Shade Finder
A Geospatial System for Enhancing Transit Comfort Through Real-Time Shade Mapping
Project Overview
Shade Finder is an applied geospatial intelligence system developed to enhance pedestrian and transit user comfort in Philadelphia by identifying shaded locations near public transit stops. Leveraging high-resolution LiDAR datasets, solar trajectory modeling, and real-time transit integration, the platform delivers dynamic shade forecasts throughout the day. This system addresses a critical but often overlooked dimension of sustainable mobility: mitigating urban heat exposure during first-mile/last-mile transit connections.
Objective
To create a real-time, user-friendly geospatial tool that assists commuters, pedestrians, and cyclists in locating shaded waiting and resting areas, improving the livability and usability of Philadelphia’s public transportation ecosystem.
Key Features and Innovations
Dynamic Shade Mapping
Real-time calculation of shaded zones based on the interaction between urban form (building heights and obstructions) and the sun’s azimuth and altitude, varying hour-by-hour.Transit System Integration
Seamless incorporation of SEPTA’s live transit stop data to align shade recommendations with actual public transportation access points.Temporal Shade Forecasting
Predictive modeling to estimate future shaded areas across different times of the day, enabling users to plan routes and waiting times adaptively.Multi-Modal Support
Targeted utility not just for transit riders, but also for pedestrians and cyclists navigating the city during periods of intense heat.
LiDAR Data Processing
Extracted and processed high-fidelity elevation and building footprint data from PASDA to construct accurate 3D models of the urban environment.
Ray-Tracing for Shadow Projection
Developed a lightweight ray-tracing engine to simulate shadow casting by urban structures based on solar angles, enabling precise real-time and forecasted shade delineation.
Transit Data Integration
Connected to SEPTA’s open API to pull live information about nearby bus, trolley, and subway stops, ensuring relevance to real-world commuter needs.
Sun Position Estimation
Deployed NOAA’s Solar Position Calculator with the SunCalc algorithm to model the sun’s trajectory and compute real-time solar angles (zenith and azimuth).
System Workflow
Route-Aware Stop Identification
Automatically detect the nearest transit stop along the user’s active route using geospatial proximity analysis.
Buffer-Based Alternative Search
Identify all transit stops within a 10-minute walkable catchment area (approximately 800 meters) using network-based spatial querying.
Environmental Data Retrieval
Pull key environmental comfort variables for each candidate stop:
Sky View Factor (SVF): Proxy for vertical exposure to the sky, inversely related to shade presence.
Tree Canopy Coverage: Percent of vegetative shading, based on urban forestry spatial layers.
Surface Temperature: Adjusted values accounting for urban heat island intensities.
Shade Score Calculation
Each stop is assigned a Shade Score to quantify overall thermal comfort.SVF (Sky View Factor): Lower values indicate denser shade; thus, 1−SVF1 - SVF1−SVF rewards more enclosed, shaded spaces.
Tree Canopy %: Higher percentages positively impact thermal comfort.
Surface Temperature Adjustment: Penalizes stops located in localized heat islands.
Ranking and Recommendation
Rank all alternative stops based on their computed Shade Scores.
Display the top 2–3 alternative stops that maximize pedestrian thermal comfort while maintaining reasonable walking detours.
Shade Score = (1 - SVF) + (Tree Canopy %) - (Surface Temp Adjustment)
Reflections
This ranking system embodies a user-centric mobility design approach that extends beyond conventional routing algorithms to include environmental equity and thermal comfort as critical layers of analysis.
By operationalizing complex spatial variables into a simple Shade Score, the platform empowers commuters to make better choices, especially during periods of high urban heat stress.
Future extensions could incorporate real-time weather data, dynamic traffic conditions, and crowdsourced comfort feedback, progressively advancing the vision for climate-adaptive urban mobility tools.
