New Publication on Social-Behavioral Dynamics During Pandemics in Major U.S. Cities

GEAR Lab is pleased to share a new publication in Transactions in Urban Data, Science, and Technology by Lei Zou, Binbin Lin, Thiagarajan Ramakrishnan, and Mingzheng Yang.

The paper, titled “Spatiotemporal Disparities of Social-Behavioral Dynamics During Pandemics: Insights from Multi-Source Big Data in Major U.S. Cities,” examines how public awareness, sentiment, mobility, policy responses, and health outcomes changed across space and time during the COVID-19 pandemic. Using ten major U.S. cities as case studies, the research integrates social media data, smartphone-based mobility metrics, and web-harvested policy evaluations to better understand how social and behavioral responses interact with pandemic progression.

This study highlights the value of multi-source geospatial big data for capturing complex human responses during public health crises. By analyzing geographic and temporal disparities from 2020 to 2021, the paper shows how pandemic responses varied across cities and how behavioral changes, mobility patterns, and policy interventions were linked to infection trends over time.

The work contributes to urban data science, public health geography, and pandemic preparedness by offering a data-driven framework for monitoring social-behavioral dynamics during crises. Its findings can help researchers, planners, and public health decision-makers design more timely, adaptive, and place-sensitive response strategies for future pandemics.

Congratulations to the authors on this important contribution to urban data science, geospatial big data, and public health resilience.

Citation

Zou, L., Lin, B., Ramakrishnan, T., & Yang, M. (2026). Spatiotemporal disparities of social-behavioral dynamics during pandemics: Insights from multi-source big data in major U.S. cities. Transactions in Urban Data, Science, and Technology, 5(1), 76-98. https://doi.org/10.1177/27541231261418712