGJ6e7vB7H0W7NYut/W8blmm3Gl7N/VdRlJQRquW7uox+eOgli1i/9CCHLzWKybgSDelkuB3F1SunqUdYHMFzUKgtzodEz/GixWm3sEMWSsqyPIEZVN3WecCAJN2ji0aeOSQUZbCKu3q7Z8j1yI1u84BWmQRegXdWKpXIza9w0QxJpTOCox7Z9CQb4L6zI2BWk3VN3VBoJ0mixiP8ISjMMKPlr4XFujFR9XnBDE6mV9i0p1c8VMeXMmMFYWB5TFKoOsO2Ijn/3+tHKfP63Y7c+8cyAFx9vJzv8LinK4I4p/0yo+wNprVfOcwGzNM47F6ypec2xeF/yr5ond87ptxu82WXjddre8Lp7pe8t1DAtyyjpkkCrzHcC6f1CPamS5PHyZ5YpImROUz/38yWNvAM9XAG09xnPG8CqGfQ4ESPTb2iMqyQA2PuYahxTg1YfUfK835BBJOMAySGC4neWUULFHZeSkWsanPAFc+22VQVzHp+5f/76uS4Upj1jXpb4sm7ovelCP/yqq89tA3X5eAncE+FKtxzSqnzuRGmY/R/mrP6WGOc9zmUh5vp/SAO5v9FtJ7X4+1NZYYfS7TRAJ7d7ievLhrj+vgyMpyGpjEV2zn/9vsKSPlvYH47EiSHxGnJkR8iubI64a87jK3R2M46QgCBX1Dw8yrepZuek37WKj7+W6IBivulC8AuAd0By4We5eBNwbtJYaPd9SORfmPkvFa2ebecMK18wiClXOnD32tdTdVjeSxXJFkBMXomnZngBavmfkbbFWBIm2IEhJWaHbgCl9qcxYF9bfjIApVc/dQjj07VdbCOsVc3CRcI8gdab19vLlKxSbFi2eBihnAgJ5HgPl5t0toCD7lJ3J55BKerbKiJHeegzXvqk5BazrQXOFuvh+2d4guhhqM70YMnPTRtxy69pvVxbLOr5/lXVpamumSzREA1cGqbWR2w71peGI1dQSso0Gw9cyvIb6IzZQk+871LboBckzXLx+DrUvfJ7LRCpiZKEIXxoK8aXekHXU0z4OBIFeHfHY/f19FBSdYqocEeq46bhNkX1Gloj4IIniCo0Pltjevr4+cURDahxNFkEOm5trBcZ3UMUBBnkEWdYqQViTtVuKcmCrM26qUpZY/QxkWC1uoxG4o+s1m1Za1EU9/Y6yvtaaZBrx37DSaisvfkOxGdVme/iPCdLsC9gW1FdkvUYSOmCsSLhstaTDAQ9zh7moqnRY3/E7oZb5QgEcnhgi2TwQyRhmwPjOtV5oQjAVlRQZMQ+94oqhx4e+6vFZ+40+gf/X9azj+1dYl1Zuu3yvL9V5vCOl0PNTypjwOGU9/SglTX+TMp99gjKffozSJz0rJXPCo5T15MOU8OF0ynrqEa73CMV/9C41MvtVu/IdqvKeQc2VxWZfYXHHOt5ytYtMVhlJhH0tFGSlCwWprq1yRrAei8W2zWKxbf62LLYV12yxtQZ6OQUeuVjSYXQCtVC2DiBKm1tLumscS1hKiCU5d6jJioXGOlIPWe9gDLc4hyIDR2V1haXtOqeAKeXuPrLfowWy/Uusy2Af1exZZQZ6VS6cRjl3/lbChtMnPi2hxEhlhEuDkMle0hr99meUPGUS5f/u51Iv8ZVJ1D3vaQkia1w6hXoaq0Za0sd7089oMggWr8qCILAmaxI7Hl5Xx4OM9z7uf7YMYtViKV+eNreWdGcEKRsdQWIccdXjRZCGhjoTCW6WDPKdX5dLvVQtCKICvSoXTZPrHRBLn/bCBLkUCjH3uMU5+/5/yPdIUpE89UXz1q3EKS9S17xnVMz90qnUbSCIkyXdFSlyS2ooOqOMYjLLKTwhi0JTirkUUWBsjmSYUAgCVaNecEaQGlcEGbkQOH2wEPpE0qlLrbwuMqfoCQGMTCf6ZMF72pcHz5Q6sc3ItqLUiWoxHYE5eKYtsoBVwFSpOQl6IeSK6naV8hSwvqBTqxaVi7XaKOrkxY1Jl8QSq/JAdRnZWPAbA3wiDoojne5PZUUZFVflU1FFHuWX5lBBWS4VMlxcVUAxyVGOkIKEcCqvK6PS6hKuW0gFpbnyTlFlPlVWIXPIJSkNDfXyf3CwX6zhenzWfqvruassat5mc0N+r9algdm/wM+oy36SOuynqDrwc4Y/o86Qz6l4yYdUNekeKQXTJ1POXb+XZHiCIH+7g/Lu+DVl3/0nyrr/bpV7+veMIK9OlqwtqJc4ZTJ1zjcQZMmr1HbxiPxOj+041WSnyTqOqsVyvcOvraOb1h4NumEURAmD3z8Kcq0GqbHjQXopNSvZRBBcGmoari5dcnJqdI6raLCwtJfc9vt6Kch3bV3a+aBoXPCiEf34stz9Lsm5F02m7CnPqc3OJfPZJykXCHK9FGTJFGparEKScbNBZcgFyRzpFkE8rwFBwkcgyMA1IMjIpA1XWwhMpuZ19e/oW5fcJQewJqWwJm24OoLUmHyvOwRRIbe140YQHXcwbgSRgKIOt+MbG0GcZZDrQ5DvzroIgsxXIcAtGkHmAUFepOxXkMOLNzuXzGeeGIkgDI+KIIxUCkEmjESQWWMiSCxTEBvLHjY3CKJVjzYnBAF/jKt6L18eFskfgiwWV4ezAnbck94pBZOPCcczCG/6yjGkwFGL0SEkFsYl9Y66j1uzBdoZTW8iTLBuGzDexTOddV0na3O0p+761u1hw6h7yVUKUSSd1jAEV/2Ovicdz1BHJ4JT8ffNbvrdKel8dPCP6rdD4E3JTDLzAoPdUirjYRH4rfUc4+uQy4gc9S6Z82Dtt+qf9f56x/jGWhdo7HTyN1znrdv6Z61LS1ke1S96WWLnGxdPleutkbm+eekrlPnaJEl8h5sJ0iZOYGS4UzLTp05+nnLuuUsQJvu+v1LWw/cxzPLJnbdR0uuvqHe4XuJrL1PHIqZMiydT/bLXGUmmyu+0zH2OyhlBermvt1yvbxWs6h77gmj5Hhtl5jnc3bW+HH76WBBMAmCcbleLKFSZFVstF+j0mAkKHBn8+iwX0VgzFJa7XNTSa2qXHBfROCLXrBkKVUSao99XjyjsNFL21JowhGWVkcW1390uF+g4Mivit2t5Q14eHuDSL7A+iR2RkM7js0bzWSMKXfttjSh0Hd9o64I2gUhmwoQu1eY/c13aKwupab66xKedkSL5sUco99c/o5zbf03pzzyuNFJMGTInPC4bX5LfPf805d11u1AXXO+AG33lgqjfGFosXBak5ZGf/Dvl/ubnlHXXH6lxzgsi9HfMepIpyHlxD7puBFk7SjzI/yZL+nfujsIbIIPAAVD/dXR0mgL7P2tdrDJI27IplPL0E+puxT//ntKfe1Ldp+lOBvmbiwzyx5EySPJrL1Hhr34s9XCJVMPcF68ug4z3z3vfBfLYG8gUxJ/Sc/KMy2Y6TT8cwDhtUQBjQnASq0tSuoSU6wt0MHGaXCttS6vTBTr6ghm9KHiG39AXsOhJ1RewaJKv6rWaMQeYdJByXU9rThz9bjVzxqoLftTFP/oyHR0HjY2GZ0ByDWMDou/a4c3ab/ymzmiOetbxufbbMT5Hv13H5+h3h+kvpvuq+41nOHiUdss6Pr0uDW7WRdlEYOeBgwAQRLNb3+q6VFXQwJUh6hu+RG01ZdS45BVqXcLywarX5eZdsFQ5jABgq3KZbULJYKoBaiHs1ovPEW5OA5x9z18o66F7BAYiJDGC5Nz6I8r59U8o4aXnqYCRBuxW1n1/o4ZF6nfaFjxPFaEXqK+/+/oRZGhomEmQKsouoBwe4Xqh3S9QB5ZJR4aKLtN7UtW5MgK+dGlQqIn1+gN9BbNOkmy9BlpdI9BvCoAqEYLjeoCrJ6/uM68vVpGQV4woQIdxUF/1gKJPXp282noNtGJ1nPttTV6troFuM/uNk9NxDbTz+Kz9dh2ftd9yy5ROXt1vSV49oNhYM3l1p+PWJ30thV4XIItuW7No6I/Kd9Zj6XfnzV8Xfqfs3FHqnP88tS96geoWvEa5f7mdCphdymcZI+2Zx9T11XfcRunMVuG6apSMpx/j734jz9KeZ8pyl7qPMeevf2B55K8CFzBS5PuspCtDg3RloJ9aursp7//+P0JRcv5+J+Vx3YI/3EoFt/4XFZ09I325bgQZz5/rHYXWaLDR33F2O75en5+xkle76tvHm17m6qGdLW4Dc8ZKXv2N7yh0yqzYMQaL9c9PKj6udeF3Ss8eFus42B11t+IdKtH2P/5Mqc8/pVisu8bHYuHauewH/mHaQfJWe5tr3NrfR7n//f8JsoHFEiRhOO/XP6bCr85IX24qggwNDkumCq0uvV4E0YH5rjlgHerEvmtWJ7rLzeuKIA41b91Nyc2rkzaYG8jisjEiN2/1NebmNV1NrPEczggCQd6KIGPlTHan5r0p6zLYR2WMIO1zJog6t3b+FMr+i1LZ5vJmTzOQAmwREETkid9Dzfu4uluRn6XB1USreXnTi6sJ1LwQzld7WRCkn3L/8/8oRLr7z8K2Ac771X8Lgly6GQgCtWM5840VTE5LKksotzCTiiryxSrc2tY6LgRBtm+d/UPFBqjJhPpPX0etnMo0X6z4fLU4beJoNjDQKzHFXV3tFit5pxlrgDYBa1Un6lgRRMUatBlxH9Vm29iASo5ok82tLq1sEzVoW34UdeeHUm9BKJXG+FO7/SS12U9RUeAX1Mn/ATfaP6dm+2mB27kUfnWSavbtkJJ78jjV7d1O1ft3UuXB3VRxcI/AdXu3Ue6p42a9wq/Vu2gDbaFNwC2Bp6guM5R6uA/deSFUU5ZvntSQLYAUClYZ6AFjLBiTnhNsYnyPAm2ShiG36Pm+vnVpN+JBlNykkXTEuvA7RWePUvOil1n2mEqVS96hzEcfEEEbl4+mTJ0scPbD91HqKyxP8OZHSZ3yImU9fK88S36DkcqAcfcJkAdwzr1/pYwlC6np6CFqPHKAir/6Qq7Gzn7gbsp88hHKeuxBgbP//ifKP76HegrDbzyCdHU7kg1rL1WdbLixpfEbs1haDWol5SMNae4z+I1lkBqLxRrdUGhhsWAvCN9p3OHnRRUnfYVNgNqwaN0c6pz99AiDVAc/z571rpyAOPlS3n6d+eQfG9qXO83rufFdyvQ3FTsBvnrRBxLbgDbQFvyIxNI85wVqCd1GQ7ijMNCT6oszvnVD4Vjr4kpBNIvlloLMfUYoiPX6arBYIl9YtFjKUHire0PhmJb0Wyn5VWctFoR8oSAsqxTtWEFDQd43B0HCjZuawhNDrxNBusb0GtX6dusNRTq/lEKQepf2Oiz6dofXqNK391gy+A07ecy6evMqO0iDmU3RKbMiNEeMINicl4M8BUHa5zwj+vuidbOpa9aTgiyNi1/h03Gy0rczG5H96XQlaDL5T5n2GhX88r/EMgweGkgC5MF3Ke+8oVgErpuz6H1GrgnSRhO3BS0Pfqd59kQDQbxoyLaKGkoyLF6xrU5e1kASzUJa7SCu3rwjMys6bBXO9o2x18U546U7O4ixLkNAkEM8d0/LmGrnvSKnfB7LD7l/YyH92cfVzVh3/kbZQTAnXGAHwbXWeJbOcoogC4RvRpRsphx4HzdoJb8ySd2mJcgymQp/8V9SL5tZsRzxAP4l5f/yv6mYEQS3rt1wBOns6qSgKFyvHERB0XbJxh2eoHL5NrU0jUuwx4mmkkLXm5euqItj8iVLHtgfIAs0QDrpNO4Z1JfO4FIaeNpC5kG2Q2s9XMmsL3nJy8sRoRbPUAexLF1dkJW65HdRR19Zrd8BOwEVNWAk1q5lVrKurpyq+X910CFqCNhKjQFbKOf0dipbPZtK18ymjJ2+VOH7KcNzGFkWUsm6+QKX8/Nk76WU+tbrXN6g2EXzKe2NVyn17Tcp+f13uEwXOO2NqRTLrAHqpE57nVLWehhtz6Fibqto/UKBS3y42PZxH7bI9c9F6bHUUFtB9dy3qopiqq4qlb5W8X+wO/rqbcRp6PHhfkF9GZC63lslIAdC1MhYq4RyWuexoCBnHOtSLVdCW+cbdUesC1Pu7Aunqdx3FpXxmArWLKDkd9+ReUh99y2KnzNLwe9Mo4TZn1Aa5oRL4iczmMq+xc/eoPh5s/hAmWbM43RK+ug9NY88d7GLF5jvxPCcpr8+VdX7YDqlvPe2avvNVyl7/1pqC9564xGksbWTVu6zSczIwu0XbmjbLS2tQo71rbL6NlWcQA51ae8Ii61WJ+KmWa1xsd4Wi/Y0b60z+IHF0tQFCKtuolV3FJrXhrW30eD5BXQ5YLmEezateJXaPn2KOj5hcj/vbcr7+X9T/q0/pqSXJlLBj/6Nhb+fUPafb6ecP/5GyHj+T/+dUniBwEIJG8ULU/izf5cTDicgVJSAC3/2fynlrdeNej/ik+8lyv/Zf8rtszl/vE3alLZv/w1Ve78nfWibNYEqPKZT+4yHqe3jR6Vvl2ye0tfes4uoo3/ANNo5bh/uFaroquZVtw+3mfcxYg00WzXyltvR12W0W26d1oXbAQXpZIoLClw35yVFGXAj7123U/qER6ngNp47Zpkynn7UnDvIEGCj8CydqUye8Y7M493qRt8CFr5TXp6o3uF6yVNeoMKf/l+e458K9cj90+8UdeE1KDt9+urxINfz18QI4nNI5XBatgsIcvmGtW29YOY7kVmxtYUG/Ja7jX3OXfyeki3cWGxzmFUQtSOzS9j4mo9O4dPvajII6ia/OVXZAlCPWTFdL+cvd1DVmhnK23XBC1TuM0PYFLkb3Xs69QWpuyi7LnhQW2/vdzOzoqnmfUbGYcogppr3yWtS816LJV2vSx4jT+mpkzcPQVYfCiBfRpLlu752QpBhFoKHhwblyt3R8vcq9fCQyA7OZYhJcL1pOcaiYrMq/X6rGfbZ2dksLJauhzqoqy/2UeGczUaYZ6HpNAdBHKwcYBVyWWbCSlvVrDKcMBvQxvx7B/P0VfUN1OnvQ91B66k9aDPVrP9ELqdpWPEmZa6aIxbcrEfukzDPnPv/TlmP3s+n25OUMeExgbMfvJsSZ7wv2pesR+6lxI/eF347ixcrk7/L4LpSjxc4ceZHUgd1E5hlkLb5GQxk2ChZj95HGU88SqWbFkgf6le+TcUb5nNfpsnnmg2zqN22nroC11DLeS+qb20zw2TBRuk5wcbHmPEZ7CT+q/mpMxNSox7mG/Ous5DcsHVhpCn8+jg1LH2N6pZPo/Ll71M6UwqMD3JGEh8OmFNop1LemGLOXeprrzAVeVieJb/1GmUirBbzw3IK7n7H+5izhA/eMd4ZfV2yeL6LznxOgzcDQWAVH7g0JKX/knM6+eMX42jtsTBacyyUztgTR7HQD1FidjxFp0VSdHokhceFyn98jkmKMq9Tdtxk1G2wS2WW6w/KLcJ3myyUEgZHhnZqoVF5urabV147h9wq6zTyKrXWFNJAEAL9V1NL+C5qnj+J2ha+QE3Lp1LeE/dTwR23idVXHOH4pMq//VeUxCdVgXlS/dnUloDsCwUxdPlZjATcSRruZRakhvlyw8CFd5OZgsj1zFwv+Q0LBfmrQUH+oE7Y9IlPcR+47T//jtImP6csw/j8xsvUi9uwehkxWutdhPR6U5B21WI5C+ntTqG52msCsfmO5NzfcF2Y5QUF6RAFB1OQeVNljFYtFtirPK3F+v2tMi8ZYgf5vTxzuLtrCvJ3ixZrHOvCFCR36UxqX/LSzTUUuv4d+jrGTHdzKiB+VASJy4w1Q1JDLCGpUYkRFoPbjbWkWy3NVou0E4vlkl6mJXQ7Nc19QbEwS4Eg9xoW21+qhQD516QcrJOLxVZYrLdfN9mELBY09V9/ayvlMYVBvYLbLGpeLsJi/U65W0jMtcFi5d7tsDTDszX1xecU+8bsRd7rr1CPJbrwm1jSXZNA66vjbrol/W5XS/pTo7BYz6iIwhGW9PGtSx6vS+6SGXIl3beOIPpqhZOjIAhYr3gDQSI1gqRqBIkUge6bqRMd7tda0HS9YGZk0oZ6JwpyiREEqlSoVJtnIyb6WWpa8jLlPXK3nFJKnfiCqb4F31v4i/801Il3qNBQqG8hNLJgjnpYlOyZHzoCz9raKP++v6p6/C7kExEg4bL92suUj4XlZzg1oaKEfSSPN1LaM4YalDdAKp+k0gf4Ib36EvViTngc2t1djxWsk1XNq2+bcqUgWl2urhGotShPHNc7qHUx3N3FF+sa12VI2UE6Zj8t81o711Dzuo7vjt8I1VBz/DPJYKLUvD8XxBFqIr5YSs179XW5k9fldjXft/435S76SGwxNxxB+vv7Ka84l/JK8yirIFP7/snfyYtRtOWEnTYft9ORr0IpNDGXQhJyKCopR/5Lic+ikLgwUQvLNW+x4ZRflE15RVmUmpFs3CdXI6pWBAepLIH1lJyWSOl5aVLSMlNkU+MZ6mChVb0GQ9VYK1ShoCBX7B36DnddD2wGeGLUw4YB74z6eFaRm0Y1exZR3d7FVHxkNZX5fEIVvjOpbO0cSnv/LUp7ZRKlvzSR4uZ+SmkvvyCfY+fNktQzgIEQqa9PYfhFZoG43rzZqh6XhCULue2dVLl7O5Xu3CYqS2lv0nMUt2CuWS9u/mz5DbSRwpQhZdqrUg/tJsz4QOC0KZMpnlk23YdUlnUKtm+ios3rKGfbFqpND6DGFD9qSLpAFSV55pyocdfIZ1xkg/Gr+akw5xv18Kyjo00KUqvqe9pruF59aqC03ZLmR4WxftSUgnKRClOjrr4uvJYZJ49Q/jt84LzH8san78kYR4xv6kssi71vzomocvkQwLMEPmhS+RCR+WZ2NJnZWHfrEscUu+bMKao69RmVnz5J6dOn8bzxurz4PGVs86b6E14315Jujw1goXzYLYu15ZjNhLeesDllcLTHhZksVmJ6gst93CNDO3ECRSdFmZkHEaU3dENDbutMFquZEbV57nNi5UX4Z8vCycqivXQK5T1+r9vQTmWx/ZHh83OXiwzymlHvl8qSjnoSKupOi/VLQwaZIqemQ4v1Z2dfJZY7oOZMffFZsw9Zj9yvfI34Wdo//ka9MduYTfSy3FF4g0Jukfc3dJOwoFfsq6g2YIdhuPSkmsQLNGj6bI3ii8XPc3ZuVWpdp347j2+kL9YTQjVkfBOtIbd3jhpym/zsEw77G7P2hU8+5GRJHw6+KZb0ThNBbC4IcpARBPHuKJuPKaQAvOW4zUx3ijtKrAiSxAiibBWGvt2NW/WVK5cEQYAcKMlAEMOyayXl7tzdre7gYA00wsB4peEmuLvDTZvrtpRkU8ucZ0WVW+n1AbUunETtsGJbWCzo2FOspHyKIuXarToX0W5Cyn8kp79mE8SSbtTLhZs21813y2K9Ikgj9aDnRz0nFuTnIqwqFuun8jn7oXuEhUC9tL/9hfrjtomleJA3rrCNxpzoG6rwWWVFUeyRytribEnXc+dwd+f57mqh/tCNNGz3JLJ7UF3AdoEvA1kSL9LQ5Utcr5d6EIsCVkzc3QeoEvIJ7CW8lrlAED4oXPttHR9kBZPF+o1isfIsLFaelcXS7u4u65Iy4TGH7Hv5MhU9/oAS4H+hLOmXg28CBenp7aaYpGiKTY4RDZSVxTofHEVHz9ro6Fc2+uxrm7BcKGf8Imj3yQC5E3E/l8AwOwVF2sUiHx4VRnn52XINcnJqkgFnUlZOhhTA+QU5worhN1HSMlOFhOMSFGQhx3W/YB2gjkxPTxUY6seMjDT5j88g8bAcA8a1zTk5mQIXc8lJjKHilFgqTo6lnHAbZa5ZQllrl1LKVl/5j5KxwYNZpPmUwCQcFt4or5WUMOcTLp9SJMOJn34sz8AqxTOLFD93FiXOmklRq1ZIHRTA+A7P4ufPofiF8xj+VN5V7X0qbQrMv6HrxXE9PItfMIdili9RMD+LWbHUbDtu8QKpCzh23lzK/2I7FZ3cRIUnNlBuWoI5J9nZatwoWVkZJlxYmC+HC+YUc6vnR9dDFkapx2xrccSXVBr+OZVzyQo4TmUBe6k8YA9lBp+l4tQ4KuJ5LETJ5N/NSaLyvBRKiwmXOS5Jjaf4g3t5fDNH9NtpfMyaxixdZI4vlmFY0AHHeCzjuZ5lzjeK23VZvpTq8/OpOiOdylNTKInnC/OWwPOdvH8zlZ7d9u0K6YkZcRSVFi4lhSfGIZlfokU7/YWa+ICCxCvqEcn1IKRryhASE8Tfh0pSNZXoIETg0ORgqq6pHlUrpnI0uVrS+8xYanxWJ2SnKURCNpETcniAanYso45PHufyGFXNmUK5v+NT5lc/Fl+gvD/eJiQZScvgMg0VIQJuUhCthlOQT3oIhkU/+Tdl2YXFFhkAYdn9+X9QKrNLqh5TkzenKssu6jELgbqAC3/6b0odzPVQNwUs2y//S9XjU1Pqwbr859spbcIjCgaleP4pUQTk3/Zjynngbsr902/kGU7lLGb18hBZ96ufUCPLbHpOdMCVoibVctoDBvWA/cqquQJbpYO+QAUwxxIl2g9hu58u8+lYF3FMWK0rQZ5UfdybOmc8TO2fPkEtiybRgL8XXQ70IApeQcU+M2WOO7lkffym+Ei59ts6PrBIGU89quaOi7Kk3yrvmJZ0rpfD85gz6rpMooL/+H8plyl0Nq9j7h3K+l7ws/+gvIUfUMesp75dBElIjzNVtkmZDjvIYH8vLd3t71YGCRkt82CSQhR3mQddL7kcz33czmpe9xn8IHeMyOA3wmJrJCgTdeIYFlut5hVL+q0OS/ptPxakQ8jo1S3pt6mkBFrNa7E0KzXvs6bVGAYwqIQFfvxBynrof1S/uY16RpCB/q4x1bwqIcXoqVXdWtJxuIQfUzJIkDdVfraa2merNDvNy1+l3sDVIqtQqDcVrZ0j8wvVauas6Wp81n5/Y0v6+Ncl77Z/kpo3LjVGkjGjQPg2T/lL/bT2cCBtPRVKO06FkD2a68SremC19DvBYLkS1DMkdo5MYmqUHEERyWFOublc41MgZKv7LZqFjdKx8mCldIJqbAycntrCC2c+gduaqeTQeqr1eldKkfcssexmPPEQJb3zBmWwoJfx5MOSSQN2hwwW9DIfe4DiP3yXMnkjol78DGX5lnq8eOCRAWc+ep9oXDKk3oMMf6Qs6PwMBj/UlXoP3yNkH3XQZvzHH/C796t6vEl0PcRlJ7LQj9+En1Liu9PMPiBeAgKugidT2tRJlMmncTqX8qQYam2spramGiovLZBxo4Bt0rCKg2mXK5T7+nrl3kWdPkglnm426+l5bGe5rjT0BLUHrqd22wYqPrWJale9TTXe71HVupnUdHEDtfmto67AtZSzZTnVeLxNtSvfppSls9T4rP12HR/LD8ksv8nccYF2ECG4N2RdHrmPMrwXynp/qwgSnxYnmxklMdPZkj5waVDi0Xt7+yg2PUZRB64XHB0k//FZWCwjby2+6+zqYOFZuaKMdusTEMQqYOr8SyqZgiP5gSPZhMovBfcLSYTA7FljxBEasHnTQKA3NXy2irL+codoTFInPyf5lyBEIug/85F7xcYBtgf5l3L///auhDuqKlv/hrfWW++13XaLONCNoLQ0KLS00MqMCMiMJkDCEGYyQRgSCAkRkNnZtrVbRbFft9qIBKFBSMhIhqqkKpWBpJJUUkNSGcgEEdhvf/vcW1MSjBhsW6y1zqpT9557655z77ln7/3t/W3hXxok7hGioA/jdqMfp4JRw6QOAC87ZKHW7iHKDl1E5kEPyD5Q1UiQD9rxtuyli8WZDm2zeNUxASREO5ASjB4h14DAoYu8aojizisLFFIhOOBrEDcM/l958z49hmoSllBd9ExybphDrk9305Uvk+gqF3vqUemz4shSfGW+pA26e78ibVBEHToKrkgp6rykDZ0sxp59T8bu6slEsn3xMnUm76CO5ERqOZZI9tggFrVmUyO/qS0vRVP9hpnk5pIfvlT1z+e6u/QP8enPPaPGBBat6VMEFOyL+2LiFan45W1yzT8IEau3SLqviHXu4lm+ka29cJ/vKmJ9M5LuI2JdaaX6c++oICQWFVw8QQqHDOzlUv5A90t5D0i6R8QaEiBi/Vq5mnQRsXqDpAeKWNwuf/QocuxYrESdKH4oP9mpxCAuzpQj0uc+iUn3FbG4wOR7/aTyQmj7PIFcWqBXU/hkKtkTIc6JNxWxRn77mPRbuS/mX/e7fQFTN11B8rQHnxXuTENGjxNEkHQo6Tln/SbIKVHSFdaBFQS5OHozQXQ37Z7d3dv8WD+UmVcp6XgLNpx/RxRNmCrrPoilQijpPIh5vIybReG+V95WGGTURZEWt+p7PEE6Jf1+Jjcd5lvlVt1fzLc5wfO1dvcod/f+Wju88cTdvb+4wAteAkUT+siCOayk363a4W04/BGFxv/uYXEHF9MuCNRYTIMyimsQZXeoMg7kPzGCXNuDxZ3cHcEP5ScJ0jf00ZX6ISvYV8SsbReGk7Yu7u4q1rzcw2qCOBGdtQXp1PR21+lrqj37Ll1PjqMbydvI/sUhopPbxOzb8Xk81cfMFnN5y9rxVLY7nJoinqFmLsY1i8iC/vldt3//zJq7exHGjgucE+F71Sf3pf9dVHp4K904veP7nSDII1JuLZdSY6/pcYKkZJ6j0yxaoZy7cJZKyoqopNRMBmMeVfADjuMrhNmv8xv/E1zBWDWUidJKRqNB6nBtgEkTEwa/IW6B0tPlgg7ilEAfbLdyu5J/HRHxoJpLyccHKZPFIrzRgWhnLV3E9YWUybJtxsplUs/mlSBlUzSLT0Fcgrm+QfAOabdiCWUiGIrrUNBTfdohgCdbb7c8VM4p5+NtMGmiDdqmbOZ2vPJIOz4Xzol61rJQuhCxTq4te8kiusDXJ+fm39BvcE7U01csJ/PhWCrZHUXFu6Op5NNXpG/oo+XE21T11ftUzaXo9BGysq6BcYAJGGZyfexgOne5nFLKykplwqh2pTyWpdp4V5ApL4uqLPlcDGROPU6244flv6zH+L/2b+aVI5ou8epxIW4T5S5bKOXC1hitf77X7d+/LBY508LXaGMSLGZhbOuL+5K98HnK3ruDyg5v+n4nSO/4tvxFLCjznRqS7usg13vqIS8JdE+sJvA9UjHp17qymnhELCUmuD7YpgKeBuuI7cNe9gyP16gCB/2Q9AcUQq7YM3SvUR8kfZAPkq7Hg4xUCLm8+fiGoo2ICUDSf+ODpI8c2oX1w4OkA2ATRPoPHiQ9/8knWMRaJJY5eCPrIhZi6V0nDogIhN+OY7uo41pnt0h6V2fFtm4T6OhsLGAprDac49Vkm5y/XUSseSJigUBaOHMD++d33d/EajLBh9Xku96Xe8gUo2JpfngTpJMnSH6qR6zKzE33YBO3MkGgpHtj0tUE8TorejlgA2PSdfeSDr6xbhaxrp9KEOS5jieIafCDXsQWBGWDuiK2wD4s4ijodYrzIOnDNCT9wa5IuuAbgqQ/4kXS7w9A0l/oBkkf1BVJhyxu8UHS5X/5+vJHjSTXjoXiAVAfOYNFrEQN7Y4nV/IBkb3RV0yQK9e/7qWzotdzwZcDwOOsCE8IniA3WNzC5Gv/nMdSi6VviZhEWSw26jH3nv4FXHcgkq5i0r3Oin5I+ne4L0U8aUwbw0QE/cFNEDykBlM+ZednccmkfBargNzCORF0Pt9+BekU9Fd3boSTnc1WJXWIWMihp9DzIg0lrpY8hNiGdjauG4++Rea3dpL5zZ2U/+YuSl+3mjLWraKUrZsoHYjv+lWUHrVOlnbUM9euVAj5upXS7hwQ8tVhsi9NQ9FRz0C7uK3SBgX1zLUrRFxI4zZwZJR2a1bQ+W2xWjucO07+A+2AtOv/i/OmbI5R7Xgfrk+/BqDtgjTLNURQzq6tYspEMf3jNSr/4nVBuws/e4tKPtpHpVyMRw6QzeHQxr5Cij6OQNYxTijFxRYeLw1JZ9FL90iAmdhgyFOIO4toxgsn+T9eFWS99NhrVHg4nswHYslyYKt4G3TpX8B1+/WPxavUmGjP2CHWPB0IeV/cFx7vrFf3UPG7u354E+T7/EDf0JV03RSsK+lgNtc/RWvCqOj+X0oxjH9KLdda7HORILb9xCSpENt+4i59EXHogtjeoyHp/ytvR7ylhIWc68WsDMLbFO08Men9tHZ8PoWk3yvbJCb9wV95kHQLkGaIIfz/hYKk99OQ5rEBSLq6BoVID1RI+pgn1FsV2wfez6tinEK7uTg+TpR4dribV0XNvUn4s8sv1lwh6W1aTHqzKO/wfcM+hbh3UI3xPFFyrKxUV1hJr4OSzm/p1vUTKGveLDEo+PXP77r9+ydIOmLSeUzg1aDHpPfFfbHc+zPWP7YSnY6/syeIb66/QDNvU1OTd4KEr+45k5EvxeXtMvP68mLdzMw7ojdm3gAk/bcPkfPIdo+OZf/bTha7potuYI15vmdyjt4g6b4BU3BqzPfqIL5mXqWDzFPX7du/bpH0W6Ae/U8x8/47PgAQW1vh/dsi5fLly/INRBjWGACEQISBAIP6X0+EgyxO0u5KBxljosgwZiSXEfJ2yx/3JBn+OJKygucJImvghy1v8tOsQE6SOkCndKDY3B7t0pct5jc24sy53ZRxlKeh48ZRwyWmQdrx+WFtAfCHffmTnpa2OAbHZqwK81xD+vJQMrLiiXZ5E//Ib9Vx8r94o2a9MEf+Eyh9Fus3+jWA8Rxt0Q4JZnKmT1bXzUpw5V8TqPFTVSo+3EW2rcFUszWIiuPDqK3OSS01lXQZyYIuN3vGrqqqSsYOBeIVwgUwjjBuqPh13zTQLonjv5RzVmLim5L3UP3ne6hqxzKyxQaTY+vzlB4W2rV/Adft2z/cAzzgauz69r4Y+B6YX0+g5pN7fvwTBEq6b6yHohtt9NBn6qzsoA5FsBfIIWBJg6u3xCzwMcaINWJJEcr9mdMkQg1vtZy5z4mFBNFocgP5zVyoEZBlLVSx1GgH9wiIQgUjfiuuJEbQW/KbDcs/zJH6ubOWLRbHPOwz4iF/epQcg21Z/NBIOz5nJq8mQIal3egRck78L9wm4Iynryp4W+rXgBziBiDuqE9VrhtyDItalzaHknPjbHJxKUtaR85N88gZM4dKd4WTS5gg58qEabRd8rDTK0pRFdOhvBMUpSgmA4gddKXeQz3K42zN/Yo6knn1SE6i5s+TqDZuoSK9i5lJGSFBYkXy7Z//dQf0j++BZJK6HfeFV1/Lq9uFBeZHP0F666zYE/VoB+soJuggfYnYBjgrficR67s6K7Jcb90YrBwFI5+lisRVQhkEVPvSztX8rWLuHTxJWuwVt077062INS9AxHqgFyLWxNuHpOvOigM0EetUwp2xguhKI5B03FjdTRtAFhRJnZnc158LznbY/jV1kilsiSC7lvvuFkc380Mwvf6Sl+9xfHMGCDmcachAuRlAaWGqxZsONnZhJJkzg0p/9T9iZkSwFN5aFpZzgZAjjNTygObGHjRP3OKxzyzu7oOljm0STAVlHormfL6x/X+u2iGBDJ9T0GEwfbBIIcRoWp6MYm6PayjEQ4L/5X0FY34v7u5izmQlvSpyATWvmyT0qJXxcPOeTM1rJ1IF6qxEg1e4jidKu6tKIjUxZnjwr13zursjaA3jBcIL/WWEbTpZ3LVrV6i2gJX0U3GCUF89kUDuzXOoOXIqtUdMpCx+cMUI4ds/v+v27x/YTCCGythxMUx4Sh58ixaGUDREHQN0/NveFwsr7mUHNxMlb/3xTxCYjR0Oh6DktbV2Tx0cWxaLWRBiyMkwW0IcUIjwJSosNCoTMIthxvcOk+VgjBTjqzuo6NBmrm+i/NcSqYi/UUwvI9hphYgKmax4p7Degm/8TomJFse4jBCg2KGUsSyEMnhFyOSHHmiutBOUl+ssAmBf+vIQkctxTCYfm7plo7SRc6Mdiwdol8FimWoXJDxPqQggQp1l7dSo9Z5rSFuzgmXuEKmnrgwj82vb5brNh7ZQweFtVLR3AxW9tIEM77xEFaffpIpTb5Dp1F/IvDuKzHs3UuGBLVRSkOsJrCoqMssYopSWlvqZy+ElrQeoIZgK+y5xPT/5n2TeuY5Mu6Ko4MUNlMbXk85jkL5gDqWgfyEB/Qu4br/+sWh6IXw1t1fHXEBAFPdZ+gfzOO9HHWMDr91vc18y+B4Y/naU7A7nT1YsL7lz9zHpHZ3tZJN4kGel2LYvoboNMwWF9sakT9Ni0p/ogtgWfVskfYAPkv7724+kw4PWti1U9S9iKlUdiqbOLxMVkn76ZapfN1X659g4x0/Ewvj0BknXczACKLSePU6NvEoJTVIU60fC56X6lw12F+H9+vZIetEtIOk3uy8IgCu/XcyK/0kfsMD7x6T7cvOqmPQrX3eQ7ZVYvrHgu51EttjFnjj0yoQwcc9ujHpW0hJLTPrA+5SNnZdvsd1rMekl9/2iZyR9oJfd3YOkP+ZF0kU0WBwUgDTfo9oN80HS+Qbr7OdFDw+QmHSLRvuDfBm+SLozfqFKoRD9nPQJeASCmaoPRvLkiKdrydvJefoQuVdNpAbejlzlrQ6rJztvIJKuZ7ntCUmH82MlT5CmtRNkMroiZ/IDr/Xvvp97yPD8+ud73QH987K7ByLpit3dD0kfpSPp/EJCFKEPkt7dfSliMa/8ow/vzAlitVaIeReofEGBUZjbIRaomGswjldJAcou4gL2XzhLlWlnyZp2hiwsJmRFLKfsiDBK3xZDWVErKTsyjLI2rKHcRBZRdkSQMT6cMg8kyLdhRzhlHEykDFbGsYSnrV5OaSuXeZbzlC0xShxYuljVRWwKkTZoK+3gWAdUXNotovNoB3FC2i2ltFXLlai1apmIVTgXTKUpGyM8504D6g5RA/+zdhUVvrKdTPs3UcH+LWT66CCV/m0/lX68jwqOv0elhnQuaWTMOk/FF85QMffbnMp9NxfImGDMlKOnVXQRsOQjaAr7ILb6oulA0uUY3g8Ry8SiHP7XuG8LpXv6F0TnpX+L/fvne92B/WO9MBUIu3YMtqPP+J26IUL24xiMTdqaMKlnLOFzI45dG5PzmljX5b7weF98/SCV/+PdO2+CqDjr1m6SYTb7Kengb9JpMpHSQf9cBqGDxqQhVhTNRRpu1fbwmfJWhlJb9uJayf+BN3PZ3nBR/IRl47EhHlIzKKTi7o6VBhRAvJoU33eX7DMNHyxtUcc2iF8SLw23+OfnKDRYy9Aq7WD1GfaIluTyXhEhIGoIEzr/hmIu/4tcGnB3jw9WSWrE3T1R0O0bXOpSj0gSG4xPrb1K3N31JJ7+7u7+STx1d3cg6XqyT2zzsLuDHI5XkOY142SMXBHPkelxrX/97+JVQ4lVfv0LuG7f/klcy5RxnrEzTBqrLIMDtfsyVN0XeCMUPjFMubHDGsgruzrmXrWy33tX1/ty/91k3hgm9++OmyCKyqZ3Zl5d3GpsbPRsb7YUUaFmWsyTXHhK1i3gAa6JmC3mUjwApbvW87fSW0r3RlBR/194ZV2Ns8mrg9zn780rsvOQ7r15H/KR0dGO/9+jgzz+DToI/newVwdxd+PN6+QJ0nGl9RZ4sdq710GqvN68XXUQvX93i0u/2cM9HKCDDFe6k2//YJ1C3AfGrqsOMtlfB0GOQpxbE3f1Memig+j3ZYDmzft9x6Tfzg/ipPXS0dF+kwliExBL5eCr0DhiwTju9vPmxQOAeHXFaN7g2d5UXkYF08aRacZ4yl0ZItxMBqDZz06i8tilVBO3mGpjF5J5/2b5rolbRKaDW8g8eQyZnptABfOnUz6QXY2JXBDy8aPFfIz4dCOQXMSasxyNCYhzY1vGmpXSBm1hLTNPG6vON3cqGRfM4OuZQIVzp1Fu2EK5NtPMSZTD12eerq7VEDxH3qzCij5rBl3aFkaVUc+TNeoFsh1/g5rPv03NKW9TVfoxQb0B+CGuQ6HlCiHXc61jEkAcVfkL3bKC6OMIBB0x/TgGbcvMedRYY6Gm2hIqPfl3qt38goxRZdwSymcdSfrHq0RO7CYq5hdECeJAIteJJ4BhwhjFiIjrBpo+bTJlLVogfZCYdFbs1ZiMEZ82bMMxiPfQ7wvGEWi8/M+4J8USJuPN7dJXLBVvYWnHYw2Gd4Og6aOo4MUtVHc45sczQfDg6/HTN3OLhxiFiYASWPc/X7Un06zvCtJqqyAHK+ZwtLPuWUfGx4fKW9zwh8eoesMCQaPrWYkv3hMp1i6kW7O8FMWK/HQ+Zg5PmiAyPTVC3lagmUGAjkQPckGQE3ylRLEELf+YkXJuiV1HTDrQZX5LFmxeSfUbZ8r54KZRGxss1+Pc/DxV7FwlddemuVSetJbbzZLfVTHBgpoj5hrodP640Spmm0WQGkOOikPn1cJWU+3hPYYFCiIT6nC/aW9v73Hs9Dq8EXSGd4QKVJ35C105sUPi0iveT6J6sFDyGNVunC9gpeQx5xXSXlMriTJAo1byzp88MfcQnYx/GK5WyidHSEw6tsMBUWLStbHLmzFVrQK8L2f2dIWq8zHisgKwEXRLPHZZQXM1R9DBKiZdW4Ux1ohfF5Sdxbeyox/J9fxoJgisKt0l8bx1C1eNBFJB7vabINXlinqUl9+qXazsPjrIK2JFzpbtuoilTMMBSTxhDh71aDfUo981iSdMtnPICrZHUBRF+STQCUiGiWyugqTj4eDz+9L+9EQ9iofel/bnZuEFehLW7ml/Jmsi1mxl5oVZ9Vf/TU6b1ypW+vab4jB426lHe6CE/VGaeRGjgHhoFKwmPX2Qz+L6dVW6ri5qOzcRVhN9BQHdjf5pqSwj+4px5Fo1gSq2s4j1e37zPP47yhs9St7Srk3z+Y09l4peipZv+DIBhHODQYT31cQuUslcRgyjgt89Qhmhi8k4/FEyPjaUMpeGiCmzkN9kktSFb5oBKdYG9KMMltGNgx8kw6AHyBC5hOrWTCbnuqlUGzmTbJuD5IFz8Ap2KWG11J1Rc6kscS25ImfJ78qoID4fv02HsqI7cQLlwreJJ14+PwxVebyCXO2gtk5+oO01krtDX0F0nQL+VZggPY2dr+eCWkEaZAWpPPs+dSTvpI6TSVRxZDevqrPUOGwKIiOLSoVYHR4eQM5qL/FfyV/fVb5To4aRgcVRjBdSOBeOHU0X+QHH9kJ+44MSSNhLuOSx8l2AFYn3gcQa5mEcA7EKJmDUQfqQCZ8vuJWw4p7Bq7cZKxhfg2E8i3Cjn1D35dHBVHa7UrD9uz5Y2pF8E+Vmbzqgu/A2RVJQmHO9Oky7vD11lnK00xnd3W5vfvfrPHsuf32DWlhdcXZcI2tcMFUnhZF19xqqSFpNlTuWUVX8EipkvQPflfEs5x7cSrZti2VfOYtAlbtWyzG2xGVUdCiWbDuXky1pORXs3UJ5/HZHPEMeL/8OftDqO9up8cZVusSij/vrDimlVZVU39FGdW0t5HDbyXn6ZXIjFcO/DlPVl6+T+8whSc1g5XrDmYPy2376Fao/9yo1pbxMri/3kJufcVBeIOql9syfyX0skdz/TOA3/vvU3NIqXs+1tbUyBig6EzuSCUHHgE9bTx94T4vXdHsHWc8dpfoTe6k+eR+VHNlLVdtC1DjEh7EoNE36mjt6hN8EKf7gPfHgzXtmnGAiOSxKAfOAc2JmyAuyHYo4vJXFC3fKWMoKCZb4F+wDH1jujMnqGNY/cmbPkDrcVGAyRnu0S1u1TBwZcQ04N1ZcuS8JoVR9/sSdiYP4WrHg0q5/rlzp8DjZ+ecHafZbQfweBH7IXKvGK5GBVwukDfMTsSJ8RKx1k7x50jdqedJhAn4x3COKFSesl6AsWebHjqLLjXVd8o1fDbC+tTbwGz9ZMRSCx8l1Yr+INPjtPHFALFOot5zcRe0nVbu2z7ZQu6ZyYS1oPP9noTXCcQ1pR7wGiabLHpbFm8Wk3+zjuvCRAI99KmINvomINThAxHq8tyLWUHJEzJRra+BrrDn12Z05QZRcrW66w2H3myAQDRQZWpNMJLRTpGkN3Z6rhR8yx/KnqJ4fdEf0LLLHzOc6y/zrp1DJi+uVJ2y0UtibVo+TdvYNs2Uy1Ucrd5VSniBu3g7GeEv8WjLdf7eIHKanR1FTg9ODPWBS6BxVukcy9rXUs1L9RaIk9YEy7Pxir9RRHBoBAybO5eTd1Jb8otRbeYK03fBOkIZzb1Mnsmad2E7u1A88fONNTc0eGh89oEwRx10WI0avdLnUI9T5RRx1Jm8n6195Aq6ZoMYhfAaLpkOE57jw5/9FjmrvhCt+63UyiSNlf8qHYyUUcaDqrGhjNQH2hAxS8NqFORsFrIvi9fvoQLFaQYkHFgJlHTkJUQdOguRDcgwI+MSc3F9MxsZRj1FtxGxxynSvf+bOnSB42PWCXOr6BxgIcqpjO77hPqHXMXm6dYSEH5Ipj9yWAnKZDWQruEi1phyym3PJfPxjssYvI2tCGBXu30xNFSXczkhOcz5V7NtI1sQVVLl9CRV9flTa4zhL1gWquZhO1dnpZL2YIcloQGAHsQ/otKL1tEldiX/VPOGtZCs1kKOigGrLjHTJnENOrjsqTFR+5n2qO7FPVhXbicNkP3FQ6tXHD5Ej9UNyX/iA6nhCVJmy5BhnRSGVFxkkoQ28mcGMr48VxkEfHxTkoO/ug3HE8RDD3A0ushYXUpOtlBq51FlLyV1klPGq5+LIzyVnbg45sjOpo9XLcdZst5PjYjbvu8ht8smRlyt1V5FZHDYxMSCeZYUGSx3iViZynM96Vn5nLA+V/ZhMF1lPwUqBOtrB+RHfaJe2diVZYxZR2aZQKo5bQTVHk8jx9yRy/l8i1eSe/8kXq6+9hvXUY0COK86dEHECDoDl25f6T0TkMufVo2ntRKo8/Zk48oFZBUrxtW7ymsDPyTeVmc7Goug+Ff6gi4OYRFc1YM6Z8oEg5BCdWlnE6jipAMEWXk2woqB+FatGtcUvBZtuxboVayCsWGDT18cBset9+ckd+6TXFwtAIfy34B0wZbwiixNfrMmeHCwSL8Org/i+/Ub5Yskx3C5z7gxqiZgsIi4CtzqOqbFCzEqd8aufJkhffsCppT/QVztbqZwnSEP4FBn8S7yS+D5A7sSlSgdhvaTy9D8lcaWeR1xHpHULWhev2KsBOgi3g37QRXe60kYOniDXtFjzlmTWQZLVBLnsM0F8M0x1nSA1tzRBdDMvzgcSvr785PAEkdyMLCJ5vHkHazoIWPe5Lh4OLI5BzxCPBD8k/TnlzMnHZfJkaYmcInojJkj7MaWvwVGzruDcTxOkr1cQxSCvcoyXpJ4m+74osh/YSJZX4kUcA8Iv1qE/JVEtb3fsi6Syr3ilaaqX4xA3Abd7oNCwskG00km1Efetn1vPKa6Y6e0elBu/EcuCdg18Tuvp96jpkwRq/CRRCKrrP0mSuvPTPWT/ZLfUG/6+g2wsmuEYgIFYnTDhoI/5urT3/kXRKV4JemSh213fp+OcOX8WGYF2w3q1ajkVQ9HmYuA60HPsg0XLwKuLEXH9rLDnzpomdURSAj2HeRnt0sKWkjMumGriQqhqx3JqOvUqtZx/g9rOvcYiYPZPE6SvVxA8GMrdu5kqWJFuYzGn7UorVWh+S4In8IPc1NJErR2sZHe2S+oxGAIgnuHh1leFpiZFHqFbjqCYK0fAJg9DvVo96mVyqNh6txgXJOaexZyKt/eIr5Ureqa4tNs3LVD1uFCqjl0kdUcUF0u+x00dTproi46M39rL4rrP8Tf6dJyRNg2oeAEr3U5DvqDdKA0lxazMD5Z9fkg6YvaBpA/XkfR5Cn0fNpgyXphLjRtniFXRvjWI2nm140unazdIsDB8/h83Q76SqxeOMAAAAABJRU5ErkJggg==' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=cdd6975c9d134e84b209054f89a2e5b6' target='_blank'><b>Hot Spots Montgomery 911 calls</b>\n",
       "                        </a>\n",
       "                        <br/>Feature layer generated from Find Hot Spots<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: June 01, 2018\n",
       "                        <br/>0 comments, 1 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Hot Spots Montgomery 911 calls\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=dd96df595ba1461aa255616bc8129b6f' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=dd96df595ba1461aa255616bc8129b6f' target='_blank'><b>Find Outliers Montgomery 911 calls</b>\n",
       "                        </a>\n",
       "                        <br/>Feature layer generated from Find Outliers<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: June 01, 2018\n",
       "                        <br/>0 comments, 1 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Find Outliers Montgomery 911 calls\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=02ebc415c5ce4c52a04349ab0d7959dc' target='_blank'>\n",
       "                        <img src='http://static.arcgis.com/images/desktopapp.png' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=02ebc415c5ce4c52a04349ab0d7959dc' target='_blank'><b>Find Locations in Montgomery 911 calls</b>\n",
       "                        </a>\n",
       "                        <br/>Feature layer generated from Find Existing Locations<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: June 03, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Find Locations in Montgomery 911 calls\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=96b60f0427e74e1bb71dfa4128fa5c94' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=96b60f0427e74e1bb71dfa4128fa5c94' target='_blank'><b>Aggregation of Montgomery 911 calls to USA Census BlockGroups</b>\n",
       "                        </a>\n",
       "                        <br/>Feature layer generated from Aggregate Points<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: June 01, 2018\n",
       "                        <br/>0 comments, 2 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Aggregation of Montgomery 911 calls to USA Census BlockGroups\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in montgomery_calls:\n",
    "    display(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the **montgomery 911 calls** item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mont_911_calls_item = montgomery_calls[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=3b723ebf35e14e4195e5dcf35685678e' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=3b723ebf35e14e4195e5dcf35685678e' target='_blank'><b>Montgomery 911 calls</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: May 30, 2018\n",
       "                        <br/>0 comments, 1 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Montgomery 911 calls\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mont_911_calls_item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the item is a Feature Layer Collection, accessing the layers property will give us a list of FeatureLayer objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mont_911_calls = mont_911_calls_item.layers[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we want to predict number of 911 calss in block group for the year 2017.<br>\n",
    "Our prediction must be based on 2016 data.<br>\n",
    "Apply filter on the layer to keep only 2016 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mont_911_calls.filter = \"timeStamp BETWEEN '2016-01-01 08:00:00' AND '2016-09-01 06:59:59'\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we want to predcit number of 911calls in each block group of the montgomery county,<br> let's use the `search` method again to find US block group layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=d3a11165055747068a5a456c2e2f0e31' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=d3a11165055747068a5a456c2e2f0e31' target='_blank'><b>USA Block Groups</b>\n",
       "                        </a>\n",
       "                        <br/>This layer presents the census block groups of the United States in the 50 states, the District of Columbia, and Puerto Rico.<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by esri\n",
       "                        <br/>Last Modified: February 08, 2017\n",
       "                        <br/>0 comments, 919 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"USA Block Groups\" type:Feature Layer Collection owner:esri>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "block_groups = gis.content.search('title: \"USA Block Groups\"', 'Feature layer', outside_org=True)[0]\n",
    "block_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mont_block_groups = block_groups.layers[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter the block groups by \"STCOFIPS = 42091\" since FIPS code for montgomery county is 42091."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mont_block_groups.filter = \"STCOFIPS = 42091\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<FeatureLayer url:\"https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Block_Groups/FeatureServer/0\">"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mont_block_groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use overlay tools to combine the 911 calls with US block groups\n",
    "overlay_layers combines two or more layers into one single layer using either intersection, union  or erase method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.features.manage_data import overlay_layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersect_blkgrp_with_calls = overlay_layers(mont_911_calls, mont_block_groups, tolerance=0, context={},\n",
    "                           output_name=\"Combine 911 calls with blkgrp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=c8a555fab468454899215691bc8c70fa' target='_blank'>\n",
       "                        <img src='http://static.arcgis.com/images/desktopapp.png' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=c8a555fab468454899215691bc8c70fa' target='_blank'><b>Combine 911 calls with blkgrp</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: June 16, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Combine 911 calls with blkgrp\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersect_blkgrp_with_calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersect_layer = intersect_blkgrp_with_calls.layers[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the features module of you gis content import SpatialDataFrame tools which converts arcgis layer into spatial dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.features import SpatialDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdf = SpatialDataFrame.from_layer(intersect_blkgrp_with_calls.layers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AGE_10_14', 'AGE_15_19', 'AGE_20_24', 'AGE_25_34', 'AGE_35_44',\n",
       "       'AGE_45_54', 'AGE_55_64', 'AGE_5_9', 'AGE_65_74', 'AGE_75_84',\n",
       "       'AGE_85_UP', 'AGE_UNDER5', 'AMERI_ES', 'ASIAN', 'AVE_FAM_SZ',\n",
       "       'AVE_HH_SZ', 'BLACK', 'BLKGRP', 'CNTY_FIPS', 'FAMILIES', 'FEMALES',\n",
       "       'FHH_CHILD', 'FIPS', 'HAWN_PI', 'HISPANIC', 'HOUSEHOLDS', 'HSEHLD_1_F',\n",
       "       'HSEHLD_1_M', 'HSE_UNITS', 'Join_Count', 'MALES', 'MARHH_CHD',\n",
       "       'MARHH_NO_C', 'MED_AGE', 'MED_AGE_F', 'MED_AGE_M', 'MHH_CHILD',\n",
       "       'MULT_RACE', 'OBJECTID_1', 'OTHER', 'OWNER_OCC', 'ObjectID',\n",
       "       'POP10_SQMI', 'POP12_SQMI', 'POP2010', 'POP2012', 'RENTER_OCC', 'SQMI',\n",
       "       'STATE_FIPS', 'STCOFIPS', 'TARGET_FID', 'TRACT', 'VACANT', 'WHITE',\n",
       "       'addr', 'desc_', 'e', 'lat', 'lng', 'timeStamp', 'title', 'twp', 'zip',\n",
       "       'SHAPE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to parse timestamp to get more information from it.<br>\n",
    "We will extend the dataset with more time related values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "sdf['datetime'] = pd.to_datetime(sdf['timeStamp'], unit='ms')\n",
    "sdf['month'] = sdf['datetime'].dt.month\n",
    "sdf['day'] = sdf['datetime'].dt.day\n",
    "sdf['dayofweek'] = sdf['datetime'].dt.dayofweek\n",
    "sdf['week'] = sdf['datetime'].dt.week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to group our dataframe by county FIPS and tract.<br>\n",
    "Framing a new column with combined code for county FIPS code and tract ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdf['cntytract'] = sdf['STCOFIPS'] + sdf.TRACT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    42091203403\n",
       "1    42091205801\n",
       "2    42091201004\n",
       "3    42091200306\n",
       "4    42091203902\n",
       "Name: cntytract, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf['cntytract'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Montgomery County, locally also referred to as Montco, is a county located in the Commonwealth of Pennsylvania. As of the 2010 census, the population was 799,874, making it the third-most populous county in Pennsylvania, after Philadelphia and Allegheny Counties, and the 71st most populous in the United States.<br>\n",
    "So let's take pop2012 as one of the significant predictor for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_columns = [\"dayofweek\", \"week\", \"month\", \"day\", \"cntytract\", \"POP2012\", \"HOUSEHOLDS\",\n",
    "                 \"FAMILIES\", \"MED_AGE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  sdf[final_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_list =   [\"dayofweek\", \"week\", \"month\", \"day\", \"cntytract\", \"POP2012\", \"HOUSEHOLDS\",\n",
    "                 \"FAMILIES\", \"MED_AGE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdf_grouped = df.groupby(groupby_list).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "week         -0.015564\n",
       "dayofweek    -0.012391\n",
       "month        -0.009381\n",
       "day           0.006395\n",
       "MED_AGE       0.047343\n",
       "FAMILIES      0.111348\n",
       "POP2012       0.121887\n",
       "HOUSEHOLDS    0.138449\n",
       "count         1.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = sdf_grouped.corr()\n",
    "corr_matrix['count'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "911 calls have more correlation with number of households and populaation in each block group.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = sdf_grouped['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdf_grouped.drop('count', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use sklearn library to split the dataframe into train and  test sets usin the **train_test split**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_X, test_X, train_y, test_y = train_test_split(sdf_grouped, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11444650121487066"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "model = DecisionTreeRegressor(max_leaf_nodes=500)\n",
    "model.fit(train_X, train_y)\n",
    "predicted = model.predict(test_X)\n",
    "r2_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2575687763457739"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=200, min_samples_leaf=6, max_features=0.6, n_jobs=-1)\n",
    "forest = forest_reg.fit(train_X, train_y)\n",
    "Predicted = forest.predict(test_X)\n",
    "r2_score(test_y, Predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2632751482579958"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(sdf_grouped,y,test_size = validation_size,random_state = seed)\n",
    "model = GradientBoostingRegressor(n_estimators=200, \n",
    "                                  learning_rate=0.1, max_depth=5, random_state=0, loss='ls', warm_start =  True)\n",
    "model.fit(X_train,Y_train)\n",
    "r2_score(Y_test, model.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "oops! The model has poor predictions.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's analyze our dataframe to understand what all factors could affect the number of 911 calls.\n",
    "Using the arcgis online organization you can navigate through the analysis variables availabe in the **enrich_layer** tools.\n",
    "We assume that the factors affecting number of 911 calls could be related to the number of smart phone buyers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enriching the **mont_block_groups** with demographic variables\n",
    "The enrich_layer tool gives you demographic and landascape data for the people, places, and businesses in a specific area, or within a selected travel time or distance from a location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.features.enrich_data import enrich_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "blkgrp_smartphones_enrich = enrich_layer(mont_block_groups, \n",
    "                              analysis_variables=[\"PhonesYellowPages.MP27002A_B\"], \n",
    "                              output_name='enrich__layer_with_1_variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=779a829b099043858068b259680158d4' target='_blank'>\n",
       "                        <img src='http://static.arcgis.com/images/desktopapp.png' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=779a829b099043858068b259680158d4' target='_blank'><b>enrich__layer_with_1_variable</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: June 16, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"enrich__layer_with_1_variable\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blkgrp_smartphones_enrich"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we use the **overlay_layers** tools from arcgis feature module.<br>\n",
    "Repeat the process to predict the mkodel on the test set.<br>\n",
    "Since we saw that month, week and dayofweek have negative as well as less correlations we are going to ignore those predictors\n",
    "and ue enriched variables as our predictors for our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "interect_911call_enrichedBlkGrp = overlay_layers(mont_911_calls, blkgrp_smartphones_enrich, tolerance=0, context={},\n",
    "                           output_name=\"interect_911call_enrichedBlkGrp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_sdf = SpatialDataFrame.from_layer(interect_911call_enrichedBlkGrp.layers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91495, 70)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_sdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AGE_10_14', 'AGE_15_19', 'AGE_20_24', 'AGE_25_34', 'AGE_35_44',\n",
       "       'AGE_45_54', 'AGE_55_64', 'AGE_5_9', 'AGE_65_74', 'AGE_75_84',\n",
       "       'AGE_85_UP', 'AGE_UNDER5', 'AMERI_ES', 'ASIAN', 'AVE_FAM_SZ',\n",
       "       'AVE_HH_SZ', 'BLACK', 'BLKGRP', 'CNTY_FIPS', 'ENRICH_FID', 'FAMILIES',\n",
       "       'FEMALES', 'FHH_CHILD', 'FIPS', 'HAWN_PI', 'HISPANIC', 'HOUSEHOLDS',\n",
       "       'HSEHLD_1_F', 'HSEHLD_1_M', 'HSE_UNITS', 'HasData', 'ID', 'Join_Count',\n",
       "       'MALES', 'MARHH_CHD', 'MARHH_NO_C', 'MED_AGE', 'MED_AGE_F', 'MED_AGE_M',\n",
       "       'MHH_CHILD', 'MP27002a_B', 'MULT_RACE', 'OBJECTID_1', 'OTHER',\n",
       "       'OWNER_OCC', 'ObjectID', 'POP10_SQMI', 'POP12_SQMI', 'POP2010',\n",
       "       'POP2012', 'RENTER_OCC', 'SQMI', 'STATE_FIPS', 'STCOFIPS', 'TARGET_FID',\n",
       "       'TRACT', 'VACANT', 'WHITE', 'addr', 'aggregationMethod', 'desc_', 'e',\n",
       "       'lat', 'lng', 'sourceCountry', 'timeStamp', 'title', 'twp', 'zip',\n",
       "       'SHAPE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_sdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_sdf['datetime'] = pd.to_datetime(en_sdf['timeStamp'], unit='ms')\n",
    "en_sdf['day'] = en_sdf['datetime'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_sdf['cntytract'] = en_sdf['STCOFIPS'] + en_sdf.TRACT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"day\", \"cntytract\", \"POP2012\", \"HOUSEHOLDS\",\n",
    "                 \"FAMILIES\", \"MED_AGE\", \"MP27002a_B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_df =  en_sdf[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_list =   [\"day\", \"cntytract\", \"POP2012\", \"HOUSEHOLDS\",\n",
    "                 \"FAMILIES\", \"MED_AGE\", \"MP27002a_B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_sdf_grouped = en_df.groupby(group_list).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day          -0.010811\n",
       "MED_AGE       0.077202\n",
       "FAMILIES      0.224070\n",
       "MP27002a_B    0.258259\n",
       "POP2012       0.261039\n",
       "HOUSEHOLDS    0.290274\n",
       "count         1.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = en_sdf_grouped.corr()\n",
    "corr_matrix['count'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_y = en_sdf_grouped['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_sdf_grouped.drop('count', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(en_sdf_grouped, en_y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6898870637738551"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "model = DecisionTreeRegressor(max_leaf_nodes=500)\n",
    "model.fit(x_train, y_train)\n",
    "predicted = model.predict(x_test)\n",
    "r2_score(y_test, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7519347455511063"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=200, min_samples_leaf=6, max_features=0.6, n_jobs=-1, oob_score=True)\n",
    "forest = forest_reg.fit(x_train, y_train)\n",
    "Predicted = forest.predict(x_test)\n",
    "r2_score(y_test, Predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7539521971735129"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(en_sdf_grouped,en_y,test_size = validation_size,random_state = seed)\n",
    "model = GradientBoostingRegressor(n_estimators=200, \n",
    "                                  learning_rate=0.1, max_depth=5, random_state=0, loss='ls', warm_start =  True)\n",
    "model.fit(X_train,Y_train)\n",
    "r2_score(Y_test, model.predict(X_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow!! We have dramatically improved results now.\n",
    "By observing the category column of our dataframe we figure out that there are three major categories of 911 calls mainly traffic, fire and emergency.Pondering upon these as our factors we will now enrich our dataframe with relative road density, day time population of workers, unemployed population, diversity index and             ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets add more demographic variables using the enrich_layer tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "blkgrp_enrich = enrich_layer(blkgrp_smartphones_enrich, \n",
    "                              analysis_variables=[\"DaytimePopulation.DPOPWRK_CY\",\"industry.UNEMP_CY\",\n",
    "                                                  \"KeyUSFacts.DIVINDX_CY\",\"Infrastructure.ROADSUM\"], \n",
    "                              output_name='enrich__layer_with_5more_variables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersect_of_enriched_blockgrp_calls = overlay_layers(mont_911_calls, blkgrp_enrich, tolerance=0, context={},\n",
    "                           output_name=\"enriched\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf = SpatialDataFrame.from_layer(intersect_of_enriched_blockgrp_calls.layers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf['category'],edf['category2'] = edf['title'].str.split(':',1).str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf = edf.drop(['title','category2'],axis = 1)\n",
    "cat_unique = pd.DataFrame(edf.category.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>AGE_10_14</th>\n",
       "      <th>AGE_15_19</th>\n",
       "      <th>AGE_20_24</th>\n",
       "      <th>AGE_25_34</th>\n",
       "      <th>AGE_35_44</th>\n",
       "      <th>AGE_45_54</th>\n",
       "      <th>AGE_55_64</th>\n",
       "      <th>AGE_5_9</th>\n",
       "      <th>AGE_65_74</th>\n",
       "      <th>AGE_75_84</th>\n",
       "      <th>...</th>\n",
       "      <th>e</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>sourceCountry</th>\n",
       "      <th>sourceCountry_1</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>zip</th>\n",
       "      <th>SHAPE</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>118</td>\n",
       "      <td>86</td>\n",
       "      <td>116</td>\n",
       "      <td>95</td>\n",
       "      <td>20</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>40.123710</td>\n",
       "      <td>-75.382390</td>\n",
       "      <td>US</td>\n",
       "      <td>US</td>\n",
       "      <td>1451635557000</td>\n",
       "      <td>WEST NORRITON</td>\n",
       "      <td>19403</td>\n",
       "      <td>{'x': -8391529.2918, 'y': 4883935.812799998, '...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>61</td>\n",
       "      <td>302</td>\n",
       "      <td>87</td>\n",
       "      <td>51</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>40.085338</td>\n",
       "      <td>-75.396135</td>\n",
       "      <td>US</td>\n",
       "      <td>US</td>\n",
       "      <td>1451635776000</td>\n",
       "      <td>UPPER MERION</td>\n",
       "      <td>19406</td>\n",
       "      <td>{'x': -8393059.3671, 'y': 4878351.154600002, '...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>117</td>\n",
       "      <td>82</td>\n",
       "      <td>233</td>\n",
       "      <td>275</td>\n",
       "      <td>316</td>\n",
       "      <td>299</td>\n",
       "      <td>104</td>\n",
       "      <td>163</td>\n",
       "      <td>283</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>40.229961</td>\n",
       "      <td>-75.313855</td>\n",
       "      <td>US</td>\n",
       "      <td>US</td>\n",
       "      <td>1451636733000</td>\n",
       "      <td>UPPER GWYNEDD</td>\n",
       "      <td>19446</td>\n",
       "      <td>{'x': -8383899.9438000005, 'y': 4899416.015000...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>127</td>\n",
       "      <td>116</td>\n",
       "      <td>139</td>\n",
       "      <td>229</td>\n",
       "      <td>203</td>\n",
       "      <td>354</td>\n",
       "      <td>299</td>\n",
       "      <td>97</td>\n",
       "      <td>160</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>40.166612</td>\n",
       "      <td>-75.067830</td>\n",
       "      <td>US</td>\n",
       "      <td>US</td>\n",
       "      <td>1451636769000</td>\n",
       "      <td>UPPER MORELAND</td>\n",
       "      <td>19006</td>\n",
       "      <td>{'x': -8356512.588300001, 'y': 4890183.4439999...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>58</td>\n",
       "      <td>109</td>\n",
       "      <td>203</td>\n",
       "      <td>123</td>\n",
       "      <td>99</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>40.113372</td>\n",
       "      <td>-75.341397</td>\n",
       "      <td>US</td>\n",
       "      <td>US</td>\n",
       "      <td>1451636825000</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>19401</td>\n",
       "      <td>{'x': -8386965.905200001, 'y': 4882430.8924999...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE_10_14  AGE_15_19  AGE_20_24  AGE_25_34  AGE_35_44  AGE_45_54  \\\n",
       "0         20         23         29        118         86        116   \n",
       "1         10         14         61        302         87         51   \n",
       "2        104        117         82        233        275        316   \n",
       "3        127        116        139        229        203        354   \n",
       "4         59         58        109        203        123         99   \n",
       "\n",
       "   AGE_55_64  AGE_5_9  AGE_65_74  AGE_75_84    ...     e        lat  \\\n",
       "0         95       20         66         16    ...     1  40.123710   \n",
       "1         24       12         16          8    ...     1  40.085338   \n",
       "2        299      104        163        283    ...     1  40.229961   \n",
       "3        299       97        160         61    ...     1  40.166612   \n",
       "4         68       83         34         19    ...     1  40.113372   \n",
       "\n",
       "         lng  sourceCountry  sourceCountry_1      timeStamp             twp  \\\n",
       "0 -75.382390             US               US  1451635557000   WEST NORRITON   \n",
       "1 -75.396135             US               US  1451635776000    UPPER MERION   \n",
       "2 -75.313855             US               US  1451636733000   UPPER GWYNEDD   \n",
       "3 -75.067830             US               US  1451636769000  UPPER MORELAND   \n",
       "4 -75.341397             US               US  1451636825000      NORRISTOWN   \n",
       "\n",
       "     zip                                              SHAPE  category  \n",
       "0  19403  {'x': -8391529.2918, 'y': 4883935.812799998, '...         0  \n",
       "1  19406  {'x': -8393059.3671, 'y': 4878351.154600002, '...         0  \n",
       "2  19446  {'x': -8383899.9438000005, 'y': 4899416.015000...         0  \n",
       "3  19006  {'x': -8356512.588300001, 'y': 4890183.4439999...         0  \n",
       "4  19401  {'x': -8386965.905200001, 'y': 4882430.8924999...         0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0 = EMS (Emergency Medical Services)\n",
    "#1 = FIRE\n",
    "# 2 =  TRAFFIC\n",
    "CATEGORIES = {'EMS':0,'Fire':1,'Traffic':2}\n",
    "edf['category'].replace(CATEGORIES,inplace=True)\n",
    "edf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf['datetime'] = pd.to_datetime(edf['timeStamp'], unit='ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf['day'] = edf['datetime'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf['cntytract'] = edf['STCOFIPS'] + edf.TRACT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"day\", \"cntytract\", \"POP2012\", \"HOUSEHOLDS\", \"category\",\n",
    "                 \"FAMILIES\", \"MED_AGE\", \"MP27002a_B\", \"DIVINDX_CY\", \"DPOPWRK_CY\", \"RoadSum\", \"UNEMP_CY\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf =  edf[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_list =   [\"day\", \"cntytract\", \"POP2012\", \"HOUSEHOLDS\",\n",
    "                 \"FAMILIES\", \"MED_AGE\", \"MP27002a_B\", \"DIVINDX_CY\", \"DPOPWRK_CY\", \"RoadSum\", \"UNEMP_CY\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf_grouped = rdf.groupby(group_list).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "vdf = edf_grouped.copy() # making a copy for use in visualization later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day          -0.010811\n",
       "DIVINDX_CY    0.060770\n",
       "MED_AGE       0.077202\n",
       "UNEMP_CY      0.119524\n",
       "FAMILIES      0.224070\n",
       "MP27002a_B    0.258259\n",
       "POP2012       0.261039\n",
       "HOUSEHOLDS    0.290274\n",
       "RoadSum       0.306760\n",
       "DPOPWRK_CY    0.400352\n",
       "count         1.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = edf_grouped.corr()\n",
    "corr_matrix['count'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "enriched_y = edf_grouped['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf_grouped.drop('count', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(edf_grouped, enriched_y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6970121511890337"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "model = DecisionTreeRegressor(max_leaf_nodes=500)\n",
    "model.fit(x_train, y_train)\n",
    "predicted = model.predict(x_test)\n",
    "r2_score(y_test, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7525701313527052"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=200, min_samples_leaf=6, max_features=0.6, n_jobs=-1, oob_score=True)\n",
    "forest = forest_reg.fit(x_train, y_train)\n",
    "Predicted = forest.predict(x_test)\n",
    "r2_score(y_test, Predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7638603774109038"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(edf_grouped,enriched_y,test_size = validation_size,random_state = seed)\n",
    "model = GradientBoostingRegressor(n_estimators=200, \n",
    "                                  learning_rate=0.1, max_depth=5, random_state=0, loss='ls', warm_start =  True)\n",
    "model.fit(X_train,Y_train)\n",
    "r2_score(Y_test, model.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x3458ccf8>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3458cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(vdf.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cluster map explains that the count for 911 calls is the most correlated to DPOPWRK_CY.<br>\n",
    "The color matrix of the \"count\" row shows that 911 calls are correlated to 'RoadSum', 'POP2012', 'MP27002a_B', 'FAMILIES' in the range of 0.3-0.4.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}