landslide_EU.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "cloudy-relevance",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "import rasterio\n",
    "from rasterio.plot import show as Show\n",
    "from rasterio import mask as mask\n",
    "import pylab as plt\n",
    "data = os.getenv('data')\n",
    "from rasterstats import point_query\n",
    "import pycountry\n",
    "import shapely\n",
    "from shapely import wkt\n",
    "import warnings\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "historical-collective",
   "metadata": {},
   "source": [
    "### Landslide analysis of POIs for EU countries\n",
    "* import POI databases created earlier\n",
    "* query school & hospital locations based on Global Landslide Hazard Map\n",
    "* merge with ADM1 boundaries\n",
    "* plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "identical-costa",
   "metadata": {},
   "outputs": [],
   "source": [
    "landslide_pth = data+'/energy/LS_RF_Mean_1980-2018.tif'  # ARUP landslide layer downloaded from DDH (rainfall trigge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "facial-value",
   "metadata": {},
   "source": [
    "#### 1. Get subnational boundaries\n",
    "ADM1 boundaries from the geoBoundaries database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "liquid-joint",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AUT', 'HRV', 'GRC', 'SVK', 'SVN', 'ITA', 'MNE', 'BIH']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = ['Austria','Croatia','Greece','Slovakia','Slovenia','Italy','Montenegro','Bosnia and Herzegovina']\n",
    "alpha3s = []\n",
    "for country in countries:\n",
    "    countries_db = pycountry.countries.get(name=country)\n",
    "    alpha3s.append(countries_db.alpha_3)\n",
    "alpha3s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "final-explosion",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_boundaries(alpha3,verbose=True):\n",
    "    r = requests.get(\"https://www.geoboundaries.org/gbRequest.html?ISO={}&ADM=ADM1\".format(alpha3))\n",
    "    dlPath = r.json()[0]['gjDownloadURL']\n",
    "    geoBoundary = requests.get(dlPath).json()\n",
    "    gdf = gpd.GeoDataFrame.from_features(geoBoundary['features'])[['geometry','shapeName']]\n",
    "    if verbose:\n",
    "        print('{}: retrieved {} features'.format(alpha3, len(gdf)))\n",
    "    return(gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "unnecessary-memorabilia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUT: retrieved 9 features\n",
      "HRV: retrieved 21 features\n",
      "GRC: retrieved 8 features\n",
      "SVK: retrieved 8 features\n",
      "SVN: retrieved 12 features\n",
      "ITA: retrieved 20 features\n"
     ]
    }
   ],
   "source": [
    "boundaries_Austria = get_boundaries('AUT')\n",
    "boundaries_Croatia = get_boundaries('HRV')\n",
    "boundaries_Greece = get_boundaries('GRC')\n",
    "boundaries_Slovakia = get_boundaries('SVK')\n",
    "boundaries_Slovenia = get_boundaries('SVN')\n",
    "boundaries_Italy = get_boundaries('ITA')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decent-mention",
   "metadata": {},
   "source": [
    "#### 2. Import POIs dataframe and query landslide map\n",
    "POIs for schools and hospitals created earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "prospective-healing",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pois_with_landslide_risk(country,poi):\n",
    "    pth = 'output/{}_{}.shp'.format(country,poi)\n",
    "    gdf = gpd.read_file(pth)\n",
    "    gdf['landslide_prob'] = point_query(gdf, landslide_pth)\n",
    "    gdf.landslide_prob = gdf.landslide_prob.fillna(0) * 100\n",
    "    return(gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "equipped-connecticut",
   "metadata": {},
   "outputs": [],
   "source": [
    "croatia_schools = get_pois_with_landslide_risk('croatia','school')\n",
    "slovakia_schools = get_pois_with_landslide_risk('slovakia','school')\n",
    "slovenia_schools = get_pois_with_landslide_risk('slovenia','school')\n",
    "greece_schools = get_pois_with_landslide_risk('greece','school')\n",
    "austria_schools = get_pois_with_landslide_risk('austria','school')\n",
    "italy_schools = get_pois_with_landslide_risk('italy','school')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "internal-nevada",
   "metadata": {},
   "outputs": [],
   "source": [
    "croatia_hospitals = get_pois_with_landslide_risk('croatia','hospital')\n",
    "slovakia_hospitals = get_pois_with_landslide_risk('slovenia','hospital')\n",
    "slovenia_hospitals = get_pois_with_landslide_risk('slovenia','hospital')\n",
    "greece_hospitals = get_pois_with_landslide_risk('greece','hospital')\n",
    "austria_hospitals = get_pois_with_landslide_risk('austria','hospital')\n",
    "italy_hospitals = get_pois_with_landslide_risk('italy','hospital')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "statistical-integration",
   "metadata": {},
   "source": [
    "#### 3. Mean landslide frequency for schools & hospitals (at country level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "forbidden-voice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>mean_annual_landslide_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>austria</td>\n",
       "      <td>0.440077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>croatia</td>\n",
       "      <td>0.211744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>greece</td>\n",
       "      <td>0.138747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>italy</td>\n",
       "      <td>0.302300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>slovakia</td>\n",
       "      <td>0.198642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>slovenia</td>\n",
       "      <td>0.532775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  mean_annual_landslide_frequency\n",
       "0   austria                         0.440077\n",
       "1   croatia                         0.211744\n",
       "2    greece                         0.138747\n",
       "3     italy                         0.302300\n",
       "4  slovakia                         0.198642\n",
       "5  slovenia                         0.532775"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs_list = [croatia_schools,slovakia_schools,slovenia_schools,greece_schools,austria_schools,italy_schools]\n",
    "names = ['croatia','slovakia','slovenia','greece','austria','italy']\n",
    "\n",
    "country_mean_schools = pd.DataFrame({'country': names, 'mean_annual_landslide_frequency': [df.landslide_prob.mean() for df in dfs_list]})\n",
    "country_mean_schools.sort_values(by='country',inplace=True)\n",
    "country_mean_schools.reset_index(drop=True,inplace=True)\n",
    "country_mean_schools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "front-federation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>mean_annual_landslide_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>austria</td>\n",
       "      <td>0.449280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>croatia</td>\n",
       "      <td>0.307820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>greece</td>\n",
       "      <td>0.246016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>italy</td>\n",
       "      <td>0.334825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>slovakia</td>\n",
       "      <td>0.358781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>slovenia</td>\n",
       "      <td>0.358781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  mean_annual_landslide_frequency\n",
       "0   austria                         0.449280\n",
       "1   croatia                         0.307820\n",
       "2    greece                         0.246016\n",
       "3     italy                         0.334825\n",
       "4  slovakia                         0.358781\n",
       "5  slovenia                         0.358781"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs_list = [croatia_hospitals,slovakia_hospitals,slovenia_hospitals,greece_hospitals,austria_hospitals,italy_hospitals]\n",
    "names = ['croatia','slovakia','slovenia','greece','austria','italy']\n",
    "\n",
    "country_mean_hospitals = pd.DataFrame({'country': names, 'mean_annual_landslide_frequency': [df.landslide_prob.mean() for df in dfs_list]})\n",
    "country_mean_hospitals.sort_values(by='country',inplace=True)\n",
    "country_mean_hospitals.reset_index(drop=True,inplace=True)\n",
    "country_mean_hospitals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "egyptian-purple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots()\n",
    "width=0.4\n",
    "height = 0.4\n",
    "ind=np.arange(len(country_mean_schools))\n",
    "ax.barh(y= ind, width=country_mean_schools.mean_annual_landslide_frequency,height=height,label='schools')\n",
    "ax.barh(y=ind + width, width=country_mean_hospitals.mean_annual_landslide_frequency,height=height,label='hospitals')\n",
    "\n",
    "ax.set(yticks=ind + width, yticklabels=country_mean_schools.country)\n",
    "ax.legend()\n",
    "\n",
    "plt.title('Mean annual landslide frequency per country (rainfall-induced)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reported-china",
   "metadata": {},
   "source": [
    "#### 4. Range of values for schools in each country\n",
    "This could also be plotted as a probability density function (line chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "abroad-definition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram to show the range of values\n",
    "\n",
    "dfs_list = [croatia_schools,slovakia_schools,slovenia_schools,greece_schools,austria_schools,italy_schools]\n",
    "names = ['croatia','slovakia','slovenia','greece','austria','italy']\n",
    "\n",
    "f,(axes) = plt.subplots(2,3,figsize=[10,8])\n",
    "\n",
    "n=0\n",
    "\n",
    "for df,country_name in zip(dfs_list, names):\n",
    "    df.landslide_prob.hist(density=True,ax = axes.flatten()[n])\n",
    "    axes.flatten()[n].set(title = country_name,xlim=[0,2.5],ylim=[0,4])\n",
    "    n+=1\n",
    "    \n",
    "plt.suptitle('Histogram: Rainfall induced landslide hazard frequency (all schools)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "jewish-female",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfs_list = [croatia_schools,slovakia_schools,slovenia_schools,greece_schools,austria_schools,italy_schools]\n",
    "names = ['croatia','slovakia','slovenia','greece','austria','italy']\n",
    "\n",
    "f,(axes) = plt.subplots(2,3,figsize=[10,8])\n",
    "\n",
    "n=0\n",
    "\n",
    "for df,country_name in zip(dfs_list, names):\n",
    "    df.landslide_prob.hist(range=[0.1,df.landslide_prob.max()],density=True,ax = axes.flatten()[n])\n",
    "    axes.flatten()[n].set(title = country_name,xlim=[0,3],ylim=[0,2.5])\n",
    "    n+=1\n",
    "    \n",
    "plt.suptitle('Histogram: Rainfall induced landslide hazard frequency (schools with non-zero landslide risk)');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooperative-canon",
   "metadata": {},
   "source": [
    "#### 5. Subnational distribution of landslide risk at schools & hospitals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "lovely-committee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get mean landslide frequency by admin unit\n",
    "\n",
    "def mean_poi_risk_by_ADM(boundaries, poi_gdf,poi_type='school',add_n_pois = True):\n",
    "    poi_gdf = gpd.sjoin(poi_gdf,boundaries)\n",
    "    poi_gdf.drop(columns=['index_right'],inplace=True)\n",
    "    mean_landslide_prob = poi_gdf.groupby('shapeName').mean().sort_values(by='landslide_prob',ascending=False)\n",
    "    boundaries = boundaries.merge(mean_landslide_prob,left_on='shapeName',right_on = mean_landslide_prob.index)\n",
    "    boundaries.rename(columns={'landslide_prob':'mean_LS_frequency_{}'.format(poi_type)},inplace=True)\n",
    "    \n",
    "    if add_n_pois:\n",
    "        counts = poi_gdf.shapeName.value_counts()\n",
    "        counts.name = 'n_pois'\n",
    "        boundaries = boundaries.merge(counts,left_on='shapeName',right_on=counts.index)\n",
    "    return(boundaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "medical-worth",
   "metadata": {},
   "outputs": [],
   "source": [
    "boundaries_Austria = mean_poi_risk_by_ADM(boundaries_Austria,austria_schools)\n",
    "boundaries_Croatia = mean_poi_risk_by_ADM(boundaries_Croatia,croatia_schools)\n",
    "boundaries_Greece = mean_poi_risk_by_ADM(boundaries_Greece,greece_schools)\n",
    "boundaries_Slovakia = mean_poi_risk_by_ADM(boundaries_Slovakia,slovakia_schools)\n",
    "boundaries_Slovenia = mean_poi_risk_by_ADM(boundaries_Slovenia,slovenia_schools)\n",
    "boundaries_Italy = mean_poi_risk_by_ADM(boundaries_Italy,italy_schools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "gross-poland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rr6VoLkgpB9Z03EzTVRXPM8uEunONrWvK6tyOaMNaDzRj3mUGe4ZCYTEMc7UCmGFdSRpEENoUhj+QgDYtuce6IilshbKpLVugwVNbCoVCobAcnXq4yM+XmjarPKLNyTqntixFc44XpFAoFBcsEtDbSFxdU2Jt2Qst1tYfhv03hBYBc78QYrGoed2HQqFQKBpIU3ttNRRTRiT3oq1O9zLsr0Zb4l8qhHgNLdTGo3V1EBAQIKOiohoip0KhUDRrdu3alSqlDDRXf1IKdNI2RiRGKRLDoq0paLFgHgCQUv5VocpWqi/3r0ZUVBQ7d+5sgJgKhULRvBFCmBJSyCj0NmJsN3ZE8i5aVM7aVinfgLbYqhpCi/VyC0BERNWIGwqFQqGoCc1ryzZGJPVKKbTAaClSyl21lD+JFjenxrADUsp5ZXFfAgPNNupTKBSKFo42tWXKZi2MGZEMBaYJIS5CCznhJYRYIKW8RmgZ/C5GCxev/IgVCoXCTLQory0p5eNSyjBD7JWrgH8MSmQSmnF9mqwcKluhUCgUZkAnhUmbtWjMOpIP0QK8rTbEhtsqpbzNLFIpFArFBY5E2IyNxCRFIrVk9WsN/7evs7JCoVAoGoW+Jbn/KhQKhaJpsSWvLaVIFIpmxKGtMSz+ZDXte0bg6uHCRXNH4uikfqYXIhLr2j1MQd2hCkUzIC+7gPsnvkJKQjqFeUVsXLYbgO9eWUqbbmFkp+Uy9OI+XHrXBJxcHJVyuUCwFa8tdTcqFM0AB0d7zsamUFpSOXNsblY+BzYdB+D00bP89PZyPH3dmThnODc8c4k1RFU0EVJiMyFSbENKhaKF4+zqxKtLHuCSO8bXWU+vl2Sl5bLo3RV89uRC0s5lNo2ACisg0Ju4WQulSBSKZkK3wR245aUr6Dumq1H1F3+yhtldHuaFOR9ZWDKFNZBgMyvblSJR2AzFRSXEH09Cr9cDEHswnuev+YgV89eX1ykLsBB3OJHPn/6Zz56sMQRcs6bHsI64uDsb30DYhkFW0XJRNhKFzfD50z+z7It/cfNw4dK7J/L968vQ6/Ts33gMnU5PSkI6qxZspFO/tpxPSCP2YAKOzg7MvH0crcL8rS2+0Vx+z0T++mETiTHJRtWXehWdqKWi3H8VikZy+uhZIjuFANpIw8nZEYD83EK+e+X38np52QV8+NB/MUO3rdxX/n9JUSmblu1hxm1jETby5v7Poq1GKxGAmH1nkFLazPUpjEMi0Cv3X4XCNPR6PWeOJnFs9ykObYth3W87uO+9azm26xTnzqRyfE9cg/r97MmFdBnYjsiOIaZNGVmJUZcOYM1PW9i7/qhR9c8npnM2NoXQdq0tLJmiqVEjEsUFS152Afk5BQSG+qEr1RF//BwFeYX4BHpRVFBMVOdQQFMcu/89jNRLnN2cOLYrji+f+6VSX6/d8oVZZHrmyvcpzC+m2+BovPzceeDD63F0ciAzNQcXNydO7o8nLLo13v61pdxpOrb8uc9oJVLGmp+2cN2TMywjkMIqSFSIFMUFys/vr2Lhu39SkFvEQ59cz28fr+bEnv8Sx9k72BPeIYiuA9tjZ2/Hsi/+bRK5stJyAdj1zyEAvP09mfv0TN65Zz77Nx6lILcI31ZeDJrci8792tKmWxjtuodjZ9e0P+SC3EJ+eudPk9s1dLSmaM5YNw+7KShFoqiV3Kx8tq3aT0lRCdnpuQghCAj1xdvfExc3ZyI7heDu5VpePz05iyWfrSE3U8sq8NrN1UcTulIdcYcTiTuc2GTXURNLPvubjct2U5hXREFuEQAZKdmsmL++3Ats0pzhdOwTxaRrhzfa/iClZM/aIxzZGcuUuSPwCfQCoLSklIXvrODY7lPodZKY/afJPJ9jVJ9DLu6Ng4M9W1fuY9yswY2ST9H8UCMSRYvg0elvcXL/mVrL7R3saRXuR352AbpSPXnZBdhSfrPUsxl1lq/8bgMrv9vA968v4+qHpzL+6iH1hiZZ+O4K9m88RofeUcSfOEe/sV3Z8udejuyIJTtdGxVtWraLLgPas23VfgryCssVrynMfXomV91/EQA6nR57e9t44ChMQ41IFDZPcUFxneW6Uh1Jp843kTTWIzUpk/cf+I6jO2N54MO55cfPnkrh3OlU/v15G5uX78HF3ZncrHyK8ovLp9A2Lq2eoTr2YAKxBxNMkqFVuD+piel06B3Fve9dW+7NBigl0kKRUqgRicL2KbMrKDT++XkrJ/adxreVF7lZ+cTsPY2+whqOvOwCi5zXzt6ON5c/jG8rbxWs8QLD3KvVhRBfoaVHT5FSdquhfDZa5luAXOB2KeW+qvWqou5KRSVys/Lx8HYjLzu/fCpGoVFaouPUoQROHWra8zo42uPu5aaUyAWGlrPd7FNb36Blt/22lvJTwEgpZYYQYjIwDxhYX6fqzmxBxJ84h5uHC/7BPiTHp7H863WkJ2cSGOLH0Kl9aNc9vE6jcezBeB686DVeWnQfGSlZTSi5oi5ahftTkFtYybFBcSEgzD4ikVKuF0JE1VG+ucLuViDMmH6VIrFxMlKyWfr5P5w+msj2vw7g7uXKsGl92bv+CIknU8rr/fjWcoZN60t6chbdBkfTrls4hflFnDwYT0lRKZ36tuGb/y2hILeIh6e+gV6nt+JVKSpSWlyqwqBcgGheWyaPSAKEEDsr7M+TUs5roAg3AiuMqWi0IhFC2AM7gUQp5cVCiMuB54DOwAAp5c662ivMz86/D/LS3E8pzCsqP5aVlsvyr9fVWL/M8Ht4W0y1soqBD5USaV7kZRdQWI/jg6Jl0oCV7alSyn6NPa8QYjSaIhlmTH1TpLwXOFJh/yBwCbC+5uoKS1JSXMqetUcqKRFFyyQvK19NNV6AlMXaMmUzB0KIHsAXwHQpZZoxbYxSJEKIMGCKoXMApJRHpJTHGiKoonEU5hXxyk3z+PWjv6wtSosmKDKAgBBfa4uBXi95+sr3OXMsydqiKJoYPXYmbY1FCBEB/AbMkVIeN7adsVNb7wKPACYHIhJC3ALcAhAREWFqc0UVDm45zjv3fmtSdFiFaXh4u3H3W9cw8pL+lBSXcsugZ0iKs+56mSvunUxEx2CryqBoWrRUu+b12hJC/AiMQrOlJADPAo7a+eSnwDOAP/CxwTGn1JipsnoViRCizOd4lxBilKmCGww98wD69eunLIYmIqVk/abjBPh70L5tKzYt36uUSB34BXnTZ1QX2nQJo/vQDgC8eO0nnE9Mr7eto7MDvq28eeqb2+jQO6r8+G2vXMmWP/eRn1PAib2n6dA7Ci9/D7at3EdKQv39NpZWYX4UFRRTUlyqXIAvMMwdRl5KOaue8puAm0zt15i7cigwTQhxEeACeAkhFkgprzH1ZIrq6HR6Fv22g8NHztImKgCdTs8l0/ri7+9BaamOzVtjePalJeX124T54RXsQ3ZSptVkbo7Y2QmGXNyHm56/lKDIwEplD340lxfmfEJ+Ts0LBiM7hXDT85fRc0Sn8pwnZTg6OTBwYk8GTuwJQGF+ES5uWij6i28YxfJv1hF/LImDW05QUlxq9uvqMqAd1zw6jT6ju5i9b0XzRrORtJCV7VLKx4HHAQwjkoeUEqkbKSV6vcTOTiCEQEpJenoe83/YRFpaLjOn9aW0VEfcmVT+WXeU4yfOAbBhszYluX7TcebMGsJ3P24mPiEde3s72ob54WAnsDuXSZJSItUYd9WQSuFLKtJrRGdeWHg3C9/+E/9gX/76fiNunq7kZuUz+rIB3PXmbNy93Iw6T5kSAU0B3fGq9oKXl53P2l+3s/zrdfgEeKHX60mISa43nlet53F35q43rmbEjP44uTjW30DRImnxsbaEEDOBD4BAYLkQYq+UcqLZJLNhjp04x2331LZwFDZtre5+W5H4hHRefuOP8n2dTo97cSlHf1ce1rXReUC7OrMEdhsUTbdF96Ir1TH3qRl4+XuQdOo8QVGBZolV5e7lxpTrRzHl+lGVjqedyyQ1MQMnV0dcPVzIy8rn1OFEfAO9yM7I5c+v13Ngc3WbZkCIL4Mm91JK5AKmgetIrIJJikRKuRZYa/h/MbDY/CLZPm6uTmbvM9XsPTZfykZxxjD4ol5Mu3kMPYZ1rHPVfnFhCVv+3MuwaX3wDvBECNEkGQX9g3zwD/KpdKxd9/+cTkbM6M/rt37But92lB974IO5jJs1uMlzoSiaGy1oakthOkePnzN7n+fOZ9Nqal8cTySRfPSs2fu3Nh37tGHEjH4Mubg3Hj5ubFm+l18+XFWry+vIS/pzxb2TKj2Ua2Pj0l189OiPZCRn4e7lSmCoH6/+/iA+AdbPhmhvb8djn9/Mvg1HyTyfQ59RXZgwe6i1xVI0EywQa8siKEViAUYM7cDW7Z35Z92R+isbSWmpnrPJWfTqGdniFEnP4R15+bcHKk0xTZg9lAmzhxJ/PIndaw9zYPMJziek02NYBwZO6km3QdFG9X1wy3Feu/ULSoo0Q3hedgF52Yk8feV7PPf9XdVGC9ZAr9Mz47ZxuLg6MXhKb2uLo2gmWML911IoRWIBXFwcefqxqUgk/64zLfd2fRw4k0bbywfhXlrKuR2xZDaB+6mlueGZS2u1U4R3CCa8QzDTbxlrcr9SSj59YmG5EqlI/PFz5GbmNwtFYu9gX56kSqGoiK1MbdmGlDaIEII5s4aYvV+dTs+J06nsO5uJfxMuUHN2dWp0utna+OalxWxevsfs/SacOEfMvpozPHp4u1VKDqVQNDesFSKlIShFYkEyG5BC1Vg83F04+XfTJMboM7oL8/e9yqKYdxg5s7/Z+9+z7ggv3/AZ21bVmz/HJMI7BPPo5zdx99vXEB4dVKnMy8+d8y1gNKdQNAfU1JYFOXHScivQCwtL6DS+OydWH7DYOewd7PEN9KTPqC7lhmlLRaEtLdHx5XO/4uzqRK8Rnc3W7+hLtZw8PYZ2ZN6TC+kxrCMjZvYnMNRXeUUpmj3K2H6B88uSnSxdvtdi/ZeU6igtLLFY//3HdePp7+7Azk7g4PjfbfLoZzdxz7j/kXDC/J5pZ44l8ezVHxLdM5L2PSK47skZuHq4mKXv8OggXlx0r1n6Uiiagha7jkRhHN8s2Mg3CzZZ/DyJTg64eLlSaMZc4Y/Ou4ns9FwcnBxwcnYkJzOPw9sOERDiS/zxc6xasNEiSqSM1uH+zLhtHPb2dtiZYaFgU5IUd56YfWfQ6/REdAqmTRejksspFLViK8Z2pUjMwJn4NDZvi8HRwZ5ePSJY8+/hJjlvdk4hHSb1xB04tXIvhdmFDe6r+9AOhLZtTWCoL6Mv+y9Fc1ZqDiu+3cDWFea1X9RGWHQQw6b2aZJzmQu9Xs9nTyzi93l/Vzru29qb21++khEWsCspLgCsbEA3BaVIGsmJmGQ++eJfdu89bZXzH4/T1rxHjOlOGwElOYXErDHdblJcUMJ9711b7XhY+yCeXXAnNw18ukmiDu9YfYDD20/SZUA7i5/LXNjZ2THykv7sWHOAs7H/pTfOSM7i75+3KUWiaBAS27GR2Ma4qRmz+t9DVlMiFTlzNoM9iRlkezbMpnBs9ylSEmpOhiaE4H8/34ujs+XfO0qKSnnu6g/ZsmIvpSXa+g9jw6VYky4D2vHF9hd56OPr6dAnitGXD+TReTfxwAfXWVs0hQ1jK+6/akTSSIqLddYWoRI5eUVET+xB0q5T5KbmmNT2+Ws+5q43rqZz/+qjAZ9AL95Z9TjpyVms/HY9m83sSGDvYI+uVEd070gGjO/BpmW72f7XAU4fPYuXnzvPfX+XWc9nCezs7Bh31RDGXWX+9UOKCw9lbL9A2L4zliXLdltbjEpkZObjG+5Hfpbpa1jsHeyI6hxaY5mLmzPte2hxrfqP68b7Dyxgxfz1jZK1jFGXDuCet+cQeyiergPbI4QgZv8Zks+kMv3mMbx77/w6I/sqFC0VW1EkoimnDfr16yd37mwZodB37o7j8Wd/oaSkeY1Iymgf4Y9dfBpnttUdsr4MB0d7Ptv8vEkRcc+dSWXbyn0s+3Jtozy5Pt/2YrUFgwBZaTm4ebri4GivlIii2SOE2GVMWlpj8e7UWg6bd6VJbf4c+YFZZTAWNSJpIH/9fbDZKhGA+OQs3I4ZH9xx5CX9TQ6rHhQRwPRbxjJ+1lDOHDvL34u2cmz3qXIXWA9vN56afxvte0aycekuju2OY9WCjeh1+kr9LPviH+547epq/Xv7Wz86r0JhTWzF2K4USQNpExVYfyUrER7ig1dWPidNCNHSfUiHBp/PzdOFTv3a0qlfWwCy03P5/OmfGXfVYHoO7wTApDnDmTRnOLMevIiTB+I5F5dKSXEJa37cwtLP/6Uwr5j7P7hOjTwUijKk7UxtKUXSAJYu38u3P2y2thg14uLkQISjA0WOznQZ34sTGw5RUlhCq7atSItPQ1fDKCqqcyhDLzbf2g0vPw8uvWsCKfHVvcBahfnTKsy/fH/0pQP54KEF/PXDJvR6Pe17RDBsWl8CQnzNJo9CYYvYkrFduf82gPyCIgosFHOqsbRv7cOhHWc4tD8BIaCksITuE3pxPl/SdlhXnGrI3jjrwSl4+rqbVY6UhHQWvL6MhJhzdbrvBob58eyCO4nsFMKan7bw6RMLuabbI9wy6BlWLdhoE66/CoWlsBX3X6VIGsCpuOaZ+NbbzZmCxCyKDDG44mJT6Ti0I0ePaIbwuBMphNUQtuP4njizy3LudCrHd8dx04Cneffeb+tUCPYO9nzwz1N07NOm/NiZ40m8c898ru/zBHFHEs0un0LR3GmRYeSFEPZCiD1CiD8M+35CiNVCiBOGvy1qLiItPZejx5LYvjOWo8eSSDqXyU+/bOOWu+ez+p+mCd9uCqH+nrim5HG2gpLLzy3i+Ml0Sg3TWV4+rpw5UD0/x/a/9ptdHhdXJ9w8XQFYtWAjO9YcrLO+k4sjVz9ycbXj506nkp/T8NAvCoUtI6UwabMWpthI7gWOAF6G/ceAv6WUrwohHjPsP2pm+axCcXEp19z4ebOdvqqElHQODyB+exy6Un2dVdNTc+kyqjuyuIT4A3HkpOUC2sP62O5TlUYEjWXC7KEUFhTz8SM/APDGbV/y5Ne31hkivtfwTnTs04Zju09VOr7mp8207RaGi5uz2eQzJ+lpufj5e1hbDEULxFa8towakQghwoApwBcVDk8H5hv+nw/MMKtkVqC0VIeUkj9W7LMNJQK09vMgbnNsvUqkjMP7EzhyNJlSb2+6jesBQElxKfNfWkJOZp5ZZZty/Uj6jO4CQE5GHo/NeJv7JrzCkk/X1JhUytnVice/vJknvroVDx+38uN71x8lJ8O8shlDSYmO03GpHD1yllOxKWzdcqLS57zmrwN8/P5fLP51R5PLpmj5SGk7NhJjRyTvAo8AFR37W0spkwCklElCiFY1NRRC3ALcAhAREdFwSZuARb/t4Mv5G9DpjHsoWxM7Ab3DA0k4ZPxakYoU5hdzKi4dV09XCnIKOLgthtfu+Y5OPSKYOHsIgcE+jZaxMK8Ie3s7OvdvR9tuYRzaGsPRnbEc3RnLp08sJCw6iHFXDa6UrzwoMpCgyEA69InikYvfICs9lztem0VgqF+j5TGFf/8+xP+eXwKAENqPGqBHrwhef/tqNm88wZuv/oG3jxv3PTi5SWVTXDhYc7rKFOpd2S6EuBi4SEp5hxBiFPCQlPJiIUSmlNKnQr0MKWWddpLmvrK9tFTHlEvfpaio1Nqi1EtogCdeeaXEHWtcbhBXd2fs7QUF+cXoSvV4eLvi5u7M/DWPNFrGqmFNpJQc2X6Sf37exoalu8hKzcHNw4X7P5hLYKhv+TqUMo7sOElBbhHRvSLN7lVWHyeOn+Pxh36sMV1yr96R7N1zGiHgu5/uJMgMSldh+5h7ZbtHh2DZ/UPTgn5unfhanTIIIb4CLgZSpJTdaigXwHvARUA+MFdKWW8cKGNGJEOBaUKIiwAXwEsIsQBIFkIEG0YjwUBKnb2Ymfj4NFxdnQgIMN/qZwcHe/737CU89fxiCossl32wGlLSNSIQRzuBXkJOUQn5xSUk1zGdk5iaQ1BEIBxr3KkL8ooq7edmFVBarCMjNQffRn62VRcXCiHoMrA9XQa25643Z1OQW0hpqY6EE+cIblN9gWdNwSObiugOQXj7uJUrEgcHOy69YiDduofx7FO/APDAw1OUElFYFAuMSL4BPgS+raV8MhBt2AYCnxj+1km9ikRK+TjwOECFEck1Qog3gOuAVw1/f6+vr4ay/I+9hIb50qtXZPmxgABP7C2QQa9fnzZccWl/iy449HJ3ItTXk8z8IpLScwnwdiN208lKdYQQ9OgRhoOzA/kOAjedJEPqycorItzPg6JSPUUWWmNRWFDMW0/8wo0PTqJNx2CLnAMoT6NrTYVRGzk5BZxPycbdwxl7ezu6dQ/n8qsG8tvPO9DrJP4BHowcY77c8gpFVSyxIFFKuV4IEVVHlenAt1KbqtoqhPApGzDU1W9jVra/CiwSQtwInAEub0RfddKlayitWnlVOuZaw8K6MqSUHDlylg4dgnBwsDfqHHq9RKfT4+hoz9xrhrHo1x0WG5VE+3lzeFMMER1aE+jrRfKZ6ivApZSc2BdfY/vjFpGqMrs2nuDgrjjue/ESRk7uccGFLikqKuV/r19Fm7aB/PT9Flb8sZfiIh1jxnXl4P54jh45y5FDifTt37b+zhSKhiD/s82ZQIAQoqL9YJ6Ucp4J7UOBig+eBMMx8ykSKeVaYK3h/zRgrCntG0qbGqY9ytDrJT/+sJmCghKyswpw93AmJiaZ3bviGDqsAyNGdGTAwHZ4ebnWeY6406nc9eACxozsjIODvcWUSPeIQE5sjwPgzHHLZxxsDEUFJbz20ELWLd/Psx/NsbY4TUpAgGf5tOn0mX3p0jWUrMw8ojsGc/PtY/j5p634NLHdRnHh0QD339RG2mlqOmG96szmY21t2HCMr76sOS/Gpo3H2b//DF/2iaq3HwcHO0pKdPxh4dzkeikpKW7+xvyK7NhwjNISHQ6Oxo3uWhqBrbwIrDAi7twllGdeuNSKEikuBCRW8dpKAMIr7IcB9bqG2nyIlAED2jJsePXIte3ateLueybw8cdz8TdisVh+fjFhoZZbnO/t5kz3iEByioppFeJjsfNYAl2pnsN7rJ9OWKFQWJylwLVCYxCQVZ99BFrAiMTV1YlRozpz4ngyyclZ5ccfeHAynTqHlO8XFZfy77ojuLs50yE6iNaGN8zMzHz+XX+UT774l2ILjRSC/TxwSSsg5mgMLq6O2sIEG2P5T9voMUDZAxSKpsP8iwyFED8Co9BsKQnAs4AjgJTyU+BPNNffGDT33+uN6dfmFQnA6DFdGDa8I9fPnUfS2UwA/vnnMMEhPnh7ayuknZ0cmDS+u2bEjknmwKEE0tJzSU7O5reluywqX3JGLj2DfDkXn05hQRO6FZuRnRuPU1xUgpOzo7VFUSguGMztmCmlnFVPuQTuNLVfm5/aKsPR0Z7/vXw5nTpp7qq//rKDG+Z+ztLfK6+lEULQITqIcaO74OToYHElAqCXkKrT0aZLSP2Vmyn5uUVsXFV34EWFQmFebCVoY4tRJACRkQF89MlcnnluJqBNWw0YWPN0jBZTa2+TyRafmo29b92eY82dz15bzvZ1x1p8jpBiG4hsoGj5SKkUiVUZObITfQyeWufOZdVYZ/vOWJybeJomNc+2w6FnZ+Tz7O3zuW3aexzafZrCfNsIbGkKKcnZ3DDnUxbM32BtURSKFhe00eYICvYG4PChxEor4ssY2L8dHaKDiE9I52xSJq++9afFZfJ3d0Hv60Z2hvG51JsjZ06m8NA1nxEQ5M29z8+kXw1ec7bK9q0x5BcU0z46yNqiKBRmt5FYihY5IgG4eGpvAJYt3cPy5XuJMyR8Ki39L2e5r4877dq04sTJZBybYI1E3PlsI5b22A6p57J48/GfyckqsLYoZqN7zwje/3gug4ZEW1sUhUJNbVmbjh2D+ebbWxg7risrlu/jkYd/5NWXl3HD9Z+TlfXfiCA+MZ0t207W0ZP5yC0sxqNDICFRAU1yvqYgKz2PR66dR0ZqTpOeNzfbMsorMiqAsPCmDVmvUNSExDQlohSJhQgP9+emm0fx4cfXce+9E8nNKyTpbCa/L/nPU6tTh2BmXNy7SUYkACfPZZLt2bJcaONOJHPvFR9z9nT1mGGWoKS4lJ8+W9sk57ImGem55Y4N51OybSJPjsK8SBM3a9FibSRVGTqsA0OHdSAhIZ34+MoPvCsuHcC+gwls2nKiSWRJzcqjx7D2OBiy7Z05kkSuDUwPObs4MmBkR+JOJBMfe75S2flzWbz15C+88Ol1uBui+poTnU7Pb99sZPTFPcnPLeLKm0dWKj+w8xTd+5kvVbA1ycrKZ+7Vn5CTU4ijoz3uHi4UFhQz75ubCQ72ueACaF6wSNtJbNWiRyQ1ERbmx+DB1ee/L5rQHWfnptGrEsG+M+fZdTaNXWfTCO0R2iTnbSzOLo7c/dxMPlt2H5Mu70/bTsEEBnmXlx/efZobJrzJkX1nzH7u9JRsFn+zkXPx6US0a4VnhVS8ACER/mY/Z1NTXFTK+ZRs/lqxvzxoaEmJjsyMPAoLS7j2qo9587U/rCylokmxkSHJBTMiqY+hg6P5+J05fL9wKxLJ2bOZpKblkpaea/FzH0vKwKuZenM5OjkwaHQn2nQMpnWoL3q9HiEE9z4/s7zOptUHeffp38jNLiQ7M58/F26nc8/Gp1UuKizhxKFEuvWNwr+1F89+NIfobjUrXf8qaQaaMzk5Bfy14gAdOweTm1NIYkIGCQlp7NpxirOJGbW2i4gM4J77JzWhpAprYysjkgtWkWRm5uNT5a22XdtWPPP4tPL9DZuO8/SLiy0qh5uzAx19PDka0zT2BVPpOaAtT7xzdZ11ho7vhoOjA8/doSVd+/ePvXTrG8XESxuXdfSrt1ZyeM9p3vr+VpycHenYI7z+Rs0cvV7y+svL2LLJ9GnUxIR0Xnnhd266bYxyCLhAUO6/zZxXX17KyhX766zTJiqA9u1aWVSOcOHA0R1xFj1HY0g8nWrUSvYe/dvg46/l59CV6vnk5WWkVgiiefZMGtkV8p/HHK47MvX3H//Nzg3HuerWUWSkWn5U2FQUF5eye+epeuv5B3jQLrp1pZwnOp2ejRuOcfP18/irnntXYfuUhZFXXlvNmOdeuJTsnAJycmo3coeF+jHvg7ncdZtl8nc5OtrjH+rbrPN8JMWns27Ffv5ZuoeiwtoDTrq6O/PGt7eU7xcVlLBp9SFKS3RsX3eM+678mDtmvk9hgbYaPuZwIu898xt5udVX+2dn5vP373uYdftodm+OodhCScasQUZ6Xr1J1q6YNYgff7mHz768iUWL76VN28ovMyXFOj79eA2bNx6npERXSy8Km0cCUpi2WYkLVpG4uDhyxRUDSU7OJiEhvdZ6dnaCy2b04/o5wxp1PjsB3p4ueHq4gJQEBXrR2sudbYcTad89rFF9W5pPX/6Dd576jTVLdqPX/+eCmhSfzscvLaW4uJSdG47zyct/4OziyIhJ3bn5kYuYevUgziWks3PDMQaM7MT81Q/jYkiRPOmy/ji7OnH18Jd57eGfyMnMJze7gLzcQr55ZxVvLriFcdP7cPezMwhva9lRYVMSHOLD6+/MZtJFPatNrQJ07hrKZVcMpKiohO1bY3jy0Z84FZtSrV52VgFvvvYHuTUoYkXLQUrTNmsh6pu2EEK4AOsBZzSbyi9SymeFED2BTwEPIA6YLaXMrquvfv36yZ07d9ZVpVnz99rDfPLFv6RWmGoZMrA9bm5OrPn3cM2NpKRr+2DiYs9TVFSCEAIfX3eyswsoNbxN+nm74Z5XTHIdhtbmQFSHIByd7Hn6/WsIDPJmwYdr2LMlhsj2rTmXmEGP/m254uYR2NkZ936i1+u5tP8LFBYU4+bhzMRL+3HLo1PqbBMfm9JiFEvMiXP8s+YQaam57NweS0SkP7feOQ69XvLoAz9QUFBzLLMRozszfWZf3n5tOf0GtuPu+yY2seSKmhBC7GpkmttKOLcNlaEvmRbR/dTsJ80qg7EYY2wvAsZIKXOFEI7ARiHECuAD4CEp5TohxA3Aw8DTFpTV6owd1YWRwzvx6Rf/8stiTSEOGdSOtm1acSY+jTZRgRw4lMDZpMzyNp4eLpyJS6XQMC0kpSQ9rfKcv7DT1mE0d+KOnwNg9eJdXH37GK64ZRRTZw/GwbDWwVTs7Oz4YsUDHNufgLefO5171W9MP7AzjqAwPxydbN9PpH10UHlML51OT2xMMu++tYJjR+tOSHfiWBJ2QnDrneN47qlfaNXKiyuvHtwUIiuaFOvaPUyh3hFJpcpCuAEbgduB1YC3lFIKIcKBVVLKLnW1t/URSRlSSh58fCHFxaW89MwleHu7cvpMGlGRAezZd5qFv2xn645YQoJ9ePyhKcSeSOb99/6qsS9/b1ecsgpJS65zMNdsCAzy5tYnLiY/pxA3D2eGju9WXial5FxCBtvXHiUrM4/Zt4/B3qH52n+aI6mpOdx+05dkZeaj19f+2xQCRo7uwtp/tJHwtz/eQYgFU0Ur6sf8I5IwGfKCaSOSuDlPNNsRCUIIe2AX0B74SEq5TQhxEJgG/A5cTuWE8S2e/PxiOkS3Jj0zDx8fN6IitfhZvXtG0qtHBMdOnCM9I48O0UF07xpGfHw6i3+rrkSzcguJCvfDw8uVxFOplYJKNkdKSkp567GfuePpaQwd3420lGzmv/sXuTmFFBeVsGvjf26tG1cd5MFXLqdjM7cBNScCAjz5Yv4tuLs7U1hYwsED8Sz9bRfbq8SDkxLWrz3Cdz/dSU5OAQGBnlaSWGExbGhlu1GKREqpA3oJIXyAxUKIbsANwPtCiGfQEsbXOKErhLgFuAUgIqLxi9SaA0IIBvZvy6lT5zl8JJGsrAKi27XCwzC9I4SgU4fgSm1qUxClOkmMIWSLX9sA/BGcNkwhNUcK80to3zWE4RO7A5ohfuNfNWdOjI89z9+/78beXtC+S9Ou3v/+4785efgsdz4z3aYWKwLl6aE9POwZNDiaQYOjSYhPIzU1l/17T+Pt7Ub76CDc3J0IDvEhGB/rCqywHDayjsSkiWYpZaYQYi0wSUr5JjABQAjRAajRSiqlnAfMA21qq1HSNiOunzOMb+dvpCivmOLCEl568XdGjuzEqlUHSE3NYebMvlw0pRfOzo7o9XoSE+o3pKdn5uGEFV347ESN0yntu4QQ1iaAkhIddz0zHU9vNzasOsCCD9aQnJhZZ5/LftjKuYQMXvj0OgtJXTPJCRls+ecIOzee4LbHp3DRlQOb9PzmJizcn7Bwf3r1rp5bR9GSaSEjEiFEIFBiUCKuwDjgNSFEKyllihDCDngKzYPrgmLgoHY88dgiMg0L7XZsjy0v+/KLdbi4OtGmTSAvPLeYuTeM4PDhxHKje40IQev2rQmKCqCkqJSU+HSy0vMsfRl07BHOnLvG0qFbGPPfX83pmGSO7D2Dh5dreVlwhD9b/znC8p+2EXc8mYS4VBKNjPabGKctamyqYINJ8ensM3wXJcWlLWpBo+ICw0ZevY0ZkQQD8w12EjtgkZTyDyHEvUKIMkvQb8DXlhKyuZGbW4izsyNrVh8kIMCzXJFUpKiolLffXIGHhwu5uYV8/OEao1aI74tJBqBNqC+tOwaRtcWyuVIcHOzp3CuC7gPa4uTkwB1PTSUtJQdPL1fsHexwdHLgm3dWseS7zRQVau7Ltz1xMQ++fBmvPbyQo/vP0LlXJLFHk2p1Xz57Jo1//9jHmKm9LHotZ8+kERLhT3C4Hx8vvod5ry/n4I44li/cjpSSS64fjpu7s0VlUCjMio0oEpO8thpLS/DaeuLxRcScSGbCxG4sWrjdIjkinJ0ccM8qtFjypmrnc3Gk56B2pCZlkZqShYODPXc9M53BYzUnvJjDZwkO98PRyZ6SEl2Nrr5SSnZuOM5rDy8kL6fmRXK9BrXjtscvJjK6tUWuI+bwWdp3CQG0kcj2dUcZMLITrz28kE2rD+Hj785L866nXecQi5xfcWFjdq+tqDAZ/Ow9JrU5fcOjVvHaumBXtjeEjz5czbatJ0lLyyUrs4DxE7ohBIwb35V337/GbOcJ9HEzWokER/oTHOnfqGmjosIStq89SuyxJLIz8kk/n8PLD/zI799tZsGHa7C3F5QUl5KSlFXrehEhBP1HdOTTpffSe3B7nGoIyb9360nuvPQDnrrl60pxuCqulm8MZUoEyqIWd2bT6kPY2Ql6DGiDQNQb40uhUJiOGpEYSWJiOtde81n5/pCh0Tz08EUUFpYQGOiFEHDdnM9INMPqdIEk1MGRlFr68gv0RK+X5GTlEz2gLftPJtO7XSsObbbMNJijkwO+AR5kpuVy8VWD8A3wICUpk9l3jsW7QlDBimSm53LflZ/UOt0lhKD3kPa4ujsRf/I8r359I74BlnFhLSosYefG4wwe09noVfcKhalYYkQS9IxpI5IzN1pnRGL7y4ObiLX/Hq20HxkZgLe3G97/5XWid58osygSiSAFSUjvcHzcnNHla57VQgiO7j6NW4QfsfFpuIf5sv9kMm4ujmQmWW5lfElxKSlnMwH4bf7G8uN6veSuZ6bX2MbHz4PXvrmJlx/4keMHEqqVSynZXSGU+gt3L+DBVy4nOMzX7IsYnV0cGTquq1n7VCiaBBuxkShFYgRSShb+tLV8397ejqAgbw4eSCAlJYsxY7WH1DVzhgBauIuVK/Y1KohacamOOMPDu4zgAE8i+kUScyYNhCDPEIspJNCTMztPN/xkDST2aBKlJTocHO05fSKZ3VtimDZ7MPb22lt/61Bf3v3pdmIOn+XR6z6nIL/m2FEAR/fFc/NFbxMY5M3ES/vh7OpEm45B+Pi58/fSPZxPysLT25W+wzvQo39bPL3rjqCrULQILLAgUQgxCXgPsAe+kFK+WqXcG1gARKDpiDellHU6UylFYgRCCOzsBH5+7qSn5+Hu7kx8fDo7d55i8uQe5fVW/LmPgvwibr19LMeOJhFbQ9TWxpCUmlMmUKXjOfnFuLg6UljQtOHWj+w9w7cfrMbN3YXDe06zf3ss65bvw8PblQ7dw5h9x1js7e2I7hrKi/Ou5/PXlnOshtFJRc6fy2LBR3/XWr7yl508+MpljJ3W29yXo1A0O4SZRyQG79uPgPFAArBDCLFUSlkx6uydwGEp5VTD8o9jQojvpZS1vgkqRWIEZxMzGDa8I4MHt2fD+mMMGRrN8BEdyc4uoLhYW7F+/Pg57O3tuezyAfj7e/DEU9O46YYvmkS+YF93Thm5psPc/PzF+kr7ZYoi7ngyY6b2JixKCx3TtU8kNz86hYcq2JkagoOjPcMmdKu/opXYsuk4e3afZtqMvrVmMdTrJTnZBej1eo4cPsv589n06duGsHC/Jltro7ABLJOHfQAQI6WMBRBC/ARMByoqEgl4Cu1m9ADSgdK6OlWKxAhKSnXcdfd4XFwc6dkrAnd3Zw4dTKRbhRhSwcHedO0aQusgzWjSlAmH9p9MJrKVF4mnUpvsnHXh7OrIVbeOKlciZXTtE8lXqx4i/XwO6/7cx7IfttbSQ+2UFJfywKxPueu56WbJC29OCgqKCQjwZMlvO/Dzc+eq2UOq1UmIT+feO+aTlVV97dFtd41j4CAtLYG/hRwPFLZEg5JVBQghKno0zTNEFykjFIivsJ8AVA378CFa2KuzgCdwpZSyTtdK5cJiBJGRAbi4OAKUx9MKbFX5h+7p6UrHTiHlcZIKC0sQAiZVmPqyFN6GgI/NhaKCEj5+aRnff/x3pfS6AMHhfnTtE8ntT06lS5+GhfuIPZbEA7M+5fPX/jSHuGZj985ThEX48/RzlzB0eMca65yKTalRiQB8+uEa7rzlK1b+uY8tm47z2cd/E3Oi+cZdUzQB0sQNUqWU/Sps86r0WJNmqjrumQjsBUKAXsCHQog6A9apEYkJ6PWS/fvPEBzkUz7ySE3NISEhnV69InE1ZP8D6NYtjHlf3MjZs5n8tepAtRhWoaG+hIX7sW1rw1127QR0a9ua0pxCYmuJk2Ut7OwFcceTuefyj+gzRFtXMmZab3ZvigHgyltG4uVdPUOgsXh4uTB6ak9ziWsWypTH8JGdaq0zbERHRo/ryr9rDtVY7u7uTM/ekXTrHk6HjsHY2at3vQsa8/+kE6gcqT0MbeRRkeuBV6W2NiRGCHEK6ARsr61TpUiMYNnS3Rw7do4JE7rx6svLmDqtD1fPHowQAl9fd3xrWEthZydo27YVrVp5ERkZwKlT5yuVp6XlcvU1Q0hLyyXmRLLJMvl7u6JPyubo5hiT2zo6OVBSXOeUZ6PRlerLowKv+HkHAL8v2AJAaKQ/Op2O3ZtP1Nq+Pnz8PZo8orA5EELw5DMzuP6mkfz283aW/Fp5XVVaWm75Whc1vaWwgCLZAUQLIdoAicBVwNVV6pwBxgIbhBCtgY5ALHWgXneMIDu7gLX/HqFd+9b07duGhT9tLQ++aG9vV+7uWhOHDycycVIPPL0qrwgvLCxh8W87iapiRzAWH0/XWkOR1IUQguvvn8DNj1xktbfdxNNpJJw6T99hHXD3dCGoFqN0XZSW6svXttgiISG+zL1xJI6OldfMuHu40N5CIWQUNoZEs5GYstXXpZSlwF3AKuAIWuzEQ0KI24QQtxmqvQgMEUIcAP4GHpVS1jl3rkYkRuDp6UpBQTHzv9nAFVcNJCzCj7/XHGLwkGj8/T3qbNu1axhnzqSRk135oe/j68aUKb3o0TOCiIgAvvl6vVFTU6Ghvowc1Yn9fx9h8NguPPTq5aSnZHNgZxzFRSX88tUGUutI29t7SHsmXz4AFzcnItu34n/3/0hBXhEAQWG+tO0UzOY1teSfNyNrl+/nvpcu4b4XL8HLx43TMckc3RdPh27aKGPjX4fIyconoLU3fYZGc2x/PN+8u4rcCp9jdmYerUJ8LC6rpfDwcOGp52by7JO/lB+ztxM4tYA0wgrzYG73XwAp5Z/An1WOfVrh/7MYUoQYi7pja+D06VR274pj5iX9kFKSk6PFvdq9O4477hzHr7/s4JuV++nQIaheReLgYMfmjZWncPr3b8ODD08h0JDVzt5OMGFid35etI1ff9GmgYJDfOjcKYSQUF9Sz+dw+HAiCPjyq5vR6/UUXjWo3PDv1iaQsDaBAEybPZgzMSl89NJSzhvCmMQcSiQ7U7uGhFPn0RliW/Ud1oG7n53OG4/+TN9h0QwZ15Wx03px3bjXyUyzfPj6BR/+ja+/B+07BREQ6MGISd1wddeuqU3HyonBgiP8WDhvbbkiORefzrZ/j9rk9FZFulfxPMvMzCf1fDYBgbaVjEthIZqP2bNOlCKpgXVrj7Lgu02cPp1KZmY+G9YfA6CVIdPe5ZcP4GRMMqmpuXSo2TmnHAcHe66/YQQ//LCZkzEpjB3XletvGFHprTMg0JOkpEw6dgzGz8+da68bxoiRnco9wEBbXZ+Sks3S33exbu1RTsWdp22bVmTnFNC/f1t69Ypg4KD2CCGIjG7N/z6/nsz0PAKDvBk3vU95PzqdvtJUXGT71rh7uhAaFcDky/sjpWTc9D6s+nUnOVmWjT6cei6Lhe/+yf7fNdtJeKdQOg1oz6CL+9JvYi9cPVzK11WcPZ3GbU9MZffmEyz/aRsAa37fzfiZfW1yVKIr1aPT6Xntf0urla379yiXXjHAClIpFA1DBW2sgpSSB+77nsBATzZuPE5R0X9G6bHjuvLoYxdz6tR52rc3bh67uLiUoqISPD2NC+lR9UFfRnZ2AU8/+QsHD9a8MtzTy4XrrhvO1Gm9EULUabepyq6Nx/Fv7UVUdFD5sZLiUtb8vocv3viT/Nwio/uqipePWzUX4DI6dQ3m4K/rayxzcLQnIMyfqbdN4PKHppUrlB8++YfEuFT+WbYXgJEX9eCxN69qsHzWIPlcFm++9gcnjp0jN/e/qToh4MlnZzJqTBcrSqdoKGYP2hgRLkMfus+kNqfufUiFkW8OzPvsX/bvjycg0IvRoyv/oP/5+xC33vwVd97+DefPZxvVn5OTg9FKBKhVAaxcub9WJQKQk13Ihx+s5upZH3Pv3d9RVGR8uJS+wzpUUiKgeXYVF5VgZydwdXMyySDu4upIdKg7nIwl1L/msPMA9vraF22Wlug4dyqFzx9dwMuz3yMvW1NGF12h2XfK8PCyvZhbJ2OS2bs7rpISAZASunYLq6WV4oLEzMZ2S6EUSQX0esnRI5pL9fFjSVx2xQDcK2TUkxJOnTpPaamel19aatLDujb27I5j9644jh8/R3EtLrmJieks/tW4kVxaai5HjpzlnbdWsmd3XKNkm37NEBZteZqftz3Dx7/dTff+bRh9cS/ufGYa194zHjcPZ9p0DMK/deX5fHt7e478vZfSEh1FOXl07dKa9p1qGMEZORpe+9Mm7uz/GAc3HcXDy5VL5w7npc+vB2Ddn/uJOZzYqOtsaoYM68Crb86qZF8bMaoTc28ciV89NjfFBYSpixGtaE9RNpIK6PV6evSM4OTJFIJDfCgoKCYiwp8jR6onQ9q/P57XX1vO08/MaNQ5t249yS8/a+t8wsL9uPnmUfTt1wZXVyd0Oj2HDibwwvNLyMgwzfi9evVB/v77EP37tyUyKgAhBG3bBjJuvGlxqrRpMoGruzMvfX49uVkF+AV6kpNVgKe3KyMn9+Dg9pPs23KClOOJHN50BPvS/26r4+u1tSTth3bBw9uVXIPdJaJNAEn763RNr0TiiSTiDsbTbWgnQiL9CY7wY/CYzmz55wj3XP4xvQa14+7nZpCZlkvnXpUN2IUFxTi7ODarOFZ9+7flqwW38ckHq4k7dZ6nn7+kWcmnaCbYiLFd2UhqoLi4FCcnB/R6yc03fkFcXO0u1HOuHcq11w3Hzq5hD4Fv529k/jcbqh0PC/dDV6onKSmzQf3WhL29HX37RdGvX1vs7QUFBSWMGt2Z4GCfRvedlpTB9y/9yvLP/qrTjbnHtMEU5+Zz9J99Rvcd0j6IWY/NZMiM/nj5aZ5uJw4l8tpDP5FYQ7DKNh2DCAr1JflsJn6BnmRl5PHuT7c326RWZfebwrYxu40kPFyG3X+/SW1iH3yweSa2EkK4AOsBZ0P9X6SUzwohegGfAi5okSHvkFLWuoTelij7UdvZCZ5+Zgbr1x/D0cmeGTP6smH9MeZ/s4FzhrUai3/bxWWXDyh3xTWFuLhUVvxZ8wM1IT694RdQCzqdnu3bYtm+7b+RwILvNjFqVGdGjOpEv35tTDLSV8TNy5WwDsH1roXZv3SLyX2/uvIpgttWnhqL7hrKoDFdiI9N4cjeM5U8zE4dO8epY1qMqtijSYyf0afZKhFAKRFF7djIiMSYO7gIGCOlzBVCOAIbhRArgBeA56WUK4QQFwGvA6MsJ6p1iGoTSJRhjQbAhIndGTI0mlUr9yOEwM3NuUFKBOC3X3eQkmKc0d5SFBaWsHLlflau3I+bmxNXXjWImZf0q2QbAsjNLWTVygMsW7qbNm1b0a9/G0JDfUlJziZ51wnWfr+eM0csY6tY/e06rn3uimrHb3p4MgAJcam8+djPHNsfX60OwKnjKvChwkZpKYrEELgr17DraNjKTDtlVlZvqgf+arF4eLhw6WWN8/PfsT2WP5fvNY9AZiI/v5ivv1rPooXbuP6GEcyY2RchBAUFxXz5xTq2bokhJSWb+Ph01q/7L/VwUHIKKUfrViLDLx1IjxFd6TWmGw6O9nx839fsWLnXKLm2Lt/FrCdm4ujkWK0s5vBZ4k+mEBjkzbH98QghcHF1rJSNMaC1d7V2CkVzR0jLrGy3BEaN94UQ9kKIvUAKsFpKuQ24D3hDCBEPvAk8XkvbW4QQO4UQO8+fP19TlQuO48fP8fRTvzQqFa8lycsr4sMPVvPmG1oUBVdXJ+69byJffXMzV1w5sFJ8KA8PZySy/MXJycURTz8PHBzt8WnljU8rb25+fQ6zHr+ELX/s5NCmo+xfd5iTe+OMlufM4YQaRzt6vZ53n/6N0lId9710Ca1DfbnxoUks3PwUl1w3DNDcmK+6dVSldmmpOXz+yT+saGaKXKGoho24/xo1OSul1AG9hBA+wGIhRDfgFuB+KeWvQogrgC+BcTW0nQfMA83Ybi7BbQkpJSUlOhwd7fn55+1889X6Jk181VBWrthPXm4Rl185gK5dw9DrJQMHtuP3JbvK6+TmFpHbujXjrxxBj1BvJlw3EnsHewrzCnFxd6GkuJSzMUlsXbYLXYmOTUu2M/nGsQy/dBDrf9lCYV4RBbl1B59093Enqlt4teN2dnZ8+Otd5fufLbuPtORsHJ0cmHbNYDLTc5l9x1hCIv3L6xzYH8/D9y2gtFSPq6sTKcnZXHfDCDN8WgqFBbCRJ6ZJVj4pZaYQYi0wCbgOuNdQ9DPQNHllrUxWVj5ff7WeqdP60K5dK6Pa7NgRy/vv/YWjgz1nzlgnJW5D2bDhGBs2HMPf34O8vCIKC0uIjPSn/4C2FOSXoNPpadM2kEsv61/JfdXVQ1so6OTsyI+vLEav03Ng/WFmPX4Jwy8dxPBLBzH5prG0jgzkpaveYddftXtx5Wfns+rrtVx009g6ZXV2cSxXGq1DfXn4tep2FS8vF0pLtVhjBQXFrP3nMCNGdSIs3B87IbB3aL5GecWFh61MbRnjtRUIlBiUiCvaqOM1NJvISGAtMAZoeHIJG6GkRMfDD/7IyZMpFBQU8/gT04xqt3rVQZJsOOQ5aHkyynB0dGDOtcOMcjKQUjJkWn92rtrLtyc/wreCvaJdzygAHvrqDm7qej95tWQOLC4obrB7dVUiowK5avYQUs9ns+avg8SfSePmuZ+Xl3/70x2EhPia5VwKxYWCMSOSYGC+EMIezaaySEr5hxAiE3hPCOEAFKJNdbVocnIKOHkyBYA1qw/h5upEp84hdOkSSniEf63tgoJ9cHFxLM9hYuskJ2exbNke/HzdycjIp0+fSDpUidZbhhCCkVcMYeQV1fOXlxEQ4sdTCx/g2RmvUVzDZ6TXS96/43OGXTIQD5/qScRM5aZbRwPQqrUXP3y3ufx4h07BNSYpUyisho2MSNSCRBPQ6fTceft8TtSQR7tr11BmzxnKwIHtam0bF5fKhvVHWbZsD5kZNb992yIODnbcdfd4pk7rU3/lWtCV6vj0wflEdArl0OZj/P195UWadnaC+z+/nUnXj26suOUUFBTz8QerWfHH3vJjU2f04d4HJpvtHE2JtoA1g43rj6GXEnd3ZwIDvRg8NLp82rG0VIeDg309PSkagrkXJLqEhsuIOx8wqc2JJx9ongsSFf9hb2/HR59cx+uv/cGa1ZVzbh86lMjvS3bRtm0ggTXkkrC3t6Ndu1a0a9eKK64cyMv/W8qWBqTJbY64uDiRmppTa3ny6fMc3XaCtLMZODg50HVox/JprTLsHey5870bAJh6+0QGTunLy1e/W16u10sWvPCzWRWJq6sTDz4yBS9PV1b+uY+srHz8/DxIOptJsA2Fps/OLmDPrji+/mJtjQtZe/WOZNCQaELDfFm6ZBc9e0XSt18bomsZRSqaETYyIlGKxETs7e249bYx7N4VR3r6f/GvPDxc8PJ05YXnlzBpUg8mX9Sz1nl9NzdnbrxpVItRJE88OZWBg9rXWn4+PpU3rv+IooJiwjuFMurK2qe5yug3sSeOzo6UVAiMec0zlwPwxWf/lk9PmYObbx9Dm3aBvPrSUpYt2cWVVw82W9+WRleq54G7vyPuVO2u9Xv3nGbvntO4uTsj9ZK4U6kMGhLdhFIqGoyNKBLlotIA/Pw8+P7HO7j19jEAuLk58ebbs3jsiam89/4cQkJ9a43kW0ZkZAB+fi1jPv6Jx3/mxx8qhz4pzC/i1WvfJzs9h27DOnPvp5oJ7ZqnLsXL37PePj19Pfh0zxuV8so7OTtw4lgS0R2C6mjZMMaM68bosV24+/5JzTJkSXFRaY0u4/YOdnh4uhAa6sttd43jqedmMvvaoTg6VZ6+srMX+Pq4cd0NI7hi1iBSz9c+glQ0H8oWJRq7WYvm94uxEZycHLjiioH4+rjxwfurkYYYU3Z2gt69I+ttfz4l2zDVBXv2nC53SbVVqubR0JXquObpy8uDLPaf1IuPd75GdJ+2RvcZFBXIFQ9PJ3Z/HLpSPdF927LvWBITJ/esVC8rM58F8zeyetUBpkztzc0GBW8KdnaCJ56ZwcED8Tz75M8APPviZRQWFuPm5lxPa8ug10vW/HWAH77bRGRUAI8/PaPSYtDcnEI8PF146tkZePu4l5eNGtOF4BBf3nz1DwA6dQnhf69eibePW43nUSgai1IkjWT8hO4MH9EJF5fq4TvqwsnZgaefnYm7uzNnEzN48YUlHLfhmFDJyVnAf4sGk04ms/TjVaSfy+C+z24lIMQPn0DTQpU4uThx48tXVzoW0j64WmDJb79ez++LtUWScXF1R0/Q6yW/L97J8BEdq+VFF0LQvUcEsSdT+PKzf5ky/jV0ej2DBmvTQBdP78OAWpwpGkvS2Uz++H03eflF2AlBSKgv69ce4fChRNq2a8U990+qdI/pSvU8+9QvvPrmrBrzu0+c3IPWrb3Jzi4gLNxPKRFbxUamtpQiMQOmKhGgkptpSKgv730wh2VL9/DTj1sq2V6shbe3K126hrJzxymjVuG//+4q0g6fYcTFfQhpF0SriABOH47nzJFEdGZcxV9Viej1kr8Njg/+/h7cde/EWtsW5Bdz9MhZMtJzca7jO5s+sx+DBkfz4Xur2LY1hs0bjwOweeNxHntqGuMmdDfDlfxH/Jk0HrjnOzKqfO/uHs7cesdYLp7WB1dDVsjDhxI5sO8MIaG+9B/QFrtacpgIIejdN8qsciqaGBuKtaUUSTPBycmBSy/rz0VTevLpJ//wx7I9VpXns89vIDDQi48+XM1vRmRnjAry4qsHv+HL+7/m1jevZfJNY7n3k1vYtHg7rSMDKSkuITMlm8Cw2tfbNAQ7O0G76NaUFJfyyBPT6vS2cnVzonffKKMesK2DvHnxlSv4YcEmVvyxl6SzmYSG+jJkWAfzCQ9kZOTx8P3fk5Geh6OjPRdP64OPrxt+fh4MHNy+WsbEjh2D8fJyZcO6owwZFq1W4rd0lCJRNARXVyfuu38i7u7OLPxpq9XkWLf2KJddPoDr5g6npERH125hBAR48u8/h9m6NYa01NxK9XOLdHS9aiTx6w7w2UPfsuSDFRQVFOPo5EDnQdFs/n0Hh7ce59lfHyK4TQ1pdxvBuAndGDm6c4NtGVJK0tNy8Q+o7gRw9TVDmXlpfzZtOEZ4hL/Z7SVr/z5MZFQgHTsFc/H0PvQfUPfUmb2DHWHhflx25UDS6nC5VrQQbESRqAWJzRQpJa+98gerVx+0yvkffOgiLprSs8ay1X8d4PN5aykoKCa/Qrh20KaewkN8cEnL5MSqXUi9xM5O0G9SL257ey7hHUKMOn9hfhGx+0/TZZDpI4DTcalERgWY1Obdt1aQkpzFy69fVW9dXamep59YxJhxXdm/7wzTZ/YjPMK/WXp7laEr1ZOYmI6vnzuenq7WFqdFYu4Fia4h4TLqJtMWJB590ToLEtW4uJkihODe+yeaJQ1uQ/h50TZqe8kYNboL87+7ldtuH8utt42hTYXEXzqdnrj4dI7m6/GbOZx2140n4NKRZJTCrT0eJP6YccmvXNycCW7TqlYZakJKybtvrWDhD6ZnYdTp9MTXE1CzzFZUqtPRqpUX7721kj+X7eWj9/4iO7ugzrbWxt7BjgP74/nzj73obNxD8IJCmrhZieb7CqXA1dWJ2+8YyzNP/9rk5z5zJo3DhxPp2jWsWpmjoz2OjvZMubgXubmFHNgfz6kKC+KCgrzRS8nw4R3JzirAXkgCC/y58cEp1VLm1oVvax+TZE5JzubEsSROHD/H+fPZvPrGLKNsCMt+38WKP/bi6GTPpg3HGDq8Y7U6BQXFfP7JP1x9zRCEncDdw4VRY7swfkJ3uvcMrxT52JoUFZWQn19cY8ywKVN7W0EiRYNRxnaFuRgyNJo2bQIrPaibin/+PkynTiG15nE/fTqV5575rVJo/BtuHMGVVw1Cr5fVpnqKC4s5fTgBZzdnwqLNH56jdZA3I0d34djRJC6/ciB29sY93MdP7MH+vWcYNCSavv1rXufi6urEhEnd2b8/nuEjO3HzbaavVTE3RUUl7NweS8fOIQQY7DtOTg6sXnmACZN64OSsft42jwUUiRBiEvAeYA98IaV8tYY6o4B30TLipkopR9bVp5raauYIIZg4qYdVzr1k8a5KSayq4u3txp13j6dVK69yrzNvbzcO7z3NwlcXs/LrfwE4F5fCq9e+z92Dn+DYjpM4WsiWUFRUwuJftjNhUg8GDGpv9CjBxcWRJ5+dydjx3ep05e7UJZQx47pWWhRoTQSCRT9tZcfWkxQXaZEUhBBMvKinUiItBTNPbRmiuH8ETAa6ALOEEF2q1PEBPgamSSm7ApfX16+622wAN3cnq517/jcbGT2mS41TJT4+bvTr14YHH5rM++//xfgJ3YiO1sKXBAd54x3oyc9vLuXUoTNcdNM4/IJ8CDPS2N4QMtLzmDqzL1ddXX8sr5ZAWlougYFeTJrSs1xpFhQU88E7q+jZO4LRY7rWqVDOJmYQEqpyrzRnLDC1NQCIkVLGAgghfgKmA4cr1Lka+E1KeQZASplSX6dKkTRzCgqK+fF7043H5iI3t5AP31/NU89Mr/EN/+j2Eyx//Xcee2R6uRIByteLXP6Qccm/zEFQsA9XXzO0yc5nbVq18mL8xO5ICWVfjaurE488MZW4U+erxduqSkFBcZ3limaA6YokQAhR0TV2niHdeRmhQHyF/QRgYJU+OgCOhmy4nsB7Uspv6zqpUiTNFCkl27fH8ubry62+0n39+qP8vCiYK66ser9BpwHRDJneH1dX642aLlTsHewYOLjmqMtRFTzpaqNde/Ou51GYmYZ5YqXW4/5b03xv1bM4AH2BsYArsEUIsVVKeby2TpUiaaZ8v2AzX3+13tpiAFoYku8XbGbU6M60alU9rtP4OXXa4Zqc4qJSFv+2g327TzP3ppG1Zm80JzqdnvVrj+Dk5FCj11dTczYxg0MHEwgN86NL11Bri6NoIBaY2kqgYlA8CENLm161TqqUMg/IE0KsB3oCtSoSZWxvpvz6yw5ri1CJwsJivLxsYyHbop+28Pkn/7B920mzrv7OzSkkJ6eg2pSQlJKvPl/L/55fwkvPLWbThmNmO2dDyczI4581B/nmy3VkZFg/dpuigZh/HckOIFoI0UYI4QRcBSytUud3YLgQwkEI4YY29XWkrk6VImmm+Ad41F+pCXF3dyY/v4jExOoZ+Job/gGe3HL7WLr1CKetGaZvzpxO5Y1XlvHhe6uY98k/pCRnAZoCWfb7Lm6e+3n5IsiSEh1ffPZvjf3s2RXHsaNVX/4sQ5duYVw1ewiOjvb8umh7k5xTYX7MnY9ESlkK3AWsQlMOi6SUh4QQtwkhbjPUOQKsBPYD29FchOsMsVHv1JYQwgVYDzgb6v8ipXxWCLEQKBvD+wCZUspe9V+KwhjCwvw4Fdv0a0dqIyurgMsv/YCoqABefvUKWrc2LSR8UzJ5Si8Arpg1qNF96XR6vv5iHRvWHQVg2IiOREYFkpGRxw/fbmLxr9VHjnZ2osbc6CWlOrIy8xstk7H07BWJm6sTOr0evSFUjcLGsMA6Einln8CfVY59WmX/DeANY/s0xkZSBIyRUuYKIRyBjUKIFVLKK8sqCCHeArKMPamibnbviqNbtzA2rLf+FElV4uJSORV7vlkrktooKCg22ilASsnRI2dZvnRPuRIBOJeUiV4v8fV1r9W1tt+AttWUCGCxXCZ1Ed0xmKysfD58dxX5BcVMn9mXzl2UzcQmsHLYE1OoV5FILdhRWahXR8NWfnlC8wm9ArD+Ut8WQs9eEfTpG0VubiHfL9iMXl/33dSmTSAjR3XSjMpSkpaWx88/b+PM6bpjRzWUz+f9y6BavIWaM3//dZBtW2K4cvZgunUPr7VeSYmOd9/6E39/D07GJJcfd3Nz4pY7xpa/2Y8d35VFP26hajiwnr3qz5DZUHSleg4ejMfZyYFORioEOzvBoKHtObAvnu++3sD4ST0YMbKTCkHfzBHU7GLVHDHKa8uwGnIX0B74SEq5rULxcCBZSnmilra3ALcARERENE7aC4SykCRzrx9BRkZ+nblJnnl2BiNGdqq0xkNKyR/LdltMPjs7O6SUzSa+lLGMndCN77/bxNOP/8xb711D23atqtUpKdERdyqFPbvieODhKcy8bACbNx7n2NGzzJo9tFK+k7btWjNydBfWrz1Sruz9/NwZNCTabDInxKeTlpZTrpxKdTpCQ32xszNeCXh6uhIa4sdTjy5Cr5fs2R1Ht+5hBNbggadoZtjIiMSou1FKqTPYP8KAAUKIbhWKZwE/1tF2npSyn5SyX2Bg/b7tisoMGxrNY09MrbU8OMS3xgd6UpLlZhpjY1OsEvursbi6OjFmbFfycgtZvepAjXVKS3WcTczkvY+vIzjEB19fd6ZM7c0DD0+pMWnWU8/NJCzcn9AwPx55fCqvvjWL1PM5/LPmkFEy6fWS5HO1f1dh4X6VRjjOzo4EBHpVS3hVHzk5Bdg72DFgUDtuuWMsDg52lJToeOXF3ytN3SkUDcGkdSRSykzDasdJwEEhhANwCdriFYUFCA3349zOU7WW+9fwQDlxIhlHR3uiogKIj09HpzN/2PBFP22rU8E1V6ZM642fvweXXjGgxnJXVydGju5sUp9PPTeDsDB/nJwdyM0pJC7uPAMHt2fdv0cYOrxDjfYS0EY/99/1LWdOpzL7umEMH9HJYiFLOnUJ5del9wNUSs516x1jy/9PTc1h04ZjTJvR1+ZGmy2VFhP9VwgRCJQYlIgrMA54zVA8DjgqpUywoIwXNCEhvry1dkWNZW5uTjUqkg4dgvju+9twcLBn165TPPHYomrz+I1l7doj3HLbaPz8mpebcn2EhPrWqkQaStt2rUlMSGfjhmMMHdaBbt3D2bs7jrTUHFLP5xBUJadMdnYBhw8msHL5PsZP7M6EST1wcXWs1xbWWKpmd9Tp9GzdcoJfftpGRmY+OYacKr8t2s7wUZ256dbRFpVHYQQtRZEAwcB8g53EDs3v+A9D2VXUMa2lMA/Dh3dk757T1Y7n5xeTnJxVoweVs7MWxXbAgHZcN3c433y9wawylZTo+H3JLq6bO6JZu5Xu2hHLqVPnGT+hO94+bmbvv6RExzNPLCKwlRcPPDylPApvrz5R9OoTVa3+2cQM7r1zPt7ebrz4yhWVpsvsjQx7bw5WrdjHpx/9Xa48hNDCqrRt14oePSMYOsL6q/MVtBxFIqXcD9SYEUdKOdfcAimqM2p0J/R6yaGDCUR3COKXn7eXr1b+9Zcd3HrbmFpzhgDMuXYYcadSWbu29sWpkZEBjJ/QDScnh/I1EElJmSz+rfbUyAu+20yvXpH0ruGB2RQkn8vi6JGzdU5FvfPmCjIz85h0UU90On2dn5MxSCk5n5JDYCtPhBA4Otrz4itXlPdbk0uwrlTPXyv3s3LFPlxcnAgM9OTp5y+t0ebSFCz8UVv5X8asa4YwfWZfAgKV8b1ZoRJbKczJzh2n6NM3inHju+Ll5Yqnpwvvv7cKgHHjuxn1cLzs8v61KpKISH8+//LGGvvZtetUnW7EBw7EW02R+Pl7VHoYSynR6fT8vfogEyf35OCBeG66bTTR0UH8vfogPy7YzLc/3NGoXB0b1x/j+ad/5fqbRnL1nKEIIWq1gZRh72CHf6Anw0d2YvrMflbJZ3L2bAY/fb8ZZydtpHrNdcM4cvgsw0Z0YOp0ZeJstihFojAXvftE4e/vwQP3fc/Lr17BlIt7YWcn6NQ5pFK+9Lro3CWUgQPbsW3byUrHnZwcGDSofa3K6MorB/HG68urHe/eI5wrrhhIWLif6RdkJhwd7csDMhYXl/LAPd8xe85QTp08z0P3LmDvntNcd8MIRo3uQmiYH2PG1Z2fozZycgpwcLDH1dWJwsIS7OwFX3+xjkMHErj1zrFERtX/HQwY2M4qCxLLiI1J5s9le3n1zVn0G1BzFkhF88NWRiRqRZINUGZQv++BSeUZ/CZf1NNoJVLGLbeOZsDAyg+R0WM6c0sdRlUnZ4calUVhQQm9+0QSEeFvkgyW4szpVI4ePsunH/2Ns4sDrm7aCvaTJ/5bUOjpaVrQydzcQv73whJunvs555IyARg3oRtvvnMNHh4ubN92spoBu7nSvUcEHTsF07N35cWSZTYdRTPF/EEbLYJSJDZEYx/aUW0C0ev+u9vs7ATdu4dz6GBirW0iI/z56OPrqq1kDwnxaVY5SA4fSmT0uK4MHNSOkFBfJk7uwfsfX8eTz82ss51eL/novb84ezajWtl332ygpKiUDz+dS5u22uJFIQQ9ekXwxruzGTSkPU3pJVuQX9xgV25vHzfe/ei6SvKmpuZw8mRy7Y1MpLiolJXL95qtP4X5gzZaCjW1dYFRcQqre/dwYmNTmHxRz1rrlyU/6t07kq1bYsqPx5xMJjMzHx8LeEI1hGkz+jJthulz/XZ2gskX92LBNxspKSnlsSenl4cOufzKgbUaoKM7BPHSq1fWWGYptm87ye+Ld3L3fRPLFZspVLXNBAR4oivV8qh07BRC66CGx09LPZ/N22/8SUREAKfjUomMCmhwXwoDNhRrS41ILjD69osq/3/W1YO57faxtVeuwGWXD+DDj64tz0mSmJDBSy8sITe30BJiNilt27XingcmMXZCN0p1uvLjzcGL6d+/D/H4wz9y8EA8er0eZ2cH/llzyGxpcn9euJUXnvmNTz9a06h+9u05Q1pqDst+30VBfpFZZFNgM1NbakRygREa+p+9IzMzzyR32M5dQpk0uQeLFmqh1vbsOc11cz5j5iX9uGaObedKd3FxZNBg88XIMhdh4f7s2BbLqdjzfP3dbYwe29Ws/d9w8yiGDOtAeHjjpk3HjO/KkOEdeO/tlQRbaHX+hYbAdoztSpFcQJw8mcKzz/xavp+RYXpujLHjuhIa5kuPHhHM++wftmyO4eTJFHOKqahAdIcg7rp3AidOnKO4pBRXzGuXcnNzpk/fNo3uRwiBq6sTjz05zQxSKcqxEUWiprYuINq2DWT6jL70NnjufPbpPyZNkSSfy2Lxrztxc3UiIsKfqdP6AJqh+99/DiPNHYelmRN7MoUF8zdafHpvxqX9efixqXh7N409Kiszn79W7KeoqKRJzqeoHSGlSZu1UIrkAkIIwR13juPNt6/mvffn0LZtq3J3YmNoHeTNw49OYcTITgD069eGWVcPJjDQkx07Ytm/L95Sojc7flm0DU9PZ6ZM7cWLz/xGcXHLcKOVUnL37d/wzVfr+OTDxtlN6mL+V+tITGj+aZutiqn2EWUjUTQ13bqH0X9A2wZFeS1byW1vb8dNN48ys2S2waDB0Xh4unJgfzwIwZOPLuSNd2bXWj/2ZAqRUQGNDtFiaYQQvPHObBwc7HjpucW8/cZyLrlsAFEmrlmqSHFxKWv/Psymjcc4sD8eB3s70tPzmDi5dm9BhYaykSiaPaYqgU1LtrNr9X7u/vDGCz7MeFi4HzEnzoGUxBw/R2Arz1rr7tt7mkfu/4HAQE8efPRiuvcMrzesSkNIOpuJf4AHTk6N+1mXuQE/88Il6PSSgIDar60usrLyWbp4F0uX7CIjPa9SWVi4H4WFauqsXpQiUTR3TInaW5BbwLu3fkbm+WwGT+1L/0k1xvG8oGjbrjXu7i5ce8Nwhg6vPVpuj54RfPrljTg5OVhEgZQRezKZ9LRcunYPM0t/vg1MEbB3z2l+XLCZ/ftOU1Ksq7HO1Ol9GzXKuVCwlRFJ8x5nK5oNLu4uTLphDAAvXvE2Xzy2gLxs072+WhJ2doLgEB+mz+xX51u7EII2bVsRGuZH6yBviymTocM7mk2JGEu2IQx9GXt3x/HoAz+wa0dsrUokukMQF03t1QTStQBsxEaiFInCKIQQ+Ab5AFCQW8jC13/ngRHPEHfowjGwKzRjfJmnX3FRKb8b0gzoSvU899QvPHz/9/WGcbli1uBmFV6n2WJieBQVIkVhE+xctbfSfkCYH0e3xxDVNdw6AimalK8+X8uZuFSSkjJ4871r8PR0Zc7c4QAcOpjAxvXH6u0jukMQQ4d1sLSoLQcbmdpSikRhNP0m9mLHyr3l+3d9cCPBbVpbTyBFkzL3xpE12tVOxabwxWf/1NCiMqGhvlx+1aBG5YO5kFAr2xUtkpn3XERRfjFb/9jJyb1x5GVe2DaSC42qSqS4qJQP3lvF338drHcdjbe3G29/MAf/BnqANRULf9jClKm98fB0sbYoGjayyFcpEoXRCCGY9fhMrnpsBiVq1fMFj5OzAxMn92D3zlMkn8uqs27vvlG4ezSTh3Md9Owd2XyUCLYzIqnX2C6EcBFCbBdC7BNCHBJCPF+h7G4hxDHD8dctK6qiuSCEwMnFCScXZTC90OnWPZxvvr+91nJnZweGDu/Agf1nbCIqcKfOIdYW4T8stLJdCDHJ8NyOEUI8Vke9/kIInRDisvr6NGZEUgSMkVLmCiEcgY1CiBWAKzAd6CGlLBJCmJ4gQaFQtFiumj2Ey68ciLePGynJ2Xg1UaywloRoWB6z2vsTwh74CBgPJAA7hBBLpZSHa6j3GrDKmH7rHZFIjVzDrqNhk8DtwKtSyiJDPRUCVqG4ADkVW/2nP+uaIdx062i8DYnPWrX2avbhYS4QBgAxUspYKWUx8BPagKAqdwO/AkY91436ZoUQ9kKIvYZOV0sptwEdgOFCiG1CiHVCiP61tL1FCLFTCLHz/PnzxpxOoVDYEH/+sbfSfkSEP3OuG24dYVoapk9tBZQ9bw3bLVV6DAUqLv5KMBwrRwgRCswEPjVWTKOM7VJKHdBLCOEDLBZCdDO09QUGAf2BRUKItrJKLHEp5TxgHkC/fv1sxHSkUCiMoaREV2n9SEioL2+8O1u5+JqJBhjbU6WU/erqsoZjVc/yLvColFJnbEw9k8aaUspMYC0wCU2T/WaY+toO6AGVqFlB3KH4Cz58iq2Rl1fE7l2nTG7n6GjPsy9eSmiYHzfdOpovv7212bv42gwSzf3XlK1+EoCKK4jDgLNV6vQDfhJCxAGXAR8LIWbU1akxXluBhpEIQghXYBxwFFgCjDEc7wA4Aan1XoaixZN+LpOHxz5ff0VFs2HZkl2cPtWwn2/bdq347KubuGr2EBwdLReU8kLEAiFSdgDRQog2Qggn4CpgacUKUso2UsooKWUU8Atwh5RySV2dGjP+DAbmG6z4dsAiKeUfBiG+EkIcBIqB66pOaykuTPqM7c6sxy9BSnnBh5u3FQJbeTU4mKS7u7OZpVGUY+YnqpSyVAhxF5o3lj3wlZTykBDiNkO50XaRitSrSKSU+4FqMcMNFv9rGnJSRctn+CUDrS2Cwkh+XbSd337ZzgsvX25tURQVsFSIFCnln8CfVY7VqECklHON6VNZxBSKC5zY2GQCAj1p117FTWtWGG/3sDpKkSgUFzB5eUUkxKfj46MWCzZHWkyIFIXC0hQUFDP/i3XleS4UTcf8L9dx9PBZbrp1jLVFUdSESmylUBjHb4u2s+DrDeTlFlpblAuK4uJSDh5M4K77JhAe4W9tcRQ1YCuJrZQiUViFo4cTy/8v8/o5cjCxturl5OUWqpGLGUhNzeGBu7/j+NEkAlt5WVscRU1IQC9N26yEUiSKJqe4qJQ1Kw+U77eP1oy8aam5tTUB4OD+eK6c9i6XT3mbT99fbVEZWzp2QpCYmAHA/55bwt7dcdYVSFEzampLoaiZI4cT2b/3TPl+h04h2NkLtm05wfp/j5CTXVCtTWmpjq8+/YeiolKKikr5bdE2smuopzAOP38PvvruVkCzUf24YLOVJVLUhK1MbSmvLUWT4+hoT/zpVD58eyU9e0dy/GgSep1k57ZYdm6LBcDbxw03NyfCIvwpLdWRfC6LiMgALps1iBNHk+g7oC1eXq5WvhLbxsHBDhdXRwoLSsgvKGbThmMMHd7R2mIpKqLcfxWKmlm75jClpXp+/3Unv/+6s8Y6WZn5ZGXmk3Q2s/zY2YQMehUU07NPJFNm9OHYkbOkns9h6AjLP/x0Oj1HDiXStXtYi1mtn5Geh6ODPZ6tXLnksv60aatSCjU3bMX9VykSRZPTKqjhxt29u0+zd/dpfvx2EyUlOjp3DTVakRTkF5ObW8i5s5kEhfjg5e2Ks7Mjer2kIL8Idw8X0lJzcHV1wq1K2I+EM2ls2Xicdu1b4+pme5khz6dkk5tbyOFDiYwa0wV3d2fOnE4lLMKfq64erEYizREr2z1MQSkSRZOSlZnPV5/92+h+Skp0AEbZSQoLS9iy8Tifvr+a9LT/DPpBwd54erlRVFRCSnIWnbqEknAmjfz8Yh5/dgaDhkaX141sE8jNd4xttNzWYOmSXbz/9koiIvyZOrNvuZfcsBGd6NI1DEcnFWixOaKFSLENTaIUiaJJ2bU9lpJindn6y8st4t81h/AP8KCkWMfB/fGcTUhH2AkKC0pIPpdF7Mlk9LrqP8hzSVmcS8oq39+7K678/5jj58oViV4v0ev1Jgc1LCoqwdnZsWEXZiaOHE7ki8/+5YabRzHzsv64ulYeTfn5e1hJMoVRmDnVrqVQikTRpPj4uWNnJ9Cbyec9MyOPl59dbJa+KrLg6w2sWXWAosISsjLz8fF1o32HYOzt7cjLK6Qgv4S8vEIeeWoanbpoCeZ0Oj07tp7kxLEktm2O4dTJFIJCfACYNKUnl1892GQ5pJSkJGfROsinQdfRuUsoS5Y/iJ1dy7DrXGioEYlCUQM7t540mxKxJDqdnsT49PL98yk5nE/JqVbv3tu+YfykHuh0enZuiyUzI69S+Zm4VCZd3It+A9uZLENGei5nTqeRnpbbYEUCKCViqygbiUJRMzfePob+g9vxyD3fA2BnL3B1dcLRwZ7s7AL6D2pH/Ok0zhoWyzV39DrJquX76q6jl3h6uZjUr65Uz1uv/EGrIG/ueXByY0RU2Cwq+q9CUSP29nb06hPFrysexN5OoNPp8TSsB8nMyMPXzwMpJfn5xezcepKgEB9OnUzhrVf+wM5OYO9gh9RLSkttZPIYaBfdmoDAmj3VpJT89N1mtm4+QfvoICZc1IM2bVvx4tO/sm1zTCWDv+LCQ7n/KhS1IISocTGhr59Hebm7uzMjx3YBoGPnEPoNbIu7uwsuro6cT8nGxdWJxPh0Nm84Rk52AT16RaLT68nLLeLzj/6muLi0Sa+pLnr0iqi0r9PpWbhgM0VFJSz9bRe5OVqwysMHElixbA8ODvbl8cR27zjF999sYPbc4U0ut6IZoEYkCoX5qPhG36q1NwBeXUPp3DW0Wt0Rozuzc9tJsrML+P3XnZyrsKjRGmzddIJ20a0RQnBg7xle/9/SWmUqKdGVuzYDuLk5KSVyoSJB2MjAu15FIoRwAdYDzob6v0gpnxVCPAfcDJw3VH3CkMJRobAqfv4eTLioJwAjx3ThndeWk5GeR052AVJK0lJz0ema7hc6/4t1/P7rTgYPi2bntpM1Gu1rI7C1isyraP4YMyIpAsZIKXOFEI7ARiHECkPZO1LKNy0nnkLROAJbefHyW7MqHSsqKuGx+37g4P74JpMjMyOPFcv2mtRGCLjm+hGWEUhhG7SUqS0ppQTKlgM7GjbbuDqFogacnR0ZP7lHkyqSijg62dOpSygBgZ74+LizavleCgqKKz0znJwcuOmOMQwZ3sEqMiqaCTbypDXKRiKEsAd2Ae2Bj6SU24QQk4G7hBDXAjuBB6WUtuGzqbjgST6X2eC29vZ22NkJuveKoLCghIz0XAoLSygqKiU0zI9+A9sSEOiJEIIOnYI5dzaTjIw84k+nMWBwe3r3jcLJ+b+f3o23j0avl5yJS+WPJbsYP7kH4RH+5c4HiguXFrUgUUqpA3oJIXyAxUKIbsAnwItoOvNF4C3ghqpthRC3ALcAREREVC1WKKxCVqZxuUyEgNAwPzIz8/HycmXQ0GhuvF3Lb+7kVPnnoyvVY+9QPcVPx84hdZ6jLIxKx84hRHcMVgsIFf/RkhRJGVLKTCHEWmBSRduIEOJz4I9a2swD5gH069fPNj4VRYumuKgUVzcnJl3ck9HjuqLXS07GJFNaqkfqJc4ujrTv0JriolL8Azxp3yGIkhIdjo51x9qqSYmYypm4VOzt7QiPVDnUL3gkLSfWlhAiECgxKBFXYBzwmhAiWEqZZKg2EzhoQTkVCrPh5OzArXeNq3SsvhAm9SmR2ijIL8be3q7SVFZdRLUNbNB5FC0PgWxRU1vBwHyDncQOWCSl/EMI8Z0Qohea3owDbrWYlIoLntTzObz1yjK8vd147NkZ1hbHaM4lZeLm7tSoWFmKC5iWokiklPuB3jUcn2MRiRSKGvAP8GDK9D6sXXOYvLyi8pwazZ027VTWQUUjaCmKRKFoDgghGDayU5Ok1VUomgUWspEIISYB7wH2wBdSylerlM8GHjXs5gK3SynrjEyqFInCpmgp+dIVCmMwt43EYKL4CBgPJAA7hBBLpZSHK1Q7BYyUUmYYlnnMAwbW1a9SJAqFQtFcMf/U1gAgRkoZCyCE+AmYDpQrEinl5gr1twJh9XWqFIlCoVA0SxqUjyRACLGzwv48wxKMMkKBiiEdEqh7tHEjsKKOckApEoVCoWieSBqiSFKllP3qKK9pbrjGkwghRqMpkmH1nVQpEoVCoWiumN/YngCEV9gPA85WrSSE6AF8AUyWUqbV16lSJAqFQtFMscCCxB1AtBCiDZAIXAVcXemcQkQAvwFzpJTHjelUKRKFQqForphZkUgpS4UQdwGr0Nx/v5JSHhJC3GYo/xR4BvAHPjZ4SZbWM12mFIlCoVA0SySgN/+CREMCwj+rHPu0wv83ATeZ0qdSJAqFQtEsaZDXllVoUkWya9euVCHE6aY8ZxUCgFQrnt+StORrg5Z9fS352qBlX1/Fa4s0e+9KkVRHSmnV0KZCiJ31zfXZKi352qBlX19LvjZo2ddn8WtTikShUCgUDcZCNhJLoBSJQqFQNEskSNvIbHWhKZJ59VexWVrytUHLvr6WfG3Qsq/PstdmI1NbQtqIoAqFQnEh4e3UWg4JmmVSm5Xx7+2yhj3qQhuRKBQKhe1gIy/6dtYWwBIIIcKFEP8KIY4IIQ4JIe41HPcTQqwWQpww/PW1tqymUse1vSGEOCqE2C+EWCyE8LGyqA2ituurUP6QEEIKIQKsJWNjqOv6hBB3CyGOGY6/bk05G0Id92YvIcRWIcReIcROIcQAa8vaEIQQLkKI7UKIfYbre95w3OafK42lRSoSoBR4UErZGRgE3CmE6AI8BvwtpYwG/jbs2xq1XdtqoJuUsgdwHHjcijI2htquDyFEOFpCnjNWlK+x1Hh9hkir04EeUsquwJvWFLKB1PbdvQ48L6XshRZ+w+aUpIEiYIyUsifQC5gkhBiEJZ8rUpq2WYkWqUiklElSyt2G/3OAI2hx+KcD8w3V5gMzrCJgI6jt2qSUf0kpSw3VjEpG0xyp47sDeAd4hFrCXtsCdVzf7cCrUsoiQ1mK9aRsGHVcmwS8DNW8qSHarC0gNXINu46GTWKx54qJSkQpEsshhIgCegPbgNZSyiTQbnqglRVFazRVrq0iN2BEMprmTsXrE0JMAxLryx1tS1T5/joAw4UQ24QQ64QQ/a0qXCOpcm33AW8IIeLRRlq2OlpGCGEvhNgLpACrpZSWe65IQK83bbMSLVqRCCE8gF+B+6SU2daWx5zUdm1CiCfRphi+t5Zs5qDi9aFdz5No0yItghq+PwfAF21K6GFgkbDRBPU1XNvtwP1SynDgfuBLa8rXGKSUOsMUXRgwQAjRzcInVCMSayKEcES7mb+XUv5mOJwshAg2lAejvVXYHLVcG0KI64CLgdnShv26a7i+dkAbYJ8QIg7tR7xbCBFkPSkbTi3fXwLwm2H6ZDtaSiObcyio5dquQ8tvAfAzWt5wm0ZKmQmsBSZhyeeKUiTWw/Am9yVwREr5doWipWg3NYa/vze1bI2ltmsTQkwCHgWmSSnzrSVfY6np+qSUB6SUraSUUVLKKLSHbh8p5Tkritog6rg3lwBjDHU6AE7YWKDDOq7tLDDS8P8Y4ERTy2YOhBCBZd6QQghXYBxwFIs9V6QWIsWUzUq01HUkQ4E5wAHDfCbAE8CraFMGN6J5/lxuHfEaRW3X9j7gDKw2zIhslVLeZhUJG0eN12fIodASqO37+wr4SghxECgGrrPBUWVt13Yz8J4QwgEoBG6xjniNJhiYL4SwR3sJXySl/EMIsQVLPFckSBsJkaJWtisUCkUzxNshUA72mmFSm1UZX6iV7QqFQqGogI286CtFolAoFM0RKa3q0msKSpEoFApFc0WNSBQKhULRGKQakSgUCoWi4Vh3bYgpKEWiUCgUzRGValehUCgUjcZG1pEoRaJQKBTNEAlINSJRKBQKRYORUo1IFAqFQtE41IhEoVAoFI3DRkYkKtaWQqFQNEOEECsxPZVAqpRykiXkqQulSBQKhULRKFpkPhKFQqFQNB1KkSgUCoWiUShFolAoFIpGoRSJQqFQKBqFUiQKhUKhaBT/B+ab5WwJ31ZeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfs_list = [austria_schools,croatia_schools,greece_schools,slovakia_schools,slovenia_schools,italy_schools]\n",
    "gdfs_list = [boundaries_Austria,boundaries_Croatia,boundaries_Greece,boundaries_Slovakia,boundaries_Slovenia,boundaries_Italy]\n",
    "names = ['austria','croatia','greece','slovakia','slovenia','italy']\n",
    "school_numbers = [len(df) for df in dfs_list]\n",
    "\n",
    "for gdf,country_name,n in zip(gdfs_list, names, school_numbers):\n",
    "    gdf.plot(column='mean_LS_frequency_school',vmax=1.5,cmap='viridis',legend=True)\n",
    "    plt.tight_layout()\n",
    "    plt.title('{}: mean LS frequency per region (n schools = {})'.format(country_name, n));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "stock-knight",
   "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>shapeName</th>\n",
       "      <th>mean_LS_frequency_school</th>\n",
       "      <th>n_pois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tirol</td>\n",
       "      <td>1.150830</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vorarlberg</td>\n",
       "      <td>0.974639</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Salzburg</td>\n",
       "      <td>0.936599</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kärnten</td>\n",
       "      <td>0.498345</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steiermark</td>\n",
       "      <td>0.469694</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Niederösterreich</td>\n",
       "      <td>0.269072</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oberösterreich</td>\n",
       "      <td>0.163012</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Burgenland</td>\n",
       "      <td>0.035989</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wien</td>\n",
       "      <td>0.011558</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          shapeName  mean_LS_frequency_school  n_pois\n",
       "0             Tirol                  1.150830     570\n",
       "1        Vorarlberg                  0.974639     235\n",
       "2          Salzburg                  0.936599     343\n",
       "3           Kärnten                  0.498345     341\n",
       "4        Steiermark                  0.469694     703\n",
       "5  Niederösterreich                  0.269072    1020\n",
       "6    Oberösterreich                  0.163012     923\n",
       "7        Burgenland                  0.035989     173\n",
       "8              Wien                  0.011558     564"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundaries_Austria.drop(columns='geometry').sort_values(by='mean_LS_frequency_school',ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "blessed-webcam",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shapeName</th>\n",
       "      <th>mean_LS_frequency_school</th>\n",
       "      <th>n_pois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dubrovnik-Neretva</td>\n",
       "      <td>1.164717</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Primorje-Gorski Kotar</td>\n",
       "      <td>0.643668</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Krapina-Zagorje</td>\n",
       "      <td>0.597965</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Split-Dalmatia</td>\n",
       "      <td>0.480084</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Po?ega-Slavonia</td>\n",
       "      <td>0.319084</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lika-Senj</td>\n",
       "      <td>0.227391</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>?ibenik-Knin</td>\n",
       "      <td>0.217740</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Zagreb County</td>\n",
       "      <td>0.179011</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Istria</td>\n",
       "      <td>0.170574</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Karlovac</td>\n",
       "      <td>0.110992</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Koprivnica-Kri?evci</td>\n",
       "      <td>0.095436</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Vara?din</td>\n",
       "      <td>0.089400</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Zadar County</td>\n",
       "      <td>0.067597</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>City of Zagreb</td>\n",
       "      <td>0.055080</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Virovitica-Podravina</td>\n",
       "      <td>0.020143</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sisak-Moslavina</td>\n",
       "      <td>0.015615</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brod-Posavina</td>\n",
       "      <td>0.006728</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Vukovar-Syrmia</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Me?imurje</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bjelovar-Bilogora</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Osijek-Baranja</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                shapeName  mean_LS_frequency_school  n_pois\n",
       "0       Dubrovnik-Neretva                  1.164717      33\n",
       "1   Primorje-Gorski Kotar                  0.643668      80\n",
       "2         Krapina-Zagorje                  0.597965      86\n",
       "3          Split-Dalmatia                  0.480084     205\n",
       "4         Po?ega-Slavonia                  0.319084      22\n",
       "5               Lika-Senj                  0.227391      38\n",
       "6            ?ibenik-Knin                  0.217740      50\n",
       "7           Zagreb County                  0.179011     135\n",
       "8                  Istria                  0.170574      57\n",
       "9                Karlovac                  0.110992      58\n",
       "10    Koprivnica-Kri?evci                  0.095436      43\n",
       "11               Vara?din                  0.089400      40\n",
       "12           Zadar County                  0.067597      88\n",
       "13         City of Zagreb                  0.055080     224\n",
       "14   Virovitica-Podravina                  0.020143      41\n",
       "15        Sisak-Moslavina                  0.015615      58\n",
       "16          Brod-Posavina                  0.006728      63\n",
       "17         Vukovar-Syrmia                  0.000000      48\n",
       "18              Me?imurje                  0.000000      47\n",
       "19      Bjelovar-Bilogora                  0.000000      65\n",
       "20         Osijek-Baranja                  0.000000     100"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundaries_Croatia.drop(columns='geometry').sort_values(by='mean_LS_frequency_school',ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "suited-month",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>shapeName</th>\n",
       "      <th>mean_LS_frequency_school</th>\n",
       "      <th>n_pois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Region of ?ilina</td>\n",
       "      <td>0.524427</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Region of Banská Bystrica</td>\n",
       "      <td>0.273607</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Region of Pre?ov</td>\n",
       "      <td>0.248292</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Region of Tren?ín</td>\n",
       "      <td>0.239884</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Region of Ko?ice</td>\n",
       "      <td>0.168503</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Region of Bratislava</td>\n",
       "      <td>0.036042</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Region of Trnava</td>\n",
       "      <td>0.017727</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Region of Nitra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   shapeName  mean_LS_frequency_school  n_pois\n",
       "0           Region of ?ilina                  0.524427     335\n",
       "1  Region of Banská Bystrica                  0.273607     283\n",
       "2           Region of Pre?ov                  0.248292     439\n",
       "3          Region of Tren?ín                  0.239884     279\n",
       "4           Region of Ko?ice                  0.168503     373\n",
       "5       Region of Bratislava                  0.036042     325\n",
       "6           Region of Trnava                  0.017727     255\n",
       "7            Region of Nitra                  0.000000     269"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundaries_Slovakia.drop(columns='geometry').sort_values(by='mean_LS_frequency_school',ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "retired-nutrition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shapeName</th>\n",
       "      <th>mean_LS_frequency_school</th>\n",
       "      <th>n_pois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zasavska</td>\n",
       "      <td>1.390975</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gorenjska</td>\n",
       "      <td>1.172633</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spodnjeposavska</td>\n",
       "      <td>0.985400</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gorivka</td>\n",
       "      <td>0.967627</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jugovzhodna Slovenija</td>\n",
       "      <td>0.855802</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Koroaka</td>\n",
       "      <td>0.772258</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Notranjsko-kradka</td>\n",
       "      <td>0.584156</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Savinjska</td>\n",
       "      <td>0.565470</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Osrednjeslovenska</td>\n",
       "      <td>0.440342</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Obalno-kra-ka</td>\n",
       "      <td>0.310879</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Podravska</td>\n",
       "      <td>0.171533</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pomurska</td>\n",
       "      <td>0.059724</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                shapeName  mean_LS_frequency_school  n_pois\n",
       "0                Zasavska                  1.390975      13\n",
       "1               Gorenjska                  1.172633      53\n",
       "2         Spodnjeposavska                  0.985400      19\n",
       "3                 Gorivka                  0.967627      79\n",
       "4   Jugovzhodna Slovenija                  0.855802      47\n",
       "5                 Koroaka                  0.772258      52\n",
       "6       Notranjsko-kradka                  0.584156      29\n",
       "7               Savinjska                  0.565470     119\n",
       "8       Osrednjeslovenska                  0.440342     193\n",
       "9           Obalno-kra-ka                  0.310879      52\n",
       "10              Podravska                  0.171533     166\n",
       "11               Pomurska                  0.059724      67"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundaries_Slovenia.drop(columns='geometry').sort_values(by='mean_LS_frequency_school',ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "federal-adjustment",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>shapeName</th>\n",
       "      <th>mean_LS_frequency_school</th>\n",
       "      <th>n_pois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dubrovnik-Neretva</td>\n",
       "      <td>1.164717</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Primorje-Gorski Kotar</td>\n",
       "      <td>0.643668</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Krapina-Zagorje</td>\n",
       "      <td>0.597965</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Split-Dalmatia</td>\n",
       "      <td>0.480084</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Po?ega-Slavonia</td>\n",
       "      <td>0.319084</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lika-Senj</td>\n",
       "      <td>0.227391</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>?ibenik-Knin</td>\n",
       "      <td>0.217740</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Zagreb County</td>\n",
       "      <td>0.179011</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Istria</td>\n",
       "      <td>0.170574</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Karlovac</td>\n",
       "      <td>0.110992</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Koprivnica-Kri?evci</td>\n",
       "      <td>0.095436</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Vara?din</td>\n",
       "      <td>0.089400</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Zadar County</td>\n",
       "      <td>0.067597</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>City of Zagreb</td>\n",
       "      <td>0.055080</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Virovitica-Podravina</td>\n",
       "      <td>0.020143</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sisak-Moslavina</td>\n",
       "      <td>0.015615</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brod-Posavina</td>\n",
       "      <td>0.006728</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Vukovar-Syrmia</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Me?imurje</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bjelovar-Bilogora</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Osijek-Baranja</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                shapeName  mean_LS_frequency_school  n_pois\n",
       "0       Dubrovnik-Neretva                  1.164717      33\n",
       "1   Primorje-Gorski Kotar                  0.643668      80\n",
       "2         Krapina-Zagorje                  0.597965      86\n",
       "3          Split-Dalmatia                  0.480084     205\n",
       "4         Po?ega-Slavonia                  0.319084      22\n",
       "5               Lika-Senj                  0.227391      38\n",
       "6            ?ibenik-Knin                  0.217740      50\n",
       "7           Zagreb County                  0.179011     135\n",
       "8                  Istria                  0.170574      57\n",
       "9                Karlovac                  0.110992      58\n",
       "10    Koprivnica-Kri?evci                  0.095436      43\n",
       "11               Vara?din                  0.089400      40\n",
       "12           Zadar County                  0.067597      88\n",
       "13         City of Zagreb                  0.055080     224\n",
       "14   Virovitica-Podravina                  0.020143      41\n",
       "15        Sisak-Moslavina                  0.015615      58\n",
       "16          Brod-Posavina                  0.006728      63\n",
       "17         Vukovar-Syrmia                  0.000000      48\n",
       "18              Me?imurje                  0.000000      47\n",
       "19      Bjelovar-Bilogora                  0.000000      65\n",
       "20         Osijek-Baranja                  0.000000     100"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundaries_Croatia.drop(columns='geometry').sort_values(by='mean_LS_frequency_school',ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "commercial-strain",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "still-return",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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