01-ACS Census Data by City Ward.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Open Data Examples** | Get American Community Survey Data by Ward\n",
    "This notebook will walk you through the following:\n",
    "* Extract American Community Survey (ACS) data from the City of Cleveland Open Data Portal using the `arcgis` API.\n",
    "* Apportion the American Community Survey data to Cleveland Wards using `cledatatoolkit`.\n",
    "* Calcuate custom proportions and margins of error for the apportioned data.\n",
    "* Visualize the data on a map using `geopandas`.\n",
    "\n",
    "For this specific example, we will be focusing on *disability* related data. More specifically, we will calculate the following:\n",
    "* Number of people with disabilities by ward.\n",
    "* Percent of each ward's population with a disability.\n",
    "* Margins of error for both the count and percentages of people with a disability.\n",
    "\n",
    "To run this notebook, you will need to install the following packages using `pip`:\n",
    "1. `numpy`\n",
    "2. `geopandas`\n",
    "3. `arcgis`\n",
    "4. `cle-data-toolkit`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "\n",
    "from arcgis import GIS\n",
    "\n",
    "from cledatatoolkit.spatial import apportion, build_aggregator\n",
    "from cledatatoolkit.census import calc_moe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract ACS Data from the Open Data Portal\n",
    "First, we want to establish a connection to the Open Data Portal using the `GIS` function from the `arcgis` API for Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Connect to the City of Cleveland ArcGIS Online service.\n",
    "gis = GIS(\"https://clevelandgis.maps.arcgis.com/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will load in the current ACS data for the City of Cleveland using the `GIS.content.get` function. Then we will convert the `Disability Status` layer from the `item` to `GeoDataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#First, let's get the ID of the ACS data in the Open Data Portal:\n",
    "acs_id = '89c639f534684dbaab9218d2227580ba'\n",
    "#Then fetch the content from ArcGIS Online\n",
    "item = gis.content.get(acs_id)\n",
    "#Get the layer by searching each layer's name. If the name matches \"Disability Status\", include it in the list. \n",
    "#Then extract the first element of that list\n",
    "ds_layer = [a for a in item.layers if a.properties.name == \"Disability Status\"][0]\n",
    "#Convert the layer to a spatially enabled dataframe\n",
    "ds_sdf = ds_layer.query().sdf\n",
    "\n",
    "#Extract the Coordinate Reference System (CRS) from the layer service definition\n",
    "ds_crs = ds_layer.properties.extent.spatialReference.latestWkid\n",
    "#Now build the geodataframe from the spatially enabled dataframe, we will standardize the coordinate reference system (CRS) to EPSG 3734, Ohio North\n",
    "ds_gdf = gpd.GeoDataFrame(ds_sdf,geometry=ds_sdf.SHAPE,crs=ds_crs).to_crs(3734)\n",
    "#Filter the geodataframe for census tracts in Cleveland\n",
    "ds_gdf = ds_gdf[ds_gdf['PLACENAME'] == 'Cleveland city']\n",
    "#Display the census tracts\n",
    "ds_gdf.geometry.boundary.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we subset the dataframe for the following fields:\n",
    "1. `GEOID`: This is the geographic identifier of each Census Tract in the dataset.\n",
    "2. `NAME`: This is the long form name of the Census Tract.\n",
    "3. `B18101_calc_numDE`: This field represents the total population with a disability in a Census Tract.\n",
    "4. `B18101_calc_numDM`: This field is the margin of error of the above estimate.\n",
    "5. `B18101_001E`: The total civilian noninstitutionalized population-- this serves as the denominator when calculating there percentage of people with a disability.\n",
    "6. `B18101_001M`: The margin of error of the above estimate.\n",
    "7. `geometry`: This field contains the actual polygons that are plotted on the map.\n",
    "\n",
    "How did I know the fields in the above list represented disability estimates and MOEs? I looked at the [service definition](https://services3.arcgis.com/dty2kHktVXHrqO8i/ArcGIS/rest/services/Census%20Data/FeatureServer/13?token=66ulAOka41ncUmGicOt-Fo1VlAE_4uSslM4TPISUrKSKTMju-Mi6aOKDOC--udHq3F9I6SvQuWDohNnilQ6HxX_DitGuUOOJ353epIThELU2xvTtYJkxKuSswCXOyhwSd9ycV8ckoJG5WrFjnKqoLSnm-KGwbyqiIP4U1qAqSrdVTsaWymiV7i8HeuQ5Ob4c4w9t9nNbzsNuY4p0qx_6mLytfdJ13I3Tj6bjmagYh00.) of the layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>TOTAL_POP</th>\n",
       "      <th>TOTAL_POP_M</th>\n",
       "      <th>POP_WITH_DISABILITY</th>\n",
       "      <th>POP_WITH_DISABILITY_M</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39035101101</td>\n",
       "      <td>Census Tract 1011.01</td>\n",
       "      <td>1977</td>\n",
       "      <td>431.0</td>\n",
       "      <td>458</td>\n",
       "      <td>142</td>\n",
       "      <td>MULTIPOLYGON (((2175866.166 661802.020, 217580...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39035101102</td>\n",
       "      <td>Census Tract 1011.02</td>\n",
       "      <td>4298</td>\n",
       "      <td>660.0</td>\n",
       "      <td>608</td>\n",
       "      <td>193</td>\n",
       "      <td>MULTIPOLYGON (((2173732.914 664642.902, 217355...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39035101300</td>\n",
       "      <td>Census Tract 1013</td>\n",
       "      <td>1519</td>\n",
       "      <td>213.0</td>\n",
       "      <td>347</td>\n",
       "      <td>100</td>\n",
       "      <td>MULTIPOLYGON (((2172701.481 661838.899, 217236...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39035101400</td>\n",
       "      <td>Census Tract 1014</td>\n",
       "      <td>1711</td>\n",
       "      <td>322.0</td>\n",
       "      <td>469</td>\n",
       "      <td>127</td>\n",
       "      <td>MULTIPOLYGON (((2171375.440 657036.740, 217137...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39035101501</td>\n",
       "      <td>Census Tract 1015.01</td>\n",
       "      <td>2013</td>\n",
       "      <td>286.0</td>\n",
       "      <td>459</td>\n",
       "      <td>131</td>\n",
       "      <td>MULTIPOLYGON (((2172883.465 661482.268, 217288...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         GEOID                  NAME  TOTAL_POP  TOTAL_POP_M  \\\n",
       "0  39035101101  Census Tract 1011.01       1977        431.0   \n",
       "1  39035101102  Census Tract 1011.02       4298        660.0   \n",
       "2  39035101300     Census Tract 1013       1519        213.0   \n",
       "3  39035101400     Census Tract 1014       1711        322.0   \n",
       "4  39035101501  Census Tract 1015.01       2013        286.0   \n",
       "\n",
       "   POP_WITH_DISABILITY  POP_WITH_DISABILITY_M  \\\n",
       "0                  458                    142   \n",
       "1                  608                    193   \n",
       "2                  347                    100   \n",
       "3                  469                    127   \n",
       "4                  459                    131   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((2175866.166 661802.020, 217580...  \n",
       "1  MULTIPOLYGON (((2173732.914 664642.902, 217355...  \n",
       "2  MULTIPOLYGON (((2172701.481 661838.899, 217236...  \n",
       "3  MULTIPOLYGON (((2171375.440 657036.740, 217137...  \n",
       "4  MULTIPOLYGON (((2172883.465 661482.268, 217288...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Subset the dataframe and rename the fields\n",
    "disability_status = ds_gdf[['GEOID', 'NAME', 'B18101_001E', 'B18101_001M','B18101_calc_numDE', 'B18101_calc_numDM', 'geometry']]\\\n",
    "    .rename(columns={\"B18101_001E\":\"TOTAL_POP\",\"B18101_001M\":\"TOTAL_POP_M\",\"B18101_calc_numDE\":\"POP_WITH_DISABILITY\",\"B18101_calc_numDM\":\"POP_WITH_DISABILITY_M\"})\n",
    "\n",
    "#View the first five rows of the result\n",
    "disability_status.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Ward Boundaries from the Open Data Portal\n",
    "Now that we've extracted the ACS data, we will need the ward boundaries in order to merge the disability prevalence information. This process is very similar to the above section. We will use the `gis.content.get` function to extract the ward boundaries item, then we will convert the item's `layer` to a `GeoDataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m00lMWXYEvcmStNk6wJXlj4fj7aqt1vnOJObYAosOjd0bTGABIrgQBEEQbgIKQAbGdmvCixGt8XV1QKNqGGsWJEli2vKj/H08EbD0dcrglte1bDQmPd9ug7XXR7a73m7WKLEUtRxiKaogCMKN4Y/Dccz47RhgqQ9x8p0Iu1GL+nIuOZf7F+8lLd8AgJezhp8f7027II9qn/NkfDYPfbefjHwDQR6OfDupJ+0a1c09SixFFQRBEP4z7u7WhGm3tgTAJMks2xdbzz2CzzafJ2L+DltgEdEugAOvDrmuwGL3hTTu+3ovGfkGOjR2Z/WUfnUWWFSFmBYRBEEQbgrTb2/NxbR81h5P5N21p/F01nB3tyZ13o/sAgP3fr2XM4m5AGhVSuZP6MKI6ywtvuLgFWb9cQKTJNOzmRffPNQTD+fSZc0bAjEtUg4xLSIIgnBjMUsys/44zm8ldhJ99taWzLi9dZ3su7HuZCLPLT+KoShps22gG7880QdP5+olbYKl4ui8jef4bIulQNYdnRvx0T2d6mXap7L3RTFyIQiCINw0VEoF79/dCSeNiqWRMQB8vuUC+Xozb95Re0mPkiTxzM+HWX8qGQCFAp67rRXPD2l9XefVm8y8svIEq47EAzBlcAteuL0NynpcTlsZIrgQBEEQbipKpYK372xPqK8LbxfVvriUlldr10vMKmTovB3k6k0A+Lho+eWJPrQOcLuu82YXGHnix4Psi85ApVTw3l0duLdX05rocq0TwYUgCIJw05AkmXmbznEpLR+pRKVOqRYTAN5ff9YWWIzsGMjn93VFqby+9RKx6QU8vGQ/l1LzcXVQ87+J3RpUHYuKiOBCEARBuGmcSsjh8y2lN+/ycal+zkNFbmvrz+qjCQBMH9L6ugOLI7GZPLb0IOlFS02/f6QnYYE3Vt6fCC4EQRCEm0ZidiFgCSaeH9IKhUKBg1rJ4DD/WrvmnZ0bM/P3ExQazcxZd4bvHu5V7XOtL0oI1Zsk2jdy57uHexLgXvHuqA2NCC4EQRCEm4Z1+qPQaObB8GZ1dt1hHQJZdSSenefTkCSpWqMXv+yP5dVVJ5BlGNzGjy/u74aLw415mxZFtARBEISbxoHLGQCE+rrU6XVfGR4GgNEs21apVMWvB2KZ9YclsBjfowlfP9Tjhg0sQAQXgiAIwk1izbEEvt0VDYDXddSVqI4Ad0eaFwU03xX1oTIkSeajDWeZudKy8dqYro35YGwn1A1kX5TqunHDIkEQBEEosvZ4ItN/PQrAsPaBtVrToizJOTqScnQApBeV+66Izmjmpd+P89cxSzLotFtbMr2Oin3VNhFcCIIgCDe0XefTmLb8CGZJ5p7uTfhwbKc6LTJlMknc/ul2CgxmFMD7YztWeExWgYHBH28js8AIwEf3dGJcj+Ba7mndubHHXQRBEIT/vF8OxGKWZAa19uODGggsJEmqUvtL6fnk6Cx1Lu7v3ZQ7Ozeu8Jgvt1+0BRYAgR433oqQ8ojgQhAEQbih5RXd2Ed1CkJ1HYHFsSuZhM/dTPNX17F0z+VKH9c6wA2non0+Knv929sGMLC1H44ay2340SUHWX8yscp9bqhEcCEIgiDc0Ixmy0iDVl29W1qBwcSjSw4weuEeErMteRObziRX6RyujpYsg72X0m2blpWnRzNvfpjci2NvDWVEx0AMZsveJCsOXqn6G2iARHAhCIIg3NCse3srq5EIuXTPZbrM3sjmsyl2z+uN5iqdx6loBOJcch7hczdjqkSAAeCgVvH5fd0Y36MJkgwv/X68SqtNGqoqBxfx8fE88MAD+Pj44OTkRMeOHTl48KDt9eTkZB5++GEaNWqEs7Mzw4YN4/z583bn0Ol0TJkyBR8fH1xdXRk7dizJyfZRYmxsLCNHjsTZ2Rl/f39eeuklTCaTXZtt27bRrVs3HBwcaNmyJUuWLCnV34ULF9KsWTMcHR3p3bs3+/fvr+pbFgRBEBowa70qSa78BiJnE3MY9OFW3lpzCoNJQgGM696EsEC3onNVrQ/jewSjLpoSSc83MGXZ4Uofq1Iq+GBsJx7tHwrA7L9Ps2DT+QqOatiqFFxkZmbSr18/NBoN69at4/Tp03zyySd4eXkBlj3n77rrLi5dusTq1as5cuQIISEhDBkyhPz8fNt5pk+fzl9//cWKFSvYvn07CQkJ3H333bbXzWYzI0eOxGAwsGfPHpYuXcqSJUt48803bW2io6MZOXIkgwcP5ujRozz//PM89thjbNiwwdbm119/ZcaMGbz11lscPnyYzp07ExERQUqKfYQqCIIg3LisIxbma0QEuTojJ+OzOXYlk8gL6Tz4zT6GLdhJTEYBAC38XNjywiA+GtcZVdG5MguMtlyOyph6aysuzBnBqE5BAGw4ncyeC2mVPl6hUPD6yLbMuN2yRfu8Tef4aW/Vi3E1FApZrnyo98orr7B792527txZ5uvnzp2jTZs2nDx5kvbt2wOWrNvAwEDmzJnDY489RnZ2Nn5+fixbtox77rkHgLNnz9K2bVsiIyPp06cP69atY9SoUSQkJBAQEADAokWLmDlzJqmpqWi1WmbOnMnatWs5efKk7fr33nsvWVlZrF+/HoDevXvTs2dPvvjiC1tfgoODefbZZ3nllVcqfL85OTl4eHiQnZ2Nu/uNtWmMIAjCf8XD3+9nW1RqqeWcBpPE9F+PsPZEUpnHOWqUvHVHe+4rsY35nV/s4nhcNgAKYMsLgwj1c61UPwoMJkYs2MnldEvQ8u7o9jxQjRLkn/wbZdt8LcTHmcUP9qBN4PVt315TKntfrNLIxZo1a+jRowfjxo3D39+frl278vXXX9te1+v1ADg6Fi+pUSqVODg4sGvXLgAOHTqE0WhkyJAhtjZhYWE0bdqUyMhIACIjI+nYsaMtsACIiIggJyeHU6dO2dqUPIe1jfUcBoOBQ4cO2bVRKpUMGTLE1uZqer2enJwcuy9BEAShYbOONpScFll3MpEus/8tM7BQKiyrNY6/FWEXWIBlTw8rGdh2LrVSfZAkiUEfbbMFFnd2blStwAJgxu2teWJgcwBi0guImL+Dp348hN5UtTyQ+lSl4OLSpUt8+eWXtGrVig0bNvD0008zbdo0li5dChQHCbNmzSIzMxODwcAHH3xAXFwciYmWJTZJSUlotVo8PT3tzh0QEEBSUpKtTcnAwvq69bXy2uTk5FBYWEhaWhpms7nMNtZzXG3u3Ll4eHjYvoKDb56CJoIgCDcrhS24sDw2mCSe+ekwBQbLzXhUpyDOvTucy++P5PL7I7k0dyRfT+pR5uqSns288XLW2B7rjZVLzEzLM5Caa/mAHeLjzGf3db2u9zNreBhvjrJUGVUrFaw/lcRX2y9V+5x1rUrBhSRJdOvWjTlz5tC1a1eeeOIJHn/8cRYtWgSARqPhjz/+4Ny5c3h7e+Ps7MzWrVsZPnz4de9vXxdmzZpFdna27evKlZtjSZAgCMLNKiPfQEKWZZt1a86FwSRhHcMY3aURX9zfrdLLVBddVdyqpX/lNkBz1hYXvI5JL6j0apFrUSgUTO4fyuX3R/LxuM4AfLPzEoWGG2P0okp3/KCgINq1s6/X3rZtW2JjY22Pu3fvztGjR8nKyiIxMZH169eTnp5O8+aWIZ7AwEAMBgNZWVl250lOTiYwMNDW5urVI9bHFbVxd3fHyckJX19fVCpVmW2s57iag4MD7u7udl+CIAhCwyNJMj/vi2Hwx9s4nWiZwm7uZwkEnLXFt7aNp6tWr8LK01nD4ge7M6Rd2feLkg7FZNJrzibb40Gt/VBXs+ZGWe7o3Ag/NwdydCZOJ2bX2HlrU5Xefb9+/YiKirJ77ty5c4SEhJRq6+HhgZ+fH+fPn+fgwYOMHj0asAQfGo2GzZs329pGRUURGxtLeHg4AOHh4Zw4ccJuVcfGjRtxd3e3BTfh4eF257C2sZ5Dq9XSvXt3uzaSJLF582ZbG0EQBOHGcyIumzFf7uG1VSfJLjQSFujGiqfC6dvCF7Dk191SlDtRYDDz2qoTbDlbuVWC1t1I3R3VDG1fcWDxxZbz3PPlHtsUzNTBLVk6uVd13tY1qZQKrGsvHNSqGj13banSxmXTp0+nb9++zJkzh/Hjx7N//34WL17M4sWLbW1WrFiBn58fTZs25cSJEzz33HPcddddDB06FLAEHY8++igzZszA29sbd3d3nn32WcLDw+nTpw8AQ4cOpV27djz44IN8+OGHJCUl8frrrzNlyhQcHBwAeOqpp/jiiy94+eWXmTx5Mlu2bOG3335j7dq1tr7MmDGDSZMm0aNHD3r16sX8+fPJz8/nkUceue4fnCAIglC3sguMfPxvFD/ti0GWwdVBzfTbWzMpPKTUFuWDWvuxLcqSjPnzvliW7YvlzOwIHLXl3/Y0Suuy1vL7kqczMfKz4uWsjholSx/pRe/mPtV8d9dmMEmk5Vl2Wg26QfYgqVJw0bNnT1atWsWsWbOYPXs2oaGhzJ8/n4kTJ9raJCYmMmPGDJKTkwkKCuKhhx7ijTfesDvPvHnzUCqVjB07Fr1eT0REBP/73/9sr6tUKv7++2+efvppwsPDcXFxYdKkScyePdvWJjQ0lLVr1zJ9+nQWLFhAkyZN+Oabb4iIiLC1mTBhAqmpqbz55pskJSXRpUsX1q9fXyrJUxAEQWi4ZFlm5eF45v5zxrad+Z2dG/HayLYEuDuWWW57XPdgvt8dTWqugUKjGRnQmyQcteVfS1M0nWEoJ7rQGUzc9uk2knMsCZytA1xZ8WQ4Hs4VnLyaCktUC9XU4HRLbapSnYv/GlHnQhAEof7EpOdzKCaTX/bHcuByJmApePV/ozvQt6VlCuRCci63z9uBWqVgQo9g3r6jvV2+Q2JWIeHvbwHg2Ju3VxgALNh8jnkbLdUxx3ZrzCfju9i9bjJJDJm33bbkdMbtrZl2W6saeb/XIssyQz7dzsXUfKYMbsFLEWG1er3y1EqdC0EQBEGobXqTmU/+jWLwx9uY8dsxDlzOxEmjYuawMNY9N9AWWIBlozAZMJplftoXS4d3NnAxNc/2evG6ESq1Ffuzg1vSNshSsGrl4Xie+flQ8bkkiTu+2GULLJ4c2LzWAwuwrBx5eZgloPhmZ7RtdUxDJoILQRAEocE4EpvJqM928fmWC7a6FcPaB7LphUE8fUuLUktKry5zoDNK/HE4vsxzK6g4uFAqlax9tj/tG1k+lf9zIon7Fu9FkiQe+HY/Z5JyAZjYuymzRrSt6turtqHtAujZzAu9SaLv+1sY/cUu3l93litFOR8NTZVyLgRBEAShNuTrTcz47RgbTlmKHPq6apk9ugP9Wvri4aS55nHWPE6VUoGDWmlbtWFV1Q3IwBJg/DW1HxO/2U/kpXQiL6XT9f82kl1o2WtkeIdA3hvTseonvg4KhYKP7unMLR9vA+BYXDbH4rL5eV8MSyf3oltTrzrtT0XEyIUgCIJQr7acTWbovB22wALgpYg2jOgYVG5gAcWlv0vMfvDH4Tj2XUq3PCiRl1mVWo5KpZJfnujD8A6W5ajWwKJXMy++fKB75U9Ug5r5unBm9jC+ndSD2aPb07mJB7k6E/d+tZctZ6tXz6O2iOBCEARBqBcpuTqmLDvM5CUHic8qpFGJZZZvrD5V5iqQqylsMx0yrQMsuRKJ2TomLN5L+NzNrDwSZ2urrUal6C8f6M7E3k1RAB0bu7P8iT5VPkdNctKquK1tAA+FN2PZ45a+GMwSk5ccZOWhuAqOrjsiuBAEQRDqlLW65m2fbGft8URUSgVPDGzOphcGsfmFQSiw1HZYsPl8hedSFQUMMvDnlH68dUc7PIv2BknM1jF/U/E5qls1870xHbk4Zzh/PTugQW1l4eKgZuP0gbbHL6w4xroTifXYo2IN56ckCIIg3PTOJuVwzyJLdc1cnYmOjT1YPaUfr45oi7NWTQs/V9tqjX9Pld5k0mSSSMvTcSW9gHPJuVwqsTIE4JF+oRx9cyj/u78bId7Otue9Xa6vBkXJoCKrwMBvB6+QVWC4rnPWhFYBbvwzbQADWllW0Ez95Qh/H0+o516JOhflEnUuBEG4WemMZub+c4YtUSl4Omlp4uXEtNta0Taodv7WFRrMfLblPF/vuIRJknHRqnhhaBseKqO65vRfj7LqiGXFh1qpYP6ELozq3AiDSaLj2xvQlzFdolBA9NyRpZ4/HpfF8v2xPDekNQHu11/d0mCSuHdxJIdjs2jp78pPj/YmsAFUzTRLMi+uOMaqI/EoFfDJ+M6M6dqkxq8j6lwIgiAIZUrL03Pv4r0sjYzhSkYhJ+KzWXcyiYe+209Krq7Gr7ctKoWh87fz5baLmCSZiPYBbHphEJP7h5YKLABuC/O3fW+SZH47ZNmhOrvQUGZgAdAusOwbXacmnsy5u1ONBBYAc/45w+HYLAAupOQx7qs9xKbXznJQndHM+pNJZBcaK2yrUir4eFxnxvdogiTDjN+O8Xs95mCIpaiCIAj/MS+uOMbRK1l4OGl4fWRbXB3UvPfPGeIyC5m67Ag/P9YbTRk3/apKydEx++/T/H3ckgcQ5OHIO3e2r3BDsFGdG9G5iScjPt9Jrs6EdXzdz82RW9oU7xny7aQe3Na27rZzWH00niV7LgMwe3R7vt0VTUx6AeO+2sPvT/UluMQ0TE34aW8M7649g6+rA+ueG4Cfm0O57VVKBe/f3QmNSsnP+2J56fdjqJTUyghGRURwIQiC8B8SeTGdbVGpqJUKfnsynDaBlvyG1oFujP5iN/ujM/hg3VleH9Wu2teQJJll+2P5YP1ZcnUmlApLLsT021vj6lC5206wjzP+bo7k6vKQiopVbD6TTOTFdFub5389yom3I651ihp1KiGbl34/Dlh2Pn0ovBnD2gfSa85mknP0PPjtPn6Y3JuMAgMZ+XrOJOYSm15Ar1BvnLQq9kdnsPF0Mh5OGloHuDKsQxBgKe1tMEuYzDJGs1T0Zfl+7rqzgGWkacRnOxnZMQiVUoFZkjFJlmNMUvFxJrOMWZI5W1ToS5bhhd+OMbCVHz6u5QcmNU0EF4IgCP8Rsizz/nrLDeu+Xk1tgQVACz9XPrqnE0//fJhvdkUT6OHIYwOaV/kaCVmFPLf8iG0vkI6NPZh7d0c6NPao8rms1brNRUMXv+yPtZsWMZb4fvOZZA7FZNLEy5l8vYkCgxmTJJGrM1keG80UFD2vN0lIsmy7iVtvzIai761LYNVKBWqVEpVSQWquZZOyfi19mH57awD83R25r1dTftkfy+X0AgZ+tLXUe/j14BW7x/FZhZxOzOHPo1VLukzN1dtGTarC20WLo6but2kXwYUgCMJ/xIZTyRy7koWTRsWzt7Us9frwjkG8MjyM99ed5f11Z+nUxJNeod6VPv/6k4nM+uMEmQVGXLQqXoxow0PhzVBVYk+PsigV1u3PLcFFCz8XNp2xvDZnTEfu7NwIgH9OJPLMz4erdY2qcNQomTOmo937eXNUO1JydByMySRXZ6xURVBfVy0hPi6AJYDSqJSoVUq0KgUalbLosQKtSombo5qJvUPYczGd6LQ8VEolGpUClVKBWqmwPdaqlaiVSlRKy88tV2fC1VHNoNZ+uFRytKgmieBCEAThP8Bklvhog2XU4rEBofi7lZ3g+OTA5pyIz2bt8UTGfxVJuyB3/n62f7mbfqXm6nlrzUn+OWFZOtq+kTtfTuxOU5/ry0FQK+2Di5nDwvhqRzQAXZp64OpouYVFp+XbjrktzB83RzXODmrUSoXle60aZ60KZ60KFwc1DmoVKqWlRoZWVXyz1qqVOKiVaFWWT/omSbJNO2hVSlr4uZb6OThpVXz7cE9k2TJFoVIoyNEZkWXwKlr+mqc3oVYqrmsEoZmvS7WPrQ8iuBAEQfgPWHk4joup+Xg5a3h84LWnOxQKBR+O7cSVjAKOx2VzOjGH+KzCMpMVZVlm9dEE3v7rFFkFxSsacnVGHvh2L5Js2dtDlmVaBbjyw+TeVeqzSmUfXJSsNfH88qP8O31QUZ8tz43v0YQP7+lcpWvUFIVCgaaov55Xbete2TyTm4lYiioIgnCT05vMzNtoqVQ5ZXBLnDUqdp5P5Z8TiRjNpZd2ujioWVRi/4yyqiGl5up58sdDPP/rUbvAAiA2o5DYjELiMgtJyCokMVvHjnNpHLuSWaV+W6cfTObiDnRt6gnAueQ8/rftAlBy+qRKpxdq0X8vnBIEQfiP2Xo2haQcS/2KD9dHMeefM7bcgNvC/Fn8UI9SeRGNPJ1wc1CTqzfZEiqt/jmRyGurLLkVGpWCabe2Yv2pJE4l5AAwoJVvUT6AApVCwYbTlk21cnWmKvVbY50WKbq+zmCyjWIAnE20rIqwdl3UhGw4RHAhCIJwk3N10KBUWKYoDFd9vN98NoWVh+MY3yO49IFX3bSzCgy8teYUq4tWOrQNcueTcZ1p18idTWcsAUSnJh78+Kj99EezV9ZazlNOH2PS81l9NIEjsZkYTBJtAt1s+4acTcrl/q/3ciI+2xagdA/x5ONxlikQ68iFJIKLBkMEF4IgCDe5/q18+e3JcO5ZFFnm6znXqABZfNO2jH7MXHmclFw9KqWCZ25pwbO3tuJUQjYPf7+fY3HZAHYjC1YKLIHFD3suM6CVH3k6E/+cTGTLmWROxOeQnKPDdNVxuy+m06FRcdXNPSXqWzzaP5Q3StThKNlPoWEQwYUgCMJNbse5VB76bn+Zrz01qAWP9Ast8zXrdMMrK49zMMaSL9HCz4VPxnehS7AnABO/2UeBwWw7pqzKnv5uDiTn6tl4JoW2b6yn0Ggu1QYsq0P83R1JyCoE4PZ2ATholOQUmpABJ42SF4e2YVAbf7vjrImUldmiXagbIrgQBEG4SRnNEh+uP8vXO6NLvaZVKfngno7llobOLErUPBiTiUIBk/uF8lJEG7sllSWLWvm4aJk1PKzUeboEe9ryLqyBhQLLUs2wQDf6NPfhjk5BhPq5AtDqtX8wmmXcnTSsfLpfhe/T2h+9qeygRah7IrgQBEG4CRnNEs8tP2KrPeHuqCanKF/B20XLoge6V1ggK8THmZj0AhzUSpZO7kWf5j6l2ljTQOeM6cj9vZuWeZ4XItpwMS0fFwcVXYO9GNougD7Nve2WlpZkmeaQ0RsrNxKhLhq5uHpqRag/IrgQBEG4CS3bF8s/J5JQKxV8OqELd3ZuRHahkdRcHQHujrg5aio8x+zRHTgZn83DfZtVWOVRkq4dCLQOcGPTjEGV7rs1h0JvrtxIhLV9oUGMXDQUIrgQBEG4yeiMZhZutdSAeH1kW1uZbA8nDR5OFQcVVoNa+zGotV+5bawFrMw1OGhgPWdlRy6srEthhfonimgJgiDcRGRZZt6mc6Tk6mns6cT9vUNq+XqW/5pqsIKVVm25NV1MyatUe+sKleZ+N1aJ7JuZGLkQBEG4CRQYTOyPzuC3g1dseRYvDG1tu1HXNINJYtovh215DlK5VSyqpnczbzacTmbnhbRKtfcqKrdtnR4R6p8ILgRBEG4gOqOZjzZEsflMMnPGdKRvS18Apv96lA2nLCsyNCoFr45oy5iujWulD78fvMLrq0+iK5q2cNGqGNWxUY2df/rtrdlwOpkCg5ntUSmllp5ezbr8taxS5kL9EMGFIAjCDWLfpXQmLN5re/zMssP8NbU/y/bH2gUWy58Ip3uIV41fPz6rgEeXHORsUq7tuTs7N+LTcZ1R1+AISViQO17OGjILjHyzK7oSwUVRnQsRXDQYIrgQBEFo4CRJ5vEfDrL5bIrd81kFRgZ8uNX2eEzXxswe3b5SK0Gqdn2Jd/4+w4+Rl21VMIO9nPj24Z60DnCr0WtZDWrtz59H4zkUU/FmZ+qikQudWC3SYIjgQhAEoQG7klFgF0Bcy7wJncstiFVdey+m89TPh2w7n6qVCl6KaMOTg1rU+LVKemxAKH8ejafAYObfU0kMbR94zbbqolKiRlHnosEQq0UEQRAaqIVbL5QKLGaPbo+2RInt7iFe/G9itxoPLHJ1RiZ+vZd7v95rCyzCm/tw6PUhtR5YAHRo7IG7o+Xz71M/HeJ0YvY121p3dFWJhM4GQ4xcCIIgNECrjsTx0YYo2+MnBzUnPrOQN1efAqBXqDfzJ3ShkadTjV/7252XeH/9WYxFxSs8nNR8cX83BrQqv+ZFTVv+ZB9Gf7Ebo1nmni8j2fPKrXgWrQwpyRpTiF1RGw4RXAiCIDRAZxOLkyadtSr+OppAQrYOtVLB9Ntb89SgFrZP7DXlQnIuk5ceIDbDsnGYQgETezVl9uj21yzVXZvaBXnw/cM9efDb/RQYzIz4bBe7Xr6lVF/ErqgNT5V/W+Lj43nggQfw8fHBycmJjh07cvDgQdvreXl5TJ06lSZNmuDk5ES7du1YtGiR3Tl0Oh1TpkzBx8cHV1dXxo4dS3Jysl2b2NhYRo4cibOzM/7+/rz00kuYTCa7Ntu2baNbt244ODjQsmVLlixZUqq/CxcupFmzZjg6OtK7d2/27y97Z0BBEITaIssyMen5ZW5Hfi0DS1TGLDCYScjW0czHmd+f7suUwS1rNLAwmSRe+v0Yt8/bYQssWvm7suPFwbw7pmO9BBZW/Vv58frItgAkZBUy7qvIUqXGrT8LWYxcNBhVGrnIzMykX79+DB48mHXr1uHn58f58+fx8ipe8jRjxgy2bNnCTz/9RLNmzfj333955plnaNSoEXfeeScA06dPZ+3ataxYsQIPDw+mTp3K3Xffze7duwEwm82MHDmSwMBA9uzZQ2JiIg899BAajYY5c+YAEB0dzciRI3nqqaf4+eef2bx5M4899hhBQUFEREQA8OuvvzJjxgwWLVpE7969mT9/PhEREURFReHvX/7SJkEQhJqgM5p5bdVJVh6O45Y2fnw7qWeFgcGZxBw2nEoq9fzaaQMq3OOjqjafSeb55UfJ1Vs+vDmolcwe3Z4JPcvehKw+PDqgOcfjsll9LIFDMVm0em09r48Kw8NJS4C7I4nZOgDMIrhoMBRyFUK9V155hd27d7Nz585rtunQoQMTJkzgjTfesD3XvXt3hg8fzrvvvkt2djZ+fn4sW7aMe+65B4CzZ8/Stm1bIiMj6dOnD+vWrWPUqFEkJCQQEBAAwKJFi5g5cyapqalotVpmzpzJ2rVrOXnypO069957L1lZWaxfvx6A3r1707NnT7744gvAspwqODiYZ599lldeeaXC95uTk4OHhwfZ2dm4u7tX9sckCIIAQGx6QVEyYvGeF9OHtOa5Ia2ueYwsy3T9v422JMqSLr8/ssb6lpFn4NEfDnAkNsv23K1hfnxxfzectQ1rxjwpW0efuZsrbOfhpOHYW0ProEf/XZW9L1ZprGvNmjX06NGDcePG4e/vT9euXfn666/t2vTt25c1a9YQHx+PLMts3bqVc+fOMXSo5R/80KFDGI1GhgwZYjsmLCyMpk2bEhkZCUBkZCQdO3a0BRYAERER5OTkcOrUKVubkuewtrGew2AwcOjQIbs2SqWSIUOG2NpcTa/Xk5OTY/clCIJQHVvPpjDq852cTszBx0XL4wNCAZi/+Rw7z6eWe6x1d8/7ezdlxu2tuS3Mn2WP9a6RfkmSxIfrz9LzvU22wMLXVcvvT4Xz3cO9GlxgAXAuOfear/m6amns6URTb2ce7tus7jollKtKv0WXLl3iyy+/ZMaMGbz66qscOHCAadOmodVqmTRpEgCff/45TzzxBE2aNEGtVqNUKvn6668ZOHAgAElJSWi1Wjw9Pe3OHRAQQFJSkq1NycDC+rr1tfLa5OTkUFhYSGZmJmazucw2Z8+eLfP9zZ07l3feeacqPxJBEAQ7kiSzYPN5Fmw+D0CXYE/+N7EbjTydyNOb+GX/FZ5bfpS10/oT5FG80iMj38CCTefYeSENvcmSUzC+RzBdgj1rtH+Tvj/AzvOWPTtUCgVPDmrOy8PCavQaNc1ax6KlvyubZgxClmVyCk24O6lRiOWnDVKVggtJkujRo4ct76Fr166cPHmSRYsW2QUXe/fuZc2aNYSEhLBjxw6mTJlCo0aNSo00NDSzZs1ixowZtsc5OTkEBwfXY48EQbiRZBUYeP7Xo2yLsoxMPNgnhNdHtcVBrQLgrTvac+xKNqcTc3h22RFWPBUOwNaoFGb/dZrL6QV258vI19d4H08XbUvuqFGya+ZgfF0da/waNc1agdOaEKtQKPBwrtkqpELNqlJwERQURLt27eyea9u2LStXrgSgsLCQV199lVWrVjFypGVusFOnThw9epSPP/6YIUOGEBgYiMFgICsry270Ijk5mcBASwW2wMDAUqs6rKtJSra5eoVJcnIy7u7uODk5oVKpUKlUZbaxnuNqDg4OODg4VOVHIgiCAMDJ+Gye+ukQcZmFOKiVzBnTkbHd7Qtb6YxmvFwsN8UjV7I4k5jDjN+O2e3VYeXrqqVNYM3nejkU7QFyS2u/GyKwgOI+G0xi75AbRZVyLvr160dUVJTdc+fOnSMkJAQAo9GI0WgstWxJpVLZlg51794djUbD5s3FyTlRUVHExsYSHm6J4sPDwzlx4gQpKcV19Ddu3Ii7u7stuAkPD7c7h7WN9RxarZbu3bvbtZEkic2bN9vaCIIg1ITfDl5h7Jd7iMsspKm3M38807dUYHEiLpuRn+1i94V0HNRKJvZqyit/nCgVWHQO9uTbST3YPOMWGtdCgSxl0RSD6QYqCmHd9VRsTHbjqNLIxfTp0+nbty9z5sxh/Pjx7N+/n8WLF7N48WIA3N3dGTRoEC+99BJOTk6EhISwfft2fvjhBz799FMAPDw8ePTRR5kxYwbe3t64u7vz7LPPEh4eTp8+fQAYOnQo7dq148EHH+TDDz8kKSmJ119/nSlTpthGFp566im++OILXn75ZSZPnsyWLVv47bffWLt2ra2/M2bMYNKkSfTo0YNevXoxf/588vPzeeSRR2rkhycIwn+b3mTm7TWn+WV/LAC3hvkzb3yXUkP2y/fH8uaaU7ZP3nqTxA97Y8o858TeTbmtbUCZr1WGJEl8uvE8eXoTb45qW/rDnuLGCy606qK9Q0RwccOoUnDRs2dPVq1axaxZs5g9ezahoaHMnz+fiRMn2tosX76cWbNmMXHiRDIyMggJCeG9997jqaeesrWZN28eSqWSsWPHotfriYiI4H//+5/tdZVKxd9//83TTz9NeHg4Li4uTJo0idmzZ9vahIaGsnbtWqZPn86CBQto0qQJ33zzja3GBcCECRNITU3lzTffJCkpiS5durB+/fpSSZ6CIAhVFZ9VyDM/HeJYXDYKBcwY0popg1vaRgbAMg3y5uqT/HYwrlLnnD+hC6O7NKp2n7acTWH6r0fILrTUrHBxUPNSRBu7NtYaG6aiG/W+S+kkZOnI1xspNJopMJoxmGTcHNQ81j+0RrdSry7ryIVRTIvcMKpU5+K/RtS5EAShLDvPpzLtlyNkFhjxdNaw4N6uDGptv+/GlYwCnv75ECfjc1Aq4NH+oXy9MxqAOzo34rH+oaw8HMe+Sxm0DXLjleFtCfSoXg5ERp6Bx388wKGYLLvn1UoFR9683W4L9ts/3c75lDzCm/vQxMuJFYeuHfj0b+nDT4/1qVafalJCViF939+CRqXg/Hsj6rs7/2mVvS82vAXNgiAIDZRZklmw6Ryfb72ALEPHxh78b2I3gr2dS7V98kdL8SxvFy2f3duV/q18+SEyBr1JYkBLXzoHe9K5BpaZfrwhii+3XbRVp/R20TL7zvZMW34EkyQzZdlhfphcXCPDug+HWZI4U6K4l1qpQKlQoFRapm1kGXZdSGfvxXT6tPC57n5eD9vIhVlGlmWx/PQGIIILQRCESkjJ0TFt+RH2XsoA4L5eTXnrjnY4alRltremOrw2oi39W/miN5lt9SteXnmcu7s2vq4ph/3R6Tzz8xHS8izLVVUKeGJgC16KaI1SqWTPxTSW7b/CjnNpnIzPpkNjD0u7okuaJNk2RdKxsTt/PTvAdm5Jkuj6f5vILjQyZdlhDr1xe7X7WRNKbjFvkmQ0KhFcNHT1P5kmCILQwO2+kMaIz3ax91IGLloVC+7twty7O14zsABsxa9OJ+ZwKiGb0V/stnv9//45U62+5OqMPPjtPsZ/tdcWWHQJ9mTfa7cxc3iYLYFz9p0dcNZa+nfv4r22fAVrQGEuEVxcvaGaUqlk3oQuAKTnG3ht1Ylq9bWmaNTFwYRI6rwxiJELQRCEazBLMp9tPs9nW84jyxAW6MbCid1o4eda4bGdm3jyE7F8uyuab3dFl3rdtRobkH21/SIf/xuF0WwJBtwc1cwb35kh7UrX7lGrlcy+sz0v/n6cPL2JVq+vQ6ko3pb8WFy2rW1ZC0duDfOnZzMvDlzO5Od9sVxIyePnR3vXS4JnyZGLzAJjgyxRLtgTIxeCIAhlSMnV8eC3+1iw2RJY3NszmD+n9KtUYAHQo5l3mc8rFfDi0NalVnGUJzlHR7/3tzB33VmMZhmFwrJk9dibt5cZWFjd0yMYbYkphGutPg1wLzuR9MfJvWgdYHm/+6Iz6DlnEzHp+ZXud01RlwgudEZznV9fqDoR/gmCIFxlz4U0pi0/SlqeHmetivfGdGBM1yYVH1hCqK8Lw9oHsr7E1um+rg4sf7w3LQPcqnSud9eeJj6rEIA2AW5883APgr1KJ5GW5eQ7w/j3VBImSSY9T09yjo5Cg5lAD0dUSiWuDirGX2N7dUetmn+nD2LWyuP8cuAKmQVGbv14O3PHdmR8j7rdGsHTWUNWgRGxwPHGIIILQRCEImZJ5ostF1iw+RySbLmRL5zYjZb+lRutKGnh1gtsKBFYjO7SiHnjO5cqalUZBqMlzyDIw5EN0wdW6VitWsmoztWvnQEwd2wnwlv6MOPXY5gkmZd/P872qBQ+v69rtd5PdRSvcqmTywnXSQQXgiAIQGqunum/HmXXBcuOoRN6BPP2ne1x0l47abM88zedQwY0KgWLHuh+XVU3VQ1gdcSdnRvTPcSLuxfuITlXz9oTSZyI38aaqf3xdNbW6rVlWbYV/ZLEyMUNQeRcCILwnxd5MZ0Rn+1k14U0nDQqPhnXmQ/u6VTtwAIsNTDAMhoSFlS1aZCrqZX2u4LWl8aezkTOupUhbf0BiM0opPeczew6n1qr1z0Wl02OzlJ1tMAgci5uBCK4EAThP0uSZD7ffJ6J3+wlNVdPK39X1kztV2rTser47uGeqJUKJBke+f7AdZ3LumS0IWwHolQq+WZST14bGYYCS8GtB77dz9c7LtbaNa1JpQCHYzJr7TpCzRHBhSAI/0lpeXomfb+fTzZa8ivu6d6E1VP70aqKyZbX4umsZeZwy4qQc8l5LN5+odrnss4ENKTClI8PaMHqqf1s26EvP1C5/VOqIz3PYPu+uZ9LrV1HqDkiuBAE4T/neFwWIz/byc7zaThqlHx0Tyc+Hte5xusntA/ywLqP2dx1UWQVGMo/4Bp0RsuUQAOKLQDo1MTTVi2zcxOPWrvOoRKjFW0Cayb4E2qXCC4EQfjP+WzzBZJz9Lg5qlkztT/janhZpcEk8diSA9z/zT7bVIYMjPnf7nKPu9q55FyGztvO+lPJgGXlR0OSlqcjT2/JgbinBqaSrqVD4+INsrIKjLV2HaHmiNUigiD859zXK5jNZ5PJ1ZnYH51B6xqaCgHYcDKJ6b8epaCo2JOHk4aOjd3ZdSGd6LQCNp1OKrfwFVh2OX32l8Psvphue87XRcv8opLcDcXR2Czb932al100rCa09HejkYcjCdk60vP1FBrMrDuZiKuDGkeNCq1aycXUPHqEeIuRjQZCBBeCIPznuDtp8HbWkp5v4Ke9MTzQJ+S6z5mnM/Ho0gPsi86wPTe2W2M+uqcTAD3e20xGvoFpy49y9M2hZY5CGEwSs/44zqoj8bYRDyeNiheHtubRAc2vu481rWR+ym2f7uDz+7raNkiracqi+aVJ35WfHDtvQucqFzwTal7DGmMTBEGoRdmFRmb9cYJxiyJJzzfg6+rArBFtr/u826NS6PHuRltg4efmwN/P9ueT8V1QKpUolUq+f7gHYFlK+fRPh+yOlySJBZvP0eHtDaw8bAksVEoFD4WHcOKtoQ0ysAAI9nKyJZlGp+Xz3trqbcZWGR5Omkq1O3Ylu+JGQq0TIxeCIPwn/HMikbfWnCI117KT6H29gnllWFs8nCt307qWV/84zrL9VwBLwuUj/Zrx+si2pSpXdg724u6ujfnjSDybz6awPSqFQW38+fNIPG+tOUl2YXHS5m1t/Zk3oQtujtfXt9qmVCqZP6ELzy0/Clh2bK0NKTk6ziXnApYgY9njvTFLMp5OWiRZRpItVUMPxmTStalnrfRBqBoRXAiCcFPL1Rl5c/UpVh2JByxLGeeO6Ujv5j7Xdd6UHB3jFkUSk1EAgLujmmWP9yl3WuDjcZ3YEpVCVoGRZ34+jLeLliuZhbbXOzXxYOF93Qj2qdy+IQ3B6C6NWXHwCrsupHMhJY/kHN01N0KrDp3BxKjPd2E0yygVsPKp8DL3Zmloya7/deJfQxCEm5YkyTz54yFWHYlHqYBnb23JuucGXHdg8fexBPp9sMUWWPRt4cPB12+vMN9AqVTyzUOW6ZF8g9kWWDTxcuK3J/uwZmr/GyqwsHp+SGsUgM4kMXzBTiSpZjYAMZkkIhbsJKVotGn2ne2vuembdedU63b0Qv0SwYUgCDet9aeS2FO04uLXJ8N5YWgbHNTVL+kNcDYxh6m/HLF9kn7rjnYse7xPpT8592jmzfgeloRDd0c188Z3YdfMW+kVen0BT33q0czblriakW/glwNXrvuckiQx+n+7iUm3BHDP3NKCB8KbXbO9pijh0yR2NmsQRHAhCMJNy6vEhlqt/a9/iWJGnoGnfz5se/zFfd14pF9olc/z4T2dOfbm7Rx/O4Ix3Rpfd78agnt6BBPg5gDA8v2xxGcVVOn4pXsu0/+DLbaCWQ9+u59TCTkA3NczmJeHhZV7vLqomJexIdRIF0RwIQjCzatPc29aFW2XvmDz+es618KtF+g5ZxPRafkANPV2Ymi76u906lHLO4nWh4Gt/QA4EZ9Dv/e38ubqk5U67qvtF3lrzSniMguZ9cdxpvx8yFbjI6JdAHPHdqrwHNZpETFy0TCIhE5BEG4qi3dcZNOZFGbc3pq315zifEoeAN/viea2tv70a+lbpfNdSM7lkSUHbPkRKgVMvbUl029vU+N9v9HNHdORC6l5HCkqrnWgRM2Pa/lq+0Xmrjtre3wuOY9zyZZ/s96h3nxVlKNSkeJpETFy0RCI4EIQhJuCzmjmk3+j+HpnNAD3Lt5r97osw/Rfj7LuuQH4uDpUeD5Jknht1UmWH7iC9XbVLsiNJY/0wr8GV0PcTNRqJaue6cf4RXvYfzkTs1z+jf7nfTG2wMLLWUN2odFWPKxtoBu/PN670tfWFI1cGMTIRYMgggtBEG54VzIKeOLHQ5xJzCm3XUquntVHE5jcv+I8iYEfbSOuaLTCQa3k/+7qwPga3oPkZmWdojBXkP/wU2SM7fv37+5EbEY+c/45S0t/V/6a2r9UrZDyr1m0Lb3IuWgQRHAhCMINbcOpJF5acYwcnQkfFy2zR3cgyNORcYsiy7y5dQvxqtR5E7N1AHg6a9jzyq01vmPqzcx6o68ouHh3TEcmfBWJSZJ56qdDfDOpB1HvDq9w5Y3OaGbh1gtcSs1Hb5IwmCWikiyBpUkEFw2CSOgUBOGGZDRLzPnnDE/+eIgcnYkuwZ6sebY/IzsF0a2pF4sf7E5jTye7Yzo0dq/01uD39rSMUmQVGCuVOyAU0ygrN3LRPcSLOXd3BCy7xi7Zc7lSS3pXHYnn8y0XWHsikU1nktlxLpXkHEstDF+3iqe8hNonQnFBEG44yTk6nl12hP2XLTf9x/qHMnN4mG3eHeC2tgHc1jYAWZZJztGz4VQSIzoGobBuhlEOk0kis8Bge/z9nssMauNf82/kJqWp5MjF6cRsXv3jBABalZLZd7av1Pkji1aS+LpqeWFoGzQqJVq1Em9nLeEtbtx6ITcTEVwIgnBD2XMxjWm/HCEtz4Crg5qP7unE8I5B12yvUCgI9HBkUt9mlTp/fFYBIxbsIrvQsk+Gm6OaVyuxuZnJJPHPqUS6NvG6Iats1iRbzkU5CZ3xWQWMWbgHkySjUir445m+hPq5VnjufZfSWXMsAYDP7utK3xZVW/0j1A0RXAiCcEPQGc18uD6K73ZbVoOEBbrx5QPdCfV1qdHrfLLhnC2wGN4hkM/v7Yq6nKH643FZfLQhisiL6ZgkGXdHNcffjqjRPt1oNBUkdGYXGBg+fyd6k4RCAUsf6Vmprdp1RjOvFI103NszWAQWDZgILgRBaPDS8/R0f3eT3XMdG3vUWGAhSZJtZYJbia29+7fyLTOwyNOZ+HzLeX4/FEd6vsHutRydiXPJubS+xh4YDYnJJDH4k22k5Rlw0Chx0arxdNLg5aJBoVAgSTImybLrqFmSkQFHtQoXBxVatRK1UomDRomTRoWrgxp3R8uxqbmWZNiygotcnZHb5+0gR2fZBXb+hC70b+VXqf7O23iO6LR8AtwdmFWJ0SSh/ojgQhCEBm3n+VSm/XKk1PMrDsVxZ5dGDKjkjakskiTx8JID7DiXxi1t/MguNNoKQKmVCno387Zr+8+JJL7acZGT8TmUvG26OqgZ0TGQVUfiMZpllu65zHtjOla7X3Vl/+UMW3GwQqOZrAIj8VmFFRxVefkGs93j7AIDt3y8jcwCy8jQayPCGN2lcuXPD8VksHjnJQDeu6sjHk4Nezv6/zoRXAiC0CBdySjg43+jWH004Zpttp5Nva7gYviCXUQl5wKwLSrV9nwzH2d+ebwPQZ5O7I9O5/MtF9h3KcOuQJNSYdki/fkhrbmlKNnzQkoeh2Oz2HEuldogSRIXUvOJyywkI09PvsGMh5MGX1ct/u6OeLto8HbWVro+hLJEcut9PYNJzdOTmqcnt9CEjOU9KhUKlAoF1lxZg1lGZzRjliyjGSZJxixJGM1F35vl4lyLEhFYns7EbZ9utwUWs4aH8fjAFpV+7/M3nUeWLcW2hlxH2XWhblQ5uIiPj2fmzJmsW7eOgoICWrZsyffff0+PHpYSrdfKxP7www956aWXAMjIyODZZ5/lr7/+QqlUMnbsWBYsWICra3Eyz/Hjx5kyZQoHDhzAz8+PZ599lpdfftnunCtWrOCNN97g8uXLtGrVig8++IARI0bYXpdlmbfeeouvv/6arKws+vXrx5dffkmrVq2q+rYFQahD+XoTAz7cavdcz2ZeHLicaXs8omMgT93SvNrXkCSJcym5ds8pgCcGhnJ/7xDeX3+WDSeT0JnsKz4GuDkwrkcwUwe3wPGq2hd3dG7E4dgs4jILMZmkcnM1qmrovO22stiVoVRgm7ZwdVDjrFXhqFHhqFHioFZRYDBhKPHeKrN/R2Ut2HyOeRvPY5ZlXv3jOJkFRvZcTLflsrw2MozHB1Q+sAC4WFTGvVeodwUthYagSsFFZmYm/fr1Y/Dgwaxbtw4/Pz/Onz+Pl1dxUZrExES7Y9atW8ejjz7K2LFjbc9NnDiRxMRENm7ciNFo5JFHHuGJJ55g2bJlAOTk5DB06FCGDBnCokWLOHHiBJMnT8bT05MnnngCgD179nDfffcxd+5cRo0axbJly7jrrrs4fPgwHTp0ACwBzWeffcbSpUsJDQ3ljTfeICIigtOnT+PoKMr3CkJDYZZk9l5KY/ZfZ+ja1JP9ZdSVSM7R4+2ixWCS+GR8ZyLaB17XNZVKJXd1acyqI/EAdGvqycTeTfn433N8tSO6VPux3Rrz/O2tCfa69kqQCT2Deeev08jA7otpNbZ8tcBgKhVYqBSgVCowSzJl5U1KsqUUtsEskVuU31BX3B0tUxZmSWbZfvvt12fc3rrKgQVYtnVfcyyBEJ+aTeAVaodCliso/l7CK6+8wu7du9m5c2elL3DXXXeRm5vL5s2bAThz5gzt2rXjwIEDttGO9evXM2LECOLi4mjUqBFffvklr732GklJSWi1Wtu1//zzT86etdShnzBhAvn5+fz999+2a/Xp04cuXbqwaNEiZFmmUaNGvPDCC7z44osAZGdnExAQwJIlS7j33nsr7HtOTg4eHh5kZ2fj7u5e6fcsCELVDPl0OxdS7G+ege6OjOgYZFsdAvD9Iz0Jb+6Do0ZVI9eVJInec7eQmqtHq1KiUSlseQIOaiVOGiVZhSZcHFScemdYpc4Z9vo6dCaJqYNb8mJE9Tc3kySJs0m57L6QxtErWaw9kQTAqmf60rVp6SqjJpNERoGBtHw9qTl6UnP1pOcbSMzWkZhVSJ7eRL7BjN5kya2wViB1d1TTs5k33z7cs9p9vVqezsTQ+TvIKTSiVilwUClp7OXEg32aVXuL+Ye/38+2qNTr/rkK16ey98UqjVysWbOGiIgIxo0bx/bt22ncuDHPPPMMjz/+eJntk5OTWbt2LUuXLrU9FxkZiaenpy2wABgyZAhKpZJ9+/YxZswYIiMjGThwoC2wAIiIiOCDDz4gMzMTLy8vIiMjmTFjht31IiIi+PPPPwGIjo4mKSmJIUOG2F738PCgd+/eREZGlhlc6PV69Hq97XFOTvn7FAiCcP0upOSVCiwA1j03AC8XLUk5hfxzIom2Qe70bOZdY4EFWEYvlj/RhyGfbi/6lF/82om3I1h/MpFpy49SoDdf+yRX8XbRkpCt47vd0SzZcxmTJPHNQz2uuSIiu8DA3ksZHIzJ4GxSLjHp+aTlGSgwlH3Na1WgVKuV+Ls7WjZVu3bZDwD2XEjj/m/2AdTKsllXRzV7Xrm1Rs+ZVzT60vQ/XkPkRlGl4OLSpUt8+eWXzJgxg1dffZUDBw4wbdo0tFotkyZNKtV+6dKluLm5cffdd9ueS0pKwt/ffqhQrVbj7e1NUlKSrU1oqP3GQgEBAbbXvLy8SEpKsj1Xsk3Jc5Q8rqw2V5s7dy7vvPNOhT8HQRBqjl8ZN0tfVwdURVUe503owvge6fQO9cFJW3OBhVULP1ce7R/KNzvtp0K+3x1t+5QtAzFp+YRUYulriI8LCdk6u+Bg0faLhHi7sPVcCkdisziXnEtito7sQmOFVSzVSgWezhoaezoxqlNQudMylaVSFufG5elMuDo2rNz+uMwCziXn4u/mSE6hZZTlYIwl3yY2vaCeeydURpV+oyRJokePHsyZMweArl27cvLkSRYtWlRmcPHdd98xceLEGya/YdasWXajITk5OQQHi10QBaE2eThpeGV4GO8Xbb0NkJan59N/z/H2ne1xUKtsqzFqy+sj27HyUJxtJQPA3kvpPDmoBUqFJX8h8lJ6pYKLt+5ox9RfjuDioCIlR09ito5dF9IZ8NHWco9z1qrwddHS1MeZsCB3eoZ407elD26ONb/kspFH8Z4rv+yPqdKqjdr20YazLNx68ZqvGyWxpfqNoErBRVBQEO3atbN7rm3btqxcubJU2507dxIVFcWvv/5q93xgYCApKSl2z5lMJjIyMggMDLS1SU5OtmtjfVxRm5KvW58LCgqya9OlS5cy35+DgwMODmLTG0Goa48PaM6WMym2vUIAfoi8zD3dm1SqcmNNGNExiJ/3xdoeny+aqnF1UJOjM3E4NpN7ezWt8DxhQe5smjEIgKV7LvPWmlO211QKcHfS0MjDiZYBrnRu4kmfFt6EBbhVaXvx61WyPPnH/57DYJaZMrhlnV2/PMfjsm3fKxRQMivw0/GdubNzo3rolVBVVQou+vXrR1RUlN1z586dIyQkpFTbb7/9lu7du9O5c2e758PDw8nKyuLQoUN0794dgC1btlgSq3r3trV57bXXMBqNaDSWqH3jxo20adPGtjIlPDyczZs38/zzz9vOvXHjRsLDwwEIDQ0lMDCQzZs324KJnJwc9u3bx9NPP12Vty0IQi3J15v4/VAc3++O5vJVw92SDG+sPsmqZ/rVej8kSSIzX2/3nKWWhMG2EuNkfHYZR5ZvUt9mKBSW7acHt/WnsWfDyRfwdNaQVWBEb5L4aEMU9/VsirertuIDa5l1Zcsrw8N4apBlREVvMuOgrvkpMaH2VCm4mD59On379mXOnDmMHz+e/fv3s3jxYhYvXmzXLicnhxUrVvDJJ5+UOkfbtm0ZNmwYjz/+OIsWLcJoNDJ16lTuvfdeGjWyRKT3338/77zzDo8++igzZ87k5MmTLFiwgHnz5tnO89xzzzFo0CA++eQTRo4cyfLlyzl48KCtLwqFgueff553332XVq1a2ZaiNmrUiLvuuquqPydBEGrIJ/9GseZYAk29nTkUk2nLTXB3VDN7dAf2Xkpn+QHL8sUjsVmYzJJtI6zacscXuzmVUDqBu9u7G23fKyuxm2pZHgpvVt1u1apPxnVm4dYLHC6qSJpZaGgQwcXRK1kArDmaYAsuRGBx46lScNGzZ09WrVrFrFmzmD17NqGhocyfP5+JEyfatVu+fDmyLHPfffeVeZ6ff/6ZqVOnctttt9mKaH322We21z08PPj333+ZMmUK3bt3x9fXlzfffNNW4wKgb9++LFu2jNdff51XX32VVq1a8eeff9pqXAC8/PLL5Ofn88QTT5CVlUX//v1Zv379DZMDIgg3G53RzOdbLgAQUzRSEerrwiP9mjG2WxNcHNQMbO2Hs4OK73ZdBuByWj4ta3mfjpKrVRw1SnTG4nl9hQL8XB14Y1S7sg69YVm3pG/2yloAjKaGkcswrH0g608l0Tqg4h1ShYarSnUu/mtEnQtBuH7peXpWHYknon0gQR6OtHxtHWC5ia94si/tG7mjVNqPCkiSRIvX1iHL8FB4CLNHdyjr1DVm/qZzzN90HoDZo9vT2t+NgzEZ3BrmT7tGdZPzUV+swcXfz/avs/yW8izceoGPNkQxvkcTPrync8UHCHWqVupcCIIgVMXG08nM+uM4aXkGvtsVzZ9T+jGglS87z6dhMss093OxCyz+PBLPtqhUDsdk2BL5+tXBttrPD2nNn0fiuZxewOy/TrPnlVvp08Kn1q/bkOgbyMhFTlGJcJWyetNQQsMgggtBEGpcrs7I//19mt8OxgGWqYWEbB39PtiC0WyJGsJb+OBUoiDWPV/usdUysGri5UREh+sr811ZPz/em4EfbMMkydz39T42vzCoTq5bXyRJYtOZ4pV7BYa6LRF+Lbl6Sz/83MT09Y2s7tY+CYJwU7mSUUBMej5Xz6zuu5TO8AU7+e1gHAoFPDGwORueH4i/mwNGs4xSAVMHt+T7h3vaRi3eXH3SFlh4OmnoEuzJ4wNC2fLCLXX2fhp7OvPqiDAALqbm8dX2a9dauBk8vOQAT/x4yPZYqqCYV13xcbEklVoTO4Ubkxi5EAShSoxmiQ/WneWbXZaKloHujvQK9aZ3c28upuTz/Z5oZNky6vDJuM70bm6ZXtj64i3svZROiI8zLf2LEzRTcnT8EBkDQJdgT/6cUvtLT6/l0QHNWbLnMlcyC/lg/Vnu7RmMh3P9r6CoDdaEWgUQ5OlUJ9NPlZFVVMhsx7lUknN0BLiLEYwbkQguBEGotMTsQqYuO8KholEGjUpBUo6ONccSWHMswdZuQo9gXh/V1q66pIuDmtvaBpQ65/pTlnL8SgX89mR4Lb+D8n22+TxxmYWApc5GfFbhTRtcqIqW1vZv5cuPj/au594U69rUkx/3WoLN3nM2076RO4Pb+DM4zI8uwV4iF+MGIYILQRAqZce5VJ7/9SgZ+QbcHNV8PK4zA1v5ceRKJq+sPEFsRgGuDmrmT+jCkHalg4hr2X0+DQBvFwe06vqZqZUkiZGf7+JMYi5g2c/j1RFtb+qVItYpKZO5YUyHWN3RuRGpuXr+OZnE8bgsTiXkcCohhy+2XsDTWcPAVn70b+VL+0butAtyR1HN+iNC7RLBhSAI5TJLMp9tPs9nW84jy9C+kTv/m9iNEB/LPht9W/iydlp/NpxK5tYwf7xdqvZJ/0ySpXhVK//6q2sgSdgCC4BOTTxodZPXWbAOABy4nEHEvB2sntIXR2393xI0KiVPDmrBk4NakJanZ8e5VLZGpbLjXCpZBUa7UbIuwZ788XTfUkuZhfon6lyUQ9S5EP7rknN0vLjiGDuLRhfu69WUt+5oV6Pbnrd+bR0Gs8S0W1syY2ibGjtvVf0QeZl3/z6DwVy8JNNBraR9I3dGd2nMhB5NGsTNt6ZM/Hovuy+m2x6/O7o9DzTQaqIAJrPE0StZbI1KYfOZFM4mWYJBd0c1747pKPYcqSOVvS+K1SKCIJRyKTWPWX8cZ8AHW9l5Pg1HjZJPxnVm7t0dazSwAGw38/oenX8ovBlH37qde3sG4+VsyRXRmyQOx2bx1ppTdJ69kQvJuRWc5cax4L4uPBQeYhvByC+xRXxDpFYp6dHMm5ciwlj//ECeHNgcjUpBjs7EtF+OsPN8an13UShBBBeC0ACk5em5kJJb78sBj17J4umfDnHbp9v5Zf8VDGaJbk09WT2lP2O7N6mVa7YNtKwcWbT9Iml5ulq5RmU5a9W8P7YTR94cyrYXb+H+3k1p7GnZnlxvkhi7aA+6BlIP4nr5ujoye3QHtEX7thjNDaOIVmXNGtHWblM7dR3uKitU7OYZ4xOEG0x6np5NZ5L591QyW6JSkGXwctYwtF0gt7cLoH8r3xofJSiLLMtsO5fKV9svsvdS8ZbnQ9r68+SgFvQI8arVpLklk3vRd+4WzJLMUz8e5ven+9bataqima8Lc8Z0BGDT6SQe++EQ2YUmHl5ygOVP1O+qlppk/bc1NJAKnVXh5+Zg+z670FCPPRGuJoILQagnL/1+nC1nU+yeyyww8uvBK/x68ApalZKod4fVejb84z8ctFVq1KgUjO7SmCcGNqd1LW8WZhXg7sgTA5vz5faLHIzJZM+FNPq2bBg1F6yGtAvkgd5N+WlfLHsvZTBs/g6+ntSDYK+Gs4V6dVmnRYz1PS9VDSVrYHQO9qy/jgiliHEkQagn6fnFn7SeHNScqHeHseyx3tzbMxiw5CK89udJDl7OID1PX2v9sK6SuDXMnx0vD+bjcZ3rLLCweu62lqiKbnJz/jlTp9eurNmj29MuyPJzOZuUy8APtvLSimOYbsBP/CVZt5I3Sjf2+9CoxO2sIREjF4JQT6w3068e7E5Ee8v+GX1b+tK3pS9Rybkcic1i2b5Ylu2LRaGArsGeDGztR0t/V0J9XQj1dcG5BlYvjOvRhPmbzpNVYCDIw+m6z1dV2QUGhi3YaUvoHNO1cZ33oTKUSiX/PDeQr7Zf5ON/ozCaZVYciuOv4wn0CvVmdOdGjO7cGHU91eqoLuvAWEOrd1FZCgXIMkhi4WODIoILQagn1gS0sv6o//ZkOPM3nWPzmRTy9CbiMgs5HJvF4dgsu3aB7o6WQMPPheZFAYf1q7LTKff3bsoXWy5wODaLY1ey6nR4eVtUCs/8fJiCopUKz9zSgkcHNK+z61fHk4NaMLF3CM8tP8LmsynojBI7zqWx41waL644TqCHpRz6hB7BDW56pyzFxbSuPXJxPC6LN1afJLfQhEmSMEtgkiRkGUySjFmSkSQZSZaRim70kiyjUip464723Nerae31X6HALMuI2KJhEcGFINQT6yetskZzNSolL0WE8VKEZSOtxOxCNp9J4XBsJpfT8olOyyezwEhSjo6kHB2Rl9Ltjg/1deH+Xk25p3sTvK5R1Co+q5BVh+P443A8pqJVKnsuptdqcPFD5GUWbDqPWZLJN5js5vlnDQ/jyUEtau3aNcnVUc23D/fkbGIOn285z75LGaTlG5CBxGwdq48msPpoAk4aFa8MD2NS32b13eVrspYBz9FdexXMG6tPcuxKdpXPbTTLbDmTXMvBBZiBuxbuppmvM0PbBhDk6USwtzPNfFxwcRC3ufogfuqCUE+swUVlRhiCPJx4oE8ID/QJsT2XVWAguijQiE7L51JaPtGp+VxMzSM6LZ/3/jnDRxui6BbiaRvNaObjQnahkT8Ox9sFJE4aFcM7BDKhKN+jtizcesEu1wQsu2B++UA3eoX61Oq1a0NYkDsLJ3YHLFuW/34ojn9OJHIqPodcvYlCo5m31pxi8Y5L/DOtf4Pcp8TVUU16voGsa6y2yCow2AILDyc1XZt6oVUp0agUqJRKHDRKHNUqHDRKNEolWrWCCyl5rD1h2TPmqVta1mr/2wS6cTI+h8RsHYnZOiIvZti9HuDuQIiPC028nGjh58qD4SG4l9jzRqgdIrgQhHpiLhot0KiqtxrE01lL16Zaujb1sns+T29izdEEft4Xw6mEHPZeyrBbYlpSeHMf7u7WmOEdg3CtxU94m88k89X2iyTnWBJTXRxUPDu4FQNb+940+3c4a9U8FN6Mh4qqXJ5LzuXpnw5zMTWP+KxCZq48zqIHe9RvJ8tgXS0SeTGdpXsulxpleeKHg7bv2wV5sOSRXhWe8+7/7QbA20VL9xCvClpfn9+f6svxuGzGfxUJQOsAV5y1amLSLaN7yTl6knP07Lds4otCAc/UcsAjiOBCEOqNtV6WgppdaurqoOb+3k25r1cwZ5NyOZOYw+WikY3L6fmYJRjRIZAx3RrTpJaXUs5aeZw/jyZQaLSv/jhnTEdGd2mYiZs1pXWAG5tfGMToL3ZxLC6bo1ey6rtLZQr1dSE6rQCjWeatNac4nZjDB2M7AXDwcgb7L2fa2lZmZKvAYLK917Hdav/f2FGjoleot+3x7Ds70KeFZRQsu8BIdHo+Men5PLf8KAA/RsaI4KIOiOBCEOqJ9RNjbWW5KxQK2ga50zaofvbFuZJewC8Hrtge+7poGd4xiGdvbYl/ifoEN7tRnYM4FpdNam7DLPL0ckQY+6IzMJpkDGaJXw9cQaNU8O6Yjvy0N9bWzs1RTWW2olqw6TySbBkhmH5769rseplK/v/k4ayhi7MnXYI9+WW/pUZJYnb9VoH9rxDBhSDUE2uuhbmeS37XlkJT8WjFpukDaVnHtTMaihEdg3hv7VnMssyF5NwG93MIC3Ln1DvDMJkkhn22kwspefy0Lxa1SsmsEWHsi04nMVtHrs7E9N+O8c2uaFY90w/tNZbcrjgUB1h2LK2JpdJWpxOyeW/tGQqNZqz/xygVltUiJcf+rhWsdw/xYu+lDAL/Q4FtfRLBhSDUk+KRi+s7z+Ql+4lJL6BTE0+GtQ/ktjD/BlFrwUldXLq8kVfd189oKBp7OqNRKTCaZf45mcS0BhZcWKnVStZPG8Dt83cQnZbPkj2XcVAriJx1G78dvMKH68+SlmfgVEIOS/dE8/jA0it7DsVkklGUsPvC7a3J1RnZcjYFjVLJiE5B19W/F1YcsxV8K4+7U9nJmutOWhJMk3LEyEVdEMGFINQTZQ2U9d5wMoktZy27QV5MzWfVkXjAskdJUx9nHg4PZUwdzHuXxc2p+M9LVFJuqcTT/5IAd0fiMgvZeymdabe1qu/uXJNarWTj8wO57dPtxGQU8NWOaJr5unJfr6ZsPp3MhtPJKBQwqlMjTCapVBD7zl+nbN8/uvQg+hLVS/9Ht+sKMPTGiiuIju7SiE5NPMt8rW2QO5dS8wHL0u76KBj3X1L/H28E4T+v+kMX3+66BFhGQTxK3MwzC4wcu5LN9N+Osu+qGhh1xdNZaxuduZiSVy99aCjaFeW9XLgBfg5qtZKNMwYRULQp2JurT3IqIYsNp5MBSzXM8Pe30PL1dYxftMfu2ONxxbUw9FeVRT+blHNd/Xq4X7MK26w/mUSntzfwxZbzpV5rVyL3yN9NTI3UNjFyIQj1RFED0yJRyXm2c/w9bQAnrmRjkiVWHopn+znLiMbfxxPp3bx+akgoimozm//j5RODvS2rcq5eNdNQadVKfn+qLwM+2orRLHPf4n1lttt/OZP90em2GiU+LlrS8w10bOzOgFZ+3Brmz4Pf7qfQaGbh1gssjYzB20VDkIcT025tRZ8WPkiShCRR4VTeQ+HNGN25ERtOJ6NSgFplqathlCTbShC9SUJvkvhmVzRTb7UfIYrLLATgljZ+qJQ1u0JLKE0EF4JQT6wJnWUloKXl6Vh9JIFRnRvZ7fx4tWduacHcdWcBGPDBVsCyN8fSyb0Ie2MdOqPEgctl17ioC9Y/4aabNGm1styLRpWM5ZTYbmiCfYqXKVv//dRKBWO6NuZUQjani/IfXlxxjE0zbkGrVnLgtdtQKu2DhGY+zpxJysUsQ3ahkexCI9FpBZxOzGHvK7fSZfZGdCYJjUqBq4MafzdHmng50dLflS5NPendzAdvV0vxMQ9nLeN7lF4O28zHmc1nUlh5OJ74rMIyk6S7h3jxy/5YUSa8jojgQhDqSXmfncYtiiQ6rYD/W3sGfzcHhnUIZMrglqUCjScHteBwTKZtyBrg7+MJzJvQhabezpxLzsPFQXX16evcjbopVk3xc7X8u91oPwe1UoFJknltZFsm9i6uDpuco6P3nM0AxGYUMv6rSP6c0q9UYAGwdlp/DsZkcjI+h/PJuaw+lkCBwYzeKBGVnIuuaPrEaJbJLDCSWWAkKjmXzWdTbOdQKsDFQY23s5YgT0dCfVxoHehG+0budAn2onPRV4HRzDc7o8sMIDLyLQXcksRS1DohggtBqCfWaZGr/xBm5BmITiuwPU7J1fNDZAw/RMbg7+ZARPtApt5qCTQupuYRk1Fgd3z7ooqXmqI/9PX5Ybl4x80b5xN7bfB3s3zyvtFGcKz/fjqD/XSOj7OWIA9HW80IbTlVZpVKJb1CfWxTJ35uDny25QJmSeZgiQJdr40I43RiLpfT80nO1pFZYERXtOxUkiFXZyJXZyImo6BUxVmNSoGbo4bsAstKlbLqcVjzXaKSK15xIlw/EVwIQj2xrha5+s/gwm0XAFApFcyb0Jkf9sRwLC4Lo1kmJVfPj3tj+HFvDG6OanJLbDbl4aTh7TvaM7pLEIlZhbZPhPV5Y7dUIJD/c9thx2cVsPlMCgcuZ3AhJY/4ovl+sJTG/uOZfvXYu8qz/I7Ktt8lK7VayfIn+jDoo20APDGw6jvZGswS/7f2jO3xg31CcCyjLkZMej67L6RzOiGbmIwC4jILycg3kK832YI1o1m2LYGF4p1eS+rZzJvfDsbRws+lyn0Vqk4EF4JQz0p+ykrJ0bHysKUIUcfGHtzZuTF3dm6MJEn8cyKJJXsu2wINa2DhqFEy7bZWGEwSL/1+jOm/HbU7f33e2K2ffI032Cf26lh56Arf7b7MhZS8UislSjocm8Xrq07w7piOddi76rEGwPoyElG/3nHJ9v1Lvx/n+SG6Su3+OqZrY77acQmDSbLNDYb6uJQZWACE+LgQ4lN2QGAwSRyOyeTwlUzOJeVxKS2PjHwDk8JDSrW1Fv1yUNf/NOF/gQguBKGeWDPWrTd/g0ki/P0ttmS0x/qH2toqlUpGdW7EqM6NkCSJjadT+GlfDK0D3Jg1LAy1Wkn/D7bYDbsrACetikdLnKeuFU+L3LzBxQfrzrI08jIFV00dqBSWBMQmXk409Xamua8zfx1PJDqtgJ/2xeLr5sDzQ+q+PHZVWFMo9KbSwUXJYlWZBUZ+3hdTqeAi1M+VqHeH10j/tGolfVr42PYSKc/uC2kAnE68viWxQuWI4EIQ6plU9CE3q8BgCywGtPJlVOdGZbZXKpVEdAgkokOg3fPWgeAeIV4serAbvq71v5bfWpj5Zp0W2RaVwpfbL9oeN/FyYkjbAO7v3ZTWZVTinHZra275ZBtxmYXM33QeXxctDxTtotoQWUcujGUEhzOGtEarVjJ/k6WmxJ3X+H1tKPL0xVOIepNZjGDUMhFcCEI9sd5v1WUkw31+X9cqn886EuKkVTWIwMJgkmxBxc06ctGpsScqpQKzJKNWKvj72f54Omuv2V6tVvLv9IEM+GAr6fkGXl99Cm8Xh+sujV1brKkLZSWiqtVK29ScUgFPlFEOvCHZc7G4mJwILGpflSt0xsfH88ADD+Dj44OTkxMdO3bk4MGDdm3OnDnDnXfeiYeHBy4uLvTs2ZPY2OLd9XQ6HVOmTMHHxwdXV1fGjh1LcnKy3TliY2MZOXIkzs7O+Pv789JLL2EymezabNu2jW7duuHg4EDLli1ZsmRJqf4uXLiQZs2a4ejoSO/evdm/f39V37Ig1KpTCTmsOhzPsv3F/49IV03ZJ2YV8vXOiyzYfI4P15/lg3Vn+b+/T/PGnyeZtfI4L/x2lMvpllUjDeFGvmjbRdq9ud6We+CkvTn/mHu7aln8YHfAcgMeOm+HJZegHM5aNRunD8K1aInws78cJiY9v9b7Wh3WWizXSgr+oyg/qGtTz2tuZNZQlEw6FctRa1+VRi4yMzPp168fgwcPZt26dfj5+XH+/Hm8vIr3DLh48SL9+/fn0Ucf5Z133sHd3Z1Tp07h6Fj8SWr69OmsXbuWFStW4OHhwdSpU7n77rvZvXs3AGazmZEjRxIYGMiePXtITEzkoYceQqPRMGfOHACio6MZOXIkTz31FD///DObN2/mscceIygoiIiICAB+/fVXZsyYwaJFi+jduzfz588nIiKCqKgo/P39r/uHJwjXIyHLsoJgcYnEOCuN2n40Y9DH2yq8aVmpGsDf+IVbL2CSZBRAr1BvHq1E6eYb1W1tA3jnzva8teYUKbl6Ri/cxbrnBpZ7jLerlnXPD2TwR9swSTLjFkWy/7UhddTjyrNOi5Q1cnE6IZvMAiMA0xt47ghgt2LHr6i0uVB7qhRcfPDBBwQHB/P999/bngsNtU8We+211xgxYgQffvih7bkWLYqHy7Kzs/n2229ZtmwZt956KwDff/89bdu2Ze/evfTp04d///2X06dPs2nTJgICAujSpQv/93//x8yZM3n77bfRarUsWrSI0NBQPvnkEwDatm3Lrl27mDdvni24+PTTT3n88cd55JFHAFi0aBFr167lu+++45VXXqnKWxeEGleyWqN162g/NwfGdmuCm6P9zo4lAwtnrQoFluV2SoUCpQJUCgVKpQIXBzUvDG1TV2/hmqy3osn9Q3ljVLt67UtdmNS3GTHp+Xy3+zJnEnOZ+89pZo0o/30Heznz8bjOPP/rUVJy9ey9mF6pxMS6pCwnIfffosJtWpWS/q386rJb1ZKaq7d9L4p/174qBRdr1qwhIiKCcePGsX37dho3bswzzzzD448/DoAkSaxdu5aXX36ZiIgIjhw5QmhoKLNmzeKuu+4C4NChQxiNRoYMKY7Sw8LCaNq0KZGRkfTp04fIyEg6duxIQECArU1ERARPP/00p06domvXrkRGRtqdw9rm+eefB8BgMHDo0CFmzZple12pVDJkyBAiIyPLfH96vR69vvgXMCdHZBULtSfEx4UrmYV8Or4Td3crXdK4LD8+2osBN8AfcuvoyY1U7vp6vXlHe/ZHZ3AyIYdfD8ZVGFwA3NW1MS//fhyDWeJgTEYDDC4st+GSyZBXv1ZGUc4GqeTgS1l1MISaVaVfi0uXLvHll1/SqlUrNmzYwNNPP820adNYunQpACkpKeTl5fH+++8zbNgw/v33X8aMGcPdd9/N9u3bAUhKSkKr1eLp6Wl37oCAAJKSkmxtSgYW1tetr5XXJicnh8LCQtLS0jCbzWW2sZ7janPnzsXDw8P2FRxcuT/4glAdSttS1Ir/0FlbVHZqpL7ZVhnUY39TcnTsvZjO38cSWLrnMseuZFZ80HV6dURbALIKjJW+nnXfkXPJDW/HVE1RlPjv6WTS8uzzFKyJyDfKQqBb2hQH5Wl5+nJaCjWhSiMXkiTRo0cPW95D165dOXnyJIsWLWLSpElIRVloo0ePZvr06QB06dKFPXv2sGjRIgYNGlTD3a9Zs2bNYsaMGbbHOTk5IsAQao21eJa5Mp/uLYUSG0SyZmVYV66UV0yquq6kF7D1XAoms4zBJJGrN5FVYCC70EhWgYEcnYlLqXnk6UvXZtg5czDBXs5lnLVm9G3pi5uDmly9iU82nuOHyb0rPMbfzYG0PEODTOqcdltLZq48AcCxK9nc1rY4d05zgwUX1oAXwNNJU05LoSZUKbgICgqiXTv7ob62bduycuVKAHx9fVGr1WW22bVrFwCBgYEYDAaysrLsRi+Sk5MJDAy0tbl6VYd1NUnJNlevMElOTsbd3R0nJydUKhUqlarMNtZzXM3BwQEHB5HoI9QundHM97svE1m0NO5YfDYTKjimKLZAb74xtuy2BheGGp4WMZgkBn28tUrb1Bft+g7A/kvpBHevveACYGj7AFYejifyYjqSJJW5mVdJwV7OnE7MJTmn4a1gGNymOPE9yMN+ebNWZVntIpcqYN8w2ec4iWmR2lal4KJfv35ERUXZPXfu3DlCQiylVrVaLT179iy3Tffu3dFoNGzevJmxY8cCEBUVRWxsLOHh4QCEh4fz3nvvkZKSYlvVsXHjRtzd3W2BS3h4OP/884/ddTZu3Gg7h1arpXv37mzevNmW7yFJEps3b2bq1KlVeduCUCPMksyfR+L55N8oEkoshcsvYz77atY/hfU5zVAVqqIbak33t9BgsgUWSgWolUrUKgValRKtWomLgxpXBxWuDho8nNTMGNqG1gFutH1jPYVGM5fSCsq/QA14cWgbVh6Ox2iWWXMskbu6Ni63fasANzacTiar0FjrfauqL7Za9rlRKxWEBdoXBbNOmRjNMm+uPsmj/UOvWaa7IfAvsUJk0EeWANVoljCaJUxmGXNRBNqpiQevDG9Ll2DPeurpzaFKwcX06dPp27cvc+bMYfz48ezfv5/FixezePFiW5uXXnqJCRMmMHDgQAYPHsz69ev566+/2LZtGwAeHh48+uijzJgxA29vb9zd3Xn22WcJDw+nT58+AAwdOpR27drx4IMP8uGHH5KUlMTrr7/OlClTbCMLTz31FF988QUvv/wykydPZsuWLfz222+sXbvW1pcZM2YwadIkevToQa9evZg/fz75+fm21SOCUFd2nEtl7rqznCkqPdzIw5HMAiOFRnPlPkUVffy+UXbVtJY8qM7IxV0Ld3MxJQ+TZNnwzCzJyLLlE3LJIfh/nx9IyzKqYJbF1VFNodHMlYzaDy6CPJ3wcNKQXWgk8lJ6hcFFx8aWXWx1xoYXOP5zwpKf1rWpZ6kRmJIbgFl37XXWqujUxIMHeodcs8JsfVGU+P/sSollqVfbeymD8V9F8tfU/rQJrNzvl1BalYKLnj17smrVKmbNmsXs2bMJDQ1l/vz5TJw40dZmzJgxLFq0iLlz5zJt2jTatGnDypUr6d+/v63NvHnzUCqVjB07Fr1eT0REBP/73/9sr6tUKv7++2+efvppwsPDcXFxYdKkScyePdvWJjQ0lLVr1zJ9+nQWLFhAkyZN+Oabb2zLUAEmTJhAamoqb775JklJSXTp0oX169eXSvIUhNpyOiGHuevOsPO8ZV8DN0c1Uwa35OG+zej/wVYKy9gQqiw32siFuuhGVNVg6HRCNkevZFXYTqVQ0MjLqdLn9XTSkJqrJyn72jeVmuTtoiW70EhsesXBTPdmnrbvr6QXEOxTvWmb3w9e4fXVJ2kd4Mabo9rRo5l3tc5jFZ2aZ0t8fKx/6V1Pezf34d3R7fntUBxnE3MxmCUKDGb2Xspg76UMzLLM6C7lB1Z1KcC9eOTij2f6olIo0KiUaFQK1ColKoWCfIOJ4Qt2YjBJrDoSzyvDw+qxxze2Kpf/HjVqFKNGjSq3zeTJk5k8efI1X3d0dGThwoUsXLjwmm1CQkJKTXtc7ZZbbuHIkSPltpk6daqYBhHqXEJWIR//G8WqI/HIsiX57aHwZkwd3BIvF0t5aOtquMrsu2H90GWogYTO+KwCkrL1hPg41VqZcOtKgiqvbikxiDN1cEtcHdQ4O6hw1qhw1Khw1Chx1Kjo2NgD52vsolkWPzcHzqfkkZpnKPWaJEnk6kzk6E3k6ozk6Uzk6c3k6Y0UGMzk680UGkxEJedyMj6Hoe38ScszoDdJ5OtNFBrN6IwSepMZvVHCYJaIK/pkXHIb8GvxdXW05dScSsypdnCx4lAcOqPE8bhs7lkUSYC7A08NasGk8JAK8z7K8uEGy/S2o0ZZah8bqwfCm9n2RjkSm8lPe2NYeTgesKzWaUicNMVVYrs08SjzZ5JVUPzvZb66TK5QJWJvEeGGlq834eJQ87/G8VkFrD+RRI7OiEmyTEeYi+ZlTWbLY0mS0aqV9Gvpw9B2gSiVCrILjXy57SLf7Y623Vjv7NyIF4e2oelVNw1rwGCuxKd7RdHt53r+4O06n8rTPx+27QcBsOqZvnRt6lXOUdWjVlo3vKpcfw0mieRcHak5xUsEnxzUvFQxserydbUEdNFp+bSY9Q/SVVMsVfHVjuhKt/VxvfY+IyVpVEoMZokzidkMu8aNvCLqq2o3JOfoeeev03yw/ix3dm7EayPa4lHOvidX23PRMto2qJJ1Vbo29aJrUy82nk4mR2fiu92XubdX0xr7N7xeLiWCUZ1JwllbOrjwcNLg5qgmV2fC363+9+e5kYngQrguidmFrDuRhEmSaB3gRu9Qn1rfR0JvMjN+USRnioZiwXIjKjSYOR6XTbC3M4lZhQxo5UdKrg4XBzUKLDdxs2wJCgxmmQKDye6TZ4HBTIHBVLSksfLJdT/ujaGlvytD2gaw/ECs7djeod68OqItna+RGGbNtahUcFF03zBeo21Kjo5XV50gPd/AuO7B5BuMZBWYkGQZF62K2IwCfjsYV+q4exZFEhboVrRduP25ZbnoZ1b0czNLllEWqehnKMmWx7JsWVYrYfmvLBdPh1RmWiQlR0f43C22hDqrAoO5xm5MjiU+tV59nbIosPzMFdYKqEpFqZyIAHcHHFRKtBoVDmqlZVRFrcJJa/nycXHg+SGtKtW/AHcHrmQWcvBy9WtxqIsSLEN8nHl1eFs+3BDFxdQ8dEaJ3w7GseJgHD2aefHWHe3pUJTncS2SJJFdaAlCR3au2qZqzw9pzey/T5OYbfl3XTdtQLVHY2qSk0NxMFFgMJU58qVQKBjaLpCVh+Mq9XsiXJsILoRq23k+lceWHrSrZeDlrGHmsDDu7dW01q57ISWPY3HZds99tb14fw7rnP3BmOsvmqRVKbi/dwhqpQJV0ZdapbQ9Ts3V88fhOC6k5HEhxVIEqZW/K7NGhDG4jb9dEllNuPrvXUqOjpd+P86Oc6m20OBIbFa55/jmoe7svpDO93suY5ZkTiXUXiXa5r6uFbbZG51e6g+5r6sDfpX81F8Zvq6W+XatSsGcMR1xcVTj6mD5cnNQ41b0idVRrSx3CqHAYCI911DjN8uwQDeuZBYSlZRb7XNYRy7MkkxEh0AiOgQSnZrH23+dZtf5VMwyHLicyajPd9HEy4lpt7VifI+y6/iU/H/n1jZVy1Gb3D+UAoOJj/89R57exJB529n/6m1VGjX5aEMUqw7H0a6RO3d3bcKwDgHVmtopyVFdfLvL15u51q+mtbpsZYJ+4dpEcCFU2xdbLqA3SbQOcKV1gBuHYjJJzNbxyh8ncHVUM6pT7WSLlxzWf/bWluTqTLZP9vl6E8fjsunRzIu4zEIc1Er83BzQqJRoVcriAEGpxMVBhauDGiet5ZOnk1aNi1aFm6OGe77cQ67exPgewbx9Z/ty+/PC0Nb8uDeGbWdTubtbY+7p3sT2KbIy5CrkXFj/4O29mM6nm85xIDqjVJUB6ydt4zXyM5r6uNCxiQdLIy/blnV6Omvo1KT0p1mNsjjhzZr8lpytY/v5NMIC3bitbQAalaLo52tpp1Vb2vq7OXBLm4o3CCw5XH1pzvDrvomURVeUOOugVnHPNW6oleGsVePsU/N/Nu/o0oiNZ1JIyzeQlqerVi6MtahVyZtiqJ8rSyf3Qmcw8dG/5/j1wBXy9CbiMgt5+ffjvL3mFPd0b8Irw8PsPsmfjM+xndPVservd+qtrWju68ozyw6jN0lsOZvKmG6VT+78blc0hUYzCdk6Np1JQaGAUB8Xlj/RB3/36k1XlNy1tbwRNesy6hulYF1DJYILodrOFn3KWnBvV9oGuWMyS8z++zQ/RMbw3PKjHIrJZNbwtmVuxXwpNY/90RkYJdluO2eTWcZglmgb5EbfFr6cSczh/FVlkf8+kQhY/ljU2iZdRTfzymwj7eao4ZlbWvLMLS2rc4lKtrW0/nlfDF/vvGQXYKmVCsZ1b8Jbd7TDscQNovmstbbgoXMTD9tojxIIcHdi8wuDGPyxpSz/Pd2b8PrIivfC2B+dzviv9gJwJbOAlyLsf/5nE3P4dOM5CgwmYtILaOLlbCti1T3Ei2EdAgkLcENd4ufqWiJnpjYCCygOSDUNdFvwUR2DmPHrMUySzKLtl675b/HrgVh2nEvlk3Gd7f6tofi9lfWJ21Gr5o1R7XhjVDtWHY5n3qYoYjMKKTCYbctIHTVKJImiKbDrv7EO61A84rH5bDKrj8WTU2jkjs6NeKRfaDlHlp66kmW4lJbP97svM7OaKzhK/tOXt+qqeARIJHReDxFcCNVmXUZp/eSpVil5eVgY+6MzOJuUy/e7L/NDZAwh3s50auLBPd2DaRXgyraoFN5Yfeq698loVovzuNbRBOunwdpUmald6z03uUTCo6ezhlEdg3h9ZNtSNxqA98Z05HRCDi8MbY27o5rmr66zez3U19WWvFYZO8+n8vB3B2yPy/pkN2XZYS6mFpexLllPIPJSOl9svYC7o5rjbxcvGa/tHB2wJHICuFfjU3hdUCqVtG/kzrG4bP45kVRmcHEls4BXVp5ABs6n5LH+uQF2wZim6HupgsBgTLfGjOnWmLOJObzz12n2RacjyWXX2bi6KmdV35PV38cTbd+fTMipMLiw/l/3yrAwJvZpSvjcLeTpTWQXVrz6pjL9KW+FlrW6rMi5uD4N8/804YYQ6uNCVHIupxOzbSshXB3U/DNtAI8uPcD+6AzyDWYupeVzKS2fP48m2B3f2NOJDo3dAdhwKrnU+a0sWzr72v7gSLKMWYZJ4SG18r4s17D8V6OqxRtfFfIx+jb3YdOZFFwd1LRr5M6rI8LoHFz+Ko/7SuS9mEoEcooydoQs+Xe0wGDiUmp+UbKmjKujhjMJOTy7/IhdO+cyggJLYqiFh5OGXJ2RYC9nYkoUr8rRmTCZJA7GZJJvMNV4ifCyWPNhrpVc2xDc37spx+JOkJBVSHaBoVSOwrgv99imwM4l5/H0T4f56qEettdtIxeVvCmGBbnzyxN9yNOZ+PNoPOn5epw1KkuVU0c1/q6OFSZ+VqR3qDdHr2ShUiowmKSiVVcV/3tbf0NNkoSbowZHjZI8fc0VGitvFZN15GLh1ot8vSOa3s298XdzxCRJ3NuzKeENbOfahkoEF0K19Qz1Iio5l33RGQzrUJxRrlQq+P6RXsiyTHKOnvMpuWw4lcQ/J5LILDAQ5O7IA+EhPD6gOWZJZsrPh23HKhRFy/JK3AwPvD4EjzreaMj6yaYy0yLXqzK3gq8n9byua5T8U1rWO9p6NoXmfi44a9TMWHH0mqMpjmolg8P8WXcyqcx5a+tNYXiHQL58oLvda4diMhn75R4A+ry/pcydKQ0mqcZ/5paVD5YVPLe0rjgHpL6M7dqEWX+cQJJhSeRlnruttd3rDmr7YG7D6WRe/v0YH97TGbAE4UCV9l0BS/XSB/rUTqD+65Phtu9XHrrCCyuOV+r33ZoIbbaNIFrem85UM3vrWCrAWqZHLB9WZIxmGaNJsk33gqXCrLUAHkBcZiErn+5bI3242YngQqi2vi18+WlvLH8dS+C521rhedUnLYVCQaCHI4Eejgxo5cf/je6ALFvmv08lZrPzfCrf7opm94V0HNRK3hjVjpEdg3DSqlhx8ApvrD6FQgFutVDHoiLWm6u2ComZ1b1IXW+hpCwxcmH99lJaPq+tOlnuca4OatY9P4AVB66wjmskvBXdFCoams/IL3vL69oILs4l59luaIPDKlezoT6o1UqaejtzOb2AHedSSwUX3UO8iMkowNdVi6+rA2eTcvntYBwZ+Qa+mdTT9nOr6GdfX5SKqv+7mot+xzRFv6i6Sla0vdrJ+Gwe/+Gg7fGMFccrfez0Ia3550QiUcm51z2V+18igguh2m5vF0BTb2diMwoY9NE2SzXAviEoFQpWHo5jx7lUYtIL8HDSoFYpMJllsgqMnEvJtftk7KRR8e2kHvRt6YssyyTl6DhdtAeHv5uD3c2wrtTFyIX1R1AXGzRKJZLTLqbm2zaYmtg7hCVFS1KtXzKWzZvWTO3P0j2XeWvNKXxctGycPghvV61tJczVq1xMJsn2XFm3t5IJctb73+1t/dl4JgWwBFm18fPeF50BWEqGXx0ANzRtg9y5nF7AlYzSZcodi4o+6U0Sf0/tz13/283JhBw2nUnhvsV76dbUE2i4uQLW33NJhpGf7cQsWUYLTJJk9/tnlmRbPpd1dMw65XMivnrLpj/+N4rE7PIrhlr/NyzZT4Bnb22Bv7sDs/44gbdLw/79aUhEcCFUm0al5PP7uvLCimNcSMnjg/VnmbfpHO6OatLKKLNcUlNvZ5y1KgI9HOkR4sX+yxl8uyuaY3HZtuFyhYLaWw1SAevfZwdN7U+L1HQtjLKUTGabvOQAvz8VTo9m3rw8LIyXh9ln35ec75/UtxlD2vkT5O5oO4c14a3kLSwtT0efOVtsN4OylteWtWPm1Ftb8sHYzvx28Aq3tPGrpeDCsrW9p3PDqBRZng6NPVh3MomUXD2/7I+lf0tfZBn2X05n2b4rABQazKjVStZM7cdD3+1n14V0Ii9ZvsCS9zL4422YzJYcB6PZUtnVVHTjloqKosmyJYj845l+dfLeCgzFicOVra0S7G3J5cosKkznUM3fD+uKtCAPR/59fiAuDqpyVybFpOcz6KNtlmMlS2AKxb/7QsVEcCFcl87Bnmx4fiB/HolnwebzxGYUkJZnwNfVgUf6NaNdI3dydSZkWUalVOCoVtEp2AOTWWb6r0fZFpXKtqhUu3OqlAraBLjxwtDW3Na27jeZ2xaVYrtJOmtq/3+Ruhi50KqVTOrbjKV7LgOW/IdrbWx1dSJhY0/7VTmqMjq8+0K6XQ7GLWGlcxsC3B1ZPaUvey9loFRYfnesSalP3dKiSu+nKqw70ZbcxbOhGtzGj4+K9vSY9ceJMttYRyiUSiU/PdaHx5cesI3+WFlXx1TkcAUF12pSyXoZXYI9bfVRrPVTNEU1UrRFj1sFuDGhRxPAspR6a1RqtacQraOfjholbpXI3yo5HVpoMNmOF4W1Kk8EF8J1UykVjO3ehDFdG3Mls4DYjAI6NfG8ZhLmibhsJn2/n4x8AwoFtPBzpVNjDzo28aBTE0/aBbnXyfLEq+XqjExecoADJUowt/Cv2RvSvqJPmCazXOEwbU175872LN8fi94klZmwKcsy+6MzCPVzKXdfheJhY5ktZ1PQGczsKpH0duLtodcs29052KvCVS41zfpzro09VGpau0YevDIsjKWRl0nK1tmNDgW6O/LuXe0Z0s5+75GvJ/VkweZzbDqdTHahEXcnDV7OWrQqJQ4aJQ5qFY5qZVFZcjUOaiUZ+QaWFAWakiTVWn2RkpRFoYFSAX9OqdpoibqM0bIqXduWC1S59iX//uhNkq1qZ2U2GRQsRHAh1BilUkGIj0uZw98l/Xk0nox8A4Hujvz2ZHipDb3qw/G4LO5dvNe2lNLbRcsLQ1vTqYlnjV0jMauQCYv3lnpeWRdDF1eRrvozbTJLzPrjBCsOxeHmoGb99IE09ix7S3Prfcholpm85ID9awoazEZVYBmKty5fHFyJaqENwVO3tLCN5GQXGFAplRVWyXzuttalEkDLE5OebwsuDCbJls9Rm1Sq6gcI1qnD6t7braNtlQ0OSk7P6c1SlfYBEiwaZrk64aY2oqNl2WpGvqFO/qhV5Jf9sdy1cDcFBjMK4LEBoRx87TYm9q7Z5Xklh6qdtSqctSp8XbU8PqD8gkK1oeTfWJ3RzDM/H2bFIcvGZrl6U7klyYe1C7LbgVNBUclxBYQ3b1g1AP48YqmtogB6hDT8kYureThrq1V+uyIlN3LT1dEKCNt0WhXuz5Ik0f3/NvLv6eSqHmp/baU1uKhce8eSwYXBjNpaElwEF5UmRi6EOtc9xItuTT05HJvFT5ExzKinpE2Al1Ycs91UtWolPzzSiz61VCQnT1+c0LbnlVvrdeWC9e98gcHEEz8cYteF4mmNUF8XmnhdezQp2MeZC3NG1HYXa8S6k5bKkP7uDnYlx//rSt48Cw1mPOtg8LA6uZA5OhPp+cXJ4V2Cq1fUy3rtyuzjA/YJ0AazVByciOCi0kRwIdSLxwY055mfD/Pj3hieGdzS7pNUXXn6p0OsO5lke3xHpyC2RiWzNSoFkBnU2p++LX2v+zp7L6bzf2tP22XIH47N4tYykh7rihIFOTojk78/wMGYTJy1KmYND+ON1adIyy27BsWNRpIkDhft7tn9Bhy1qE1OJcrFH4rJxC+jgIQsHQVGEzqDmXyDmcTsQnJ1Jtun9l6h3ni5aDCYJIwmCZ3RjN4koTNJGExmDCbLvkAGk4TeZMZolouelzBKMk2uMc1WnpKrM1Y907faeTPFIxdVDw4KjZJtpE6MXFSeCC6EejG0XQBNvJyIyyxkxaE4HqylCoHluTqjfuXheLvHX+2I5uTbEdUalpYkia93RvP1zmi7SpRqpYIx3RrXW2Bh/duabzAx8et9nIjPxt1RzZLJvQgLdOON1afI1ZvILjTWeVXUmvb8r8fIL8qhebR/83ruTcNSMqdg6i9HKnXMn0fjK25Uw0oGF3d/uYfH+ofyWiU22LuadbVHWp6Bp386xML7u1Y6idVokmz5IiaxmVmlieBCqBdqlZJH+4fyzl+n+eTfKCLaB5S7QqE2fP1gD55ZdoiELB1m2bL+X5Jl8vTFVQDlKs7yZhcYmP33af46lmi3Z4aXs4aH+zVjyqCW9To8b30/n2+5AICPi5YfHu1F+0aW4WbrRmY/7Y3h6UHN62QVQW2Y+88Z1hyz5FvcFuYvRi7K0NjTifis4mJdSoUluVihsORHlMzF8HLWkF1oRMaSv6LAkmSpKDrG8mW5iVu/V6uUqBSWT/sl695U5f8oR7USlVJhKe4mWz4AVCe48HSyTEGaJJl1J5P443A89/QIrtSxepO5eOSihrZhl4vKjUtF9UasJcglayExWbbtUGt9ziTJNPV2rpMtCWqCCC6EejOxdwgrDsZxOjGH11adZPGD3eukoJRVsI8zfz07wO657AIDPd7bhNEs08LPtUorH3J1Rrq/u8lu6LR1gCuvjmjLLXW8UmHDySReX30SfVGlQ1nGVlDJKsDdgZ8f60NLf9dSx3+0IYrPNp9nSFt/Xh3ZtlSti4ZKkiQe++EgW85aaqcEuDvw9UPdKzjqv2n3K7eW+/rgj7cSnVZA9xBPVj5d/UJbey6kcf83+6p1rFKpZMfLt/Dwdwc4n5JX7aWg04e0Jjotj10XLIXGkisx9afAEgjpTZLt78DZpFzGLdpTXE1UljGZLTd+k1myBQxmSUaSLUFE8QcXy8iHtX11hAW6sf75gdU6tq6J4EKoN1q1ko/HdWb0wl1sPJ3M6qMJ3NW1cb326ZElBzCaZZQKWDK5apuFnU7Isf3RuC3Mn9l3ta+3m/L8zedILecPaCNPR5Y/XnoZcJcmnuwsSu7UmyTWnkhi7YkkQn1dmHJLC+7u1rhBjmZIksSKQ3F8tOGcbRqqdYAra6b0a5D9vRF0buJFdFoBF1IqV5DrWlwc7POpqvrxobGnM31b+lqCiwpuyheSc3nvnzPk6Iy2jciMUvGnf2vAUJnpDYXCEpQbTBJNvJxsj0vWwalNKqUClUKBUlm8G2xUcm4FRzUcIrgQ6lW7Ru5Mu7UVn2w8x1trThHewocA97qdHrFafTTeVrHwqUEtCC5nxURFvn34+nYxvV7Wv51qpYKHwkNIytHxzwlL8mpTb2d+ezKcQI/SP+dWAW7svJDG3V0bU2AwseVsKgazRHRaPi/+fpzXV58kon0gr45oW2//TiXpDCYe//EQkRftK4QObRfAoge6icDiOgwO8+PPo/FkFxqvq9BW52Av+rf04UJKHmqVkpEdAys+6Coaa42MCj7wv/D7MY5dya7wfJWZgrUGQXqTmWBvZ/58ph+xGQWolQqUSoXtvxqlZepGo1KgVilRK4umlkpMEVmnjlRKBVq1pY1GrUSlUCBTPCVVHFDYh2CZ+Qa6/t9GZNlSa+NGKEMugguh3j11Swv+PZ3MifhsZv1xgm8n9ajT6RGw3KRmrrTslBjk4Vhqv40bjfVvT9emntzVtTEPfbcfgHZB7vzwaC98XR3KPK6Rp+WPrsEssejBHkiSxE/7Ylm84xJxmYXojBKrjyaw+mgCLfxcmTq4JWO61d9o04/7Yuy2xG7q5cSbd7QrVcWyoZFly+ZcBQYzBXoz+QYTBQaT5bHBMsffr6VvvayisrotrLj0/t5LGde1cuqnx/pcV180Sut28uVHF/kl8qU6NfGwlRVXK4tv/KG+Lray4uUqGqowFE0lWsrVe1b7PVwPa0IpWEZdVMr6+72oLBFcCPVOrVTQraknJ+Kz2XI2hawCI151vPvgkz8dRmeUUADfV3PUoUFVBi76W3TgciajF+5Gli2BxpKHe+FRzgZeTbwsywUTihL9lEolD4U346HwZkSn5vHeP2fYfi4Vo1nmYmoe0387yqurTvBo/1BejKj7eiVJ2cVTPwf/v737Do+i+voA/t2+6ZU0CCHUUAJEAiFUlfwARQELCgZERFEEAbEgKliQLoqIgqCivFIUFUSkBZAeQkKAECAhQGiBEFI3ddvc94/ZHbLUlNmScD7Pk4fs7t3Zu8Nm58yde8/5qA98XcUfTcnRVEBTYUCplv+pMBhh5PgDncHIkFeqRX6pDhzHoDVyQrBQblrSWaY18P+agwetAWV6430/L6N7hGLaE9WfvCgWVzWfKlxr4HD4Qu2Ci9oyn8mX6owYuHg/isr0MLKbVVQ5joEDn5gP4JfN/v5adO1eUwIYAYcos165nk9dWbBCwQWxqwq9EVP+TMHfx/iZ/a/3bmbzwGJPeg72nOEnAA7tHIywQPcabceRlqk19nbG6Wv89VnGgK5NvfHDyM5wVd37Tz7IlIug8ioCs9AGrvhhZGdwHIefD17Ej/szkVVYjnK9EYv/O4sdp69j3evRNk3/bZ5X0sBNZZXAYs6WNCzdc0707VbmpJDBRSWDs1IOZ6UMadn8/9u95szYitIUXBRXGO7f2IqcKlUnTrly/8seYiS7kphmaOiMxvu2tbbKl0GMDnUWc3cUXBC7yS3RYszKJCRfKoRcKsGMwe0wrEtjm/bBYOAwfjW/zt/TWYGZT7Wr8bbMw6f2vhparjPiYl6ZcDuyiRd+HtWlSkPs5uAip1gLnYG747I3qVSKl3uE4uUeoThzvRjvrDuOlCtFSMsuRvtPtsPPXYWoUB+8/nBTtAmsWUbFqsozTd50u0/QVFMnr/IHMmelDF7OSrioZFArZBbXx71dlPB2VUIh5YfgnVVyuJjSuzsr5XBR8QXDXCxuy+CilMNJIbvt+vr3e85h9pY0yGX2/iTdLBhWXKG3az9e6haKf45fQ36ZDt7OSgR4qKGQmecv8FVUzXlqJJKbJQbuh+M4lOk4FFfoYTAycOBXcmgNRmHZts5g/4N55avEdaW+CQUXxC4YYxi/OhnJlwrhrpZj6fBOdhl2nbzuOIpNabmXjehUqwmABqP9Ry6KK/QY/UsS0rKL4aSQYdmLndCzRYMqP9/Hha+mqTNyuK6pQLD3vSe1tvR3w8bxPTB/WzqW7D4LjgHXNVpsPH4VG49fxVMRDfHV8x1r+a7uLr+MHwb3vMelntoIME1afePhZhj/aAurvMatzBNT/0rOQliAG9zUCng5KxDT2h9ymW0nqJpLj5fr7Xv27qqWY9tb916CueC5jvfdzgd/pWDN4cvVyrWhd4C/a3ml76W6koKcggtiF7vP3MCh8/lQyqX4641uaO7nZvM+pF3TCImWHmsXgC6htaspIiTYsdMJZ2GZDiNXJOL45UK4qeRYMaozIpt4V2sbEokEgZ5qXMwrw7Wi+wcXZu/2a4U3H2mG35KuIO5UNpIuFqBCz2H90SwUlumwYlSX255z6FwevF2VaOlf8/9783C9t5UupZlXxGRrKqyy/Ttxr5QZddbmNOH3pcMfQv92VTsjF4vCNHJVrrP/pQEx7DidU63Awkkhw6AOQVbrT1VVHtyiyyKE3AXHMczbmg4AeKlbE7sEFgDwxqpkAPwXyNdDI2q9PQPjz3DsEVvklmgx/IcEpGUXw8tZgZUvRyG8Uc0uSQR6mIOL2+dd3ItaKcfIbk0wslsTcByHZ5fGI/lSIf5Lv4Hnlh7E2jFdhZGhPek5GLmCL9fews8Vnw9uh6gaVFQ1F4Pzucvql9ryNy3Xva6x3fyHIZ0aQSoB5m5JQ9sgD8Sf5xM/HTyXZ/Pgor4xz12IbuqDaU+0hlImhUoug5uTHHKpFHIpf9nP/K+jMGdDNWfzrAsouCA290/KVZy+poGbSo6xvZvZpQ9/H8vCeVNtkY+eaC1KSl17/c1fLSzH8B8TcP5GKRq4qfDr6Ci0CrhzwHb8ciEOZ+ZXStvMf+FKTHMIpBJ+2SEAZBfV/GxdKpXirze648WfErD3TC4OXyhAxIw4zBwcjic6BGFt4mWhbUZOCZ5fdgiNvZzw8cC26NPa/x5btlRhOqM2L6EVm/myyHUbjlyoFTLERoUgNoqvtzPVNJS/Mv4icku0eK9fGJr4utikLyrT30XlJZ51mTm4yCosQ5sg684HEptCJuWLwImUgtzaKLggovpiWzp2nL4OnZHD2N7NMOSW/P06A4cF288A4PNb2HplCMBP4vpofSoAvr6C+Uu89tsVZTPVknypAONXJeNqUQWCPNRY9WpXhN7jwPPiT4dRVF61yXmVS8TX1MqXozBhzVFsPH4VReUGjF9zFF/GnRH60NDTCaU6AwrL9LhUUI7RvyTB302FV3qGon/bQAT73PuyjLl+S2Mv6xxszcHF1cJy6I0cFDae8wDwqatLtUZsPH4Vm09kY/OJbESGeGHd69FWzwfj767GuRuluFxQdv/GdYA5WLqUX47LeWX3/Xw5EqUpuHCEpbFVQcEFEY3ByOHb3WeFM/j3/kxBeCMPhAXcXNr588FMXMovg6+rCqO6N7FLP2duThMmcS5+ofaXQ8yMnO0uizDG8MO+TMzdmgYDx9DU1wX/90oUGt6nrLX5oN6/bQAUcqmQFpmBwcjx2+UYg5NShkEdxbnWvGhYBGKjGuOt347halGFMGIEAEMiG2FSTEv8kXQZ87alI6dYi+vFWszcnIaZpvkGaoUUnk4KSCQSaE1frnojB4OpXgOA29KYiyXY2wlKmRS5JTrE/pCA38Z0tXmCNz93NRYNi8AT7QMx5v+OAACSLhagTGeEi5VWyZh1b+6Lg+fyajWK5Ugm9GmBiWuPAQAu5pfWqeDCnKXUESaYVkW1w/CsrCwMHz4cPj4+cHJyQnh4OJKSkoTHX3rpJdP1oZs//fv3t9hGfn4+YmNj4e7uDk9PT4wePRolJSUWbVJSUtCzZ0+o1WoEBwdj3rx5t/Vl3bp1CAsLg1qtRnh4ODZv3mzxOGMM06dPR2BgIJycnBATE4OMjIzqvmVSRXKZFH5u/LVvlVwKxoC3fjuOHaeu41pROd5YdUSYoDaxT3M4K20f2xaW6fDzgQsAgKhQb0Q0Fq9a5s2JVtY9+JTpDBjzf0cwc/NpGDiGJ9oH4u/x3e8bWFT2+VPt8M2wCHwb+xCWjuiE70dE4oeRkfjxpc5YMaoLvovtJOpcmKimPjg4tQ9mPtUOzpWWxC7ckYGRPx2GUiHF1om9sGxEJzTycrKYwFah55Ct0eJaUQXyS3Uo0RqgNXDCkjyZVILWgdaZt+PprERsV3559OmrGpsHFpX1bRuAE5/0FW7bYmLfQFOAaeAYMm+U3Ke1/WUVliF2+SH0+2ovHv1iN3rO3YWus3Yg8vM4dPxsOz7++6TQtm5cXLhJavrs1cs5FwUFBejevTseeeQRbNmyBQ0aNEBGRga8vCy/oPv3748VK1YIt1Uqy8lWsbGxuHbtGuLi4qDX6zFq1CiMGTMGq1evBgBoNBr07dsXMTExWLp0KU6cOIGXX34Znp6eGDNmDADg4MGDGDZsGGbPno0nnngCq1evxuDBg5GcnIx27fhcBfPmzcOiRYvwyy+/IDQ0FNOmTUO/fv1w6tQpqNX2r4tQHwV7OeO6RovXejXFD/szcfqaBq+sTBImI8mkErzSMxQviHQporrGr06GkfGFyZbEilstk7NBbFFUpsfLvyTiyMUCKOVSTH+iDWKjGlf7oGev76fYqBA83ykYb6xOxo7T18ExYM+ZG0ISMwn4yo9xk3vDw0mO5IuFOH6lCOdyisEY4O6sgIeTAl7OSni7KOHjokSXUG+rBqrm/CCPhNm2su2dVM5VYpMliZVOkv9JuYYJfWyzHLem5mxJw4FzeVVq27iKK6EchflPvI7EFtULLubOnYvg4GCLwCE0NPS2diqVCgEBd87tf/r0aWzduhWJiYmIjIwEAHzzzTd4/PHH8cUXXyAoKAirVq2CTqfDTz/9BKVSibZt2+LYsWP48ssvheDi66+/Rv/+/fHuu+8CAGbMmIG4uDgsXrwYS5cuBWMMCxcuxEcffYRBgwYBAFauXAl/f39s2LABQ4cOrc5bJ1VkXrrn7aLEf+88jJ8OZGL1oUso1hoQ0dgTMweHo01QzTJg1lZqVpFQcjk2KgTeruLO9zCfUYgRWxSV6eGmllskWCoq02P4jwk4kVUEdzW/1LRTSHWXmt79y8nIMWw+cQ0nsorQJtAdAzsE3ZbgSQxyuRTLXoxEcYUen/1zCnGnr6OwjL9cwwCczi5GnwV70KulLxYNjUDftravE6Kp0OOHfZm4UlCGvabAJ6pp9fa1NUgrBZG2OMjsP3ezbsv6o1kOH1yYJ/gq5VJ0aeIFlVwGlUIKtVwGlZwvMKZSyNCtmQ9CfGwzKVY8VSve5iiqFVxs3LgR/fr1w5AhQ7Bnzx40bNgQb7zxBl599VWLdrt374afnx+8vLzw6KOP4vPPP4ePD7/MLD4+Hp6enkJgAQAxMTGQSqVISEjAU089hfj4ePTq1QtK5c0v/379+mHu3LkoKCiAl5cX4uPjMXnyZIvX7devHzZs2AAAyMzMRHZ2NmJiYoTHPTw8EBUVhfj4+DsGF1qtFlrtzSVnGo2mOruH4GZwkVVYDn93NaY+1hrjHmmOjOvFiAj2ssrBqqreWMVfr3ZRyvDpQHFrNhRX6LFopziX3JbuOYe5W9PQvpEnlo3oBH93NXI0FXjxp8NIyy6Gt4sSq16JQusapikHIGQfFG4zhlE/JwoHUgBYc/gSFjzXAY1qUR32XtzUCswf0gEAP8n2ukaLv45m4ZtdGajQc9h7JhcPzYjDkMhgzBjUTpQVPVVRrjPilV+ScDgzX7gv0EON/nYIcm5VaEoaBgAqhfX3x/ORjfDtf2dxpaAcmbml+C3xEp7vbNssutVh/ox4OytqXSzN0djxilyNVOvTef78eSxZsgQtWrTAtm3bMHbsWEyYMAG//PKL0KZ///5YuXIldu7ciblz52LPnj147LHHYDTlZ8/Ozoafn+Xwolwuh7e3N7Kzs4U2/v6Wy9HMt+/XpvLjlZ93pza3mj17Njw8PISf4ODgO7YjdxfagD8bOH/j5qQ9d7UCnUK87RpY/J50GZfy+bwNnw5sK/oa9sW7zgq5EGqatpkxhtlbTmPOljQwxi8bXbgjA5fzyzDk+3ikZRfDz02F1a/WPLAQenbL2c+5GyVCYDGoYxCcFDIkZObjsYX7sOPU9Rq9VnVIpVIEejph3CPNkfpxP8RGNYZMIgHHgN8SLyP8k234bvdZq/cDAGb8ewqHM/PhopRh/CPN8VqvpvhjbDer5dKoDvM8C4mEz89ibVKpFOsqFQBbtjfT6q9ZG0oZv0/qyJzHajF/fdbLORccxyEyMhKzZs0CAERERCA1NRVLly7FyJEjAcBiRCA8PBzt27dHs2bNsHv3bvTp00fErotv6tSpFqMhGo2GAoxq0prSBMvsGEjciuM4fLqRn8jV2NsJz0aK/3+aW3JzxOuzQdWvT2Iwcnj/rxP448gVi/uvFJRhyNJ4ZGsqEOzthF9HR4kynDtw8QE4q2TQGTg8GuaHF6NDhEsmHz7eGm//rxWe+GYfNBUGvLIyCd+P6IR+Njpzl8ulmPlUON7r1woTfzuG3ek3oDVwmLc1Hcv3nsfMweF4vL31kkltS+VPPqY90QZDbVzr5n5UpoMnY/wkS4WV649cKyzH/77aK9x+K8axL4vI5XVr0mN1mCuj1pW3Vq3Tt8DAQLRpYzmc3Lp1a1y6dOmuz2natCl8fX1x9ix/1hEQEICcnByLNgaDAfn5+cI8jYCAAFy/bnm2ZL59vzaVH6/8vDu1uZVKpYK7u7vFD6kec8XC9jXMDmkNH288hVLTtdjvRJ7EaWbOt+DnpsJz1QxejBzDe3+m4I8jVyCTShDR2FN4bF9GLrI1FWju54p1r3WrdWBhTpOdranA+RuluFJQjpXxFzH6lyS0MqXhPnguD419nBE3ubfwvLM5tl8p4OGsxM+juuC/t3ujjWk1SEGZHm+sTsbD8//D8csFVnndZn6uAIBL+Y6X26HypRCtlfMdXC4oQ58v9wj5TmY/zSdAc2QKaf0NLsyTtutK+u9qBRfdu3dHenq6xX1nzpxBSMjdZ/5fuXIFeXl5CAzkzzSio6NRWFiII0eOCG127doFjuMQFRUltNm7dy/0+pvJfuLi4tCqVSthZUp0dDR27txp8VpxcXGIjuaH8EJDQxEQEGDRRqPRICEhQWhDxFdguibsCEPIAJBbUoFVCRcBAD1b+KJdQ+sEPXrTF311R2y0BiMmrD2Kv5KzIJNK8GrPpjh6qdCiTXhDD/z+WjQCPGq/wumHkZ3RswVfIK5vG398PbQjAOBiXplwRnTQNInPw0khDL33sENRObPQBq7YPLEXVr8ShUDTPriQV4ZB3x7E1D9TRH+93i35Qm/2CKjuR1kpiZc1kyllFZah75d7UaYzQgLgq+c62rxicU2YC7vVx+DCfCW3rry3agUXb731Fg4dOoRZs2bh7NmzWL16NZYtW4Zx48YBAEpKSvDuu+/i0KFDuHDhAnbu3IlBgwahefPm6NevHwB+pKN///549dVXcfjwYRw4cADjx4/H0KFDERTER8UvvPAClEolRo8ejZMnT+K3337D119/bXHJYuLEidi6dSsWLFiAtLQ0fPLJJ0hKSsL48eMB8FHepEmT8Pnnn2Pjxo04ceIEXnzxRQQFBWHw4MFi7DtyB21McwFSs4rs3BPe6/+XDM60BHbxMPESZt2qcs6FqirRGvDyz4n4N+UaFDIJvhkWgSGRjSzauKnkWPlyF9EKc3UM9oSbmr8auv3UdSGhEAA8HMYfVM21LG4Ua1GuN0IulTjESFS35r6In9oHs58OF+7bcvLO86dqo5lp3tDejBvIKrxZX6WgVIdjlwtxo9h2dUZuJZVKhDLo1gouzl4vRsyCvSjX84HFty88hKceamiV1xLbzVwQdu6IFdy8LFI33ly1govOnTtj/fr1WLNmDdq1a4cZM2Zg4cKFiI2NBQDIZDKkpKRg4MCBaNmyJUaPHo1OnTph3759FrkuVq1ahbCwMPTp0wePP/44evTogWXLlgmPe3h4YPv27cjMzESnTp3w9ttvY/r06cIyVADo1q2bENx06NABf/zxBzZs2CDkuACA9957D2+++SbGjBmDzp07o6SkBFu3bqUcF1bUvpEngJuXR+xp5+nrSLrID52/0iMUHs7WSzVurMEy1HGrknHAtDR2xUtd8Hh4IJr6uqB7c35llbeLEu/2byV6inRzIGQelXBSyPDRgNZ4tBU/0fpyfjmKyvUWqa7tmTzqVsO6NBYu4QSJMJpzq35tA9CliTcq9Bwm/3YMKVcKEfvDIUTMiMPgbw+gy6wdeO77eBy/XCj6a1eFeUWE1iB+vY8jFwvw2KJ9QmDx9bCOVp3fIjYhuK8bx99qMQdOdWWyarUzzzzxxBN44okn7viYk5MTtm3bdt9teHt7Cwmz7qZ9+/bYt2/fPdsMGTIEQ4YMuevjEokEn332GT777LP79omI46EQT0glwImsIhzOzEeXUPvkBuA4DpN+OwYA8HVRYkr/VjZ53aoeghljOH6lEADwcvdQ9DBdqpBIJFj1SlcYjJwwxCs284nPBwNao3kDV1wrKsc/x6/i839PAwCa+7lCrZAKBy9Hu8ZbUmHAmZxiAMDDrcRPbCWRSDBjcDs8/d0BJGTmY+DiA8JjDdxUuFGsxeHMfDz3fTx2TO5d5bL0YjGPXBhEPj3fefo6Xl2ZxI/0SST48aVIq+xfazKvVr51qbUjKtcZsSstB6U6A3SmjLN6I1+YzGDkYGQMnCnFvZEx5JsuOdeVyyJUW4SIKtDDCUO7NMbqhEv4eONJbHqzh11Wjkxdn4riCn4i2tIRnaxePlliTnBT1fYSCTo38UbcqesI9b394GStwKKyaRtSoZJLhYmBEgk/D2PWU+FQyWWQSvj952jfZbO3nAZj/NK88Y82t8prtApww6pXu+LVlUm4UaxFt2Y+mPdsezTycsYP+87j839Pw8gxu+QeMP89iZmh888jl/HOuhQw8DUsfn8tWtTU+LYilfB/N+Y9Y+QYtAYjtHpOqEsjkQCNvJzsPhq3ZM+5GuXGcVZafwmyGCi4IKJ7p28rbDrOl1Vfm3hJtKqjVZV5owS/m0p6P9yqASKbWH/0pCajsebn2PpLrmcLX2w35a7QGjh4OiswqEMQXuoealFR1dGCCoAfkTIv1+3WzMeqab87Bnvi4PuPIq9EB393FSQSCQpKdVi65xwAYOzDzayWYOxepCKvGli+95xQJM5JIcOmCT3QrIGrKNvenZ6DjOslMDJTgTzG+IJzHP+v0eJf/uzdfF+F3ohynRE6Iwe9gUFn5MCZCuuZKxAbTL8YTWf4maaieKVaI5p/sPmuozvv9G2J8Y/ad1ltUaWEaH3b+EMuk0Ahk0IulUIhk0AqlUAmkUAmlUAqkUAmBRr7uCDcSpPSxUbBBbmjHaeu4+RVDQI91Aj0VCPQwwlBnuoqfZl7uygxslsTfLPrLFYn2D64GP1LEhj4a9PfxT5kk9fUmyd0ViNQMB8bbH0CNSK6CSIaeyEztxRtgtwR6uNi1wRn1bFo11lhpGXmU+H3aV17CpnUYpXOPylXkVuig1wqwfCu9qmPY55zkXihADeKtSiuMAjD6SUVesikEgyKaAh3teKe2+E4hme/j0eyaV6SUibF7ncfFrLs1tbVwnK8tCJRlG3VxK2BhVwqEe47fa3YHl2yYK4T82rPUHw4QNyMwY6Aggtym62p2Xj91yN3fMzfXYVXezbFiOgQqOR3Hp7jOIbtJ/kz416mZX228mv8BaGk9/QBrW1WedU8SbI6B+krBXwehXKt+BPz7qddQ49qLctljNl9GBkA/jl+Tfj9bE6JzetDPBrmh292ncWNYi1GrUjEqleiRJ9wez/mMuvTNqTetU2J1oixDze753Z0Rk4ILMy3zWn7xWCuFwMAzzzUCDIpTGfg/IoXmVQKuezmbanEdL+MP2NXyKRwVcmhlEuhlEuhkEmFbZg/i3KpRCiIKJNKoJBJoNVz8HFVwk2tgMr0XJVcCrlMiv87dBHTNqQKf6/2pBIm5taRGZrVRMEFuc28bfwQadem3lDKZbhWWI5rRRUo0RpwXaPF5/+exsr4i3j/sTA81i7gtoPOPylXkX69GG5qOV7vde8vODGV6Qz4zDQpsamvC4ZHN7HZa1d3edgX29JxynT2lFVUfp/W9lE5+6ORYzVOay6mt//XEm+uPQojxzD6lyRMG9Aao3s2tdnrN/JyxppXu2LoskM4dU2D4T8mYNUrUfCstBIpr0SL35Ou4NQ1DV6MDkFnkS/LPdupEb7fcw5OChncnRRwVcmhUvDD6ebqsnklty+XzdHwf8N6I0OZzoCDlaqHmufe6EU80JmnOfm6KrHguQ6ibbc2rDUZtiZUppGLCr3tTy5sgYILYiG3RCvUBfl+eCQ8nG8OrWoq9Niccg0L4s7gUn4Z3liVjE4hXvhwQGs8ZJr8df5GCb6MOwMAeL13M4vnW9ubq4/yE7YA/Dgy8r7txVSds/rvdp/F4v9u1slo7ifO9W2xVR6F+WL7GSjlUnQM9sCjYf73eJZ1Pd4+EC39XTHouwMo1Rox49/TyMwtxec2uERi1tzPFWvHRGHoskM4eVWDp787iKUjOqGlvxu0BiMGfXsAVwr4gDG7qBzrXu8m6uu/3rsZXu9956B9wfZ0fLPrLPZm3MB/aTnQGvh5Cn8fy8K2k3euEdOzhS9yNFqkXy+G3ijeQdcRc07cDC7sP1ogrWPpvKuLggtiwTxM2tLf9bbAwF2twNAujfFkhyAs23sey/aex5GLBXj6u4N4PDwAZTojdqfzZ06+rkq81K2JzfqdcqUQO9P4tPLPRjZCqEgT0qpKuCxynxhjxYFMzNtqmeVWYeWVLDUlNw1XGzgmTGKUSyU49nFfuKrs99XR3N8NB6c8iv5f78O1ogr8mnAJF/JKsfLlLlZfFST0wc8Na17tihd/OozzuaUYtPgAxj3SDL6uKiGwAIC8Ut09tiI+83X8M9dLMOrnO8938HZRwlkpg1ohg5ezAtOfaIN3/+AznZaLeBZtXtVicKDEDOb5KnoH6lN9RcEFsXDEFFx0Crn7MjQXlRxv/a8lhnVpjC/j0rHuyBVsPsFnSpRIgD5hfni7byvh2rAtvP5rMt83pQyzB9vuLNbMvPZceo8RjFUJF/HpP6cA8F/wYQFu/NC0/a823JGzUo4vn++IIxfywQCsjL8IA8dQpjXYNbgA+Loj+959BE8vPYiUK0XYfzYPjy7Yg80Te9psnk0LfzdserMHJq49hv1nc/HF9jPCY5EhXki6WIDM3FJsPnEND7dqYJN+Pdk+CPHn8pBTXAGVXAalXGpabQAEuKvx+VPhd/y/MwcCZTqDaH0xjxI40sjFzYDHgTpVT1FwQSzcDC7uf504wEONec92wKjuofj5wAW4qeUY3jUETXxtO8lu+d5zuGpK0zzr6XDI5bYfCeC4ewcXszefxvd7zwPga3b893ZvIcmXg8YWAICBHYIw0FSsanXCJX7ZoIOM48rlUmwc3wPjVydjU8o1XMgrQ7c5u3Dw/UdtFmD4uKrwy8td8FfyFaxLuoLDF/LRws8VP43qjMm/HceO09fxxqpkSCRAY29ntPJ3Q7uGHnj6oYZWWcba2McZv74SVe3nnbzKZ9TVlOvv07LqzAdyRxolMAc8jjCh08xxeiIuCi6IQGfgkGKqCXKvkYtbtQ50x9xn21urW/dUUmHAvG38ZYawADcM6mifGgjmA+6dRuXf++M4fk/iczP4uiix9a2e8HBW1rkvFalUApjyEDiSxS88hKa+6Vi06ywKy/T4aH0qvny+o81eXyaVYEhkMIZEBiOnuAJuKgWclDLMeSYc87YqsCvtBnJLtLiYV4aLeWXYfuo6vt6ZgZmD2zlMSff2DT1x+EI+dCKe0QvJvhwkGAUAuekPVO8An2Fznjwxk6E5EgouiMDAcUIxpMv5ZRYJlRzVuNXJ0BsZJACW23gSZ2Xmk7NbRy4mrj2Kv49dBQCE+rpgy4QeUJvOqm/muXDksYub5FIJdAAcYC7cbSb3bYVT1zTYcToHG45lYfqTbSxWcNiKn9vNZZy+rirMe5ZfJZFbosWZ7GKkXy/GP8evIvlSITalXHOY4MLJlPXRw0m8Cdjmpep6I0OOpgJ+Ii1xrQ3ziidHmAdi/q6osEKNGEfgmDPJiF04K+Xwd+cLzC2IO+PwEfXhzDxh6d1znYMRbIdsibeqHCaM/fWIEFi0CXTDzsm9hMACuDkcWjdCi5sJwhxhpv2dLBjSATJ+cAVvmS45OQpfVxW6NffFqO6heLdfGABYVFy1N5UViqFVntux5vBl0bZbG+bRlJNXNThwNhe70q7j72NZ2J2eY/MROfOlu80nsjH8hwQcPJdbZyqeVgWNXBAL/zc6Cn2/2ovjlwux7shlPN/ZMc6sbsVxHN4wTeJ0U8sx+6l293mGbZi/Gl75JRE7TvOrVzo08sD6N7rddSVDHRm4EJamvvXbMYv00JFNvPFClP0/Jx7OSjzTqRF+T7qC3ek3kFVYhoae9g84b9XIywkAH1xwHHOI7KgKU3AhZhn3y6YkcQDQ5A71c+xBXulvMPaHBIvHlo3ohL5tA2zWlyc6BCL5UgHWH83C/rO52H82FxGNPbFoaITNi+FZAwUXxEJLfzd8+HhrzNx8Gl/FZWBQx4bC8jZHMn/bGeSalvktfL6jzZYg3k3lAOHFnxKw90wuAH7VwO+vdb1j/+raWYq3ixJF5Xocv1KE41eKhPs3HMvC4Iggm02ivJfPBrbF+qNZ0BsZJqw5ij/Hdrd3l24T4KGGVMIfyHNLtRaXUuylXMePWHyz6yxyirXQGzgYOGaq68EJ6cV1RvPvN+t9CLeNfCE3tVwGnZHD2ZwSYfv2mgt1q47BnujZwhdZheVQmDKEnryqAQBcL7498Zg1uasV+GJIB0zs0wLL953HyviLOHqpEBuOZuHNPvateyIG+38bEIczIjoEKw5k4mpRBdYlXcYIG2a6rIocTQW+38vnXXiosSf6tLZfUqdbmb+oACC6qQ/WjOl63+fUlZGLb194CHszbkACvs9GDpi7NQ0cAyr0HOwwxeE2aqUcz0cG49eESzhxRXP/J9gYYwzf7MwQlmeaD+r25qbmDwX5pTos2X1O1G23b+Q4hbaclDL832jL1TTjViXj3xPX7HYZONjbGZ8NagdNuR4bjl2tN+nAKbggt1ErZHiuczAW7sjA7vQbDhdcvLIyCZypnsAPL3a2d3cA3D4jvlszH6x+9f6BBXCzXLujaxPkjjZB7hb3fbXjDHQGDmU6A7xtXGPjbpqaLtkwB1uPY+QYpv+dilUJlwDwqcxtXRvlbsY/0hzuagUYGBQyKZQyqVClUynnbyuEH4lQ68P8uEImgVImRYWeg5ExKCvd39Lfzd5v756kDrI81TwB2ZFW19QGBRfkjs6ZUoAfv1Jo347cYnPKNaSYhuTffLQ5vF0d44BW+Qy0U4hnlQKL+vAdYl5BYu8v5sqKtXyuBpkN5zIwxl8iqNBz0BqM0Jr+5W/zwdeKAxewKy0HEgnw2aB2GGGnqqp30sLfDTMGO8a8JVuTO8iSWfPn1VHyyNQWBRfkjnxNB20xl6bVls7A4e11xwEA/m4qTIppaece3TT24WZ4Z10K2jV0x7rXoqv0HPOZdV25LHIn5gJQtjyQ30+RqRqnQib+PBzGGN7/8wQSL+SjQm9EhYFDuc6ICoOxSsGiQibBV893xBPtg0TvG6kZqbAKisFg5FBYrofeyMFgZMgprsAP+zJh5BgMHBPuV8qleLdfq2pVFr4f899QPYktKLggwHVNBU5f0yAi2EuoJ2K+/vhYu0B7ds3C2F+PCLUPfnzJMS6HmD0V0QiPtQus1uTXevEl4oC5Om6YKoI6WWEi8o0SLX5LuveySvOkRpWCL/WtVsiglEnh4aTAW/9rie7NfUXvF6k5c1w8Z0sa5mxJq/LzGns7ixpcmP+EHGkUsDYouHjAbTuZjYlrj6JCz8FNJcfrDzfDGw83EyYVOcIyOQDYn3FDKEz2zEMNRf2jFktNV9U40oG5uqRSAEbHyjJYYbpEZY2Ri8pF5v56oxtcVXKo5TKolXwQoZbLoJBJ6vT/6YOm6R2KHCplfE0WmVSCcr0R3Zr54In2gZBLpVi+7zzSsotxtbAch87nQSaVICLYE/Jaft5kEse4PCMWCi4eYDoDh4//PokKPQeZVIJirQHzt6Uj/lweDp7jl1K6q+3/EeE4Dm+s4nNaeDjJMd9OqcbFVh++Q/i8AZxDnW1VmAJjpRVqzKgUN7cZFuDmEMtvSe283rsp/tfGHwaOg1wqRWNv53t+dvJKtZi1OQ0703KEE55xjzQTkqPVlJAu3YH+lmqDMnQ+wL7fcw7Zmgr4ualw8tN++GhAawDA/rO54BgwpFMjDHeASWdvr0uBpoKv1vj98Ei757QQizDnws79qA2FzPGKU5kDnQoRy4ebmVNa89t3nPdMak4ikaC5nyvCAtzR3M/1vkHpo2F+aBPojhAfZ2Fu2oW8sns+p6r9AGhCJ6lj0rI1+OyfUyiuMMBoKj6Vfr0YADAxpgXUChle6dkUJVoDNhzNwuS+rYRqmPZ0/DKfwQ4A/tfaD12b+di5R+LhhPkK9u1HbfAHW71Drc0PMNWwuFZUgQGL9mHZi51Ey9Qpk0qgkEmgNzJRU2WTuqO5nxs2T+wJgK8U/MH6E9DWItC8rqlAQZkO+aX8XKF6MnBBwcWDYl3SFRw8l3fb/RP6tEBs1M3RiUkxLR1mFQbHcRj9cxIAwFkpw7exnezcI3GZM3TK6nB0oTZdJrDGKEFNffxkG+w7m4sbxVqcvKpBz7n/YWjnYHw2sB3kIlwqUctl0BsNNHJBhJosuhqO3P2XloOXf0m0uERa1zL33g0FFw+IPNMM+mFdgtG/XSBkEgn83VVo4cAJbj7ddNoixbc1rqHbk3n43lEmzdaEedKkmDUpasvDWYnED2OweFcGvt6ZAb2RYfXhy/jraBbe/l9LvNqrWa22r1JIUawVt8gXqZvMc3C0NQyuz90oAWP8/CBvZyWcVbJ6s0yZgosHhPksq22QB3q3bGDn3tzfuRslWHnwAgA+jbYtCwrZitF0glKXRy7MHHF1xPhHW2BktyYYtzoZe8/kokLPYebmNHy/9zy+er4jerao2d+Bed4FjVwQpax2IxfmE6aY1n74rp6NzNavU0FyV+blTVIHPAjcyaifDoOB/+Nb/mKkvbtjFebhz7o8P1UYfXHQj5WbWoGVL0dhx1u90DqAH6XLLdFhxI+HMXDxfmQVVn8iXm3PVkn9UdsVHkKp+3oYqNbhrzVSHebPvqMeBCpbvCsDlwrKAQCfD24HVwdYDmsN5kurdaW2yL044shFZc393bBlUi/89FJneJkSxaVcKUKPOf/hzTXJKNMZqrwt88iFI01iJfYhrWXKbmUt52w4svr5rU1uw+rIyEVxhR5f7cgAALQJdMNzkcF27hG5F3PisPI6chb/aJgfjk7vi4U7zuDb/85Cb2T45/g1bE3Nxis9muLdfi3vu9TZESexEvswX9K8UazF0j3n+PL0pjTildOF640cynRGYaWekfH/XiuqAFB/EmdVRsFFPccY/wHOzOULkTl4bIF3fj8OI8cglQA/j+pi7+5YlVC108H/T+6lrib+mRTTEq/3aorJ645jy4ls6I0MS/acw8pDFzC1fxiG36MSsDCUTSMXDzxzcH1do61W6vBbNXBVidUlh0HBRT03fs1R/JtyTbjtyMPX526UYPup6wCAgR0aws+Ur6C+c9z/kfszDwsb6lhwAQBqpRzfxXZCVmEZJq45hqSLBSjVGvHR3yexcOdZzHm6HWLa3D6R2HxAoZEL8lBjT7zeuxmuFZVDLuVLzMtlEsilfMl5uVQCuUwKhVQCJ6UMCpkUUqkEcqkEMokEEgl/aeThln72fiuio+CinjMHFk4KGdzUcnRp4m3nHt3dmJVHwMCfGc6rJym+76WujIQePJeLWZtPo1RrhM7AQW80/zCUaPm5Co60FLW6Gno644+x3ZCaVYSJa4/h3I0S5JZo8crKIwjxccaioR3RIdhLaE8jF8RMLpPi/cdql/a7vqLgoh5LzSoSfv/1lS7oFOK4gcX65Cycu1ECAPjg8db1LqfFvTjyaBIA/HHkClKzNHd93FkpQ9sgdxv2yDraNfTAzrd7Y1daDqb+mYLrxVpczCvDoG8PonMTL3z7wkPwc1fTyAUhVVDtb/CsrCwMHz4cPj4+cHJyQnh4OJKSku7Y9vXXX4dEIsHChQst7s/Pz0dsbCzc3d3h6emJ0aNHo6SkxKJNSkoKevbsCbVajeDgYMybN++27a9btw5hYWFQq9UIDw/H5s2bLR5njGH69OkIDAyEk5MTYmJikJGRUd23XGcFeNy8rPDqyiPYc+aGHXtzdwYDhw83nAAANPJywshuTezbIRupKyMX5n72a+uPDeO6498JPRD3Vi/sfudhHHj/USR9FIMmvi727aSIHg3zQ8KHMZj3bHu4qfjzr8QLBeg6eyeeXXIQfx+7CqB+TsIjRCzVCi4KCgrQvXt3KBQKbNmyBadOncKCBQvg5eV1W9v169fj0KFDCAq6PdtYbGwsTp48ibi4OGzatAl79+7FmDFjhMc1Gg369u2LkJAQHDlyBPPnz8cnn3yCZcuWCW0OHjyIYcOGYfTo0Th69CgGDx6MwYMHIzU1VWgzb948LFq0CEuXLkVCQgJcXFzQr18/VFRUVOdt11m+riqsez0aTX1dkF+qw7vrjjtUgSmzqetPoMxUJnvpcMdIJMMYg6ZCj9wSLQpKdSgq16NUa0CFnr80IMYERvMWHHvc4uZKo85NvNEx2BNtgzzQwt8NTXxd0NDTqd5WBn0uMhjHP/4fXusVCrlUAo4BSRcLAPCjNTGt/e3cQ0IcV7W+FebOnYvg4GCsWLFCuC80NPS2dllZWXjzzTexbds2DBgwwOKx06dPY+vWrUhMTERkJJ8c6ZtvvsHjjz+OL774AkFBQVi1ahV0Oh1++uknKJVKtG3bFseOHcOXX34pBCFff/01+vfvj3fffRcAMGPGDMTFxWHx4sVYunQpGGNYuHAhPvroIwwaNAgAsHLlSvj7+2PDhg0YOnRodd56ndW5iTc2T+yJ7nN2IadYi+0nr6N1oBuaNnC1d9cAAEcvFWDdkSsAgEdaNUC7hh42e23GGI5fKcKGo1m4nF+GgjIdCsv1KCrTo7BcX6Uy4jLTxCyZVIK+bf3x9dAIG/TctoxCjhRHD4PEJ5VKMfXxNhgS2RhPfrNfWHKrVsgc5m+IEEdUrZGLjRs3IjIyEkOGDIGfnx8iIiKwfPlyizYcx2HEiBF499130bZt29u2ER8fD09PTyGwAICYmBhIpVIkJCQIbXr16gWlUim06devH9LT01FQUCC0iYmJsdh2v379EB8fDwDIzMxEdna2RRsPDw9ERUUJbW6l1Wqh0WgsfuoDtUKGRt58Vchxq5Px6II9OJtTbOde8Z+VUT8nAuDPBJfEPmSz1z5ysQBPLt6Pwd8ewM8HL2BnWg6SLxXi/I1S5JXqqhRYAHyGSp2RQ7neiH+OX61W0SFzW0c/Zhs5frRLVhcysImoQm/EvynXMPKnw+j71R6LXB6v9Lz9pIoQclO1Ri7Onz+PJUuWYPLkyfjggw+QmJiICRMmQKlUYuTIkQD40Q25XI4JEybccRvZ2dnw87NcdiOXy+Ht7Y3s7Gyhza0jIv7+/sJjXl5eyM7OFu6r3KbyNio/705tbjV79mx8+umn990Pdc3l/DIcv1xocd/pa8Vo7mffomWTfj+OwjI9AOB/bfyxJvEynBQyqBUyqBVSqBQyi9tOChlUchnkMgkUUilkMn5Jl0ImrfKBr6hMj7nb0rDm8CUwxs/8fzw8EFGh3vB0VsLTWcH/OPG/q+RSIekNx/GZ+IzGm0lwOMZwIbcUzy87BGel3OEnZ9aEOTWxqh5NstUbOVTojSjTGVGqNaBcb0SFnkO5zojCch3+S7uBbSezhdUwANAl1BvPRwbjsfCAenspiBCxVOsvhOM4REZGYtasWQCAiIgIpKamYunSpRg5ciSOHDmCr7/+GsnJyXXyS3bq1KmYPHmycFuj0SA4uO5niNx4/Opt99l7MtqVgjJsqtSvv49dFSbK1YREAiikUshl/CUKhcy0xlwqQYWB4+dJMCbM7QCAZzs1wtTHwuBznwQ2cpnknn8oN4r5irOVD0TV6ruDz7qoMFX/dFLKrPo6BiOHEtOBXqvnUGHgD/7lOv7fCr0ReiMnLIfVGRm0Bj440Oo5FJrmxZSb2ukN/KiSzsCPLJXpDML2qpqXI9BDjaciGmJIZDBC69GkVUKsrVrBRWBgINq0aWNxX+vWrfHnn38CAPbt24ecnBw0btxYeNxoNOLtt9/GwoULceHCBQQEBCAnJ8diGwaDAfn5+QgI4BPWBAQE4Pr16xZtzLfv16by4+b7AgMDLdp07Njxju9PpVJBpap/mdK6hHqjfSMPaPUc0q/zl0PsvYzu039OgWOAWi5FAzcVwgLdoZRLoTWfQer5g0mF6bb5d62Bu+OBgTE+P7+uCm+raQMXzBwcjuhmPqK8lysFfPErc4XE+sZc/XPziWs4eVUj5Lng0xqbUhxzHHQGfiTHPKLDmUZ7zL8bOb59ud4IjmMwcAwVej440JnyZtiaVAK4KOVQK/nRMWeFHC4qGcIC3TG4Y0NEhngJicIIIVVXreCie/fuSE9Pt7jvzJkzCAkJAQCMGDHijvMgRowYgVGjRgEAoqOjUVhYiCNHjqBTJ35lwK5du8BxHKKiooQ2H374IfR6PRQKvshQXFwcWrVqJaxMiY6Oxs6dOzFp0iThteLi4hAdHQ2An2gaEBCAnTt3CsGERqNBQkICxo4dW523XWcxxvDF9nScvlaMXi0aQCaVVAourLtq5ND5PJy6qsF1TQVKdQZhOSMDkHmjFPHn8yCXSrDxzR5o6V+9yzOM8QcmY+Xc/Rz/r8HIYOD4AMRg5Nso5Xy2PJlEgjK9AU19XUXNo5FbogPABzeDvz0gTPKEhH/DwoGW8X1n4O+rKynZzfUTtp28fp+W4lDIJFDJZVDJpXBSyuCslMFJKYeTQgqlXAaljB+ZMv+4qeVQyqXwdFbAVSWHWi6DSiGt1EYCZ6XctB3T9kyX21RyaZ0cZSXE0VUruHjrrbfQrVs3zJo1C8899xwOHz6MZcuWCUtEfXx84ONjeTaoUCgQEBCAVq1aAeBHOvr3749XX30VS5cuhV6vx/jx4zF06FBh2eoLL7yATz/9FKNHj8aUKVOQmpqKr7/+Gl999ZWw3YkTJ6J3795YsGABBgwYgLVr1yIpKUnoi0QiwaRJk/D555+jRYsWCA0NxbRp0xAUFITBgwfXeIfVJb8cvIBv/zsHANiVZjlaJJdZ7wv1v7QcYaLmvYzuEVrtwALg/28VMgkUspupmO3JUGl577Fb5rZURaCHY6c5/2BAa6xPvgJp5ctNppTGctPBWyGTCnNhJBJ+8qfUlN648ooauUwCtUIGuZSfJ6OSS+GqkkOlkEIll8FVJX+gEqgRUl9VK7jo3Lkz1q9fj6lTp+Kzzz5DaGgoFi5ciNjY2Gq96KpVqzB+/Hj06dMHUqkUzzzzDBYtWiQ87uHhge3bt2PcuHHo1KkTfH19MX36dItcGN26dcPq1avx0Ucf4YMPPkCLFi2wYcMGtGvXTmjz3nvvobS0FGPGjEFhYSF69OiBrVu3Qq127C9zMaRmFWHWZr6QTtsgd0SGeMHIGCSQwNtFiSc73J5/RCz3G0ae/L+WcFfLMSyq8T3b1RUjopsgtIErynVGyKQS4RIAY/ywu8R0kJVKJKbbpvsANPJydvgljR2DPdEx2NPe3SCE1CESVp21cw8YjUYDDw8PFBUVwd297qQ3LtMZ8MSi/TifW4qY1v5Y/mInmw796o0c1h6+hIycEuSV6iwKpx3+sA/83Op/cEcIIfVRVY+LtJ6qHjp4Ng/nc0vhopRh/rPtbX5NWSGTYoSpZPWGo1n4N+UalDIp/hgbTYEFIYQ8AOjiZj1krinSrbkv3NT2ix/1Rg6f/3sKADChT3O0b+Rpt74QQgixHQou6hm9kcMnG08Kt+V2XB556Hweckt08HVV4rXezezWD0IIIbZFwUU9M3dLGpIuFsBNLcdHA1rbtS9bUvlMqH3bBkBRT3NAEEIIuR1949cjW05cww/7MwEAXwzpgBAf+2UUNHIM2015Efq3DbBbPwghhNgeBRf1RGZuKd79IwUA8Fqvpuhn5wN68qUC5JZo4a6Wo2tTcTJhEkIIqRsouKgHynVGjP31CEq0BnRp4o13+rWyd5ew1XRJJKa1PyVFIoSQBwx969dxjDF8tCEVadnF8HVV4ZsXIuw+v4ExJgQX/dvRJRFCCHnQUHBRx/2edBl/Jl+BVAIsGtYR/u72zyMxe0sasgrLAQC9Wjawc28IIYTYGgUXddjJq0WY9je/7PTtvq3QrZmvnXsEFJbpsGzveQB8NVZHqP1BCCHEtii4qKM0FXq8sSoZOgOHR1o1wFgHySMxY9Np4fefR3W2Y08IIYTYCwUXdRBjDFP+SMHFvDI09HTCl891vG+xMFvYc+YG/ky+AokE+HNsNzgrKbs8IYQ8iCi4qANurS234sAFbEnNhkImwbexD8HLRWmnnt1UojXgg79OAABe6tYEnUK87NwjQggh9kLBhQNjjGH636noPHMHTl/TAAD+S8vBZ5v4eh0fPN7aYUphf7EtHVmF5Wjk5YR3+tp/KSwhhBD7oXFrB7Y7/QZWxl8EABy/XAgAGPVzIgCga1NvvNStiZ16ZunIxXz8En8BADD76XC4qOhjRQghDzIauXBQmgo9vtpxRritkElxIbdUuB0Z4m3zUup3ojNwmPrXCTAGPNupEXq2oKWnhBDyoKPgwkFN/esEUq4UCbeNHMOGY1nC7Z4t7L/sFACW7zuPM9dL4O2ixIeP27dQGiGEEMdA49cOKDWrCP+mXINEAkgAcAyY8e8pFFcYoJRJMX9Ie0Q5QL2Oi3mlWLQzAwDw0YDWDjGxlBBCiP1RcOGAUrP4EQvGgAZuKuQUa1FcYYCnswLLRkSiS6i3nXt4M+241sChWzMfPBXR0N5dIoQQ4iAouHBAYYHuwu85xVoAQIiPM1a81BlNG7jaq1sAAI5jkEolWHHgAvZl5EIpl+Lzwe0cYv4HIYQQx0DBhQNq39ADvq5K5JboAPB5I958tDl8XFV27dfvSZfxnqmsu9m7fVvZPeAhhBDiWCi4cDBFZXq89fsxIbB489HmeNtB8kaU64wWt1/q1gSv9Ay1U28IIYQ4Klot4kCOXy5E/6/3YldaDpRyKeY+E+4wgQUAvBgdgjamSzYt/FzxycC2dDmEEELIbWjkwkH8l5aDcauTUaYzIsTHGd++8BDaNfSwd7csnLyqwelsPlPonGfC7dwbQgghjoqCCztjjGH5vvOYvSUNjAE9mvti6YhOcHWwLJeMMXz2zykwBgzsEIROIfZfsUIIIcQxOdYR7AGjNRjx4fpU/HHkCgBgWJfG+HRgWyjljne16t8T13D4Qj7UCinefyzM3t0hhBDiwCi4sJOiMj3G/F8SEjLzIZNKMP2JNngxOsQh5zBU6I2YvTkNAPBar2YI8nSyc48IIYQ4Mgou7EBrMOKVlYlIvFAAV5Uc38U+hF4tHbcmx/K955FVWI5ADzVe793M3t0hhBDi4Ci4sIMf9mUi8UIB3NRy/DYmGm2C3O//JDvJLqrAd7vPAQDefywMTkqZnXtECCHE0VFwYWMFpTosMR2sZwxq59CBhd7IoevsnQCAhxp7YmCHIDv3iBBCSF3geDMH67m/j2WhRGtAWICbQx+sGWN4/88Twu2Pn6ScFoQQQqqGggsbS7xQAAAY1LEhpFLHPVh/t/sc/kzmV7GM6BqCDsGe9u0QIYSQOoMui9jYlYIyAECor4ude3J3vxy8gPnb0gEAnw5si5Hdmti3Q4QQQuqUao9cZGVlYfjw4fDx8YGTkxPCw8ORlJQkPP7JJ58gLCwMLi4u8PLyQkxMDBISEiy2kZ+fj9jYWLi7u8PT0xOjR49GSUmJRZuUlBT07NkTarUawcHBmDdv3m19WbduHcLCwqBWqxEeHo7NmzdbPM4Yw/Tp0xEYGAgnJyfExMQgIyOjum9ZVOaaIX7u9i1Cdjf/d+giPt54EgAw/pHmFFgQQgiptmoFFwUFBejevTsUCgW2bNmCU6dOYcGCBfDy8hLatGzZEosXL8aJEyewf/9+NGnSBH379sWNGzeENrGxsTh58iTi4uKwadMm7N27F2PGjBEe12g06Nu3L0JCQnDkyBHMnz8fn3zyCZYtWya0OXjwIIYNG4bRo0fj6NGjGDx4MAYPHozU1FShzbx587Bo0SIsXboUCQkJcHFxQb9+/VBRUVGjnSWGcj1f/Espq15cV6E34pudGfj0n5OY/ncq5m9Lu62QWG2tSriIaRv4/fdar6Z4u29LUbdPCCHkAcGqYcqUKaxHjx7VeQorKipiANiOHTsYY4ydOnWKAWCJiYlCmy1btjCJRMKysrIYY4x99913zMvLi2m1WovXbtWqlXD7ueeeYwMGDLB4raioKPbaa68xxhjjOI4FBASw+fPnC48XFhYylUrF1qxZU62+FxUVVes938vD8/9jIVM2scW7Mqr1vG92nmEhUzZZ/Lz121G26fhVll1UXut+rU64KGz3800nGcdxtd4mIYSQ+qWqx8VqnT5v3LgRkZGRGDJkCPz8/BAREYHly5fftb1Op8OyZcvg4eGBDh06AADi4+Ph6emJyMhIoV1MTAykUqlw+SQ+Ph69evWCUqkU2vTr1w/p6ekoKCgQ2sTExFi8Xr9+/RAfHw8AyMzMRHZ2tkUbDw8PREVFCW1updVqodFoLH7ENu6R5gCAb3Zl4GJe6X3bM8bww77z+GL7mdse+ys5C+NWJ+OlFYk16kvC+TyM/OkwXlh+CFP/4leGvNw9FB883ppWhhBCCKmxak3oPH/+PJYsWYLJkyfjgw8+QGJiIiZMmAClUomRI0cK7TZt2oShQ4eirKwMgYGBiIuLg6+vLwAgOzsbfn5+lp2Qy+Ht7Y3s7GyhTWhoqEUbf39/4TEvLy9kZ2cL91VuU3kblZ93pza3mj17Nj799NPq7JJqe+ahhvgr+QoOnsvDB+tP4NfRURYHcp2BQ/KlAkgATP/7JNKvF1s8/9DUPpi1+TTO5pSAYwxp2cXILdHiSkEZUrOKoJRLERHshQ3HsrAvIxcGjmFwxyAo5VJ0CfWGn5saAGAwcpjyZwou5JUJ236pWxNMe4ICC0IIIbVTreCC4zhERkZi1qxZAICIiAikpqZi6dKlFsHFI488gmPHjiE3NxfLly/Hc889h4SEhNuCCkczdepUTJ48Wbit0WgQHBws6mtIJBLMfjocfb/aiwNn87ArLQd9WvujqFyPJbvPYV3SZeSV6u76/AAPNRYNiwAAHL9ciEHfHoBMIkHMl3tQoefu+Jy9Z/j5LmEBbtg6qRcA4O9jV3EhrwxezgpMf7IN/N3ViG7qQ4EFIYSQWqvWZZHAwEC0adPG4r7WrVvj0qVLFve5uLigefPm6Nq1K3788UfI5XL8+OOPAICAgADk5ORYtDcYDMjPz0dAQIDQ5vr16xZtzLfv16by45Wfd6c2t1KpVHB3d7f4sYYQHxe8ZFqFMfqXJIz4MQEdPt2OpXvOIa9UB3e1HJ7Oitue9/e47ha3DRwfTGRrKm4LLFyUMjT0dEKfMD/4uPCXl9KyizFo8X6czSnBN7v4VTNjejXDUxGN0K2ZLwUWhBBCRFGtkYvu3bsjPT3d4r4zZ84gJCTkns/jOA5arRYAEB0djcLCQhw5cgSdOnUCAOzatQscxyEqKkpo8+GHH0Kv10Oh4A+ycXFxaNWqlbAyJTo6Gjt37sSkSZOE14mLi0N0dDQAIDQ0FAEBAdi5cyc6duwIgB+JSEhIwNixY6vztq1iSGQwvt97HgCwLyNXuH9J7EP4Xxt/yGVSVOiNyC/VoUxnRHM/19u2EeztDLVCKgQW3Zr5IDWrCMHezlgS2wmNfZwBAPmlOvScuwulOiOOXynCM0sOoqhcDy9nBV6Mvvf/HSGEEFJt1ZklevjwYSaXy9nMmTNZRkYGW7VqFXN2dma//vorY4yxkpISNnXqVBYfH88uXLjAkpKS2KhRo5hKpWKpqanCdvr3788iIiJYQkIC279/P2vRogUbNmyY8HhhYSHz9/dnI0aMYKmpqWzt2rXM2dmZff/990KbAwcOMLlczr744gt2+vRp9vHHHzOFQsFOnDghtJkzZw7z9PRkf//9N0tJSWGDBg1ioaGhrLy8aqsrrLFaxIzjuNtWfyzbc67a2yks07Ez2RpWqtUzxhgr1xnuuNKjqFzHRvyYYPF6Px/IrO3bIIQQ8gCp6nGxWsEFY4z9888/rF27dkylUrGwsDC2bNky4bHy8nL21FNPsaCgIKZUKllgYCAbOHAgO3z4sMU28vLy2LBhw5irqytzd3dno0aNYsXFxRZtjh8/znr06MFUKhVr2LAhmzNnzm19+f3331nLli2ZUqlkbdu2Zf/++6/F4xzHsWnTpjF/f3+mUqlYnz59WHp6epXfqzWDC8YYK9Ma2OBv97OQKZvYk9/ss/ryz98OX2LNpv7LQqZsYu//mWLV1yKEEFL/VPW4KGGMMfuOnTgujUYDDw8PFBUVWW3+RbnOiJQrhYhs4g2ZDWqNGIwctAYOLirK/E4IIaR6qnpcpCOMnTkpZYhq6mOz15PLpJBXMzsoIYQQUh10lCGEEEKIqCi4IIQQQoioKLgghBBCiKgouCCEEEKIqCi4IIQQQoioKLgghBBCiKgouCCEEEKIqCi4IIQQQoioKLgghBBCiKgouCCEEEKIqCi4IIQQQoioKLgghBBCiKgouCCEEEKIqKgq6j2Yq9FrNBo794QQQgixP/Px0Hx8vBsKLu6huLgYABAcHGznnhBCCCGOo7i4GB4eHnd9XMLuF348wDiOw9WrV+Hm5gaJRGK119FoNAgODsbly5fh7u5utdd5UNH+tR7at9ZD+9a6aP/WDGMMxcXFCAoKglR695kVNHJxD1KpFI0aNbLZ67m7u9OH3Ipo/1oP7VvroX1rXbR/q+9eIxZmNKGTEEIIIaKi4IIQQgghoqLgwgGoVCp8/PHHUKlU9u5KvUT713po31oP7Vvrov1rXTShkxBCCCGiopELQgghhIiKggtCCCGEiIqCC0IIIYSIioILQgghhIiKgguRzZ49G507d4abmxv8/PwwePBgpKen3/M5J0+exDPPPIMmTZpAIpFg4cKF92w/Z84cSCQSTJo0SbyO1wHW3LdZWVkYPnw4fHx84OTkhPDwcCQlJVnhXTgua+1fo9GIadOmITQ0FE5OTmjWrBlmzJhx39oE9UlN9u3y5cvRs2dPeHl5wcvLCzExMTh8+LBFG8YYpk+fjsDAQDg5OSEmJgYZGRnWfCsOyRr7V6/XY8qUKQgPD4eLiwuCgoLw4osv4urVq9Z+O/UCBRci27NnD8aNG4dDhw4hLi4Oer0effv2RWlp6V2fU1ZWhqZNm2LOnDkICAi45/YTExPx/fffo3379mJ33eFZa98WFBSge/fuUCgU2LJlC06dOoUFCxbAy8vLWm/FIVlr/86dOxdLlizB4sWLcfr0acydOxfz5s3DN998Y6234nBqsm93796NYcOG4b///kN8fDyCg4PRt29fZGVlCW3mzZuHRYsWYenSpUhISICLiwv69euHiooKW7wth2GN/VtWVobk5GRMmzYNycnJ+Ouvv5Ceno6BAwfa6m3VbYxYVU5ODgPA9uzZU6X2ISEh7KuvvrrjY8XFxaxFixYsLi6O9e7dm02cOFG8jtZBYu3bKVOmsB49eojcu7pPrP07YMAA9vLLL1vc9/TTT7PY2FgxulknVXffMsaYwWBgbm5u7JdffmGMMcZxHAsICGDz588X2hQWFjKVSsXWrFkjep/rEjH2750cPnyYAWAXL14Uo5v1Go1cWFlRUREAwNvbu9bbGjduHAYMGICYmJhab6s+EGvfbty4EZGRkRgyZAj8/PwQERGB5cuXi9HFOk2s/dutWzfs3LkTZ86cAQAcP34c+/fvx2OPPVbrPtZVNdm3ZWVl0Ov1wnMyMzORnZ1t8X3g4eGBqKgoxMfHi9vhOkaM/Xu37UokEnh6eta2i/UeFS6zIo7jMGnSJHTv3h3t2rWr1bbWrl2L5ORkJCYmitS7uk3MfXv+/HksWbIEkydPxgcffIDExERMmDABSqUSI0eOFKnHdYuY+/f999+HRqNBWFgYZDIZjEYjZs6cidjYWJF6W7fUdN9OmTIFQUFBQjCRnZ0NAPD397do5+/vLzz2IBJr/96qoqICU6ZMwbBhw6jQWRVQcGFF48aNQ2pqKvbv31+r7Vy+fBkTJ05EXFwc1Gq1SL2r28TatwD/ZRQZGYlZs2YBACIiIpCamoqlS5c+sMGFmPv3999/x6pVq7B69Wq0bdsWx44dw6RJkxAUFPRA7t+a7Ns5c+Zg7dq12L17N30H3Ic19q9er8dzzz0HxhiWLFkiZnfrL3tfl6mvxo0bxxo1asTOnz9frefd6br1+vXrGQAmk8mEHwBMIpEwmUzGDAaDiD13fGLuW8YYa9y4MRs9erTFfd999x0LCgqqTTfrLLH3b6NGjdjixYst7psxYwZr1apVbbpZJ9Vk386fP595eHiwxMREi/vPnTvHALCjR49a3N+rVy82YcIEMbpb54i5f810Oh0bPHgwa9++PcvNzRWrq/UezbkQGWMM48ePx/r167Fr1y6EhobWept9+vTBiRMncOzYMeEnMjISsbGxOHbsGGQymQg9d3zW2LcA0L1799uWrZ05cwYhISGibL+usNb+LSsrg1Rq+VUjk8nAcZwo268Larpv582bhxkzZmDr1q2IjIy0eCw0NBQBAQHYuXOncJ9Go0FCQgKio6NF7b+js8b+BW6OWGRkZGDHjh3w8fERu+v1l11Dm3po7NixzMPDg+3evZtdu3ZN+CkrKxPajBgxgr3//vvCba1Wy44ePcqOHj3KAgMD2TvvvMOOHj3KMjIy7vo6D+JqEWvt28OHDzO5XM5mzpzJMjIy2KpVq5izszP79ddfbfr+7M1a+3fkyJGsYcOGbNOmTSwzM5P99ddfzNfXl7333ns2fX/2VJN9O2fOHKZUKtkff/xh8Zzi4mKLNp6enuzvv/9mKSkpbNCgQSw0NJSVl5fb9P3ZmzX2r06nYwMHDmSNGjVix44ds2ij1Wpt/h7rGgouRAbgjj8rVqwQ2vTu3ZuNHDlSuJ2ZmXnH5/Tu3fuur/MgBhfW3Lf//PMPa9euHVOpVCwsLIwtW7bMNm/KgVhr/2o0GjZx4kTWuHFjplarWdOmTdmHH374QH1B12TfhoSE3PE5H3/8sdCG4zg2bdo05u/vz1QqFevTpw9LT0+33RtzENbYv3f7bANg//33n03fX11EJdcJIYQQIiqac0EIIYQQUVFwQQghhBBRUXBBCCGEEFFRcEEIIYQQUVFwQQghhBBRUXBBCCGEEFFRcEEIIYQQUVFwQQghhNQTe/fuxZNPPomgoCBIJBJs2LCh2ttgjOGLL75Ay5YtoVKp0LBhQ8ycObNa26CqqIQQQkg9UVpaig4dOuDll1/G008/XaNtTJw4Edu3b8cXX3yB8PBw5OfnIz8/v1rboAydhBBCSD0kkUiwfv16DB48WLhPq9Xiww8/xJo1a1BYWIh27dph7ty5ePjhhwEAp0+fRvv27ZGamopWrVrV+LXpsgghhBDygBg/fjzi4+Oxdu1apKSkYMiQIejfvz8yMjIAAP/88w+aNm2KTZs2ITQ0FE2aNMErr7xS7ZELCi4IIYSQB8ClS5ewYsUKrFu3Dj179kSzZs3wzjvvoEePHlixYgUA4Pz587h48SLWrVuHlStX4ueff8aRI0fw7LPPVuu1aM4FIYQQ8gA4ceIEjEYjWrZsaXG/VquFj48PAIDjOGi1WqxcuVJo9+OPP6JTp05IT0+v8qUSCi4IIYSQB0BJSQlkMhmOHDkCmUxm8ZirqysAIDAwEHK53CIAad26NQB+5IOCC0IIIYQIIiIiYDQakZOTg549e96xTffu3WEwGHDu3Dk0a9YMAHDmzBkAQEhISJVfi1aLEEIIIfVESUkJzp49C4APJr788ks88sgj8Pb2RuPGjTF8+HAcOHAACxYsQEREBG7cuIGdO3eiffv2GDBgADiOQ+fOneHq6oqFCxeC4ziMGzcO7u7u2L59e5X7QcEFIYQQUk/s3r0bjzzyyG33jxw5Ej///DP0ej0+//xzrFy5EllZWfD19UXXrl3x6aefIjw8HABw9epVvPnmm9i+fTtcXFzw2GOPYcGCBfD29q5yPyi4IIQQQoioaCkqIYQQQkRFwQUhhBBCREXBBSGEEEJERcEFIYQQQkRFwQUhhBBCREXBBSGEEEJERcEFIYQQQkRFwQUhhBBCREXBBSGEEEJERcEFIYQQQkRFwQUhhBBCREXBBSGEEEJE9f9OK4JnZ/+sZwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#First, let's get the ID of the Ward boundaries from the Open Data Portal:\n",
    "ward_id = 'af288688b25345e8b7d5dcf750f21fc1'\n",
    "#Then fetch the content from ArcGIS Online\n",
    "item = gis.content.get(ward_id)\n",
    "\n",
    "#Now extract the layer, and convert it to GeoDataFrame\n",
    "w_layer = item.layers[0]\n",
    "w_sdf = w_layer.query().sdf \n",
    "#Extract the Coordinate Reference System (CRS) from the layer service definition\n",
    "w_crs = w_layer.properties.extent.spatialReference.latestWkid\n",
    "w_gdf = gpd.GeoDataFrame(w_sdf,geometry=w_sdf.SHAPE,crs=w_crs)\n",
    "#Subset for relevant information\n",
    "wards = w_gdf[['WARD','NAME','COUNCILMEMBER','geometry']].rename(columns={\"WARD\":\"WARD_NO\",\"NAME\":\"WARD_NAME\"})\n",
    "\n",
    "#Plot the results\n",
    "wards.geometry.boundary.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apportion ACS Data to Wards\n",
    "The aim of this section is to calculate the number of people with a disability per ward and the margin of error for that estimate. Importantly, census tracts do not align perfectly with the ward boundary edges, so we will have to determine a strategy for resolving these overlapping edges. There are a number of ways to do this, but the method we use we call `largest overlap`. The `largest overlap` method does the following:\n",
    "\n",
    "1. Overlay the census tracts on the wards.\n",
    "2. If a census tract falls across two different wards, calculate the area of each census tract for each ward.\n",
    "    * For example, if a Census Tract falls on the boundary of Ward 16 and 17, we calculate that 60% of its area is in Ward 16, and the remaining 40% is in Ward 17.\n",
    "3. Whichever ward contains a majority of the Census Tract's area, assign that Census Tract's data to said Ward.\n",
    "    * Continuing the example above, we would assign the Census Tract's data to Ward 16.\n",
    "\n",
    "Once this process is complete, we can aggregate all the data across census tracts within each Ward. For example, if Ward 16 is assigned 8 Census Tracts from the `largest overlap` process, and each ward has a population of 1,000, we determine Ward 16's population to be 8,000.\n",
    "\n",
    "### The `calc_moe` function\n",
    "Margins of Error are aggregated in a different way than regular estimates. Instead of simply summing up all the values for each Census Tract, we use the following formula:\n",
    "<p align='center'><img src='resources/01/moe_sum.png'></img></p>\n",
    "\n",
    "Where $MOE(\\hat{x}_{1})$ represents the Margin of Error for estimate 1, $MOE(\\hat{x}_{2})$ represents the Margin of Error for estimate 2, and so on.\n",
    "\n",
    "To learn more about how Margins of Error are calculated for derived estimates, please checkout [this](https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018_ch08.pdf) article from the US Census Bureau. There are different ways to calculate the Margin of Error depending on if you are summing, averaging, or taking a proportion of your estimates.\n",
    "\n",
    "The `calc_moe` function will calculate the Margins of Error for estimates based on some aggregation strategy. It takes the following parameters:\n",
    "1. `array`: This is a list, or lists of values that are required to calculate a margin of error for a derived estimate. For example, using the above formula, the array we would pass through would look like this: [$\\hat{x}_{1}$,$\\hat{x}_{2}$...$\\hat{x}_{n}$]\n",
    "2. `how`: This is the aggregation strategy of the estimates. It takes either `sum`, `mean`, or `proportion` and will calculate the margin of error accordingly.\n",
    "\n",
    "The function will return the value of the margin of error for all estimates within the passed arrays.\n",
    "\n",
    "\n",
    "### The `apportion` function\n",
    "The entire aggregation process listed above is wrapped into the function `apportion` from the `cledatatoolkit` package. The `apportion` function takes the following arguments:\n",
    "1. `left`: The GeoDataFrame containing the data of interest. In this case, this will be our `disability_status` DataFrame from the ACS data.\n",
    "2. `right`: The GeoDataFrame containing the geography to which you will apportion the data from `left`. This is the `wards` DataFrame containing the ward information.\n",
    "3. `group_key`: The ID column of the `right` GeoDataFrame. This will be the `WARD` column from the `wards` DataFrame.\n",
    "4. `target_key`: The ID column of the `left` GeoDataFrame. This will be the `GEOID` column from the `disability_status` DataFrame.\n",
    "5. `aggregator`: A python dictionary assigning the aggregation strategy to relevant columns from the `left` GeoDataFrame. In our case, the `aggregator` should look something like this:\n",
    "\n",
    "```\n",
    "aggregator = {\n",
    "    TOTAL_POP: 'sum',\n",
    "    TOTAL_POP_M: lambda x: calc_moe(x, 'sum'),\n",
    "    POP_WITH_DISABLITY: 'sum',\n",
    "    POP_WITH_DISABILITY_M: lambda x: calc_moe(x,'sum')\n",
    "}\n",
    "```\n",
    "\n",
    "This function will return a GeoDataFrame with the apportioned estimates. In this case, we will receive a GeoDataFrame with one row for each ward, and the sum of `POP_WITH_DISABILITY`, and the margin of error for that sum, listed under the `POP_WITH_DISABILITY_M` column. The same will apply for the `TOTAL_POP` and `TOTAL_POP_M` columns.\n",
    "\n",
    "### The `build_aggregator` function\n",
    "The above `aggregator` dictionary can be constructed using `build_aggregator`. The function will iterate through every column in the inputted DataFrame. If the column contains numeric data, it is added to the returned dictionary. Furthermore, if a column name ends with `_M` (the default naming mechanism for Margins of Error in ACS data), the function will assign the appropriate `calc_moe` implementation. Using this function is ideal for apportioning larger ACS datasets. The function takes the following parameters:\n",
    "1. `df`: The DataFrame for which you want to build the aggregator. In our case we will use the `disability_status` DataFrame.\n",
    "2. `exclude`: A list of columns to exclude from the aggregator build process.\n",
    "3. `default`: The default aggregation strategy you are using. In this case we will use `sum`.\n",
    "\n",
    "First we will build the aggregator for our Disability Status data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'TOTAL_POP': 'sum', 'TOTAL_POP_M': <function build_aggregator.<locals>.<lambda> at 0x000001744E3D4180>, 'POP_WITH_DISABILITY': 'sum', 'POP_WITH_DISABILITY_M': <function build_aggregator.<locals>.<lambda> at 0x000001740680E700>}\n"
     ]
    }
   ],
   "source": [
    "aggregator = build_aggregator(disability_status,exclude=None)\n",
    "print(aggregator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will apportion the ACS data to wards using the `apportion` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        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>TOTAL_POP</th>\n",
       "      <th>TOTAL_POP_M</th>\n",
       "      <th>POP_WITH_DISABILITY</th>\n",
       "      <th>POP_WITH_DISABILITY_M</th>\n",
       "      <th>WARD_NO</th>\n",
       "      <th>WARD_NAME</th>\n",
       "      <th>COUNCILMEMBER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27952</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>4169</td>\n",
       "      <td>468</td>\n",
       "      <td>3</td>\n",
       "      <td>Ward 3</td>\n",
       "      <td>Kerry McCormack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26372</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>4769</td>\n",
       "      <td>556</td>\n",
       "      <td>13</td>\n",
       "      <td>Ward 13</td>\n",
       "      <td>Kris Harsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24994</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>3962</td>\n",
       "      <td>526</td>\n",
       "      <td>17</td>\n",
       "      <td>Ward 17</td>\n",
       "      <td>Charles J Slife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24648</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>5454</td>\n",
       "      <td>612</td>\n",
       "      <td>12</td>\n",
       "      <td>Ward 12</td>\n",
       "      <td>Rebecca Maurer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24294</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>5291</td>\n",
       "      <td>518</td>\n",
       "      <td>15</td>\n",
       "      <td>Ward 15</td>\n",
       "      <td>Jenny Spencer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23257</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>4682</td>\n",
       "      <td>507</td>\n",
       "      <td>10</td>\n",
       "      <td>Ward 10</td>\n",
       "      <td>Anthony T Hairston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22273</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>4298</td>\n",
       "      <td>484</td>\n",
       "      <td>16</td>\n",
       "      <td>Ward 16</td>\n",
       "      <td>Brian Kazy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20268</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>4805</td>\n",
       "      <td>565</td>\n",
       "      <td>7</td>\n",
       "      <td>Ward 7</td>\n",
       "      <td>Stephanie D Howse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20265</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>3844</td>\n",
       "      <td>461</td>\n",
       "      <td>2</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>Kevin L Bishop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20094</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>4683</td>\n",
       "      <td>613</td>\n",
       "      <td>5</td>\n",
       "      <td>Ward 5</td>\n",
       "      <td>Richard A Starr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19721</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>4231</td>\n",
       "      <td>630</td>\n",
       "      <td>8</td>\n",
       "      <td>Ward 8</td>\n",
       "      <td>Michael D Polensek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19207</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>3277</td>\n",
       "      <td>386</td>\n",
       "      <td>11</td>\n",
       "      <td>Ward 11</td>\n",
       "      <td>Danny Kelly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19173</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>4334</td>\n",
       "      <td>571</td>\n",
       "      <td>6</td>\n",
       "      <td>Ward 6</td>\n",
       "      <td>Blaine A Griffin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19019</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>4360</td>\n",
       "      <td>503</td>\n",
       "      <td>14</td>\n",
       "      <td>Ward 14</td>\n",
       "      <td>Jasmin Santana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18581</td>\n",
       "      <td>1564.0</td>\n",
       "      <td>3133</td>\n",
       "      <td>442</td>\n",
       "      <td>9</td>\n",
       "      <td>Ward 9</td>\n",
       "      <td>Kevin Conwell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17565</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>3654</td>\n",
       "      <td>532</td>\n",
       "      <td>1</td>\n",
       "      <td>Ward 1</td>\n",
       "      <td>Joseph T Jones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16003</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>3212</td>\n",
       "      <td>441</td>\n",
       "      <td>4</td>\n",
       "      <td>Ward 4</td>\n",
       "      <td>Deborah A Gray</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TOTAL_POP  TOTAL_POP_M  POP_WITH_DISABILITY  POP_WITH_DISABILITY_M  \\\n",
       "10      27952       1310.0                 4169                    468   \n",
       "3       26372       1713.0                 4769                    556   \n",
       "7       24994       1465.0                 3962                    526   \n",
       "2       24648       1535.0                 5454                    612   \n",
       "5       24294       1366.0                 5291                    518   \n",
       "0       23257       1531.0                 4682                    507   \n",
       "6       22273       1409.0                 4298                    484   \n",
       "13      20268       1272.0                 4805                    565   \n",
       "9       20265       1640.0                 3844                    461   \n",
       "12      20094       1681.0                 4683                    613   \n",
       "15      19721       1416.0                 4231                    630   \n",
       "1       19207       1592.0                 3277                    386   \n",
       "14      19173       1268.0                 4334                    571   \n",
       "4       19019       1236.0                 4360                    503   \n",
       "16      18581       1564.0                 3133                    442   \n",
       "8       17565       1325.0                 3654                    532   \n",
       "11      16003       1489.0                 3212                    441   \n",
       "\n",
       "   WARD_NO WARD_NAME       COUNCILMEMBER  \n",
       "10       3    Ward 3     Kerry McCormack  \n",
       "3       13   Ward 13        Kris Harsh\n",
       "  \n",
       "7       17   Ward 17     Charles J Slife  \n",
       "2       12   Ward 12    Rebecca Maurer\n",
       "  \n",
       "5       15   Ward 15       Jenny Spencer  \n",
       "0       10   Ward 10  Anthony T Hairston  \n",
       "6       16   Ward 16          Brian Kazy  \n",
       "13       7    Ward 7   Stephanie D Howse  \n",
       "9        2    Ward 2      Kevin L Bishop  \n",
       "12       5    Ward 5   Richard A Starr\n",
       "  \n",
       "15       8    Ward 8  Michael D Polensek  \n",
       "1       11   Ward 11       Danny Kelly\n",
       "  \n",
       "14       6    Ward 6    Blaine A Griffin  \n",
       "4       14   Ward 14      Jasmin Santana  \n",
       "16       9    Ward 9       Kevin Conwell  \n",
       "8        1    Ward 1      Joseph T Jones  \n",
       "11       4    Ward 4    Deborah A Gray\n",
       "  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apportion the census data by ward\n",
    "wards_ds = apportion(left=disability_status, right=wards, group_key=\"WARD_NO\", target_key=\"GEOID\", aggregator=aggregator)\n",
    "#Show the data sorted by number of people with a disability:\n",
    "wards_ds.sort_values('TOTAL_POP',axis=0,ascending=False).drop('geometry',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Proportions\n",
    "Now that we have the number of people with a disability and the corresponding margin of error for each ward in the city, we will now calculate the percentage of people with a disability. In this section we will calculate four new fields:\n",
    "1. `PROP_WITH_DISABILITY`: This is the proportion of the population within a ward that has a disability.\n",
    "2. `PROP_WITH_DISABILITY_M`: This is the margin of error for the proportion of the population with a disability.\n",
    "3. `PERCENT_WITH_DISABILITY`: `PROP_WITH_DISABILITY` * 100\n",
    "4. `PERCENT_WITH_DISABILITY_M`: `PROP_WITH_DISABILITY_M` * 100\n",
    "\n",
    "Calculating the first of these four variables is easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "wards_ds.loc[:,'PROP_WITH_DISABILITY'] = wards_ds['POP_WITH_DISABILITY'] / wards_ds['TOTAL_POP']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will calculate the margin of error for the above proportion. The [ACS](https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018_ch08.pdf) calculates margins of error for derived proportions using the following formula: \n",
    "<p align='center'><img src='resources/01/moe_prop.png'></img></p>\n",
    "\n",
    "Where $\\hat{P}$ represents the calculated proportion (`PROP_WITH_DISABILITY`), $\\hat{X}$ the numerator of the proportion (`POP_WITH_DISABILITY`), and $\\hat{Y}$ the denominator (`TOTAL_POP`). $MOE()$ represents the margin of error for the associated estimate.\n",
    "\n",
    "`calc_moe` can calculate the margin of error for proportions by simply setting the `how` argument to `proportion`. However, the `arrays` argument needs to be formatted as 2D array, consisting of the following:\n",
    "1. The denominators of the proportion ($\\hat{Y}$ from the above formula).\n",
    "2. The proportions themselves ($\\hat{P}$).\n",
    "3. The margins of error of the numerator ($MOE(\\hat{X})$).\n",
    "4. The margins of error of the denominator ($MOE(\\hat{Y})$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Arrange the arrays to match the required structure for calc_moe\n",
    "arrays = [wards_ds['TOTAL_POP'], wards_ds['PROP_WITH_DISABILITY'], wards_ds['POP_WITH_DISABILITY_M'], wards_ds['TOTAL_POP_M']]\n",
    "#Calculate the MOE\n",
    "wards_ds.loc[:,'PROP_WITH_DISABILITY_M'] = calc_moe(arrays,how='proportion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we represent the columns from above as percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WARD_NO</th>\n",
       "      <th>PERCENT_WITH_DISABILITY</th>\n",
       "      <th>PERCENT_WITH_DISABILITY_M</th>\n",
       "      <th>POP_WITH_DISABILITY</th>\n",
       "      <th>POP_WITH_DISABILITY_M</th>\n",
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       "      <th>TOTAL_POP_M</th>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7</td>\n",
       "      <td>23.71</td>\n",
       "      <td>2.36</td>\n",
       "      <td>4805</td>\n",
       "      <td>565</td>\n",
       "      <td>20268</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>Ward 7</td>\n",
       "      <td>Stephanie D Howse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>23.31</td>\n",
       "      <td>2.35</td>\n",
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       "      <td>613</td>\n",
       "      <td>20094</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>Ward 5</td>\n",
       "      <td>Richard A Starr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>22.92</td>\n",
       "      <td>2.19</td>\n",
       "      <td>4360</td>\n",
       "      <td>503</td>\n",
       "      <td>19019</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>Ward 14</td>\n",
       "      <td>Jasmin Santana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>2.58</td>\n",
       "      <td>4334</td>\n",
       "      <td>571</td>\n",
       "      <td>19173</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>Ward 6</td>\n",
       "      <td>Blaine A Griffin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>22.13</td>\n",
       "      <td>2.07</td>\n",
       "      <td>5454</td>\n",
       "      <td>612</td>\n",
       "      <td>24648</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>Ward 12</td>\n",
       "      <td>Rebecca Maurer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>21.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>5291</td>\n",
       "      <td>518</td>\n",
       "      <td>24294</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>Ward 15</td>\n",
       "      <td>Jenny Spencer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>21.45</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4231</td>\n",
       "      <td>630</td>\n",
       "      <td>19721</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>Ward 8</td>\n",
       "      <td>Michael D Polensek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>20.8</td>\n",
       "      <td>2.59</td>\n",
       "      <td>3654</td>\n",
       "      <td>532</td>\n",
       "      <td>17565</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>Ward 1</td>\n",
       "      <td>Joseph T Jones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20.13</td>\n",
       "      <td>1.73</td>\n",
       "      <td>4682</td>\n",
       "      <td>507</td>\n",
       "      <td>23257</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>Ward 10</td>\n",
       "      <td>Anthony T Hairston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>20.07</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3212</td>\n",
       "      <td>441</td>\n",
       "      <td>16003</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>Ward 4</td>\n",
       "      <td>Deborah A Gray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>19.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4298</td>\n",
       "      <td>484</td>\n",
       "      <td>22273</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>Ward 16</td>\n",
       "      <td>Brian Kazy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>18.97</td>\n",
       "      <td>1.68</td>\n",
       "      <td>3844</td>\n",
       "      <td>461</td>\n",
       "      <td>20265</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>Kevin L Bishop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>18.08</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4769</td>\n",
       "      <td>556</td>\n",
       "      <td>26372</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>Ward 13</td>\n",
       "      <td>Kris Harsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>17.06</td>\n",
       "      <td>1.43</td>\n",
       "      <td>3277</td>\n",
       "      <td>386</td>\n",
       "      <td>19207</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>Ward 11</td>\n",
       "      <td>Danny Kelly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>16.86</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3133</td>\n",
       "      <td>442</td>\n",
       "      <td>18581</td>\n",
       "      <td>1564.0</td>\n",
       "      <td>Ward 9</td>\n",
       "      <td>Kevin Conwell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>15.85</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3962</td>\n",
       "      <td>526</td>\n",
       "      <td>24994</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>Ward 17</td>\n",
       "      <td>Charles J Slife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>14.91</td>\n",
       "      <td>1.52</td>\n",
       "      <td>4169</td>\n",
       "      <td>468</td>\n",
       "      <td>27952</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>Ward 3</td>\n",
       "      <td>Kerry McCormack</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WARD_NO  PERCENT_WITH_DISABILITY  PERCENT_WITH_DISABILITY_M  \\\n",
       "13       7                    23.71                       2.36   \n",
       "12       5                    23.31                       2.35   \n",
       "4       14                    22.92                       2.19   \n",
       "14       6                     22.6                       2.58   \n",
       "2       12                    22.13                       2.07   \n",
       "5       15                    21.78                       1.75   \n",
       "15       8                    21.45                        2.8   \n",
       "8        1                     20.8                       2.59   \n",
       "0       10                    20.13                       1.73   \n",
       "11       4                    20.07                       2.03   \n",
       "6       16                     19.3                        1.8   \n",
       "9        2                    18.97                       1.68   \n",
       "3       13                    18.08                       1.75   \n",
       "1       11                    17.06                       1.43   \n",
       "16       9                    16.86                       1.91   \n",
       "7       17                    15.85                       1.89   \n",
       "10       3                    14.91                       1.52   \n",
       "\n",
       "    POP_WITH_DISABILITY  POP_WITH_DISABILITY_M  TOTAL_POP  TOTAL_POP_M  \\\n",
       "13                 4805                    565      20268       1272.0   \n",
       "12                 4683                    613      20094       1681.0   \n",
       "4                  4360                    503      19019       1236.0   \n",
       "14                 4334                    571      19173       1268.0   \n",
       "2                  5454                    612      24648       1535.0   \n",
       "5                  5291                    518      24294       1366.0   \n",
       "15                 4231                    630      19721       1416.0   \n",
       "8                  3654                    532      17565       1325.0   \n",
       "0                  4682                    507      23257       1531.0   \n",
       "11                 3212                    441      16003       1489.0   \n",
       "6                  4298                    484      22273       1409.0   \n",
       "9                  3844                    461      20265       1640.0   \n",
       "3                  4769                    556      26372       1713.0   \n",
       "1                  3277                    386      19207       1592.0   \n",
       "16                 3133                    442      18581       1564.0   \n",
       "7                  3962                    526      24994       1465.0   \n",
       "10                 4169                    468      27952       1310.0   \n",
       "\n",
       "   WARD_NAME       COUNCILMEMBER  \n",
       "13    Ward 7   Stephanie D Howse  \n",
       "12    Ward 5   Richard A Starr\n",
       "  \n",
       "4    Ward 14      Jasmin Santana  \n",
       "14    Ward 6    Blaine A Griffin  \n",
       "2    Ward 12    Rebecca Maurer\n",
       "  \n",
       "5    Ward 15       Jenny Spencer  \n",
       "15    Ward 8  Michael D Polensek  \n",
       "8     Ward 1      Joseph T Jones  \n",
       "0    Ward 10  Anthony T Hairston  \n",
       "11    Ward 4    Deborah A Gray\n",
       "  \n",
       "6    Ward 16          Brian Kazy  \n",
       "9     Ward 2      Kevin L Bishop  \n",
       "3    Ward 13        Kris Harsh\n",
       "  \n",
       "1    Ward 11       Danny Kelly\n",
       "  \n",
       "16    Ward 9       Kevin Conwell  \n",
       "7    Ward 17     Charles J Slife  \n",
       "10    Ward 3     Kerry McCormack  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wards_ds.loc[:,'PERCENT_WITH_DISABILITY'] = np.round(100*wards_ds['PROP_WITH_DISABILITY'],2)\n",
    "wards_ds.loc[:,'PERCENT_WITH_DISABILITY_M'] = np.round(100*wards_ds['PROP_WITH_DISABILITY_M'],2)\n",
    "\n",
    "#Reorder columns for displaying\n",
    "cols = ['WARD_NO','PERCENT_WITH_DISABILITY','PERCENT_WITH_DISABILITY_M','POP_WITH_DISABILITY','POP_WITH_DISABILITY_M','TOTAL_POP','TOTAL_POP_M','WARD_NAME','COUNCILMEMBER']\n",
    "wards_ds[cols].sort_values('PERCENT_WITH_DISABILITY',axis=0,ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map Results with `geopandas`\n",
    "We will use the apportioned `wards_ds` DataFrame from above to map the percent of individuals with a disability by ward. To do this, we will use the `explore()` method on the GeoDataFrame. Please note that to visualize the map, you will need the following libraries in addition to `geopandas`:\n",
    "1. `folium`\n",
    "2. `matplotlib`\n",
    "3. `mapclassify`\n",
    "\n",
    "The below code will generate an interactive HTML map, feel free to play around with the styling parameters to your liking. You can learn more about the `explore()` method [here]('https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.explore.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;html&gt;\n",
       "&lt;head&gt;\n",
       "    \n",
       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
       "    \n",
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       "            L_DISABLE_3D = false;\n",
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       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
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       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
       "    \n",
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       "                    width: 100.0%;\n",
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       "    &lt;script&gt;$( function() {\n",
       "        $( &quot;.maplegend&quot; ).draggable({\n",
       "            start: function (event, ui) {\n",
       "                $(this).css({\n",
       "                    right: &quot;auto&quot;,\n",
       "                    top: &quot;auto&quot;,\n",
       "                    bottom: &quot;auto&quot;\n",
       "                });\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "    &lt;/script&gt;\n",
       "    &lt;style type=&#x27;text/css&#x27;&gt;\n",
       "      .maplegend {\n",
       "        position: absolute;\n",
       "        z-index:9999;\n",
       "        background-color: rgba(255, 255, 255, .8);\n",
       "        border-radius: 5px;\n",
       "        box-shadow: 0 0 15px rgba(0,0,0,0.2);\n",
       "        padding: 10px;\n",
       "        font: 12px/14px Arial, Helvetica, sans-serif;\n",
       "        right: 10px;\n",
       "        bottom: 20px;\n",
       "      }\n",
       "      .maplegend .legend-title {\n",
       "        text-align: left;\n",
       "        margin-bottom: 5px;\n",
       "        font-weight: bold;\n",
       "        }\n",
       "      .maplegend .legend-scale ul {\n",
       "        margin: 0;\n",
       "        margin-bottom: 0px;\n",
       "        padding: 0;\n",
       "        float: left;\n",
       "        list-style: none;\n",
       "        }\n",
       "      .maplegend .legend-scale ul li {\n",
       "        list-style: none;\n",
       "        margin-left: 0;\n",
       "        line-height: 16px;\n",
       "        margin-bottom: 2px;\n",
       "        }\n",
       "      .maplegend ul.legend-labels li span {\n",
       "        display: block;\n",
       "        float: left;\n",
       "        height: 14px;\n",
       "        width: 14px;\n",
       "        margin-right: 5px;\n",
       "        margin-left: 0;\n",
       "        border: 0px solid #ccc;\n",
       "        }\n",
       "      .maplegend .legend-source {\n",
       "        color: #777;\n",
       "        clear: both;\n",
       "        }\n",
       "      .maplegend a {\n",
       "        color: #777;\n",
       "        }\n",
       "    &lt;/style&gt;\n",
       "    \n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    \n",
       "    \n",
       "    &lt;div id=&#x27;maplegend PERCENT WITH DISABILITY&#x27; class=&#x27;maplegend&#x27;&gt;\n",
       "        &lt;div class=&#x27;legend-title&#x27;&gt;PERCENT WITH DISABILITY&lt;/div&gt;\n",
       "        &lt;div class=&#x27;legend-scale&#x27;&gt;\n",
       "            &lt;ul class=&#x27;legend-labels&#x27;&gt;\n",
       "                &lt;li&gt;&lt;span style=&#x27;background:#f7fbff&#x27;&gt;&lt;/span&gt;14.91, 15.85&lt;/li&gt;\n",
       "                &lt;li&gt;&lt;span style=&#x27;background:#c6dbef&#x27;&gt;&lt;/span&gt;15.85, 18.08&lt;/li&gt;\n",
       "                &lt;li&gt;&lt;span style=&#x27;background:#6baed6&#x27;&gt;&lt;/span&gt;18.08, 20.13&lt;/li&gt;\n",
       "                &lt;li&gt;&lt;span style=&#x27;background:#2171b5&#x27;&gt;&lt;/span&gt;20.13, 22.13&lt;/li&gt;\n",
       "                &lt;li&gt;&lt;span style=&#x27;background:#08306b&#x27;&gt;&lt;/span&gt;22.13, 23.71&lt;/li&gt;\n",
       "            &lt;/ul&gt;\n",
       "        &lt;/div&gt;\n",
       "    &lt;/div&gt;\n",
       "    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_0be4f79e82bcbd405c1772e5f4882b83&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;\n",
       "    \n",
       "    \n",
       "            var map_0be4f79e82bcbd405c1772e5f4882b83 = L.map(\n",
       "                &quot;map_0be4f79e82bcbd405c1772e5f4882b83&quot;,\n",
       "                {\n",
       "                    center: [41.49510041098689, -81.70621507435578],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 10,\n",
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       "            L.control.scale().addTo(map_0be4f79e82bcbd405c1772e5f4882b83);\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_2134f0e54a65a7107f3e9016c3990f4b = L.tileLayer(\n",
       "                &quot;https://cartodb-basemaps-{s}.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;\\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://cartodb.com/attributions\\&quot;\\u003eCartoDB\\u003c/a\\u003e, CartoDB \\u003ca target=\\&quot;_blank\\&quot; href =\\&quot;http://cartodb.com/attributions\\&quot;\\u003eattributions\\u003c/a\\u003e&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_0be4f79e82bcbd405c1772e5f4882b83);\n",
       "        \n",
       "    \n",
       "            map_0be4f79e82bcbd405c1772e5f4882b83.fitBounds(\n",
       "                [[41.39072793774375, -81.87912902731605], [41.59947288423004, -81.53330112139552]],\n",
       "                {}\n",
       "            );\n",
       "        \n",
       "    \n",
       "        function geo_json_74739cba7a1dcd20bfa3df87c40d9d99_styler(feature) {\n",
       "            switch(feature.id) {\n",
       "                case &quot;8&quot;: case &quot;2&quot;: case &quot;5&quot;: case &quot;15&quot;: \n",
       "                    return {&quot;color&quot;: &quot;#2171b5&quot;, &quot;fillColor&quot;: &quot;#2171b5&quot;, &quot;fillOpacity&quot;: 0.75, &quot;weight&quot;: 2};\n",
       "                case &quot;1&quot;: case &quot;3&quot;: case &quot;16&quot;: \n",
       "                    return {&quot;color&quot;: &quot;#c6dbef&quot;, &quot;fillColor&quot;: &quot;#c6dbef&quot;, &quot;fillOpacity&quot;: 0.75, &quot;weight&quot;: 2};\n",
       "                case &quot;4&quot;: case &quot;12&quot;: case &quot;14&quot;: case &quot;13&quot;: \n",
       "                    return {&quot;color&quot;: &quot;#08306b&quot;, &quot;fillColor&quot;: &quot;#08306b&quot;, &quot;fillOpacity&quot;: 0.75, &quot;weight&quot;: 2};\n",
       "                case &quot;7&quot;: case &quot;10&quot;: \n",
       "                    return {&quot;color&quot;: &quot;#f7fbff&quot;, &quot;fillColor&quot;: &quot;#f7fbff&quot;, &quot;fillOpacity&quot;: 0.75, &quot;weight&quot;: 2};\n",
       "                default:\n",
       "                    return {&quot;color&quot;: &quot;#6baed6&quot;, &quot;fillColor&quot;: &quot;#6baed6&quot;, &quot;fillOpacity&quot;: 0.75, &quot;weight&quot;: 2};\n",
       "            }\n",
       "        }\n",
       "        function geo_json_74739cba7a1dcd20bfa3df87c40d9d99_highlighter(feature) {\n",
       "            switch(feature.id) {\n",
       "                default:\n",
       "                    return {&quot;fillOpacity&quot;: 0.75};\n",
       "            }\n",
       "        }\n",
       "        function geo_json_74739cba7a1dcd20bfa3df87c40d9d99_pointToLayer(feature, latlng) {\n",
       "            var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};\n",
       "            \n",
       "            let style = geo_json_74739cba7a1dcd20bfa3df87c40d9d99_styler(feature)\n",
       "            Object.assign(opts, style)\n",
       "            \n",
       "            return new L.CircleMarker(latlng, opts)\n",
       "        }\n",
       "\n",
       "        function geo_json_74739cba7a1dcd20bfa3df87c40d9d99_onEachFeature(feature, layer) {\n",
       "            layer.on({\n",
       "                mouseout: function(e) {\n",
       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
       "                        geo_json_74739cba7a1dcd20bfa3df87c40d9d99.resetStyle(e.target);\n",
       "                    }\n",
       "                },\n",
       "                mouseover: function(e) {\n",
       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
       "                        const highlightStyle = geo_json_74739cba7a1dcd20bfa3df87c40d9d99_highlighter(e.target.feature)\n",
       "                        e.target.setStyle(highlightStyle);\n",
       "                    }\n",
       "                },\n",
       "            });\n",
       "        };\n",
       "        var geo_json_74739cba7a1dcd20bfa3df87c40d9d99 = L.geoJson(null, {\n",
       "                onEachFeature: geo_json_74739cba7a1dcd20bfa3df87c40d9d99_onEachFeature,\n",
       "            \n",
       "                style: geo_json_74739cba7a1dcd20bfa3df87c40d9d99_styler,\n",
       "                pointToLayer: geo_json_74739cba7a1dcd20bfa3df87c40d9d99_pointToLayer\n",
       "        });\n",
       "\n",
       "        function geo_json_74739cba7a1dcd20bfa3df87c40d9d99_add (data) {\n",
       "            geo_json_74739cba7a1dcd20bfa3df87c40d9d99\n",
       "                .addData(data)\n",
       "                .addTo(map_0be4f79e82bcbd405c1772e5f4882b83);\n",
       "        }\n",
       "            geo_json_74739cba7a1dcd20bfa3df87c40d9d99_add({&quot;bbox&quot;: [-81.87912902731605, 41.39072793774375, -81.53330112139552, 41.59947288423004], &quot;features&quot;: [{&quot;bbox&quot;: [-81.59114011147066, 41.42425591716355, -81.54560712551674, 41.47176790894343], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[-81.59111011163961, 41.44721191405275], [-81.59114011147066, 41.44900591367086], [-81.58750211273934, 41.44905591357543], [-81.5863601130766, 41.44907391342857], [-81.58516911344913, 41.44907391342214], [-81.58402211370593, 41.4490839133885], [-81.58401911370183, 41.449940913174466], [-81.58401511360117, 41.450713913059104], [-81.5845671134825, 41.450715913099344], [-81.58459811327778, 41.45297291271702], [-81.5813161142704, 41.45298591252649], [-81.58009211455611, 41.45300491249077], [-81.57717011559917, 41.452993912329276], [-81.57549911604531, 41.45304391227172], [-81.57553911563583, 41.45753491137168], [-81.57559811505838, 41.46438291018288], 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&quot;16&quot;, &quot;properties&quot;: {&quot;COUNCILMEMBER&quot;: &quot;Kevin Conwell&quot;, &quot;PERCENT_WITH_DISABILITY&quot;: 16.86, &quot;PERCENT_WITH_DISABILITY_M&quot;: 1.91, &quot;POP_WITH_DISABILITY&quot;: 3133, &quot;POP_WITH_DISABILITY_M&quot;: 442, &quot;PROP_WITH_DISABILITY&quot;: 0.16861309940261557, &quot;PROP_WITH_DISABILITY_M&quot;: 0.019090035547408873, &quot;TOTAL_POP&quot;: 18581, &quot;TOTAL_POP_M&quot;: 1564.0, &quot;WARD_NAME&quot;: &quot;Ward 9&quot;, &quot;WARD_NO&quot;: &quot;9&quot;, &quot;__folium_color&quot;: &quot;#c6dbef&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
       "\n",
       "        \n",
       "    \n",
       "    geo_json_74739cba7a1dcd20bfa3df87c40d9d99.bindTooltip(\n",
       "    function(layer){\n",
       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
       "    \n",
       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
       "    let fields = [&quot;WARD_NAME&quot;, &quot;PERCENT_WITH_DISABILITY&quot;, &quot;PERCENT_WITH_DISABILITY_M&quot;];\n",
       "    let aliases = [&quot;WARD_NAME&quot;, &quot;PERCENT_WITH_DISABILITY&quot;, &quot;PERCENT_WITH_DISABILITY_M&quot;];\n",
       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
       "        String(\n",
       "        fields.map(\n",
       "        (v,i)=&gt;\n",
       "        `&lt;tr&gt;\n",
       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
       "            \n",
       "            &lt;td&gt;${handleObject(layer.feature.properties[v])}&lt;/td&gt;\n",
       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
       "    +&#x27;&lt;/table&gt;&#x27;;\n",
       "    div.innerHTML=table;\n",
       "    \n",
       "    return div\n",
       "    }\n",
       "    ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});\n",
       "                     \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x17406307190>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set the tooltip specification to show these fields.\n",
    "tooltip = [\"WARD_NAME\",\"PERCENT_WITH_DISABILITY\",\"PERCENT_WITH_DISABILITY_M\"]\n",
    "#Set the opacity of the polygons to 75%\n",
    "style = {\"fillOpacity\":0.75}\n",
    "#Set the legend label to \n",
    "legend = {\"caption\":\"PERCENT WITH DISABILITY\", \"colorbar\":False}\n",
    "\n",
    "wards_ds.explore(\"PERCENT_WITH_DISABILITY\",cmap=\"Blues\", scheme='NaturalBreaks', tiles=\"CartoDB dark_matter\", tooltip=tooltip, style_kwds = style, legend_kwds = legend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a static capture of the map if the above cell does not render:\n",
    "![Disability Prevalence by Ward](resources/01/static_ward_map.PNG)"
   ]
  }
 ],
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