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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

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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.

Keywords:
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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.