Annals of GIS (Apr 2024)

A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction

  • Yi-Jie Wang,
  • Cheng-Zhi Qin,
  • Peng Liang,
  • Liang-Jun Zhu,
  • Zi-Yue Chen,
  • Cheng-Long Wu,
  • A-Xing Zhu

DOI
https://doi.org/10.1080/19475683.2024.2324398
Journal volume & issue
Vol. 30, no. 2
pp. 199 – 214

Abstract

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ABSTRACTSpatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies.

Keywords