A mobile route-intelligence map showing emergency response corridor density, station activity, and demand intensity across an urban street network.

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Emergency Mobility

Emergency Route Intel

Mobile response intelligence for route activity, station coverage, response intensity, and high-frequency emergency vehicle corridors.

Year

2025

Duration

5 months

Location

New York, United States

Surfaces

Stack

Architecture

GCP

Mapping

Mapbox Maps SDK for iOSMapbox Directions APIVector tile layersRoute-density heatmapsCAD/AVL/GPS telemetry

Code

SwiftUISwiftiOS

Gemini on Vertex AIVertex AI Agent BuilderSpatial clusteringRoute anomaly detectionDemand-pattern summarization

Conceptual application work, created with the listed platforms and APIs and tested across the indicated surfaces.

Details

Built as a mobile-first emergency response mapping product for dispatch, planning, and operational review teams that need to understand response movement patterns quickly.

Ingests CAD, AVL, and GPS-style response telemetry through a streaming pipeline before aggregating route activity into geospatial corridor-density layers.

Transforms route telemetry into corridor-density overlays so users can distinguish routine travel paths from high-demand emergency response corridors.

Uses a dark vector basemap and high-contrast route gradients to make critical movement patterns visible in low-light dispatch and field environments.

Supports station-level inspection with selected markers, availability context, live response volume, ETA indicators, and corridor activity tied to a specific response node.

Designed around origin, destination, and corridor analysis so teams can compare where responses begin, where incidents concentrate, and which routes carry the most operational load.

Uses clustering and heatmap layers to keep station density, incident nodes, and high-frequency corridors readable on small mobile screens.

Supports time-window based analysis for comparing peak demand periods, recent response activity, and recurring movement patterns.

Uses AI-assisted spatial interpretation to summarize high-activity corridors, identify emerging coverage gaps, and recommend areas for deeper operational review.