A hierarchical constraint-based graph neural network for imputing urban area data

Abstract

Urban area data are strategically important for public safety, urban management, and planning. Previous research has attempted to estimate the values of unsampled regular areas, while minimal attention has been paid to the values of irregular areas. To address this problem, this study proposes a hierarchical geospatial graph neural network model based on the spatial hierarchical constraints of areas. The model first characterizes spatial relationships between irregular areas at different spatial scales. Then, it aggregates information from neighboring areas with graph neural networks, and finally, it imputes missing values in fine-grained areas under hierarchical relationship constraints. To investigate the performance of the proposed model, we constructed a new dataset consisting of the urban statistical values of irregular areas in New York City. Experiments on the dataset show that the proposed model outperforms state-of-the-art baselines and exhibits robustness. The model is adaptable to numerous geographic applications, including traffic management, public safety, and public resource allocation.

Keywords:

• Urban area
• spatial prediction
• hierarchical constraint
• spatial interpolation

Acknowledgements

We sincerely acknowledge Prof. May Yuan, Prof. Bo Huang, and the anonymous reviewers for their insightful comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and code that support the findings of this study are available in Figshare with the identifiers at the link: https://doi.org/10.6084/m9.figshare.20101202.v2

Additional information

Funding

This research was supported by the National Natural Science Foundation of China, grant numbers 42071382.

Notes on contributors

Shengwen Li

Shengwen Li received the B.S. degree in computer science and the Ph.D. degree in cartography and geographic information engineering from China University of Geosciences, Wuhan, China, in 2000 and 2010, respectively. He is currently an Associate Professor at the School of Computer Science, China University of Geosciences. His research interests include deep learning, spatial-temporal data mining, and knowledge graph.

Wanchen Yang

Wanchen Yang received the B.S. degree in Information Engineering from China University of Geosciences (Wuhan) in 2019 and the M.S. degree in Software Engineering from China University of Geosciences (Wuhan) in 2022. He is currently a data analyst. His main interests include data mining and deep learning.

Suzhen Huang

Suzhen Huang is a graduate student at the School of Computer Science, China University of Geosciences. Her research focuses on deep learning and traffic prediction.

Renyao Chen

Renyao Chen is a Ph.D. student at the School of Computer Science at the China University of Geosciences, Wuhan, China. His research focuses on knowledge graph, natural language processing, and geographic artificial intelligence.

Xuyang Cheng

Xuyang Cheng received the B.S. degree in software engineering from China University of Geosciences (Wuhan) in 2020 and the M.S. degree in software engineering from China University of Geosciences (Wuhan) in 2023. He is currently working in the Internet industry, and his main interests include traffic prediction and natural language processing.

Shunping Zhou

Shunping Zhou is a Ph.D supervisor at the School of Computer Science, China University of Geosciences, and his main research interests are spatial database technology research and software development.

Junfang Gong

Junfang Gong is an associate professor at the the School of Geography and Information Engineering, China University of Geosciences. She received her Ph.D. degree from the China University of Geosciences. Her research interest is geographic artificial intelligence, and spatial data analysis.

Haoyue Qian

Haoyue Qian is a Ph.D student at the School of Computer Science, China University of Geosciences (Wuhan). Her main research interests include cartographic synthesis and multi-scale representation of GIS data.

Fang Fang

Fang Fang is an associate professor at the School of Computer Science, China University of Geosciences (Wuhan). Her main research interests are urban visual intelligence, intelligent information processing, spatial data mining and spatial decision-making.