The Urban Data Research Lab uses novel big data and spatial data science to understand urban inequality at the scale of the neighborhood in order to inform housing policy and opportunities for more equitable cities.
Our primary areas of research are:
Measuring Neighborhood Dynamics with Big Data
Much of how researchers, advocates, and policy-makers understand the geography of opportunity is largely based on the residential neighborhood context. To study neighborhood dynamics, especially at a large scale, we have traditionally relied on administrative data such as those produced by the U.S. Census Bureau. This area of research aims to think, measure, and generate data products to describe the wide range of activities and socio-spatial dynamics that make up how people experience their social and environmental context and how these can change across the day or seasonally. We investigate the possibilities of using novel, unstructured data sources such as newer iterations of cell phone location data, in a ground-truthed and statistically rigorous manner, to shift how we think about context in a more holistic, representative way.
Legacies of Housing and Real Estate in the 20th Century
Public policy in the U.S. is at a critical turning point where the need to address historical and ongoing housing discrimination calls for more nuanced understandings of longstanding spatial inequalities and their impacts on residential segregation and stratification. We study large-scale historical housing discrimination through such as practices as federal-level redlining and urban renewal in the mid-20th century United States and its impacts on housing and socioeconomic outcomes. In this area of research, we aim to add nuance to historical narratives through the discovery and analysis of historical big data at the scale of the neighborhood and the individual, with experiences that may not be measurable by traditional data sources.
10:10am - 11:25am
CRP and DESIGN 4680/5680
2:45 pm - 4:00 pm
Students must also enroll in:
Practioners' Talk Series
4:30 pm - 5:30 pm
Ghost cities of China: Identifying urban vacancy through social media data
Williams, Sarah, Xu, Wenfei, Tan, Shin Bin, Foster, Michael J., and Chen, Changping
Cities, 94, 275-285.
A roundtable discussion: Defining urban data science
Organizers, Kang, Wei, Oshan, Taylor, Wolf, Levi J., Discussants, Boeing, Geoff, Frias-Martinez, Vanessa, Gao, Song, Poorthuis, Ate, and Xu, Wenfei
Environment and Planning B: Urban Analytics and City Science, 46(9), 1756-1768
Russell Sage Foundation Grant
April 24, 2023
Along with Jacob Faber, Kate Thomas, and Thomas Storrs, we are so pleased to receive a Russell Sage Foundation grant to continue our work on Federal Housing Administration's mortgage insurance activities.
Annoucing the Urban Data Hub at Cornell AAP
April 3, 2023
The Cornell AAP Urban Data Hub is a cross-disciplinary initiative aimed to catalyze longer-term urban big data infrastructure within AAP to accelerate our research, teaching, and engagement with contemporary and critical discourse in design and planning practice.
Collaboration with HKUST: Measuring the Evolution of Urban Renewal in the United States using Satellite Imagery and Deep Learning
November 28, 2022
Cornell Global Hub
CCSS Grant for A New Picture of Segregation
November 14, 2022
Cornell Center for Social Sciences awards us grant for A New Picture of Segregation
Wenfei Xu is an assistant professor in the Department of City and Regional Planning at the College of Architecture, Art, and Planning at Cornell University. She is also the director of the Urban Data Research Lab. Her research questions how housing policies, practices, institutions, and technologies have shaped urban inequality, with an orientation toward methods in urban analytics. She works on topics in social-spatial stratification, segregation, race and ethnicity, data science, mapping, and neighborhood change in the United States. Her work has ranged 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.
Xu received her Ph.D. in urban planning from Columbia University's Graduate School of Architecture, Planning, and Preservation, a Dual Masters in architecture and urban planning from MIT's Department of Architecture and Department of Urban Studies and Planning, and a Bachelor's of Arts in Economics from the University of Chicago. After her Ph.D., was a Fellow at the Mansueto Institute for Urban Innovation at the University of Chicago.
Director, Urban Data Research Lab
I am Houpu Li, currently pursuing a Master's degree in Regional Science at Cornell University. I hold a bachelor's and master's degree in Architecture from China, with a focus on parametric design in architecture and quantitative analysis of urban spatial data.
Research Assistant, Regional Science
Tung Chen is a graduate student pursuing a Master of Science in Advanced Architectural Design at Cornell University, with a minor in City and Regional Planning. He holds a bachelor's degree in architecture from Tunghai University. His research interests focus on the space of migration, movements and mobilities during displacements, emphasizing the formation and design of refugee camps nowadays
Advanced Architectural Design
Charlotte Verity is a senior at Cornell University studying computer science, with a minor in human development. She is passionate about data science, software, and machine learning.
Leah Chen is currently a second-year master of regional planning student at Cornell University and received her Bachelor of Engineering degree in China. Her research interests include urban data analysis for urban design and urban transportation equity analysis
Research Assistant, City and Regional Planning
Yucheng Zhang is a masters student in City and Regional Planning with a background in data science. Yucheng research interest includes using spatial data to understand the urban environment and explore the potential of automated urban data analysis algorithms to alleviate the administrative cost of understanding cities.
City and Regional Planning
Michael Cao is a third-year student at Cornell University studying statistical science with a minor in operations research. He is passionate about developing efficient algorithms to extract meaningful insights from large and complex datasets. Upon graduation, he plans to pursue a Master of Engineering degree in operations research.
Zhuojun Wang is a current Ph.D. student specializing in integrating GIS techniques, data science, spatial analysis, and machine learning to address urban problems. With prior internships at municipalities in the US and China, she brings practical experience in urban policies. Her background in Regional Science and GIS provides a strong foundation for her research.
I am Moheng Ma, a graduate student in regional science at Cornell University, My research interests concentrate on using spatial data to understand relationship between human activity and the built environment & association between urban inequality and socio economic attributes. I hold a master degree in urban planning from Columbia University.
Ari is a sophomore studying in Cornell University’s College of Arts & Sciences and minoring in Public Policy in the Brooks School. He is interested in housing policy and understanding how local governments can best meet housing affordability needs.
Arts and Sciences
Su Jeong Jo
Please reach out to email@example.com if you're interested in learning more or collaborating with us. Our ongoing lab projects touch on issues of housing and neighborhood change through urban big data (ranging from archival to cell phone data) and urban data science methods.
For prospective Master's and PhD level students who are interested in working with us, we are particularly interested in students with a social science background and strong coding skills. You can apply to our Master of Regional Planning and PhD in City and Regional Planning programs.