HGGNN data and code

<strong>Abstract</strong> Urban area data are strategically important for public safety, urban management, and planning. Previous research has attempted to recover values from unsampled areas, while minimal attention has been paid to the values of irregular areas, which are naturally formed by road networks or administrative 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 from 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 showed that the proposed model outperformed the baselines by 30.34%, 14.43%, and 14.47% in RMSE, MAE, and MRE, respectively. This research enriches datasets on spatial missing value research and provides a methodological reference for the completion and prediction of a wide variety of geographic big data. It can be used in numerous spatial decision applications, including traffic management, public safety, and public resource allocation. <br> <strong>Keywords:</strong> urban area, spatial prediction, hierarchical constraint, spatial interpolation <br> This is the code and data of "A hierarchical constraint-based graph neural network for imputing"