Hacklytics 2025 Entry for GrowthFactorAI Challenge

DevPost Submission

This project estimates the total impressions a storefront receives based on its visibility from nearby road segments and associated traffic data. Using provided traffic volume data and spatial analysis techniques, this solution generates a visibility score that can guide commercial realtors in evaluating prime storefront locations.

• Estimates visibility scores using spatial data and road geometry.
• Integrates traffic volume, direction of travel, and road classifications to improve accuracy.
• Computes total impressions by combining visibility scores with traffic data.
• Provides scalable functionality with OpenStreetMap (OSM) and Google Maps API integration.

The solution follows these steps:

1. Data Preparation:

   • Load and preprocess the provided traffic dataset.
   • Extract key features like trips_volume, match_dir, segment_length_m, and geom.

2. Visibility Estimation:

   • Identify road segments within a defined visibility radius (e.g., 50-100 meters).
   • Use spatial analysis tools (e.g., GeoPandas, Shapely) to calculate visibility scores.
   • Account for factors like distance, angle of view, and potential obstructions.

3. Impressions Calculation:

   • For each store location, combine visibility scores with corresponding traffic volume.
   • Sum impressions from all relevant segments to compute the total score.

4. Evaluation & Tuning:

   • Adjust parameters for optimal results.
   • Validate against sample storefront locations.

• Traffic Data: Provided dataset with road segments, traffic volume, and directional details.
• Geospatial Data: OpenStreetMap (OSM) API for road geometry and storefront locations.
• Satellite Imagery: (Optional) Google Maps API for enhanced visibility estimation.

• Python 3.x
• Geopandas
• Shapely
• Folium (for visualization)
• Requests (for API integration)
• Pandas

The solution outputs a visibility score for each storefront, which reflects its estimated total impressions based on nearby traffic patterns and visibility conditions.

• Integrate pedestrian traffic data for improved accuracy.
• Incorporate seasonal and time-of-day effects to refine impressions estimates.
• Develop a user interface for intuitive data visualization.

• Hyeokjin Jin
• Lucas Chen
• Krystal Wu