Bio

Dr.  Kang directs the GISense Lab at the University of South Carolina. He was a postdoctoral researcher at the MIT SENSEable City Lab, received his Ph.D. from the University of Wisconsin-Madison, and obtained his bachelor's degree from the Wuhan University. He had working experience at the Google X and MoBike. Dr. Kang’s research mainly focuses on Human-centered Geospatial Data Science to understand human experience at place and develop ethical and responsible geospatial artificial intelligence (GeoAI) approaches. He was the recipient of the Waldo-Tobler Young Researcher Award by the Austrian Academy of Sciences, CaGIS Rising Award, CPGIS Education Excellence Award, etc. He was the founder of the non-profit educational organization GISphere.

Research

The GISense lab focuses on modeling and understanding human-environment relationships at place and tackling real-world challenges (e.g., spatial-socio inequity, public health, housing, crime, climate change) in urban systems with spatiotemporal big data analytics, geospatial artificial intelligence (GeoAI), and maps and geovisualizations. 

There are three key research themes of the GISense Lab:
(1) an emphasis on place to observe human dynamics and subjective experiences including but not limited to emotion, perception, cognition, and creativity;
(2) a recognition of the need to develop ethical cartography and GeoAI methods (e.g., machine learning, deep learning, generative AI) for modeling geographic phenomenon and urban environments; 
(3) a focus on human-environment interactions in urban systems to solve practical
problems and inform decision-making processes including but not limited to spatial-socio inequity, public health, housing, crime, climate change, and transportation.

As the director of the GISense Lab, Dr. Kang has published over 50 peer-reviewed papers on journals such as the International Journal of Geographical Information Science, Transactions in GIS, Cartography and Geographic Information Science, Landscape and Urban Planning, Computers, Environment and Urban Systems, and Proceedings of the National Academy of Sciences. His work has been recognized as best papers in Annals of GIS and AAG GISS/CyberGIS/EPBG groups. He has served as the associate editor of the Computational Urban Science and has been reviewers of over 30 academic journals. Dr. Kang has served on multiple academic committees such as the AAG GISS/Cartography/CyberGIS and CPGIS.

Teaching

Dr. Kang teaches GIS courses that offers practical skills such as geospatial analytical and technical skills to understand human-environment interactions. His classes provide hands-on tutorials coupled with exercise labs with most up-to-date technologies in the industry. Students will learn GIS for solving real-world geographic problems, which will benefit careers in GIS, geography, environmental science, urban planning, etc.

• GEOG 105: Digital Earth
• GEOG 556: WebGIS and Geovisualization
• GEOG 564: GIS-based Modeling

Representative Publications

Kang, Y., Abraham, J., Ceccato, V., Duarte, F., Gao, S., Ljungqvist, L., Zhang, F., Näsman, P. and Ratti, C., 2023. Assessing differences in safety perceptions using GeoAI and survey across neighborhoods in Stockholm, Sweden. Landscape and Urban Planning, 236, p.104768.

Wang, Y., Kang, Y., Liu, H., Hou, C., Zhou, B., Ye, S., Liu, Y., Rao, J., Pei, Z., Ye, X. and Gao, S., 2023. Choosing GIS graduate programs from afar: Chinese students' perspectives. Transactions in GIS, 27(2), pp.450-475.

Kang, Y., Wu, K., Gao, S., Ng, I., Rao, J., Ye, S., Zhang, F. and Fei, T., 2022. STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity. International Journal of Geographical Information Science, 36(8), pp.1518-1549.

Kang, Y., Zhang, F., Gao, S., Peng, W. and Ratti, C., 2021. Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling. Cities, 118, p.103333.

Kang, Y., Zhang, F., Peng, W., Gao, S., Rao, J., Duarte, F. and Ratti, C., 2021. Understanding house price appreciation using multi-source big geo-data and machine learning. Land Use Policy, 111, p.104919.

Kang, Y., Zhang, F., Gao, S., Lin, H. and Liu, Y., 2020. A review of urban physical environment sensing using street view imagery in public health studies. Annals of GIS, 26(3), pp.261-275.

Kang, Y., Jia, Q., Gao, S., Zeng, X., Wang, Y., Angsuesser, S., Liu, Y., Ye, X. and Fei, T., 2019. Extracting human emotions at different places based on facial expressions and spatial clustering analysis. Transactions in GIS, 23(3), pp.450-480.

Kang, Y., Gao, S. and Roth, R.E., 2019. Transferring multiscale map styles using generative adversarial networks. International Journal of Cartography, 5(2-3), pp.115-141.

Zhang, F., Fan, Z., Kang, Y., Hu, Y. and Ratti, C., 2021. “Perception bias”: Deciphering a mismatch between urban crime and perception of safety. Landscape and Urban Planning, 207, p.104003.

Kruse, J., Kang, Y., Liu, Y.N., Zhang, F. and Gao, S., 2021. Places for play: Understanding human perception of playability in cities using street view images and deep learning. Computers, Environment and Urban Systems, 90, p.101693.

Hou, X., Gao, S., Li, Q., Kang, Y., Chen, N., Chen, K., Rao, J., Ellenberg, J.S. and Patz, J.A., 2021. Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race. Proceedings of the National Academy of Sciences, 118(24), p.e2020524118.

Yang, J., Rong, H., Kang, Y., Zhang, F. and Chegut, A., 2021. The financial impact of street-level greenery on New York commercial buildings. Landscape and Urban Planning, 214, p.104162.

Gao, S., Rao, J., Kang, Y., Liang, Y. and Kruse, J., 2020. Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSpatial Special, 12(1), pp.16-26.

Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J., Dopfer, D., Sethi, A.K., Reyes, J.F.M., Yandell, B.S. and Patz, J.A., 2020. Association of mobile phone location data indications of travel and stay-at-home mandates with COVID-19 infection rates in the US. JAMA network open, 3(9), pp.e2020485-e2020485.

Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J., Dopfer, D., Sethi, A.K., Reyes, J.F.M., Yandell, B.S. and Patz, J.A., 2020. Association of mobile phone location data indications of travel and stay-at-home mandates with COVID-19 infection rates in the US. JAMA network open, 3(9), pp.e2020485-e2020485.

Zhang, F., Zu, J., Hu, M., Zhu, D., Kang, Y., Gao, S., Zhang, Y. and Huang, Z., 2020. Uncovering inconspicuous places using social media check-ins and street view images. Computers, Environment and Urban Systems, 81, p.101478.

Rao, J., Gao, S., Kang, Y. and Huang, Q., 2020. LSTM-TrajGAN: A deep learning approach to trajectory privacy protection. arXiv preprint arXiv:2006.10521.