Faculty Profile

Wei Kang

Title
Assistant Professor
Department
Geography and the Environment
College
College of Liberal Arts and Social Sciences

    

Education

PhD, Arizona State University, 2018.
Major: Geography
Dissertation Title: Issues in the Distribution Dynamics Approach to the Analysis of Regional Economic Growth and Convergence: Spatial Effects and Small Samples

Current Scheduled Teaching*

GEOG 5550.001, Advanced Geographic Information System, Spring 2024
GEOG 4900.703, Special Problems, Spring 2024

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching*

GEOG 5560.002, Application Development with Python Programming, Fall 2023 SPOT
GEOG 4900.004, Special Problems, Fall 2023
GEOG 5550.001, Advanced Geographic Information System, Spring 2023 Syllabus SPOT
GEOG 4560.001, Introduction to Python Programming, Spring 2023 Syllabus SPOT
GEOG 4900.703, Special Problems, Spring 2023
GEOG 5560.002, Application Development with Python Programming, Fall 2022 Syllabus SPOT

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Publications

Published Intellectual Contributions

Book Chapter
Kang, W. (2022). Spatial dynamics. Handbook of Spatial Analysis in the Social Sciences. 277-290. Edward Elgar Publishing. https://www.elgaronline.com/view/book/9781789903942/book-part-9781789903942-24.xml
Kang, W., Wilson, J. P. (2020). PySAL and Spatial Statistics Libraries. The Geographic Information Science & Technology Body of Knowledge. (3rd Quarter 2020), . UCGIS. https://doi.org/10.22224/gistbok/2020.3.1
Conference Proceeding
Rey, S., Knaap, E., Han, S., Wolf, L., Kang, W., Akici, F., Lippa, D., Niederhut, D., Pacer, M. (2018). Spatio-temporal analysis of socioeconomic neighborhoods: The Open Source Longitudinal Neighborhood Analysis Package OSLNAP. Proceedings of the 17th Python in Science Conference. 121 - 128.
Journal Article
Oselin, S., Ross, J., AU - Wang, Qingfang, Q., Kang, W. (2023). Fair Chance Act failures? Employers’ hiring of people with criminal records. Criminology & Public Policy. https://onlinelibrary.wiley.com/doi/full/10.1111/1745-9133.12655
Hatch, E., Wang, C., Kang, W., Karna, B., Sabinsky, N., Ferguson, K. (2023). Geographies of Opportunity for Youth Across the Contiguous United States. The Professional Geographer. https://www.tandfonline.com/doi/full/10.1080/00330124.2023.2261516
Wang, Q., Kang, W. (2023). Small businesses and government assistance during COVID-19: Evidence from the paycheck protection program in the U.S.. Environment and Planning A. https://journals.sagepub.com/doi/10.1177/0308518X231166407
Luo, W., Zhou, Y., Liu, Z., Kang, W., He, S., Zhu, R., Li, R., Huang, B. (2023). Cross-regional analysis of the association between human mobility and COVID-19 infection in Southeast Asia during the transitional period of “living with COVID-19”. Health & Place. https://www.sciencedirect.com/science/article/abs/pii/S1353829223000370
Kang, W., Wang, Q. (2022). The Impact of COVID-19 on Small Businesses in the US: A Longitudinal Study from a Regional Perspective. International Regional Science Review. https://journals.sagepub.com/doi/10.1177/01600176221132230
Kang, W., Knaap, E., Rey, S. (2021). Changes in the economic status of neighbourhoods in US metropolitan areas from 1980 to 2010: Stability, growth and polarisation. Urban Studies. 00420980211042549. https://doi.org/10.1177/00420980211042549
Rey, S. J., Anselin, L., Amaral, P., Arribas-Bel, D., Cortes, R. X., Gaboardi, J. D., Kang, W., Knaap, E., Li, Z., Lumnitz, S., Oshan, T. M., Shao, H., Wolf, L. J. (2021). The PySAL Ecosystem: Philosophy and Implementation. Geographical Analysis. https://onlinelibrary.wiley.com/doi/abs/10.1111/gean.12276
Wang, Q., Kang, W. (2021). What Are the Impacts of COVID-19 on Small Businesses in the U.S.? Early Evidence based on the Largest 50 MSAs. Geographical Review. Routledge. https://doi.org/10.1080/00167428.2021.1927731
Lumnitz, S., Arribas-Bell, D., Cortes, R., Gaboardi, J., Griess, V., Kang, W., Oshan, T., Wolf, L., Rey, S. (2020). `splot` - visual analytics for spatial statistics. Journal of Open Source Software. 5(47), 1882. The Open Journal. https://doi.org/10.21105/joss.01882
Rey, S., Han, S. Y., Kang, W., Knaap, E., Cortes, R. X. (2020). A Visual Analytics System for Space--Time Dynamics of Regional Income Distributions Utilizing Animated Flow Maps and Rank-based Markov Chains. Geographical Analysis. https://onlinelibrary.wiley.com/doi/abs/10.1111/gean.12239
Kang, W., Rey, S. J. (2020). Inference for Income Mobility Measures in the Presence of Spatial Dependence. International Regional Science Review. 43(1-2), 10-39. https://doi.org/10.1177/0160017619826291
Alyousifi, Y., Ibrahim, K., Kang, W., Zin, W. Z. (2020). Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model. Environmental Monitoring and Assessment. 192(11), 1--24. Springer.
Kang, W., Rey, S., Wolf, L., Knaap, E., Han, S. (2020). Sensitivity of sequence methods in the study of neighborhood change in the United States. Computers, Environment and Urban Systems. 81, 101480.
Shao, H., Li, W., Kang, W., Rey, S. J. (2020). When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics. Journal of Geovisualization and Spatial Analysis. 4(2), 1--16. Springer.
Wei, R., Grubesic, T. H., Kang, W. (2020). Spatiotemporal patterns of alcohol outlets and violence: A spatially heterogeneous Markov chain analysis. Environment and Planning B: Urban Analytics and City Science. https://doi.org/10.1177/2399808320965569
Han, S. Y., Rey, S., Knaap, E., Kang, W., Wolf, L. (2019). Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents. ISPRS International Journal of Geo-Information. 8(11), . https://www.mdpi.com/2220-9964/8/11/509
Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., Wolf, L. J. (2019). Inference in Multiscale Geographically Weighted Regression. Geographical Analysis. 52(1), 87-106. https://onlinelibrary.wiley.com/doi/abs/10.1111/gean.12189
Rey, S. J., Kang, W., Wolf, L. J. (2019). Regional inequality dynamics, stochastic dominance, and spatial dependence. Papers in Regional Science. 98(2), 861-881. https://rsaiconnect.onlinelibrary.wiley.com/doi/abs/10.1111/pirs.12393
Alyousifi, Y., Ibrahim, K., Kang, W., Zin, W. Z. (2019). Markov chain modeling for air pollution index based on maximum a posteriori method. Air Quality, Atmosphere & Health. https://doi.org/10.1007/s11869-019-00764-y
Organizers,, Kang, W., Oshan, T., Wolf, L. J., Discussants,, 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
Kang, W., Rey, S. J. (2019). Smoothed Estimators for Markov Chains with Sparse Spatial Observations. Geographical Analysis. 1-22. https://onlinelibrary.wiley.com/doi/abs/10.1111/gean.12222
Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., Fotheringham, A. S. (2019). MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. ISPRS International Journal of Geo-Information. 8(6), . https://www.mdpi.com/2220-9964/8/6/269
Oshan, T., Wolf, L. J., Fotheringham, A. S., Kang, W., Li, Z., Yu, H. (2019). A comment on geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science. 33(7), 1289-1299. Taylor & Francis. https://doi.org/10.1080/13658816.2019.1572895
Kang, W., Rey, S. J. (2018). Conditional and joint tests for spatial effects in discrete Markov chain models of regional income distribution dynamics. The Annals of Regional Science. 61(1), 73--93. https://doi.org/10.1007/s00168-017-0859-9
Fotheringham, A. S., Yang, W., Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers. 107(6), 1247-1265. Taylor & Francis. http://dx.doi.org/10.1080/24694452.2017.1352480
Rey, S. J., Kang, W., Wolf, L. (2016). The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics. Journal of Geographical systems. 18(4), 377--398. http://dx.doi.org/10.1007/s10109-016-0234-x

Awarded Grants

Contracts, Grants and Sponsored Research

Grant - Research
Wang, Q. (Principal), Kang, W. (Co-Principal), "Forced Displacement and Community Resilience: Housing Insecurity under COVID-19 in Inland Southern California," Sponsored by National Science Foundation, Federal, $336050 Funded. (July 1, 2022June 30, 2025).
Wang, Q. (Principal), Link, B. (Co-Principal), Kang, W. (Co-Principal), "Identifying the Effect of Housing policy on Mental Health Outcomes Among Low-Income Renters and their Children During the COVID-19 Pandemic," Sponsored by California Department of Public Health, State, $200000 Funded. (July 1, 2023June 30, 2024).
Kang, W. (Principal), Balachandran, S. (Co-Principal), "Fair Housing Reforms in Low Income Housing Tax Credits: An investigation of implications for neighborhoods and communities in the DFW region," Sponsored by University of North Texas Division of Research and Innovation, University of North Texas, $10000 Funded. (June 1, 2023May 31, 2024).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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