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Abstract
Extreme heat events are becoming more frequent and intense, and pose a growing danger to urban residents. Understanding urban heat dynamics and thermal exposure is critical for identifying and mitigating heat hazards. Most studies of urban heat dynamics use satellite-derived or weather station data that are limited in temporal and spatial resolutions. Also current heat vulnerability indices provide insight into heat sensitivities within given communities but do not account for the dynamic nature of human movement as people travel for different activities. Improving urban sustainability requires new ways to capture hyperlocal ambient air temperature (AAT) information and varying heat exposure within complex built environments. The novel paradigm of drive-by-sensing offers great potential to “move the dial” in capturing the spatiotemporal dynamics of urban heat. Then for human thermal exposure studies, the generated human movement pattern from large-scale, anonymized smartphone location data was incorporated in the dynamic thermal exposure index. This study demonstrates the broader application of temporally dense hyperlocal AAT data obtained using a novel drive-by-sensing framework. Using 4 million data points collected between May-December 2018 and 2019, we examine the thermal complexities of a mid-sized city, including the spatio-temporal dynamics of hotspots and areas of extreme heat exposure. Firstly, these novel high spatiotemporal datasets reveal widely different heat profiles of the city under different weather conditions and times of the day. The temperature between hot and cold spots within the city at a given time can be as high as 12oC. We also found that some hotspots are consistent in space and time while others vary. Secondly, we used machine learning models to quantify the relationship between hyperlocal AAT and biophysical parameters to predict temperature all over the city. Lastly, high-temperature spots associated with a large volume of foot traffic are successfully identified through the proposed D ynamic T hermal Ex posure index(DTEx). We observed the hottest spots at shopping plazas but not specifically in the urban center. This novel approach for monitoring, analyzing and interpretating urban thermal conditions can be scaled to other cities and used by governmental entities to make our cities more heat resilient and sustainable.