Di Zhu

he/him

[email protected]

612-625-2056

Geography, Environment & Society

473 Social Sciences Building

267 19th Ave S
Minneapolis, MN 55455

Curriculum Vitae

Collegiate Affiliation

Assistant Professor,

Geography, Environment & Society

Links

Google Scholar

Geospatial Data Intelligence Lab

Personal Website

Minnesota Population Center

Data Science Initiative

Research Gate

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 (geodi.umn.edu) directed by Prof. Zhu aims at generating theoretical and actionable insights from big geo-data by exploring the frontiers across GIScience, Geospatial Artificial Intelligence, and Social Sensing.

Prof. Zhu's research uses integrated spatial thinking and cross-disciplinary methods to facilitate Intelligent Spatial Analytics (ISA) and Geographic Knowledge Discovery, focusing on the human-environment interactions within population, urban dynamics, public health, human mobility, spatial networks, socioeconomic sustainability, crime, business optimization, etc.

Educational Background & Specialties

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Educational Background

Visiting Lecturer: SpaceTimeLab, University College London
PhD: Cartology and Geographic Information Science, Peking University
BSc: Cartology and Geographic Information Systems, Peking University
BA: Economics, Peking University

Specialties

Geographic Information Science (GIScience)
Geospatial Artificial Intelligence (GeoAI)
Spatial Networks
Human-Environment Interactions
Social Sensing
Human Mobility
Urban Complexity

Publications

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Publications

Ma, Z., & Zhu, D.* (2024). Collective flow-evolutionary patterns reveal the mesoscopic structure between snapshots of spatial network. International Journal of Geographical Information Science, 1-32.
Wang, Y., & Zhu, D.* (2024). A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation. Information Fusion, 104, 102149.
Zhu, D.*, & Cao, G. (2024). Intelligent Spatial Prediction and Interpolation Methods. In Handbook of Geospatial Artificial Intelligence (pp. 121-150). CRC Press.
Zhu, D.*, Gao, S., & Cao, G. (2022). Towards the intelligent era of spatial analysis and modeling. In Proceedings of the 5th ACM SIGSPATIAL international workshop on AI for geographic knowledge discovery (pp. 10-13).
Zhu, D.*, Liu, Y., Yao, X., & Fischer, M. M. (2022). Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica, 26(4), 645-676.
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.

Activities

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Activities

Research

Sensing Geospatial Communities in Mobility Networks: How Human Movements Drive Dynamic Community Structures within the Twin Cities Metro Area: https://www.cura.umn.edu/project/sensing-geospatial-communities-mobility-networks-how-human-movements-drive-dynamic. 2022-2023
Carbon Emission Efficiency and Human Mobility: https://rc.umn.edu/project/carbon-emission-efficiency-and-human-mobility. 2024-2025
Faculty Member at Minnesota Population Center: https://pop.umn.edu/people/di-zhu. since 2021
DSI affiliate faculty: https://cse.umn.edu/dsi/di-zhu. since 2022

Professional

Early Career Teaching and Learning Program: https://cei.umn.edu/programs/early-career-teaching-and-learning-program. 2024-2025
Guest Editor at Computational Urban Science: https://link.springer.com/collections/cihadjhbib. 2024 -
Program Chair at GeoAI'24 in ACM Sigspatial: The 7th International Workshop on AI for Geographic Knowledge Discovery (GeoAI’24). 2024
Local Organization Chair for ACM Sigspatial 2025 2025
Board Member for CPGIS : Board Member in the International Association of Chinese Professionals in Geographic Information Sciences (CPGIS). 2024-2026

Awards

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Awards

Medium/Large Seed Grant, Data Science Initiative, UMN, 2023, Quantifying Carbon Emission Efficiency through the Lens of Human Mobility, 2023-2024
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

Courses

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