Author Image

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

I am an Assistant Professor in GIScience at the University of Minnesota, Twin Cities since 2020.
I obtained my PhD in Cartology and GIScience from Peking University (PKU) in Jun. 2020. I have a B.S. in Geographic Information Systems and a dual B.S. in Economics both from PKU.
From 2014 - 2020, I was a research assistant at the Spatial-temporal Social Sensing (S3) Lab, PKU. I have also worked as a visiting lecturer/researcher at SpaceTimeLab, University College London (UCL) between 2018 -2019.
My research interests include Geospatial Modelling, Applied Artificial Intelligence, Social Sensing, and Spatiotemporal Data Mining.
P.S. I'm a big fan of photography, music and NBA :)

Contact

I am currently seeking exceptional candidates to compete for the funded full-time PhD/MsC program at the University of Minnesota, Twin Cities (UMN).

The [Ms/PhD Assistantship] can be expected to start from Sep. 2021, feel free to reach out at:
dizhu@umn.edu

Academics

# for co-first author, * for correspondence

Peer-Reviewed Journal Articles

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

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

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

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

Di Zhu, Xin Yao, Ximeng Cheng, Fan Zhang, Yang Zhang, Zhou Huang, Yu Liu*. 2019. Estimating spatial configuration of intra-urban human activities using graph convolutional neural networks. EPJ Data Science. (under review)

Selected conference papers and invited talks

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, 2019, 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, 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.

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.

Projects

[2018.06-Present] 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.

[2019.01-2023.12] 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).

[2017.01-2021.12] 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.

[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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