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Dr. Di Zhu is an Assistant Professor of Geographic Information Science in the Department of Geography, Environment, and Society at the University of Minnesota, Twin Cities.

The Geospatial Data Intelligence (GeoDI) Lab directed by Prof. Zhu aims at generating both theoretical and actionable insights from spatiotemporal data by exploring the frontiers that bridge geospatial information, artificial intelligence, and social sensing.

Prof. Zhu's research interests center around Geospatial Artificial Intelligence (GeoAI), Spatial Analytics, Social Sensing, and Urban Complexities. He uses integrated spatial thinking and cross-disciplinary methods to facilitate Intelligent Spatial Analytics (ISA) and Geographic Knowledge Discovery, focusing on the human-environment complexities within population, urban dynamics, public health, human mobility, spatial networks, socioeconomic sustainability, crime, business optimization, etc. 

Education

Visiting Lecturer: SpaceTimeLab, University College London
Ph.D., Cartology and Geographic Information Science, Peking University
B.Sc., Cartology and Geographic Information Systems, Peking University
B.A., Economics, Peking University
 

Teaching Subjects

• GEOG 3531/5531 - Numerical Spatial Analysis
• GEOG 8980 - Seminar in Geospatial Artificial Intelligence
• GEOG 5533 - Advanced Spatial Analysis
• GIS 5555 - Basic Spatial Analysis

Honors and Awards

• FIRP, Sensing Geospatial Communities in Mobility Networks: How Human Movements Drive Dynamic Community Structures within the Twin Cities Metro Area, Center for Urban & Regional Affairs, 2022 - 2023
• Seed Grants for Social Sciences Research, A GeoAI-based Model for Human Mobility Flow Generation, CLA, UMN, 2023-2024
• Early Career Award , GISRUK, UK, 2019
• Rising Star Award, College GIS Forum, China, 2018
• Lixin Tang Award, 2017 - 2020
• Presidential Fellowship, PKU

Selected Publications

• Zhu, D.*, Liu, Y., Yao, X., & Fischer, M. M. (2021). Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica, 1-32.
• Zhu, D.*, Ye, X., & Manson, S. (2021). Revealing the spatial shifting pattern of COVID-19 pandemic in the United States. Scientific reports, 11(1), 1-9.
• Zhu, D., Zhang, F., Wang, S., Wang, Y., Cheng, X., Huang, Z., & Liu, Y. (2020). Understanding place characteristics in geographic contexts through graph convolutional neural networks. Annals of the American Association of Geographers, 110(2), 408-420.
• Zhu, D., Cheng, X., Zhang, F., Yao, X., Gao, Y., & Liu, Y. (2020). Spatial interpolation using conditional generative adversarial neural networks. International Journal of Geographical Information Science, 34(4), 735-758.
• Zhu, D., Huang, Z., Shi, L., Wu, L., & Liu, Y. (2018). Inferring spatial interaction patterns from sequential snapshots of spatial distributions. International Journal of Geographical Information Science, 32(4), 783-805.
• Zhu, D., Wang, N., Wu, L., & Liu, Y. (2017). Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data. Applied Geography, 86, 152-164.