  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.
