Quantifying Community Resilience Based on Fluctuations in Visits to Point-of-Interest from Digital Trace Data

TL;DR

This paper presents a method to quantify community resilience by analyzing fluctuations in visits to various POIs during disasters, demonstrated through a case study of Hurricane Harvey in Houston.

Contribution

The study introduces a novel approach to measure community resilience using digital trace data of POI visits, applicable across different disaster contexts.

Findings

Certain POI categories like grocery stores showed high resilience.

Medical facilities and entertainment had lower resilience values.

Spatial impact extended beyond flooded areas.

Abstract

This study aims to quantify community resilience based on fluctuations in the visits to various Point-of-Interest (POIs) locations. Visit to POIs is an essential indicator of human activities and captures the combined effects of perturbations in people lifestyles, built environment conditions, and businesses status. The study utilized digital trace data of unique visits to POIs in the context of the 2017 Hurricane Harvey in Houston (Texas, USA) to examine spatial patterns of impact and total recovery effort and utilized these measures to quantify community resilience. The results showed that certain POI categories such as building materials and supplies dealers and grocery stores were the most resilient elements of the community compared to the other POI categories. On the other hand, categories such as medical facilities and entertainment were found to have lower resilience values. This result suggests that these categories were either not essential for community recovery or that the community was not able to access these services at normal levels immediately after the hurricane. In addition, the spatial analyses revealed that many areas in the community with lower levels of resilience experienced extensive flooding. However, some areas with low resilience were not flooded extensively, suggesting that spatial reach of the impacts goes beyond flooded areas. The results demonstrate the importance of the approach proposed in our study. While this study focused on Houston and only analysed one natural hazard, the approach can be applied to other communities and disaster contexts. Applying this approach, emergency managers and public officials can efficiently monitor the patterns of disaster impacts and recovery across different spatial areas and POI categories and also identify POI categories and areas of their community that need to be prioritized for resource allocation.