Managing Urban Crises via the Lens of Human Mobility Data: Measurement and Intervention

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Managing Urban Crises via the Lens of Human Mobility Data: 
Measurement and Intervention"

By

Miss Lin CHEN


Abstract:

Cities are complex sociophysical systems marked by heterogeneity, 
interconnectivity, and dynamicity, where people, infrastructures, and 
opportunities are unevenly distributed, mutually influenced, and constantly 
evolving. These features pose unique challenges in tackling urban crises 
such as pandemics and natural disasters, which require a comprehensive 
understanding of what people experience and how they react in complex urban 
environments. Human mobility data, collected from widely-adopted 
GPS-equipped mobile devices, provide a powerful study lens with its 
large-scale, fine- grained, and real-time nature. In this thesis, I develop 
data-driven methodologies that harness human mobility data for sensing, 
understanding, and intervening in urban crises.

First, to sense vulnerable populations in urban crises, I construct 
VulnerabilityMap, an open data framework that integrates large-scale 
mobility patterns with demographic, environmental, and behavioral 
indicators. Drawing on a taxonomy of shocks and stresses inspired by 
Maslow's hierarchy of needs, VulnerabilityMap produces fine-grained 
measures of community disadvantage and crisis impact. Machine learning 
models trained on this data demonstrate strong predictive performance and 
reveal key social factors that trap populations in vulnerable circumstances.

Second, to understand the progression of urban crises, I conduct a thorough 
data-driven analysis across different phases of the COVID-19 pandemic at 
scale. I identify two dominant mobility response patterns: daily 
life-centered venues experience smaller reductions and quicker recovery, 
while work-centered venues face larger reductions that persist. These 
changes exert deeper impacts on the underlying social fabric, significantly 
correlating with income and racial segregation. Clustering analysis further 
uncovers geographical heterogeneity in mobility responses, linked to 
partisan inclinations.

Third, to understand the role of mobility network structure in shaping 
social resilience during urban crises, I construct city-wide mobility 
networks based on visitation preference dependencies. Network centrality 
emerges as a robust predictor of social resilience, explaining over 80% more 
variance in both segregation and mobility changes than traditional metrics. 
Moreover, core and peripheral places identified from the network do not 
distinguish each other by their static geographic distribution, but instead 
differ dramatically in how, when, and from where people visit them—a result 
with direct implications for targeting crisis-response interventions.

Fourth, to intervene effectively and equitably, I develop a demographic- and 
mobility‑aware epidemic simulation model and an optimization framework for 
resource allocation, demonstrated through COVID-19 vaccine distribution. 
Simulation results show that prioritizing disadvantaged communities improves 
both overall social utility and equity even amid vaccine hesitancy, and that 
multi-objective optimization can reconcile conflicts between different 
equity dimensions.

Finally, I conclude this thesis with future research directions and 
challenges related to data-driven urban crisis management.


Date:                   Friday, 25 April 2025

Time:                   2:45pm - 4:45pm

Venue:                  Room 5562
                        Lifts 27/28

Chairman:               Prof. Ajay JONEJA (IDEA)

Committee Members:      Dr. Tristan BRAUD (Supervisor)
                        Prof. Pan HUI (Supervisor, EMIA)
                        Prof. James KWOK
                        Prof. Qiong LUO
                        Dr. Gareth TYSON (ECE)
                        Prof. Flora SALIM (UNSW)