| description abstract | Improving urban resilience to disasters becomes well-recognized in both industry and academia, but resilience remains challenging to be operationalized, especially in complex urban contexts. Currently, longitudinal empirical studies on measuring resilience at fine-grains of space and time are lacking. Few methods can quantify resilience on an urban scale based on collective responses of individuals in a real disaster and can be adopted in distinct disaster contexts with crowdsourced data. We explored the potential advantages of network analysis to describe a complex human–spatial system (HSS). We integrated insights from the research field of socio-environmental systems, finding Fisher information (FI) to be an effective tool to quantify the dynamics of resilience. Consequently, we propose a quantitative framework, combining network analysis and FI, to measure resilience of HSS to disasters. We generated spatial networks with aggregated geolocations from a Twitter streaming API, and computed and compared network-wide metrics before, during, and after a disaster. FI was employed to detect mobility perturbations and to reveal the dynamic process of resilience over time. We applied our spatial-network analysis and FI framework to examine Hurricane Harvey and the subsequent flood in Greater Houston, Texas, in 2017. The analysis uncovers changed statuses and durations in the spatial network and suggests an intrinsic resilience of the HSS. The data-driven analytical framework contributes to an enhanced spatiotemporal understanding of urban resilience through a human-mobility perspective and to improved management of integrated cyber, human, and infrastructure systems. | |
