{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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 which can be whatever, such as:\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 use Travel Time Matrix data from Helsinki to classify some features of the data based on map classifiers that are commonly used e.g. when doing visualizations, and our own self-made classifier where we determine how the data should be classified.\n",
    "\n",
    "1. use ready made classifiers from pysal -module to classify travel times into multiple classes.\n",
    "\n",
    "2. use travel times and distances to find out\n",
    "\n",
    "   - good locations to buy an apartment with good public transport accessibility to city center\n",
    "   - but from a bit further away from city center where the prices are presumably lower.\n",
    "\n",
    "*Note, during this intensive course we won't be using the Corine2012 data.*\n",
    "\n",
    "## Download data\n",
    "\n",
    "For this lesson, you should [download a data package](https://github.com/AutoGIS/data/raw/master/L4_data.zip) that includes 3 files:\n",
    "\n",
    " 1. Helsinki_borders.shp\n",
    " 2. Travel_times_to_5975375_RailwayStation.shp\n",
    " 3. Amazon_river.shp\n",
    " \n",
    "```\n",
    "$ cd /home/jovyan/notebooks/L4\n",
    "$ wget https://github.com/AutoGIS/data/raw/master/L4_data.zip\n",
    "$ unzip L4_data.zip\n",
    "```\n",
    "\n",
    "## Classifying data\n",
    "\n",
    "### Classification based on common classifiers\n",
    "\n",
    "[Pysal](http://pysal.readthedocs.io/en/latest) -module is an extensive Python library including various functions and tools to do spatial data 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 -module are ([see here for more details](http://pysal.readthedocs.io/en/latest/library/esda/mapclassify.html)):\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 into memory from a GeoJSON file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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    15981       36    15988       41  6002702   14698      65       73   \n",
      "1    16190       34    16197       39  6002701   14661      64       73   \n",
      "\n",
      "   pt_r_d  pt_r_t  pt_r_tt    to_id  walk_d  walk_t    GML_ID   NAMEFIN  \\\n",
      "0   14698      61       72  5975375   14456     207  27517366  Helsinki   \n",
      "1   14661      60       72  5975375   14419     206  27517366  Helsinki   \n",
      "\n",
      "       NAMESWE NATCODE          area  \\\n",
      "0  Helsingfors     091  62499.999976   \n",
      "1  Helsingfors     091  62499.999977   \n",
      "\n",
      "                                            geometry  \n",
      "0  POLYGON ((391000.0001349226 6667750.00004299, ...  \n",
      "1  POLYGON ((390750.0001349644 6668000.000042951,...  \n"
     ]
    }
   ],
   "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",
    "print(acc.head(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there exist 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": 3,
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wo7oDTgEVRiACqAqQOVcIu6OpDIKnMYTqyqcOyu1wZDuED4GIMQid62cVSRZw\nGFDcG4SGxBC8pMUNQqMMXm8EtV0q9eGt/PXiH4+QtM9MbrkkzEeQ2D+WWSNrVypbFTs5pVZw3Hsa\nW+XrTE3p6QrCY6vqVpxv9p7gSTyhWvsa78e50tlPryPYv/7K58agyV//SVElHI6iykRU/NH5tN6E\nGoRj3kIBn/ZQdAyshcjwvuDftbY8tpICwoD21fWG5nMZVRAXHsATif08qjWw2SUGIZDngOy3twT4\ntu3vrUezE0KEAu8BfVDlum+RUv4khLgbuAt1TbpaSvmwEMLoaDvAMf5iKeVcIYQ/8BlwnqP9Kinl\no47xbwb+C1REWV6XUr7nODcb+Kfj+LNSykWO412BpahRxj3ATVLKpnvUaONIWQziV6AUiOFgtrOM\ndEevxvI8O8i79i5XArVwuIxMQLyztpEZNUumX3Ul0GO7weKiDqEFVgWe4LxycLtaqOtpv1ncYM37\nROotQqjCic1BQ/YiqMDdSsDdisJtezfaRz4GPXpdW43lqXhqrhYA66SU04QQJsBfCHEJkAj0k1KW\nCyEqwunXAj5Syr4OI3BICLEE1Y/xopRyi2OMTUKIK6SUFRUly6SUf3e+qBCiHfAUMAjVEO0RQqyU\nUuYCLwDzpZRLhRBvAbcCbzb0gzibkIXJoM8C/8bJR7d9CoEDyAJ/0PtDYTIoDZW7+DPTthRT9X/C\nlQGAT5tN7Kii3hkKIUKA0cD7AFJKi5QyD7gTmCelLHccr3BcSyBACGFAFV6xAAVSyhIp5ZaKMYC9\nQN25aHA5sEFKmeMwAhuACUJNOr4U+NzRbhEw1cP3fNYipUSe2QGpa8BuR33yM8E5qaVf8d7KIH2d\nuqlM/kHQ+4IhQP3RcI80OH78oJWVUGvyZzMIQgh0QpwVBsGTFUJX1IqXhUKIfqjumXtRtYtHCSGe\nQ00kf1BKuQv1Jp2IWkHkD9wvpay2xne4oCajrjwquEYIMQY1Yni/lPIUEIMaMa3A7DgWDuRJKW01\njtdCCHE7cDtA5861S8jPFqTdAhmbIXeveqD0BAR2cpw9Sd/wky77eRJUbio8cxF5ipP3r4fTtoyt\n6Bpq0gK0Cjx9Pw0NoAf3QHQa4v28vCQlu5hFW4+StD+V3FI7YX46EhNimD3ivFpBZYlaCGZ3BJXd\nuViaMtjs7VieBJ49LWSTUqLT6dCdBYbQE5NlQI0HvCml7I/q9H3UcbwdMAx4CFjueHIfghojiEY1\nJg8IISrJ8gOyAAAgAElEQVQd3I6VwxLgVSnlMcfhVUAXKWVf1FXAoiZ4bwBIKd+RUg6SUg7q0MF1\nrnRbR1ry4NhiKPe+vF7jT45ffYvwxrPl8Gmuev07fAv38cX4Xzl8w898Mf5XfAv3cdXr37HlcPXv\nrU22tCZrW+DseMeeGAQzYJZSVlRnfI5qIMzAl1JlJ2AH2gM3oMYbrA430lbUGEAF7wDJUspXKg5I\nKbMrXE+oAemKaFwq6sa7FcQ6jmUDoQ7j4nz8nEOWnYFjH6qbsiOcfjQ06sLxPfGLbtarpGQX88DS\n3bw75g8eSkglLsiCQQdxQRYeSkjl3TF/8MDS3dXkrz399paXlXHNlZcw+bIRXHHJUBa8+HzluXvv\nuLlSYvrioX2ZPG6kyzG+37KR8aMG1pK/zs3J4brEKxssf13Bnl27CPb1YdWKL6sdVxSFy0YO48Zr\nrwaotrkRqKuGe+65h/j4eC688EL27t1beW7dunX06NGD+Pj4Fpe/rtdlJKXMEEKcEkL0kFIeBsYC\nh1DzHS8Btgghzkd1+J5B3frpUuAjIUQA6griFQAhxLOo5ZO3OV9DCBElpUx3/DoF+M3xej3wvBCi\nYq/F8cBjUkophNgCTEPNNJoNJDXkA/AG9bkmkyqBta7NvlOVsvVL7Ad+AMA43ukpo0MnNz2aB3fu\noJZ0SZ3VuMsYakgmkUduJglCB35R3o/vBYu2HmVGfBYDOpS4PD+gQwnTz8ti8bZjPDG5L6CK1jnX\nPzm7c5zdR+Htg/hqzXoCAgPJySlmxlWXM/qScfQfOJgFb31Y2W7uvx8nMLj2/hiKovCvxx/gwyUr\nGNDvfEYNG8aMGdcQHdGLea+9zNjLxvLgw4/w4n9e4O3XXubZud7dfBVF4Z//eIyx48bhZzJUcyG9\nOv91evbqSWFBgcu+a9euJTk5meTkZHbs2MGdd97Jjh07UBSFu+66iw0bNhAbG8vgwYOZMmUKvXrV\nLQ/SVHga5bgb+EQIcQBIAJ4HPgC6CSF+wXFTlur/8htAoBDiV2AXsFBKeUAIEQs8DvQC9goh9gsh\nKgzDPUKIX4UQPwP3ADcDOGIPzzjG2QU87RSPeASYI4Q4ghpTeL/Bn4LH5AAZqAuU01C4DZm1DSmb\npxJU2kqQ+efkwkejJfDp6LbAr6lI2p/KdefVVhp1Znp8Fkn7q+SvTXrPbjtCCAIC1Qwpm82KzWqt\nJWInpWTNqq+YnFhbJqKmlPS06dfx9SpVgG71qlXMvGkWADNvmsXXDZCYfvP115l61dV0qKFXlGo2\ns27tGm6+5ZbKY/YaBcBJSUnMmjULIQTDhg0jLy+P9PR0du7cSXx8PN26dcNkMjFjxgySkpr9WbcS\nj74tUsr9VHf7VHCji7ZFqKmnNY+bcbNalFI+Bjzm5twHqMan5vFjqPGKFqSGxrxU4PS3kP8LMmoC\nIqCJg9Z5P6N64jQ0GoB/87qLAHJL7cQE1B2wjQ6wkFtS9T3W6wS+Rj1l1vofpBRFYfzoizh+9Cgz\nb76NhAHVb0O7dmyjfYcOdOlWe6OlmvLXMTGx7N65E4DTmZlERamrp8jISE5nZtY7F2fSUlNZlbSC\ntRs3see2ag4PHn5gDs/NnUdhUZU+l12qq6IKg5aamkqnTlWr/NjYWFJTU10e37HD/eZDTU3bLptr\nc5SiZtI6dGKCTU5L/uPI1IPQ8VKEwa/RV5LSDrkHMPTwgx4uCpxqug3cuB6cXT2uN7pvHNXG8TRj\nxiMp56bB08wgd3LWzsc9kbxuUumKxkhz6HzAGNJ0c3FDmJ+O1GITcUHujUJasYkw/+qrAl+DjnKH\nvlZdm9jr9Xo2bd1Bfl4ef5k5HfOpZGI7da88//WKz7ni8qkuJSvahfji72usJnPtCnfy2RX4GWq7\nwx578F6eeX4uuhqFZmtXf03HiAgGDBzI9999iwCC/YxnRYYRaAbBS4qoc2OWvJ+hMBnZcSyE9qnz\nS1b/pY6Cpfk3ONE4R7GXqyvX8KGN+x7WQ2JCDMuPnuahBPeuzWVHOpCYUD3bSSdEtThCfYSEhjJi\n1Bi2bPyGm/6iGgSbzcY3a1exfPk3LvtER0djNldlraemmomKUVdNFfLXUVFRbuWv//XEP1m3Zi06\nYWff/upGePfufcy+cSYA2WfOsH7dWgwGA7t27uDrVatYu3YtZWVlFBYUcMusWSz59JNq/WNiYjh1\nqmpuZrOZmJgYrFary+MtRduvlGgjqAFl14GzaiglkPU9pHyKLG/EDT373BLh02gFyrJANm81++wR\n57H0SAf2Zvm7PL83y59lRzsw66JuLs/XxZkzWeTn5QFQWlrK91s2Ed+9R+X5bT98S7f484mMdO0a\nGzh4MEePHOHE8eNYLBY+X7acKyepMteeyF//65ln2b5nTy1jAHDs+EF+O3KU344cZerV1/DKa68z\nOTGRp597nmMpJ/ntyFE++uRTLr30Uj795ONa/adMmcLixYuRUrJ9+3ZCQkKIiopi8ODBJCcnc9wx\nZ03+ug0iKUdNnjLikQ215gMSUpZiD+6DaD8cYfBcT0aWn1FXB8ZgsLrOUqhFa0lBN+XuZJ68h2ZS\nNXXnAmqUa8hpro1213n7Xo3B4BuF0DWvjlFceAAvzRjE/y2F6edlMT0+i+gAC2nFJpYd6cCyox14\nacagWsVpBp0g2NeIHbBYFRTHakFx2tGpKPcMt99yC4qiYJd2rpk2jWuuTqw8v3l9EjfeeANduldt\nmpR8LIU5f/8bn36xAoPBwEsLFpB45UQURWHWzTfTyyElXSF//dHC94mL68yy5R9Wm58rN5GnWBU7\npVaFUoutWtzgrbfeAuCOO+5g4sSJrFmzhvj4+Er5awCDwaDJX7cUDZW/lhyhqswhCrUI2wUu/mil\nYsC2JwXDpbeji6uuBCmRLvOTSV8Hufu8nmclbm6mzRFDaLbKYU+MmgfXbpbqYlrIIDT2s+04FtG+\n7t2+6sJr+ettx0jabya3xE6Yv47EhFhmXdStljGoQEqJxS4pLrdhq5GKCt7LVEP1imFP+jvf+Ett\n/i6Pu8O5vTOKIimxqCIKAT4GAoz6ZnXb1USTv252vP9iViMvA9uXTyM69UXXqQ8i+gKEfwiEnUHq\nYoEwNZko/1fI3oFWeKbRJHi02VDTEBcewBOT+1bWGniCEAKTDgy+RnQCCspslNmabzOf1sJql5j0\nZ8fftGYQPMIXVZYJPBWSc/VkKk8dRDl1sPJ3EdcHLKUQEIZhyHAo+EM9YXGd112ntHIL7xjm8roN\neaL1dt5NuCpIXXmk3jYxU5r+ptqUKzW3Lq2rrkX4tP39p4UQVNwrQ/yM+Cn6yn3A7Q2Qe4gIqloh\nlNpcP8i5e/p3t1rwFr1eVNc5KrcR7GPA6GH9RWuiGQSP8KUqoNx0qXzy9EkodcQIzg8GW37dHTQ0\nPMXX+x3S2gJGncBql9jOIVe2TbFTWG4jzM/Yoq6jhtD2TVaboAXkg8+d779GWyCw5dxFTUmFVPS5\nhlWxU2aze5Vq2xpoKwRPkEY4UwDYwegLofV38aSIqXJ1AKDUvzqoM5DpzpXi5JLxyD3RlNlKTZQR\nVM3Fgutxmit47DUefGZ1/j94+TlV+04EOQq2dD7g3/wqp86kZBezaNtxkn5OJbfYRliAgcR+Mcy+\nqKvboLI7dAJ1i02nYyZ9sdv27nAbGC4tcmpUtXlQY9xEdVHhPpJAuSIBiVEn0OvanuHTVggeIIQO\n8sxw+lco0YrFNNoohcnqj7Q2u4aRM1sOn+aq//2ArzGdL+40cfjZAL6404SvMZ2r/vdDLfnr+tAJ\ngdFxs1SkrExJPRewI7HTdt+TZhA8pQVkADQ0moQWdBelZBfzwPK9vDvLxEMTTMSF6zDoBXHhOh6a\nYOLdWSYeWL63mvy1JwhRlZCtKAoD+o9k8qRaEmm89NJr6EQwZ864flBbt24DF/QYQPf4fsybVyV/\nnZOTy/hJ0zm/ewLjxyWS66X89ffffUtUeDuGDRzIsIEDmfvsMwCUlZUxevgwhg4YwKB+F/Lsv//l\nsr9iP0vlrzUcdOgFIeHgEwnkVR1vI5u7NxnNkPXT2GtUc7GE1++G8shd18I0KJvIg89JMAYAaS2G\nU1+oGcsB3lcFN5RF244zY7CeAXGus+8GxOmZPljP4p+O88SkPl6NrRcKOiGZv+AVevY8n4KCwmrn\nT50ys+GbTXTuFKO6gUp9qrmAFEXh73c9wDcbkoiNjWHI4IuZMmUiXc8fwLPzX2LU2Al884+7mTfv\nZebNm88LLzxd2dcT99FFI0fyRVJ1lVQfHx/WbNhIYGAgVquVy8aMZvzlExgybFi1dmvXreGPP85e\n+WuNgDAI8wX/xgvXaWg0OQIoNYPdijDVobfVxCT9nMp1g+t+rpw+2EDSfu9l3HU6G+lpx1i3Zg23\n3ja71vk59z/GC/95xm3mzs6du4mP70a3bqr89fQZ15CUtBqoLn89e/YNJK342uv5uUIIQaBDsttq\ntWK12lzOb/XKVVw/88Y2J3+tGQSP8WntCWho1E8LFqMB5BbbiAmtOzgaHSrILbHV2cY1gvvve5QX\n/vN0LVXRpKTVRMdE0a+f+0K41NR0YjtVBddjY6NJTU0DaspfdyQzs+49HVyx46efGNK/P1MnXcmh\nX3+tPK4oCsMGDqRLdBSXXjaWwUNrV4unpaUS6STNXZf8dWpqy+2JormMPMYXtQahkVXLHlBnAZqL\nNnW1axSNzTjyRLPIGXfX8NIt5ambyNuiM48+48a6EL38nOWvVdtK4t9ZzTBqQcICDKTmSeLC3RuF\ntDxJmL/3t5qvv95Ah4iODBw4iG/XrwVFgdIiSqSOuc+/yPpvVqgNhU51FTm5i2pSavPHovig2I21\njoP6ZO9NllFC/wH8fuw4gYGBrFu7hhnTruHAb78DqmT39j17yMvL4/pp1/DrL7/Qu09td5mhDVYv\naysEj/FF3QehedUjNTQaTMlJyNyMzD3QYpdM7BfD8l11P/0v22UjMcF7CedtW7exauVqunbpyfWz\n7mTzdz9y0y1/5+jR4xw/nkJCvxF07dIHszmVgQNGkZFRfZObmJgozKeqdmpzJX8N1Cl/XRE0rklw\ncHCla2jCFROxWq2cOXOmWpvQ0FBGX3wxG75ZX6t/dHQMp9PTKn+vkLl2J4vdUmgGwWM0l5HGWULa\namTB4Ra51OyLurJ0l8LeFNcaRHtTFJbtUpg1vKvXY8+dO5dT5pMcP/ELSxa/yaVjRvLRB6/Tt29v\nMk8f4/iJXzh+4hdiY2PYs/cHIiM7Vus/ePBAkpOPcfz4iUbJX2/fU3vFmZGRUVlktnvnTux2O+Hh\n4WRlZZHnJNm9eeNGevToUav/lZMnafLXZzMCgSSclnAZaWg0iHAnX3X2bqTeFxEQ16yXjAsP4KXr\nBvB/i/cyfbCe6YMNRIcK0vIky3bZWLZL4aXrBnhdnAYg7VaQ5V71SUtL5/9u+zur13yBwWDgtdf/\ny4TLr8KmSJfy14sXLqRT5858tGSpV9dZ8cUXvPfO2+j1Bvz8fFn08ScIIchIT68l2X3FlZMAeO/t\ntwG47a9/5fIrJrJp/XpN/ro1aaj8dQWS/agid07y1574w920acqUSG/lmD2iCX33zRHjcHc9Z9G6\nphSna5Y4TY3/E7fCdw2JTeiMEHcDwr9hLgev5a9/Ok7S/lRyS2yE+RtITIhh1nDvK5XBYQys+WB3\nuGjriA8405hqY2fpbHc0RJLb3TU6BjWPJI4mf91iaCmnGmcRdiucXI7sfitCH9ysl4oLD+CJSX28\nrjVwhbQrYMlF82i3PJpB8IoAwP1m4hoabYZQp3TMgj1I3wsQflGtNx8PkNIG0ga2UtAZQOhBareo\nlsSjT1sIEQq8B/RB1Wi6RUr5kxDibuAuQAFWSykfFkIYHW0HOMZfLKWc6xhnIPAh6qP2GuBeKaUU\nQvgAi4GBQDYwXUp5wtFnNvBPx1SelVIuchzvCiwFwoE9wE1Syma+WxvxaF9laFT6oTud/rrcH56k\nqnqEJ/N24w6r87qeCN01g5utudxHjcLNTmrQBG4iB1anLSGNV4wE5SekbySEDYSQHgh9Cyj41oFE\nAWyoO0MJQIKwqSmkptq3pbpcQZ7sbuaJO6iw0PXtIyioahvSE8lVEhnOW3c647xjmr9RT5BvlZvJ\nP8iz/VRaC0/XZAuAdVLKC4B+wG9CiEuARKCflLI38KKj7bWAj5SyL+oN/q9CiC6Oc28C/wd0d/xM\ncBy/FciVUsYD84EXAIQQ7YCngKHAEOApIUSYo88LwHxHn1zHGM1M6/4RaWg0irIMOLMVDr+KNK9C\nFptbXI5ZSom05IMsQV1t25x+NFqbelcIQogQYDRwM4DjKdwihLgTmCelmgYgpayQNJRAgBDCgLoS\nsAAFQogoIFhKud0x7mJgKrAW1bD8y9H/c+B1odZ7Xw5skFLmOPpsACYIIZYClwI3OPoscvR/s0Gf\ngsf4oHnZNM46AruC4nj61fuAUg5KKfYj69X7cGBXROxAdAENF3BMyS7mw20nSNqfRl6JhVB/E4kJ\n0dx8UZfqQWWl1BEodn7id6wQKl9Xyto1eD6tiUEn8DcZ8DHoMLRBieu68OTu1hXIAhYKIfqhumfu\nBc4HRgkhngPKgAellLtQb+iJqKk4/sD9UsocIcQgwOw0rhmoSH+IAU4BSCltQoh8VFdQ5fEafcKB\nPCmlzcVY1RBC3A7cDtC5c2cP3m5d+HC2fkmBxrkgnF1SzXXtJhIK9NQ15IlrzZ2LyiMXXWP3RvBg\nHh4J+eUfctlGRPdEpv3meH0B9qBwdH3HI2J7e7Wz15bDp7l/2X7G9Y1i7vX9iQjx4XR+ORsPppP4\nxlbmT0/gkh4R6mrEVgRSAdF0f0fNtY9BBc6uJFNIlZfA2X3Uq2ftwrazEU9cRgbUeMCbUsr+QDHw\nqON4O2AY8BCw3PFUPwT1rhmNakweEEK0nPxiDaSU70gpB0kpB3Xo0KFRY6mCvFqBmsY5iF3Bfngr\nts+fwvb1iyh7VoK019stJbuY+5ft5x9T+3DT6G5Ehfmh1+mICvPjptHd+MfUPty/bL8qf20rVo2B\nF3Tt0ocL+w5j2MCBjHTSBHr6qScZ0r8/wwYOZPIVE0hPS3PZ/5v160jo3Yth/Xrz2sv/rTyem5PD\ndYlXMjyhD9clXkmel/LXADt3buXqqy5lyuTRjBkzpto5RVHo378/kyZNctlXyrYpf+2JQTADZinl\nDsfvn6MaCDPwpVTZiRodao/qxlknpbQ63EhbgUFAKuC8jVOs4xiOfzsBOFxNIajB5crjNfpkA6GO\ntjXHajYkdjSDoHGuINrFICK7IyK7Q3BE5WthMGH/YxuypABZnIu0lLqNNXy47QTj+kZxQYxrd9MF\nMSFc1jeKD7edALtFrY3QebfG3LxlNdv37OHHHTsqj933wIPs3LeP7Xv2cMXEK5n77LO1+imKwpx7\n7uGrVV/z/a59fPX5Zxz+XV0RvTb/RUaNuZif9v/CqDEX89r8F2v1r4uC/DyeefpRXn9jMStXfc9n\nn31W7fyCBQvqrOFYu3Ytycmq/PU777zDnXfeWTnnu+66i7Vr13Lo0CGWLFnCoUOH3I7T1NTrMpJS\nZgghTgkhekgpDwNjgUPAUeASYIsQ4nzABJwBTqL69z8SQgSgriBekVKmCyEKhBDDgB3ALOA1x2VW\nArOBn4BpwGZH9tF64HmnQPJ44DHHuS2OtksdfVtAIzYXVc+oaXC33G9sNkyTZRy1Ip64SDxp70nf\nhszJ67GacmtSJ5zn5C6jyt28rd9srOr7ztpaYytPjQNpR+amgt4E/iHgG4jQVWXKJO1PY+71/euc\n47i+UfxjyT6enOzaZVuXy0ciKLX5UbO0LTi4qq4iN78Qq11SWGqtVji2e+dOup13Hl27daOw1MrU\na65l/eqvGdT/QjasXc3ajZsoLLQwccp0bpx2Jfc99FSd78OZVV99xuWTptClp/o5R0RUzcdsNrN6\n9Woef/xxXn75ZZf9k5KSmDVrVi356xMnTlTKXwOV8tdtbT+Eu4FPhBAHgATgeeADoJsQ4hccN2Wp\nPka8AQQKIX4FdgELpZQValt/Q01JPYJqUCq+he8D4UKII8AcVJcUjmDyM45xdgFPVwSYgUeAOY4+\n4Y4xmhlT/U00NM5FFAuyrAiZdQKZn4m0liGlJK/EQkRI3avmDsE+5JU0LCNcCMGkyy9nxJAhfPDu\nu9XO/euJf3J+1y58sXwpDz/+RK2+aWlpxMZWORiiomNIT1MdCc7y1x0iOnLmjHfy18ePHaUgP4+Z\n065k6oTRLF68uPLcfffdx3/+859akt3OuJO5Pivkr6WU+1HdPjW50UXbItTUU1fj7EatZah5vKyO\nPh+gGp+ax4+hxitaEB+06kmNPxVCD3rHU7dOB3oD0lqGKBdg1xPqb+R0fjlRYe6r+LMKygn1b5jk\nw8ZvvyM6JobTp08zecIEzr+gByNHjQZU8bl/PfMszz37PB+8/ZZLo+AJQgi8iKEDoCg2fjmwn8XL\nV1JWVsbMq8YzYMAgkpOT6dChAwMHDuTbb79t0HxaEy2H0iscX+pm2DazubZ9bIybo1EZNh6O5S3u\nrt0Qd1Bzb69pnf9q5evmct25K2L0xH3kCbLISdJZqSruksVWRHAAU/qEsfFgGjeNPs/tGBsOpjEl\noXoWjjs3UdrpGnsvG0NJO11MdEQEU6YmsnvXrkqDUMHV101n5rSrePjxJ6oVoEVHR2M2q0mKQX5G\nsrPSiYtTn74r5a/1wZzOzCA8vHbCycvznmbLpm8AWLXhx2rnIqOiCQ1rh79/AP7+AQwaNJwNG7Zx\n6NABVq9eydq1aykrK6OgoIAbb7yRjz/+uFp/dzLXVqtVk78+W1CzjDS3kYZGBTcP6cCGA+n8nuo6\ntvZ7aj4bD2Yw+6Jor8cuKSmmqEjdR7m4uJhNGzZUqpUeSU6ubLdu9dfEn39+rf4DBw/m6JEjnHBI\nSbuTv/7qs08Ze/nEWv3nPPokqzb8WMsYAIy9/Er27PwJm81GaWkJ+/btptt53bl/zj85cDCZEydO\nsHTpUi699NJaxgBgypQpmvz1OYG9YUtfDY1zD0FcO1/mX3Me939xkMsujGJc32g6BPuQVVDOhoNp\nbDyYwfzpPYgL96eq+MwzzmSd5q5bVa+0EHaumzGD8Zer4gZPPv4P/vjjD3RCR3RsJ/7ziroay0hP\nY87f/8anX6zAYDDw0oIFJF45EUVRXMpff/Du+8TEdmLBWx96Nbf47j0YdcllTLrsInQ6HTfeMIse\nPXoRHORDOzfus7feeguAO+64g4kTJ7JmzRpN/ro1aaz8NYAs2AWnNqi/BJ0PhX80wcyq01yyzo3J\n0GlK3Lk5nHF+f56878YUmXmKJ59fo2XIvdRw8vaz9MY1eeSie7mgi/snexFclfuTklPOop1ZrPwl\nl7wSK6H+Rqb0CWP26DjiwmvfID12GTlw1hNqCM7ZR43RNYqPrsomOmXOp7BQ3a8hKjKIsDriKC2J\nJn/dktjPrlJ0DY2WIK6dD09OiOXJCbHVT/i1jZtkc6LX6/D1PTdupefGu2hJ7FrYRUNDQ8XHx0Cn\n2BBMpratYuopmkHwlrAYCL1IfS3aw6GGu4w8cWF44hJoyus1hppzdef2achYro57Vnzl2Xv2RHK8\nUa4hZ1dQE2apeetac6YhLkRnN5EzsqC43jaeaA411jXkDk/cRJ6QeqaYMosqv9Ex3J8AX4NXuk9t\nHe1x11tEMOjOA50er5OXNTQ0zmqk4ycsyOecMwagrRC8RuAHdELiWkxLQ+PPSEqehYX78kj6vYCC\ncoUQXwOJfcP4y5guLoPKZys6IYhq50eA77mZbagZhIZiNkMdu055q63jSfvGahw19+5hdY3pifvo\np2/LKl8Pv7jhmxE11k3k7TWquV7cZRB5smMcjXPrNer/1Gl+Fbc6oZjcun+c+TY5n3u+PEmv2FCm\nDosj2M9IQamV3835TH5tH69efwEX92jX8Ll5gXN2kDv3kycZROWlNuyODEy9TqDYJUa9jvYhPpgM\n50a8wBWay6ih2CVYS1t7FhoarUpKTjn3fHmCKwbEMqxHB0IDTOh0gtAAE8N6dOCKATHcs+R3UrIb\n9reSn5fHrTddz8iB/Rg1KIHdO7ZXO//ma68QGexHdvYZl/03b/iGEQMurCV/nZebw+wZiVw2oj+z\nZySSn1dd/tpml9gU9cdul5gMOiLb+VUag//+978kJCSQkJBAnz590Ov15OSoMmvz58+nd+/e9OnT\nh+uvv56ysjJqcjbLX2u4wlD/k5OGxrnOwh2n6RUb6lbLKCrMj56xISzc2jCBtn8+8iCXXjaeH/f8\nzKZtO+ne44LKc6nmU3y3aRMxnTq57KsoCo89cB+ffpFUS/767Tfmc9HIMWzcuo+LRo7h7Tfmu51D\nkL+JqHb+6J3E6h566CH279/P/v37mTt3LmPGjKFdu3akpqby6quvsnv3bn755RcURWHp0qW1xjxr\n5a813GAMQRZmoex1PJkEtYdCx+uQ6rotnmTGuGvjqRvAkywZb4u6WrpgzRM3kSdFVp664hqToeMW\nL2WuG+Le8nZ+HmVENTDzKelgLolD696JsGdsCEk7T/JIbXWIWjgXkKVmnGH7th959S1V5dRkMmEy\nVbmBnnzsYZ545jlmX+9SF5Ot3/9Ep7iutGsfQ3k5XDHpKlZ+uYI77n6ATevX8PHnq4mPDmbO3X/l\n4osvBl6p7GvUC6RO0C7Ih0C/uuMFS5Ys4frrr6/8XZWzKMVoNFJSUkJ0dO3ivrNd/lqjJo69aeWZ\nFPWn8AwyNxUR1w8R1Lid2TQ0zhbyy2wE13PDDPIzUlBqq7ONK06mnCA8vD333nk7l40cxpy/30lx\nsZreum71KqKiound90K3/TMy0oiKrhKGi4yKITMjHYAzZ7KI6BipHo+MJDMzs1pfKaFjqF+9xqCk\npNwbgAEAACAASURBVIR169ZxzTXXAKpo3YMPPkjnzp2JiooiJCSE8ePH1+rXVuWvNYPQUGq4jITR\nF8N1z6EfNBWZkeymk4bGuUWIr4GCenL8C0utBPt574yw2Wwc/Hk/N9/6f2z8cTv+/v68/vKLlJSU\nsODF//Dw4082dNrVUOWvq6ePBvga8PGg2GzVqlWMGDGCdu3UoHlubi5JSUkcP36ctLQ0iouLXYrb\ntVU0l1FDKTdgPZpDzhETOj9/wq//K7rIOGw/fgJ+wdWaepJh0+X16ZWvT/x9mcs2nhZ6NaYoqSnx\nxI3lrRaPJ3i621pjdqlz+/m5cxM1YTFac3xmDSWxbxi/m/MZ1sP9qvg3cz6TLozBz1Di8ry7grXu\n53UhJjaWMaPVz/S66dfy0n/+Q1baSU6dTOGykeoey+mpqVw++iK+2/YTQSHhlf27devMl8vSCQoy\nUVhoISM9lY6R6qY47dt34HRmBvHRwaSnpxMRUd3NG+xv4vHHH2f16tUA7N+/3+Ucly5dWs1dtHHj\nRrp27UrF/u1XX30127Zt48Ybq28do8lfn2sY/JFnsrCcPEXYnY9giIxD5qVj37MSmZ/R2rPT0GgR\n/jI0gkPmPNJzXWcRpeeW8ps5nxuHx3k9dmRkJLGxsfxx+DAA327ezAU9e9Knb19S0tL57chRfjty\nlJjYWLbu3EVkZGS1/gkDB3Hs2BFSTpzAYrGwOulLxo5XAxmXjr+Crz77FIBFixaRmJhYra/RoOO5\n556rDBy7Ij8/n++++65a386dO7N9+3ZKSkqQUrJp0yaXeytr8tfnGn4hCIORjnNfx3SeqsVu++Ej\nsHvvK9XQOFuJa+fDq1d34Z4vT9AzNoSesaEE+RkpLLXymzmP38wF/Pe6fnQO9wdcrxDq4sVXFnDL\nrFlYLBa6duvKW+/VvVNuTfnr5/87n+uvmozVZmPa9Bvp3kO9Of/1rjnce8dsunf/hLi4OJYvX+71\n3L766ivGjx9PQECV+3jo0KFMmzaNAQMGYDAY6N+/P7fffjugyV+3OZpC/roCKSUU5yIC21X+bv//\n9s48Pqvq2vvflYlJICQyIxBs0SqjBodWcKBXKW0B64Ra3yha+1oHqlWE1l6tiq9e9VoVr0MBBUWj\npYgDKHItjlUQJKKCVsGBKGPCJEJCkvX+cXaSk+Q5yXmGzOv7+TwfzrPO3vvsfU549tm/vfbay++H\nslJo24mkNpWTVPHELIolHlCiJKP6irUUj5wRz85yidzdLVGSW23XimfhXJA3VjR8UnoKh3U/uIot\naKHaV4VFPLpiK8+t2cHu/SV0apvC+CFduOiYbvTLcHsutzsoYt4wMY5iwR+/qHvH2Bc6Njcs/HUj\nICJwkG/1Zclekjp+5x2ntYfY9hQ3jGZJv4w23DTmEG4aE3lNgNE8sDmERJHSztuQ3DAMo5kSaoQg\nIunATGAQXrC/Sar6johcCVwOlAKLVHWKiJwPXOfLPgQ4ClgPvOmz9wGeUNXfi8iFwJ1AucPtDFWd\n6a6dA9zg7Leq6hxnzwJygUxgFXCBqjbae7lIMtqhP5QVQ3J7KC6oOBdm4VeQNBSLbBC0iKmKPSBv\ntPJCPCGuY6Eh5Jz68MCKZZFfmHubteyDiuPie86I6tphFvaFiWUUL37vo0TKRx3rWENg1CSsZHQv\n8LKqnikiaUB7ETkZGA8MVdUiEekGoKrzgHkAIjIYWKiq5dP0w8oLFJFVwALfNZ5W1Sv8FxWRDOBG\nIBuvI1olIs+r6g7gDuAeVc0VkYeAi4EHo2l8ItGSffDdF0AZpGXWmd4wDKOpUWeHICKdgVHAhQDu\nLbxYRC4DblfVImffGiH7uXhv8dXLHAh0o+qIIRKnAUtVtdDlWwqMEZFc4BTgPJduDnATjdgh8N16\noKzRLm8YjclXhUXMemcLCz8s5LuiMg5qk8SEwRlcfHz3ykllo8kTZg4hC9gGPCoiq0Vkpoh0AAYC\nI0VkuYi8LiIjIuQ9B3gqgn0i3ojA7+J0hoh8KCLzRaR8Zqo3sNGXJt/ZMoGdqlpSzd54JBVB1xO8\nT8ZRjVoVw2hIXvtsFz9/aB2vfriDPpLMkHap9JFkXv1wBz9/aB2vfbarsatohCSMZJSCNwdwpaou\nF5F7ganOngEcB4wAnhGRAeU/8iJyLPC9qn4UocyJwAW+7y8ATznp6bd4b/ynxNooPyJyKXApeItG\n6gOlGDolw8a3ElJekFtmvBp9GHfPqPXmq6+qTPT8VTQkgQHfQgSVC02UK4zrKyBg0LMLmjcIQ9x1\nbXcQXxXs44r5X9ArKYkOyZXvl20EupFMB8q4Yv4XLJp8NP2i3Cfn6/UfMPGcCwEo0yS+/GIDN9x4\nE1dMnswHeXlMvvx37N9fRFpqEg/8z90cc0x2jTmIV5a8zJRrrqG0tJScSZO4dsr17Nl3gB2Fhfz2\nogvY+NVXHNKvH4889gSH9e0Zum47duxg0qRJrF+/nrZt2zJ79mwGDRoEQP/+/enYsSPJycmkpKQQ\nydVdVZk8eTKLFy+mffv2PPbYYxx1lPci+fLLLzN58mRKS0u55JJLmDp1anQ3Lg7CjBDygXxVXe6+\nz8frIPKBBeqxAk8v8TstTyTC6EBEhgIpqlrx16iqBeXSE97kdfmvzzeA34+tj7MVAOkiklLNXgNV\nfURVs1U1u3w5eeIpwJviMIzWxaw38+ksUqUz8NMhOYnOIsx+Kz/qsg877Iesznub1Xlv8/aKFbRr\n355xEyYAcMO0qUz78595d9Uq/nLzH7l+Ss24RqWlpVxz1VU8+8KLrFrzIX/PfZp1LpT0/ffcxcgT\nT+KdvI8YeeJJ3H/PXVHV7bbbbmPYsGGsWbOGuXPnMnny5Crnly1bRl5eXsTOAJpu+Os6OwRV3Qxs\nFJHDnGk0sBZYCJwMFXMCacB29z0JOJsI8wd48wpVOgoR8XfN44B17ngJcKqIdBGRLsCpwBI3ClkG\nnOnS5QDP1dWW+qMUaDnbBBpGWBau3kpnqf1npLMksXB1pCnG8Cz756sMGDCAvv28EBgiwp7dewDY\ntWs3vXr1qJFn5YoVDDj0ULIGDCAtLY0zzzmbF194HoAli17k7PO8+EJnn/drXn7xhajqs3btWk45\nxRMxDj/8cL788ssaEVNrIyj89YoVKyrCX6elpVWEv24ownoZXQnMcx5GG4CLgL3AbBH5CG8ZVo5v\nTmAUsFFVN0Qo62ygemT0q0RkHFACFFI5gV0oIrcA77l0N5dPMAPXA7kiciuwGqh9TXu9UgTsizqw\nWbzx/OsDvwzjD7jnx1+/L08OlonCSF/xuK0Grsb13e8Pe1R9c/MzODPEqvV6DlZXW8C9eAjlehzL\nivFqq42/KyolrV3tHUKawHf7S8MVHxAAb/7Tz3DWORMrvv/X3f/N+J+P5Y/XT6G0tJQXli5j655U\nOvrey7799lv69PEEhm+37qVDx658sNp75tu2ba0IdNetew+2bYuuwxo6dCgLFixg5MiRrFixgq++\n+or8/Hy6d++OiPDTn/6U5ORkfvvb31aErvATTfjr5cuX18hfX4TqEJzbaKRlz7+OYENVX8ObW4h0\nbkAE2zRgWkD62cDsCPYNwDGBlW5Qwv2xG0ZL46A2yRSrN2cQRLF66WKluLiYxS++wF+mT6+wzXz4\nYe64624m/OpXPDHvKa654jL+/vzimMqPFP66LqZOncrkyZMZNmwYgwcPZvjw4SQne21866236N27\nN1u3buU//uM/OPzwwxk1alRMdWtobKVyQkgFWk+sFMMoZ8LwbuzS2t2td2kZE4Z3qzVNbbz00lKG\nDh9O9+7dK2zzHp/L+NNPB2Dc6WewelXN0V6vXr3Iz690UvSHv+7atVvFZjlbNm/i4IO7sv9AKcUl\nlW3505/+VLFvcnU6derEo48+Sl5eHnPnzmXbtm0Vu5yVh6vu1q0bp59+OitWrKiRPyj8dZC9obBY\nRgmhFKi5kXYFIWSHMDJRLNtpRivPBG3xWV80xOrmID4sqBz0BspHAdJQtIH/gp5d9XKC/g7iuU8J\nvcf7vqs8bncQF4/sw4L3t9ChtCzixPLe0jJ2lSmTsjOq5vUTEPSunNyn/s755/2qQk7aV9Kenr16\n8eYbrzPqxJN46/XXGHBozft79IgRrP/8c7784gs6ZnTjpRef5X9mPcaePcWc9NMxPP7oHK6dNpVn\nnnyC037+C3btPwAidG2fRlKSMH36dKb7RiV+du7cSfv27UlLS2PmzJmMGjWKTp06sXfvXsrKyujY\nsSN79+7llVde4T//s+aE97hx45gxYwYTJ05k+fLlFeGvu3btWhH+unfv3uTm5vLkk0/Wen8SiXUI\nCSG64aZhtBT6ZbZjxvlHcMUTa+mM0lmSSBNPJtqlXmcw46ysmBen7d27l6VLl/HQw/dWsc948CGu\nu+YaSkpKSE1L4857ZwCw6dtv+d1vL+XZF14kJSWFu++9l/E/H8uBAyWce0EOh//oCPbsKa4If/2P\np5+gT9++PPKY29VMle8PlHJQm9p/GtetW0dOTg4iwpFHHsmsWd4U5pYtWzjdjVxKSko477zzGDNm\nDNA8wl9bh5AASsraAqWkJO1u7KoYRoNz0mEZLPq/P2L2u1tYuGYH3xWVclBaMhOGdGHScfGtVO7Q\noQPbC76qYf/xCSfwtpNi/GGue/bqxbMvvFjxfczPxjLmZ2OrpAHokpHB3GdeoGPHtApbqhvhlJQp\nqlrrvMLxxx/Pv//97xr2AQMG8MEHH0TI4XUE5YgIDzzwQMR0Y8eOZezY6n43DYN1CAmgYH9XvjuQ\nwaGdN9WdOEHeKbHsjdDQ8kyQZNKQdfJLQX6JCGBIpj9w24kVR/rxbRHLimd/iLDePQ3tVVYn1eUc\nv+zjO+6X0Ya/jO3LX8bGsPizmgxVZ5WqeyK1S0xAvMKdlbJveq9OtaRsudiksmEYhgFYh5AQkgVS\nonRbMwzDaGqYZJQAFCipvhVpgqSheAkjybzzWuVQ+XgiSxuJXCyXKJko7ep/VBwHyTx+qnsSrfFJ\nSFXlo+hIpAdQGDnN/7z8HH9SpetztIvOgvaA0Gqrb6PdH0F3741oDywnRk+kSFSfNyjHP2+wZ0/l\nFio/aKUykR/rEBKAmJeR0cr5qrCImW9vZuGaQvaWKB1ShAlDMrh4aCf6pafVXYDRJLAOIQEkSRkp\nYsHtjNbJa5/t4vLc9SR9vZvkjXtILyqhrE0KC7/cxYK8TswY15uTsqJ/wzcaHusQEkBG2+1ktI0v\neFdDECRHBEkNVSSLRlxA5idICvHHLAoVo4j4ZKIgwsg58cYT8pcVD3Vt7ao/PqHGueoS0Fc7i7k8\n9wuS39tE6u5K+SV5fwnJn+3gwJa9XAEsysmqMlIIKyXd+8BMZj46D0W45Dc5/P73l7OvpD3Tb/4L\nj86axcEHexGMb7r1Fsb8rKar5j+XvsKfr7+W0tJSzs+5kCuv8Xb3LQ9//e3Gr+nfvz/PPPNMxPoE\n8cknn3DRRRfx/vvvM336dK699tqKc0Hhr2+66Sb+9re/UR51+bbbbovoXhoU/rqwsJBzzjmHL7/8\nsqLOXbp0iaredWGTygnBJCOjdTLrvQKSvt5dpTPwk7q7mKSNu5m9sjDi+dr46ONPmPnoPJa/sYi8\nD/7FoheX8Pnn6yvOXzF5Mu+uWsW7q1ZF7AxKS0uZ9off8+Q/nuON91bz7Py/8+knXiDl8vDXn332\nGaNHj+b222+Pqm4ZGRncd999VToCP0Hhr6+++mry8vLIy8uL2BnUFv769ttvZ/To0THXOQzWISSE\nUqxTMFojC9d5MlFtJH+9h4Xrol+0ue7Tzzgmezjt27cnJSWFUSf+hAULwoepXr3yPbIGHEq/rCzS\n0tKYcMZZLFnkLVrzh7/Oyclh4cKFUdWtW7dujBgxgtTU1Kjy1UVt4a+fe+45cnJyYq5zGEwySgg7\nCbtBTrQeOnGHK46S+looFsZjJh4pJGiRWSwEhSL3E+19iqWd9bGAL6g9QRzYWRR4LjW9DXtLIb2o\nJDANQFJRCbtLtEpZqel1r14edMTh3HDTHRQUFNIuI4mXFr/C0dnDaZfyPSlJB3j4gRnkPjGXYUeN\n4P/deWcN+WTfzu0cmtWP7h29+334of1Zvnw53Tu2Zfu2rQz5YRYAPXr0iGovg7qoLfz1/fffz9y5\nc8nOzubuu++uUefawl9v2bKFnj171kudy7ERQpwopXgdgmG0PjokQ1kdcX/K2qRwUEr0I+gfHf5D\nplzzO0775bn8bMyvGDpsSEWI6csuu4T1G9awOu9tevTswbTrroup/hBb+OvaeOutt8jLy+Oll17i\ngQce4I033gDgsssuY8OGDeTl5dGzZ0/+8Ic/xHyNRNe5HOsQ4mYn3u6hhtH6mPCjTpQe0rHWNKV9\nOzLhR7H5+F984Xms/NcSXn/jZbp0SWfgQG/U1L17N5KTk0lKSuKiiy9h5cr3auStLZR09+7d2bTJ\nCzWzadMmunWrGZ67tvDXtREU/rp79+4Vdf7Nb34TVVjssHWOF5OM4qYL4IW3Ft/tVGJfmNYQsk20\nckQY75lYCJO/IaSyIKINQZ0oD6DqxHMPgmQiOfKPFcfF95xRI72U1r1+4OIRmSxYu5sDW/ZGnFg+\n0CmNskM6MSk7g9SA9QiBi9TaHcTWrdvo1q0rX3+9kWcXPM87774KwKZNm+nZ09s28/mFCyNGBB0x\nYkRgKOlx48YxZ84cpk6dypw5cxg/fnyN/LWFvw6itvDXmzZtqpB8nn32WQYNGpTwOseLdQhxIiRh\nAy2jtdIvPY0Z43pzBaAbd5P89R6S3DqE0r4dKTvEW4cQ6+K0M8/4NQUFhaSmpjLjgbtJT08H4Pop\nfyYv70NEhEP6ZXHf/zwI1Ax/HRRKeurUqZx99tnMmjWLfv36Re12unnzZrKzs9m9ezdJSUn89a9/\nZe3atWzfvj0w/PWUKVPIy8tDROjfvz8PP/ww4G31eckll7B48eJ6rXMYrEMwDCMuTso6iEU5Wcxe\nWcjCdbvZXaIclCKcPjSDSUPiW6n8xptLItrnPv63iuN9JZXRTquHvw4KJZ2Zmcmrr74ac7169OhB\nfn5+DXunTp0Cw18//vjjEe29evVi8eLK7T/rq85hsA6hnvAPx8PE2QkjR4SVkqL1aInHmyVeL6FE\neRnFgv+5+KUhf4ykL04emvDr1pcEFirelG/3Pn/7I+XVH59AavdgbyC/1NO/Uwdu7tuFm6OudfT4\nO4Da2LKn8m+r3NPIqB3TOgzDMAzAOgTDMALxdg4zmg/xPq9QkpGIpAMzgUF4K7Amqeo7InIlcDne\nUt1FqjpFRM4H/E7BQ4CjVDVPRF4DegL73LlTVXWriLQB5gJHAwXAOar6pbt2DnCDS3+rqs5x9iwg\nF8gEVgEXqGrk9fONjF8+SvM5Q9SHHBGWqMNi++ScsDJPkGdSovB7xvhlHj+1yXVBskqY5xKPLBcL\nYRaUBf2d+VFeD32tdqVFFJZBZnr7Cp/3oBhEcREitHVYmSiI1iAfqSoFBQW0bRt7+8LOIdwLvKyq\nZ4pIGtBeRE4GxgNDVbVIRLq5Ss0D5gGIyGBgoarm+co6X1WrRx+7GNihqj8QkYnAHcA5IpIB3Ahk\n43VEq0TkeVXd4dLco6q5IvKQK+PB6G+BYRiR6J20hm+2w/btHSkPzaL7Kt+5pF2Cwlqn1r1quTiE\nC2x19h8ojWgvbJvYcBNNibZt29KnT5+Y89fZIYhIZ2AUcCGAewsvFpHLgNtVtcjZI4X7PBfvLb4u\nxgM3ueP5wAzxXklOA5aqaqGry1JgjIjkAqcA57k8c1x+6xAMI0GkSjH9k6u+ux34V91hPaLmRz+p\nM0n1/bDD8K+1myParx5+aNRltRbCjBCygG3AoyIyFE+emQwMBEaKyHRgP3CtqlZfLngO3o+9nzki\ncgD4B54EpEBvYCOAqpaIyC48KajC7sh3tkxgp6qWVLPXQEQuBS4F6Ns3hg3AG4F4Y9jEIu/UVU4Y\ne9h6REvQPWjoBWuJksBiiU/ll4PiQXxxnjRAVqptt7/ATuDIyD/qH8axK92agsTJU1ePtE4gDGEm\nlVOAo4AHVXU4sBeY6uwZwHF4cwbPiC+4hogcC3yvqh/5yjpfVY8ERrrPBQlpRS2o6iOqmq2q2eVx\nyA3DMIyahOkQ8oF8VV3uvs/H6yDygQXqsQIvoM/BvnwTgaf8BanqN+7fPcCTwDHu1DfAIQAikgJ0\nxptcrrA7+jhbAZDu0vrthmEYRozUKRmp6mYR2Sgih6nqp8BoYC2wHjgZWCYiA4E0YDuAiCQBZ+ON\nAnC2FCBdVbeLSCrwC+B/3enngRzgHeBM4J+qqiKyBLhNRMpjxJ4KTHPnlrm0uS7vc/HciGhRtgIH\nkMhKVSjCyAWJDIFcX/GIor1emPRh5KbjqbwfWVdHLjPsAsF47m0YWS7eZ+eve7TyUZD0MjgzIEOA\n/FOdKvJTCO+le95cX2cav7Tjl5hqk49eDZorMJkoasJ6GV0JzHMeRhuAi/Cko9ki8hFQDORopRPs\nKGCjqm7wldEGWOI6g2S8zqB8/fks4HER+RwoxBtdoKqFInILUD43cXP5BDNwPZArIrcCq10ZDchu\noIiAqQvDMIxmR6gOwbmNRprm/3VA+tfw5hb8tr146wwipd8PnBVwbjYwO4J9A5WSk2EYhhEnFsso\nZlIIu0takFQRKvaMj1jkozDSS6LiCSVyIVqiynpSDguVLky7E+W91RDeUX55ZvQRPSKmCXLl9Es1\n1aUgCdiNLoxbqL8eQTJPELV5KNVvuLfWhYWuiJkSPKXMMAyjZWAdQsxYjBfDMFoWJhnFTBe8UYJh\nGEbLwDqEGJEqSy5qEjRvEDRX4J9PCHIr9AdzC6tDN/T+Bg1J1rLIG5HEEjQw2nbX17xBmO0uo8Wv\n1/t1/DArh4PmDBKJuYc2HUwyMgzDMADrEAzDMAyHSUZxEkYa8ssA8UgC/pj/1a8bxm012mBz9b2f\nQW2cp59WHAe5jjbEfhKJXCkeLYkKaOcn2gBzYQmSpYLSJBKTnBKHjRAMwzAMwEYIMaP7t4JG3oDD\nMAyjOWIdQqwUvAel+wJP14e3SG3lpBI5+Jk/6FtjbtlZH4SRcxLpKRXGUyioHrHsgRAPDS2jBF0v\nTEA7o+lgkpFhGIYB2AghdpLr3gfWMAyjOWEdQqyU7vc+IagPb5HGuEZ945d3/PKW3x6t3BKLl5A/\nj9+zy09Q/P/e3BfRHvbafm+xtKAtLpsRYaQk/7F5DDUuJhnFjMUyMgyjZWEjhFhJbg9it88wjJaD\n/aLFiPQYDYDuWF1p9G092BAxYOIh2kVqDUE8i8Di9dwJyh+4NeTHb0c0R7vHRW35WzImDTVNTDIy\nDMMwAOsQDMMwDIdJRvHik4n8BEkNTV1KaioESThBkkoV75wAzyD/Ir3aiFYmCkNDLEYzjHixEYJh\nGIYBWIdgGIZhOEJJRiKSDswEBuE54E9S1XdE5ErgcqAUWKSqU0TkfOA6X/YhwFHAv4G/A4e69C+o\n6lRX/oXAncA3Ls8MVZ3pzuUANzj7rao6x9mzgFwgE1gFXKCqDb7rvV8CCpQafDSElOTfWS3IW6ep\neBb5CYr3k3r1VZWJ/LKNT65LDZDuwhKPTBTkTdTQ8YsMI17CjhDuBV5W1cOBocA6ETkZGA8MVdUj\ngbsAVHWeqg5T1WHABcAXqprnyrnLlTEc+ImI/Mx3jafL8/k6gwzgRuBY4BjgRhHp4tLfAdyjqj8A\ndgAXx3QHDMMwDCBEhyAinYFRwCwAVS1W1Z3AZcDtqlrk7FsjZD8X7y0eVf1eVZeVlwG8D/Sp4/Kn\nAUtVtVBVdwBLgTEiIsApwHyXbg4woa62GIZhGMGEkYyygG3AoyIyFE+emQwMBEaKyHRgP3Ctqr5X\nLe85eKOIKjgJ6pd4I49yzhCRE4FPgatVdSPQG9joS5PvbJnATlUtqWavgYhcClwK0Ldv3xDNjZ0q\n8lHATmpBXknx4peJmhNB4amr7A7nl3MC7l8sktuagr0Vx4MzAxIFPa8QUlIYmai1LEQzmgdhJKMU\nvDmAB1V1OLAXmOrsGcBxeHMGz7g3dwBE5Fjge1X9yF+YiKQATwH3qeoGZ34B6K+qg/FGAXPiapUP\nVX1EVbNVNbtr166JKtYwDKPFEaZDyAfyVXW5+z4fr4PIBxaoxwqgDDjYl28i3g9/dR4BPlPVv5Yb\nVLWgXHrCm7wuf236BjjEl7ePsxUA6a5z8dsNwzCMGKlTMlLVzSKyUUQOU9VPgdHAWmA9cDKwTEQG\nAmnAdgARSQLOBkb6yxKRW4HOwCXV7D1VdZP7Og5Y546XALf5JpJPBaapqorIMuBMvDmKHOC5qFpe\nz/g9T/yyQH0tTPNLLEE7ozVFzyJ/nc7TTyuO/W3IWvZBxXFVmWdlPdeuEfBJVGG81oKwBZBGLIRd\nqXwlME9E0oANwEV40tFsEfkIKAZyVLU8JvQoYKNPEkJE+gB/Aj4B3nfqUrl76VUiMg4oAQqBCwFU\ntVBEbgHK5yZuVtVCd3w9kOs6mdW4SW/DMAwjNkJ1CM5tNDvCqV8HpH8Nb27Bb8sHJCD9NGBawLnZ\nwOwI9g14rqiGYRhGArBYRjFS8MCdJLXvQIedrya87KCFZX7ppDp+iaUpSkNheFIOi2h/x2f3y0ok\nUBb5sKDyfSdaKSrakNexeBZVqd9mn3NegBeU7UJmxIJ1CDFS9OnHJKd3oUOXutMahmE0ByyWUawc\nONDYNTAMw0goNkKIAS0tgdQ0kjO78s3Cf1bYgxYiVZERXql7AVlQ/KEg76Ha8EssQZJMUydo8Vp9\n4ZdnghhM5cK0WHZGi5YqMtZm34mA2E6jj+hR73UyWh7WIcRA8WefUPTh+ySnpzd2VQzDMBKGSUYx\nsH/N+41dBcMwjIRjI4QYSGEn6WNGkdoviy5dIstE0W4SHy9BnkVNUSYKkrGCpKHavKvqgyGZlP6w\nNQAADPtJREFUHSLa/YviPuwxueJ48Kn3Rkoem5QUIAGFiZ0UJHX56x3UNsMA6xCiRlVJLVxNatpu\npMMhaN1ZDMMwmgUmGUXLjm9h3+7GroVhGEbCsRFClGhZKUnHngWAZPah9PPldeQwDMNoHliHECVJ\nB/cl6eDKfRWSDzuh4ti/wjjIBTXM3EKYlcbV9fbm6l5adeVxw/Lq2kr/zfp204xp34OgeYOgPRoK\nor+EYfgxycgwDMMArEMwDMMwHCYZNUHiXZnrz99cA901NH75aEiIYHBV0/+x4jhw69Qgqss/QW6n\ncWCupkZYbIRgGIZhANYhGIZhGA6TjBJItHHxG4Iw8pNfVjrzvwdVHAd5RIWVoYKuHbQ9ZkMT7T4B\nfuklSFaSIyvlo1QC5KPapKAQMlGY4HuGEQs2QjAMwzAA6xAMwzAMh0lG9USQfORfsBZtALygxW4N\nTVgvKL8cFMteDs0R5fWo0kst24D6g9KFwbyJjHixEYJhGIYBWIdgGIZhOEJJRiKSDswEBgEKTFLV\nd0TkSuByoBRYpKpTROR84Dpf9iHAUaqaJyJHA48B7YDFwGRVVRFpA8wFjsaLyHKOqn7prp0D3ODK\nulVV5zh7FpALZAKrgAtUtTi225AY/B4m/gVKaVf/I2L63lTGPkrk/gl+qcYfXynoGv54Qv70QWXG\nW6eWRrQykZ/aZKEw+zIYRiIJO0K4F3hZVQ8HhgLrRORkYDwwVFWPBO4CUNV5qjpMVYcBFwBfqGqe\nK+dB4DfAD91njLNfDOxQ1R8A9wB3AIhIBnAjcCxwDHCjiHRxee4A7nF5drgyDMMwjBips0MQkc7A\nKGAWgKoWq+pO4DLgdlUtcvatEbKfi/cWj4j0BDqp6ruqqngjggku3XhgjjueD4wWEQFOA5aqaqGq\n7gCWAmPcuVNcWlze8rIMwzCMGAgjGWUB24BHRWQonjwzGRgIjBSR6cB+4FpVfa9a3nPwfuwBegP5\nvnP5zlZ+biOAqpaIyC48KajCXi1PJrBTVUsilFUFEbkUuBSgb9++kZLUC375KAi/lBQkHwV5FsUS\nTjnI6ydIJmpor6YW4VUTZcjqwZkrq3z3LzoLuh/+OErRLq4zjNoIIxmlAEcBD6rqcGAvMNXZM4Dj\n8OYMnnFv7gCIyLHA96r6UcJrHQWq+oiqZqtqdteuXRuzKoZhGE2aMB1CPpCvquVbg83H6yDygQXq\nsQIoAw725ZsIPOX7/g3Qx/e9j7OVnzsEQERSgM54k8sV9mp5CoB0l7Z6WYZhGEYM1CkZqepmEdko\nIoep6qfAaGAtsB44GVgmIgOBNGA7gIgkAWcDI33lbBKR3SJyHLAc+D/A/e7080AO8A5wJvBP5320\nBLjNN5F8KjDNnVvm0ua6vM/FcyMag6BQyWGkmjCSFNTi4RTiGkF5mwpBkkp9SUmhvImiDFltcYmM\npkTYlcpXAvNEJA3YAFyEJx3NFpGPgGIgx00WgzcJvVFVN1Qr53dUup2+5D7gTVg/LiKfA4V4owtU\ntVBEbgHK5yZuVtVCd3w9kCsitwKrXRmGYRhGjITqEJzbaKRXmV8HpH8Nb26hun0l3lqG6vb9wFkB\nZc0GZkewb8BzRTUMwzASgMUyaiLEtAl7HDR1OahZEWKXs3ilIb9nkZ973lxfcWweR0a8WOgKwzAM\nA7AOwTAMw3CYZNSIhPUUai34vYP8UsjoI3o0RnWAqp5MgzMDEkXpWVQbQdKQYTQENkIwDMMwAOsQ\nDMMwDIdULh1o+WRnZ+vKlSvrTmg0WfwSTmPG9AmzSC2MZ1GTjMdktDhEZJWq1vkHaSMEwzAMA7AO\nwTAMw3CYl5HRrGhML5yqsZMqR9/+ENbRLkDze1OBLS4zGhcbIRiGYRiAdQiGYRiGwyQjw4iTRIaw\ntthERmNiIwTDMAwDsA7BMAzDcJhkZDQrGlNG8Xs4xRNfqTZPKZOJjMbERgiGYRgGYB2CYRiG4bAO\nwTAMwwBsDsEwYiKewHpDbJ7AaKLYCMEwDMMArEMwDMMwHKEkIxFJB2YCgwAFJqnqOyJyJXA5UAos\nUtUpLv0Q4GGgE1AGjABSgTd9xfYBnlDV34vIhcCdwDfu3AxVnenKygFucPZbVXWOs2cBuUAmsAq4\nQFWLo74DhhEScwk1Wjph5xDuBV5W1TNFJA1oLyInA+OBoapaJCLdAEQkBXgC7wf6AxHJBA6o6n5g\nWHmBIrIKWOC7xtOqeoX/oiKSAdyIF1pSgVUi8ryq7gDuAO5R1VwReQi4GHgw6jtgGIZhACEkIxHp\nDIwCZgGoarGq7gQuA25X1SJn3+qynAqsUdUPnL1AVUurlTkQ6EbVEUMkTgOWqmqh6wSWAmNERIBT\ngPku3RxgQl1tMQzDMIIJM4eQBWwDHhWR1SIyU0Q6AAOBkSKyXEReF5ERLv1AQEVkiYi8LyJTIpQ5\nEW9E4N+/8wwR+VBE5ovIIc7WG9joS5PvbJnATlUtqWavgYhcKiIrRWTltm3bQjTXMAyjdRKmQ0gB\njgIeVNXhwF5gqrNnAMcB1wHPuDf3FOAE4Hz37+kiMrpamROBp3zfXwD6q+pgvFHAnJhbVA1VfURV\ns1U1u2vXrokq1jAMo8URpkPIB/JVdbn7Ph+vg8gHFqjHCrzJ44Od/Q1V3a6q3wOLXXoARGQokKKq\nq8ptTlYqcl9nAke742+A8tECeBPR3wAFQLqbr/DbDcMwjBips0NQ1c3ARhE5zJlGA2uBhcDJUDEn\nkAZsB5YAg0WkvfvBPtGlL+dcqo4OEJGevq/jgHXueAlwqoh0EZEuePMTS5zUtAw406XLAZ4L1WLD\nMAwjImG9jK4E5jkPow3ARXjS0WwR+QgoBnLcD/UOEflv4D08z6DFqrrIV9bZwNhq5V8lIuOAEqAQ\nuBBAVQtF5BZXFsDNqlrojq8HckXkVmA1btLbMAzDiA2pOq/bssnOztaVK1fWndAwDKMFISKrVLXO\nrf1spbJhGIYBWIdgGIZhOFqVZCQi24CvYsx+MN6keWuiNbYZWme7W2ObofW0u5+q1ul336o6hHgQ\nkZVhNLiWRGtsM7TOdrfGNkPrbXcQJhkZhmEYgHUIhmEYhsM6hPA80tgVaARaY5uhdba7NbYZWm+7\nI2JzCIZhGAZgIwTDMAzDYR2CYRiGAbTwDkFE2orIChH5QEQ+FpG/OPswEXlXRPLcXgnH+PJME5HP\nReRTETnNZz/a7dfwuYjc50J9IyJtRORpZ18uIv19eXJE5DP3yWmq7RaR/iKyz9nz3A50zardtbR5\nqIi849rwgoh08uVpyc86YrtbwrP2XTtZvD1aXnTfM0RkqavLUvECYpanbfbPukFQ1Rb7AQQ4yB2n\nAsvx9m94BfiZs48FXnPHRwAfAG3wNgZaDyS7cytcXgFe8uX/HfCQOy7f+Ae8vSI2uH+7uOMuTbTd\n/YGPAspqFu2upc3vASc6+yTgllbyrIPa3eyfta++1wBPAi+67/8FTHXHU4E7WtKzbohPix4hqMd3\n7muq+6j7lL8pdga+dcfjgVxVLVLVL4DPgWPEC8/dSVXfVe+vYi6VW3aOp3JDn/nAaPeWEXH7z/pq\nq58Y2h2R5tTuWto8EHjD2ZcCZ/jq35KfdVC7I9Lc2i0ifYCf4+2fUo6/nv5tdVvEs24IWnSHABXD\nyjxgK96DXA78HrhTRDYCdwHTXPKgLTt7u+Pq9ip51NvScxfeFp9BZTUIUbYbIMtJCK+LyEhna1bt\nDmjzx3j/uQHOonLDpZb+rIPaDS3gWQN/BabgbcxVTndV3eSONwPd3XGLedb1TYvvEFS1VFWH4e2q\ndoyIDAIuA65W1UOAq2mBeylE2e5NQF+X/hrgSb/W3lwIaPMk4HcisgroiLd3R4siynY3+2ctIr8A\ntqpv18XquDd+86mPkhbfIZSjqjvxdlkbg7fD2gJ36u9A+aRy0Jad37jj6vYqecTbIa4z3hafQWU1\nKGHa7YbSBe54FZ7GOpBm2m5/m1X1E1U9VVWPxtupb71L1qKfdVC7W8iz/gkwTkS+BHKBU0TkCWCL\nk4HKJbCt1etfrZ7Nqc0NQ2NPYtTnB+gKpLvjdsCbwC/wtug8ydlHA6vc8ZFUnXzaQPDk01hnv5yq\nk0/PaOXk0xd4E09d3HFGE213V187B+D9gWc0p3bX0uZuzpaEpxFPaiXPOqjdzf5ZV2v/SVROKt9J\n1Unl/2pJz7pB7mdjV6Ce/1iG4G2vuQb4CPhPZz8BWOX+SJYDR/vy/AnvrelTnMeBs2e7MtYDM6hc\n5d0W7237c/fHNcCXZ5Kzfw5c1FTbjTfh+DGQB7wP/LK5tbuWNk8G/u0+t5fXvxU864jtbgnPulr7\nT6KyQ8gEXgU+A/4X3w91S3jWDfGx0BWGYRgG0IrmEAzDMIzasQ7BMAzDAKxDMAzDMBzWIRiGYRiA\ndQiGYRiGwzoEwzAMA7AOwTAMw3D8f1hcE/zpBOaxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2403c33aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot using 9 classes and classify the values using \"Fisher Jenks\" classification\n",
    "acc.plot(column=\"pt_r_tt\", scheme=\"Fisher_Jenks\", 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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mzbhw4QLR0dF06NCBfv368cknnzBnzhyef/55Ro0ahV6vHDAMDg7m3LlzBAQE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\nMcYYwBoEY4wxLmsQjDHGANYgGGOMcf1/Jki3WgszdTcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24036cc72e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot walking distance\n",
    "acc.plot(column=\"walk_d\", scheme=\"Fisher_Jenks\", 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",
    "- 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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pysal as ps\n",
    "\n",
    "# Define the number of classes\n",
    "n_classes = 9\n",
    "\n",
    "# Create a Natural Breaks classifier\n",
    "classifier = ps.Natural_Breaks.make(k=n_classes)"
   ]
  },
  {
   "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>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_r_tt\n",
       "0        7\n",
       "1        7\n",
       "2        6\n",
       "3        7\n",
       "4        7"
      ]
     },
     "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": "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",
    "- Now we want to join that reclassification into our original data but let's first rename the column so that we recognize it later on:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>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>...</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>area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>nb_pt_r_tt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15981</td>\n",
       "      <td>36</td>\n",
       "      <td>15988</td>\n",
       "      <td>41</td>\n",
       "      <td>6002702</td>\n",
       "      <td>14698</td>\n",
       "      <td>65</td>\n",
       "      <td>73</td>\n",
       "      <td>14698</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>5975375</td>\n",
       "      <td>14456</td>\n",
       "      <td>207</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>62499.999976</td>\n",
       "      <td>POLYGON ((391000.0001349226 6667750.00004299, ...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16190</td>\n",
       "      <td>34</td>\n",
       "      <td>16197</td>\n",
       "      <td>39</td>\n",
       "      <td>6002701</td>\n",
       "      <td>14661</td>\n",
       "      <td>64</td>\n",
       "      <td>73</td>\n",
       "      <td>14661</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>5975375</td>\n",
       "      <td>14419</td>\n",
       "      <td>206</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>62499.999977</td>\n",
       "      <td>POLYGON ((390750.0001349644 6668000.000042951,...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15727</td>\n",
       "      <td>33</td>\n",
       "      <td>15733</td>\n",
       "      <td>37</td>\n",
       "      <td>6001132</td>\n",
       "      <td>14256</td>\n",
       "      <td>59</td>\n",
       "      <td>69</td>\n",
       "      <td>14256</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>5975375</td>\n",
       "      <td>14014</td>\n",
       "      <td>200</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>62499.999977</td>\n",
       "      <td>POLYGON ((391000.0001349143 6668000.000042943,...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15975</td>\n",
       "      <td>33</td>\n",
       "      <td>15982</td>\n",
       "      <td>37</td>\n",
       "      <td>6001131</td>\n",
       "      <td>14512</td>\n",
       "      <td>62</td>\n",
       "      <td>73</td>\n",
       "      <td>14512</td>\n",
       "      <td>58</td>\n",
       "      <td>...</td>\n",
       "      <td>5975375</td>\n",
       "      <td>14270</td>\n",
       "      <td>204</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>62499.999976</td>\n",
       "      <td>POLYGON ((390750.0001349644 6668000.000042951,...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16136</td>\n",
       "      <td>35</td>\n",
       "      <td>16143</td>\n",
       "      <td>40</td>\n",
       "      <td>6001138</td>\n",
       "      <td>14730</td>\n",
       "      <td>65</td>\n",
       "      <td>73</td>\n",
       "      <td>14730</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>5975375</td>\n",
       "      <td>14212</td>\n",
       "      <td>203</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Helsingfors</td>\n",
       "      <td>091</td>\n",
       "      <td>62499.999977</td>\n",
       "      <td>POLYGON ((392500.0001346234 6668000.000042901,...</td>\n",
       "      <td>7</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    15981       36    15988       41  6002702   14698      65       73   \n",
       "1    16190       34    16197       39  6002701   14661      64       73   \n",
       "2    15727       33    15733       37  6001132   14256      59       69   \n",
       "3    15975       33    15982       37  6001131   14512      62       73   \n",
       "4    16136       35    16143       40  6001138   14730      65       73   \n",
       "\n",
       "   pt_r_d  pt_r_t    ...        to_id  walk_d  walk_t    GML_ID   NAMEFIN  \\\n",
       "0   14698      61    ...      5975375   14456     207  27517366  Helsinki   \n",
       "1   14661      60    ...      5975375   14419     206  27517366  Helsinki   \n",
       "2   14256      55    ...      5975375   14014     200  27517366  Helsinki   \n",
       "3   14512      58    ...      5975375   14270     204  27517366  Helsinki   \n",
       "4   14730      61    ...      5975375   14212     203  27517366  Helsinki   \n",
       "\n",
       "       NAMESWE NATCODE          area  \\\n",
       "0  Helsingfors     091  62499.999976   \n",
       "1  Helsingfors     091  62499.999977   \n",
       "2  Helsingfors     091  62499.999977   \n",
       "3  Helsingfors     091  62499.999976   \n",
       "4  Helsingfors     091  62499.999977   \n",
       "\n",
       "                                            geometry nb_pt_r_tt  \n",
       "0  POLYGON ((391000.0001349226 6667750.00004299, ...          7  \n",
       "1  POLYGON ((390750.0001349644 6668000.000042951,...          7  \n",
       "2  POLYGON ((391000.0001349143 6668000.000042943,...          6  \n",
       "3  POLYGON ((390750.0001349644 6668000.000042951,...          7  \n",
       "4  POLYGON ((392500.0001346234 6668000.000042901,...          7  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the column so that we know that it was classified with natural breaks\n",
    "classifications.columns = ['nb_pt_r_tt']\n",
    "\n",
    "# Join with our original data (here index is the key\n",
    "acc = acc.join(classifications)\n",
    "\n",
    "# Let's see how our data looks like\n",
    "acc.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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4imNLzyeRUDbua2Z14xpOrXxftqeVMtnc9DaDEcLG/cN4q3YXJw4Plwluf+CV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\nVd/062wGwzAMYwiRzhWG69ma9MsudF+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2403c345fd0>"
      ]
     },
     "metadata": {},
     "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": [
    "### 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 takes into account two criteria:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_classifier(row, src_col1, src_col2, threshold1, threshold2, output_col):\n",
    "   # 1. If the value in src_col1 is LOWER than the threshold1 value\n",
    "   # 2. AND the value in src_col2 is HIGHER than the threshold2 value, give value 1, otherwise give 0\n",
    "   if row[src_col1] < threshold1 and row[src_col2] > threshold2:\n",
    "       # Update the output column with value 0\n",
    "       row[output_col] = 1\n",
    "   # If area of input geometry is higher than the threshold value update with value 1\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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_m_d</th>\n",
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       "      <th>car_r_d</th>\n",
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       "      <th>pt_r_t</th>\n",
       "      <th>...</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>area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>nb_pt_r_tt</th>\n",
       "      <th>suitable_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>14698</td>\n",
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       "      <td>...</td>\n",
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       "      <td>27517366</td>\n",
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       "      <td>62499.999976</td>\n",
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       "      <td>7</td>\n",
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       "      <td>64</td>\n",
       "      <td>73</td>\n",
       "      <td>14661</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>14419</td>\n",
       "      <td>206</td>\n",
       "      <td>27517366</td>\n",
       "      <td>Helsinki</td>\n",
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       "      <td>62499.999977</td>\n",
       "      <td>POLYGON ((390750.0001349644 6668000.000042951,...</td>\n",
       "      <td>7</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 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    15981       36    15988       41  6002702   14698      65       73   \n",
       "1    16190       34    16197       39  6002701   14661      64       73   \n",
       "\n",
       "   pt_r_d  pt_r_t      ...       walk_d  walk_t    GML_ID   NAMEFIN  \\\n",
       "0   14698      61      ...        14456     207  27517366  Helsinki   \n",
       "1   14661      60      ...        14419     206  27517366  Helsinki   \n",
       "\n",
       "       NAMESWE NATCODE          area  \\\n",
       "0  Helsingfors     091  62499.999976   \n",
       "1  Helsingfors     091  62499.999977   \n",
       "\n",
       "                                            geometry  nb_pt_r_tt suitable_area  \n",
       "0  POLYGON ((391000.0001349226 6667750.00004299, ...           7             0  \n",
       "1  POLYGON ((390750.0001349644 6668000.000042951,...           7             0  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "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, src_col1='pt_r_tt', \n",
    "                src_col2='walk_d', threshold1=20, threshold2=4000, \n",
    "                output_col=\"suitable_area\", axis=1)\n",
    "\n",
    "# See the first rows\n",
    "acc.head(2)"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3808\n",
       "1       9\n",
       "Name: suitable_area, dtype: int64"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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enhHsqgSEiHDu1b9ixezVZW9cA/3m9+cEuwpHsYDgp4Q28bViQXBT8/3rD68x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\nxhjj/D9kmx71Cw9UCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24036c545c0>"
      ]
     },
     "metadata": {},
     "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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}