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Boyeong Hong – Using Big Data to Quantify Neighborhood Disparities in Urban Crisis Risks
Enrico Ubaldi – Using Mobilkit for Disaster Risk Management
Andrea Garcia Tapia (Community Manager, Disaster Mobility Data Network)
While conceptual definitions provide a foundation for the study of disasters and their impacts, the challenge for researchers and practitioners alike has been to develop objective and rigorous measures of resilience that are generalizable and scalable, taking into account spatiotemporal dynamics in the response and recovery of localized communities. This research analyzes mobility patterns of more than 1M anonymized mobile devices to quantify neighborhood gaps in urban crisis risks. This project measures exposure density at high spatial and temporal resolution to understand neighborhood disparities in transmission risk of COVID-19. By integrating geolocation data and granular land-use information, this project establishes both the extent of activity in a particular neighborhood and the nature of that activity across residential, nonresidential, and outdoor activities. Then we analyze the differential behavioral response to social-distancing policies based on local risk factors, built-environment characteristics, and socioeconomic inequality. The results highlight the significant disparities in health outcomes for racial and ethnic minorities and lower-income households. Exposure density provides an additional metric to further explain and understand the disparate impact of COVID-19 on vulnerable communities.