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

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.

Spatial structure of city population growth

“…3 reveals the heterogeneity among cities in terms of polycentric organization, but with a primary, central urban county 47 , 48 . Recent multi-scale modeling analyses of urban mobility and growth 49 , 50 ; 51 , 52 are noteworthy in combining diverse data sources and theoretical approaches, but there is a need for empirical data analysis at a more disaggregated level 53 , 54 .…”

Reimagine fire science for the anthropocene

“…Furthermore, many measures of fire processes and impacts are inferred from static datasets (126), while fires and their effects are inherently dynamic; collecting observations that capture these dynamics, such as the response of wind during a fire event, would greatly reduce uncertainties in forecasting the impacts of fire on social-ecological systems. For fast-paced, local processes like fire behavior and the movement of water and smoke, we need more high frequency observations from laboratory and field-based studies, such as the role of flamegenerated buoyancy in fire spread (127), to update empirical relationships, some established by decades-old research and still used in models (128,129).…”

“…Nested, coupled modeling frameworks that integrate across physical, biological, and social systems will not only enhance our understanding of the connections, interactions, and feedbacks among fire, humans, and the Earth system, but also enable adaptation and resilience planning if we create metrics to gauge the response of social-ecological systems to fire (e.g. (126,190)). These metrics would include fire impacts on ecosystem services, human health, ecosystem health, and sustainable financing through policies on fire suppression, air and water quality, and infrastructure stability.…”

Gray Measure and Spatial Distribution Exploration of Local Emergency Resilience on Compound Disasters

“…The fourth takes resilience (the social and ecological vulnerability) as the capacity to cope with uncertainty and surprise by mobilizing diverse sources of resilience [ 22 ]. We contend that the core content of resilience is the capability to adapt to external disturbances, maintain system balance, and show dynamic learning in disasters, and self-organization in chaos, which should be systematic and dynamic [ 2 ], by synthesizing these views.…”

“…In the past decades, the rising frequency and severity of disasters have posed a severe threat to human society [ 1 ]. The hazard-inducing factors, evolutionary mechanisms, and hazardous forms of disasters have changed significantly owing to human society’s digitization, networking, and urbanization [ 2 ]. Compound disasters, which are highly complex and can involve different types of disasters [ 3 ], pose complex coordination and recovery challenges [ 4 ], and bring long-term devastation and shocks [ 5 ], have become the primary manifestation mode of disaster events and means the disaster prevention at the grassroots faces difficult situations.…”