Open AccessQuantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data
Author(s) -
Cristian Podesta,
Natalie Coleman,
Amir Esmalian,
Faxi Yuan,
Ali Mostafavi
Publication year - 2021
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2021.0158
Subject(s) - community resilience , resilience (materials science) , duration (music) , metropolitan area , natural hazard , population , geography , environmental resource management , proxy (statistics) , hazard , natural disaster , computer science , trace (psycholinguistics) , data science , environmental science , resource (disambiguation) , sociology , ecology , demography , meteorology , art , computer network , linguistics , philosophy , physics , literature , archaeology , machine learning , biology , thermodynamics
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.