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A post-disaster recovery process necessitates significant financial and time investment. Previous studies have found the importance of post-disaster spatial recovery heterogeneity, but the recovery heterogeneity has not been extended to the directed recovery relationships despite the significance of sequential recovery plans. Identifying a causal structure between county-level time series data can reveal spatial relationships in the post-disaster recovery process. This study uses a causal discovery method to reveal the spatiotemporal relationships between counties before, during, and after Hurricane Irma in 2017. This study proposes node aggregation methods at different time scales to obtain internally validated causal links. This paper utilizes points of interest data with daily location information from mobile phones and county-level daily nighttime light data. We find intra-regional homogeneity, inter-regional heterogeneity, and a hierarchical structure among urban, suburban, and rural counties based on a network motif analysis. Subsequently, this article suggests county-level post-disaster sequential recovery plans using the causal graph methods. These results help policymakers develop recovery scenarios and estimate the corresponding spatial recovery impacts.

 

We propose a systems model for urban population growth dynamics, disaggregated at the county scale, to explicitly acknowledge inter and intra-city movements. Spatial and temporal heterogeneity of cities are well captured by the model parameters estimated from empirical data for 2005–2019 domestic migration in the U.S. for 46 large cities. Model parameters are narrowly dispersed over time, and migration flows are well-reproduced using time-averaged values. The spatial distribution of population density within cities can be approximated by negative exponential functions, with exponents varying among cities, but invariant over the period considered. The analysis of the rank-shift dynamics for the 3100+ counties shows that the most and least dense counties have the lowest probability of shifting ranks, as expected for ‘closed’ systems. Using synthetic rank lists of different lengths, we find that counties shift ranks gradually via diffusive dynamics, similar to other complex systems.

 

Abstract In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses ( what if the disaster did not occur? ), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore, hierarchical Bayesian models are used to reveal the effect of business characteristics such as location and category on the long-term resilience of businesses. The study presents a novel and more efficient method to quantify business resilience, which could assist policy makers in disaster preparation and relief processes. 

Despite the rising importance of enhancing community resilience to disasters, our understandings on when, how and why communities are able to recover from such extreme events are limited. Here, we study the macroscopic population recovery patterns in disaster affected regions, by observing human mobility trajectories of over 1.9 million mobile phone users across three countries before, during and after five major disasters. We find that, despite the diversity in socio-economic characteristics among the affected regions and the types of hazards, population recovery trends after significant displacement resemble similar patterns after all five disasters. Moreover, the heterogeneity in initial and long-term displacement rates across communities in the three countries were explained by a set of key common factors, including the community’s median income level, population, housing damage rates and the connectedness to other cities. Such insights discovered from large-scale empirical data could assist policymaking in various disciplines for developing community resilience to disasters. 

Understanding how information use contributes to uncertainties surrounding evacuation decisions is crucial during disasters. While literature increasingly establishes that people consult multiple information sources in disaster situations, little is known about the patterns in which multiple media and personal network sources are combined simultaneously and sequentially across decision‐making phases. We address this gap using survey data collected from households in Jacksonville, Florida affected by 2016's Hurricane Matthew. Results direct attention to perceived consistency of information as a key predictor of uncertainty regarding hurricane impact and evacuation logistics. Frequently utilizing National Weather Service, national and local TV channels, and personal network contacts contributed to higher perceived consistency of information, while the use of other local and online sources was associated with lower perceived consistency. Furthermore, combining a larger number of media and official sources predicted higher levels of perceived information consistency. One's perception of information amount did not significantly explain uncertainty. This study contributes to the theorizing of individuals' information environment from the perspective of media and network multiplexity and provides practical implications regarding the need of information coordination for improved evacuation decision‐making.