Spatial model for predictive recovery monitoring based on hazard, built environment, and population features and their spillover effects

Abstract

The ability to proactively monitor the trajectory of post-disaster recovery is valuable for resource allocation prioritization. Existing knowledge, however, lacks models and insights for quantifying and proactively monitoring post-disaster community recovery. This study examines models that could predict population activity recovery at the scale of the census block group (CBG). Population activity recovery is measured by using location-based human mobility visitation patterns to essential points-of-interest (POIs) in the context of the 2017 Hurricane Harvey in Harris County, Texas. The study examined the association between the population activity recovery duration and 32 features split into four categories: (1) physical vulnerability and access, (2) hazard exposure and impact, (3) proactive actions and (4) population features. Several types of spatial regression models were evaluated to determine their ability to capture this relationship. The Spatial Durbin Model was identified as the best fit for assessing direct, spillover, and total effects of features on population activity recovery at the CBG level. The results show the extent of physical vulnerability, measured by road network density, prolongs the duration of population activity recovery by a combination of direct and spillover effects. Also, the extent of access to essential facilities, measured based on the number of POIs, shortens the duration of population activity recovery. Correspondingly, the extent of flooding is not a significant feature in explaining the population recovery duration in CBGs. The results show that better preparedness, measured by extent of POIs visitations prior to hurricane landing, is associated with faster population activity recovery. In terms of population attributes, the total number of people, the percentage of minorities, and the percentage of Black and Asian subpopulations are significant features in the model for predicting the duration of population activity recovery. The study outcome offers data-driven insights for understanding the determinants of population activity recovery and provides a new model tool for predictive recovery monitoring based on evaluating the direct, spillover, and total effects of features. These findings can identify areas with slower or more rapid recovery to inform emergency managers and public officials in ensuring equitable resource allocation prioritization.