Transfer Learning-Based Region Statistical Data Completion via Double Graphs

Region statistical data is widely utilized as fundamental spatial information for urban status observation, urban planning, and the formulation of urban policies. In practice, the collected region statistical data is usually incomplete due to insufficient sensors, recording errors or privacy restrictions. Transfer learning-based methods make great advancement on completing missing region statistical data in the data-sparse city by training model on the data-rich city. However, these methods employ all available attributes of regions in their dataset to learn transferred knowledge, even if the values of some attributes in the source and target cities differ significantly. To address this issue, this paper proposes a double graph neural network model to improve completion of the region statistical data on insufficient data cities. The model first introduces spatial distribution of attributes to identify transferable attributes, then utilizes hierarchical graph structure to characterize the spatial relationships of regions, finally designs a double graph network to transfer knowledge for completing the missing values of insufficient data cities. Experiments on two datasets show that the proposed model outperforms the baseline models and is robust. This study presents a promising method for spatial data completion, and provides a method reference for spatial applications with insufficient data.