Abstract
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. In this paper, we analyze mobility patterns of more than 800,000 anonymized mobile devices in Houston, Texas, representing approximately 35% of the local population, in response to Hurricane Harvey in 2017. Using changes in mobility behavior before, during, and after the disaster, we empirically define community resilience capacity as a function of the magnitude of impact and time-to-recovery. Overall, we find clear socioeconomic and racial disparities in resilience capacity and evacuation patterns. Our work provides new insight into the behavioral response to disasters and provides the basis for data-driven public sector decisions that prioritize the equitable allocation of resources to vulnerable neighborhoods.
Highlights
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
After data preprocessing in order to ensure data confidentiality, we focus exclusively on data points representing smartphone activity falling within the boundaries of Harris County for the 2-month period between August 1st, 2017 and September 30th, 2017
Mobility patterns during the hurricane are clustered into distinct neighborhood groups, demonstrating that predominantly low-income and minority neighborhoods are most impacted by the hurricane while least able to evacuate to safer areas outside of the impact area
Summary
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. In an effort to capture the spatiotemporal dynamics of event response, some studies have used social media data like Facebook or Twitter to understand disaster-related online behavior changes and detect crisis regions during natural disaster events[15,42,43,44,45,46,47] While research using these digital traces can help to measure the overall impact of a disaster, social media data are characterized by representativeness bias and often require aggregate spatial resolutions, such as the county or city scale, to capture sufficient geotagged samples[15,42,43,44,45,46,47]. Despite the increased interest in mobility data by scholars in disaster management fields, limited attention has been paid to neighborhood-level evacuation and recovery patterns at scale and the disparate behavioral responses across communities with divergent socioeconomic and demographic characteristics
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