Wei Kang is a Research Scientist at UCR Inland Center for Sustainable Development (ICSD). Her research interests are methodological - spatial statistics/econometrics, as well as empirical - housing, neighborhood change, inequality, growth, and convergence. Her current projects focus on affordable housing and housing policies. She is the core developer of the widely used open-source spatial analysis python library – PySAL.


  • Housing & Poverty & Inequality
  • Local and Regional Development & Urban Data Science
  • GIScience & Spatial Statistics & Spatial Econometrics


  • PhD in Geography, 2018

    Arizona State University

  • MSc in Cartology and GISystem, 2014

    Peking University

  • BSc in Geographic Information System, 2011

    Wuhan University

Academic Appointment


Graduate Research Assistant

School of Geographical Sciences and Urban Planning, Arizona State University

Jan 2015 – Apr 2018 Tempe, Arizona
Participated in the NSF project New Approaches for Spatial Distribution Dynamics.

Graduate Teaching Assistant

School of Geographical Sciences and Urban Planning, Arizona State University

Aug 2014 – Dec 2014 Tempe, Arizona
Teaching assistant for “GIS 470: Statistics for Geographers”.

Graduate Research Assistant

Institute of Remote Sensing and GIS, Peking University

Sep 2011 – Jun 2014 Beijing, China
Participated in the project “Application of Temporal GIS to Spatio-Temporal Land & Resources Data Management” funded by Beijing Municipal Bureau of Land and Resources.

Recent Publications

(2020). Spatiotemporal patterns of alcohol outlets and violence: A spatially heterogeneous Markov chain analysis. Environment and Planning B: Urban Analytics and City Science.


(2020). PySAL and Spatial Statistics Libraries. The Geographic Information Science & Technology Body of Knowledge (3rd Quarter 2020 Edition).


(2020). When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics. Journal of Geovisualization and Spatial Analysis.


(2020). Sensitivity of sequence methods in the study of neighborhood change in the United States. Computers, Environment and Urban Systems.


(2020). `splot` - visual analytics for spatial statistics. Journal of Open Source Software.



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