School-Zone Sign Evidence Explorer

A Python and Leaflet workflow that connects historical sign detections with school-zone maps to identify streets needing field review.

Why it matters

A mapped school zone does not show whether its signs are current. Connecting sign observations to streets helps focus field inspections where evidence is outdated, missing, or inconsistent.

My contribution

I connected school-zone GIS data, existing Mapillary sign detections, and ordinance records in a Python workflow. I kept each detected sign separate from its street associations to avoid double counting, then built a searchable map and reports for review.

Result

The assessment covered 214 school-zone street segments and prioritized 12 for inspection: seven had conflicting speed detections, and five had only regular road-speed sign evidence. These are review priorities, not confirmed violations.

Tools

Python
ArcGIS REST
Mapillary API
Leaflet
GeoJSON and shapefiles

All sign detections predate September 2023. They do not establish current sign conditions or legal compliance. Mapillary supplied the detections.

Project gallery

Figures from the September 14, 2026 assessment. Map data: Mapillary (CC BY-SA 4.0), City of Lewisville GIS, and Esri. 2 images. Select an image to view it full size.

Interactive evidence map of historical school-zone sign candidates, school-zone segments, and photo evidence markers at sign-feature locations in Lewisville, with layer and speed filters. September 14, 2026 assessment; map data from Mapillary (CC BY-SA 4.0), City of Lewisville GIS, and Esri.

Report figure: historical sign candidates and school-zone segments around Old Town Lewisville, a local view rather than the full assessment extent. Basemap: Esri; candidate data: Mapillary; school zones: City of Lewisville GIS.

Approach

The workflow links mapped streets, ordinance records, and historical sign observations. It counts each detected candidate once, keeps observation dates visible, and carries conflicting or missing evidence into the review queue.

The explorer supports street search, evidence filters, map layers, and accessible tables. A standalone copy includes the evidence and photos for offline review.

School-zone geometry and Mapillary evidence are collected, normalized, and reconciled with an ordinance table before feeding a review queue, maps, and reports — A simplified data flow. The diagram contains no source records or service addresses. Scroll horizontally to explore the diagram.

What the findings mean

On 173 segments, older sign observations showed the expected speed value. Another 21 needed manual matching between mapped streets and ordinance records. The remaining categories identify conflicting speeds or gaps in the available sign evidence; the chart shows where field review should start.

Of 214 segments, 173 show the expected value only in stale evidence, 21 need manual legal and GIS matching, and 12 are priority 1: 7 with a conflicting numeric candidate and 5 with regular road-limit evidence only — Segment assessment from the September 14, 2026 run. Priority ranks where to look first; it is not a finding of non-compliance. Scroll horizontally to explore the diagram.

Technical details

Professional · Geospatial Software Development and Data Science · September 14, 2026 assessment

School-zone maps, ordinance records, and street-level imagery need to be connected to identify gaps and conflicting sign evidence.

Connect school-zone street geometry with nearby sign detections and imagery, keeping unique candidates separate from their street links.
Compare detected speed values with configured ordinance records and retain observation dates, conflicts, and missing evidence.
Use the same assessment to produce field-review priorities, a Leaflet map, GIS exports, and reports.

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