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

Takahiro Yabe; P Suresh C Rao; Satish V Ukkusuri; Susan L Cutter

doi:10.1073/pnas.2111997119

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

Proc Natl Acad Sci U S A. 2022 Feb 22;119(8):e2111997119. doi: 10.1073/pnas.2111997119.

Authors

Takahiro Yabe^{1

2}, P Suresh C Rao^{1

3}, Satish V Ukkusuri⁴, Susan L Cutter⁵

Affiliations

¹ Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907.
² Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142.
³ Department of Agronomy, Purdue University, West Lafayette, IN 47907.
⁴ Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907; sukkusur@purdue.edu.
⁵ Hazards & Vulnerability Research Institute, Department of Geography, University of South Carolina, Columbia, SC 29208.

Abstract

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

Keywords: big data; complex systems; disaster resilience; urban science.