Boettcher Center West, 2050 E. Iliff Ave. Denver, CO 80208
What I do
I teach and research in the field of Geographic Information Science.
Volunteered Geographic Information (VGI), Spatial Analysis, High-Performance Geo-computing
I obtained my Ph.D. in GIScience from the University of Wisconsin-Madison in 2018. During my Ph.D. study, I also obtained a M.S. in Computer Sciences in 2016. My current research is focused on the theory and application of volunteered geographic information (VGI) and computing technologies to support spatial big data analytics.
Ph.D., Geographic Information Science, University of Wisconsin-Madison, 2018
MS, Computer Sciences, University of Wisconsin-Madison, 2016
MS, Geographic Information Science, Beijing Normal University, 2013
BS, Geography, Beijing Normal University, 2010
American Association of Geographers
International Association of Chinese Professionals in Geographic Information Sciences
My research falls in geospatial big data analytics. I am particularly interested in VGI and its applications. VGI has been an important source of geospatial big data. However, it suffers from certain data quality issues in analysis (e.g., spatial bias). My research focuses on mitigating spatial bias in VGI (and other non-traditional data) to improve the quality of inferences made from them. In addition, geospatial big data (e.g., VGI) is often massive, it is urgent to address the computational challenges facing geospatial big data analysis. A side-line of my research focuses on developing algorithmic optimizations and utilizing cutting-edge computing technologies (e.g., high-performance computing) to support geospatial big data analytics.
Zhang, G., Zhu, A. -xing, Huang, Z. -pang, Ren, G., Qin, C. -Z., & Xiao, W. (2018). Validity of historical volunteered geographic information: Evaluating citizen data for mapping historical geographic phenomena. TRANSACTIONS IN GIS, 22(1), 149-164.
Zhang, G., & Thomson, S. -M. (2020). Integrating VGI and authoritative data for wildlife habitat mapping. The 2020 Annual Meeting of the American Association of Geographers (AAG). Virtual Meeting: American Association of Geographers (AAG).
Zhang, G. (2019). Modelling species habitat suitability from presence-only data using kernel density estimation. The 2019 Annual Meeting of the American Association of Geographers (AAG). Washington, D.C., USA.
Zhang, G., & Zhu, A. -xing. (2018). Representativeness-directed sample spatial bias mitigation for predictive mapping. The 2018 Annual Meeting of the American Association of Geographers (AAG). New Orleans, Louisiana, USA.
Whitbeck Graduate Dissertator Award, Department of Geography, University of Wisconsin-Madison
Best Paper Competition Award (Runner Up), The 2nd International Symposium on Spatiotemporal Computing
Campus-Wide Capstone Ph.D. Teaching Award, University of Wisconsin-Madison
Trewartha Conference Travel Award, Department of Geography, University of Wisconsin-Madison