2_result_assessment/indicator-assessment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bicycle_infrastructure    0.2\n",
       "designated_route          0.1\n",
       "road_category             0.3\n",
       "max_speed                 0.1\n",
       "car_parking               0.1\n",
       "pavement                  0.1\n",
       "gradient                  0.1\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>bicycle_infrastructure</th>\n",
       "      <th>designated_route</th>\n",
       "      <th>road_category</th>\n",
       "      <th>max_speed</th>\n",
       "      <th>car_parking</th>\n",
       "      <th>pavement</th>\n",
       "      <th>gradient</th>\n",
       "      <th>traffic_lights_or_stops</th>\n",
       "      <th>natural_environment</th>\n",
       "      <th>car_traffic_volume</th>\n",
       "      <th>width_cycle_together</th>\n",
       "      <th>lighting</th>\n",
       "      <th>safe_parking_night</th>\n",
       "      <th>curve_x</th>\n",
       "      <th>short_cuts</th>\n",
       "      <th>tram_tracks</th>\n",
       "      <th>priority_at_intersections</th>\n",
       "      <th>urban_environment</th>\n",
       "      <th>accidents_near_misses</th>\n",
       "      <th>social_encounters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.84</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bicycle_infrastructure  designated_route  road_category  max_speed  \\\n",
       "0                     0.70              0.04           0.02       0.62   \n",
       "1                     0.70              0.09           0.03       0.64   \n",
       "2                     0.72              0.15           0.10       0.68   \n",
       "3                     0.73              0.21           0.13       0.75   \n",
       "4                     0.76              0.23           0.14       0.76   \n",
       "5                     0.76              0.27           0.23       0.76   \n",
       "6                     0.79              0.37           0.27       0.79   \n",
       "7                     0.85              0.50           0.47       0.80   \n",
       "8                     0.86              0.50           0.48       0.82   \n",
       "9                     0.91              0.51           0.58       0.82   \n",
       "10                    0.92              0.59           0.65       0.85   \n",
       "11                    0.92              0.60           0.85       0.87   \n",
       "12                    0.93              0.67           0.98       0.90   \n",
       "13                    0.95              0.84            NaN       0.96   \n",
       "14                    0.96              0.87            NaN       1.00   \n",
       "15                    0.97              0.88            NaN        NaN   \n",
       "16                    0.99              0.93            NaN        NaN   \n",
       "17                    1.00               NaN            NaN        NaN   \n",
       "\n",
       "    car_parking  pavement  gradient  traffic_lights_or_stops  \\\n",
       "0          0.01      0.24      0.22                     0.20   \n",
       "1          0.18      0.28      0.33                     0.49   \n",
       "2          0.32      0.31      0.50                     0.52   \n",
       "3          0.34      0.34      0.50                     0.60   \n",
       "4          0.43      0.39      0.51                     0.78   \n",
       "5          0.45      0.41      0.57                     0.82   \n",
       "6          0.50      0.57      0.59                     0.83   \n",
       "7          0.56      0.60      0.62                     0.85   \n",
       "8          0.60      0.65      0.66                     0.92   \n",
       "9          0.68      0.75      0.69                      NaN   \n",
       "10         0.68      0.75      0.73                      NaN   \n",
       "11         0.93      0.78      0.73                      NaN   \n",
       "12          NaN      0.79      0.74                      NaN   \n",
       "13          NaN      0.83      0.78                      NaN   \n",
       "14          NaN      0.83      0.84                      NaN   \n",
       "15          NaN      0.83      0.87                      NaN   \n",
       "16          NaN      0.85      0.99                      NaN   \n",
       "17          NaN      0.99       NaN                      NaN   \n",
       "\n",
       "    natural_environment  car_traffic_volume  width_cycle_together  lighting  \\\n",
       "0                  0.28                0.68                  0.03      0.29   \n",
       "1                  0.44                0.68                  0.43      0.53   \n",
       "2                  0.45                0.75                  0.52      0.54   \n",
       "3                  0.50                0.76                  0.54      0.57   \n",
       "4                  0.51                0.85                  0.58      0.60   \n",
       "5                  0.53                0.85                  0.60      0.67   \n",
       "6                  0.61                0.86                  0.62      0.69   \n",
       "7                  0.61                0.87                  0.67      0.71   \n",
       "8                  0.61                0.87                  0.68      0.71   \n",
       "9                  0.66                0.93                  0.68      0.72   \n",
       "10                 0.78                0.97                  0.84      0.73   \n",
       "11                 0.79                0.98                  0.87      0.75   \n",
       "12                 0.88                0.98                  0.91      0.79   \n",
       "13                 0.92                0.98                  0.91      0.83   \n",
       "14                 0.95                0.98                  0.93      0.84   \n",
       "15                 0.96                1.00                  0.95      0.89   \n",
       "16                 0.96                 NaN                  0.97       NaN   \n",
       "17                  NaN                 NaN                  0.98       NaN   \n",
       "\n",
       "    safe_parking_night  curve_x  short_cuts  tram_tracks  \\\n",
       "0                 0.85     0.85        0.50         0.13   \n",
       "1                 0.87     1.00        0.55         0.29   \n",
       "2                 0.90      NaN        0.91         0.46   \n",
       "3                 0.93      NaN        0.99         0.51   \n",
       "4                 0.93      NaN        0.99         0.75   \n",
       "5                 1.00      NaN        1.00         0.76   \n",
       "6                 1.00      NaN        1.00         0.81   \n",
       "7                  NaN      NaN         NaN         0.94   \n",
       "8                  NaN      NaN         NaN          NaN   \n",
       "9                  NaN      NaN         NaN          NaN   \n",
       "10                 NaN      NaN         NaN          NaN   \n",
       "11                 NaN      NaN         NaN          NaN   \n",
       "12                 NaN      NaN         NaN          NaN   \n",
       "13                 NaN      NaN         NaN          NaN   \n",
       "14                 NaN      NaN         NaN          NaN   \n",
       "15                 NaN      NaN         NaN          NaN   \n",
       "16                 NaN      NaN         NaN          NaN   \n",
       "17                 NaN      NaN         NaN          NaN   \n",
       "\n",
       "    priority_at_intersections  urban_environment  accidents_near_misses  \\\n",
       "0                        0.77               0.59                   0.84   \n",
       "1                        0.80                NaN                   0.95   \n",
       "2                        0.80                NaN                    NaN   \n",
       "3                         NaN                NaN                    NaN   \n",
       "4                         NaN                NaN                    NaN   \n",
       "5                         NaN                NaN                    NaN   \n",
       "6                         NaN                NaN                    NaN   \n",
       "7                         NaN                NaN                    NaN   \n",
       "8                         NaN                NaN                    NaN   \n",
       "9                         NaN                NaN                    NaN   \n",
       "10                        NaN                NaN                    NaN   \n",
       "11                        NaN                NaN                    NaN   \n",
       "12                        NaN                NaN                    NaN   \n",
       "13                        NaN                NaN                    NaN   \n",
       "14                        NaN                NaN                    NaN   \n",
       "15                        NaN                NaN                    NaN   \n",
       "16                        NaN                NaN                    NaN   \n",
       "17                        NaN                NaN                    NaN   \n",
       "\n",
       "    social_encounters  \n",
       "0                 NaN  \n",
       "1                 NaN  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                 NaN  \n",
       "5                 NaN  \n",
       "6                 NaN  \n",
       "7                 NaN  \n",
       "8                 NaN  \n",
       "9                 NaN  \n",
       "10                NaN  \n",
       "11                NaN  \n",
       "12                NaN  \n",
       "13                NaN  \n",
       "14                NaN  \n",
       "15                NaN  \n",
       "16                NaN  \n",
       "17                NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "pd.options.plotting.backend = \"plotly\"\n",
    "\n",
    "### SETTINGS ###\n",
    "dir = os.path.join(\"..\", \"..\", \"..\", \"4_results\")\n",
    "output_dir = \"output\"\n",
    "f_ind_workshop = \"4_indicators_workshop.csv\"\n",
    "\n",
    "export_html = False\n",
    "export_svg = False\n",
    "export_pdf = True\n",
    "export_png = True\n",
    "\n",
    "plot_title = False\n",
    "plot_yAxTitle = False\n",
    "\n",
    "# weights used in NetAScore (for reference)\n",
    "d_ind_netascore = dict(\n",
    "    bicycle_infrastructure = 0.2,\n",
    "    designated_route = 0.1,\n",
    "    road_category = 0.3,\n",
    "    max_speed = 0.1,\n",
    "    car_parking = 0.1,\n",
    "    pavement = 0.1,\n",
    "    gradient = 0.1\n",
    ")\n",
    "ind_netascore = pd.Series(d_ind_netascore)\n",
    "display(ind_netascore)\n",
    "ind_workshop = pd.read_csv(os.path.join(dir, f_ind_workshop), sep=\";\", decimal=\",\")\n",
    "display(ind_workshop)\n",
    "\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bicycle_infrastructure    0.666667\n",
       "designated_route          0.333333\n",
       "road_category             1.000000\n",
       "max_speed                 0.333333\n",
       "car_parking               0.333333\n",
       "pavement                  0.333333\n",
       "gradient                  0.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-scale NetAScore weights to range 0..1\n",
    "ind_netascore_norm = ind_netascore/max(ind_netascore)\n",
    "ind_netascore_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bicycle_infrastructure</th>\n",
       "      <th>designated_route</th>\n",
       "      <th>road_category</th>\n",
       "      <th>max_speed</th>\n",
       "      <th>car_parking</th>\n",
       "      <th>pavement</th>\n",
       "      <th>gradient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.856667</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.379231</td>\n",
       "      <td>0.801333</td>\n",
       "      <td>0.473333</td>\n",
       "      <td>0.621667</td>\n",
       "      <td>0.639412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.106771</td>\n",
       "      <td>0.290928</td>\n",
       "      <td>0.315844</td>\n",
       "      <td>0.107562</td>\n",
       "      <td>0.245443</td>\n",
       "      <td>0.235853</td>\n",
       "      <td>0.193729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.755000</td>\n",
       "      <td>0.335000</td>\n",
       "      <td>0.395000</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.885000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.945000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bicycle_infrastructure  designated_route  road_category  max_speed  \\\n",
       "count               18.000000         17.000000      13.000000  15.000000   \n",
       "mean                 0.856667          0.485294       0.379231   0.801333   \n",
       "std                  0.106771          0.290928       0.315844   0.107562   \n",
       "min                  0.700000          0.040000       0.020000   0.620000   \n",
       "25%                  0.760000          0.230000       0.130000   0.755000   \n",
       "50%                  0.885000          0.500000       0.270000   0.800000   \n",
       "75%                  0.945000          0.670000       0.580000   0.860000   \n",
       "max                  1.000000          0.930000       0.980000   1.000000   \n",
       "\n",
       "       car_parking   pavement   gradient  \n",
       "count    12.000000  18.000000  17.000000  \n",
       "mean      0.473333   0.621667   0.639412  \n",
       "std       0.245443   0.235853   0.193729  \n",
       "min       0.010000   0.240000   0.220000  \n",
       "25%       0.335000   0.395000   0.510000  \n",
       "50%       0.475000   0.700000   0.660000  \n",
       "75%       0.620000   0.820000   0.740000  \n",
       "max       0.930000   0.990000   0.990000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_workshop[ind_netascore.index].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "dta = ind_workshop[ind_netascore.index]\n",
    "plot = dta.boxplot(points=\"all\", title=\"Workshop: Indicator importance\" if plot_title else None)\n",
    "plot.update_layout(width=800, height=500, yaxis=dict(range=[-0.02,1.02], title=\"importance\" if plot_yAxTitle else None))\n",
    "plot.update_traces(marker_color=\"#15459C\", line_width=1, marker_size=2)\n",
    "plot.add_scatter(name=\"bikeability\", y=ind_netascore_norm, x=ind_netascore_norm.index, marker_size=50, line_color=\"#EB0F09\", showlegend=False, mode=\"markers\", marker_symbol=\"line-ew-open\")\n",
    "plot.show()\n",
    "if export_html: plot.write_html(os.path.join(output_dir, \"indicators_1.html\"))\n",
    "if export_png: plot.write_image(os.path.join(output_dir, \"indicators_1.png\"), scale=6)\n",
    "if export_svg: plot.write_image(os.path.join(output_dir, \"indicators_1.svg\"))\n",
    "if export_pdf: plot.write_image(os.path.join(output_dir, \"indicators_1.pdf\"))"
   ]
  },
  {
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   "source": [
    "dta = ind_workshop\n",
    "plot = dta.boxplot(points=\"all\", title=\"Workshop: Indicator importance (full set)\" if plot_title else None)\n",
    "plot.update_layout(width=1200, height=500, yaxis=dict(range=[-0.02,1.02], title=\"importance\" if plot_yAxTitle else None))\n",
    "plot.update_traces(marker_color=\"#15459C\", line_width=1, marker_size=2)\n",
    "plot.add_scatter(name=\"bikeability\", y=ind_netascore_norm, x=ind_netascore_norm.index, marker_size=30, line_color=\"#EB0F09\", showlegend=False, mode=\"markers\", marker_symbol=\"line-ew-open\")\n",
    "plot.show()\n",
    "if export_html: plot.write_html(os.path.join(output_dir, \"indicators_2.html\"))\n",
    "if export_png: plot.write_image(os.path.join(output_dir, \"indicators_2.png\"), scale=6)\n",
    "if export_svg: plot.write_image(os.path.join(output_dir, \"indicators_2.svg\"))\n",
    "if export_pdf: plot.write_image(os.path.join(output_dir, \"indicators_2.pdf\"))"
   ]
  },
  {
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     "text": [
      "Excluded indicators (with less than 5 ratings): ['curve_x', 'priority_at_intersections', 'urban_environment', 'accidents_near_misses', 'social_encounters']\n"
     ]
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   ],
   "source": [
    "ind_ws5 = ind_workshop.loc[:,ind_workshop.count()>4]\n",
    "plot = ind_ws5.boxplot(points=\"all\", title=\"Workshop: Indicator importance (indicators with min. 5 ratings)\" if plot_title else None)\n",
    "plot.update_layout(width=1200, height=500, yaxis=dict(range=[-0.02,1.02], title=\"importance\" if plot_yAxTitle else None))\n",
    "plot.update_traces(marker_color=\"#15459C\", line_width=1, marker_size=2)\n",
    "plot.add_scatter(name=\"bikeability\", y=ind_netascore_norm, x=ind_netascore_norm.index, marker_size=30, line_color=\"#EB0F09\", showlegend=False, mode=\"markers\", marker_symbol=\"line-ew-open\")\n",
    "plot.show()\n",
    "if export_html: plot.write_html(os.path.join(output_dir, \"indicators_3.html\"))\n",
    "if export_png: plot.write_image(os.path.join(output_dir, \"indicators_3.png\"), scale=6)\n",
    "if export_svg: plot.write_image(os.path.join(output_dir, \"indicators_3.svg\"))\n",
    "if export_pdf: plot.write_image(os.path.join(output_dir, \"indicators_3.pdf\"))\n",
    "print(f\"Excluded indicators (with less than 5 ratings): {ind_workshop.loc[:,ind_workshop.count()<5].columns.to_list()}\")"
   ]
  },
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     "text": [
      "Excluded indicators (with less than 5 ratings): ['curve_x', 'priority_at_intersections', 'urban_environment', 'accidents_near_misses', 'social_encounters']\n",
      "Excluded indicators (manual filter): ['short_cuts', 'traffic_lights_or_stops']\n"
     ]
    }
   ],
   "source": [
    "# additionally filter out indicators which are out of scope (connectivity, intersections)\n",
    "manual_exclude = [\"short_cuts\", \"traffic_lights_or_stops\"]\n",
    "in_sel = list(ind_ws5.columns)\n",
    "for iname in manual_exclude:\n",
    "    in_sel.remove(iname)\n",
    "ind_sel = ind_workshop[in_sel]\n",
    "\n",
    "# sort by response count and indicator name\n",
    "#cols = pd.DataFrame(ind_sel.count(), columns=[\"n\"])\n",
    "#cols[\"colname\"] = cols.index\n",
    "#cols = cols.sort_values(by=[\"n\", \"colname\"], ascending=[False, True])\n",
    "#ind_sel = ind_workshop[cols.index]\n",
    "\n",
    "# sort by median rating\n",
    "ind_sel = ind_workshop[ind_sel.median().sort_values(ascending=False).index]\n",
    "\n",
    "# plot\n",
    "plot = ind_sel.boxplot(points=\"all\", title=\"Workshop: Indicator importance (filtered)\" if plot_title else None)\n",
    "plot.update_layout(width=900, height=500, \n",
    "                   yaxis=dict(range=[-0.02,1.02], title=\"importance\" if plot_yAxTitle else None), \n",
    "                   xaxis=dict(title=None),\n",
    "                   margin=dict(t=15, l=10, r=10, b=100)\n",
    ")\n",
    "plot.update_traces(marker_color=\"#15459C\", line_width=1, marker_size=2)\n",
    "plot.add_scatter(name=\"bikeability\", y=ind_netascore_norm, x=ind_netascore_norm.index, marker_size=30, line_color=\"#EB0F09\", showlegend=False, mode=\"markers\", marker_symbol=\"line-ew-open\")\n",
    "for indicator in ind_sel.columns:\n",
    "    plot.add_annotation(x=indicator, y=1, yshift = 15, showarrow = False,\n",
    "                        text = f\"n={ind_sel[indicator].count()}\")\n",
    "plot.show()\n",
    "if export_html: plot.write_html(os.path.join(output_dir, \"indicators_sel.html\"))\n",
    "if export_png: plot.write_image(os.path.join(output_dir, \"indicators_sel.png\"), scale=6)\n",
    "if export_svg: plot.write_image(os.path.join(output_dir, \"indicators_sel.svg\"))\n",
    "if export_pdf: plot.write_image(os.path.join(output_dir, \"indicators_sel.pdf\"))\n",
    "print(f\"Excluded indicators (with less than 5 ratings): {ind_workshop.loc[:,ind_workshop.count()<5].columns.to_list()}\")\n",
    "print(f\"Excluded indicators (manual filter): {manual_exclude}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "safe_parking_night         7\n",
       "bicycle_infrastructure    18\n",
       "car_traffic_volume        16\n",
       "max_speed                 15\n",
       "lighting                  16\n",
       "pavement                  18\n",
       "width_cycle_together      18\n",
       "gradient                  17\n",
       "tram_tracks                8\n",
       "natural_environment       17\n",
       "designated_route          17\n",
       "car_parking               12\n",
       "road_category             13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_sel.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "safe_parking_night        0.930\n",
       "bicycle_infrastructure    0.885\n",
       "car_traffic_volume        0.870\n",
       "max_speed                 0.800\n",
       "lighting                  0.710\n",
       "pavement                  0.700\n",
       "width_cycle_together      0.680\n",
       "gradient                  0.660\n",
       "tram_tracks               0.630\n",
       "natural_environment       0.610\n",
       "designated_route          0.500\n",
       "car_parking               0.475\n",
       "road_category             0.270\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_sel.median().sort_values(ascending=False)"
   ]
  }
 ],
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