{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e0bf8629",
   "metadata": {},
   "source": [
    "# RA2CE feature: Equity analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb251586",
   "metadata": {},
   "source": [
    "This notebook is a rather technical notebook. The recommended user is a user with background knowledge of network criticality. \n",
    "\n",
    "In this notebook, we will perform criticality analysis with three different distributive principles: utilitarian, egalitarian and prioritarian principles. For more background knowledge on these principles and the application on transport network criticality analysis, please read: https://www.sciencedirect.com/science/article/pii/S0965856420308077 \n",
    "\n",
    "The purpose of the equity analysis performd in this notebook is providing insight into how different distributive principles can result in different prioritizations of the network. While we usually prioritize network interventions based on the number of people that use the road, equity principles allow us to also take into account the function of the network for for example underpriviliged communities. \n",
    "Depending on the equity principle applied, your network prioritization might change, which can change decision-aking.\n",
    "\n",
    "The RA2CE analysis is set up generically. The user can determine the equity weights themselves. This can for example be GINI-coefficients or social vulnerability scores. The user-defined equity weights will feed into the prioritarian principle.\n",
    "\n",
    "The three applied principles are explained below:"
   ]
  },
  {
   "attachments": {
    "image.png": {
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    }
   },
   "cell_type": "markdown",
   "id": "fe73ccb4",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb771623",
   "metadata": {},
   "source": [
    "### For new users\n",
    "\n",
    "If you have not checked out the previous RA2CE examples and you want to run your own RA2CE analysis, we advise you to first familiarize yourself with those notebooks. In this current notebook we will not provide extensive explanations as to how to run RA2CE and create the correct setups. We will assume the user has this knowledge already. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7256f804",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3f682f",
   "metadata": {},
   "source": [
    "Packages import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e67d001f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import folium"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d38a12",
   "metadata": {},
   "source": [
    "Set the path to your equity analysis data folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99350e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_dir = Path(\"data\", \"equity_analysis\")\n",
    "assert root_dir.exists()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb60c4e8",
   "metadata": {},
   "source": [
    "## Inspect data and get familiar with the use-case"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ad1f48",
   "metadata": {},
   "source": [
    "We will first show you the data that will be used in this use case on Pontianak Indonesia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675c06b8",
   "metadata": {},
   "source": [
    "### 1. Using equity weights to delineate more vulnerable areas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184a1fc2",
   "metadata": {},
   "source": [
    "For the equity analysis, the user can assign equity weights which can be used in the criticality analysis. In this analysis we use a region weights file (region_weight.csv) which holds weights for specific areas in Pontianak Indonesia. You then also need a shapefile delineating the regions in question. We will inspect both files in this example and see which areas of Pontianak are more vulnerable according to this user-specified equity-weight file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50e0e257",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the files\n",
    "path_network = root_dir / \"static\" / \"network\"\n",
    "path_region_weights = path_network / \"region_weight.csv\"\n",
    "path_regions = path_network / \"region.shp\"\n",
    "\n",
    "region_weigths = pd.read_csv(path_region_weights, sep=';')\n",
    "regions = gpd.read_file(path_regions, driver = \"SHP\")\n",
    "\n",
    "#Merge the shapefile and the weights csv file:\n",
    "region_weights_plot = pd.merge(regions, region_weigths, left_on='DESA', right_on='region')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3625082a",
   "metadata": {},
   "source": [
    "Which regions are more vulnerable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "900749e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Inspect the regions\n",
    "region_weights_plot.explore(column='weight', cmap='Reds', tiles=\"CartoDB positron\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21e3726",
   "metadata": {},
   "source": [
    "### 2. Inspect origins, destinations and the road network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a484934e",
   "metadata": {},
   "source": [
    "Load the origins and destinations data and the road network of our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae73f50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_network = root_dir / \"static\" / \"network\"\n",
    "path_origins = path_network / \"origins_points.shp\"\n",
    "path_destinations = path_network / \"osm_health_point.shp\"\n",
    "origins = gpd.read_file(path_origins, driver = \"SHP\")\n",
    "destinations = gpd.read_file(path_destinations, driver = \"SHP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bbbc39b",
   "metadata": {},
   "source": [
    "Inspect origins (blue) and health destinations (red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37051457",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = origins.explore(color='blue', tiles=\"CartoDB positron\", name='WorldPop origins')\n",
    "m = destinations.explore(m=m, color=\"red\", name=\"Health destinations\")\n",
    "# this is completely optional\n",
    "folium.LayerControl().add_to(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6163a416",
   "metadata": {},
   "source": [
    "## Running the RA2CE analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6071f5fd",
   "metadata": {},
   "source": [
    "### 1. Make sure your data is created and stored correctly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ec6ce7",
   "metadata": {},
   "source": [
    "See the origins/destinations example to familiarize yourself with how to set up origins and destinations files. Inspect the regions file and the region_weight file and make sure they are saved in the correct folder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195ad33f",
   "metadata": {},
   "source": [
    "### 2. Set up your network and analysis ini"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf330d14",
   "metadata": {},
   "source": [
    "Before you start running, you need to make sure that your .ini files contain the right parameters. Check out the analysis and network.ini files in the data folder to check the additional parameters that need to be set to run this example! Below we paste a snippet of these files as example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5909031d",
   "metadata": {},
   "source": [
    "**Network Ini update section origins_destinations**<br>\n",
    "\n",
    ">[origins_destinations]<br>\n",
    "origins = origins_points.shp # Must be in the static/network folder, belongs to this analysis <br>\n",
    "destinations = osm_health_point.shp # gis_osm_pois_pontianak.shp # Must be in the static/network folder, belongs to this analysis<br>\n",
    "origins_names = A<br>\n",
    "destinations_names = B<br>\n",
    "id_name_origin_destination = OBJECTID #default if origins is a tif file = OBJECTID<br>\n",
    "origin_count = values #necessary if traffic on each edge would be recorded in optimal_route_origin_destination<br>\n",
    "origin_out_fraction = 1<br>\n",
    "category = category<br>\n",
    "region = region.shp #necessary for distributional / equity analysis<br>\n",
    "region_var = DESA #necessary for distributional / equity analysis<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97f55bb",
   "metadata": {},
   "source": [
    "**analysis_ini update optimal_route_origin_destination analysis**<br>\n",
    "\n",
    ">[analysis1]<br>\n",
    "name = optimal route origin destination<br>\n",
    "analysis = optimal_route_origin_destination<br>\n",
    "weighing = length<br>\n",
    "save_traffic = True #True if you want to record the traffic in each edge<br>\n",
    "equity_weight = region_weight.csv #equity-weighted factors for each region, should be stored in static/network. Note that 'region' and 'region_var' should present in network.ini<br>\n",
    "save_gpkg = True<br>\n",
    "save_csv = True<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de8e703",
   "metadata": {},
   "source": [
    "### 3. Find your folder and initalization files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65d045f",
   "metadata": {},
   "source": [
    "Now we can start our RA2CE analysis. Let's initialize RA2CE first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d6562dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ra2ce.ra2ce_handler import Ra2ceHandler #import the ra2cehandler to run ra2ce analyses\n",
    "\n",
    "_network_ini_name = \"network.ini\" #set the name for the network.ini\n",
    "_analysis_ini_name = \"analyses.ini\" #set the name for the analysis.ini"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f92d24",
   "metadata": {},
   "source": [
    "Check if the network.ini and analysis.ini indeed exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a017a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "network_ini = root_dir / _network_ini_name\n",
    "assert network_ini.is_file()\n",
    "\n",
    "analysis_ini = root_dir / _analysis_ini_name\n",
    "assert analysis_ini.is_file()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e187eb",
   "metadata": {},
   "source": [
    "### 4. Run RA2CE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd2b259",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "handler = Ra2ceHandler(network=network_ini, analysis=analysis_ini)\n",
    "handler.configure()\n",
    "handler.run_analysis()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a29bba14",
   "metadata": {},
   "source": [
    "## Post-processing results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f08ac47",
   "metadata": {},
   "source": [
    "The result of the equity analysis will be saved into the 'output' folder, under the specified analysis 'optimal_route_origin_destination'. This is a .csv file without geometry. To plot the results below, we will first add geometry to this data prior to showing the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a35997",
   "metadata": {},
   "source": [
    "####  1. Load the traffic analysis output data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e484c96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_route = root_dir/'output'/'optimal_route_origin_destination'\n",
    "optimal_route_graph = optimal_route / \"optimal_route_origin_destination_link_traffic.csv\"\n",
    "traffic = pd.read_csv(optimal_route_graph)\n",
    "df = traffic.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0104a74",
   "metadata": {},
   "source": [
    "#### 2. Load the origin_destination graph edges that hold geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6df0d73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_output_graph = root_dir / \"static\" / \"output_graph\"\n",
    "base_graph_edges = path_output_graph / \"origins_destinations_graph_edges.gpkg\"\n",
    "edges_gdf = gpd.read_file(base_graph_edges, driver = \"GPKG\")\n",
    "gdf = edges_gdf.copy()\n",
    "edges_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eebcab54",
   "metadata": {},
   "source": [
    "#### 3. Merge analysis data with geometry "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76685748",
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in ['traffic', 'traffic_egalitarian', 'traffic_prioritarian']:\n",
    "    gdf[var] = 0\n",
    "    \n",
    "    # Create a dictionary to store the values for faster lookup\n",
    "    var_values = dict(zip(zip(df['u'], df['v']), df[var]))\n",
    "\n",
    "    for u, v in zip(df['u'], df['v']):\n",
    "        if u != v:\n",
    "            # Check if the edge (u, v) exists in the DataFrame\n",
    "            if (u, v) in var_values:\n",
    "                gdf.loc[(gdf['u'] == u) & (gdf['v'] == v), var] = var_values[(u, v)]\n",
    "                \n",
    "                # Check if the reverse edge (v, u) exists and hasn't been assigned a value yet\n",
    "                if (v, u) not in var_values:\n",
    "                    gdf.loc[(gdf['u'] == v) & (gdf['v'] == u), var] = var_values[(u, v)]\n",
    "            else:\n",
    "                # If the edge doesn't exist in the DataFrame, check if the reverse edge does\n",
    "                if (v, u) in var_values:\n",
    "                    gdf.loc[(gdf['u'] == v) & (gdf['v'] == u), var] = var_values[(v, u)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4512585",
   "metadata": {},
   "source": [
    "#### 4. Rank the edges based on the different traffic analysis results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8d7ff80",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf['traffic_ranked'] = gdf['traffic'].rank(method='min', ascending=False)\n",
    "gdf['traffic_egalitarian_ranked'] = gdf['traffic_egalitarian'].rank(method='min', ascending=False)\n",
    "gdf['traffic_prioritarian_ranked'] = gdf['traffic_prioritarian'].rank(method='min', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a291bb1",
   "metadata": {},
   "source": [
    "## Visualize results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd661ba6",
   "metadata": {},
   "source": [
    "The darker the edge, the higher its ranking, the more critical the network part \n",
    "\n",
    "Ranking 1 is the highest ranking, higher values represent lower rankings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37f909c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
    "gdf.plot(column='traffic_ranked',cmap='gist_heat',ax=axs[0],legend=True)\n",
    "axs[0].set_title('Utilitarian principle ranking')\n",
    "gdf.plot(column='traffic_egalitarian_ranked', cmap='gist_heat', ax=axs[1], legend=True)\n",
    "axs[1].set_title('Egalitarian principle ranking')\n",
    "gdf.plot(column='traffic_prioritarian_ranked', cmap='gist_heat',ax=axs[2], legend=True)\n",
    "axs[2].set_title('Prioritarian principle ranking')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f302147",
   "metadata": {},
   "source": [
    "What does this tell us?\n",
    "\n",
    "The higher the ranking of an edge (values closer to 1), the more critical that part of the network, given the equity principle applied. In the utilitarian principle, this means the most people using that edge. In the egalitarian principle it means having each origin-destination pair having equal opportunity to acces, regardless of how many people live in one origin. In the last principle we correct for the provided equity weight and base the travel calculations on that. This prioritizes more vulnerable areas. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0318f0f8",
   "metadata": {},
   "source": [
    "These insights can be used to for example prioritize network investments. Depending on the service of the network, the prioritization of investments can shift. If investments were to be done based on serving underpiviliged communities, the prioritarian ranking principle can be used, utilizing weights that represent priviliged and underpriviliged areas/communities/regions. As the user is free to specify their own 'equity weights', they can determine their own criteria for prioritization of areas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd6495a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.explore(column='traffic_prioritarian_ranked', tiles=\"CartoDB positron\", cmap='gist_heat')"
   ]
  }
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