RouteTO

RouteTO: The map that guides you safely, not just quickly. Helps pedestrians choose safer routes using 441k+ crime reports across GTA

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Inspiration Walking in downtown Toronto can often feel unsafe, especially as crime rates continue to rise. Many of us have experienced moments of vulnerability — being followed, harassed, or simply feeling uneasy in certain areas. We built RouteTO to address this real problem: ensuring pedestrians can reach their destinations with confidence and peace of mind.

What it does RouteTO is an intelligent, safety-first route planning web application. Unlike traditional navigation apps that prioritize speed, RouteTO integrates over 441,000+ crime records from the Toronto Police Service to:

  1. Visualize crime data on an interactive map through markers, heatmaps, and clustering 2.Filter incidents by crime type (Assault, Auto Theft, Robbery, etc.) and time range (14 days, month, 6 months, year)
  2. Compare multiple routes with metrics like distance, duration, incident density, and an aggregated safety score
  3. Provide scenario-based personalization, such as avoiding assault-heavy areas at night or theft-prone zones when parking

Challenges we ran into: Processing and optimizing large-scale geospatial datasets (~441k records) efficiently in real time Implementing viewport-based data loading and bounding box queries to prevent performance bottlenecks Coordinating development across a distributed team (merge conflicts, version control workflows)

Accomplishments that we're proud of: Delivering a fully functional safety-first navigation system that’s immediately applicable in daily life Implementing dynamic route analysis with crime clustering and visual risk scoring Designing a unique product that combines open data, mapping, and routing in a novel way

What we learned: Leaflet.js and its ecosystem (clustering, heatmaps, GeoJSON integration) OSRM for route optimization and buffer-based spatial analysis Building data pipelines: CSV parsing → spatial indexing → temporal filtering → GeoJSON conversion → frontend rendering How to balance data processing performance with frontend interactivity

What's next: Expand to mobile app development for broader accessibility Scale the platform to other cities with similar open crime data Build a crime data library for longitudinal safety trend analysis Add community reporting features for real-time incident updates Integrate real-time alerts and notifications for nearby crimes Explore machine learning models for predictive crime risk analysis

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