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    "# Data reclassification\n",
    "\n",
    "Reclassifying data based on specific criteria is a common task when doing GIS analysis. The purpose of this lesson is to see how we can reclassify values based on some criteria. We could, for example, classify information based on travel times and housing prices using these criteria:\n",
    "\n",
    "```\n",
    "1. if travel time to my work is less than 30 minutes\n",
    "\n",
    "    AND\n",
    "\n",
    "    2. the rent of the apartment is less than 1000 € per month\n",
    "\n",
    "    ------------------------------------------------------\n",
    "\n",
    "    IF TRUE: ==> I go to view it and try to rent the apartment\n",
    "    IF NOT TRUE: ==> I continue looking for something else\n",
    "```\n",
    "\n",
    "In this tutorial, we will:\n",
    "\n",
    "1. Use classification schemes from the PySAL [mapclassify library](https://pysal.org/mapclassify/) to classify travel times into multiple classes.\n",
    "\n",
    "2. Create a custom classifier to classify travel times and distances in order to find out good locations to buy an apartment with these conditions:\n",
    "   - good public transport accessibility to city center\n",
    "   - bit further away from city center where the prices are presumably lower\n",
    "\n",
    "## Input data\n",
    "\n",
    "We will use [Travel Time Matrix data from Helsinki](https://blogs.helsinki.fi/accessibility/helsinki-region-travel-time-matrix/) that contains travel time and distance information for \n",
    "routes between all 250 m x 250 m grid cell centroids (n = 13231) in the Capital Region of Helsinki by walking, cycling, public transportation and car.\n",
    "\n",
    "In this tutorial, we will use the geojson file generated in the previous section:\n",
    "`\"data/TravelTimes_to_5975375_RailwayStation_Helsinki.geojson\"`\n",
    "\n",
    "Alternatively, you can re-download [L4 data](https://github.com/AutoGIS/data/raw/master/L4_data.zip) and use `\"data/Travel_times_to_5975375_RailwayStation.shp\"` as input file in here.\n",
    "\n",
    "\n",
    "\n",
    "## Common classifiers\n",
    "\n",
    "### Classification schemes for thematic maps\n",
    "\n",
    "\n",
    "[PySAL](https://pysal.org/) -module is an extensive Python library for spatial analysis. It also includes all of the most common data classifiers that are used commonly e.g. when visualizing data. Available map classifiers in [pysal's mapclassify -module](https://github.com/pysal/mapclassify):\n",
    "\n",
    " - Box_Plot\n",
    " - Equal_Interval\n",
    " - Fisher_Jenks\n",
    " - Fisher_Jenks_Sampled\n",
    " - HeadTail_Breaks\n",
    " - Jenks_Caspall\n",
    " - Jenks_Caspall_Forced\n",
    " - Jenks_Caspall_Sampled\n",
    " - Max_P_Classifier\n",
    " - Maximum_Breaks\n",
    " - Natural_Breaks\n",
    " - Quantiles\n",
    " - Percentiles\n",
    " - Std_Mean\n",
    " - User_Defined\n",
    "\n",
    "- First, we need to read our Travel Time data from Helsinki:"
   ]
  },
  {
   "cell_type": "code",
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       "\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>car_m_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>from_id</th>\n",
       "      <th>pt_m_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>GML_ID</th>\n",
       "      <th>NAMEFIN</th>\n",
       "      <th>NAMESWE</th>\n",
       "      <th>NATCODE</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29476</td>\n",
       "      <td>41</td>\n",
       "      <td>29483</td>\n",
       "      <td>46</td>\n",
       "      <td>5876274</td>\n",
       "      <td>29990</td>\n",
       "      <td>76</td>\n",
       "      <td>95</td>\n",
       "      <td>24984</td>\n",
       "      <td>77</td>\n",
       "      <td>99</td>\n",
       "      <td>5975375</td>\n",
       "      <td>25532</td>\n",
       "      <td>365</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((402250.000 6685750.000, 402024.224 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29456</td>\n",
       "      <td>41</td>\n",
       "      <td>29462</td>\n",
       "      <td>46</td>\n",
       "      <td>5876275</td>\n",
       "      <td>29866</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "      <td>24860</td>\n",
       "      <td>75</td>\n",
       "      <td>93</td>\n",
       "      <td>5975375</td>\n",
       "      <td>25408</td>\n",
       "      <td>363</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((402367.890 6685750.000, 402250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36772</td>\n",
       "      <td>50</td>\n",
       "      <td>36778</td>\n",
       "      <td>56</td>\n",
       "      <td>5876278</td>\n",
       "      <td>33541</td>\n",
       "      <td>116</td>\n",
       "      <td>137</td>\n",
       "      <td>44265</td>\n",
       "      <td>130</td>\n",
       "      <td>146</td>\n",
       "      <td>5975375</td>\n",
       "      <td>31110</td>\n",
       "      <td>444</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((403250.000 6685750.000, 403148.515 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36898</td>\n",
       "      <td>49</td>\n",
       "      <td>36904</td>\n",
       "      <td>56</td>\n",
       "      <td>5876279</td>\n",
       "      <td>33720</td>\n",
       "      <td>119</td>\n",
       "      <td>141</td>\n",
       "      <td>44444</td>\n",
       "      <td>132</td>\n",
       "      <td>155</td>\n",
       "      <td>5975375</td>\n",
       "      <td>31289</td>\n",
       "      <td>447</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((403456.484 6685750.000, 403250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29411</td>\n",
       "      <td>40</td>\n",
       "      <td>29418</td>\n",
       "      <td>44</td>\n",
       "      <td>5878128</td>\n",
       "      <td>29944</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "      <td>24938</td>\n",
       "      <td>76</td>\n",
       "      <td>99</td>\n",
       "      <td>5975375</td>\n",
       "      <td>25486</td>\n",
       "      <td>364</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((402000.000 6685500.000, 401900.425 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_m_d  car_m_t  car_r_d  car_r_t  from_id  pt_m_d  pt_m_t  pt_m_tt  \\\n",
       "0    29476       41    29483       46  5876274   29990      76       95   \n",
       "1    29456       41    29462       46  5876275   29866      74       95   \n",
       "2    36772       50    36778       56  5876278   33541     116      137   \n",
       "3    36898       49    36904       56  5876279   33720     119      141   \n",
       "4    29411       40    29418       44  5878128   29944      75       95   \n",
       "\n",
       "   pt_r_d  pt_r_t  pt_r_tt    to_id  walk_d  walk_t    GML_ID   NAMEFIN  \\\n",
       "0   24984      77       99  5975375   25532     365  27517366  Helsinki   \n",
       "1   24860      75       93  5975375   25408     363  27517366  Helsinki   \n",
       "2   44265     130      146  5975375   31110     444  27517366  Helsinki   \n",
       "3   44444     132      155  5975375   31289     447  27517366  Helsinki   \n",
       "4   24938      76       99  5975375   25486     364  27517366  Helsinki   \n",
       "\n",
       "       NAMESWE NATCODE                                           geometry  \n",
       "0  Helsingfors     091  POLYGON ((402250.000 6685750.000, 402024.224 6...  \n",
       "1  Helsingfors     091  POLYGON ((402367.890 6685750.000, 402250.000 6...  \n",
       "2  Helsingfors     091  POLYGON ((403250.000 6685750.000, 403148.515 6...  \n",
       "3  Helsingfors     091  POLYGON ((403456.484 6685750.000, 403250.000 6...  \n",
       "4  Helsingfors     091  POLYGON ((402000.000 6685500.000, 401900.425 6...  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "fp = \"data/TravelTimes_to_5975375_RailwayStation_Helsinki.geojson\"\n",
    "\n",
    "# Read the GeoJSON file similarly as Shapefile\n",
    "acc = gpd.read_file(fp)\n",
    "\n",
    "# Let's see what we have\n",
    "acc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there are plenty of different variables (see [from here the description](http://blogs.helsinki.fi/accessibility/helsinki-region-travel-time-matrix-2015) for all attributes) but what we are interested in are columns called `pt_r_tt` which is telling the time in minutes that it takes to reach city center from different parts of the city, and `walk_d` that tells the network distance by roads to reach city center from different parts of the city (almost equal to Euclidian distance).\n",
    "\n",
    "**The NoData values are presented with value -1**. \n",
    "\n",
    "- Thus we need to remove the No Data values first.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Include only data that is above or equal to 0\n",
    "acc = acc.loc[acc['pt_r_tt'] >=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's plot the data and see how it looks like\n",
    "- `cmap` parameter defines the color map. Read more about [choosing colormaps in matplotlib](https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html)\n",
    "- `scheme` option scales the colors according to a classification scheme (requires `mapclassify` module to be installed):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot using 9 classes and classify the values using \"Natural Breaks\" classification\n",
    "acc.plot(column=\"pt_r_tt\", scheme=\"Natural_Breaks\", k=9, cmap=\"RdYlBu\", linewidth=0, legend=True)\n",
    "\n",
    "# Use tight layout\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from this map, the travel times are lower in the south where the city center is located but there are some areas of \"good\" accessibility also in some other areas (where the color is red).\n",
    "\n",
    "- Let's also make a plot about walking distances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3RWSXa3W+GPgRI9zPBLzj6vNFpdTPrj6HAQ+4+toNfIzxxbAc+D8dOaLx5kyuDauz+n7y2hxCWgV0JNI+/A/n57xZxGADSF4u5+e8SdqH/yl337m5uQwZMsQtW1pAXl4e/fr1c0ub/vnPf3Zfe/LJJ+nZsyeJiYmMGjWKEyeMH7OpqakMGzaM6Oho7r777iL9LVy4kJ49e9K9e3cefvhhn+N57rnn6NChA507d2bFisJo3e3bt3PxxRfToUMH7r333jKLKR09epRRo0bRtWtXunXr5pZTPXToEP3796djx45Mnz4dq9WKzeZgy5bv6d+vA1MmD2fK5OG89dY/3H2NvKIvk64awpTJw5l2TfHkGzB84n9/9jHGjO7P5ElD2bNnp/vahg1rGTb0EsaM7s9//vNPABo3iuSVV54hLi6Ol19+ucQ+K4OA4rRrOzpO+8IiI8/GiRwrTtf2h93j33BZV9qBUOBCaRBm4eqEllg8lOUCidN2ZmVy9JqRxQy2Jyo8gtaLV2OKKrsL5l//+hd2u5377ruvSLmIkJ2dTXR0NDabjcsvv5zXX3+dAQMGkJGR4VbQ++c//8mePXuYPXs22dnZ7Nixg127drFr1y53NmNqaiq9evVi+/btNGnShJkzZ3LTTTcxYsSIIvfcs2cPM2bMYMuWLZw4cYIrrriC3377DbPZTL9+/dz3HzduHPfeey9jx44NeJ5Dhw7l8ccfp1evS7HZ8qhfP4LIyEimTZvGlClTuPbaa7nzzjtp374rV026gY0bN/D+e2/x1tvzivU18oq+fLxoBQ0aNPJ5v/XfrmbevDnM/vdH7Ny5nef+/gQLFi7H4XAwftxA5i9YSr16Tbhm6ijee38uAwf0BuAvf/kL0dHR/PGPfwx4blURp63R1Dg2hxObUziVZ8cqUML+NCO6NS9Tn4EY+QIXioiN3SczSWhRv0z3yP52lV+DDcaKO/vbVcSMm1ymvgHmzZvHRx99VKxcKUV0tPElYLPZsNlsbvdOgcEGyM7OdpdHRUVx+eWXs3///iJ9HTx4kE6dOlGw6X/FFVfwySefFDPaS5cu5dprryUsLIy2bdvSoUMHtmzZQps2bcjIyGDgwIEA3HTTTSxZsiRgo71nzx7sdjsjR47kyNE0wIzFEoqIsHbtWj766COsVgfjxk/lxRf/zpUTrguoX3+sXbuciVddg1KKhIS+ZGZmcObMKY4fP0qr1m1p1eoisrKsTJs2jXXfrHAb7apGa49ogoIsm4N96Xnsz8gj11H6BmNVoID2jfwLP5WE/VxxH3lJOAKs54nVauXgwYO0adOm5D4dDhITE2natCkjR46kf//+7muPP/44rVq1Yt68eTzzzDN+79OhQwd+/fVXDh8+jN1uZ8mSJaVKnkJR+db4+Phi5YHy22+/ERsby+TJUxgzZhDPPvskR46e58yZs8TGxpKb6+DQ4XM0adKc06dS3O2SkrYzefIw7rxjBvv3/eouVwp+d/t0rpk6ko8/nlviPU+fTqF588Jo42bN4jh1KoVTp04S17xQEKtBw6YkJycHPJeKoo22plbhdDrJtdnJsNrJszuxOpycy7NxLCsfs4IQkyKkmuOlo0PNRIeaiQgxczbA7DlPLA0bB1TPHGA9T86eNYyWzz7NZpKSkkhOTmbLli1F0t2fffZZjh07xvXXX1+qqFODBg14++23mT59OoMGDaJNmzZlljwtqTxQ7HY7GzZs4K9/+zuffLKKY0eP8PHH8zhzJgsRSDmZ6ZZGNZlMWCwmunXryarV2/nss2+4/vrbuOeem939fTjvCxZ/sprZ//6I+fPfY9u24rl7JXmOlVLuC2azcR+bzUl+vqNKDjwoCe0e0QRMrt1JnsOBWSliQsw4ASVS7PSQ8iIiHMu2km6tvn3nsrhTrHYHm4+l0SQ6jKjQwP90ooaMJPXNF0v1aUcNGRlwnwVEREQU0Z72RWxsLEOHDmX58uXFzoG87rrrGD9+PE8//bTfPiZMmMCECYbmxjvvvIPZXFzNzp98q+dqtKzyrfHx8fTq1YumTVtx9mw2I0eNY+vWLUyaNIPz58+Tl2fFYrFw6NARGjdpht3uJNojlHLwkCv4618f4fz5VBo0aETTpsbn3qhRE64YMY6fd+6gb9+BRe7ZrFkcJ08W/ho4dSqFpk2bY7NZSTl5gvR0431POXGcRo2akptrJzKyaPJOVaBX2ppSyXc4OZGdz770XI5lWUnJtrL7fA67z+WQUokncziFajXY5eFERh7bk9PK1MYUHUPs9bf5rRN7/W3l2oRs0KABDoejRMN95swZ0tLSACPCZPXq1W5p03379rnrLVu2rFTJU4DTp43swfPnz/PWW29x++23F6szceJEFixYQH5+PocOHWLfvn3069ePuLg4YmJi2LRpEyLC3LlzueqqqwD47LPPePTRR/3e+5JLLuH8+fMcO2pEuWz8YT3t23cyjj4beDkrV34OwNIlHzN8+BjX/E+7V787d/6I0+kkNrYhOTnZZGdnAZCTk80PP6yjQ8fi8x82fDTLli5CRPjpp21Ex8TQpEkzevToxdEjB0lOPoLVauWrr5cwcuRYdxp7VaNX2hqf2J1O0vIdnM2zYlGFbgmLSeF0glkZm4MOpxNzJay2j2fnY1Fgr6UBTSKGq+RcjpV9Z7Lo2CQaESNm3FTKT/2COGzvOG0VHlHhOO1Ro0bx3XffccUVVxQpT0lJYebMmTgcDpxOJ9OmTePKKw2xo0ceeYS9e/diMpm46KKLihyGULBpaLVaWbJkCStXrqRbt27cd999/PTTT4Ahc9qpUyfAMPrbtm3jmWeeoXv37kybNo1u3bphsVj417/+5V6Rv/3229x8883k5uYyduxY9ybkgQMHimyMloTZbOaFF17k+usnISL07JnIjBkzCQkx89ijz3DX/93CG/98gS5denD11cYm5MqVn7NwwQeYLWbCw8J5+R//RilFauoZ7r33FgAcdgfjx09m0KDhACxcYGhsT792JoMHX8H69WsYO6Y/4eER/O3Z1wFDivbxx5/jjt9di9PpYPLkGXTu0q1KE2o80SF/mmJk2RyczbNhdTjJKyX9N9xsIt/hJDbMQnxUaIWST35OzS4hHqT2YLU72PCboSTcNCqUxlFhNMg9RZcuXagXHtjPYmd2FtnfrsJx7izmgozIcqywPdmxYwevvPIK//vf/yrUT01xww038Oqrr+JPjsJmc3A+LY+zZ2uPnIB3cs1FrWMxm0065E9T+YgIDqfgAGxOIcSkEIE0q50cu4MsV4hbeIA/9wQ4n2+neWQoIeW02Q6n1GqD7U1mvp3T2VYujRby7U7sDqdfedcCTFHR5Qrr80evXr0YNmwYDoejRD9zbefDDz8ssVxEyM21ce58LhkZ+dXiL64ISikeeughPvvsMx588MEqu4822hcYdqeT41lWHCLujMJQs8LqWlGHmhShHm6QUJcl9ZV9aFEQosBsUljKabCdTienc23u+1ZnpmNZUChiXSvqULMizGK4RUxKkW1zUM91wCsYBsfuFJSiSDJOVXHrrbdW+T2qkvNpueTk2IiJDiUiIoTcXBtpro0+a76D0BAzZrOJUNeZi1Zbze99mEwmQiwmLCEm94G+L730Ei+99FKV3lcb7QuIcwWZhF420dNI+nruC7uATSDSbCqXa0RESMmxkepS66vNhFhM9GlXmEG3Zs9JnBbB7nRid0K+3UmI2fjVYncKCrCYDUNfEbdRXccpQlaOjcz0PNLT84iMDCEnp/gGd20w1J44nU5sdidxcTGEV+NnrI12HcbuNH665zqcZNoc2J1SzGBXFlHlPMw0w+oICoMdCCKC3YFbZVAphdXu5HS2lVCTsSI3m4wVmcPjs7A7nZiUon54yAWn2S0ipGdbqy3GuSo4lpxOgwaRNIgNr9ITawrQRruOIQhZ1nzSrIocu4NQkwmrK4Mw1GwiTAyjkF9J1ttighBnoaEqK3kOJyHKWK0HMwXG1qQUyjUXz+cC2JxObHk5hB7aAtnnUZGxONoNgNAInCJk5tuICrUQEoBvPNixO5zk5tvJzLVhcwgWs4nQUOOL3+zx3FqLQ0BDLGb3OPPz7SQfT6dJ4yjq1Sv7GZFlQRvtOoKIDZQZOMihzDh3eafYzb4blYGfU4ttYgNgdxoG93y+nSYRZf+J6JTgNdgjujVHUg4TExFCpite3eGxner9PHzHEsJ/WoayF55yIj/MJS9hInm9JuFwCvkOG/XCLESEBN+GYmmICPk2B+nZVrLy7MRGhZKTbxhlG0CEsV+QmV56slBt4HxaYehmaIiZ/HwHycczaJxnp3HjqCoLAaz7X+l1HJvTSHyxOfcCG4DA9Rwqk3ynlDkxRkTIddTelVRlEr5jCRHbFxUx2ADKnk/E9kWE71hiFIiQkWcjK98esMvAlzQrQFpaGlOnTqVLly507dqVjRuNdO1z584xcuRIOnbsyMiRIzl/3tCePnz4MBERESQmJpKYmMisWbPcfQ0dOpTOnTu7rxUk23jjLc3qdBoukK/WfEdiYgL9evfg6SceLrNL5OWXn2HihMFMuPJy/v7sY8XaP/u3R+nbp6379do1XzN50lC3/Or27YULmA0b1jJ+3KVFpFW9KU2ataT2L7/0NN27t+fFF6tuM1KvtIMQwQqkcT4vjLN5oQhCo/AwjHOTq5cws0JcEqmZtiyiQiIIMQV2qmm2zUG+w0mICQI4ZCZ4seYQ/tMyv1XCf1pGXvdRmMOjjCYOJ458ISbUUuqK7b///S9TpkwpMdzvvvvuY8yYMSxevBir1UpOTg4Azz//PCNGjOCRRx7h+eef5/nnn+eFF14AoH379j5Pdp83bx59+5b8qwsMNb4FCxawe/duDh89xphRo/huy084RPH4Qw/w8qtvcnFiX2ZedzXfrFlFrwFD/c6tgB07trJjxxY+W/INADfeMIGtW3+gX7/LANi1K4nMzIwibfoPGMyw4WNQSrF3724e/MMdfPHl9zgcDp792yP8592PadasBdOnj2bYsNF06NC5SPsN69dw5Mghvl6+iZ07t/PM0w+7pVm920+4cgKtWnfg0ceeITY2Boul6tbD2mjXcoRcIB9wAlYgB2M17aRBODQowX12caPSE4083R2B1A+kjnFQkcI4LrR0UvPtdcZYx0SUHEOcmWsj9NCWYitsb5Q9n9BDW7B2HgoY7hSbw8g4rR8e4tfP7UuaNSMjg/Xr1/P+++8DEBoaSmiocejs0qVLWbduHQAzZ85k6NChbqNdEZYsWcLkq68hPdeJOaop8Re1ZWfSduo3bkFmZgZdLu6DzSFcdfW1fPLpZ0WMttWPW0QpsObnY7MZm5Z2u51GjYxkHIfDwcsvP81LL81m9eqv3G0atCiM9MnNzXG77n7++UeXtGobAMaNncQ3a5cXM9oF0qz16oWXKM3atWsnsrKsjBs7ia+//oJbbr0HgJAqdm1p90it5wRwCOPgn71AOoYBr61kEWiaTEQVrkZqE6ac86VXAkw5acXKHE4h24/byZ8068GDB2nSpAm33HILvXr14vbbbyc728goPHXqFHFxxt5HXFxcEVfHoUOH6NWrF0OGDGHDhg1F+rzllltITEzkr3/9axH3hN3hJC0rn1/3HyamQTOsrkORm8e1ICXlBKdOnqB5XKFAVPO4Fpw6mUKgJCZeQr9+lzF0SE+GDunJZZcNpX17I43+o4/mMGzYaJo0aVas3cqvP2f04L78ftYN/PVvr7rmXlRatVnzFpw6XVxXPVBp1ubNW3Dm7CmaNomiXdsGhIdX7Vr4wvirCVIM45eH8YMoyvUI9Xhe2wgBwoHTARnuJuEhNIswUS8kD1Wrv4gqhjOyQYD1You8tphMWEwmlJ/30p80q91u58cff+T3v/89O3bsICoqiueff97vGOLi4jh69Kg7Nf66664jI8NwO8ybN4+ff/6ZDRs2sGHDBubOnYvV5uB8Vj4p53LIc325mM0Ki1kRGmLCZFKYzaYKS7MeOXKIgwf3sWZtEmu/+YnNm79j27aNnD59khUrPuf664uLVwGMGjuBFeu38cab7/PGP12/JEoaSwltC6qZzYqwMMNN5SnNajIpWsTF0Lx5NDHRYTRuHFUt8draPVJLMYzeYcBbGL9ytBcCc3eUFRvGl8yvQB5Ca1SJfw4GSimaRuZTeKZz4PiKZqmNWNv2I3LjXL8uErGEYW3br0hZwQnzdif4OivHnzRrfHw88fHx7oMPpk6d6jbazZo1IyUlhbi4OFJSUmjatCkAYWFhhIUZexJ9+vShffv2/Pbbb/Tt25eWLY1VZ1RUNJOunsba9T8w9qppZLgSYfJtTpo2iyP52DGyco3Y+xPHjxMZ05jmcS05mXLCPbaTKSdo2ixwWdw1q7+iZ0IfoqKMxcrlg0bw00/byc7O4uiRQ4wdMwCAvLxcxo4bwJrvk4q079t3IMeOHeb8+VSaNY8j5WThWE6dPOGWavWkQJrV4RAaNowgNfUUl1/Wg8OHo1m+fBFxzWMwmRQnTpwok8xsRdEr7VqIoQqyHzha00OpACeBXQh+DBWplMdgBx2hkeQlTPRbJS9hIoT63kh2+oir9yfN2rx5c1q1asXevXsBWLNmDd26dQMMCdUPPjAU7T744AO3TOqZM2fcUSgHDx5k3759tGvXDrvdztmzxgLi1Pksli37nPYlyJmOGDWOL5d+Sn5+PseOHubwoQP07NWHps2aExUdzY7tWxERliyezxWjxwOGC+Pl5/7i9/2Ja9GSbVt/wG63Y7PZ2Lb1B9q168iQISNZv2EXq1ZvY9XqbURERLoN9pFDB9wr/D17dmKz2YiNbViitOqwYaOL3bNAmhWE3bt2UL9+feLi4rjkkkvYt28fR48ewWazsWDBAiZO9P/5ViZ6pV3LEPIwDDZAgfpbVg2NpqxEUvhDMwRj4/RXhEZAUxShXvWTwV3m/wzFYCev1ySA4nHaljB3nLY/HCKYfPxq8SXNCvDGG29w/fXXY7VaadeuHe+99x5gSLNOmzaNOXPm0Lp1axYtWgTA+vXreeqpp7BYLJjNZmbPnk3Dhg3Jzs5m9OjR2Gw2rDY7lw4ayk0334rFbGLNyq/4+acd3P/Q43Ts3JWxEyYxdlg/LGYLf3n2H+6olqefe4U/PXAXeXm5DBk2kiHDjUMfjh45VOTAgpLnOIHNm75j8qShgOLyQcNKNLSeLP9qGUsWL8BiCSEsJJSX//EOSqkSpVUL9LRLkmYdPKg30dFR7vfOYrHw5ptvMnr0aBwOB7feeivdu3f3O5bKJCBpVqVULPAu0AMjuetWEdnoVWco8BrGX+tZERniKn8AuN3V7mfgFhHJU0q9BEzA+Ms+4CpPU0q1AX7B2HUD2CQihcGiJVBXpFmFdGA3rlSDOkQEYAd6oYhwlwo/ApnVMoKqcqdIymE6BnCAgBtrDmr/JshOg6hYpMMAMh3+1evMJkXDyFCfmt3VLc2alWvj5PnK+5J98J7f8dhfniPGUjGJWoDQ+mXLRvQVsVKvXhgZGcaXa8sW9ahfhn6rWpo1UPfI68ByEekCJGAYVc/OY4G3gIki0h24xlXeErgX6CsiPQAzcK2r2Sqgh4j0BH4DPI+uOCAiia6HX4NdVxB3lEhdM9gF2ICfEFIQziL8hvF9fYERGol0G45cMgXpNtyvS6QA5VIS9IWnNGt1YKnkE1r+8cZ/aNSo7OdjVgdKGbrZgfLQQw/x4Ycfun3vVUGp7hGlVD1gMHAzgIhYKf7Xdh3wqYgcddXxTJWyABFKKRvG7+cTrjorPepsAqaWbwrBjeDE2HBMxXh7Cn4mVs8KtHqIovCf2hmMVTcY8y34g6hL860cCvRMQgPQIqlOadbSTumpC5hNhv5JREQI5jJowdQWadZ2GH9p7ymlEoDtwH0i4hnG0AkIUUqtw7A6r4vIXBE5rpR6GWNHLRdY6WWsC7gVWOjxuq1SageQATwhIhu8Gyil7gDuAGjdunUA06h9iNszVPAdl1ODo6lKvCNgqp+yJhxVJ56JOZkeZ246XJuPllqk/CcipOcUrtnqRYa4o0eCHU/XisPpxGp1EBMdWHZvdRLIV4gF6A28LSK9MGLOHimhTh9gPDAaeFIp1Ukp1QC4CmgLtACilFI3eDZUSj2OsfSa5ypKAVq77vUH4CPXar8IIvKOiPQVkb7+jimqrQiZwI/gJ7pCowEIqaYDYwPB6RTy60oaawCUV72yKglkpZ0MJItIgdrKYoob7WSMzcdsIFsptR7D9w1wSETOACilPgUuBT50vZ4JXAmMENeOqIjk47JkIrJdKXUAYyUf/DuNLoRsDDe+GeMjKPhOyvDZRlNHcORjzvwV5chGzFE4YrqAueTVnLlAg7sWuSNMJkMTPNSVzVpdh9lWN5YQM+GRIYSE1j61xVKNtoicVEodU0p1FpG9wAhgj1e1pcCbSikLhpOyP/AqhjNzgFIqEsM9MgKX8VVKjQH+BAwREbdfQCnVBDgnIg6lVDugI3CwgvP0P0dsGB6ck0AY0AFFbBXeMYXCML666hIJPjxdKFXhKrGk/oAldRNKCt0JcnoN9kYDsDe6tFh9h1OwWFStOvVGKYXJpNxp6uFVfDRcWaNBKq3/EBNOuxlqobZ5oCO6B5inlNoJJAJ/V0rNUkrNAhCRX4DlwE5gC/CuiOxyrc4XY/gBfnbd7x1Xn29i+L9XKaWSlFKzXeWDgZ1KqZ9cbWeJyLkKzrMUzmFshNkxvD+HEX51GfPKxeiz9pworakeLKk/EHJ2QxGDDaDERsjZDVhSfyixXSAHIviTZn311Vfp3r07PXr0YMaMGcWScF5++WWUUu7EmS1btrilVxMSEvjss8/cdbdv387FF1/MwD49eOZJ39Kqs9/4ByMuS2TUoD5sWLfaXb5r5w7GjxjIiMsS/bYvIC3tHDdMvZKEji14+vGi4XO++vpk4Tz6XdyOCSMvZ8LIy/n4IyPuetP3691lE0ZeTvd2TVm1/IsifT7zxEMkdCya2ViQlQpGElLHjh3p2LGjOzEJDK2W/v3707FjR6ZPn47Vavj8Fy5cSIcOHbjyyiv9zrOsBGS0RSTJ5T/uKSKTROS8iMwWkdkedV4SkW4i0kNEXvMo/7OIdHGV3+hyfyAiHUSklXdon4h8IiLdRSRBRHqLyOeVOuMSycf40VHf9QjHSMfei5AWsABSYJz2upemzuPIx5K6yW8VS+omcBTd37CYTIQE4H7wJc16/Phx/vnPf7Jt2zZ27dqFw+FgwYIF7uvHjh1j1apVRTbye/TowbZt20hKSmL58uXceeed2O1GtM/vf/973nnnHfbt28exwwf5Yf2aYu6Rfb/9ypdLP+WrtZuZM+8T/vzYg+4vkz8/+gf+9sLrrP5uB0cOHWD9N6vxR2hoGPc//Dh/evKvxa7562v8xCl8vuo7Pl/1HdOumwnAgMsGu8v+9/EyIiIiuHzIcHebn3/6kYz0dPdrpQwXUMHszp07x9NPP83mzZvZsmULTz/9tFuD/E9/+hMPPPAA+/bto0GDBsyZMweA6dOn8+677/qdY3mofWv/GiEDw1Cnux6nXP9PBX7CiC+u+OrYMP7JGNEUBfeqAnZ/X/KjrG01lYI589diK2xvlNgwZ+0tUmZ3OgNaLsybN8+dhu6N3W4nNzcXu91OTk5OEY2MBx54gBdffLGI+yUyMhKLxfCa5uXlua+lpKSQkZHBwIEDMZtMTJ9xHV9/+Xmx9Po1K75k/FVTCAsLo1XrNlzUph07d2zn9KmTZGVm0qtvP5RSTJo6g1XLv8Canud+FBBaP5zQ+uHExjWib7+BhIUVdWH46itQln+5lMHDRhIRYcTIOxwOXvjrUzz9t2fddWx2J0pB/SgjJHXFihWMHDmShg0b0qBBA0aOHMny5csREdauXcvUqUbE8syZM1myZEnAYykP2mgDpbsr0oHtCEdduiDl5RzGCl5zIaEcgX3hK3txuYLcUk4g9yfN2rJlS/74xz/SunVr4uLiqF+/PqNGjQJg2bJltGzZkoSEhGLtNm/eTPfu3bn44ouZPXs2FouF48ePEx8f767TPK5lidKqp06mENfCs14LTp6suDRr0Xv472vFV8u48opLuft3N5JyPLlY+y+XfsKVVxWmhfzvvXcYMWoszZobUrUWs7HKjo0KJTzU+AI7fvw4rVq1creJj4/n+PHjpKamEhsb6/6iKyivSi54oy3YMaI4/O0S18eI8MgEdiMEpo9cnLPUTklVTVUi5sA+c/FM41aKMIsZi8n/n6g/adbz58+zdOlSDh06xIkTJ8jOzubDDz8kJyeHZ599lmeeeabEdv3792f37t1s3bqV5557jry8vGL+5xCLCYvFVMw94kuCtaLSrIHcA2D4yLF8s+lnvlj9A5cOGsrD9xdNqD596iR7f93DoKEjAONLZvkXS7jx1jvdyUwxESE0jY0gJrIwE7I65hUoF7RglHAWI/TOBn6jRbzdGOcRWgCtUQQWfC/kYESnVCJldWH4qt/9ssqprykRR0wX5PQavy4SUSE4ojsTYy6qQxJaik/bnzTr6tWradu2LQV5DFOmTOGHH34gISGBQ4cOuVfZycnJ9O7dmy1bttC8eaFEadeuXYmKimLXrl3Ex8eTnFy4ak07e4oWLVvg8DBa9aNCjEMPThTWO5lygmbN4nxKs5YnOsSfzGuDhg3d5dOvv5mX/v6XIm2/+vwzRo29kpAQ433es2snRw4f5IrLeqGUccJN74Ru/PLrb0SGFZrH+Ph490k/YLxnQ4cOpXHjxqSlpWG327FYLCQnJ1e5TOsFvtLOofxaH9nAVoTkIlEmQr7LQONRlkVwy6xqKoQ5DHujAX6r2BsNKDFeu7QYbX/SrK1bt2bTpk3k5OQgIqxZs4auXbty8cUXc/r0aQ4fPszhw4eJj4/nxx9/pHnz5hw6dMi98XjkyBH27t1LmzZtiIuLIyYmhk2bNiEifPjh/7hmymTCQ8w0rhdGhCueuaqkWT3x19fpU4ULozUrv6J9h05F2n6xZHER18iwK0azMWkf6zb/zNaffnFLu6Zl5eNwFEaOjB49mpUrV3L+/HnOnz/PypUrGT16NEophg0bxuLFi4GiMrdVxQW90jZisgt+ugb6VsS6/h+OIUOaCuQgmDBcKIbfWogFmgNHMNwqkR5t0yo2bE3QURCHXSxOW4X4jNM2q8BitH1Js/bv35+pU6fSu3dvLBYLvXr14o477vDb13fffcfzzz9PSEgIJpOJt956i8aNDTGnt99+m5tvvpnc3FzGjh3L+HHjUEqxbNkytmzZyp8ee7LSpVmH9r+YrKwMbFYbq5Z/yXvzP6Njpy4++5r739msWfk1FrOF+rENeOG1t919JR87wsmU4/QbeHmJ9wq1mEFBRJgZnMLab39g4Ufv8+6779KwYUOefPJJLrnkEgCeeuopGrpW9S+88ALXXnstTzzxBL169eK2227z+x5XlICkWWs75ZVmFdIwokMAWmIcmFtRWgHHXM+bUqgrUgXUVITHBeYe8ZdoU2ZpVkc+5qy9KHsWYonGEd3ZZ0ZkiEkRUopPG6pfmtUXdoeTM+l5ZOfZqR8VQnp2YL9iC6RZa5PSX7gIGRn5tG3TgAgfhzYHwrp163j55Zf54gv/0S1VIc1aR6nabCutK6IphjkMR/2e2BtdiqN+T58GG0p3jRRQ3dKsvrCYTTSNjaCs+3C1WZo15WRmqUlAvli4cCF33XUXDRoEdkZooGj3iPt0mMp6KyKAgg8pnMLvxfJGnGguVMpi+6pTmtUfCgizBP9aMDzMgiPKMNanT2djMinCwszUqxf4Qm/69OlMnz690sd2QRtthXJtIuZTedmJv5W/abAktFT1OGuZ+6WqNUlKwhKgP7u2YTIpWjSK4mxG4cZobFQoadm1/8ALzwSftPrhUCCKZXOQmWn8au4cFVomfe2qIPi/EitMVbtINJqyU5uU/cqKyaSKaITXJXJqgXb4Bb3SNogBHOi3QlM92DGrsyisCKE4pDGe//YK1C6CXfHUIUJUhDEviyW4JxMRbnH7tW32mt03AG2pMDIhs6hW8aZgcYPUFGV9f6rRneJ9As6vp+sRYckh1176WY8WdQyL6RhKFcb/ihxEqdYoLsIhQr7DGXCoX23GYjZhcrkXzBYTMTGF2YWZmYWuEl/lNYGvRJ/TZwplCMLDat5kaveIdo9oqgGLOkaI+UgRgw24Xh9GOOIuK+sq21uadcyYMcTGxhaTBPUlIbpu3Trq16/vlmQtSG/fu3evuywxMZF69erx2muvFenTW9rVm7WrVtL74u4MSOjOG68Unp2Ydv4cM6+9ioGJPZh21XjSzpe+Uf/IH/6P/j3bM2540USl5//6BKMH9+XKKy7lrtuuJyM9zX3t1z27uGbCFYwd1p/xIwaSn5dHbm4Ot994DaMH92XssP689Pc/u+vn5+dz36ybGXFZIldfOZzkY0eK3Cvf6sBmc7hlajt06MC9997rXonn5+czffp0OnToQP/+/Tl8+DAABw4cIDExkejoip84r402YRgukpr/BtXUVexYTMdKqXMUxI4JhalMcSPFpVkfeuihEmO2fUmIAgwaNIikpCSSkpJ46qmnAOjcubO7bPv27URGRjJ58mR3m5KkXQsItZiItCge++P9fLr0C9Zv3cFnixex99dfAPj3v17l0suHsDFpF4OGDOWNV18udZ5Tpl3Hf+d9Uqz8ssHD+HLtJr5Y/QNt2rVn9puvAIbC4R/vvYNnnn+Vr7/ZzIeLvsTiSl+/fdY9rFi/jaUrNvDj1s18u3YVAIvnz6Ve/VjWfJ/ELb+7i5ee/XORe0WEW7DZnEVkavft28fy5csBmDNnDg0aNGD//v088MAD/OlPfwKgffv2JCUllTrHQNBGm3AKD0DwgS+p0/I+NJVLLXifIyw57oc3ZnW22Aq7OE7schongtXpLNPZhN7SrCNGjCAmpmiGYUUlRNesWUP79u256KKL3GUlSbsWYFKKn3dsp0279rRp15bQ0FAmXX0NK778gpiYUL5Z9TU33mJoXU+77gaWf1G6bH6/AZdRP7Z4zPOgISPcKnuJvS9x65J89+1aOnftTtfuFwOGLonZbCYiIpIBlw0GIDQ0lG4XJ3AyxUisW73yK6Zccx0AY8ZPYuN33xaJ027cOIr09LNumVqlFDfddJP7vVy6dCkzZxrzmjp1KmvWrCl3nLcvtNEOUPBJoykvisB8tcqlYSOA1eEM6I/dnzSrJ6VJiG7cuJGEhATGjh3L7t27i7VfsGABM2bMcL/2J+1awIkTJ2jpIeca16IlKSeMe545c9othdqseRxnz54pda6BsHjBhwwZZqS0Hzq4H4Xilusmc9XoQbzz1mvF6mekp7F21dcMvHwIYKj+NW/REgCLxUJ0vXqkpRU9OMtbptbzvfSUcLVYLNSvX5/U1NRKmVsBF7xPQGFCaIB+KzRVhRBaeiVACCkS6md1CqEm/1Kf/qRZi/TtR0K0d+/eHDlyhOjoaL766ismTZrEvn37CsdhtbJs2TKee+45ALe068qVK/3eL9dmR7n+875nVfDW6y9hsViYOGUaAA6Hne1bN/LJV+uIiIjgpmkT6XFxIpcOGgoY7pMH/u82brp1Fq0vausetzfeY/ZXpzqkWvVKGzBC/rzcI9qdoSkDufZI98MbhzRGxP+fmogJhzTGIVLkke8Qvytuf9KsnnhKiAJFJETr1avn3iAbN24cNputyMbi119/Te/evWnWrBlgbKoVSLu2adPGLe168uRJ11yEzHw7DZs0J/nYMfdxfSknjrsPL2jSpKn74IJTJ1No3LhJqXPwx6cff8Q3q1fwjzf/4zaSzeNacMmAy2nYsBEREZEMGT6K3bt+crd54uH7uKhte2753V3usuZxLTjp+jVgt9vJysigfv2iLhlvmVrP9zI+Pp5jx46526enp7uFpSoLbbQBI/Vco6kqLNidrfzWMK4X/7Vn+Lh9G25/0qye+JMQPXnypLv/LVu24HQ6adSokbvt/Pnzi7hG/Em7Fhhsu1NI7NOXgwf3c/jQIaxWK0s+WcSocYaE6qhx4/n4ow8B+PijDxk93oh0OZlygpumTfA7F2/Wf7Oad956jdnvL3AfIQaGr3vvL7vIzc3BbrezddN3dOhoiHu98sJfycxM54mnny/S14hR4/h00UcALP9yCQMuG1xspewtUzt37lz3ezlx4kT3ob+LFy9m+PDhNbPSVkrFKqUWK6V+VUr9opQaWEKdoa5T1Xcrpb71KH/AVbZLKTVfKRXuKm+olFqllNrn+n8DjzaPKqX2K6X2KqVGV8ZE/ROFdo9oqhK7tMLmuKjYilvEhM1xEXYpbtTNSrndJTY/hrtAmrWAQYMGcc0117BmzRri4+NZsWIFYEiIvvLKK3To0IHU1FS3hOjixYvp0aMHCQkJ3HvvvSxYsMBtaHJycli1ahVTpkzxOz+H00m+3cGvB49y9VUTMCtFVHgYL73yOlMmXMmgvolMnHw1Xbp2A+CeB/7It9+sZWBiD779Zi33PGCctn7m9EnMlpL/Fu+/61amTRzJoQP7uLxPVxbNnwvA00/8keysLG6+dhITRl7Ok3+6H4D6sQ249Y67mTJuGBNHXU63ixMYdsVoUk4c5+1/vsz+3/Zy1ejBRU5tv+baG0k7f44RlyXy3jv/4o+P/cV9/ymTCw8Cfvvtt7n99tvp0KED7du3Z+zYsQDcdtttpKam0qFDB1555RWef77ol0JlEJA0q1LqA2CDiLyrlAoFIkUkzeN6LPADMEZEjiqlmorIaaVUS+A7oJuI5CqlPga+EpH3lVIvAudE5Hml1CNAAxH5k1KqGzAf6Ae0AFYDnUTEZypSeaVZCxBOYiTYeEizardI3aUSk3F+/aUeXbp28Hm9uLukICPShhBSLCOyNCwuQ25Shb7S6pZmFRHy7E5sDqdb0S/PbiQF2VwHB0SEmH2eb+kvieZ/771Di5bxjBg1rtLHXRrRERaycg33UbMGEVWSih8dHU1WVvGzQMsizVrqvxalVD1gMHAzgIhYodh2+HXApyJy1FXHU0TaAkQopWwYJwEUnBN0FTDU9fwDYB3wJ1f5AhHJBw4ppfZjGPCNpY21/IRjGG2Npqqx4JDmpVfzgVMEuwgKsJgUFqWKSLMWxGpXFXaHkyyrg3xXOneI2eQ21GZzxd0AN97i/5CGYOXAgQNcffXV7n2BihDIV3w74AzwnlIqAdgO3CcinkdMdwJClFLrMDJVXheRuSJyXCn1MsZZW7nAShEp2HJuJiIpACKSopRq6ipvCWzy6DvZVVaFhAN1U+BGUzcocJMoCiVbRSDfKZiUMPPmmzFVQ+p7ltUOKMItxpeDyQRm5XquCp+HmE2lniRf24gItRAeaiYy1IKlkuVlKzO5JhCjbQF6A/eIyGal1OvAI8CTXnX6ACMwdvU2KqU2YRj7q4C2GGdsLVJK3SAiH/q5X0n/8or5cJRSdwB3ACVmZJWNMMB+QblEbCu3u5+HjOpTanmdIggPLHb4cWM6xXgIYrhPTKpKDHie3UG+3enX9eFZt7biKcHarWtTPzVrJ4F8nSQDySKy2fV6MYYR966zXESyReQssB5IAK4ADonIGRGxAZ8CBYfhnVJKxQG4/n/aoy/PXZl4Cl0qbkTkHRHpKyJ9C06bLi9GHKlOstEENwLYRLA6DOEph5/NyzL3LUJWvp+sYU21UarRFpGTwDGlVGdX0Qhgj1e1pcAgpZRFKRUJ9Ad+wXCLDFBKRSpj12SEqxxgGTDT9Xymq4+C8muVUmFKqbZAR2BLuWYXIMZBCNo9oql6HCJkWO2cy7OTYbX7XUEHgtm9Mak8nhvX7CLYnIKtjGnxJWFzCCFmE+EhZkJq+BCAC51At63vAea5IkcOArcopWYBiMhsEflFKbUc2Ak4gXdFZBeAUmox8CNG9soO4B1Xn88DHyulbsMw7te4+tvtijLZ42rzf/4iRyqHDAz9kbqBp4ujIvV9lV8QbpNKcpV4apGczoEzuQqnZ4Zgrp0GYRYahpcv5DQgoy9gK3CdeEWeBILTaazc81wukbwK+qprWo7VlwRrsBDQV6aIJLlcET1FZJKInHcZ69kedV4SkW4i0kNEXvMo/7OIdHGV3+iKCkFEUkVkhIh0dP3/nEebZ0WkvYh0FpGvK3G+PtCuEU3VcjoHTuWaihhsMFwa5/KNlXd5yc3NZfTwwoN9rxo/jhaNG3H1VROL1Lvphhvo2rULPS6+mJtvuYW8/EKDuW7dOhITE+nevTtDhhg6HMeOHWPYsGF07daVfr168p+33nTXP3/uHNOuGl+qtOraVSu5rHfPYtKsBe2vuKwXM6+9ivS08kuzFvDu7H/SsWV9zp0ztD5sNhsP3zeL8SMGMnrIJcx+4x/uurt27mD8iIHFpFWPHj3KsGHD6NWrFz179uSrr74q8V5amrWmkVDIKT0VWKMpDw6nscL2x/l8e7ldGHPfe4+Jkya7w/3uf/BB3n3/ffd1izJCA2dcdx0/797Dj0k/kZOTy9vvvEOe3UHquXPcddddLFu2jN27d7No0SKjncXCSy+/zMYfd7Jm/Xe8959/u6VV33j1ZQYNGepXWtXhcPDog/fz0SdLi0mzFrRf/f0OLr18CP/+16ulztOXNCtAyvFkvl//DS1aFm6Hff3FEqzWfL5cs5Ely79lwYfvu/Wx//zoH/jbC6+T9NNu9uz5lcWLl2K12vnb3/7GtGnT2LFjBwsWLOCuu+4q8X41Kc2q0wABCIHMkkXcawNldXdUNWV1mwRlpEolukrSrRRbYXsjQJbNQb3Qsv9JLpz/Ee/9rzAga9jwEaz/dp37td31ZTBy7FgKHBt9LrmElBPHcQIfzvuIiZMm0cKlXNe0qRFRERcXR4PGTUnPsxEVHUPHzl04eeIEnbt0ZcWXX/DpV0am5bTrbmDKuNE8+cyzRca1Y9tW2rZrz0VtDTGmAmlW7/aTr7mOG6aO5+HHn/E7z34DLit2KEEBz/7lUR5+/Bl+f+t17jKlFDk5Rgp7Xm4eISEhREfHcPrUSbIyM+nVtx/p6fmMHjOFRYs+ZdSoMSilyMjIACA9Pd2tKeJJSkqKW5oVcEuzjh07lqVLl/KXv/wFMKRZ7777bkSkUlPZ9Uobl38vJKb0ihpNObCXJqVdxnqeWK1WDh06xEWlSLN6YrPZmD9vHiNHGwoR+/bt4/z5NIYPG0av3n34z3/fw+6Shi0I7Tty5DC7dibRu+8lQGDSqikpJ9xfBOBbmrVps+akppZfmnXNyq9oFtfCrZtdwJjxVxEZGcmlvToxpF93bpt1D7ENGnLq5Am3cBVA82YtOH06BUuIib/85S98+OGHxMfHM27cON54441i99PSrLWFShYq12gKCDRPozz5HKkBSrN6cv/dd3PZoEEMGWQcBOBw2En68UeWr1pFdnYOQwZdTp9L+tG5c2fMJoUjJ5eZM67lmedfIqZevYDvUx0ypbm5Obz1z5d5/6PPil3bmbQdk9nM9z/uJSM9jRmTx3DpoKE47MYXksNqJzQ6jOiYUCIjQ4mKDOXfs+dz88038+CDD7Jx40ZuvPFGdu3ahclU+OHUtDSrNtoFWNMKnzfqD6mbfVatDmqbSyQQAhlzIJEqdc1VUj8UUrLFr4tEAdEhZU9BDw9QmrWAv//1Gc6ePcP8t992u03iWrbkikaNCIuMJCwykssHDWLP7l106dqFjKxcbpw+jUnXTGP8xEnufgqkVZs1jysireoZDdKiRUtOeEiYliTNWtC+SZPyJbkcPXyI5KNHmDDycgBOphxn0ujBfPLlWj7/bBGDh15BSEgI/Xu2Z+iQQZw9tpeOHRM5eeIEjlw7IbER5OWep1UrY+U8Z84ct3964MCB5OXlcfbsWbfLCAKTZo2Pj9fSrFWOWUeQaKoGswmaRPj/JdcgzFKuLMZApVkB3p8zh9UrV/L+h/OKrByvnDCR77/7DrvdTk5ODlu3bqFzly6ICA/83yw6du7MrLvvK9KXL2lVTwqkWY8cPlxl0qydu3Zn884DrNv8M+s2/0zzuJYsWbGeJk2bEdcyno3fr0dEyM7OZtOmTXTp0oVmzZoTGRXN/n0/ExMTWkRatXXr1qxZswYwRJzy8vLwTt4LCmnWCwJzNES2Mh7m4I7j1NQ+mkZCswgnJi9FBhNCwwrEaQOMuGIkP3xfKM06cugQbrz2WtatXUvHNhexaqWx4Xfv/93F6dOnGXb55Qzo04fn/vZXALp07crI0aPp37sXgy8dyM233Er3Hj3Y+P33LF7wEd+t/5YRl/VnxGX9Wb3CWIX6klY9dTKF2280zqG0WCz8/aVXmTF5QpVKs/rihpt/R052FuOGD6Bv30uYcf2NXNS+C2ERFl5/7Q0ee+w+OnXqVERa9R//+Af/+c9/SEhIYMaMGbz//vtuo5uYmOjuu9ZLs9Z2KirNCiDpv8DxpSDOSnOPBKOLozZTK9wmXu6R0qRZvXE4jWgSu9PwYdcPNVbiBZR08k1pJO3YwRuvvcYc1wqvMsnMtVV6nz7vlWmtMmnWepEhZOTYMClo0SiS8HJE6VQG1SLNesFQrwvU6wx7XqjpkWjqMGYTNKzkH3KJvXoxeOjQapFmrWqqUpo11GKiecMIQi3V/x5VtzTrBYHxE0ghUe20e0QTdMy85ZaaHkKtJtRiolG9cMymyvUvB4pOrqlKHJng0NmRtZELQQvFU6ukPK6Sysbz9JbqdJWUhQ4tCsMQz6Tnkp5tjDM81Eye1UGD6FDqR4VW6Unw1Yk22t6Yo2p6BBqNphJQCpo3iCC6Co4Nq0m00fYmvCk4a+eKQhP85Nud7DubT7bVSVSoiY6Nwwir5FNSLmRMJkV4qBmTSdEoJoywcsS+13a00S6JfI+U2sjWkHMUwptD/e5wak2x6jpKpGap8sScSpJp3XI0m63HcrA5CyO2vj2QxSWtIunXuvgvPE9XiS+q2oUSDC4RT5xO4/zMZvXDMddR3e+6OauKYPH641EWaHU1tJwAzvyaGZMm6NlyNJsfjmQXMdgANqfww5FsthzN9tGydAKVZi3gwfvuo2lsfffr9PR0pk66iv69e9M3oSdzPRQCfUmr7tr5E+OGD2bEZf0ZNeQyfty2tcR7lSbNWpq0qye+pFmnT59OYmIiiYmJ9OnZhfFXXIbZbGLevHnu8sTEREwmE0lJSWRmZhYpb9y4Mffffz8Ar7zyCt26daNnz56MGDGCI0dKFqjS0qy1CUs9MIUbSTZRbaDpMFS9znDuR1ChpTbXaLzJtzvZesz/qnnrsRzyy6MYRenSrJ78uG0baWlpRcreefstunTtyuYff+Tr1Wt47OGHsFqtfqVV//rk4zz4yOOs+X4zDz/2JH996vFi9wpEmtWftKs3vqRZFy5cSFJSEklJSUyePIUpU6YAcP3117vL//e//9GmTRsSExOJiYlxlyclJXHRRRe52/Tq1Ytt27axc+dOpk6dysMPP1ziWGpSmlUbbW8s4eDMg7xT0ORyVGQzJPsInN8OzjxsK7cXe/giZFQf90NTdVT5+7z7e98PWz7kFk+W8GTf2fxiK2xvbE5h/9ny/ZJbOP8jrpxYuKoeNnwE0THFVSsdDgePP/In/uadpacUWZlZRrp3VhYNGjbEYrGwbcsWt7RqaGioW1rVaKLIzDQkTDMz0mnuUuzzxFOa1bv9ii+/YNp1NwCGtOvyLz4nJia02MOTfgMuo35sA5/vg4jw6SeLmTFjRrFr8+fPL7F83759nD59mkGDBhnv3bBhREYaLqcBAwYU0RgpwFOaVSnllmYFWLp0KTNnGqcoTp06lTVr1lTaOZ0FaJ+2N+ZIwAStrkZFtTbe8JPF/dgaTaBkWwNbQQdaz5OySLPO/te/GHflBOLiihrYWXf9H9MmT6J961ZkZWYy96OPMJlMnDhRXFr1x23Gca3PvPASMyZP4JknHsXpdPL5qm+K3a8kadaC9oFIu5aVDRs20KxZMzp27Fjs2sKFC1m6dGmx8vnz5zN9+vQSwwHnzJnjTk/3pDzSrI0bNy73vLzRK21vQutD8ytQ0YZwO/lnwBSiNUk05SYqNLA/s0DreRKoNGvKiRN89slifn/33cWurV65kosTEjhw9Bgbt23nD/fdR0ZGhl+Z0Q/efYenn3uRH3/Zz9PPvcgf7v59sbrVIVPqia/V9ObNm4mMjKRHjx7Fri1YsKDENh9++CHbtm3joYceKnZNS7PWMpQ5AiIiCgus5yHnmPE8slXJjXygo0qqh1ot6wp0bBzGtwey/LpIQkyKDo3LrjTZIEbIz8slwpLjN5Lkp6QdHDhwgIu7dAYgJyeHi7t05udf9/K/D97nwYcfRilF+w4duKhNG3779VdatmzJqZTj7gSb1DMptG4dT0xECB/Pn8ffXjTOXJw4+WoevKf4sVxlkWYtkHb1xjtKxGKNIbSEEEm73c6nn37K9u3F/+Z8GeaffvoJu91Onz5F/82sXr2aZ599lm+//ZawsOKfiZZmre1oyVZNBQmzmLiklf/QvEtaRZYrXjtQadYx48ZzKPk4v+w/wC/7DxAZGcnPv+4FoFWr1qxbuxaAU6dOse+332jTrh19LrmEA/v3c/jQIaxWK4sXfsz4Kw3Z1ObN4/jhuw0AfPftOtq1Ly6aVR5p1pQTx5k6obhLojRWr15Nly5dirgtAJxOJ4sWLeLaa68t1qaklfmOHTu48847WbZsWRENbU+CQppVKRWrlFqslPpVKfWLUmpgCXWGKqWSlFK7lVLfuso6u8oKHhlKqftd1xZ6lB9WSiW5ytsopXI9rs32vld1IhKJ41QIjlMhODNqZ8yqpvbTr3UUl14URYiX9kWISXHpRVElxmkHyshRw/nuu42Fr31Is/rikccfZ9PGjVySmMj40aP469+fo3HjxlgsFv7x+utcNX4cvS/uwdXXTKVb9+4AvPzGv3j68UcYfmk//v70U7z0unFS+8mUE1x39SSgfNKsp06exGIu2QEwY8YMBg4cyN69e4mPj2fOnDnua75W0+vXryc+Pp527doVu/bxxx8Xa/PQQw+RlZXFNddcQ2JiIhM9NniDSppVKfUBsEFE3lVKhQKRIpLmcT0W+AEYIyJHlVJNReS0Vx9m4DjQX0SOeF37B5AuIs8opdoAX4hIcQeUDypDmtUXziM/Yf/UOHDU1O9qnFtKPg26LnJ82X7385YTA5cfrS1Uh6vkV8dwunS8qGhhhO9Y3Hy7k/0eGZEdGocRbimMmRYyyzyGHTt+4tVX3mTu//5T4vWqSMCpqqSbOf9+m/hWrRg9zlh5N4upW/tI1SLNqpSqBwwGbgYQEStg9ap2HfCpiBx11TlNcUYAB0ow2AqYBgwvbSw1gfPErpoegqYOEWYx0b15ROkVy0CvXgkMHTa4Tkiz3nZn8Q3NukBlSrMG4h5pB5wB3lNK7VBKvauU8v4t1wlooJRap5TarpS6qYR+rgXml1A+CDglIvs8ytq67vWtUmpQIBOpCkQcqMb1MPWfajziu9fUUDQav9x6641Bb7DrMgXJNQcOHKhwX4FEj1iA3sA9IrJZKfU68AjwpFedPhir6Qhgo1Jqk4j8BuByqUwEHi2h/xkUNeYpQGsRSVVK9QGWKKW6i0iGZyOl1B3AHWCc61YlZB3EVC8a2xfvA1C+fLXqJxC3hmedyuqzNlArokdKSbYBirhQyuMSuVA4lVnyBqvnAcK+dEjqKoGstJOBZBEpOH9rMYYR966zXESyReQssB5I8Lg+FvhRRE55NlJKWYApwMKCMhHJF5FU1/PtwAGMlXwRROQdEekrIn29D96sNDLLZtg0Go2mqil1pS0iJ5VSx5RSnUVkL8Zqeo9XtaXAmy4jHAr0B171uO69mi7gCuBXEXEHPSqlmgDnRMShlGoHdAQOlmVSlYXzZDpE1t6oyEBWy2VdUVfkvp4r8LLeN5DVu69VdDDFw2fnO9i4N53z+Wk0iAllYNeGRFXgUN+6jueK2jut/UIl0H8t9wDzXG6Og8AtSqlZACIyW0R+UUotB3ZieBHeFZFdAEqpSGAkcGcJ/Zbk5x4MPKOUsgMOYJaInCvjvCoF54E9qObFU2I1mvKwaONpPt18ljxboaNtzvLDTLm8JdcMalmDI9MEEwEtI0UkyeWK6Ckik0TkvMtYz/ao85KIdBORHiLymkd5jog0EpH0Evq92bMPV9knItJdRBJEpLeIfF6B+VUMu3eQjEZTPhZtPM1H350uYrAB8mxOPvrmGIs2HC9337m5uQwdMtYtzTp2zGQaxLZiwpXXFKk3cugQBvTpw4A+fWjfuhXTrzaU7V79x8vu8r6JCcSEhXLu3Dl+27vXXT6gTx+aN2zAm6+/DsDTTzzK5X0SGDbwEm65bhrpXsqBBVSWNGvK8WSmjB/NoL6JDO7Xm/fffdt9bdGiRXTv3h2TyURJob9Hjx4lOjqal18uVBL0Ja26fv16evfujcViYfHixT7Ho6VZaysN4yGs4m9yeTi+bH+pj9pGTY2ttqspZuc7+HTzWb91Pv3uODn59nL1/9///o/JUya4o0f++NB9zP3fO+7rEZYcIiw5fPfd12zavp1N27fTf8AAJk6aDMADD/7RXf7M3/7G5YMH07BhQzp17uwu/37LFiIiI5k4aRIAY8aMYvvOnWxLSqJdh47808MgF1AZ0qwFan/1YyN4+PG/8tU3W1i4dBXz3v8P+377FYB2HTszZ+58Bg0aXGIfDzzwQDHhJ1/Sqq1bt+b999/nuuuu8/uea2nW2krGacgPIBJAo/HDxr3pxVbY3uTZnPywp3xewI/mfcxVV413vx4xYigxMb4XG5mZmXz7zTdMcKVee/LxwoVMm1485fubtWto164drS8yEomuGDkKi8Xwrva5pB8px4v/UiirNKs/mjWPo/vFiQBER8fQvmNnTp08AUDrtp1oHNcGu7P4e7xkyRLatWtH9+6F4br+pFXbtGlDz549MZl8m8aalmbVRtsfJh33qqk457MDW0Gfzyp7lqHVauXgwcO0aXNR6ZVdLFuyhKHDh1OvXtFQuZycHFavWMFVrgMBPFm88GOuKcGYA8z/31yGjxxdrLwkadaUE4Zxr4g0a/KxI+zZtZOEXkWTBR0OweEhypWdnc0LL7zAn//85yL1/EmrBkJ5pFkrE71t7Y/6zavcPVIb3Rw1QSBx4L6iREIfKFlaQHb/vUz9VJV7pUFUYH9mDcLKnglw9mwqsR5Hh5WEdxr7ooULuPnW24rV++qLLxhw6aXFVOmsVitfffE5Tz/7bLE2Lz73dywWM1eXYNCrQqY0OzuLu393I48//RwxMcaXTnq2sfckGAJRBfz5z3/mgQceKOZHrui4tDRrbSYrFcKq9uBUTd1nYOf6zFl70q+LJDzExKWdyp4kEhERTl5e4CfepKamsn3rVhYsLv5Ft/jjhSWuplcuX05Cr17FUrA/nDuXr7/8kgVLvyrRMFWGNKsnNpuNu393IxMnT2P0uJLPvwyxFP463rx5M4sXL+bhhx8mLS0Nk8lEeHg4V199tU9p1UDQ0qy1Ge0e0VQCUWFmpvT3f3LJlP6NiQwr+7+3QKVZC/hs8WLGjBtPeHhRIab09HS+W7++yLFlBSxauKCYMV+5YjmvvvwSH3+2xH08lzeVKc0qIjz24N2079CZW+8sfpBDSWzYsIHDhw9z+PBh7r//fh577DHuvvtuv9KqgRAU0qwXKqpeEwgvv2SmL2pzBEhN0fabn9yPsmJ99Wr3oyIEcu5neblmYFOuu7wp4SFF/+TCQ0xcd3lTrhlYsnZzIHhLsw4eNJpp19zEmjXf0iq+SxFp1sUfL+Saa6cX62PZkiWMGDmSqKii/95zcnJYu3o1V02eXKT8wfvuIzMzkwljxjDy8gE8/uB9xESEVJk065ZNP7DkkwVs2bSBSWMGMWHk5axbsxKAnzevYWi/biRt38L48eMZPbq4f90bX9KqW7duJT4+nkWLFnHnnXcW2cAMKmnW2k5VSbPav3wF6jXBuW1JpfarjXVxPI11RYyvp3+7rD5tTwL1b5cozeqHnHwHP/yWwfksGw2iQ7i0U72iK2w/sq6+qAlpVl9UVLLVW5rV532CVHukWqRZL2i0e0RTyUSGmbniYt8nipcHLc1a+6lMaVZttP0R3RDCKyd6RK+uqw5f0SMVcXNUd4SJG0+FwDKsum+99Uaf1yIsOYXdV/Gqu+A8Sai6gxIguFbXULnJNdpo+yNPJ9ZoNJrahd6I9IfSb49Go6ld6JW2P2z5UL9sWt3aDVJxfLk7agOebpPyuEoys2ys+fY4Z1PzaNwonBFDWhITHVJ6wwuIunYuZGWjjbYPJDcT594NmKJLDuLXaMrKnP/t5b15e8nNc7jLXn5jJ7dc35nbbuxcgyPTBBP6978PnEeSQILlgDFNbWfO//by1pw9RQw2QG6eg7fm7GHO//aWu29PadakpJ1cOnAEPbr3I6HnQBYuLPzV8uab/+biLp2JCrFw9mxx1cHtW7cSExbKZ58Utlm5YjmJ3btxcZfOvPziC+7yTxcvpm9CT6JDQ/jRT7htZUmz5uXl0a9fPxISEujevXsRPRFf0qyHDx8mIiKCxMREEhMTmTVrFmDEno8fP54uXbrQvXt3HnnkEXcbX9Kqxd4rLc1a+1D1mmAe/jtMHfoT+sAn7kdJ6GQZjT8ys2y8N8+/UX5v3l6ysr2iLXKzCh8+UMTw3n8XMWXKNVglBlNoQ/7937ls/WkXn33xNfff/yhpLq3ryy4bwBfLV7iV+jxxOBw88dijXDFqVJGyP9x7L599/gXbd/7MogUL+WWPcWhVt+7d+ejjRVw+qOi525m5NvejMqRZCwgLC2Pt2rX89NNPJCUlsXz5cjZt2gRAjx49+PTTTxk8uLg0a0HURlJSErNnF0r3//GPf+TXX39lx44dfP/993z99deAb2lVb7Q0ay3E1KIL5oQxmOKKHU+p0ZSJNd8eL7bC9iY3z8Gab8t3EMK8efPcadQdO3WiQ0fjtKW4Fi1o0qQpZ84Yq+pevRK4qE2bEvt4+803mTR5Ck2aFGZmbtuyhXbt29O2XTtCQ0OZOn0aX3y+DIAuXbvSqbN/l05lSrMqpdyrVJvNhs1mc6eHd+3alc6ljMWTyMhIhg0bBkBoaCi9e/d2a4kEIq2qpVk1mjrO2dTAdEHOpgYu/FSAIc16kDYlGONtW7Zgs1lp376d3z5OHD/O50uXcPudRU8EPHHiBPHxrdyvW7aMJ+X4iYDHVtnSrA6Hg8TERJo2bcrIkSPp379/qW0OHTpEr169GDJkCBs2bCh2PS0tjc8//5wRI0YAgUmramnWIMPTRVKQbl2RA201FcdX2ntJn1VN0LhRYNEQjRuFlbnvYydPUT82lhx70ZV8SkoKt99yM+/M+S/5zmjj5FYfPPzgH/jr358rlk1ZlRKm/jiVWfgl5xlJYjabSUpKIi0tjcmTJ7Nr1y569Ojhs5+4uDiOHj1Ko0aN2L59O5MmTWL37t1uHXG73c6MGTO49957adeuXcBjrmlpVr3S1miqmBFDWhIR7j+9PCLczIghZT/cNzwiopjCX0ZGBldPnMhTTz9DvwEDSu3jx+3bmXnD9XTt0J4ln37C/ffczedLl9KyZUuSk4+56x0/nkzzFnEBjy0QaVYgYGnWAmJjYxk6dKjbj+yLsLAwGjVqBECfPn1o3749v/32m/v6HXfcQceOHbn//vvdZQXSqoBPadVApFn9ta8o2mhrNFVMTHQIt1zv3+d6y/WdiY4qe7y2tzSr1Wrl2qlXc90NNzBl6tSA+tizbz+/7D/AL/sPMGnK1bz2xptMuOoq+lxyCQf27+fwoUNYrVYWL/yY8VdOCHhslSnNeubMGfeGam5uLqtXr6ZLly5+73/mzBn3YccHDx5k37597hX1E088QXp6Oq+99lqRNoFIq9a0NGtA7hGlVCzwLtAD44CIW0Vko1edocBrQAhwVkSGKKU6Aws9qrUDnhKR15RSfwF+BxQ4sx4Tka9cfT0K3AY4gHtFZAW1kBKjSZYlVP9AysHGdSX7WQcOrZnEhkPDCt83X/KsZXVxVLVLxLZyO3Lp5UhGNqqefwnfgjhs7zjtiHBzkThtFVH6StbbFTLiipH88P13DB9xBZ8sWsT3GzZwLvUcH86dC8C/58whITGRt954g1f/8TKnTp6kf+9ejB4zlrfeeaekWwCGT/Yfr7/OVePH4XA4uOnmm+nmkipdtmQJD95/H2fPnGHyxIn0uLgnC5Z8zsmUE/zh7rv46JMlRaRZHQ4HM26cWUSa9Y6bb+CjuR/QslUr/vPBPKC4NGuBq2TP/sP84a47cDgcOJ1Opk2bxpVXGob+s88+45577uHMmTOMHz+exMREVqxYwfr163nqqaewWCyYzWZmz55Nw4YNSU5O5tlnn6VLly707t0bgLvvvpvbb7+d2267jRtvvJEOHTrQsGFDFixY4B5LYmKiOwLk7bff5uabbyY3N5exY8cWkWb11b6yCEiaVSn1AbBBRN5VSoUCkSKS5nE9FvgBGCMiR5VSTUXktFcfZuA40F9EjriMdpaIvOxVrxswH+gHtABWA51ExOf2e1VJs5YHT+NTm6ltRtuTyjLa1cH+S++jS5sWpRrtArKyCzIi82ncKIwRQ1oWWWGXx2gn7djBG6+9xhzXCq+6qUxhKH/SrHUhU7JapFmVUvWAwcDNACJiBaxe1a4DPhWRo646pynOCOCAiBwp5ZZXAQtEJB84pJTaj2HAN/pvptHUfqKjQrhqXJtK7TOxVy8GDx2qpVlrMdUtzdoOw4XxnlIqAdgO3Cci2R51OgEhSql1QAzwuojM9ernWowVtCd3K6VuArYBD4rIeaAlsMmjTrKrrAhKqTuAOwBat24dwDQ0nqtrzxW1Z7mvFbgvasPKvGYRRIRAvJaBrKIDIdLjHMSCVffMW26plL4DvW+R8piSyz2jQMpLXVhdg//kmrLGcQeyEWkBegNvi0gvIBt4pIQ6fYDxwGjgSaWUOyvF5VKZCCzyaPM20B5IBFKAfxRUL2EMxWYlIu+ISF8R6dukSdlEnTSayiIs6zTnsvIqPYFCc2EgIqSmphY7s9Mfgay0k4FkEdnser2Y4kY7GWPzMRvIVkqtBxKAgviascCPInLKY7Du50qp/wBfePRVGNEP8UDgEf0aTTUS98syUoCzOfGUvN7wICSt0u9vdVSfPk6ouWzBZhl5Ffd1nwuv+wqI4eHhRZJ1SqNUoy0iJ5VSx5RSnUVkL4Zveo9XtaXAm0opCxAK9Ade9bg+Ay/XiFIqTkRSXC8nA7tcz5cBHymlXsHYiOwIbAl4RhVEUreBOQIV2730yjVMWV0ZldW2rP1UlgultiTLeGKx5dBqZ9EIAZ+SrR72VXV/rEz3eXXDAffzEd2aF/ZTpl6K0rNR4ebpztRsPzUNlu85WWqdBwa1L7Hcc/zl7UNjEGhG5D3APJeb4yBwi1JqFoCIzBaRX5RSy4GdGP803xWRXQBKqUhgJHCnV58vKqUSMVwfhwuui8hupdTHGF8MduD//EWOVDo5yRASA9R+o63RaC48AjLaIpIEeIeezPaq8xLwklcdRCQHaFRCuc9D7UTkWeDZQMZW+WjfpEajqb1o7RFvLFFgCuznfEk/0bUOSXF8Ra2UlUBi4AN5/z3r1GZ8uUQCwZfrw7O8OqkMt4nGQBttb+xZoII71lWj0dRdtPZIMbR7RKPR1F70StubRv3BFOrzcmlRC75+kvtKzQ6WtPfKwperpLLen9rsEilrxIgna3xEbgQSaRGIS8RX/4Ggoz2qF220vVCRgcdLajQaTXWj3SMajUYTROiVtg/Km7zh+TPc10HAnni6BQJxBVRWUkx1cp0UHmrra47B7ibylVBTEZeIL+qSO6IuzaW60CttjUajCSK00dZoNJogQrtHvHBs/BhiiiVw+iUQN0gg+HKVeJa39aj/kfJ/hJUmuKlO14FOfgketNH2wnlqH8qWW9PD0Gg0mhLR7hFvtC6yRqOpxeiVtjdRDVChkQFVrSy3SEn4SjYJRmpDZIivz0p2/73EctvK7aX26VOCtQ4RiNvE87mOBql69Erbm6xUxJpT06PQaDSaEtFGW6PRaIII7R7xwnzZDajQcJzbl5V4vSpdInWJ2nbgry83iC8uBNdHRdBukJpDG20vTM3a1fQQNBqNxifaPaLRaDRBhF5pl5GSNEm0y6TqqEgUTVldIhpNMKBX2hqNRhNEaKOt0Wg0QURA7hGlVCzwLtAD4zyuW0Vko1edocBrQAhwVkSGKKU6Aws9qrUDnhKR15RSLwETACtwALhFRNKUUm2AX4ACPc9NIjKrPJOrCJ4uj9JkWn1dryy3STBqjPg6ocbXwbuVdbKM8G2l9KPR1FYC9Wm/DiwXkalKqVCgSMqgy6i/BYwRkaNKqaYAIrIXSHTVMQPHgc9czVYBj4qIXSn1AvAo8CfXtQMikljeSWk0Gk1dpVT3iFKqHjAYmAMgIlYRSfOqdh3wqYgcddU5XUJXIzCM8RFXnZUiYndd2wToc740Go2mFAJZabcDzgDvKaUSgO3AfSKS7VGnExCilFoHxACvi8hcr36uBeb7uMetFHWjtFVK7QAygCdEZIN3A6XUHcAdAK1btw5gGuXH081Rko5GVRwaG4wuEV94RoB4upJ8vW9ldStpl4jmQiKQjUgL0Bt4W0R6AdnAIyXU6QOMB0YDTyqlOhVcdLlUJgKLvDtXSj0O2IF5rqIUoLXrXn8APnKt9osgIu+ISF8R6dukSZMApqHRaDTBTyBGOxlIFpHNrteLMYy4d53lIpItImeB9YDnknQs8KOInPJspJSaCVwJXC9iaKKKSL6IpLqeb8fYpOyERqPRaEp3j4jISaXUMaVUZ9fG4ghgj1e1pcCbSikLEAr0B171uD4DL9eIUmoMxsbjEBHJ8ShvApwTEYdSqh3QEThY9qlVH1WRXON5GG4wukp8jd+zvLyHJ5eL7pcVPt/9fbXdtkiCj+cYfPBzal/3856NoqpiSJogJ9DokXuAeS43x0HgFqXULAARmS0ivyillgM7ASfwrojsAlBKRQIjgTu9+nwTCANWKaWgMLRvMPCMUsoOOIBZInKuIpPUaDSaukJARltEkoC+XsWzveq8BLxUQtscoNihiyJS4i6UiHwC6LxwjUajKQGtPeKF7fMXMbXsxtEHH6/Ufn2d3uJLWyMYXSKe+Bq/L1dJlRCIS6SG3Ca+uLjRNvfzndpVoikBncbuhZw5gmSl1vQwNBqNpkS00fbGYa3pEWg0Go1PtHvEA2fWOQgJh5CwMrWryMG1ZW0b7FElNUYAkRt16r6aOoteaXtyYi+cPwG2/JoeiUaj0ZSINtoeOI97h59rNBpN7UK7RzyQ88ehfnOwlM09Up3UNpdIWd01VR4x4oFiSInlPrVKKiuSpAIuEc/kGk92phZK/ehIkgsbbbRdSH4OcvRnECfYtXtEo9HUTrR7xIWk7DUMtkaj0dRi9ErbhWrekZCbXjdehEfT9puZ7msViQ4pL8EYJVKdro8qx5erpIqjQTyTa3y5SjQXNtpou1Dh0RAeXdPD0Gg0Gr9o94hGo9EEEXqlrdG48Iw2KRJhUgsSZHTEiKYAvdLWaDSaIEIbbY1GowkitHtEUyGqVWrVB76SaCpS36erRKOpYfRKW6PRaIIIbbQ1Go0miNDuEU1ABGOyT0Uoq0ukrDonvhJnPKNEehY7pE+j0SttjUajCSq00dZoNJogIiD3iFIqFngX6AEIcKuIbPSqMxR4DQgBzorIEKVUZ2ChR7V2wFMi8ppSqqHrWhvgMDBNRM67+noUuA1wAPeKyIpyza6S8Dx811OHpKRDeatCp6Qi8qe+6lck0qNOaYx4UJEoEc+2Pxc5kNdXpEq2j3KNxj+BrrRfB5aLSBcgAfjF86LLqL8FTBSR7sA1ACKyV0QSRSQR6APkAJ+5mj0CrBGRjsAa12uUUt2Aa4HuwBjgLaWUubwT1Gg0mrpEqUZbKVUPGAzMARARq4ikeVW7DvhURI666pwuoasRwAEROeJ6fRXwgev5B8Akj/IFIpIvIoeA/UC/QCek0Wg0dZlA3CPtgDPAe0qpBGA7cJ+IeP6+6wSEKKXWATHA6yIy16ufa4H5Hq+biUgKgIikKKWauspbAps86iW7yoqglLoDuAOgdevWAUyjcijJJeLrelW4SgYODS/xXp5ukJqQkvWmrBEmdcnl4imvujMAeVWtK6IpC4G4RyxAb+BtEemF4Yx7pIQ6fYDxwGjgSaVUp4KLSqlQYCKwKID7qRLKpFiByDsi0ldE+jZp0iSAbjUajSb4CcRoJwPJIrLZ9XoxhhH3rrNcRLJF5CywHsP3XcBY4EcROeVRdkopFQfg+v9pj75aedSLB04EMhmNRqOp65TqHhGRk0qpY0qpziKyF8M37X1s+VLgTaWUBQgF+gOvelyfQVHXCMAyYCbwvOv/Sz3KP1JKvQK0ADoCW8o0qxqkIq6J0lwv/up4uk18UZtdEFUR5eILz0NyPV0ZlYU+cUZTlQSaEXkPMM/l5jgI3KKUmgUgIrNF5Bel1HJgJ+AE3hWRXQBKqUhgJHCnV5/PAx8rpW4DjlIYcbJbKfUxxheDHfg/EXFUZJIajUZTVwjIaItIEuC9fJjtVecl4KUS2uYAxRJyRSQVY9Ve0v2eBZ4NZGwajUZzIaG1R+oIvtwmbat5HMGGpyvDl6ukrHXKiqe7RkeSaEpDp7FrNBpNEKGNtkaj0QQR2j1SyQQSAXIhUNukXD1dEL4IxMWhI0M0NY1eaWs0Gk0QoY22RqPRBBHaPaKpcnwlyFRnQo2vqIxA3CYV6V+jqWz0Sluj0WiCCG20NRqNJojQ7hHNBUdluUR89aldJZqqRK+0NRqNJojQRluj0WiCCO0e0WgqGe0q0VQleqWt0Wg0QYQ22hqNRhNEaPeIpsaozSfpVATtEtFUJXqlrdFoNEGENtoajUYTRGj3iEbjQrs1NMGAXmlrNBpNEKGNtkaj0QQRAblHlFKxwLtAD0CAW0Vko1edocBrQAhwVkSG+GurlFoIFGhzxgJpIpKolGoD/AIUhBZsEpFZ5ZmcRlMS2g2iCWYC9Wm/DiwXkalKqVAg0vOiyzC/BYwRkaNKqaaltRWR6R7t/wGke7Q5ICKJZZ2MRqPR1HVKNdpKqXrAYOBmABGxAlavatcBn4rIUVed04G2VUopYBowvPzT0Gg0mguDQHza7YAzwHtKqR1KqXeVUt6/LzsBDZRS65RS25VSN5Wh7SDglIjs8yhr66r/rVJqUEmDUkrdoZTappTadubMmQCmodFoNMFPIEbbAvQG3haRXkA28EgJdfoA44HRwJNKqU4Btp0BzPd4nQK0dtX/A/CRa8VeBBF5R0T6ikjfJk2aBDANjUajCX4CMdrJQLKIbHa9XoxhiL3rLBeRbBE5C6wHEkprq5SyAFOAhQVlIpIvIqmu59uBAxgreY1Go7ngKdVoi8hJ4JhS7lNYRwB7vKotBQYppSxKqUigP/BLAG2vAH4VkeSCAqVUE6WU2fW8HdAROFj2qWk0Gk3dI9DokXuAea7oj4PALUqpWQAiMltEflFKLQd2Ak7gXRHZ5autR7/XUtQ1AsbG5TNKKTvgAGaJyLlyzE2j0WjqHEpEanoMFaZv376ybdu2mh6GRqPRVBpKqe0i0te7XGdEajQaTRChjbZGo9EEEXXCPaKUOgMcKWfzxsDZShxObeZCmivo+dZlLoS5XiQixeKZ64TRrghKqW0l+Y3qIhfSXEHPty5zIc3VG+0e0Wg0miBCG22NRqMJIrTRhndqegDVyIU0V9DzrctcSHMtwgXv09ZoNJpgQq+0NRqNJojQRluj0WiCiKA02kqpcKXUFqXUT0qp3Uqpp13liUqpTUqpJJfWdj+PNo8qpfYrpfYqpUZ7lPdRSv3suvZP16EMKKXClFILXeWbXcegFbSZqZTa53rMrG3zVUq1UUrlusqTlFKz68h8E5RSG13j/9xTsjdYP9+yzjXYP1uP+5qVoZn/het1Q6XUKtc4VimlGnjUDcrPtsoQkaB7AAqIdj0PATYDA4CVwFhX+Thgnet5N+AnIAxoiyH3anZd2wIMdPX5tUf7u4DZrufXAgtdzxtiCF81BBq4njeoZfNtA+zy0Vcwz3crMMRVfivw12D/fMsx16D+bD3G+gfgI+AL1+sXgUdczx8BXgj2z7aqHkG50haDLNfLENdDXI+C1Vd94ITr+VXAAjG0ug8B+4F+Sqk4oJ6IbBTjU50LTPJo84Hr+WJghOubfDSwSkTOich5YBUwpoqmCpRrviVSB+bbGUOrHdc4rvYYe1B+vuWYa4kEw1w9xhqPcWDKux7FnmP8gKJjD8rPtqoISqMN7p9XScBpjA9iM3A/8JJS6hjwMvCoq3pL4JhH82RXWUvXc+/yIm1ExI5x8HAjP31VKWWcL5R8ZFuwz3cXMNFV5RqglffYvcYYFPMt41whyD9b4DXgYQwZ5wKaiUiKa4wpQMHh4EH92VYFQWu0RcQhxont8RjfvD2A3wMPiEgr4AFgjqu6KqkLP+XlbVNllHG+vo5sC/b53gr8n1JqOxBD4SHRQf35lnGuQf3ZKqWuBE6LcSpVQE1KKAuaz7YqCFqjXYCIpAHrMH7mzAQ+dV1aBBRsRCZTdKUSj+FKSHY99y4v0kYZx6LVB8756ataCGS+4vvItqCer4j8KiKjRKQPxuEZB1zV6sTnG8hc68BnexkwUSl1GFgADFdKfQiccrk8Clw9p73H7jXGYJlv5VPTTvXyPIAmQKzreQSwAbgS+AUY6iofAWx3Pe9O0c2MgxRuZmzF2Pgp2MwY5yr/P4puZnwshZsZhzA2Mhq4njesZfNt4jG/dsDxgjEG+XybuspMGD7MW4P98y3HXIP6s/Wa+1AKNyJfouhG5IvB/tlW2ftW0wMo54fdE9iBcbzZLuApV/nlwHbXh7wZ6OPR5nGMVcleXLvMrvK+rj4OAG9SmCUajrF63Y+xS93Oo82trvL9wC21bb4Ym1a7XeU/AhPqyHzvA35zPZ4vGHswf75lnWuwf7Zecx9KodFuBKwB9rn+39CjXlB+tlX10GnsGo1GE0QEvU9bo9FoLiS00dZoNJogQhttjUajCSK00dZoNJogQhttjUajCSK00dZoNJogQhttjUajCSL+H4HJi+TYOI+oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot walking distance\n",
    "acc.plot(column=\"walk_d\", scheme=\"Natural_Breaks\", k=9, cmap=\"RdYlBu\", linewidth=0, legend=True)\n",
    "\n",
    "# Use tight layour\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, from here we can see that the walking distances (along road network) reminds more or less Euclidian distances. \n",
    "\n",
    "### Applying classifiers to data\n",
    "\n",
    "As mentioned, the `scheme` option defines the classification scheme using `pysal/mapclassify`. Let's have a closer look at how these classifiers work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mapclassify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Natural Breaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaturalBreaks           \n",
       "\n",
       "    Interval       Count\n",
       "------------------------\n",
       "[  0.00,  19.00] |   196\n",
       "( 19.00,  28.00] |   437\n",
       "( 28.00,  36.00] |   809\n",
       "( 36.00,  43.00] |   835\n",
       "( 43.00,  53.00] |   688\n",
       "( 53.00,  65.00] |   423\n",
       "( 65.00,  78.00] |   202\n",
       "( 78.00,  95.00] |   163\n",
       "( 95.00, 155.00] |    54"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapclassify.NaturalBreaks(y=acc['pt_r_tt'], k=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Quantiles (default is 5 classes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quantiles               \n",
       "\n",
       "    Interval       Count\n",
       "------------------------\n",
       "[  0.00,  30.00] |   792\n",
       "( 30.00,  37.00] |   779\n",
       "( 37.00,  44.00] |   821\n",
       "( 44.00,  56.00] |   685\n",
       "( 56.00, 155.00] |   730"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapclassify.Quantiles(y=acc['pt_r_tt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It's possible to extract the threshold values into an array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.,  31.,  38.,  44.,  52.,  62.,  76.,  95., 155.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = mapclassify.NaturalBreaks(y=acc['pt_r_tt'], k=9)\n",
    "classifier.bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's apply one of the `Pysal` classifiers into our data and classify the travel times by public transport into 9 classes\n",
    "- The classifier needs to be initialized first with `make()` function that takes the number of desired classes as input parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Natural Breaks classifier\n",
    "classifier = mapclassify.NaturalBreaks.make(k=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now we can apply that classifier into our data by using `apply` -function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>pt_r_tt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_r_tt\n",
       "0        8\n",
       "1        7\n",
       "2        8\n",
       "3        8\n",
       "4        8"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classify the data\n",
    "classifications = acc[['pt_r_tt']].apply(classifier)\n",
    "\n",
    "# Let's see what we have\n",
    "classifications.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(classifications)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so now we have a DataFrame where our input column was classified into 9 different classes (numbers 1-9) based on [Natural Breaks classification](http://wiki-1-1930356585.us-east-1.elb.amazonaws.com/wiki/index.php/Jenks_Natural_Breaks_Classification).\n",
    "\n",
    "- We can also add the classification values directly into a new column in our dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>pt_r_tt</th>\n",
       "      <th>nb_pt_r_tt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>146</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>155</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_r_tt  nb_pt_r_tt\n",
       "0       99           8\n",
       "1       93           7\n",
       "2      146           8\n",
       "3      155           8\n",
       "4       99           8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the column so that we know that it was classified with natural breaks\n",
    "acc['nb_pt_r_tt'] = acc[['pt_r_tt']].apply(classifier)\n",
    "\n",
    "# Check the original values and classification\n",
    "acc[['pt_r_tt', 'nb_pt_r_tt']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now we have those values in our accessibility GeoDataFrame. Let's visualize the results and see how they look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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"
    }
   ],
   "source": [
    "# Plot\n",
    "acc.plot(column=\"nb_pt_r_tt\", linewidth=0, legend=True)\n",
    "\n",
    "# Use tight layout\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here we go, now we have a map where we have used one of the common classifiers to classify our data into 9 classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a histogram\n",
    "\n",
    "A histogram is a graphic representation of the distribution of the data. When classifying the data, it's always good to consider how the data is distributed, and how the classification shceme divides values into different ranges. \n",
    "\n",
    "- plot the histogram using [pandas.DataFrame.plot.hist](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hist.html)\n",
    "- Number of histogram bins (groups of data) can be controlled using the parameter `bins`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for public transport rush hour travel time\n",
    "acc['pt_r_tt'].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also add threshold values on thop of the histogram as vertical lines.\n",
    "\n",
    "- Natural Breaks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "# Define classifier\n",
    "classifier = mapclassify.NaturalBreaks(y=acc['pt_r_tt'], k=9)\n",
    "\n",
    "# Plot histogram for public transport rush hour travel time\n",
    "acc['pt_r_tt'].plot.hist(bins=50)\n",
    "\n",
    "# Add vertical lines for class breaks\n",
    "for value in classifier.bins:\n",
    "    plt.axvline(value, color='k', linestyle='dashed', linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Quantiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "# Define classifier\n",
    "classifier = mapclassify.Quantiles(y=acc['pt_r_tt'])\n",
    "\n",
    "# Plot histogram for public transport rush hour travel time\n",
    "acc['pt_r_tt'].plot.hist(bins=50)\n",
    "\n",
    "for value in classifier.bins:\n",
    "    plt.axvline(value, color='k', linestyle='dashed', linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Check your understanding**\n",
    "\n",
    "Select another column from the data (for example, travel times by car: `car_r_t`). Do the following visualizations using one of the classification schemes available from [pysal/mapclassify](https://github.com/pysal/mapclassify):\n",
    "    \n",
    "- histogram with vertical lines showing the classification bins\n",
    "- thematic map using the classification scheme\n",
    "\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a custom classifier\n",
    "\n",
    "**Multicriteria data classification**\n",
    "\n",
    "Let's create a function where we classify the geometries into two classes based on a given `threshold` -parameter. If the area of a polygon is lower than the threshold value (average size of the lake), the output column will get a value 0, if it is larger, it will get a value 1. This kind of classification is often called a [binary classification](https://en.wikipedia.org/wiki/Binary_classification).\n",
    "\n",
    "First we need to create a function for our classification task. This function takes a single row of the GeoDataFrame as input, plus few other parameters that we can use.\n",
    "\n",
    "It also possible to do classifiers with multiple criteria easily in Pandas/Geopandas by extending the example that we started earlier. Now we will modify our binaryClassifier function a bit so that it classifies the data based on two columns.\n",
    "\n",
    "- Let's call it `custom_classifier` that does the binary classification based on two treshold values:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_classifier(row, src_col1, src_col2, threshold1, threshold2, output_col):\n",
    "    \"\"\"Custom classirifer that can be applied on each row of a pandas dataframe (axis=1).\n",
    "    \n",
    "    This function classifies data based on values in two source columns and stores the output value in the output column.\n",
    "    Output values is 1 if the value in src_col1 is LOWER than the threshold1 value AND the value in src_col2 is HIGHER than the threshold2 value. \n",
    "    In all other cases, output value is 0.\n",
    "    \n",
    "    Args:\n",
    "        row: one row of data\n",
    "        src_col1: source column name associated with threshold1\n",
    "        src_col2: source column name associated with threshold2\n",
    "        threshold1: upper threshold value for src_col1\n",
    "        threshold2: lower threshold value for src_col2\n",
    "        output_col: output column name\n",
    "\n",
    "    Returns:\n",
    "        updated row of data.\n",
    "    \"\"\"\n",
    "\n",
    "    # If condition is true, assign 1 into output column\n",
    "    if row[src_col1] < threshold1 and row[src_col2] > threshold2:\n",
    "        row[output_col] = 1\n",
    "    \n",
    "    # Else, assign 1 into output column\n",
    "    else:\n",
    "        row[output_col] = 0\n",
    "\n",
    "    # Return the updated row\n",
    "    return row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have defined the function, and we can start using it.\n",
    "\n",
    "- Let's do our classification based on two criteria and find out grid cells where the **travel time is lower or equal to 20 minutes** but they are further away **than 4 km (4000 meters) from city center**.\n",
    "\n",
    "- Let's create an empty column for our classification results called `\"suitable_area\"`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <td>44</td>\n",
       "      <td>5878128</td>\n",
       "      <td>29944</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "      <td>24938</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>5975375</td>\n",
       "      <td>25486</td>\n",
       "      <td>364</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>POLYGON ((402000.000 6685500.000, 401900.425 6...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_m_d  car_m_t  car_r_d  car_r_t  from_id  pt_m_d  pt_m_t  pt_m_tt  \\\n",
       "0    29476       41    29483       46  5876274   29990      76       95   \n",
       "1    29456       41    29462       46  5876275   29866      74       95   \n",
       "2    36772       50    36778       56  5876278   33541     116      137   \n",
       "3    36898       49    36904       56  5876279   33720     119      141   \n",
       "4    29411       40    29418       44  5878128   29944      75       95   \n",
       "\n",
       "   pt_r_d  pt_r_t  ...    to_id  walk_d  walk_t    GML_ID   NAMEFIN  \\\n",
       "0   24984      77  ...  5975375   25532     365  27517366  Helsinki   \n",
       "1   24860      75  ...  5975375   25408     363  27517366  Helsinki   \n",
       "2   44265     130  ...  5975375   31110     444  27517366  Helsinki   \n",
       "3   44444     132  ...  5975375   31289     447  27517366  Helsinki   \n",
       "4   24938      76  ...  5975375   25486     364  27517366  Helsinki   \n",
       "\n",
       "       NAMESWE NATCODE                                           geometry  \\\n",
       "0  Helsingfors     091  POLYGON ((402250.000 6685750.000, 402024.224 6...   \n",
       "1  Helsingfors     091  POLYGON ((402367.890 6685750.000, 402250.000 6...   \n",
       "2  Helsingfors     091  POLYGON ((403250.000 6685750.000, 403148.515 6...   \n",
       "3  Helsingfors     091  POLYGON ((403456.484 6685750.000, 403250.000 6...   \n",
       "4  Helsingfors     091  POLYGON ((402000.000 6685500.000, 401900.425 6...   \n",
       "\n",
       "  nb_pt_r_tt  suitable_area  \n",
       "0          8              0  \n",
       "1          7              0  \n",
       "2          8              0  \n",
       "3          8              0  \n",
       "4          8              0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create column for the classification results\n",
    "acc[\"suitable_area\"] = None\n",
    "\n",
    "# Use the function\n",
    "acc = acc.apply(custom_classifier, \n",
    "                src_col1='pt_r_tt', \n",
    "                src_col2='walk_d', \n",
    "                threshold1=20, \n",
    "                threshold2=4000, \n",
    "                output_col=\"suitable_area\", \n",
    "                axis=1)\n",
    "\n",
    "# See the first rows\n",
    "acc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okey we have new values in `suitable_area` -column.\n",
    "\n",
    "- How many Polygons are suitable for us? Let's find out by using a Pandas function called `value_counts()` that return the count of different values in our column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3798\n",
       "1       9\n",
       "Name: suitable_area, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get value counts\n",
    "acc['suitable_area'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so there seems to be nine suitable locations for us where we can try to find an appartment to buy.\n",
    "\n",
    "- Let's see where they are located:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7FjzKzaqf1W6bJ8fTJjWZE8V1uyIsN2sHbVKT/XYyqb6U/VzjWsYRFdsMgPjk+PJZJSePn2Dj0i1sXLqFpLYJ5fOKW7RLpEkToSBvL2NGvcrtT9zEGf06+6UPDalwzwGWzlzJzP/OYc+2fFJ7p5T/DJsnx9f776kvtGiXVB7nphXZ/PX60fz8yR8z6LqBPn1fWzAqRBw+UETTqEj+88d3mPLS5w32vmec3ZlNy7fSsXs7Jqx9gSZNaneR7YSH3+GDZ6b6KLrgE9ksgt9NuIdLbhvs71DqnaqSuWgjn76cwZyP5nPDg1c32B3Mfe2cK89m8fTlANz26A3c9qfrTnsaqC0YFaL27drP+09/QtbiTaybv8FvceRm7WDOpPkMvflCr/dRVTYs3eLDqIJP8bET/OP2l8hem8vP/nJzSNzi7GjRMb6Z+C2fvvIFMfFRrJqT6e+QfGrR9GVc/tMhPlvGN/h/Ixqh/XsKWfHNWraty2PBZ0soOVFCh25tSe3luuFP9trcBoulbZfWnHRKI/8b/yWdenWkcy/vpgqu/GYtu7P30K5La3Zs3l3zDo1A2We4fvEmnvvFK9zzwh3EJ8X5Oaq6yd2wg2/em0fOuly2rcsDIKF1QnkfE1uHTv0+KqYp7bq4VhzseGZ7n667buWRALdj8y7ycws4eaKEfTsL2bYul0/+Nb18Kl4g6dwnhfCIMMYuecar9n/78fMBf9m6v7Xr0pq/fvJQeaILdCUnS1gwbSmLpi/n8/98CcAND1zFxy9Mq2HPwDX4hoHM/XjhKdsru3MNwL0vjeBHv77ytN/XyiNB6rNXZpC5cANr52UB0POC7gGZsMtsXpGNqno1/emMfp0taddgx+bd/PfJj3j8gwf9HUq19u8uZNaH3/HRs5+Sn7uXPoN7+DukBhMWHkan3h0ZfOP5nHNFP9LO6uTT97OkHcBKS0vZvS2f+JZx5b8ILdsllT8OtGVCk9omUHzsBLM//I6Lbr6gxsT944eu5XBhEfM+WcT2jYGx6E8gKfucwyICc463qpKbtZ3vpi5h7scLaJvWmrjEWOISY0lqm1gef4u2wb0iYkzzGFJ7dyQ8IoxNy7PLtzeLacrv37iXwdcPJDquYdYdASuPBCxV5c3H32fi3yf7OxSvde6TwtbVOQCM+Ptt3PLQj2pM3KvmrON3Q//cEOEFtZmlH/k7hHJHDh8tP7HYY2A3/jd+pr9D8qnBNwzknCvO5sq7vr87japytOgYTZtF1stNfCtTVXnEboIQgI4dOc6/7389qBK2p+mvfcVjV/+jwlrUnhZNX84T1/6zAaMKXgf3HvJ3COUmPjWZF0eNZ8vKwPqm5ythEeFceF3FOxaJCNGxUT5L2NWx8kiA2ZOTz7/vfwOaCF37uy6y2Lhsq5+j8k5Kj/aEO1/lE1o1Z/+uQp4d8W/Ov/ocLr7tQpq3iK/QfvKYaSS1TSCpbQK56wP3IopAULB9L/EtAmMWyeHCovLfzbLlekNZn0E9AmoGj1dJW0QSgAlAb0CBEao636PNUOBFIAIoUNWLnO0PAL9w9lsN3Kmqx0RkNHA1UAxsdrYXikgqkAlkOYdeoKqj6tzDILJm3nr+esOzPr+LtK/M/rDyk4p7cgqY+PePGfPdU7Tt/P2NWA/vL7Jk7YXU3h1p2yVwFp1K/0Ff/veqqyQSLAOK2hr64wuY9cF3AMQkRPs5moq8HWmPATJU9UYRiQQq9MJJ6mOBK1Q1R0RaOdvbA78BeqrqURH5ELgFeBOYCTyiqidF5J/AI8BDziE3q2q/0+pZkPls3AxmvT8vaBN2TfbvPsDvL/4Ltz16PYmtE1g8fTn7dhX6O6ygEB0XRVRMM3+HUa5l+yR/h9BgIiLDOW94f3+HUUGNSVtE4oEhwB0AqlqMa3Ts7jZgsqrmOG32eLxHlIicwJXsdzhtZri1WQDcWLcuBLcTxSd458lJfDtlISk92tP9nC4AfrvjuC907pNCjLOM6beTF3Fon6s+m9KjA0ltEoDQ6m99SenhWp+7rotx+UpUA86U8JfYhBhSerTnzHO7EtM8xt/hVODNSDsNyAfeEJG+wFLgflUtcmvTDYgQkVlAHDBGVd9W1e0i8iyQAxwFZngk6zIjgA/cnncWkeXAQeAxVZ3ruYOIjARGAqSkeHcFXqDZv7uQcb97i68nfgtATuZ2P0fkG5VdmGBqVvb78JNHb/BzJN8rLS0tL40ADB95eUjOHjlcWERO5nYG/jCwRtng3eyRcKA/8Iqqng0UAQ9X0mYAMBwYBjwuIt1EJBG4FugMtANiROR29x1F5FHgJPCus2knkOK814PARGe0X4GqjlfVdFVNT05O9q63ASRryWbuPedh8nOrnl1hDECP831/h29vHdp3uFGtFxOIF7J5M9LOA/JUtWy4NIlTk3YerpOPRUCRiMwB+jqvbVXVfAARmQxcALzjPP85cBVwqToTxlX1OHDcebxURDbjGsmHzETs3KztjBn1KjEJ0SS0iqen84/Snws+mcCT2rsj0XFRtElt5e9QysW3iCM+yXWxCVBe9go1cUmxpPbuSMv2LfwdyilqHGmr6i4gV0S6O5suBTxvKzEVGCwi4SISDQzENQMkBzhPRKLFdZXFpc52ROQKXCcer1HV8pX5RSRZRMKcx2lAV8Cn/7Uf3HuIV//wNjckj+Dusx5k1Zy1vnw7pr06k43LtpK9Jpe5Hy9k3fwNlrDNKbLX5NI8Ob7B7ojiDREhvmU82WtyyV6T67ObavjboX2HyV6Ty4GCg/4O5RTeXlxzH/CuiKwC+gF/F5FRIjIKQFUzgQxgFbAImKCqa5zR+SRgGa7pfk2A8c4xX8ZV/54pIitEZJyzfQiwSkRWOvuOUtXvb73sA0u+WEHWok0c3HuI7DW5vPXEBzxz58s++cAO7T8ccJefm8DVY2DglEbK9BkU2DfYrU/7dxf6O4RTeDXlT1VXAJ6XU47zaDMaGF3Jvn8GTrlOWVXPqOK9PgY+9iau+rI7p4DmyfHli9y0TWvDjs27eGHkOK67/4f0Gdyz1ov7V2XWB98R3yKu/L1Wzw3ttYVN3aWd1Smg6tllLr51ENPGf0nx0WJiQ7Q8EhkVSdpZnQic7zjfsysigcwFG2jbuTXfTnaV7d0T6bxPFtNncA/uf+VuOvU8veUxS0tLmfTcp7Z2tPHKllXbOF50zN9hnCKyWSTtz2jDV+/Opcd5gfefSl1dctug8plcuet3EBbehOt+e5WfozqVrT2Cq3ZYndVzM/llvz/w/j+ncOxI3W9AujhjhSVsUyufT/jK3yFUquhgaNayAZI7tuCMs1O58cGr6Z7exd/hnKLRj7QPFxYR3TyK6PiqLxjoO7QX4Lpk98kbn+WGB65iwOV9q2xflXlTFtKlbyqbV2bXNVzTiHTpm0qic/FRoEnr04n8nL00CQvEAkLdxCfF0aVvKucO78+I/7vV3+FUqVGPtOd9sog7z7yfzcuzOXLwaJXtVs5ay8pZa5nz0XwWZ6zg4WH/x9jfvkHBDu/Pj+Zt2MH01762hG28tnllNr0u6F5zQz+45t4rCI8MK1+KF+Dqe4b5MaLTd3DfITavzOb9f0xh2Zer/B1OlRp10t6WmVfntT42r8jmrp6/ZcpLn3PAbdnMgh37yNtQcRGkTSu28t4/QuOu06bhiAjnXnm2v8OoVIu2iYz++i+kD+vHqOd+zlkXBdal9qdDVflw9KcBtRyuu0ZdHmndsSVnnJ0KQExz79ZT6H9ZHwDadG5NeGQYC6YtIXttLk2jIul1QXeWzFzJjs276Te0F1eOvIw3H32PzSu20rFbu/J9l3252if9MaFl0PUDad7ylIuBA0ZUTLPyS+wvvX0wy79azWevfOHnqOoutVdK+b/RZjGRTHp+GiOeCrwySaNO2i07tCi/fdBZQ3p5tc/3Cdc98boeT3npc2556EdkvPY1q2atJW/DjvKz0e63KTLGG90GpPk7BK8lJDen79BeXHDtOXw3dTE/uu9KPvnXdH+HVSuvPzqx/PHQH1/A+09P4YJr0znz3K5+jOpUjbo80jrVt2uW5Of59JogE+IGXjXA3yHUSlKbRP741q+JiAyNsaCq8q9fv0ZJSYm/Q6kgNH66dZTcoQXd0rugpaX1toZC+65tSR/mmlnSulMrmkZFALDki5X1cnzTeKT2Or3rAvwhPCKMM/p3xnXPk+CVdlYqhwtdC5lOePhdYuKjSenRniE3nu/nyBp50g4LD+NA/kF2b8uvtzWLn/vFK/VyHNO4XX3PsIBac8RbTaOa8vQXj/PKA2+Wb7vhgav4+IVp/guqDtxLJVGxzZj78UKahDVhwA/6EhPv36tAG3V5BHxfIjGmLgb+MDBnjXgjOi6KYXde7O8w6l1pSSmrZnuuldfwGvVIG1wne44VHQ+4+8CZxqlbeheahDWh78W9/R3KadmTu5duztWEwb58a9f+XSg+dgKA/Dz/r3/f6JN2VGwUG5ZspveFjWflMhNYZpZ+BMDGZVv4VfpDpA/rS7Popn6O6vQIsGGJ6xZyZYujBSv3UkmXvqn+C8TR6MsjrTpZecQEloHDg2vWSGUim0X4OwSf2LYul7079/s1hkaftNukJtP9nC5WHjH+J9D9nC6kndXJ35Gctv6X9eG+l++i+zldiA2hf1vdz+nK7uw9NTf0oUZfHmmV0pKsxZvpdYGVR4x/XN7kpgrPn779JV789m+0Sgneb4FRsVFcfc8wPn7xfxwuDJ0VAW995Dp/h2Aj7eSOLWjSJPimVpnQlZ+3l7//ZAyF+XVbFydQiAiRTUOzTOJPjT5pR0RG0P+ys4hJjPF3KMYw6PqBDLp+IG06t+bf979RfoFHsDlRfIJJL3xG8+T4kCqPBIJGXx4BOHbkOEX7g/MfhwktZXdPKrN3+z7+Pv3RoJpNUlJSwrgH3+bTsRkAnHF2Zz9HFFoa/UgboE3nVv4OwZhKrZ6byegR/+bkiZP+DsUrpaWljHvgLbautptX+4pXSVtEEkRkkoisF5FMETnlAnwRGercVX2tiMx22/6As22NiLwnIs2c7UkiMlNENjp/J7rt84iIbBKRLBHx+crqaWelEmvlERNgLrr5fC66+XyaNYvktUcmBnSpJD9vL0tmrODp218ia8kmWnZsUR5/IMxtDiXelkfGABmqeqOIRAIVilQikgCMBa5Q1RwRaeVsbw/8BuipqkdF5EPgFuBN4GHgK1V9WkQedp4/JCI9nTa9gHbAlyLSTVV9ttRWQnI8mwLgSidj3M3+cH6F55+OzeCKEZdw0c0X0GdwD7+tTVJysoSvJ35L1uJNREZFoqXKnEnzadUpmTXOTbG7n3NG+dKsnv0IVO439v3TxN9y8S0X+jmiytWYtEUkHhgC3AGgqsVAsUez24DJqprjtHGfyBgORInICVzJvuy2LtcCQ53HbwGzgIec7e+r6nFgq4hsAs4FfPbJt+rUkk3Lt/rq8MbUi+JjJ1g9N5NPx35Bh25tufqeYVx86yASWzVvsBhy1m/ntT+9y3efLAag96AzWfPtesAuVGso3oy004B84A0R6QssBe5XVffvat2ACBGZBcQBY1T1bVXdLiLPAjnAUWCGqs5w9mmtqjsBVHVn2egcaA8scDt2nrPNZ1p3SrbyiAloF9/qGvXFJ8WR2tu1ZOuWVduY+/ECug7owjlX9KX/ZWcRFhbm0zhef3QiTZtFlseT1CaR5I4tvn/cIQmArv2D5wYOZXqe350e53Wj/2Vn0TYtcM9zeZO0w4H+wH2qulBExuAqZTzu0WYAcCkQBcwXkQW4kv21QGegEPhIRG5X1Xeqeb/KvvOdsjiviIwERgKkpKR40Y2qJXdowdFDVd/Y1xh/++a9eVW+tubb9ayas44xo/7DD+++jGF3XkyLtolVtq+r+Z8tYd6URVwx4hIyXv+62rbVxRtIytZ9CSbeJO08IE9Vy+YiTcKVtD3bFDij7yIRmQP0dV7bqqr5ACIyGbgAeAfYLSJtnVF2W2CP27HcV3/vwPcllXKqOh4YD5Cenn5aK66HhYeR3LHl6RzCGL/bvS2fNx57j3mfLKRVSjI/vPsyBlx+Fk2anP4ksZKTJYx78M3TD9KcthqTtqruEpFcEemuqlm4RtOei8pOBV4WkXAgEhgIvADEAOeJSDSu8silwBJnn0+BnwNPO39Pdds+UUSex3UisiuwqO5drNmBgoM0bxnny7cwxicu++kQAOKS4ujslE3iW8RxcO8hvp44l9Vz1tE8OZ6hP77wtEbfWYs30/OC7q4/53ercaRtfMfb2SP3Ae86M0e2AHeKyCgAVR2nqpkikgGsAkqBCaq6BkBEJgHLgJPAcpzRMa5k/aGI3IWr5n2Tc7y1ziyTdc4+9/py5gjA+oUbyVq82ZdvYYxPfPnfOV61+88f3+HKuy5h0PUDOfvSPrUafR/ce4j5ny4ufy9v39P4hldJW1VXAOkem8d5tBkNjK5k3z8Df65k+15cI+/K3u8p4ClvYqsPLTu0aKi3MsYvSk6WsHHZFqa9OpN2XVrzo/t+yMW3XkhCcs0zT9752ySKDobOok/Bzi5jB5I7JBGbFOvvMIzxiR/cMRSA5i3i6OTcLDh7bS5/u/l5ug1I4/xr0ukzuGel876PHTnO1tU55XehCTWr5qxjx+bdAJw1pAfturTxc0Q1s6SNqx5YfNRz6rkxoWHGm7OqfG3V7HWs/S6LogNHuP7+4QwfeXmF1xdnrGDFN2to1Sk0T9RnvP41M992XcD98H9/Y0k7WIgIyVYiMY1YTuZ2xtzzH1bPzaRL31TOu3oAHbq1Y9qrM2reOQSEhYfRKiU4/mOypO3QU6eCGxPSrrzLdUopvmUcqb1c1zo0bxlHbtYOcrN20KF7O1qnJHPlXZfS84Lu1Y7Yg1WHbu248q5LufxnFwXNvSwtaTt2bNpV/vim313NR8995sdojPG96a995ZO2gcj9Iprn7hpLxhvfANCyfRK3/el6f4VVJ7Y0qyOmuS3Ubkxjcsltgxhy0ykLlgY8G2k7WrRLpPcg130iYxNtJokxoWTl7LXkZG4HoH3Xttz38i+4+p4f+G2lxNNhSdsRmxDL+oUbOXmihB4Du/o7HGNMPfr63bl8PuErYppH88+ZT9A9iKcwWnnEMfiGgUw78q7d5NeYEJXSoz0vL3o6qBM22Ei7XNllvenD+tmFNsaEmM59Uxn57M+IiQ/+c1c20vZQsGMfh/cd9ncYxhgvzSz9qPzPtfdeUb69bArfTx67gWvu+UFIJGywkfYpEhrwLiDGGN+JaBrOEx/9jsE3nOfvUOqVJW0PXfqmcrzomL/DMMbUQXzLOPoM7kFM82jufOpW0vp08ndI9c6StidVstfmlj89a0gPVs3JpGv/zlx862DG/+FtPwZnjIGq7zhzsOAQYRFh/OGNe4lvEZpr5FvS9pDYOqHC84hmkfxl8h/o0K1t0NxV2pjGqmlUJE9nPEZYuG/vlelPlrQ9JHdsQUzzGPoM6UF4eDgjR99OWp9U/vXrCbRODdybfRpj4Nzh/UM6YYPNHjlFTEIMRQeK2Lw8m5/++SbS+qSycvZaPh37BUWFRTUfwBjjN30v6uXvEHzOkraHxFbNaRIWxp8//j19BvegtLSUV39vdWxjTGCw8oiH1qnJ3PPCHaSd5TrrvC0zj2YxzegzpCcxCTF+js4Y09hZ0vYQlxhLnNuCUds37GT1HNfN5/s4C0oZYxpGVbNEGjMrj9Qg1kbXxpgA4tVIW0QSgAlAb0CBEao636PNUOBFIAIoUNWLRKQ78IFbszTgCVV9UUQ+ALo72xOAQlXtJyKpQCaQ5by2QFVH1bZj9aVlhyT6DOkJYOURY4zfeVseGQNkqOqNIhIJVLiI30nqY4ErVDVHRFoBqGoW0M9pEwZsB6Y4r/3Ybf/ngANuh9ysqv3q0J96t2dbgZVHjE+5lwAub3KTHyMxwaDGpC0i8cAQ4A4AVS0GPG9dfhswWVVznDZ7KjnUpbiS8TaP4wtwM3BJbYNvCEtmrPB3CMYYU86bmnYakA+8ISLLRWSCiHjWCboBiSIyS0SWisjPKjnOLcB7lWwfDOxW1Y1u2zo77zVbRAZ70xFfOHniJKWlyk8eu4GfPHYDfYeG/hxQY0xgE9Xq70IuIunAAuBCVV0oImOAg6r6uFubl4F0XKPpKGA+MFxVNzivRwI7gF6qutvj+K8Am1T1Oed5UyBWVfeKyADgE2e/gx77jQRGAqSkpAzYtq3CAL5eLJi2lDXfZvLBM1Pr/djGmPoRqjNMRGSpqqZ7bvdmpJ0H5KnqQuf5JKB/JW0yVLVIVQuAOUBft9evBJZVkrDDgetxO1mpqsdVda/zeCmwGddIvgJVHa+q6aqanpyc7EU3am/BtKU+Oa4xxtRVjUlbVXcBuc5MEHCNptd5NJsKDBaRcBGJBgbimgFS5lYqL41cBqxX1byyDSKS7Jy0RETSgK7AFi/7U68imoXbTX6NMQHF29kj9wHvOmWOLcCdIjIKQFXHqWqmiGQAq4BSYIKqrgFwkvjlwC8rOW5lde4hwJMichIoAUap6r5a9qtebF2VQ9Nmkf54a2OMqZRXSVtVV+CqWbsb59FmNDC6kn2PAC2qOO4dlWz7GPjYm7h87fhRz0kyxhjjX3ZFZDVSerQnNtEuqDEmkC3831Jef3QihfkHam4cAixpV2N3dj6H99tyrMYEsmVfrua9f0zhk5em+zuUBmFJuxph4fbjMSZYfPLydIoOHvF3GD5nq/xVo12XNjZ7JAhUNU/XLglvHCaP+R8ARQeOULB9HzHx0TXsEdwsaVcjP28vMc1D+xfAmFDSqUcHf4fgc/b9vxrhEaF9rzljTPCxkXY1WnVKtvJIAzmdle7c24fqJc2mao3tM7ekXY39uwqJbBrh7zCMMaaclUeqERZu5RFjTGCxkXY1WnZoQWyCnYgMdI3t67Fp3J+5Je1qHCw4BDUsXWuMMQ3JyiPVaBJmPx5jTGCxkXY1jh85TpvOrfwdRqMTaF99v9ixsvzxsHZ9q2lp6kOgff6BxoaSVTi49xBfT5zL4f2H/R2KMcaUs6RdhSUzVlJaavVsY0xgsfJIFVp3SuY3//4FXc7uzC+evr18u61n0fgEcknkdC5KMsHJknYVel3QnV4XdK+5oTHGNCArjxhjTBCxkXYt2dfRwFPV59AYPqtQ7JetJVM9G2kbY0wQsaRtjDFBRNSLy7RFJAGYAPQGFBihqvM92gwFXgQigAJVvUhEugMfuDVLA55Q1RdF5C/A3UC+89qfVPVz51iPAHcBJcBvVPWL6uJLT0/XJUuW1NiPhhCKX1cbWmO+E4035YDG8HOoTGMrlYjIUlVN99zubU17DJChqjeKSCRQYRUlJ6mPBa5Q1RwRaQWgqllAP6dNGLAdmOK26wuq+qzHsXoCtwC9gHbAlyLSTVVLvIzVGGNCVo3lERGJB4YArwGoarGqFno0uw2YrKo5Tps9lRzqUmCzqm6r4S2vBd5X1eOquhXYBJxbU5zGGNMYeDPSTsNVwnhDRPoCS4H7VbXIrU03IEJEZgFxwBhVfdvjOLcA73ls+7WI/AxYAvxOVfcD7YEFbm3ynG0ViMhIYCRASkqKF90w5lQNua5IfX2999esmNrGXx+xNbaSiDe8OREZDvQHXlHVs4Ei4OFK2gwAhgPDgMdFpFvZi05J5RrA/RN4BeiCq3yyE3iurHklMZxSeFfV8aqarqrpycnJXnTDGGOCnzdJOw/IU9WFzvNJuJK4Z5sMVS1S1QJgDuA+bLkSWKaqu8s2qOpuVS1R1VLgP3xfAskDOrrt2wHY4W2HjDEmlNVYHlHVXSKSKyLdnROLlwLrPJpNBV4WkXAgEhgIvOD2+q14lEZEpK2q7nSeXgescR5/CkwUkedxnYjsCiyqXbfqbuq/M4hLjOGS2wY31FsaL9VXWcC9JNKQTueiEV+UQWr78/Smjfsx6/p5WUmket7OHrkPeNcpc2wB7hSRUQCqOk5VM0UkA1gFlAITVHUNgIhEA5cDv/Q45jMi0g9X6SO77HVVXSsiH+L6j+EkcG9DzhxZM289ye2TLGkbYwKSV0lbVVcAnvMFx3m0GQ2MrmTfI0CLSrb/tJr3ewp4ypvY6puWlvrjbY0xxiu29oiHxDaJxCTEeNW2sV7k4C/uP++qShzezAAJ5KVW3Z3O71dVpQl/lR4a8wVT9c2Stoe9O/YRERHm7zCMMaZStvaIJ7v7ujEmgNlI28OND15Ns5hmVb5e169z9vWweo3h5+OL0oQ3x/R1ScRmezQsS9oeep5vd6sxxgQuK48YY0wQsZF2FRpq3YTGcHcVb/p4On0P5Nkg/iqJBItQ6ktDsZG2McYEEUvaxhgTRKw84uG/f/2I5I6nXMBZLV8vudkYZlbUViCXROpLQ5YO7HcseFjS9rB+8UaOHj7q7zCMMaZSVh7xYNfWGGMCmY20PbRom0h08+iaG+Lbr6+hdFY9EL5i29f/uvHm5xYIa5s0JjbS9pCft5cjB474OwxjjKmUJW1jjAkiVh7xcNffbyMqthkfPfdZpa/b17/gZGWQ+mX/DvzHkraHrv3T/B2CMcZUycojxhgTRGykXUuVfc22r4q+czo/WyuJmFBkI21jjAkilrSNMSaIeFUeEZEEYALQG1BghKrO92gzFHgRiAAKVPUiEekOfODWLA14QlVfFJHRwNVAMbAZuFNVC0UkFcgEspx9FqjqqLp07nTUZsnUql6vr7KJfc33nv2sTKjztqY9BshQ1RtFJBKocMmgk9THAleoao6ItAJQ1Sygn9MmDNgOTHF2mwk8oqonReSfwCPAQ85rm1W1X107ZYwxoarG8oiIxANDgNcAVLVYVQs9mt0GTFbVHKfNnkoOdSmuZLzNaTNDVU86ry0AOtSpB8YY04h4M9JOA/KBN0SkL7AUuF9Vi9zadAMiRGQWEAeMUdW3PY5zC/BeFe8xgopllM4ishw4CDymqnM9dxCRkcBIgJSUFC+6UXf+uLtMKH3Nr+3Pr7ZlpVD6WRlTE29ORIYD/YFXVPVsoAh4uJI2A4DhwDDgcRHpVvaiU1K5BjjlX6OIPAqcBN51Nu0EUpz3ehCY6Iz2K1DV8aqarqrpycnJXnTDGGOCnzdJOw/IU9WFzvNJuJK4Z5sMVS1S1QJgDuC+Sv2VwDJV3e2+k4j8HLgK+Imqa1FUVT2uqnudx0txnaTshjHGmJrLI6q6S0RyRaS7c2LxUmCdR7OpwMsiEg5EAgOBF9xevxWP0oiIXIHrxONFqnrEbXsysE9VS0QkDegKbKl91xqOr2/eGoxf/725C08w9qu2TqePdtGWqYy3s0fuA951yhxbgDtFZBSAqo5T1UwRyQBWAaXABFVdAyAi0cDlwC89jvky0BSYKSLw/dS+IcCTInISKAFGqeq+0+mkMcaECq+StqquANI9No/zaDMaGF3JvkeAU266qKpnVPFeHwMfexOXMcY0Nrb2iIe/3vgsfQb14JUH36zX49b2ApxgLx1UFb/d5cR79rMylbHL2D1sWZlNwfa9/g7DGGMqZUnbQ/GxE/4OwRhjqmTlETd7d+6nWUxTmsU0rdV+p1PKqO2+jW32hTGmIhtpu1k7bz15G3ZyrOi4v0MxxphKWdJ2s3pupr9DMMaYall5xE1u1g7adWlD0+jalUcaUqCVRHy9rsjpCKWZOTaTxJSxpO0oOniE5V+uorRUOX7EyiPGmMBk5RHHuvkbKC1Vf4dhjDHVspG248xzz2DCWtdyKfFJsYwc/bPy1/zxdToYZ4nY13ZjfM+StiMuMZa4xFh/h2GMMdWy8ogxxgQRG2kb4wjkkpSVnkwZG2kbY0wQsaRtjDFBxMoj5rQEwkUftX1fb9oHcqnENG420jbGmCBiSdsYY4KIlUeMVxpbueB0lsz1xXGMKWMjbWOMCSKWtI0xJoh4VR4RkQRgAtAbUGCEqs73aDMUeBGIAApU9SIR6Q584NYsDXhCVV8UkSTntVQgG7hZVfc7x3oEuAsoAX6jql/UqXf1pKrSQGVfZX1ROjid5U9re0Ph2sYTSnxxB6JQWh7WBAZvR9pjgAxVPRPoC1S4W4CT1McC16hqL+AmAFXNUtV+qtoPGAAcAaY4uz0MfKWqXYGvnOeISE/gFqAXcAUwVkTC6tpBY4wJJTUmbRGJB4YArwGoarGqFno0uw2YrKo5Tps9lRzqUmCzqm5znl8LvOU8fgv4kdv291X1uKpuBTYB53rbIWOMCWXelEfSgHzgDRHpCywF7lfVIrc23YAIEZkFxAFjVPVtj+PcArzn9ry1qu4EUNWdItLK2d4eWODWLs/ZVoGIjARGAqSkpHjRjfpRU2mgIWdZVPVegfDV22ZNuATa3XxM8POmPBIO9AdeUdWzgSKcUoZHmwHAcGAY8LiIdCt7UUQigWsAb347pZJtp9ydQFXHq2q6qqYnJyd7cVhjjAl+3iTtPCBPVRc6zyfhSuKebTJUtUhVC4A5uGrfZa4Elqnqbrdtu0WkLYDz9x63Y3V0a9cB2OFNZ4wxJtTVWB5R1V0ikisi3VU1C1dtep1Hs6nAyyISDkQCA4EX3F6/lYqlEYBPgZ8DTzt/T3XbPlFEngfaAV2BRbXqlR+dTmmitmti+OL4/uKLWS61fS9jgoG3V0TeB7zrlDm2AHeKyCgAVR2nqpkikgGsAkqBCaq6BkBEooHLgV96HPNp4EMRuQvI4fsZJ2tF5ENc/zGcBO5V1ZLT6aQxxoQKr5K2qq4A0j02j/NoMxoYXcm+R4AWlWzfi2vUXtn7PQU85U1sxhjTmNjaIyEikEsfpnqBsLytCR52GbsxxgQRS9rGGBNErDxSz+zrrUugLeUaCDEYUx9spG2MMUHEkrYxxgQRK48Yn6vt8qS+KDH5eolUK4uZhmIjbWOMCSKWtI0xJohYecQ0Or6YSWIXyJiGYiNtY4wJIpa0jTEmiFh5xJh6ZqUS40s20jbGmCBiSdsYY4KIlUeM34Rq6SBU+2UCg420jTEmiFjSNsaYIGLlEWMcVtYwwcBG2sYYE0QsaRtjTBDxqjwiIgnABKA3oMAIVZ3v0WYo8CIQARSo6kXV7SsiHwDdnd0TgEJV7SciqUAmkOW8tkBVR9Wlc8ZUxsogJph5W9MeA2So6o0iEglEu7/oJOaxwBWqmiMirWraV1V/7Lb/c8ABt302q2q/2nbGGGNCXY1JW0TigSHAHQCqWgwUezS7DZisqjlOmz3e7isiAtwMXFL3bhhjTOPgTU07DcgH3hCR5SIyQURiPNp0AxJFZJaILBWRn9Vi38HAblXd6Lats9N+togMriwoERkpIktEZEl+fr4X3TDGmODnTdIOB/oDr6jq2UAR8HAlbQYAw4FhwOMi0s3LfW8F3nN7vhNIcdo/CEx0RuwVqOp4VU1X1fTk5GQvumGMMcHPm6SdB+Sp6kLn+SRcidizTYaqFqlqATAH6FvTviISDlwPfFC2TVWPq+pe5/FSYDOukbwxxjR6NSZtVd0F5IpI2UyPS4F1Hs2mAoNFJFxEooGBQKYX+14GrFfVvLINIpIsImHO4zSgK7Cl9l0zxpjQ4+3skfuAd53ZH1uAO0VkFICqjlPVTBHJAFYBpcAEVV1T1b5ux72FiqURcJ24fFJETgIlwChV3VeHvhljTMgRVfV3DKctPT1dlyxZ4u8wjDGm3ojIUlVN99xuV0QaY0wQsaRtjDFBJCTKIyKSD2yr4+4tgYJ6DCeQNaa+gvU3lDWGvnZS1VPmM4dE0j4dIrKksrpRKGpMfQXrbyhrTH31ZOURY4wJIpa0jTEmiFjShvH+DqABNaa+gvU3lDWmvlbQ6GvaxhgTTGykbYwxQcSStjHGBJGgTNoi0kxEFonIShFZKyJ/dbb3E5EFIrLCWWv7XLd9HhGRTSKSJSLD3LYPEJHVzmsvOTdlQESaisgHzvaFzm3Qyvb5uYhsdP78PND6KyKpInLU2b5CRMaFSH/7ish8J/7P3JfsDdbPt7Z9DfbP1u19w8S1Zv4053mSiMx04pgpIolubYPys/UZVQ26P4AAsc7jCGAhcB4wA7jS2f5DYJbzuCewEmgKdMa13GuY89oi4HznmNPd9v8VMM55fAvwgfM4CdfCV0lAovM4McD6mwqsqeJYwdzfxcBFzvYRwN+C/fOtQ1+D+rN1i/VBYCIwzXn+DPCw8/hh4J/B/tn66k9QjrTV5bDzNML5o86fstFXc2CH8/ha4H11rdW9FdgEnCsibYF4VZ2vrk/1beBHbvu85TyeBFzq/E8+DJipqvtUdT8wE7jCR10F6tTfSoVAf7vjWqsdJ44b3GIPys+3Dn2tVDD01S3WDrhumDLBbbN7jG9RMfag/Gx9JSiTNpR/vVoB7MH1QSwEfguMFpFc4FngEad5eyDXbfc8Z1t757Hn9gr7qOpJXDceblHNsXyqlv2Fym/ZFuz9XQNc4zS5CejoGbtHjEHR31r2FYL8swVeBP6IaxnnMq1VdacT406g7ObgQf3Z+kLQJm1VLVHXHds74PqftzdwD/CAqnYEHgBec5pLZYeoZntd9/GZWva3qlu2BXt/RwD3ishSII7vbxId1J9vLfsa1J+tiFwF7FHXXam82qWSbUHz2fpC0CbtMqpaCMzC9TXn58Bk56WPgLITkXlUHKl0wFVKyHMee26vsI+4bovWHNhXzbEahDf91apv2RbU/VXV9ar6A1UdgOvmGZudZiHx+XrT1xD4bC8ErhGRbOB94BIReQfY7ZQ8yko9ezxj94gxWPpb//xdVK/LHyAZSHAeRwFzgauATGCos/1SYKnzuBcVT2Zs4fuTGYtxnfgpO5nxQ2f7vVQ8mfGhfn8yYyuuExmJzuOkAOtvslv/0oDtZTEGeX9bOdua4Kphjgj2z7cOfQ3qz9aj70P5/kTkaCqeiHwm2D9bn/3c/B1AHT/ss4DluG5vtgZ4wtk+CFjqfMgLgQFu+zyKa1SShXOW2dme7hxjM/Ay318l2gzX6HUTrrPUaW77jHC2bwLuDLT+4jpptdbZvgy4OkT6ez+wwfnzdFnswfz51ravwf7ZevR9KN8n7RbAV8BG5+8kt3ZB+dn66o9dxm6MMUEk6GvaxhjTmFjSNsaYIGJJ2xhjgoglbWOMCSKWtI0xJohY0jbGmCBiSdsYY4LI/wOJHz/jS5ukKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "acc.plot(column=\"suitable_area\", linewidth=0)\n",
    "\n",
    "# Use tight layour\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A-haa, okay so we can see that suitable places for us with our criteria seem to be located in the\n",
    "eastern part from the city center. Actually, those locations are along the metro line which makes them good locations in terms of travel time to city center since metro is really fast travel mode.\n",
    "\n",
    "**Other examples**\n",
    "\n",
    "Older course materials contain an example of applying a [custom binary classifier on the Corine land cover data](https://automating-gis-processes.github.io/2017/lessons/L4/reclassify.html#classifying-data>)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}