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This study aims to quantify community resilience based on fluctuations in thevisits to various Point-of-Interest (POIs) locations. Visit to POIs is anessential indicator of human activities and captures the combined effects ofperturbations in people lifestyles, built environment conditions, andbusinesses status. The study utilized digital trace data of unique visits toPOIs in the context of the 2017 Hurricane Harvey in Houston (Texas, USA) toexamine spatial patterns of impact and total recovery effort and utilized thesemeasures to quantify community resilience. The results showed that certain POIcategories such as building materials and supplies dealers and grocery storeswere the most resilient elements of the community compared to the other POIcategories. On the other hand, categories such as medical facilities andentertainment were found to have lower resilience values. This result suggeststhat these categories were either not essential for community recovery or thatthe community was not able to access these services at normal levelsimmediately after the hurricane. In addition, the spatial analyses revealedthat many areas in the community with lower levels of resilience experiencedextensive flooding. However, some areas with low resilience were not floodedextensively, suggesting that spatial reach of the impacts goes beyond floodedareas. The results demonstrate the importance of the approach proposed in ourstudy. While this study focused on Houston and only analysed one naturalhazard, the approach can be applied to other communities and disaster contexts.Applying this approach, emergency managers and public officials can efficientlymonitor the patterns of disaster impacts and recovery across different spatialareas and POI categories and also identify POI categories and areas of theircommunity that need to be prioritized for resource allocation.

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