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Spatial process-based transfer learning for prediction problems

Author

Listed:
  • Daisuke Murakami

    (Singular Perturbations Co. Ltd.
    Institute of Statistical Mathematics)

  • Mami Kajita

    (Singular Perturbations Co. Ltd.)

  • Seiji Kajita

    (Singular Perturbations Co. Ltd.)

Abstract

Although spatial prediction is a versatile tool for urban and environmental monitoring, the predictive accuracy is often unsatisfactory when limited samples are available from the study area. The present study was conducted to improve the accuracy in such cases through transfer learning, which uses larger datasets from external areas. Specifically, we proposed the SpTrans method, which pre-trains map patterns for each area using spatial process models. These patterns are then used in transfer learning to distinguish between unique patterns in the study area and common patterns across areas. The performance of the proposed SpTrans method was examined using land price prediction, with empirical results suggesting that the model achieves higher prediction accuracy than conventional learning, which does not explicitly consider local spatial dependence.

Suggested Citation

  • Daisuke Murakami & Mami Kajita & Seiji Kajita, 2025. "Spatial process-based transfer learning for prediction problems," Journal of Geographical Systems, Springer, vol. 27(1), pages 147-166, January.
    • Handle: RePEc:kap:jgeosy:v:27:y:2025:i:1:d:10.1007_s10109-024-00455-y
    DOI: 10.1007/s10109-024-00455-y
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    References listed on IDEAS

    as
    1. Ye Tian & Yang Feng, 2023. "Transfer Learning Under High-Dimensional Generalized Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(544), pages 2684-2697, October.
    2. Mansheng Lin & Shuai Teng & Gongfa Chen & David Bassir, 2023. "Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network," Land, MDPI, vol. 12(3), pages 1-26, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Spatial prediction; Transfer learning; Crime; Spatial process; Gradient boosting;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other

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