diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 00000000..ca443103 --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,42 @@ +# AI Coding Assistant Instructions for SEPTA Service Cuts Engagement Project + +## Project Overview +This is a client-side web application that visualizes the impact of SEPTA bus service cuts on Philadelphia residents. Users enter their address or use geolocation to see how cuts affected bus routes in their Pennsylvania Senate district. The app uses Leaflet maps, Turf.js for geospatial analysis, and Census geocoding API. + +## Architecture +- **Modular ES6**: Code split into `js/` modules (main.js, map.js, location.js, utils.js) with event-driven communication via a shared EventTarget bus. +- **Data Flow**: User location → geocoding → district lookup → filter/display route impacts. +- **No Build System**: Static files served directly; use ES modules and CDN libraries (Leaflet, Turf.js, Lodash). +- **Data Sources**: Pre-processed GeoJSON files in `data/` (routes, stops, districts) generated from R scripts. + +## Key Conventions +- **Event Bus Pattern**: Use `events.dispatchEvent(new CustomEvent('eventName', { detail: data }))` for inter-module communication (e.g., `userLocationAcquired`, `districtData`). +- **Async Data Loading**: Fetch JSON with `await fetch('data/filename.json')` in `utils.js`. +- **Geospatial Ops**: Use Turf.js for point-in-polygon (district lookup) and masking. +- **Debounced Inputs**: Apply Lodash `_.debounce()` to address search for performance. +- **CORS Handling**: Route external API calls through `https://corsproxy.io/?url=` for Census geocoding. + +## Developer Workflows +- **Linting**: Run `npm run js-lint` (ESLint) and `npm run css-lint` (Stylelint) before commits. +- **Data Processing**: Use R scripts in `r/` to transform raw CSV arrivals data into GeoJSON (e.g., `export_to_geojson.R`). +- **Local Development**: Open `index.html` in browser; no server needed for static assets. +- **Testing**: Manually test geolocation, address input, and map interactions across districts. + +## Common Patterns +- **Map Styling**: Define Leaflet styles as functions returning objects (e.g., `standardStyle(feature)` in `map.js`). +- **District Overlay**: Use Turf `turf.mask()` to create inverted polygons for focus areas. +- **Error Handling**: Alert users for geolocation failures; log console errors for debugging. +- **Responsive Design**: CSS in `styles.css` handles sidebar/map layout. + +## Integration Points +- **Leaflet Map**: Initialized in `map.js` with CartoDB basemap; add GeoJSON layers with custom styles. +- **Census API**: Geocode addresses via `https://geocoding.geo.census.gov/geocoder/locations/address` with Philadelphia bounds. +- **Turf.js**: Perform spatial queries like `turf.booleanPointInPolygon(point, polygon)`. + +## File Structure Highlights +- `js/main.js`: Entry point, loads data, sets up event listeners. +- `js/location.js`: Handles geolocation and address search with debounced API calls. +- `js/map.js`: Manages Leaflet map, legend, and district masking. +- `data/`: Static GeoJSON (septa_routes.json, arrivals_and_stops.json, districts.json). +- `r/`: Data processing scripts for generating JSON from raw CSVs. +/Users/chkim/Documents/Projects/classes/musa-javascript/septa-cuts-engagement/.github/copilot-instructions.md \ No newline at end of file diff --git a/.gitignore b/.gitignore index 12ac6472..4a02da5a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,4 @@ node_modules/ -.DS_Store \ No newline at end of file +.DS_Store +*.csv +.Rhistory diff --git a/README.md b/README.md index 40a45209..70986a9a 100644 --- a/README.md +++ b/README.md @@ -1 +1,19 @@ -Add a readme for your engagement tool here. Include content overview, data citations, and any relevant technical details. \ No newline at end of file +## Introduction + +Due to a structural funding shortfall, SEPTA (the transit agency for the Philadelphia area) was forced to enact significant service cuts in the summer of 2025. The service cuts lasted two weeks, starting August 24. Though the cuts were reversed after the agency shifted funds from its capital budget to its operating budget, the underlying funding shortfall still remains, and similar cuts may return if a long-term funding agreement is not enacted by the Pennsylvania legislature. + +## Purpose + +This online tool is intended to help SEPTA riders (or potential SEPTA riders) understand what the day-to-day impacts of these service cuts were. The tool, based on actual service data rather than scheduled trips, allows users to select specific trips (for example, their commuting trips) to see how bus service to their own stop at particular times of day were impacted by the service cuts. + +This tool focuses specifically on reduced bus service. Though the media and riders (understandably) paid much attention to the 32 eliminated and 19 shortened bus routes, as well as the threat of elimination for several Regional Rail lines, the degradation in service for routes which still existed—and continued to serve hundreds of thousands of riders—received less attention. This tool alleviates this information gap by quantifying the actual decrease in service as well as estimating the amount of time lost to riders because of the service cuts. + +## Data and methods + +The underlying data are real-time bus arrival data collected by SEPTA (and generously shared with the author). The data include arrival times for 41 bus routes which continued operations during the service cuts; these routes include high, medium, and low ridership routes, as well as a mix of urban and suburban routes. The time period of the data spans the week immediately before the cuts (August 17-23), as well as the first week of the cuts (August August 24-30). + +The number of bus arrivals both before and during the cuts is calculated at each stop, separately for each route, each direction, day of the week (weekday vs weekend), and time of day. (The count is normalized by day of week, so that the values are comparable for weekdays and weekends. Further, any trip with a bus arrival count of zero either before or during the cuts is excluded from comparison.) Based on these counts, the percentage difference during the cuts vs before the cuts is calculated. + +The estimates of waiting times are calculated by dividing the number of minutes in the time window by the number of arrivals, and dividing this value by two. For example, if 3 buses arrived at the stop during the morning rush period (7-10 am), the average waiting time would be 30 minutes (i.e., with a frequency of one bus per hour, if you arrive at a random time the expected value of of your waiting time would be 30 minutes). The difference in waiting time due to the cuts is simply the waiting time during the cuts minus the waiting time before the cuts. + + diff --git a/css/styles.css b/css/styles.css new file mode 100644 index 00000000..2ec862e4 --- /dev/null +++ b/css/styles.css @@ -0,0 +1,113 @@ +html { + font-size: 24px; + font-family: 'Gill Sans', sans-serif; +} + +body { + padding: 0; + margin: 0; +} + +.content { + display: flex; + flex-direction: row; + min-height: 100vh; +} + +.map-container { + position: relative; + min-width: 0; + background-color: silver; + flex: 1 1 50%; +} + +#map { + position: absolute; + inset: 0; +} + +.marker-pin { + width: 30px; + height: 30px; + border-radius: 50% 50% 50% 0; + background: #000000; + position: absolute; + transform: rotate(-45deg); + left: 50%; + top: 50%; + margin: -15px 0 0 -15px; +} + +.marker-pin::after { + content: ''; + width: 24px; + height: 24px; + margin: 3px 0 0 3px; + background: #fff; + position: absolute; + border-radius: 50%; +} + +.custom-div-icon i { + position: absolute; + width: 22px; + font-size: 22px; + left: 0; + right: 0; + margin: 10px auto; + text-align: center; +} + +.custom-div-icon i.awesome { + margin: 12px auto; + font-size: 17px; +} + +.sidebar { + position: relative; + background-color: #e9c46a; + padding: 16px; + display: flex; + flex-direction: column; + flex: 0 0 50%; + justify-content: space-around; +} + +.sidebar-container { + background-color: white; + padding: 12px; + margin: 5px; +} + +#trip-search-container { + display: flex; + flex-direction: column; +} + +.input { + flex: 0 0 auto; + padding: 8px; + border: none; + border-radius: 4px; + margin-right: 10px; +} + +.input label { + font-weight: bold; +} + +.citations { + flex: 1 0 auto; + display: flex; + flex-direction: column; + justify-content: flex-end; + align-items: flex-end; + font-size: 0.6em; +} + + + + + + + diff --git a/data/arrivals_and_stops.json b/data/arrivals_and_stops.json new file mode 100644 index 00000000..1d797d36 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a/index.html b/index.html new file mode 100644 index 00000000..c2ad5271 --- /dev/null +++ b/index.html @@ -0,0 +1,104 @@ + + + + + + + + + + + + + + + + + + SEPTA service reductions simulator + + + + + +
+
+
+
+
+ +
+ + + + + + + + + +
+

Datasource: Archived real-time arrival data for 41 bus routes shared by SEPTA. For our methodology, see here.

+
+ +
+
+ +
+ + \ No newline at end of file diff --git a/js/dropdown.js b/js/dropdown.js new file mode 100644 index 00000000..a8b54281 --- /dev/null +++ b/js/dropdown.js @@ -0,0 +1,83 @@ +import * as utils from './utils.js'; + +/** +* Populate options of a drop-down menu +* +* @param {EventTarget} eventBus - The event bus to dispatch the event on +* @param {object} geojsonData - The GeoJSON FeatureCollection with data +* @param {string} dropdownId - The id of the dropdown element +* @param {string} propertyName - The property name which contains option values +* @param {string} defaultText - The text to display for the default option +*/ +export function populateDropdown(eventBus, geojsonData, dropdownId, propertyName, defaultText = 'Please choose an option') { + // Get the dropdown element + const dropdownElement = document.getElementById(dropdownId); + // Clear existing options + dropdownElement.innerHTML = ''; + // Turn disabled off + dropdownElement.disabled = false; + + // Add a default option + const defaultOption = document.createElement('option'); + defaultOption.value = ''; + defaultOption.textContent = defaultText; + dropdownElement.appendChild(defaultOption); + + // Create array of unique property values + const optionsArray = utils.getUniquePropertyValues(geojsonData, propertyName); + + // Populate new options + optionsArray.forEach(value => { + const option = document.createElement('option'); + option.value = value; + option.textContent = value; + dropdownElement.appendChild(option); + }); + + // Dispatch filtered data to event bus once selection is made + dispatchData(eventBus, geojsonData, dropdownId, propertyName); +} + +/** +* Listen for changes in a dropdown menu and dispatch an event containing the data filtered by the +* value of the selection +* +* @param {EventTarget} eventBus - The event bus to dispatch the event on +* @param {object} geojsonData - The GeoJSON FeatureCollection with data to be filtered +* @param {string} dropdownId - The id of the dropdown element +* @param {string} propertyName - The property name to filter on +*/ +export function dispatchData(eventBus, geojsonData, dropdownId, propertyName) { + const dropdownElement = document.getElementById(dropdownId); + dropdownElement.addEventListener('change', (event) => { + const selectedValue = event.target.value; + const filteredData = utils.filterGeojsonByProperty(geojsonData, propertyName, selectedValue); + const customEvent = new CustomEvent(`${dropdownId}Data`, { detail: { value: selectedValue, data: filteredData } }); + eventBus.dispatchEvent(customEvent); + // For debugging, print to console the name and details of the event dispatched + console.log(`Event dispatched: ${customEvent.type}`, customEvent.detail); + + }); +} + +/** +* Populate a new dropdown menu with data filtered from a previous dropdown selection passed via event bus, +* with values based on a specified property. +* +* @param {EventTarget} eventBus - The event bus to listen on +* @param {string} dataEventName - The name of the event to listen for, containing filtered data +* @param {string} dropdownId - The id of the dropdown element to be populated +* @param {string} propertyName - Name of the property with values to be shown as options +* @param {string} defaultText - The text to display for the default option +*/ +export function cascadeDropdown(eventBus, dataEventName, dropdownId, propertyName, defaultText) { + eventBus.addEventListener(dataEventName, (event) => { + const filteredData = event.detail.data; + // Print to console name and details of the received event for debugging + console.log(`Event received: ${dataEventName}`, event.detail); + // Populate the new dropdown with the filtered data + populateDropdown(eventBus, filteredData, dropdownId, propertyName, defaultText); + // Logging for debugging + console.log(`New dropdown populated for: ${dropdownId}, filtering by ${propertyName}`); + }); +} diff --git a/js/htmlDisplay.js b/js/htmlDisplay.js new file mode 100644 index 00000000..376d198c --- /dev/null +++ b/js/htmlDisplay.js @@ -0,0 +1,80 @@ +import * as storage from './storage.js'; + +/** +* When user selection is made, display the relevant data in the results section +* +* @param {EventTarget} eventBus - The event bus to listen for the event on +*/ +export function displayUserData(eventBus) { + eventBus.addEventListener('stopData', (event) => { + const resultElement = document.querySelector('#user-results-contents'); + const resultData = event.detail.data; + + // Clear previous results + resultElement.innerHTML = ''; + + if (resultData.features.length === 0 | resultData.features.length > 1) { + resultElement.textContent = `Please make a valid selection using the drop-down menus above.`; + return; + } + + // Pull out relevant fields from the result + const resultsFields = resultData.features[0].properties; + const routeId = resultsFields.route_id; + const tripHeadsign = resultsFields.trip_headsign; + const weekPeriod = resultsFields.week_period; + const timeOfDay = resultsFields.time_of_day; + const stopName = resultsFields.stop_name; + const arrivalsBeforeCuts = resultsFields.arrivals_before_cuts; + const arrivalsDuringCuts = resultsFields.arrivals_during_cuts; + const arrivalsChange = resultsFields.percent_change; + const waitTimeDifference = resultsFields.expected_wait_time_difference; + + // Some derived calculations for display + const arrivalsChangePercent = (arrivalsChange >= 0) ? `an increase of ${Math.round(arrivalsChange * 100)}%` : `a decrease of ${Math.abs(Math.round(arrivalsChange * 100))}%`; + const waitTimeDifferenceText = (waitTimeDifference >= 0) ? `${waitTimeDifference} minutes longer` : `${Math.abs(waitTimeDifference)} minutes less`; + + // Display the results + resultElement.innerHTML = + `

For your trip (Route ${routeId} going towards ${tripHeadsign} during ${weekPeriod}s starting at ${stopName}), + an average of ${arrivalsBeforeCuts} buses came in the ${timeOfDay} before the August 2025 service cuts.

+ +

However, during the cuts, an average of ${arrivalsDuringCuts} buses came, ${arrivalsChangePercent} from the pre-cut average. + This meant that on average you waited ${waitTimeDifferenceText} for your bus during the period of the service cuts.

`; + + }); +}; + +/** +* Display stored community data summary statistics in the community-summary section +* +* @param {string} context - 'init' for initial display, 'update' for after user data stored +*/ +export async function displayCommunityDataSummary(context) { + const displayData = await storage.calculateSummaryStatistics(); + + // Values for display + const contextText = (context === 'init') ? 'trips' : 'trips (including your own)'; + const numTrips = displayData.totalSubmissions; + const averagePercentChange = (displayData.averagePercentChange >= 0) ? `${Math.round(displayData.averagePercentChange * 100)}% increase` : `${Math.abs(Math.round(displayData.averagePercentChange * 100))}% decrease`; + const totalWaitTimeAverage = (displayData.totalWaitTimeDifference / displayData.totalSubmissions); + const totalWaitTimeDisplay = (totalWaitTimeAverage >= 0) ? `${Math.round(totalWaitTimeAverage)} additional minutes` : `${Math.abs(Math.round(totalWaitTimeAverage))} fewer minutes`; + + const summaryElement = document.querySelector('.community-results'); + summaryElement.innerHTML = ` +

Affects on community trips

+

Riders on the ${numTrips} ${contextText} summitted by users of this tool saw a ${averagePercentChange} in the number of buses arriving during their travel window during the August 2025 service cuts.

+

This meant that the riders of these trips waited ${totalWaitTimeDisplay} for their bus to come compared to before the service cuts, on average.

+ `; +}; + +/** + * Listen for user data storage event and update community data summary display + * + * @param {EventTarget} eventBus - The event bus to listen for the event on + */ +export function updateCommunityDataSummary(eventBus) { + eventBus.addEventListener('userDataStored', async () => { + await displayCommunityDataSummary('update'); + }); +}; diff --git a/js/main.js b/js/main.js new file mode 100644 index 00000000..8fd2e44b --- /dev/null +++ b/js/main.js @@ -0,0 +1,50 @@ +import * as utils from './utils.js'; +import { initMap } from './map.js'; +import * as dropdown from './dropdown.js'; +import * as htmlDisplay from './htmlDisplay.js'; +import * as storage from './storage.js'; + +// Events in event bus: +// routeData - dispatched when route is selected +// directionData - dispatched when trip direction is selected +// weekdayData - dispatched when day of week is selected +// timeData - dispatched when time of day is selected +// stopData - dispatched when stop is selected +// userDataStored - dispatched when user data is stored in Firestore +const events = new EventTarget(); + +const mapElement = document.querySelector('#map'); + +// Read geoJSON data +const septaNetwork = await utils.getGeojsonCollection('septa_routes.json'); +const arrivalsAndStops = await utils.getGeojsonCollection('arrivals_and_stops.json'); + +// Initialize map +initMap(events, mapElement, septaNetwork); + +// Display existing user data summary statistics +htmlDisplay.displayCommunityDataSummary('init'); + +// Populate route dropdown +dropdown.populateDropdown(events, arrivalsAndStops, 'route', 'route_id', 'Please choose your route'); + +// Populate trip direction dropdown based on selected route +dropdown.cascadeDropdown(events, 'routeData', 'direction', 'trip_headsign', 'Please choose your trip direction'); + +// Populate day of week dropdown based on previous selections +dropdown.cascadeDropdown(events, 'directionData', 'weekday', 'week_period', 'Please choose the day of the week'); + +// Populate day of week dropdown based on previous selections +dropdown.cascadeDropdown(events, 'weekdayData', 'time', 'time_of_day', 'Please choose your stop'); + +// Populate stop dropdown based on previous selections +dropdown.cascadeDropdown(events, 'timeData', 'stop', 'stop_name', 'Please choose your stop'); + +// In user-results section, listen for final stop selection and display results +htmlDisplay.displayUserData(events); + +// Store user data to Firestore +storage.storeUserData(events); + +// Update user data summary statistics when user's own data is stored +htmlDisplay.updateCommunityDataSummary(events); diff --git a/js/map.js b/js/map.js new file mode 100644 index 00000000..5cb9b6cc --- /dev/null +++ b/js/map.js @@ -0,0 +1,106 @@ +import * as utils from './utils.js'; + +/** +* Initialize the Leaflet map with base layers and controls +* @param {EventTarget} eventBus - The event bus to listen for data events +* @param {HTMLElement} mapElement - DOM element to attach the map to +* @param {Object} initialData - GeoJSON data to display on the map +*/ +async function initMap(eventBus, mapElement, initialData) { + // Initialize the Leaflet map + map = L.map(mapElement, { + center: [39.95, -75.19], // Philadelphia coordinates + zoom: 10, + }); + + // Add base tile layer + L.tileLayer('https://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}{r}.png', { + attribution: '© OpenStreetMap contributors © CARTO' + }).addTo(map); + + // Load initial data + const initialLayer = L.geoJSON(initialData, { + style: feature => { + return { color: '#be9b0eff' }; + } + }); + initialLayer.addTo(map); + map.fitBounds(initialLayer.getBounds()); + + // When route is selected, update map to show only that route + eventBus.addEventListener('routeData', (event) => { + const routeData = event.detail.data; + const routeId = utils.getUniquePropertyValues(routeData, 'route_id')[0]; + const routeMapData = utils.filterGeojsonByProperty(initialData, 'route_id', routeId); + console.log('Updating map for route:', routeId, routeMapData); + + // Highlight the selected route on the map + updateRouteMap(routeMapData); + + // Change opacity of other routes + initialLayer.eachLayer(layer => { + if (layer.feature.properties.route_id !== routeId) { + layer.setStyle({ opacity: 0.2}); + } + }); + }); + + // When stop is selected, update map to show that stop + eventBus.addEventListener('stopData', (event) => { + const stopMapData = event.detail.data; + console.log('Updating map for stop:', stopMapData); + + // Highlight the selected stop on the map + updateStopMap(stopMapData); + }); +} + +/** +* Show selected route on map +* @param {Object} routeData - GeoJSON data to display on the map +*/ +function updateRouteMap(routeData) { + // Remove existing route layer if present + if (map.routeLayer) { + map.removeLayer(map.routeLayer); + } + + // Add new route layer + map.routeLayer = L.geoJSON(routeData, { + style: feature => { + return { color: '#ff7800ff', weight: 5 }; + } + }); + map.routeLayer.addTo(map); + map.fitBounds(map.routeLayer.getBounds()); +} + +/** +* Show selected stop on map +* @param {Object} stopData - GeoJSON data for the selected stop +*/ +function updateStopMap(stopData) { + // Define custom icon for stop marker + const icon = L.divIcon({ + className: 'custom-div-icon', + html: "
departure_board", + iconSize: [30, 42], + iconAnchor: [15, 42] + }); + + // Remove existing stop marker if present + if (map.stopMarker) { + map.removeLayer(map.stopMarker); + } + + // Add new stop marker + const stopFeature = stopData.features[0]; + const [lat, lng] = stopFeature.geometry.coordinates.slice().reverse(); // GeoJSON uses [lng, lat] + + map.stopMarker = L.marker([lat, lng], { + icon: icon + }).addTo(map); + map.setView([lat, lng], 15); +} + +export { initMap }; diff --git a/js/storage.js b/js/storage.js new file mode 100644 index 00000000..3681abfb --- /dev/null +++ b/js/storage.js @@ -0,0 +1,107 @@ +// Import the functions you need from the SDKs you need +import { initializeApp } from 'https://www.gstatic.com/firebasejs/12.7.0/firebase-app.js'; +import { getFirestore, collection, addDoc, getDocs } from 'https://www.gstatic.com/firebasejs/12.7.0/firebase-firestore.js'; + +// Your web app's Firebase configuration +const firebaseConfig = { + apiKey: 'AIzaSyAp8afrw8k9_kyPgGD0NuthJVvcNSfk8GU', + authDomain: 'septa-cuts-impacts.firebaseapp.com', + projectId: 'septa-cuts-impacts', + storageBucket: 'septa-cuts-impacts.firebasestorage.app', + messagingSenderId: '260659001862', + appId: '1:260659001862:web:e3ba37f5b6071dfe4ce187', +}; + +// Initialize Firebase +const app = initializeApp(firebaseConfig); +const db = getFirestore(app); + +/** +* When stopData event is dispatched, store the user selection data in Firestore +* +* @param {EventTarget} eventBus - The event bus to listen for the event on +*/ +export function storeUserData(eventBus) { + eventBus.addEventListener('stopData', async (event) => { + const resultData = event.detail.data; + if (resultData.features.length === 0 | resultData.features.length > 1) { + console.log('Invalid selection, data not stored.'); + return; + } + const resultsFields = resultData.features[0].properties; + try { + await addDoc(collection(db, 'stop_data_storage'), { + route_id: resultsFields.route_id, + trip_headsign: resultsFields.trip_headsign, + week_period: resultsFields.week_period, + time_of_day: resultsFields.time_of_day, + stop_id: resultsFields.stop_id, + stop_name: resultsFields.stop_name, + arrivals_before_cuts: resultsFields.arrivals_before_cuts, + arrivals_during_cuts: resultsFields.arrivals_during_cuts, + percent_change: resultsFields.percent_change, + expected_wait_time_difference: resultsFields.expected_wait_time_difference, + timestamp: new Date() + }); + console.log('User data successfully stored.'); + } catch (e) { + console.error('Error adding document: ', e); + } + + // After storing user data, dispatch an event to indicate data were stored + const updateEvent = new Event('userDataStored'); + eventBus.dispatchEvent(updateEvent); + }); +}; + +/** +* Get stored community data from Firestore +* +* @returns {object} Firestore collection +*/ +async function getCommunityData() { + const communityDataCollection = collection(db, 'stop_data_storage'); + const communityData = await getDocs(communityDataCollection); + const allCommunityData = []; + communityData.forEach((doc) => { + allCommunityData.push({ id: doc.id, ...doc.data() }); + }); + return allCommunityData; +}; + +/** +* From the retrieved community data, calculate the following summary statistics: +* - Total number of submissions +* - Total number of arrivals before cuts +* - Total number of arrivals during cuts +* - Average percent change in arrivals +* - Sum of expected wait time difference +* +* @returns {object} summary statistics +*/ +export async function calculateSummaryStatistics() { + const communityDataArray = await getCommunityData(); + const totalSubmissions = communityDataArray.length; + let totalArrivalsBeforeCuts = 0; + let totalArrivalsDuringCuts = 0; + let totalWaitTimeDifference = 0; + + communityDataArray.forEach((data) => { + totalArrivalsBeforeCuts += data.arrivals_before_cuts; + totalArrivalsDuringCuts += data.arrivals_during_cuts; + totalWaitTimeDifference += data.expected_wait_time_difference; + }); + + const averagePercentChange = totalSubmissions > 0 ? (totalArrivalsDuringCuts / totalArrivalsBeforeCuts) - 1 : 0; + + const returnData = + { + totalSubmissions, + totalArrivalsBeforeCuts, + totalArrivalsDuringCuts, + averagePercentChange, + totalWaitTimeDifference + }; + + return returnData; +}; diff --git a/js/utils.js b/js/utils.js new file mode 100644 index 00000000..623df6e9 --- /dev/null +++ b/js/utils.js @@ -0,0 +1,55 @@ +/** +* Read geojson file +* +* @param {string} filename - The name of the geojson file to load +* @return {object} The FeatureCollection as loaded from the data file +*/ +export async function getGeojsonCollection(filename) { + const resp = await fetch(`data/${filename}`); + const data = await resp.json(); + return data; +} + +/** +* Find all unique values of a specified property in GeoJSON features. +* +* @param {object} geojsonData The GeoJSON object (FeatureCollection). +* @param {string} propertyName The name of the property to check. +* @returns {Array} An array of unique values. +*/ +export function getUniquePropertyValues(geojsonData, propertyName) { + if (!geojsonData || !Array.isArray(geojsonData.features)) { + throw new Error("Invalid GeoJSON FeatureCollection"); + } + + const values = geojsonData.features + .map(feature => feature.properties?.[propertyName]) + .filter(value => value !== undefined && value !== null); + + return [...new Set(values)]; +} + +/** +* Filter a geoJSON FeatureCollection so that only features with specified property value are included +* +* @param {object} geojsonData - The GeoJSON FeatureCollection +* @param {string} propertyName - The property name to filter on +* @param {any} propertyValue - The property value to match +* @returns {object} - A new GeoJSON FeatureCollection with filtered features +*/ +export function filterGeojsonByProperty(geojsonData, propertyName, propertyValue) { + if (!geojsonData || !Array.isArray(geojsonData.features)) { + throw new Error("Invalid GeoJSON FeatureCollection"); + } + + const filteredFeatures = geojsonData.features.filter(feature => + feature.properties?.[propertyName] === propertyValue + ); + + const returnData = { + type: "FeatureCollection", + features: filteredFeatures + }; + + return returnData; +} diff --git a/r/export_to_geojson.R b/r/export_to_geojson.R new file mode 100644 index 00000000..921b4175 --- /dev/null +++ b/r/export_to_geojson.R @@ -0,0 +1,162 @@ +# Purpose ------------------------------------------------------------------------------------- +# Read SEPTA's GTFS feeds pre- and post-cuts and export needed subsets of data as geojson files. + +# Preliminaries ------------------------------------------------------------------------------- + +library(tidyverse) +library(tidylog) +library(janitor) +library(tidytransit) +library(sf) + +# Set path relative to location of this script +setwd(dirname(this.path::this.dir())) + +data_dir <- str_c(getwd(), "/data") + +# Arrivals data ----------------------------------------------------------------------------- + +arrivals_pre_raw <- data.table::fread(str_c(data_dir, "raw", "arrivals_data", "stop_observations_week1.csv", sep = "/")) %>% + clean_names() + +arrivals_post_raw <- data.table::fread(str_c(data_dir, "raw", "arrivals_data", "stop_observations_week2.csv", sep = "/")) %>% + clean_names() + +# Check which route/stop combinations are present in both data +stops_pre <- arrivals_pre_raw %>% + distinct(route_id, stop_id, stop_name) + +stops_post <- arrivals_post_raw %>% + distinct(route_id, stop_id, stop_name) + +stops_good <- inner_join(stops_pre, stops_post) + +# inner_join: added no columns +# > rows only in stops_pre (1,430) +# > rows only in stops_post ( 154) +# > matched rows 6,809 +# > ======= +# > rows total 6,809 + +# Get arrival times for routes/stops which were served both pre- and post-cuts +arrivals_filtered_pre <- arrivals_pre_raw %>% + # filter: removed 161,960 rows (7%), 2,069,834 rows remaining + filter(route_id %in% stops_good$route_id) %>% + # filter: removed 28,827 rows (1%), 2,041,007 rows remaining + filter(stop_id %in% stops_good$stop_id) %>% + mutate(period = "before_cuts") + +arrivals_filtered_post <- arrivals_post_raw %>% + # filter: no rows removed + filter(route_id %in% stops_good$route_id) %>% + # filter: removed 20,020 rows (1%), 1,830,552 rows remaining + filter(stop_id %in% stops_good$stop_id) %>% + mutate(period = "during_cuts") + +arrivals_ready <- arrivals_filtered_pre %>% + bind_rows(arrivals_filtered_post) %>% + mutate(day_of_week = wday(service_date, label = TRUE)) %>% + mutate(week_period = if_else(day_of_week %in% c("Sat", "Sun"), "Weekend", "Weekday")) %>% + mutate(arrival_hour = as.numeric(str_extract(observed_time, "^\\d{2}"))) %>% + mutate(time_of_day = + case_when(arrival_hour <= 6 ~ "Early morning (00:00 to 6:59)", + arrival_hour <= 9 ~ "Morning rush (07:00 to 9:59)", + arrival_hour <= 14 ~ "Midday (10:00 to 14:59)", + arrival_hour <= 18 ~ "Afternoon rush (15:00 to 18:59)", + arrival_hour <= 23 ~ "Evening (19:00 to 23:59)")) %>% + mutate(time_of_day = + fct_relevel(time_of_day, + "Early morning (00:00 to 6:59)", + "Morning rush (07:00 to 9:59)", + "Midday (10:00 to 14:59)", + "Afternoon rush (15:00 to 18:59)", + "Evening (19:00 to 23:59)")) + +# Directions often have multiple headsigns; pick the most common per direction +directions <- arrivals_ready %>% + count(route_id, direction_id, trip_headsign) %>% + arrange(route_id, direction_id, desc(n)) %>% + slice_max(order_by = n, by = c(route_id, direction_id), with_ties = FALSE) + +# Clean order of routes +routes_order <- gtools::mixedsort(unique(arrivals_ready$route_id)) + +# Aggregate number of arrivals +arrivals_aggregated <- arrivals_ready %>% + mutate(stop_id = as.character(stop_id)) %>% + group_by(period, route_id, direction_id, week_period, stop_id, stop_name, gtfs_stop_sequence, time_of_day) %>% + summarize(arrivals = n()) %>% + ungroup() %>% + # Join prototypical direction headsigns + left_join(directions %>% select(-n)) %>% + # Normalize counts to be arrivals per day + mutate(arrivals = if_else(week_period == "Weekday", arrivals / 5, arrivals / 2)) %>% + # Pivot periods to top + pivot_wider(names_from = period, + values_from = arrivals, + names_prefix = "arrivals_", + values_fill = 0) %>% + # Remove rows with no arrivals + # filter: removed 1,759 rows (3%), 64,820 rows remaining + filter(arrivals_during_cuts > 0 & arrivals_before_cuts > 0) %>% + # Percentage loss + mutate(percent_change = (arrivals_during_cuts / arrivals_before_cuts) - 1) %>% + mutate(percent_change = round(percent_change, 2)) %>% + # Average waiting time + mutate(time_numerator = + case_when(time_of_day == "Early morning (00:00 to 6:59)" ~ 420, + time_of_day == "Morning rush (07:00 to 9:59)" ~ 180, + time_of_day == "Midday (10:00 to 14:59)" ~ 300, + time_of_day == "Afternoon rush (15:00 to 18:59)" ~ 240, + time_of_day == "Evening (19:00 to 23:59)" ~ 300)) %>% + mutate(headway_before_cuts = time_numerator / arrivals_before_cuts) %>% + mutate(headway_during_cuts = time_numerator / arrivals_during_cuts) %>% + mutate(expected_wait_time_difference = (headway_during_cuts - headway_before_cuts) / 2) %>% + mutate(expected_wait_time_difference = round(expected_wait_time_difference, 1)) %>% + # Order and select + mutate(route_id = fct_relevel(route_id, routes_order)) %>% + arrange(route_id, week_period, time_of_day, trip_headsign, gtfs_stop_sequence) %>% + select(route_id, week_period, time_of_day, trip_headsign, stop_id, stop_name, + arrivals_before_cuts, arrivals_during_cuts, + percent_change, expected_wait_time_difference) + +# Post-cut network data -------------------------------------------------------------------------------- + +network_raw <- read_gtfs(str_c(data_dir, "raw", "septa_cut", "google_bus.zip", sep = "/")) + +# Read shapes and stops as sf objects for mapping +network_sf <- gtfs_as_sf(network_raw) + +# Route linestrings +network_geometry <- network_sf[["shapes"]] + +network_trips <- network_raw[["trips"]] + +network_stop_times <- network_raw[["stop_times"]] + +network_stops <- network_raw[["stops"]] %>% + select(stop_id, stop_lat, stop_lon) %>% + st_as_sf(coords = c("stop_lon", "stop_lat"), crs = "EPSG:4326") + +# Collate data -------------------------------------------------------------------------------- + +# Associate linestrings with route numbers +network_routes_mapping <- network_geometry %>% + left_join(network_trips %>% distinct(shape_id, route_id)) %>% + # Only routes in arrivals data + filter(route_id %in% arrivals_aggregated$route_id) + +# Arrivals data with stop coords +arrivals_export <- inner_join(arrivals_aggregated, network_stops) + +# Export -------------------------------------------------------------------------------------- + +st_write(network_routes_mapping, + str_c(data_dir, "/septa_routes.json"), + driver = "GeoJSON") + +st_write(arrivals_export, + str_c(data_dir, "/arrivals_and_stops.json"), + driver = "GeoJSON") + +