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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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\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,  21.00] |   263\n",
       "( 21.00,  30.00] |   529\n",
       "( 30.00,  37.00] |   779\n",
       "( 37.00,  45.00] |   914\n",
       "( 45.00,  53.00] |   480\n",
       "( 53.00,  63.00] |   356\n",
       "( 63.00,  76.00] |   251\n",
       "( 76.00,  94.00] |   178\n",
       "( 94.00, 155.00] |    57"
      ]
     },
     "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([ 21.,  30.,  38.,  45.,  53.,  63.,  77.,  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": [
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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>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>7</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>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_r_tt  nb_pt_r_tt\n",
       "0       99           7\n",
       "1       93           7\n",
       "2      146           8\n",
       "3      155           8\n",
       "4       99           7"
      ]
     },
     "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>7</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          7              0  \n",
       "1          7              0  \n",
       "2          8              0  \n",
       "3          8              0  \n",
       "4          7              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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\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
}