Mobile phone data reveals the importance of pre-disaster inter-city social ties for recovery after Hurricane Maria

Abstract

Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the recovery process of cities, where inter-city dependencies and intra-city coupling of social and physical systems may affect the outcomes in unforeseen ways. Despite the large implications of understanding the recovery processes of cities after disasters for many domains including critical services, disaster management, and public health, little work have been performed to unravel this complexity. Rather, works are limited to analyzing and modeling cities as independent entities, and have largely neglected the effect that inter-city connectivity may have on the recovery of each city. Large scale mobility data (e.g. mobile phone data, social media data) have enabled us to observe human mobility patterns within and across cities with high spatial and temporal granularity. In this paper, we investigate how inter-city dependencies in both physical as well as social forms contribute to the recovery performances of cities after disasters, through a case study of the population recovery patterns of 78 Puerto Rican counties after Hurricane Maria using mobile phone location data. Various network metrics are used to quantify the types of inter-city dependencies that play an important role for effective post-disaster recovery. We find that inter-city social connectivity, which is measured by pre-disaster mobility patterns, is crucial for quicker recovery after Hurricane Maria. More specifically, counties that had more influx and outflux of people prior to the hurricane, were able to recover faster. Our findings highlight the importance of fostering the social connectivity between cities to prepare effectively for future disasters. This paper introduces a new perspective in the community resilience literature, where we take into account the inter-city dependencies in the recovery process rather than analyzing each community as independent entities.

Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach

“…An increase in hospital visits reflect the large number of injuries and casualties caused by the flooding and severe winds caused by the hurricane. Significant increase in visits to hotels in San Juan reflect the large number of residents who evacuated from the rural areas in Puerto Rico to the capital city, which agrees with previous studies that observe the influx of population movements in San Juan from the suburban and rural areas of the island [44]. Minor details are captured in the figures as well, for example, how weekly fluctuations are estimated more vividly in universities (students do not attend classes on weekends) compared to other business types, and also how the impacts of Hurricane Irma, although minimal compared to Hurricane Maria, are captured in the time series data.…”

“…Second, we see a significant increase in gasoline stations in metropolitan areas (green) after Hurricane Maria. This reflects the high travel demand from the rural areas towards the metropolitan areas in the island due to evacuation mobility [44]. Third, in some business categories such as hospitals and hotels, we see an increase in visits after the hurricanes compared to before, especially in the San Juan region (blue).…”

High-resolution human mobility data reveal race and wealth disparities in disaster evacuation patterns

“…Recently, GPS-enabled devices which generate unprecedented amounts of mobility data have enabled more nuanced studies of behaviour (Alessandretti et al, 2018(Alessandretti et al, , 2017González et al, 2008;Hasan et al, 2013), including in large-scale disasters and extreme events (Bagrow et al, 2011;Bengtsson et al, 2011), allowing the construction of probabilistic models (Song et al, 2014(Song et al, , 2013, and highlighting the importance of social networks (Finch et al, 2010;Metaxa-Kakavouli et al, 2018;Yabe et al, 2019) and socioeconomic factors . By comparing multiple disasters, certain typical behaviours have been established, including an exponential return rate .…”

Wind disasters adaptation in cities in a changing climate: A systematic review

“…Data derived from social media such as Twitter is used for analyzing mobility patterns [171,172,174] and for evaluating economic damages [175]. Research contents of articles under this topic are closely linked to those under topic 7 which discussed the reconstruction of cities [176][177][178] and recovery of markets [179] since respondence can often occur after disasters.…”