Toward data-driven, dynamical complex systems approaches to disaster resilience

• DOI: 10.1073/pnas.2111997119
• Publication Date: 2022-02-08T21:40:26Z
• Authors: Takahiro Yabe; P. Suresh C. Rao; Satish V. Ukkusuri; Susan L. Cutter
• Keywords: Resilience

With rapid urbanization and increasing climate risks, enhancing the resilience of urban systems has never been more important. Despite availability massive datasets human behavior (e.g., mobile phone data, satellite imagery), studies on disaster have limited to using static measures as proxies for resilience. However, metrics significant drawbacks such their inability capture effects compounding accumulating shocks; dynamic interdependencies social, economic, infrastructure systems; critical transitions regime shifts, which are essential components complex process. In this article, we argue that literature needs take opportunities big data move toward a different research direction, is develop data-driven, dynamical models Data-driven modeling approaches could overcome allow us quantitatively model recovery trajectories intrinsic characteristics communities in generic manner by leveraging large-scale granular observations. This approach brings paradigm shift process its linkage with process, paving way answering important questions policy applications via counterfactual analysis simulations.