2022/11/19 by Daisuke Murakami, Murakami, Daisuke, Mami Kajita +3 · 1 citation
Economics, Econometrics and Finance · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Housing Market and Economics #Human Mobility and Location-Based Analysis #Spatial and Panel Data Analysis
paper · pdf · doi:10.48550/arxiv.2211.10693
openalex publication_date 2022/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Although spatial prediction is widely used for urban and environmental monitoring, its accuracy is often unsatisfactory if only a small number of samples are available in the study area. The objective of this study was to improve the prediction accuracy in such a case through transfer learning using larger samples obtained outside the study area. Our proposal is to pre-train latent spatial-dependent processes, which are difficult to transfer, and apply them as additional features in the subsequent transfer learning. The proposed method is designed to involve local spatial dependence and can be implemented easily. This spatial-regression-based transfer learning is expected to achieve a higher and more stable prediction accuracy than conventional learning, which does not explicitly consider local spatial dependence. The performance of the proposed method was examined using land price and crime predictions. These results suggest that the proposed method successfully improved the accuracy and stability of these spatial predictions.