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k-nearest neighbors prediction and classification for spatial data

Mohamed-Salem Ahmed (), Mamadou N’diaye (), Mohammed Kadi Attouch () and Sophie Dabo-Niange ()
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Mohamed-Salem Ahmed: Alicante
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

Journal of Spatial Econometrics, 2023, vol. 4, issue 1, 1-34

Abstract: 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.

Keywords: Regression estimation; Prediction; Spatial process; Supervised Classification; k-nearest neighbors (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43071-023-00041-2

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