Journal article
An explainable spatial interpolation method considering spatial stratified heterogeneity
International journal of geographical information science : IJGIS, Vol.39(3), pp.600-626
03/04/2025
DOI: 10.1080/13658816.2024.2426067
Abstract
Spatial interpolation is essential for handling sparsity and missing spatial data. Current machine learning-based spatial interpolation methods are subject to the statistical constraints of spatial stratified heterogeneity (SSH), normally involving separate modeling of each stratum and simple weighted averaging to integrate intra-stratum and inter-strata features. However, these models overlook the different contributions of inter-strata features to different locations within a stratum (heterogeneous inter-strata associations, HIA) and the explanation of spatial effects on the interpolation process, leading to suboptimal and unreliable interpolation outcomes. This article proposes a novel explainable spatial interpolation method considering SSH (X-SSHM). Spatial and environmental features are utilized to describe intra-stratum and inter-strata information, which are fed into random forest-based learners to achieve high-level semantic feature mapping. Geographically weighted regression is employed to integrate intra-stratum and inter-strata features to achieve a unified expression of SSH and HIA, obtaining the final interpolation result. Geographically weighted Shapley (GSHAP) is proposed to decompose the marginal contributions of intra-stratum and inter-strata features. Model performance is evaluated on simulated and soil organic matter datasets. X-SSHM outperformed five baselines regarding interpolation accuracy. Moreover, statistical methods validated X-SSHM's ability to elucidate the mechanisms by which SSH, spatial autocorrelation and HIA affect the model interpolation process.
Details
- Title: Subtitle
- An explainable spatial interpolation method considering spatial stratified heterogeneity
- Creators
- Shifen Cheng - Institute of Geographic Sciences and Natural Resources ResearchWenhui Zhang - South China Agricultural UniversityPeng Luo - Technical University of MunichLizeng Wang - Institute of Geographic Sciences and Natural Resources ResearchFeng Lu - Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
- Resource Type
- Journal article
- Publication Details
- International journal of geographical information science : IJGIS, Vol.39(3), pp.600-626
- DOI
- 10.1080/13658816.2024.2426067
- ISSN
- 1365-8816
- eISSN
- 1362-3087
- Publisher
- Taylor & Francis
- Number of pages
- 27
- Grant note
- XDB0740100-02 / Strategic Priority Research Program of the Chinese Academy of Sciences 42371469; 42101423 / National Natural Science Foundation of China KPI003; YPI002 / Innovation Project of LREIS
- Language
- English
- Date published
- 03/04/2025
- Academic Unit
- School of Earth, Environment, and Sustainability
- Record Identifier
- 9985219305002771
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