Predicting bike sharing demand using recurrent neural networks. - Yan Pan; Ray Chen Zheng; Jiaxi Zhang 002; Xin Yao 006 (IIKI, 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)

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)

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)

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)

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)

Generating population migration flow data from inter-regional relations using graph convolutional network. - Yuxia Wang; Xin Yao 006; Yu Liu 003; Xia Li 001 (Int. J. Appl. Earth Obs. Geoinformation, 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)