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
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.whugis20170383Conference 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/10.1145/3557918.3565863Luo, 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/10.1145/3557918.3565862Wang, 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/10.1145/3557918.3565866Zhang, 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/10.1145/3534678.3539239Chen, 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.