Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper). - Di Zhu 004; Yu Liu 003 (GIScience, 2018)

A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends. - Xin Yao 006; Di Zhu 004; Yong Gao 003; Lun Wu; Pengcheng Zhang; Yu Liu 003 (IEEE Access, 2018)

The Scale Effect on Spatial Interaction Patterns: An Empirical Study Using Taxi O-D data of Beijing and Shanghai. - Shiliang Zhang; Di Zhu 004; Xin Yao 006; Ximeng Cheng; Huagui He; Yu Liu 003 (IEEE Access, 2018)

Modelling Irregular Spatial Patterns using Graph Convolutional Neural Networks. - Di Zhu 004; Yu Liu 003 (CoRR, 2018)

Inferring spatial interaction patterns from sequential snapshots of spatial distributions. - Di Zhu 004; Zhou Huang; Li Shi; Lun Wu; Yu Liu 003 (Int. J. Geogr. Inf. Sci., 2018)

Visualizing spatial interaction characteristics with direction-based pattern maps. - Xin Yao 006; Lun Wu; Di Zhu 004; Yong Gao 003; Yu Liu 003 (J. Vis., 2019)

A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data. - Lun Wu; Ximeng Cheng; Chaogui Kang; Di Zhu 004; Zhou Huang; Yu Liu 003 (Int. J. Digit. Earth, 2020)

Spatial interpolation using conditional generative adversarial neural networks. - Di Zhu 004; Ximeng Cheng; Fan Zhang 011; Xin Yao 006; Yong Gao 003; Yu Liu 003 (Int. J. Geogr. Inf. Sci., 2020)

Mapping Human Activity Volumes Through Remote Sensing Imagery. - Xiaoyue Xing; Zhou Huang; Ximeng Cheng; Di Zhu 004; Chaogui Kang; Fan Zhang 011; Yu Liu 003 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2020)

Uncovering inconspicuous places using social media check-ins and street view images. - Fan Zhang 011; Jinyan Zu; Mingyuan Hu; Di Zhu 004; Yuhao Kang; Song Gao 001; Yi Zhang 064; Zhou Huang (Comput. Environ. Urban Syst., 2020)

Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London. - Nilufer Sari Aslam; Di Zhu 004; Tao Cheng 004; Mohamed R. Ibrahim; Yang Zhang 039 (Ann. GIS, 2021)

Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases. - Xiao Huang 003; Di Zhu 004; Fan Zhang 011; Tao Liu; Xiao Li 015; Lei Zou 002 (CoRR, 2021)

Sensing Population Distribution from Satellite Imagery Via Deep Learning: Model Selection, Neighboring Effects, and Systematic Biases. - Xiao Huang 003; Di Zhu 004; Fan Zhang 011; Tao Liu; Xiao Li 015; Lei Zou 002 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021)

Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks. - Xin Yao 006; Yong Gao 003; Di Zhu 004; Ed Manley; Jiaoe Wang; Yu Liu 003 (IEEE Trans. Intell. Transp. Syst., 2021)

Sensing overlapping geospatial communities from human movements using graph affiliation generation models. - Peng Luo 001; Di Zhu 004 (GeoAI@SIGSPATIAL, 2022)

SHGCN: a hypergraph-based deep learning model for spatiotemporal traffic flow prediction. - Yi Wang 132; Di Zhu 004 (GeoAI@SIGSPATIAL, 2022)

Towards the intelligent era of spatial analysis and modeling. - Di Zhu 004; Song Gao 001; Guofeng Cao (GeoAI@SIGSPATIAL, 2022)

MetroGAN: Simulating Urban Morphology with Generative Adversarial Network. - Weiyu Zhang; Yiyang Ma; Di Zhu 004; Lei Dong; Yu Liu 003 (KDD, 2022)

Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. - Di Zhu 004; Yu Liu 003; Xin Yao 006; Manfred M. Fischer (GeoInformatica, 2022)

A generalized heterogeneity model for spatial interpolation. - Peng Luo 001; Yongze Song; Di Zhu 004; Junyi Cheng; Liqiu Meng (Int. J. Geogr. Inf. Sci., 2023)

Correction to: Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. - Di Zhu 004; Yu Liu 003; Xin Yao 006; Manfred M. Fischer (GeoInformatica, 2023)

Edge Activating Module: Learning edge-to-edge features for mobility flow generation. - Sheng Wang; Di Zhu 004 (GeoAI@SIGSPATIAL, 2024)

Uncover the nature of overlapping community in cities. - Peng Luo 001; Di Zhu 004 (CoRR, 2024)

A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation. - Yi Wang 132; Di Zhu 004 (Inf. Fusion, 2024)

Next track point prediction using a flexible strategy of subgraph learning on road networks. - Yifan Zhang 009; Wenhao Yu 001; Di Zhu 004 (Int. J. Geogr. Inf. Sci., 2024)

Collective flow-evolutionary patterns reveal the mesoscopic structure between snapshots of spatial network. - Zhongfu Ma; Di Zhu 004 (Int. J. Geogr. Inf. Sci., 2025)

A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction. - Yi Wang 132; Zhenghong Wang; Fan Zhang 011; Chengling Tang; Chaogui Kang; Di Zhu 004; Zhongfu Ma; Sijie Ruan; Weiyu Zhang; Yu Zheng 004; Philip S. Yu; Yu Liu 003 (CoRR, 2025)