Hi, this is Di Zhu. Welcome to my website!

I am an Assistant Professor of Geographic Information Science (GIScience) in the Department of Geography, Environment and Society (GES), University of Minnesota, Twin Cities (UMN), and the director of Geospatial Data Intelligence (GeoDI) Lab.

I have a PhD in Cartology and GIScience from Peking University (PKU), a B.S. in Geographic Information Systems, and a B.Ec. in Economics also from PKU. My research focuses on GIScience, Geospatial Artificial Intelligence (GeoAI), Spatial Networks, Human-Environment Interactions, and Urban Complexities.

Besides the main appointment, I serve as a faculty at the Minnesota Population Center (MPC), an executive committee member of the Master of GIS program at UMN, an affiliated professor at UMN Data Science Initiatives (DSI), a Board Member at the International Association of Chinese Professionals in Geographic Information Sciences (CPGIS).

Before becoming a faculty, I was a graduate research assistant at the Spatial-temporal Social Sensing (S3) Lab, PKU. I was also a visiting researcher at SpaceTimeLab, Department of Civil, Environmental, and Geomatic Engineering, University College London (UCL), between 2018 -2019.

Contact

GeoDI Lab is seeking PhD/MA students to join us in the Twin Cities.

Please feel free to reach out with your CV, transcript, and one writing sample at:

dizhu@umn.edu

Inquiries without transcript and the writing sample(s) would not be considered

Academics

# for co-first author, * for correspondence

(NOT updated since June 2022. Please visit google scholar for recent publications)

Journals and Books

Di Zhu * and Guofeng Cao. 2023. Intelligent Spatial Prediction and Interpolation Methods. in Handbook of Geospatial Artificial Intelligence (GeoAI) edited by Song Gao, Yingjie Hu and Wenwen Li

https://doi.org/10.1201/9781003308423-7

Di Zhu * and Yingjie Hu. 2023. Artificial Intelligence. in Concise Encyclopedia of Human Geography edited by Loretta Lees and David Demeritt. 32-36.

https://doi.org/10.4337/9781800883499.ch07

Peng Luo, Yongze Song*, Di Zhu, Junyi Cheng, Liqiu Meng. 2022. A generalized heterogeneity model for spatial interpolation. International Journal of Geographical Information Science. 37:3, 634-659.

https://doi.org/10.1080/13658816.2022.2147530 [code]

Yifan Zhang, Wenhao Yu*, Di Zhu. 2022. Terrain feature-aware deep learning network for digital elevation model superresolution. ISPRS Journal of Photogrammetry and Remote Sensing. 189, 143-162.

https://doi.org/10.1016/j.isprsjprs.2022.04.028

Tongxin Chen, Kate Bowers, Di Zhu*, Xiaowei Gao, Tao Cheng. 2022. Spatio-temporal stratified associations between urban human activities and crime patterns: a case study in San Francisco around the COVID-19 stay-at-home mandate. Computational Urban Science. 2(1), 1-12.

https://doi.org/10.1007/s43762-022-00041-2 [code]

Di Zhu*, Yu Liu, Xin Yao, Manfred M. Fischer. 2021. Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica. 1-32

https://doi.org/10.1007/s10707-021-00454-x [code]

Di Zhu*, Xinyue Ye, Steven Manson. 2021. Revealing the spatial shifting pattern of COVID-19 pandemic in the United States. Nature Scientific Reports. 11(8396)

https://www.nature.com/articles/s41598-021-87902-8 [code]

Xiao Huang*, Di Zhu, Fan Zhang, Tao Liu, Xiao Li, Lei Zou. 2021. Sensing population distribution from satellite imagery via deep learning: Model selection, neighboring effects, and systematic biases. Journal of Selected Topics in Applied Earth Observations and Remote Sensing,14, 5137-5151

https://doi.org/10.1109/JSTARS.2021.3076630

Di Zhu, Fan Zhang, Shengyin Wang, Yaoli Wang, Ximeng Cheng, Zhou Huang, Yu Liu*. 2020. Understanding place characteristics in geographic contexts through graph convolutional neural networks. Annals of the American Association of Geographers, 110:2, 408-420.

https://doi.org/10.1080/24694452.2019.1694403

Yaoli Wang, Di Zhu, Zhou Huang*, Yu Liu. 2020. A unified spatial multigraph analysis for sustainable urban transportation. Nature Scientific Reports, 10, 9573.

https://doi.org/10.1038/s41598-020-65175-x

Nilufer Sari Aslam, Di Zhu, Tao Cheng, Mohamed Ibrahim and Yang Zhang. 2020. Semantic enrichment of secondary activities using smart card data and point of interests: A case study in London. Annals of GIS

https://doi.org/10.1080/19475683.2020.1783359

Xin Yao, Yong Gao*, Di Zhu, Ed Manley, Jiao'e Wang and Yu Liu. 2020. Spatial origin-destination flow imputation using graph convolutional networks. IEEE Transactions on Intelligent Transportation Systems

https://doi.org/10.1109/TITS.2020.3003310

Xiaoyue Xing, Zhou Huang*, Ximeng Cheng, Di Zhu, Chaogui Kang, Fan Zhang, Yu Liu. 2020. Mapping Human Activity Volumes Through Remote Sensing Imagery. Journal of Selected Topics in Applied Earth Observations and Remote Sensing

https://doi.org/10.1109/JSTARS.2020.3023730

Fan Zhang, Jinyan Zu, Mingyuan Hu*, Di Zhu, Yuhao Kang, Song Gao, Yi Zhang, Zhou Huang. 2020. Uncovering inconspicuous places using social media check-ins and street view images. Computers, Environment and Urban Systems. 81, 101478.

https://doi.org/10.1016/j.compenvurbsys.2020.101478

Di Zhu, Ximeng Cheng, Fan Zhang, Xin Yao, Yong Gao, Yu Liu*. 2019. Spatial interpolation using conditional generative adversarial neural networks. International Journal of Geographic Information Science, 1-24.

https://doi.org/10.1080/13658816.2019.1599122 [code]

Fan Zhang, Lun Wu, Di Zhu, Yu Liu*. 2019. Social sensing from street-level imagery: a case study in learning urban mobility patterns. ISPRS Journal of Photogrammetry and Remote Sensing, 153: 48-58.

https:// doi.org/10.1016/j.isprsjprs.2019.04.017

Lei Chen, Yong Gao*, Di Zhu, Yihong Yuan, Yu Liu. 2019. Quantifying the scale effect in geospatial big data using semi-variograms. Plos One, 1-18

https:// doi.org/10.1371/journal.pone.0225139

Di Zhu, Zhou Huang, Li Shi, Lun Wu, Yu Liu*. 2018. Inferring spatial interaction patterns from sequential snapshots of spatial distributions. International Journal of Geographic Information Science, 32(4): 783-805

https://doi.org/10.1080/13658816.2017.1413192 [code]

Di Zhu, Yu Liu*. 2018. Modelling irregular spatial patterns using graph convolutional neural networks. arXiv preprint: 1808.09802

https://arxiv.org/abs/1808.09802

Di Zhu, Ninghua Wang, Lun Wu and Yu Liu*. 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.

https://doi.org/10.1016/j.apgeog.2017.07.001

Shiliang Zhang, Di Zhu#,*, Xin Yao, Ximeng Cheng, Huagui He, Yu Liu. 2018. The scale effect on spatial interaction patterns: an empirical study using taxi OD data of Beijing and Shanghai. IEEE Access, 6, 51994-52003.

https://doi.org/10.1109/ACCESS.2018.2869378

Xin Yao, Di Zhu, Yong Gao, Lun Wu, Pengcheng, Zhang, Yu Liu*. 2018. Visualizing spatial interaction characteristics with direction-based pattern maps. Journal of Visualization, pp 1-15.

https://doi.org/10.1007/s12650-018-00543-4

Xin Yao, Lun Wu, Di Zhu, Yong Gao, Yu Liu*. 2018. A stepwise spatio-temporal flow clustering method for discovering mobility trends. IEEE Access, 6, 44666-44675.

https://doi.org/10.1109/ACCESS.2018.2864662

Lun Wu, Ximeng Cheng, Chaogui Kang, Di Zhu, Yu Liu*. 2018. A framework for mixed use decomposition based on temporal activity signatures extracted from big geo-data. International Journal of Digital Earth, pp 1-19

https://doi.org/10.1080/17538947.2018.1556353

Di Zhu, Yu Liu*. 2017. An Incremental Map-Matching Method Based on Road Network Topology. GEOMATICS AND INFORMATION SCIENCE OF WUHAN UNIVERS, 42(1): 77-83.

http://ch.whu.edu.cn/CN/10.13203/j.whugis20150016

Yu Liu, Zhaohui Zhan, Di Zhu, Yanwei Chai, Xiujun Ma, Lun Wu*. 2018. Incorporating Multi-source Big Geo-data to Sense Spatial Heterogeneity Patterns in an Urban Space. GEOMATICS AND INFORMATION SCIENCE OF WUHAN UNIVERS, 43(3): 327-335.

https://doi.org/10.13203/j.whugis20170383

Conference papers & presentations

Zhu, D.* (2023). Learning Spatial Heterogeneity via Explainable Deep Spatial Regression. In AAG Annual Meeting. Mar. 23-27, Denver, USA.

Ma, Z., and Zhu, D.* (2023). Collective Flow Evolution as a Mesoscopic Structure of Spatial Network, 30th International Conference on Geoinformatics. Jul. 20-22, London, UK.

Ma, Z., and Zhu, D.* (2023). Collective Flow Evolution Pattern: A mesoscopic exploration of spatial network dynamics, In AAG Annual Meeting. Mar. 23-27, Denver, USA.

Hendrickson, R., and Zhu, D.* (2023). Exploring the Scaling Relationships between Human Mobility and Air Pollutants in the Twin Cities, In AAG Annual Meeting. Mar. 23-27, Denver, USA.

Zhu, D.*, Gao, S., Cao, G. (2022). Towards the Intelligent Era of Spatial Analysis and Modeling, Proceedings of The 5th ACMSIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI ’22), Nov, Seattle, WA, United State.

https://doi.org/10.1145/3557918.3565863

Luo, P., and Zhu, D.* (2022). Sensing overlapping geospatial communities from human movements using graph affiliation generation models, Proceedings of The 5th ACMSIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI ’22), Nov, Seattle, WA, United State.

https://doi.org/10.1145/3557918.3565862

Wang, Y., and Zhu, D.* (2022). SHGCN: A hypergraph-based deep learning model for spatiotemporal traffic flow prediction, Proceedings of The 5th ACMSIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI ’22), Nov, Seattle, WA, United State.

https://doi.org/10.1145/3557918.3565866

Zhang, W., Ma, Y., Zhu, D., Dong, L., and Liu, Y.*. (2022). MetroGAN: Simulating Urban Morphology with Generative Adversarial Network, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), Aug. 14 - 18, Washington DC, United States.

https://doi.org/10.1145/3534678.3539239

Chen, T., and Zhu, D.*. (2021). The Spatio-temporal Stratified Association between Human Activities and Crime Patterns during the COVID-19 Stay-at-home Mandate, Proceedings of the 2021 ACM SIGSPATIAL China Annual Conference on Space Intelligence (SpatialDI 2021), Apr. 23 - 24, Hangzhou, China.

Chen, T.*, Cheng, T., and Zhu, D. (2021). The exploration of human activity zones using geo-tagged big dataduring the COVID-19 first lockdown in London, UK, Proceedings of the 29th Conference on GIS Research UKApr. 13-16, Cardiff University, United Kingdom.

Zhu, D.*, Cheng, T., and Liu, Y. (2019). Geo-propagation from Incomplete Spatial Distribution Data: A Case Study of House Price Estimation, Proceedings of the 27th Conference on GIS Research UK, Newcastle upon Tyne, United Kingdom.

Soundararaj, B.*, and Zhu, D. (2019). Estimating pedestrian flow from footfall counts using geo-propagation. In Annual conference on complex systems (ccs 2019). Sep. 30 - Oct. 4, Singapore.

Wang, Y.,* Zhu, D., Yin, G., Huang, Z., and Liu, Y. (2019). Investigating local travel speed with spatial network structures and properties. In Proceedings of the 2nd international conference on urban informatics. June 24-26, Hong Kong, China.

Zhu, Di.* (2019). Spatial interpolation based on conditional generative adversarial neural network. In AAG Annual Meeting. Apr. 5, Washington D.C., USA.

Zhu, D. and Liu, Y.* (2018). Modelling spatial patterns using graph convolutional networks, Leibniz International Proceedings in Informatics (LIPIcs), 10th International Conference on Geographic Information Science, 2018, Melbourne, Australia.

Zhu, D., Shi, L., Wang, Y., Cheng, X., and Liu, Y.* (2017). Infer Spatial Interaction Patterns from Spatial Distributions, 25th International Conference on Geoinformatics, 2017, Buffalo, United States of America.

Zhu, D., Wang, N. and Liu, Y.* (2016). Street perspective: a novel spatial unit in urban social sensing, 17th International Symposium on Spatial Data Handling, 2016, Beijing, China.

Zhu, D. and Liu, Y.* (2016). The Distance Effect in Spatial Interaction and Spatial Similarity: a Big Data View of Tobler’s First Law, 33rd International Geographical Congress, 2016, Beijing, China.

Invited Talks

Zhu, D.* (2020). Intelligent spatial prediction in incomplete-data scenarios. Invited talk in the CPGIS GeoAI Seminar Series@China University of Geosciences, May 6, online livestream.

Zhu, D.* (2020). Intelligent spatial prediction: Rethinking geospatial modeling in the era of GeoAI. Invited talk in the Annual Conference of Geomatics and GIScience@Central South University, Dec. 26, Changsha, China.

Zhu, D.* (2019). Inferring national migration flows from sequential population snapshots. Invited talk in Geospatial Seminar@UCL, Department of Civil Environmental and Geomatic Engineering, UCL. Feb. 21, London, United Kingdom.

Projects

[2022.07-2023.07] FIRP, Center for Urban & Regional Affairs (1801-10964-21584-5672018). Sensing Geospatial Communities in Mobility Networks: How Human Movements Drive Dynamic Community Structures within the Twin Cities Metro Area (PI).

Detect geospatial boundaries of communities, describe dynamic community profiles, and identify key transitions within our community structure during the COVID-19 pandemic within the Twin Cities Metro Area.

[2021.09-present] Start-up Funding for New Faculty at University of Minnesota (1000-10964-20042-5672018)}. Intelligent Spatial Models and Analytical Methods (PI).

Explore the frontier of Geospatial Artificial Intelligence, and bridge the methodological linkage between deep/machine learning models and spatial analytical models with a focus on human-environment complexities within socioeconomic and population data.

[2020.09-present] National Spatiotemporal Population Research Infrastructure (2R01HD057929-11). National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Collaboration with Minnesota Population Center

[2019.01-2021.09] The Major Program of the National Natural Science Foundation of China (no. 41830645). Theoretical and analytical methods of spatial interaction networks in geospatial big data (SI).

Investigate systematic methods for analyzing multi-modal spatial networks at different spatio-temporal scales. Develop a WebGIS platform and apply to: city (Shenzhen), megalopolis (Guangdong-Hong Kong- Macau Big Bay Area), and nation (China).

[2017.01-2021.09] National Science Fund for Distinguished Young Scholars (no. 41625003). Geo-spatial models and analytical methods (SI).

Investigate human behavior characteristics from the perspective of the interaction between people and geographical environment with the support of big geo-data using deep learning methods using PyTorch on Linux.

[2017.07-2021.07] The National Key Research and Development Program of China (no. 2017YFB0503600). Big geo-data mining and spatio-temporal pattern discovery (SI).

Represent and model diverse geospatial semantics of locations and develop spatial prediction approaches incorporating locations' relatedness.

[2018.10-2019.10] The China Scholarship Council funding (no. 201806010077). Modelling spatial heterogeneity and spatial interactions from the big geo-data perspective (PI).

Develop a spatio-temporal Geo-propagation method for sparse geospatial data prediction with an application of the house price estimation in Beijing from 2011-2018.

[2018.06-2021.09] A 2C location recommender and time planning Map App for offline meetup (Startup project). Co-Founder and Chief Product Officer(CPO) at Beijing Jikewenqing(GeekArt) Technology Co. Ltd.

Integrate existing algorithms of location-related schedule planning and location recommendation in the context of clients’ business scenarios: negotiate time according to every participant’s schedule and activity preference.

[2015.01-2016.12] National Natural Science Foundation of China (no. 41428102). Spatial optimizing of urban facilities to mitigate traffic congestion: a case study of Beijing (SI).

[2013.01-2016.12] National Natural Science Foundation of China (no. 41271386). Investigating human mobility pattern based on massive spatio-temporal data (SI).

Investigate the GPS-enabled taxis' origin and destination (OD) distributions, mobility patterns and relations with urban structure, street networks. Develop spatio-temporal data mining algorithms for processing large-scale geo-data using Python and PostgreSQL.

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