Extreme heat poses significant environmental and health risks. These risks often disproportionately affect marginalized and disenfranchised communities. Neighborhood-scale planning is essential for addressing climate change through adaptation strategies. Currently, there is a lack of long-term weather data at the neighborhood level, limiting the ability to analyze localized weather trends and compare variations across areas. This gap persists due to the challenges of collecting fine-scale local data, including significant time demands, limited resources, and potential privacy concerns. To bridge this gap, this project employs the Urban Weather Generator model to generate neighborhood-scale temperature and relative humidity data for the city of Houston. The neighborhood size is defined using a 500-by-500-m grid. Based on the comprehensive analysis of heat stress variation across these neighborhoods, we identify areas with notably higher or lower heat stress duration. This dataset is intended to inform targeted interventions in microclimate design and personal-level heat adaptation.
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