Publications

2026

  • Zhu, D.*, Wang, S., and Luo, P. (2026). Uncovering spatial process heterogeneity from graph-based deep spatial regression. ISPRS Journal of Photogrammetry and Remote Sensing, 232: 509-523. [url]
  • Cheng, X., Wang, T., Zhu, D., & Ma, J. (2026). Uncertainty explanation of artificial intelligence models by SHAP. Knowledge-Based Systems, 115437. [url]

  • Du, J., Mugambi, T., Zhu, D., Betway, B., & Odhiambo, N. (2026). Modelling uncertainty in data fusion: A knowledge graph approach. Expert Systems with Applications, 131550. [url]

2025

  • Wang, S., and Zhu, D.* (2025). A Context-Enhanced Graph Neural Network Operator for Edge-to-Edge Learning in Human Mobility Networks. Transactions in GIS, 29 (7): e70154. [url] 
  • Zhu, D.*, & Ma, Z. (2025). Gravity-informed deep flow inference for spatial evolution modeling in panel data. International Journal of Geographical Information Science, 1-29. [url]
  • Wang, S., & Zhu, D. (2025). A Deep Origin-Destination Flow Imputation Model Informed by the Visitation Law in Human Mobility. In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems (pp. 1266-1269). [url]
  • Wang, Y., Wang, Z., Zhang, F., Tang, C., Kang, C., Zhu, D., Ma, Z., Ruan, S., Zhang, W., Zheng, Y., Yu, P., Liu, Y. (2025). A Gravity-informed spatiotemporal transformer for human activity intensity prediction, Transactions on Pattern Analysis and Machine Learning, 3625859. [url]
  • Xiong, M., & Zhu, D.* (2025). A Graph-based Deep Population Downscaling Model on Irregular Spatial Units. In Proceedings of the 8th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (pp. 37-44). [url]
  • Ma, Z., & Zhu, D.* (2025). A Neural Fitting Method for Potential Differential Equations in Urban Dynamics. In Proceedings of the 8th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (pp. 45-48). [url]
  • Song, Y.*, Xiong, M., Zeng, X., & Zhu, D. (2025). A Behavior-Informed and Geo-Context-Aware Home Detection Framework for Mobile Phone Positioning Data. In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems (pp. 1008-1018). [url]
  • Xiong, M., Zhu, D.*, & Van Riper, D. (2025). A visitor-enriched census in the U.S. cities using large-scale mobile positioning data. Scientific Data, 12(1), 1106. [url]
  • Xiong, M., & Zhu, D.* (2025). Lakeplace: Sensing interactions between lakes and human activities. Environment and Planning B: Urban Analytics and City Science. [url]
  • Zhu, D.*, & Ma, Z. (2025). Toward the Spatial Evolution Between Distribution Snapshots: A Network Perspective. In Urban Human Mobility (pp. 177-192). CRC Press. [url]
  • Wang, S., & Zhu, D.* (2025). Inferring human movements after snowfall: A weather-informed graph learning model for flow redistribution in mobility networks. International Journal of Geographical Information Science, 1–29. [url]
  • Guo, H., Wang, H., Zhu, D., Wu, L., Fotheringham, A. S., & Liu, Y. (2025). RegionGCN: Spatial-Heterogeneity-Aware Graph Convolutional Networks. Annals of the American Association of Geographers, 1-17. [url]
  • Zeng, X., Song, Y., & Zhu, D.* (2025). Spatially explicit knowledge in geo-embeddings: Interpreting location representation derived from human movement trajectories. Transactions in Urban Data, Science, and Technology, 27541231251333348. [url]
  • Luo, P., Song, C., Li, H., Zhu, D., & Duarte, F. (2025). Modeling shared micromobility as a label propagation process for detecting the overlapping communities. Computers, Environment and Urban Systems. 1-22. [url]
  • Gao, S., Mai, G., Newsam, S., Yang, L., Lunga, D., Zhu, D., Martins, B. and Arundel, S., (2025). Report of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. The SIGSPATIAL Special, 15(1), 3-4. [url]
  • Wang, S., Zhao, C., Jiang, Q., Zhu, D., Ma, J., & Sun, Y. (2025). Application of Graph Convolutional Neural Networks and multi-sources data on urban functional zones identification, a case study of Changchun, China. Sustainable Cities and Society, 106116. [url]
  • Jiang, B., Cheng, T., Tsou, M. H., Zhu, D., & Ye, X. (2025). Advancing translational human dynamics research: bridging space, mind, and computational urban science in the era of GeoAI. Computational Urban Science, 5(1), 1-9. [url]

2024

  • Wang, S., & Zhu, D.* (2024). Edge Activating Module: Learning edge-to-edge features for mobility flow generation. In Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (pp. 11-14). [url] 
  • 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. [url]
  • Zhu, D.,* & Cao, G. (2024). Intelligent Spatial Prediction and Interpolation Methods. In Handbook of Geospatial Artificial Intelligence (GeoAI) edited by Song Gao, Yingjie Hu, and Wenwen Li. [url]
  • 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. [url]
  • Zhang, Y., Yu, W., & Zhu, D. (2024). Next track point prediction using a flexible strategy of subgraph learning on road networks. International Journal of Geographical Information Science, 1-26. [url]
  • Luo, P., & Zhu, D.* (2024). Uncover the nature of overlapping community in cities. [url]
  • Zhang, G., Gong, X., & Zhu, D. (2024). Geographic proximity and homophily effects drive social interactions within VGI communities: an example of iNaturalist. International Journal of Digital Earth, 17(1), 2297948. [url]
  • Xiong, M., & Zhu, D*. From Lake to Lakeplace: A Case Study of Lake-Related Human Activities in the Twin Cities.

2023

  • Zhu, D., * & Hu, Y. (2023). Artificial Intelligence. In Concise Encyclopedia of Human Geography edited by Loretta Lees and David Demeritt. 32-36. [url]
  • Luo, P., Song, Y., Zhu, D., Cheng, J., & Meng, L. (2023). A generalized heterogeneity model for spatial interpolation. International Journal of Geographical Information Science, 37(3), 634-659. [url]
  • Chen, T., Zhu, D., Cheng, T., Gao, X., & Chen, H. (2023). Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic. Plos one, 18(1), e0277913. [url]
  • Liu, Y., Wang, K., Xing, X., Guo, H., Zhang, W., Luo, Q., ... & Zhu, D. (2023). Spatial effects in Geographical analysis, 地理分析中的空间效应. Acta Geographica Sinica, 78(3), 517-531. [url]

2022

  • 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, 1-32. [url]
  • Wang, Y., & Zhu, D.* (2022). SHGCN: a hypergraph-based deep learning model for spatiotemporal traffic flow prediction. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (pp. 30-39). [url]
  • Luo, P., & Zhu, D.* (2022). Sensing overlapping geospatial communities from human movements using graph affiliation generation models. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (pp. 1-9). [url]
  • 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). [url]
  • Zhang, W., Ma, Y., Zhu, D., Dong, L., & Liu, Y. (2022). Metrogan: Simulating urban morphology with generative adversarial network. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2482-2492). [url]
  • Zhang, Y., Yu, W., & Zhu, D. (2022). Terrain feature-aware deep learning network for digital elevation model superresolution. ISPRS Journal of Photogrammetry and Remote Sensing, 189, 143-162. [url]
  • Chen, T., Bowers, K., Zhu, D.*, Gao, X., & Cheng, T. (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), 13. [url]

2021

  • Zhu, D.*, Ye, X., & Manson, S. (2021). Revealing the spatial shifting pattern of COVID-19 pandemic in the United States. Scientific Reports, 11(1), 8396. [url]
  • Huang, X., Zhu, D., Zhang, F., Liu, T., Li, X., & Zou, L. (2021). Sensing population distribution from satellite imagery via deep learning: Model selection, neighboring effects, and systematic biases. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 5137-5151. [url]
  • Sari Aslam, N., Zhu, D., Cheng, T., Ibrahim, M. R., & Zhang, Y. (2021). Semantic enrichment of secondary activities using smart card data and point of interests: A case study in London. Annals of GIS, 27(1), 29-41. [url]
  • Chen, T., Cheng, T., & Zhu, D. (2021). The exploration of human activity zones using geo-tagged big data during the COVID-19 first lockdown in London, UK. GIS Research UK (GISRUK). [url]

2020

  • 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.
  • Xing, X., Huang, Z., Cheng, X., Zhu, D., Kang, C., Zhang, F., & Liu, Y. (2020). Mapping human activity volumes through remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5652-5668.
  • Yao, X., Gao, Y., Zhu, D., Manley, E., Wang, J., & Liu, Y. (2020). Spatial origin-destination flow imputation using graph convolutional networks. IEEE Transactions on Intelligent Transportation Systems, 22(12), 7474-7484.
  • Wu, L., Cheng, X., Kang, C., Zhu, D., Huang, Z., & Liu, Y. (2020). A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data. International Journal of Digital Earth, 13(6), 708-726.
  • Wang, Y., Zhu, D., Yin, G., Huang, Z., & Liu, Y. (2020). A unified spatial multigraph analysis for public transport performance. Scientific reports, 10(1), 9573.
  • Zhang, F., Zu, J., Hu, M., Zhu, D., Kang, Y., Gao, S., ... & Huang, Z. (2020). Uncovering inconspicuous places using social media check-ins and street view images. Computers, Environment and Urban Systems, 81, 101478.
  • Liu, Y., Yao, X., Gong, Y., Kang, C., Shi, X., Wang, F., ... & Zhu, X. (2020). Analytical methods and applications of spatial interactions in the era of big data, 大数据时代的空间交互分析方法和应用再论. Dili Xuebao/Acta Geographica Sinica, 75(7), 1523.

2019 and Before

Please refer to Google Scholar Page