Transit Trips.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Equity of Induced Transit Trips\n",
    "\n",
    "In this workbook we analyze how changes in travel time and demand brought on by the introduciton of additional transit infrastructure is distributed accross populations.\n",
    "\n",
    "First, we set up our modules and load the appropriate data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import numpy as np\n",
    "\n",
    "# Replace with your data folder paths\n",
    "mx_folder = r\"data/matrices/input\"\n",
    "counts_folder = r\"data/counts/input\"\n",
    "link_folder = r\"data/counts/interim\"\n",
    "output_folder = r\"data/counts/output\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we load in our dataset, let's sum demand by each origin and display the total differences in transit trips made to learn about how much is induced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario A: 13733.152638852596 extra demand\n",
      "Scenario B: 4025.5698905665195 extra demand\n",
      "Scenario C: 2771.1263431145344 extra demand\n"
     ]
    }
   ],
   "source": [
    "dtypes = {'i':int, 'j':int, 'travel_time':float, 'demand':float}\n",
    "mx_BAU = pd.read_csv(os.path.join(mx_folder, 'bau_times_flows.csv'), dtype=dtypes)[['i', 'demand']].groupby('i', as_index=False).sum()\n",
    "mx_A = pd.read_csv(os.path.join(mx_folder, 'scenario_A_times_flows.csv'), dtype=dtypes)[['i', 'demand']].groupby('i', as_index=False).sum()\n",
    "mx_B = pd.read_csv(os.path.join(mx_folder, 'scenario_B_times_flows.csv'), dtype=dtypes)[['i', 'demand']].groupby('i', as_index=False).sum()\n",
    "mx_C = pd.read_csv(os.path.join(mx_folder, 'scenario_C_times_flows.csv'), dtype=dtypes)[['i', 'demand']].groupby('i', as_index=False).sum()\n",
    "\n",
    "print(\"Scenario A:\", (mx_A.demand.sum()-mx_BAU.demand.sum()), \"extra demand\")\n",
    "print(\"Scenario B:\", (mx_B.demand.sum()-mx_BAU.demand.sum()), \"extra demand\")\n",
    "print(\"Scenario C:\", (mx_C.demand.sum()-mx_BAU.demand.sum()), \"extra demand\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are using induced trips directly, we can join together our demand data for each scenario and calculate the deltas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13733.1526388526\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i</th>\n",
       "      <th>demand_BAU</th>\n",
       "      <th>demand_A</th>\n",
       "      <th>demand_B</th>\n",
       "      <th>demand_C</th>\n",
       "      <th>delta_A</th>\n",
       "      <th>delta_B</th>\n",
       "      <th>delta_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>513.622080</td>\n",
       "      <td>480.545055</td>\n",
       "      <td>466.507274</td>\n",
       "      <td>472.622201</td>\n",
       "      <td>-33.077024</td>\n",
       "      <td>-47.114806</td>\n",
       "      <td>-40.999879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.748341</td>\n",
       "      <td>8.560731</td>\n",
       "      <td>9.641028</td>\n",
       "      <td>9.808405</td>\n",
       "      <td>-2.187610</td>\n",
       "      <td>-1.107312</td>\n",
       "      <td>-0.939935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>104.356331</td>\n",
       "      <td>96.522627</td>\n",
       "      <td>90.797636</td>\n",
       "      <td>86.584327</td>\n",
       "      <td>-7.833704</td>\n",
       "      <td>-13.558695</td>\n",
       "      <td>-17.772003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>102.040002</td>\n",
       "      <td>240.352959</td>\n",
       "      <td>229.777685</td>\n",
       "      <td>230.856723</td>\n",
       "      <td>138.312957</td>\n",
       "      <td>127.737683</td>\n",
       "      <td>128.816722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>28.774700</td>\n",
       "      <td>486.080989</td>\n",
       "      <td>391.578174</td>\n",
       "      <td>383.648842</td>\n",
       "      <td>457.306289</td>\n",
       "      <td>362.803474</td>\n",
       "      <td>354.874142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   i  demand_BAU    demand_A    demand_B    demand_C     delta_A     delta_B  \\\n",
       "0  1  513.622080  480.545055  466.507274  472.622201  -33.077024  -47.114806   \n",
       "1  2   10.748341    8.560731    9.641028    9.808405   -2.187610   -1.107312   \n",
       "2  3  104.356331   96.522627   90.797636   86.584327   -7.833704  -13.558695   \n",
       "3  4  102.040002  240.352959  229.777685  230.856723  138.312957  127.737683   \n",
       "4  5   28.774700  486.080989  391.578174  383.648842  457.306289  362.803474   \n",
       "\n",
       "      delta_C  \n",
       "0  -40.999879  \n",
       "1   -0.939935  \n",
       "2  -17.772003  \n",
       "3  128.816722  \n",
       "4  354.874142  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta = pd.merge(mx_BAU[['i', 'demand']], mx_A[['i', 'demand']], on='i')\n",
    "delta.columns = ['i', 'demand_BAU', 'demand_A']\n",
    "delta = pd.merge(delta, mx_B[['i', 'demand']], on='i')\n",
    "delta.columns = ['i', 'demand_BAU', 'demand_A', 'demand_B']\n",
    "delta = pd.merge(delta, mx_C[['i', 'demand']], on='i')\n",
    "delta.columns = ['i', 'demand_BAU', 'demand_A', 'demand_B', 'demand_C']\n",
    "\n",
    "delta['delta_A'] = delta['demand_A'] - delta['demand_BAU']\n",
    "delta['delta_B'] = delta['demand_B'] - delta['demand_BAU']\n",
    "delta['delta_C'] = delta['demand_C'] - delta['demand_BAU']\n",
    "delta.head()\n",
    "print(delta.delta_A.sum())\n",
    "\n",
    "# Write the file for mapping\n",
    "delta.to_csv(os.path.join(output_folder, 'trips_added.csv'), index=False)\n",
    "delta.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can load in our demographic data and calculate the distributions of the score across various demographic groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13733.1526388526\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>value</th>\n",
       "      <th>scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pop_2016</td>\n",
       "      <td>5.029686</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pop_2016</td>\n",
       "      <td>1.474341</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pop_2016</td>\n",
       "      <td>1.014909</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vm_minority</td>\n",
       "      <td>5.558348</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>income_lim</td>\n",
       "      <td>4.684783</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>labour_unemployed</td>\n",
       "      <td>5.556481</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>vm_minority</td>\n",
       "      <td>1.474980</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>income_lim</td>\n",
       "      <td>1.250315</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>labour_unemployed</td>\n",
       "      <td>1.422330</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>vm_minority</td>\n",
       "      <td>0.861685</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>income_lim</td>\n",
       "      <td>0.814844</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>labour_unemployed</td>\n",
       "      <td>0.881940</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             category     value scenario\n",
       "0            pop_2016  5.029686        A\n",
       "1            pop_2016  1.474341        B\n",
       "2            pop_2016  1.014909        C\n",
       "3         vm_minority  5.558348        A\n",
       "4          income_lim  4.684783        A\n",
       "5   labour_unemployed  5.556481        A\n",
       "6         vm_minority  1.474980        B\n",
       "7          income_lim  1.250315        B\n",
       "8   labour_unemployed  1.422330        B\n",
       "9         vm_minority  0.861685        C\n",
       "10         income_lim  0.814844        C\n",
       "11  labour_unemployed  0.881940        C"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taz_da = pd.read_csv(os.path.join(link_folder, 'taz_da_link.csv'), dtype={'DAUID': int, 'taz_id': int, 'frac_da_in_taz': float})\n",
    "da_demo = pd.read_csv(os.path.join(counts_folder, 'da_census_profile.csv'))\n",
    "print(delta.delta_A.sum())\n",
    "\n",
    "# Let's construct our TAZ to DA demographic link separately\n",
    "taz_da_link = pd.merge(taz_da, da_demo, on='DAUID')\n",
    "taz_da_link = pd.concat([taz_da_link[['taz_id']], taz_da_link[taz_da_link.columns[-10:-1]].multiply(taz_da_link['frac_da_in_taz'], axis=\"index\")], axis=1)\n",
    "taz_demo = taz_da_link.groupby('taz_id', as_index=False).sum()\n",
    "\n",
    "delta_demo = pd.merge(taz_demo, delta, left_on='taz_id', right_on='i')\n",
    "\n",
    "# Now let's assume that the proprotion of trips taken by a given group is proportional to their population makeup\n",
    "# E.g. if there are a 100 net new trips in a zone with 60% visible minority population, we assume 60% of those trips were taken by visible minorities\n",
    "\n",
    "delta_demo['A_vm_minority'] = (delta_demo['vm_minority']/delta_demo['vm_total'])*delta_demo['delta_A']\n",
    "delta_demo['A_income_lim'] = (delta_demo['income_lim']/delta_demo['income_total'])*delta_demo['delta_A']\n",
    "delta_demo['A_labour_unemployed'] = (delta_demo['labour_unemployed']/delta_demo['labour_total'])*delta_demo['delta_A']\n",
    "\n",
    "delta_demo['B_vm_minority'] = (delta_demo['vm_minority']/delta_demo['vm_total'])*delta_demo['delta_B']\n",
    "delta_demo['B_income_lim'] = (delta_demo['income_lim']/delta_demo['income_total'])*delta_demo['delta_B']\n",
    "delta_demo['B_labour_unemployed'] = (delta_demo['labour_unemployed']/delta_demo['labour_total'])*delta_demo['delta_B']\n",
    "\n",
    "delta_demo['C_vm_minority'] = (delta_demo['vm_minority']/delta_demo['vm_total'])*delta_demo['delta_C']\n",
    "delta_demo['C_income_lim'] = (delta_demo['income_lim']/delta_demo['income_total'])*delta_demo['delta_C']\n",
    "delta_demo['C_labour_unemployed'] = (delta_demo['labour_unemployed']/delta_demo['labour_total'])*delta_demo['delta_C']\n",
    "\n",
    "# What we want to do now is get the total trips by a demographic and divide by the total population of that demographic\n",
    "sums = delta_demo[[\n",
    "    'A_vm_minority', 'B_vm_minority', 'C_vm_minority', \n",
    "    'A_income_lim', 'B_income_lim', 'C_income_lim', \n",
    "    'A_labour_unemployed', 'B_labour_unemployed', 'C_labour_unemployed',\n",
    "    'vm_minority', 'income_lim', 'labour_unemployed'\n",
    "]].sum()\n",
    "\n",
    "# Finally we take ratios to get trips per 1,000 people.\n",
    "to_plot = pd.DataFrame({\n",
    "    'category': ['pop_2016', 'pop_2016', 'pop_2016', 'vm_minority', 'income_lim', 'labour_unemployed', 'vm_minority', 'income_lim', 'labour_unemployed', 'vm_minority', 'income_lim', 'labour_unemployed'],\n",
    "    'value': [\n",
    "        delta.delta_A.sum()/(delta_demo['pop_2016'].sum()/1000),\n",
    "        delta.delta_B.sum()/(delta_demo['pop_2016'].sum()/1000),\n",
    "        delta.delta_C.sum()/(delta_demo['pop_2016'].sum()/1000),\n",
    "        sums['A_vm_minority']/(sums['vm_minority']/1000),\n",
    "        sums['A_income_lim']/(sums['income_lim']/1000),\n",
    "        sums['A_labour_unemployed']/(sums['labour_unemployed']/1000),\n",
    "        sums['B_vm_minority']/(sums['vm_minority']/1000),\n",
    "        sums['B_income_lim']/(sums['income_lim']/1000),\n",
    "        sums['B_labour_unemployed']/(sums['labour_unemployed']/1000),\n",
    "        sums['C_vm_minority']/(sums['vm_minority']/1000),\n",
    "        sums['C_income_lim']/(sums['income_lim']/1000),\n",
    "        sums['C_labour_unemployed']/(sums['labour_unemployed']/1000),\n",
    "        ],\n",
    "    'scenario': ['A', 'B', 'C', 'A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C']\n",
    "    })\n",
    "to_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we assemble our data into a plottable form and generate plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>value</th>\n",
       "      <th>scenario</th>\n",
       "      <th>demo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pop_2016</td>\n",
       "      <td>5.029686</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pop_2016</td>\n",
       "      <td>1.474341</td>\n",
       "      <td>B</td>\n",
       "      <td>Total Population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pop_2016</td>\n",
       "      <td>1.014909</td>\n",
       "      <td>C</td>\n",
       "      <td>Total Population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vm_minority</td>\n",
       "      <td>5.558348</td>\n",
       "      <td>A</td>\n",
       "      <td>Visible Minority</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>income_lim</td>\n",
       "      <td>4.684783</td>\n",
       "      <td>A</td>\n",
       "      <td>Low Income (LIM)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category     value scenario         demo_name\n",
       "0     pop_2016  5.029686        A  Total Population\n",
       "1     pop_2016  1.474341        B  Total Population\n",
       "2     pop_2016  1.014909        C  Total Population\n",
       "3  vm_minority  5.558348        A  Visible Minority\n",
       "4   income_lim  4.684783        A  Low Income (LIM)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_names = {'income_lim': \"Low Income (LIM)\", 'pop_2016': 'Total Population', 'vm_minority': \"Visible Minority\", 'labour_unemployed':\"Unemployed\"}\n",
    "to_plot['demo_name'] = to_plot.category.map(pretty_names)\n",
    "to_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
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       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"strokeWidth\": 0}, \"axis\": {\"grid\": false}, \"font\": \"Roboto\", \"title\": {\"fontSize\": 18}}, \"data\": {\"name\": \"data-688aee02b50fdc3fe943489635c63b7c\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"scenario\", \"title\": \"Scenario\", \"type\": \"nominal\"}, \"row\": {\"field\": \"demo_name\", \"sort\": [\"Total Population\"], \"spacing\": 35, \"title\": \"\", \"type\": \"nominal\"}, \"x\": {\"field\": \"value\", \"title\": \"Trips per 1,000 Individuals\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"scenario\", \"title\": null, \"type\": \"nominal\"}}, \"height\": 80, \"title\": \"Average Additional Transit Demand for SmartTrack Scenarios\", \"width\": 600, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-688aee02b50fdc3fe943489635c63b7c\": [{\"category\": \"pop_2016\", \"value\": 5.0296856460908606, \"scenario\": \"A\", \"demo_name\": \"Total Population\"}, {\"category\": \"pop_2016\", \"value\": 1.474341080185471, \"scenario\": \"B\", \"demo_name\": \"Total Population\"}, {\"category\": \"pop_2016\", \"value\": 1.0149085762023744, \"scenario\": \"C\", \"demo_name\": \"Total Population\"}, {\"category\": \"vm_minority\", \"value\": 5.558348079397956, \"scenario\": \"A\", \"demo_name\": \"Visible Minority\"}, {\"category\": \"income_lim\", \"value\": 4.684782998927865, \"scenario\": \"A\", \"demo_name\": \"Low Income (LIM)\"}, {\"category\": \"labour_unemployed\", \"value\": 5.556481169212995, \"scenario\": \"A\", \"demo_name\": \"Unemployed\"}, {\"category\": \"vm_minority\", \"value\": 1.4749796462740483, \"scenario\": \"B\", \"demo_name\": \"Visible Minority\"}, {\"category\": \"income_lim\", \"value\": 1.2503150025197554, \"scenario\": \"B\", \"demo_name\": \"Low Income (LIM)\"}, {\"category\": \"labour_unemployed\", \"value\": 1.4223300023250265, \"scenario\": \"B\", \"demo_name\": \"Unemployed\"}, {\"category\": \"vm_minority\", \"value\": 0.8616847111760476, \"scenario\": \"C\", \"demo_name\": \"Visible Minority\"}, {\"category\": \"income_lim\", \"value\": 0.8148438127787136, \"scenario\": \"C\", \"demo_name\": \"Low Income (LIM)\"}, {\"category\": \"labour_unemployed\", \"value\": 0.8819397240994697, \"scenario\": \"C\", \"demo_name\": \"Unemployed\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(to_plot).mark_bar().encode(\n",
    "    alt.Y('scenario:N', title=None),\n",
    "    alt.X('value:Q', title='Trips per 1,000 Individuals'),\n",
    "    alt.Color('scenario:N', title='Scenario'),\n",
    "    alt.Row('demo_name:N', title='', sort=['Total Population'], spacing=35)\n",
    ").properties(\n",
    "    title=\"Average Additional Transit Demand for SmartTrack Scenarios\",\n",
    "    width=600,\n",
    "    height=80\n",
    ").configure(font='Roboto').configure_axis(grid=False).configure_view(strokeWidth=0).configure_title(fontSize=18)"
   ]
  }
 ],
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