GIS-Based Urban Heat Island Analysis Tool

Design an advanced GIS platform that analyzes urban heat islands (UHIs) using multi-source geospatial data, remote sensing imagery, and IoT temperature readings. The solution should follow established geospatial data standards (e.g., OGC standards, ISO 19157 for data quality) and provide actionable heat mitigation strategies.

Urban heat islands pose significant health and energy challenges, particularly in rapidly growing cities. Understanding spatial and temporal heat distribution patterns is key to crafting effective mitigation strategies. This project will incorporate data from multiple sources—such as Landsat satellite imagery, IoT-enabled temperature sensors, and municipal land-use datasets—and process it using industry-standard GIS frameworks (e.g., QGIS, ArcGIS). By adhering to open geospatial data standards and providing standardized output formats (GeoTIFF, shapefiles), the platform will facilitate integration with city planning tools.

The project will produce an open-source GIS tool that maps UHIs with high spatial and temporal resolution. It will provide detailed analysis and recommendations for urban planners, helping reduce heat exposure and improve city livability. By leveraging OGC-compliant data formats and publishing all algorithms and workflows, the tool will ensure reproducibility, scalability, and broad adoption.

Target Outcomes:

• A GIS platform that meets OGC and ISO standards for geospatial data quality.
• Demonstrated case studies showing the effectiveness of recommended heat mitigation strategies.
• Open-source code, data workflows, and documentation.

10 Steps

• Collect and preprocess high-resolution thermal data from satellite imagery (e.g., Landsat, Sentinel-3), ensuring data alignment and quality using standard geospatial processing libraries (e.g., GDAL)
• Develop geospatial workflows to merge satellite data with on-the-ground IoT temperature readings, standardizing all datasets according to ISO 19115 metadata standards
• Build and validate machine learning models (e.g., Random Forests, Gradient Boosting) to predict urban heat island intensity and spatial distribution patterns
• Implement advanced geospatial analytics, including topographic and vegetation indices (e.g., NDVI, NDBI) to assess their correlation with urban heat island effects
• Integrate the processed data into a scalable geospatial database (e.g., PostgreSQL/PostGIS), ensuring optimized spatial indexing for rapid query responses
• Develop a user-friendly GIS interface using industry-standard mapping APIs (e.g., OpenLayers, Leaflet) to visualize heat maps, trend analysis, and intervention scenarios
• Perform comprehensive field validation, using mobile temperature sensors and heat sensors to ground-truth model predictions and refine algorithms
• Conduct scenario-based analyses to model the impact of proposed mitigation strategies (e.g., green roofs, reflective surfaces) on heat distribution patterns
• Build automated reporting tools that generate detailed urban heat island assessments, including environmental impact metrics and economic cost-benefit analyses
• Publish all source code, model configurations, and data processing scripts in an open-source repository, along with an extensive user manual and deployment guide for urban planners and researchers