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Updated: Oct 4, 2025
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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Toward data-driven, dynamical complex systems approaches to disaster resilience
Takahiro Yabe1,2, P Suresh C Rao1,3, Satish V Ukkusuri4
1Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907.
Summary
Urban resilience is crucial amid climate risks. This study advocates for data-driven complex systems models to better understand disaster impacts, moving beyond static measures for dynamic insights.
Area of Science:
- Urban resilience and disaster management
- Complex systems science
- Big data analytics
Background:
- Urban systems face increasing climate risks and require enhanced resilience.
- Current disaster resilience studies rely on static measures, limiting their ability to capture dynamic complexities.
- Static metrics fail to account for compounding shocks, system interdependencies, and critical transitions.
Purpose of the Study:
- To advocate for a paradigm shift in disaster resilience research.
- To promote the development of data-driven, dynamical complex systems models.
- To leverage big data for a more comprehensive understanding of urban resilience.
Main Methods:
- Utilizing massive datasets of human behavior (e.g., mobile phone data, satellite imagery).
- Developing data-driven complex systems models.
- Quantitatively modeling dynamic recovery trajectories and resilience characteristics.
Main Results:
- Identified limitations of static measures in capturing disaster resilience dynamics.
- Proposed data-driven complex systems modeling as a superior approach.
- Highlighted the potential for generic modeling of community resilience.
Conclusions:
- A move towards data-driven dynamical complex systems models is essential for advancing disaster resilience research.
- This approach overcomes the limitations of static metrics by leveraging big data.
- Enables quantitative modeling of recovery trajectories and policy-relevant simulations.
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