Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

Abstract

This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.

Community-scale Big Data Reveals Disparate Impacts of the Texas Winter Storm of 2021 and its Managed Power Outage

“…Several methods have been proposed to evaluate the community impact of crisis events using new technology that provides large-scale digital trace data. (10,18) Detailed spatiotemporal data offer unique insights into the interdependence of disaster problems, human activity, and urban mobility. The new insights can help us assess the impact of disaster more accurately (19), respond to disaster promptly (20), and enhance preparedness for future events (21).…”

“…Analysis of visits to POIs, such as restaurants, gas stations, and grocery stores, reveals insights into how people move around a city and where people visit during a disaster event. Several studies have been conducted to assess the impact or determine the signal of disasters, such as the COVID-19 pandemic (20,28), Hurricane Harvey (18), and Hurricane Irma (29), by implementing the visit data of POIs. The fluctuations in POI visits could provide reliable insights regarding disrupted access to critical facilities, such as grocery stores and restaurants.…”

Unraveling the Temporal Importance of Community-scale Human Activity Features for Rapid Assessment of Flood Impacts

“…These community-scale big datasets have become more commonly available on the same day and at fine spatial resolution affording capture of human activities, such as daily activity indexes, transaction activities, online communications, and mobility. Large-scale flood-caused perturbations cause disruptions at the smaller scale of human activities (Podesta et al 2021). Hence, variations in human activities in a flood-affected community signal impacts on the community (Farahmand et al 2021), which can be further used for the rapid assessment of flood impacts (e.g., Fan et al 2020;Yuan et al 2021b).…”

“…Fluctuations in the density of population activities (obtained through aggregate cell phone signals) or Waze road flooding reports could indicate local flood inundation status in an area outside a floodplain (Farahmand et al 2021;Praharaj et al 2021). Podesta et al (2021) compared the fluctuations in visits to points of interest (POIs) during normal periods to the Hurricane Harvey period and found that such fluctuations can be used to assess flood impacts. Yuan et al (2021b) assessed the flood impacts of Hurricane Harvey through the analysis of variations in credit card transactions.…”

“…While these studies point to the promise of garnering insights from community-scale big data about heterogeneous human activity-based features, little is known about the temporal importance of these features across flood stages. For instance, changes in human mobility before an event might be due to preparedness and evacuation response of people and might not be a strong indication of flood impacts (Podesta et al 2021). Changes in human mobility during and in the immediate aftermath could be a stronger signal about of flood disruption (Yabe et al 2020a).…”