ArticleProceedings of the National Academy of Sciences of the United States of America2022
Toward data-driven, dynamical complex systems approaches to disaster resilience.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Reliable Hardware Sensor and Large Language Model Fusion for Intelligent Short-Term Market Risk Sensing and Prediction.Sensors (Basel, Switzerland) · 2026Article
- Behaviour-based dependency networks between places shape urban economic resilience.Nature human behaviour · 2025Article
- MetaCity: Data-driven sustainable development of complex cities.Innovation (Cambridge (Mass.)) · 2025Review
- Article
- Healthcare system resilience and adaptability to pandemic disruptions in the United States.Nature medicine · 2024Article
- Preparing for the next outbreak: A review of indices measuring outbreak preparedness, vulnerability, and resilience.Preventive medicine reports · 2023Review
- Resilience patterns of human mobility in response to extreme urban floods.National science review · 2023Article
- National resilience assessment and improvement based on multi-source data: Evidence from countries along the belt and road.International journal of disaster risk reduction : IJDRR · 2023Article
- Spatial structure of city population growth.Nature communications · 2022Article
- Gray Measure and Spatial Distribution Exploration of Local Emergency Resilience on Compound Disasters.International journal of environmental research and public health · 2022Article
- Reimagine fire science for the anthropocene.PNAS nexus · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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