Wenfei Xu

Biography

Wenfei Xu is an assistant professor in GIScience and Urban Data Science in the Department of Geography at UCSB and the Director of the Urban Data Research Lab. Her research examines how housing policies, practices, institutions, and technologies have influenced urban inequality, with a focus on methods in spatial data science. She works on topics in social-spatial stratification, segregation, race and ethnicity, data science, mapping, and neighborhood change in the United States.
 
Wenfei's work, which has been funded by the National Science Foundation, the Russell Sage Foundation, and the Washington Center for Equitable Growth, ranges from an interest in the historical legacies of structural housing discrimination and its contemporary spatial-temporal manifestations to exploring the uses of big data in characterizing human activity for urban social science research. Wenfei has published in several journals, including The Proceedings of the National Academies of Science, The Annals of the Association of American Geographers, Urban Studies, Nature Scientific Data, and Housing Policy Debate, among others. She currently serves on the editorial board of Nature Scientific Data and the Annals of the AAG.
 
Wenfei was previously an assistant professor at Cornell University and a postdoctoral scholar at the University of Chicago, Mansueto Institute for Urban Innovation. She holds a Ph.D. in Urban Planning from Columbia University, dual M.Arch and MCP degrees from MIT, and a B.A. in economics from the University of Chicago. Before her doctorate, Wenfei was a data scientist at the Department of Homeless Services and at CARTO.

Research

My research investigates enduring questions of neighborhood change through advances in spatially-explicit computational techniques and novel spatial data sources in two related streams. The first stream clarifies the mechanisms of discriminatory housing practices throughout history and their impacts on contemporary urban inequality. The second stream investigates how novel forms of unstructured big spatial data can be used to update our understanding of racial composition, neighborhood diversity, segregation, and change.