EVALUATION OF MOBILITY-INDUCED HEAT RISK USING NUMERICAL WEATHER SIMULATION AND QUASI-REAL TIME CONGESTION DATA

Abstract

 Heat-risk evaluations are needed by societies for adapting to and mitigating the social/health impacts attributed to climate change and urbanization. Meanwhile, big data and numerical models are advancing.

 In this study, we propose an integrated approach to utilize both big data and existing models for estimating heat-related risk. By combining products of a 3D-City irradiance model and Google big data services, a geospatial framework for mapping heat-related risks(Hazard×Exposure×Vulnerability) was developed .Hazard and exposure(individual HR factor) were estimated using thresholds of wetbulb globe temperature(WBGT) and mobility from frequented locations to nearest station exits; Vulnerability(HR probability factor) was estimated using popular times of frequented locations. Through this framework, multiple perspectives for understanding heat risks may be observed at various districts. For example, ATMs were found to have high heat risks mainly because of its high HR probability factor.