process_output.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('lsoa_metrics_new.pkl', 'rb') as f:\n",
    "    metrics = pickle.load(f)\n",
    "\n",
    "lsoa_list = []\n",
    "lsoa_exp_list = []\n",
    "lsoa_od_list = []\n",
    "\n",
    "for k in metrics.keys():\n",
    "    lsoa_list.append(metrics[k]['LSOA'])\n",
    "    lsoa_exp_list.append(metrics[k]['LSOA_Expanded'])\n",
    "    lsoa_od_list.append(metrics[k]['LSOA_OD'])\n",
    "    \n",
    "lsoa_metrics = pd.DataFrame(lsoa_list)\n",
    "lsoa_exp_metrics = pd.DataFrame(lsoa_exp_list)\n",
    "lsoa_od_metrics = pd.DataFrame(lsoa_od_list)\n",
    "\n",
    "index = metrics.keys()\n",
    "\n",
    "lsoa_metrics['LSOA'] = index\n",
    "lsoa_metrics = lsoa_metrics.set_index('LSOA')\n",
    "\n",
    "lsoa_exp_metrics['LSOA'] = index\n",
    "lsoa_exp_metrics = lsoa_exp_metrics.set_index('LSOA')\n",
    "\n",
    "lsoa_od_metrics['LSOA'] = index\n",
    "lsoa_od_metrics = lsoa_od_metrics.set_index('LSOA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lsoa_exp_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsoa_exp_metrics['Increase in LTS 1 Centrality Percent'] = lsoa_exp_metrics['Increase in LTS 1 Centrality Percent'].replace([np.inf, -np.inf], np.nan)\n",
    "lsoa_exp_metrics['Increase in LTS 2 Centrality Percent'] = lsoa_exp_metrics['Increase in LTS 2 Centrality Percent'].replace([np.inf, -np.inf], np.nan)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with NaN values in the specified column\n",
    "lsoa_exp_metrics = lsoa_exp_metrics.dropna(subset=['Increase in LTS 1 Centrality Percent'])\n",
    "lsoa_exp_metrics = lsoa_exp_metrics.dropna(subset=['Increase in LTS 2 Centrality Percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Increase in Cycleways KM</th>\n",
       "      <th>Increase in Cycleways %</th>\n",
       "      <th>% Increase in Cycleways KM</th>\n",
       "      <th>% Increase in Cycleways %</th>\n",
       "      <th>Increase in LTS 1 and 2 KM</th>\n",
       "      <th>Increase in LTS 1 and 2 %</th>\n",
       "      <th>% Increase in LTS 1 and 2 KM</th>\n",
       "      <th>% Increase in LTS 1 and 2 %</th>\n",
       "      <th>Increase in LTS 1 Centrality</th>\n",
       "      <th>Increase in LTS 1 Centrality Percent</th>\n",
       "      <th>Increase in LTS 2 Centrality</th>\n",
       "      <th>Increase in LTS 2 Centrality Percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSOA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E01013335</th>\n",
       "      <td>137.104</td>\n",
       "      <td>-0.201646</td>\n",
       "      <td>0.043504</td>\n",
       "      <td>-0.049485</td>\n",
       "      <td>669.272</td>\n",
       "      <td>-5.515374</td>\n",
       "      <td>0.012233</td>\n",
       "      <td>-0.077969</td>\n",
       "      <td>-0.003021</td>\n",
       "      <td>-0.172620</td>\n",
       "      <td>-0.000747</td>\n",
       "      <td>-0.058970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013336</th>\n",
       "      <td>201.708</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.092025</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>964.547</td>\n",
       "      <td>-4.517725</td>\n",
       "      <td>0.017859</td>\n",
       "      <td>-0.067657</td>\n",
       "      <td>-0.001585</td>\n",
       "      <td>-0.099479</td>\n",
       "      <td>-0.000563</td>\n",
       "      <td>-0.058127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013337</th>\n",
       "      <td>894.658</td>\n",
       "      <td>0.708732</td>\n",
       "      <td>0.310454</td>\n",
       "      <td>0.186091</td>\n",
       "      <td>1607.986</td>\n",
       "      <td>-4.258643</td>\n",
       "      <td>0.032623</td>\n",
       "      <td>-0.065375</td>\n",
       "      <td>-0.005068</td>\n",
       "      <td>-0.236278</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013338</th>\n",
       "      <td>257.540</td>\n",
       "      <td>-0.715296</td>\n",
       "      <td>0.041098</td>\n",
       "      <td>-0.088559</td>\n",
       "      <td>1627.390</td>\n",
       "      <td>-6.301376</td>\n",
       "      <td>0.032101</td>\n",
       "      <td>-0.096435</td>\n",
       "      <td>-0.003381</td>\n",
       "      <td>-0.288716</td>\n",
       "      <td>-0.000502</td>\n",
       "      <td>-0.051421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013339</th>\n",
       "      <td>918.776</td>\n",
       "      <td>-1.045216</td>\n",
       "      <td>0.075475</td>\n",
       "      <td>-0.085803</td>\n",
       "      <td>2134.339</td>\n",
       "      <td>-7.118628</td>\n",
       "      <td>0.035849</td>\n",
       "      <td>-0.119486</td>\n",
       "      <td>-0.007775</td>\n",
       "      <td>-0.331585</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013450</th>\n",
       "      <td>169.645</td>\n",
       "      <td>-2.798298</td>\n",
       "      <td>0.006548</td>\n",
       "      <td>-0.319564</td>\n",
       "      <td>6002.521</td>\n",
       "      <td>-6.484641</td>\n",
       "      <td>0.083664</td>\n",
       "      <td>-0.267432</td>\n",
       "      <td>-0.007514</td>\n",
       "      <td>-0.302552</td>\n",
       "      <td>-0.001257</td>\n",
       "      <td>-0.287214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013451</th>\n",
       "      <td>1.744</td>\n",
       "      <td>-0.057390</td>\n",
       "      <td>0.015502</td>\n",
       "      <td>-0.422290</td>\n",
       "      <td>4222.224</td>\n",
       "      <td>-4.449194</td>\n",
       "      <td>0.299132</td>\n",
       "      <td>-0.260936</td>\n",
       "      <td>-0.007793</td>\n",
       "      <td>-0.648603</td>\n",
       "      <td>-0.002465</td>\n",
       "      <td>-0.282579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01033067</th>\n",
       "      <td>1775.103</td>\n",
       "      <td>0.305806</td>\n",
       "      <td>0.127576</td>\n",
       "      <td>0.038565</td>\n",
       "      <td>3198.599</td>\n",
       "      <td>-2.001702</td>\n",
       "      <td>0.039095</td>\n",
       "      <td>-0.042931</td>\n",
       "      <td>-0.000919</td>\n",
       "      <td>-0.099286</td>\n",
       "      <td>0.000799</td>\n",
       "      <td>0.172549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01033068</th>\n",
       "      <td>930.584</td>\n",
       "      <td>-0.130511</td>\n",
       "      <td>0.068389</td>\n",
       "      <td>-0.015643</td>\n",
       "      <td>3953.268</td>\n",
       "      <td>-1.291917</td>\n",
       "      <td>0.054082</td>\n",
       "      <td>-0.028826</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.044953</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.117876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01033069</th>\n",
       "      <td>509.242</td>\n",
       "      <td>-0.050941</td>\n",
       "      <td>0.065834</td>\n",
       "      <td>-0.006622</td>\n",
       "      <td>3008.653</td>\n",
       "      <td>-0.342255</td>\n",
       "      <td>0.064966</td>\n",
       "      <td>-0.007431</td>\n",
       "      <td>-0.000352</td>\n",
       "      <td>-0.054147</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.038568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Increase in Cycleways KM  Increase in Cycleways %  \\\n",
       "LSOA                                                           \n",
       "E01013335                   137.104                -0.201646   \n",
       "E01013336                   201.708                 0.000751   \n",
       "E01013337                   894.658                 0.708732   \n",
       "E01013338                   257.540                -0.715296   \n",
       "E01013339                   918.776                -1.045216   \n",
       "...                             ...                      ...   \n",
       "E01013450                   169.645                -2.798298   \n",
       "E01013451                     1.744                -0.057390   \n",
       "E01033067                  1775.103                 0.305806   \n",
       "E01033068                   930.584                -0.130511   \n",
       "E01033069                   509.242                -0.050941   \n",
       "\n",
       "           % Increase in Cycleways KM  % Increase in Cycleways %  \\\n",
       "LSOA                                                               \n",
       "E01013335                    0.043504                  -0.049485   \n",
       "E01013336                    0.092025                   0.000277   \n",
       "E01013337                    0.310454                   0.186091   \n",
       "E01013338                    0.041098                  -0.088559   \n",
       "E01013339                    0.075475                  -0.085803   \n",
       "...                               ...                        ...   \n",
       "E01013450                    0.006548                  -0.319564   \n",
       "E01013451                    0.015502                  -0.422290   \n",
       "E01033067                    0.127576                   0.038565   \n",
       "E01033068                    0.068389                  -0.015643   \n",
       "E01033069                    0.065834                  -0.006622   \n",
       "\n",
       "           Increase in LTS 1 and 2 KM  Increase in LTS 1 and 2 %  \\\n",
       "LSOA                                                               \n",
       "E01013335                     669.272                  -5.515374   \n",
       "E01013336                     964.547                  -4.517725   \n",
       "E01013337                    1607.986                  -4.258643   \n",
       "E01013338                    1627.390                  -6.301376   \n",
       "E01013339                    2134.339                  -7.118628   \n",
       "...                               ...                        ...   \n",
       "E01013450                    6002.521                  -6.484641   \n",
       "E01013451                    4222.224                  -4.449194   \n",
       "E01033067                    3198.599                  -2.001702   \n",
       "E01033068                    3953.268                  -1.291917   \n",
       "E01033069                    3008.653                  -0.342255   \n",
       "\n",
       "           % Increase in LTS 1 and 2 KM  % Increase in LTS 1 and 2 %  \\\n",
       "LSOA                                                                   \n",
       "E01013335                      0.012233                    -0.077969   \n",
       "E01013336                      0.017859                    -0.067657   \n",
       "E01013337                      0.032623                    -0.065375   \n",
       "E01013338                      0.032101                    -0.096435   \n",
       "E01013339                      0.035849                    -0.119486   \n",
       "...                                 ...                          ...   \n",
       "E01013450                      0.083664                    -0.267432   \n",
       "E01013451                      0.299132                    -0.260936   \n",
       "E01033067                      0.039095                    -0.042931   \n",
       "E01033068                      0.054082                    -0.028826   \n",
       "E01033069                      0.064966                    -0.007431   \n",
       "\n",
       "           Increase in LTS 1 Centrality  Increase in LTS 1 Centrality Percent  \\\n",
       "LSOA                                                                            \n",
       "E01013335                     -0.003021                             -0.172620   \n",
       "E01013336                     -0.001585                             -0.099479   \n",
       "E01013337                     -0.005068                             -0.236278   \n",
       "E01013338                     -0.003381                             -0.288716   \n",
       "E01013339                     -0.007775                             -0.331585   \n",
       "...                                 ...                                   ...   \n",
       "E01013450                     -0.007514                             -0.302552   \n",
       "E01013451                     -0.007793                             -0.648603   \n",
       "E01033067                     -0.000919                             -0.099286   \n",
       "E01033068                      0.000213                              0.044953   \n",
       "E01033069                     -0.000352                             -0.054147   \n",
       "\n",
       "           Increase in LTS 2 Centrality  Increase in LTS 2 Centrality Percent  \n",
       "LSOA                                                                           \n",
       "E01013335                     -0.000747                             -0.058970  \n",
       "E01013336                     -0.000563                             -0.058127  \n",
       "E01013337                      0.000008                              0.000701  \n",
       "E01013338                     -0.000502                             -0.051421  \n",
       "E01013339                     -0.000005                             -0.000933  \n",
       "...                                 ...                                   ...  \n",
       "E01013450                     -0.001257                             -0.287214  \n",
       "E01013451                     -0.002465                             -0.282579  \n",
       "E01033067                      0.000799                              0.172549  \n",
       "E01033068                      0.000579                              0.117876  \n",
       "E01033069                      0.000321                              0.038568  \n",
       "\n",
       "[106 rows x 12 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsoa_exp_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(10, 5))\n",
    "\n",
    "# Plot histograms\n",
    "ax[0].hist(lsoa_exp_metrics['% Increase in LTS 1 and 2 %']*100, bins=30, alpha=0.5, label = 'Change in Percent')\n",
    "ax[0].hist(lsoa_exp_metrics['% Increase in LTS 1 and 2 KM']*100, bins=30, alpha=0.5, label = 'Change in KM')\n",
    "ax[0].legend()\n",
    "ax[0].set_title('Change in LTS 1 and 2 between 2016 and 2021', fontsize = 10)\n",
    "ax[0].set_xlabel('Percentage Change')\n",
    "ax[0].set_ylabel('LSOA Count')\n",
    "\n",
    "# Plot histograms\n",
    "ax[1].hist(lsoa_exp_metrics['Increase in LTS 1 Centrality Percent']*100, bins=30, alpha=0.5, label='LTS 1')\n",
    "ax[1].hist(lsoa_exp_metrics['Increase in LTS 2 Centrality Percent']*100, bins=30, alpha=0.5, label='LTS 2')\n",
    "ax[1].legend()\n",
    "ax[1].set_title('Change in Mean Centrality of LTS Roads Between 2016 and 2021', fontsize = 10)\n",
    "ax[1].set_xlabel('Percentage Change')\n",
    "ax[1].set_ylabel('LSOA Count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace inf values with the maximum value of the column\n",
    "max_value = lsoa_od_metrics['Best path LTS1/2 Proportion Increase Percent'].replace([np.inf, -np.inf], np.nan).max()\n",
    "lsoa_od_metrics['Best path LTS1/2 Proportion Increase Percent'] = lsoa_od_metrics['Best path LTS1/2 Proportion Increase Percent'].replace([np.inf, -np.inf], max_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace inf values with the maximum value of the column\n",
    "max_value = lsoa_od_metrics['Dangerous road penalty increase percent'].replace([np.inf, -np.inf], np.nan).max()\n",
    "lsoa_od_metrics['Dangerous road penalty increase percent'] = lsoa_od_metrics['Dangerous road penalty increase percent'].replace([np.inf, -np.inf], max_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSOA\n",
       "E01013335   -0.000000\n",
       "E01013336    7.589231\n",
       "E01013337    4.334312\n",
       "E01013338   -0.000000\n",
       "E01013339   -0.000000\n",
       "               ...   \n",
       "E01013445    7.296756\n",
       "E01013446    6.451225\n",
       "E01013447    7.108346\n",
       "E01013448    7.198107\n",
       "E01013449    6.591114\n",
       "Name: Dangerous road penalty increase percent, Length: 77, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsoa_od_metrics.dropna(subset=['Dangerous road penalty increase percent'])['Dangerous road penalty increase percent'] * -100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "ax.hist(lsoa_od_metrics['Best path LTS1/2 Proportion Increase Percent']*100, bins=30, alpha=0.5)\n",
    "ax.set_title('Change in LTS 1 and 2 Composition of Best Route to Work', fontsize = 10)\n",
    "ax.set_xlabel('Percentage Change')\n",
    "ax.set_ylabel('LSOA Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "ax.hist(lsoa_od_metrics.dropna(subset=['Dangerous road penalty increase percent'])['Dangerous road penalty increase percent'], bins=30, alpha=0.5)\n",
    "ax.set_title('Change in Dangerous Road Penalty to Work', fontsize = 10)\n",
    "ax.set_xlabel('Penalty Change')\n",
    "ax.set_ylabel('LSOA Count')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Increase in Cycleways KM</th>\n",
       "      <th>Increase in Cycleways %</th>\n",
       "      <th>% Increase in Cycleways KM</th>\n",
       "      <th>% Increase in Cycleways %</th>\n",
       "      <th>Increase in LTS 1 and 2 KM</th>\n",
       "      <th>Increase in LTS 1 and 2 %</th>\n",
       "      <th>% Increase in LTS 1 and 2 KM</th>\n",
       "      <th>% Increase in LTS 1 and 2 %</th>\n",
       "      <th>Best path LTS1/2 Proportion Increase</th>\n",
       "      <th>Best path LTS1/2 Proportion Increase Percent</th>\n",
       "      <th>Dangerous road penalty increase</th>\n",
       "      <th>Dangerous road penalty increase percent</th>\n",
       "      <th>LTS 3 Penalty Increase</th>\n",
       "      <th>LTS 3 Penalty Increase percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSOA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E01013335</th>\n",
       "      <td>3515.366</td>\n",
       "      <td>0.197434</td>\n",
       "      <td>0.130387</td>\n",
       "      <td>0.026682</td>\n",
       "      <td>4062.443</td>\n",
       "      <td>-3.746464</td>\n",
       "      <td>0.021493</td>\n",
       "      <td>-0.072221</td>\n",
       "      <td>8.025992</td>\n",
       "      <td>0.162753</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-35.5035</td>\n",
       "      <td>-0.038655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013336</th>\n",
       "      <td>3515.366</td>\n",
       "      <td>0.197434</td>\n",
       "      <td>0.130387</td>\n",
       "      <td>0.026682</td>\n",
       "      <td>4062.443</td>\n",
       "      <td>-3.746464</td>\n",
       "      <td>0.021493</td>\n",
       "      <td>-0.072221</td>\n",
       "      <td>0.096652</td>\n",
       "      <td>0.008692</td>\n",
       "      <td>-315.312</td>\n",
       "      <td>-0.075892</td>\n",
       "      <td>314.1310</td>\n",
       "      <td>0.298895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013337</th>\n",
       "      <td>3349.380</td>\n",
       "      <td>0.136661</td>\n",
       "      <td>0.117058</td>\n",
       "      <td>0.018710</td>\n",
       "      <td>4157.419</td>\n",
       "      <td>-3.721218</td>\n",
       "      <td>0.019926</td>\n",
       "      <td>-0.069870</td>\n",
       "      <td>0.035440</td>\n",
       "      <td>0.011036</td>\n",
       "      <td>-212.918</td>\n",
       "      <td>-0.043343</td>\n",
       "      <td>206.2220</td>\n",
       "      <td>0.194202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013338</th>\n",
       "      <td>4728.069</td>\n",
       "      <td>0.241507</td>\n",
       "      <td>0.146412</td>\n",
       "      <td>0.029141</td>\n",
       "      <td>4474.518</td>\n",
       "      <td>-4.243381</td>\n",
       "      <td>0.022272</td>\n",
       "      <td>-0.082300</td>\n",
       "      <td>13.227598</td>\n",
       "      <td>0.295012</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-114.1235</td>\n",
       "      <td>-0.129494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013339</th>\n",
       "      <td>6092.811</td>\n",
       "      <td>0.297936</td>\n",
       "      <td>0.144568</td>\n",
       "      <td>0.035295</td>\n",
       "      <td>6902.166</td>\n",
       "      <td>-3.912114</td>\n",
       "      <td>0.025565</td>\n",
       "      <td>-0.072346</td>\n",
       "      <td>7.906501</td>\n",
       "      <td>0.197038</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-88.7520</td>\n",
       "      <td>-0.079624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013451</th>\n",
       "      <td>10318.305</td>\n",
       "      <td>-1.148925</td>\n",
       "      <td>0.127890</td>\n",
       "      <td>-0.130157</td>\n",
       "      <td>54047.032</td>\n",
       "      <td>-4.264205</td>\n",
       "      <td>0.153308</td>\n",
       "      <td>-0.110555</td>\n",
       "      <td>-0.001895</td>\n",
       "      <td>-0.001711</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01013452</th>\n",
       "      <td>10404.335</td>\n",
       "      <td>-1.313697</td>\n",
       "      <td>0.118395</td>\n",
       "      <td>-0.151339</td>\n",
       "      <td>54757.322</td>\n",
       "      <td>-4.809786</td>\n",
       "      <td>0.146342</td>\n",
       "      <td>-0.130133</td>\n",
       "      <td>1.332408</td>\n",
       "      <td>1.825975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01033067</th>\n",
       "      <td>8959.358</td>\n",
       "      <td>-1.685504</td>\n",
       "      <td>0.101587</td>\n",
       "      <td>-0.159968</td>\n",
       "      <td>46545.435</td>\n",
       "      <td>-5.039924</td>\n",
       "      <td>0.142270</td>\n",
       "      <td>-0.128944</td>\n",
       "      <td>-0.012801</td>\n",
       "      <td>-0.028845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01033068</th>\n",
       "      <td>8067.860</td>\n",
       "      <td>-1.759289</td>\n",
       "      <td>0.093878</td>\n",
       "      <td>-0.166548</td>\n",
       "      <td>45839.313</td>\n",
       "      <td>-4.957840</td>\n",
       "      <td>0.145004</td>\n",
       "      <td>-0.127594</td>\n",
       "      <td>-0.276318</td>\n",
       "      <td>-0.035571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E01033069</th>\n",
       "      <td>6907.735</td>\n",
       "      <td>-1.778759</td>\n",
       "      <td>0.094939</td>\n",
       "      <td>-0.175754</td>\n",
       "      <td>47798.495</td>\n",
       "      <td>-4.276055</td>\n",
       "      <td>0.177103</td>\n",
       "      <td>-0.113902</td>\n",
       "      <td>-0.105755</td>\n",
       "      <td>-0.027879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Increase in Cycleways KM  Increase in Cycleways %  \\\n",
       "LSOA                                                           \n",
       "E01013335                  3515.366                 0.197434   \n",
       "E01013336                  3515.366                 0.197434   \n",
       "E01013337                  3349.380                 0.136661   \n",
       "E01013338                  4728.069                 0.241507   \n",
       "E01013339                  6092.811                 0.297936   \n",
       "...                             ...                      ...   \n",
       "E01013451                 10318.305                -1.148925   \n",
       "E01013452                 10404.335                -1.313697   \n",
       "E01033067                  8959.358                -1.685504   \n",
       "E01033068                  8067.860                -1.759289   \n",
       "E01033069                  6907.735                -1.778759   \n",
       "\n",
       "           % Increase in Cycleways KM  % Increase in Cycleways %  \\\n",
       "LSOA                                                               \n",
       "E01013335                    0.130387                   0.026682   \n",
       "E01013336                    0.130387                   0.026682   \n",
       "E01013337                    0.117058                   0.018710   \n",
       "E01013338                    0.146412                   0.029141   \n",
       "E01013339                    0.144568                   0.035295   \n",
       "...                               ...                        ...   \n",
       "E01013451                    0.127890                  -0.130157   \n",
       "E01013452                    0.118395                  -0.151339   \n",
       "E01033067                    0.101587                  -0.159968   \n",
       "E01033068                    0.093878                  -0.166548   \n",
       "E01033069                    0.094939                  -0.175754   \n",
       "\n",
       "           Increase in LTS 1 and 2 KM  Increase in LTS 1 and 2 %  \\\n",
       "LSOA                                                               \n",
       "E01013335                    4062.443                  -3.746464   \n",
       "E01013336                    4062.443                  -3.746464   \n",
       "E01013337                    4157.419                  -3.721218   \n",
       "E01013338                    4474.518                  -4.243381   \n",
       "E01013339                    6902.166                  -3.912114   \n",
       "...                               ...                        ...   \n",
       "E01013451                   54047.032                  -4.264205   \n",
       "E01013452                   54757.322                  -4.809786   \n",
       "E01033067                   46545.435                  -5.039924   \n",
       "E01033068                   45839.313                  -4.957840   \n",
       "E01033069                   47798.495                  -4.276055   \n",
       "\n",
       "           % Increase in LTS 1 and 2 KM  % Increase in LTS 1 and 2 %  \\\n",
       "LSOA                                                                   \n",
       "E01013335                      0.021493                    -0.072221   \n",
       "E01013336                      0.021493                    -0.072221   \n",
       "E01013337                      0.019926                    -0.069870   \n",
       "E01013338                      0.022272                    -0.082300   \n",
       "E01013339                      0.025565                    -0.072346   \n",
       "...                                 ...                          ...   \n",
       "E01013451                      0.153308                    -0.110555   \n",
       "E01013452                      0.146342                    -0.130133   \n",
       "E01033067                      0.142270                    -0.128944   \n",
       "E01033068                      0.145004                    -0.127594   \n",
       "E01033069                      0.177103                    -0.113902   \n",
       "\n",
       "           Best path LTS1/2 Proportion Increase  \\\n",
       "LSOA                                              \n",
       "E01013335                              8.025992   \n",
       "E01013336                              0.096652   \n",
       "E01013337                              0.035440   \n",
       "E01013338                             13.227598   \n",
       "E01013339                              7.906501   \n",
       "...                                         ...   \n",
       "E01013451                             -0.001895   \n",
       "E01013452                              1.332408   \n",
       "E01033067                             -0.012801   \n",
       "E01033068                             -0.276318   \n",
       "E01033069                             -0.105755   \n",
       "\n",
       "           Best path LTS1/2 Proportion Increase Percent  \\\n",
       "LSOA                                                      \n",
       "E01013335                                      0.162753   \n",
       "E01013336                                      0.008692   \n",
       "E01013337                                      0.011036   \n",
       "E01013338                                      0.295012   \n",
       "E01013339                                      0.197038   \n",
       "...                                                 ...   \n",
       "E01013451                                     -0.001711   \n",
       "E01013452                                      1.825975   \n",
       "E01033067                                     -0.028845   \n",
       "E01033068                                     -0.035571   \n",
       "E01033069                                     -0.027879   \n",
       "\n",
       "           Dangerous road penalty increase  \\\n",
       "LSOA                                         \n",
       "E01013335                            0.000   \n",
       "E01013336                         -315.312   \n",
       "E01013337                         -212.918   \n",
       "E01013338                            0.000   \n",
       "E01013339                            0.000   \n",
       "...                                    ...   \n",
       "E01013451                              NaN   \n",
       "E01013452                              NaN   \n",
       "E01033067                              NaN   \n",
       "E01033068                              NaN   \n",
       "E01033069                              NaN   \n",
       "\n",
       "           Dangerous road penalty increase percent  LTS 3 Penalty Increase  \\\n",
       "LSOA                                                                         \n",
       "E01013335                                 0.000000                -35.5035   \n",
       "E01013336                                -0.075892                314.1310   \n",
       "E01013337                                -0.043343                206.2220   \n",
       "E01013338                                 0.000000               -114.1235   \n",
       "E01013339                                 0.000000                -88.7520   \n",
       "...                                            ...                     ...   \n",
       "E01013451                                      NaN                     NaN   \n",
       "E01013452                                      NaN                     NaN   \n",
       "E01033067                                      NaN                     NaN   \n",
       "E01033068                                      NaN                     NaN   \n",
       "E01033069                                      NaN                     NaN   \n",
       "\n",
       "           LTS 3 Penalty Increase percent  \n",
       "LSOA                                       \n",
       "E01013335                       -0.038655  \n",
       "E01013336                        0.298895  \n",
       "E01013337                        0.194202  \n",
       "E01013338                       -0.129494  \n",
       "E01013339                       -0.079624  \n",
       "...                                   ...  \n",
       "E01013451                             NaN  \n",
       "E01013452                             NaN  \n",
       "E01033067                             NaN  \n",
       "E01033068                             NaN  \n",
       "E01033069                             NaN  \n",
       "\n",
       "[107 rows x 14 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsoa_od_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace inf values with the maximum value of the column\n",
    "max_value = lsoa_od_metrics['LTS 3 Penalty Increase'].replace([np.inf, -np.inf], np.nan).max()\n",
    "lsoa_od_metrics['LTS 3 Penalty Increase'] = lsoa_od_metrics['LTS 3 Penalty Increase'].replace([np.inf, -np.inf], max_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "ax.hist(lsoa_od_metrics.dropna(subset=['LTS 3 Penalty Increase'])['LTS 3 Penalty Increase'], bins=30, alpha=0.5, label = 'Change in Percent')\n",
    "ax.set_title('Change in Uncomfortable Road Penalty', fontsize = 10)\n",
    "ax.set_xlabel('Penalty Change (m)')\n",
    "ax.set_ylabel('LSOA Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsoa_metrics.to_csv('lsoa_metrics.csv')\n",
    "lsoa_exp_metrics.to_csv('lsoa_exp_metrics.csv')\n",
    "lsoa_od_metrics.to_csv('lsoa_od_metrics.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ate",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}