I mapped almost every USA traffic death in the 21st century

Original Article ↗ Hacker News Discussion ↗

An interactive map that plots nearly every U.S. traffic death since 2001, built from the federal FARS database, has impressed readers while immediately buckling under heavy traffic. Commenters probe data quality and missing cases, suggest technical improvements like vector tiles, filters and heatmaps, and share ways to independently query or download the raw crash data. The project also catalyzes a much broader debate over American road design, speed, “stroads,” traffic calming, enforcement, and the trade‑off between driver convenience and the country’s unusually high rate of road fatalities.

Data source & scope

  • Map uses NHTSA’s Fatality Analysis Reporting System (FARS), aggregated from state and local law-enforcement crash reports.
  • Coverage is 2001‑01‑01 to 2023‑01‑01; only fatal crashes are included, not all crashes or injuries.
  • Several commenters cross‑checked local incidents: many matched well, some were missing or had incorrect attributes (e.g., ages, seatbelt use, locations).

Site performance & implementation

  • The site was repeatedly “hugged to death” by HN traffic: slow loads, 500s, partial data loading, and mobile issues.
  • Backend is PostgreSQL/PostGIS serving GeoJSON; performance concerns around generating large responses on the fly.
  • Suggestions:
    • Pre‑generate vector tiles (Tippecanoe, Planetiler, FlatGeobuf, MBTiles) and host on S3 or similar.
    • Consider MapLibre/Mapbox instead of pure Leaflet.
    • Move some filtering to the frontend; use expressions instead of regenerating GeoJSON each change.
    • Use caching, CDN (e.g., Cloudflare), and proper Postgres tuning.

Desired features & UI feedback

  • Common requests:
    • Filters (time of day, season, DUI, speeding, pedestrian/cyclist, multi‑vehicle, medical events, vehicle type).
    • Heatmap or density visualization instead of (or in addition to) individual pins.
    • Normalization by traffic volume or population to distinguish “busy” vs “dangerous” roads.
    • High‑level statistics and rankings (dangerous corridors, intersections).
    • Clearer entry into the map (many users didn’t realize the title image is a link).
  • Some users want routing or “risk scores” by road segment; others want a per‑place search to find specific incidents.

Data quality & interpretation

  • Data is shaped by a multi‑stage pipeline (local → state → federal); quality varies widely by jurisdiction.
  • Problems mentioned: inaccurate coordinates, mis‑classified locations (e.g., Manhattan as Flushing), inconsistent cause coding, missing incident types, and non‑standardized narratives.
  • Self‑reported and human‑entered fields (e.g., speeding, substance use, some questionnaires) are especially unreliable.
  • Commenters note that fatality locations do not always align with “highest crash” locations; low‑frequency but severe sites differ from everyday fender‑bender hotspots.

Traffic safety debates

  • Many use the map to argue that US road design is unusually dangerous versus other rich countries, citing:
    • High speeds, wide “stroads,” car‑centric planning, driveways and frequent access points, and lack of traffic calming.
  • Others emphasize trade‑offs:
    • Desire for fast, wide roads and shorter commutes vs. increased fatalities and injuries.
    • Environmental impacts of slower, stop‑and‑go traffic vs. benefits of reduced crashes and mode shift.
  • Strong debate over:
    • Road design vs enforcement (automated speed cameras, tech‑based limits).
    • Metrics: per‑capita deaths vs per‑mile/per‑vehicle vs absolute counts.
    • Feasibility and timescale of redesigning cities, adding transit, or building Dutch‑style bike networks.