Subject Area
Civil Engineering, Computer Science, Engineering, General/Other, Mathematics, Applied, Physical Sciences, General/Other, Physics, Statistics, Sustainability and Development, Urban Planning
Abstract
Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.
A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of individuals. This metric is derived from large-scale cell-phone GPS data and traffic-flow measurements. Analyses of multiple U.S. metropolitan areas subjected to major natural hazards—including hurricanes and the 2021 North American winter storm—reveal that, despite significant disruptions, MSD time histories consistently revert to their pre-event steady-state behavior immediately or within a short time, indicating an inherent resilience of large cities.
A complementary analysis using traffic-flow data demonstrates that probability density functions p(x,t) yield MSD responses that closely match those derived from GPS tracking, providing independent validation. Building on these observations, a mechanical model rooted in Langevin dynamics is introduced, establishing the proportionality ⟨r²(t)⟩ = WJ(t) between MSD and creep compliance. The model accurately predicts post-event recovery, offering a deductive framework with predictive capabilities.
Overall, this work establishes a unified approach that bridges stochastic mobility behavior with deterministic system response, providing a rigorous foundation for quantifying and predicting urban resilience.
Degree Date
Spring 5-16-2026
Document Type
Dissertation
Degree Name
Ph.D.
Department
Civil and Environmental Engineering
Advisor
Professor Nicos Makris
Second Advisor
Professor Khaled Abdelghany
Third Advisor
Professor Usama El Shamy
Fourth Advisor
Professor Emerita Sara Lynne Stokes
Fifth Advisor
Associate Professor Brett. A. Story
Number of Pages
187
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Chatzikyriakidis, Georgios, "An Analytical Framework for Quantifying Urban and Community Resilience to Natural Hazards from Cell-Phone GPS-Location and Traffic-Flow Data" (2026). Civil and Environmental Engineering Theses and Dissertations. 42.
https://scholar.smu.edu/engineering_civil_etds/42
Included in
Applied Mechanics Commons, Applied Statistics Commons, Civil Engineering Commons, Data Science Commons, Dynamics and Dynamical Systems Commons, Dynamic Systems Commons, Engineering Mechanics Commons, Engineering Physics Commons, Fluid Dynamics Commons, Other Computer Sciences Commons, Other Engineering Science and Materials Commons, Other Mechanical Engineering Commons, Other Physical Sciences and Mathematics Commons, Statistical, Nonlinear, and Soft Matter Physics Commons, Statistical Theory Commons, Structural Engineering Commons, Structural Materials Commons, Sustainability Commons, Systems Engineering Commons, Transportation Engineering Commons, Urban, Community and Regional Planning Commons
