Urban Data Science: Theories, Methods, Models, and Applications for Our Changing Cities

Type: Virtual Panel
Sponsor Groups: Spatial Analysis and Modeling Specialty Group, Geographic Information Science and Systems Specialty Group, Urban Geography Specialty Group
Poster #:
Day: 4/11/2021
Start / End Time: 8:00 AM / 9:15 AM (PDT)
Room: Virtual 48
Organizers: Qunshan Zhao
Chairs: Qunshan Zhao


The city is the darling of geographical data science. Population density often begets data density, so data science methods and perspectives now are increasingly relevant to analyze our changing cities. The city provides fertile ground for the development of new theories, methods, and models across many problem domains that span sociology, economics, political science, epidemiology, urban planning, public policy, and geography. Further, the development of a new "city science" is emerging from these fields, co-opting both theory and methods for new inquiry.

To this end, urban data science is experiencing a significant bout of high-profile attention as exciting new dynamics are captured with increasing detail via sensor networks, user-generated content, and many already existing new forms of urban big data in the business and administrative systems. This emergence of a new city science provides an immense opportunity for cutting-edge quantitative geographical and urban research, with recent books, high-profile papers, and new research institutes & environments springing up at multiple institutions. Thus, we aim to help define this new research frontier in three sessions showcasing novel geographic data science for dynamic urban processes and one panel exploring the progress in the field of urban data science. Opportunities are available for any folks interested in many different geographic topics at the core of urban data science, including but not limited to:

Analysis, modelling, and prediction of movement in and across cities
New methods or applications for social, network, or spatial interaction
Econometrics, counterfactuals, & causal inference for urban studies
New methods or applications in geodemographic analysis
Place detection, regionalization, clustering, or boundary identification
Segregation, sorting, & place choice in and among cities
Spatial-temporal dynamics of neighborhood demographics
Identification & validation of neighborhood/contextual effects
Environmental risk and resilience in complex urban systems
Analysis of structure, form, & complexity in the built environment
Methods and applications for new forms of urban big data or streaming data
Critical empirical analysis and validation of "accidental" urban data
Building better theory for a data-intensive urban science

Please submit your abstracts to levi.john.wolf@bristol.ac.uk, weikang@ucr.edu, toshan@umd.edu, or Qunshan.Zhao@glasgow.ac.uk by November 19, 2020.

These sessions are hosted in conjunction with the University of Bristol Quantitative Spatial Sciences Research Group, the University of Maryland Center for Geospatial Information Science, the University of California, Riverside Inland Center for Sustainable Development, and the University of Glasgow Urban Big Data Centre.


Kang, W., Oshan, T., Wolf, L. J., Boeing, G., Frias-Martinez, V., Gao, S., Poorthuis, A., & Xu, W. (2019). A roundtable discussion: Defining urban data science. Environment and Planning B: Urban Analytics and City Science, 46(9), 1756–1768. https://doi.org/10.1177/2399808319882826


Type Details Minutes Start Time

To access contact information login