Harnessing Geospatial Data for Urban Climate Resilience: Insights from a Fine Scale Ambient Temperature Analysis in an Urban Heat Island

Background: Cities face increasing risks from the urban heat island effect. Our ability to assess fine-scale differentials in urban heat is limited to the sparse spatial density of temperature monitoring. This study aims to assess ambient temperature variations at high resolution within an urban heat island in the northeastern United States, focusing on spatial disparities in heat exposure and implications for local climate planning and policy. Methods: The authors carried out a community-based research project in which 60–80 ambient temperature sensors were deployed in the city of Chelsea, Massachusetts, from 2015 to 2023 and compared to the National Weather Service temperature data from Logan International Airport. Data were analyzed for warm seasons, hot weeks, and heat waves. Warm season, yearly, and day–night ambient heat was analyzed and mapped using geospatial regression and kriging, incorporating natural/built environment variables. Results: Local sensors were up to 10°F (5.6°C) higher than National Weather Service readings during hot weeks with heat waves. Within Chelsea, spatial analyses identified approximately 5°F or 2.8°C (average) and 9°F or 5.0°C (maximum) higher temperatures in hot spots compared to cooler areas. Neighbourhoods with higher temperatures were in the more residential, urbanized areas of Chelsea. Day–night mapping further highlighted areas with prolonged heat exposure, crucial for health implications. Conclusion: This study highlights the value of local fine spatial ambient temperature data in urban climate planning. Geospatial modelling of outputs can assist policymakers in developing climate interventions. The research supports targeted efforts to mitigate urban heat, underscoring the importance of integrating environmental data with local insights for effective and equitable heat management strategies.