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
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.
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