RouteTO

** Crime-Aware Dynamic Route Planner for Toronto ** Ignition Hacks V5 Winner - 3rd Place

Features

Interactive Crime Mapping

• Real-time Crime Visualization: Display Toronto crime incidents on an interactive map
• Multiple View Modes: Switch between individual markers, crime clusters, and heatmap density views
• Crime Type Filtering: Filter by specific crime categories (Assault, Auto Theft, Break and Enter, Robbery, Theft Over)
• Temporal Filtering: View crimes from specific time periods (Last 14 Days, Month, 6 Months, Year)
• Dynamic Loading: Crime data loads based on current map viewport for optimal performance

Smart Route Analysis

• Safety-First Routing: Analyze multiple route options and identify the safest path
• Crime Risk Assessment: Calculate crime density and safety ratings for each route
• Visual Route Comparison: Color-coded routes (Green: Low Crime, Yellow: Medium, Red: High)
• Detailed Route Metrics: Distance, duration, crime density, and nearby incident counts

Location Services

• Address Search: Search for Toronto addresses using OpenStreetMap geocoding
• Current Location: Get user's current location using browser geolocation API
• Quick Locations: Fast navigation to popular Toronto destinations (CN Tower, Downtown, Airport)
• Smart Positioning: Automatically position map view under control panels

User Interface

• Responsive Design: Optimized for desktop and mobile devices
• Draggable Controls: Movable view mode controls for customized layout
• Navigation Bar: Clean top navigation with Home and Map sections
• Filter Indicators: Visual feedback showing active crime type and date filters

CSV Download Process

Due to GitHub's 100MB file size limit, the crime data CSV must be downloaded separately:

1. Visit Toronto Open Data Portal:

   • Navigate to: https://open.toronto.ca/dataset/major-crime-indicators/
   • Look for "Major Crime Indicators" dataset

2. Download the CSV File:

   • Click on the CSV download link (typically ~200-500MB)
   • File format: Major_Crime_Indicators_Open_Data_[ID].csv

3. Place in Data Directory:

   RouteTO/
   └── data/
       └── Major_Crime_Indicators_Open_Data_-3805566126367379926.csv
   

4. Alternative Data Sources:

   • Any Toronto crime CSV with required columns will work
   • Minimum required fields: LAT_WGS84, LONG_WGS84, MCI_CATEGORY, OCC_DATE

Performance Features

• Async Processing: Non-blocking I/O for concurrent requests
• Response Caching: HTTP cache headers for map data
• Data Pagination: Configurable result limits
• Bounding Box Optimization: Efficient spatial queries

Data Processing Pipeline

1. Request Validation: Pydantic models ensure data integrity
2. Spatial Filtering: Pandas operations for bounding box queries
3. Crime Analysis: NumPy calculations for risk assessment
4. Route Optimization: OSRM integration for path finding
5. Response Formatting: GeoJSON serialization for frontend

Installation & Setup

Prerequisites

• Node.js 16+ and npm
• Python 3.8+ and pip
• Git for version control

Frontend Setup

cd frontend
npm install
npm run dev

Backend Setup

cd backend
pip install -r requirements.txt
python main.py

Data Setup

1. Download Toronto crime CSV (see Data Setup section above)
2. Place in data/ directory
3. Backend will automatically load and process the data

Future Enhancements

Planned Features

• Real-time Crime Alerts: Push notifications for nearby incidents
• Historical Trends: Long-term crime pattern analysis
• Community Integration: User-submitted safety reports
• Mobile App: Native iOS/Android applications
• Advanced Routing: Multi-modal transportation options

Technical Improvements

• Machine Learning: Predictive crime modeling
• WebSocket Support: Real-time data updates
• Offline Capabilities: Progressive Web App features
• Advanced Analytics: Crime trend visualization

Links

• Devpost Submission: https://devpost.com/software/rainbolt-ai

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License

Proprietary. Built for Hack the Valley X 2025.