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k-nearest neighbors prediction and classification for spatial data

Author

Listed:
  • Mohamed-Salem Ahmed

    (Alicante
    Univ. Lille, CHU Lille, ULR 2694 - METRICS Evaluation des technologies de santé et des pratiques ḿedicales)

  • Mamadou N’diaye

    (University Cheikh Anta Diop of Dakar & Senegalese Institute of Agricultural Research)

  • Mohammed Kadi Attouch

    (University Sidi Bel Abbes)

  • Sophie Dabo-Niange

    (Université Lille, CNRS)

Abstract

This paper proposes a spatial k-nearest neighbor method for nonparametric prediction of real-valued spatial data and supervised classification for categorical spatial data. The proposed method is based on a double nearest neighbor rule which combines two kernels to control the distances between observations and locations. It uses a random bandwidth in order to more appropriately fit the distributions of the covariates. The almost complete convergence with rate of the proposed predictor is established and the almost sure convergence of the supervised classification rule was deduced. Finite sample properties are given for two applications of the k-nearest neighbor prediction and classification rule to the soil and the fisheries datasets.

Suggested Citation

  • Mohamed-Salem Ahmed & Mamadou N’diaye & Mohammed Kadi Attouch & Sophie Dabo-Niange, 2023. "k-nearest neighbors prediction and classification for spatial data," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-34, December.
    • Handle: RePEc:spr:jospat:v:4:y:2023:i:1:d:10.1007_s43071-023-00041-2
    DOI: 10.1007/s43071-023-00041-2
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    Keywords

    Regression estimation; Prediction; Spatial process; Supervised Classification; k-nearest neighbors;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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