A generalized heterogeneity model for spatial interpolation

Spatial heterogeneity refers to uneven distributions of geographical variables. Spatial interpolation methods that utilize spatial heterogeneity are sensitive to the way in which spatial heterogeneity is characterized. This study developed a Generalized Heterogeneity Model (GHM) for characterizing l...

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Bibliographic Details
Published in:International journal of geographical information science : IJGIS 2023-03, Vol.37 (3), p.634-659
Main Authors: Luo, Peng, Song, Yongze, Zhu, Di, Cheng, Junyi, Meng, Liqiu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
area-to-area kriging
Boundaries
Chlorophyll
Estimates
Heterogeneity
Interpolation
Machine learning
Spatial distribution
Spatial heterogeneity
Spatial interpolation
spatial statistics
Spatial variations
stratified heterogeneity
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Summary:Spatial heterogeneity refers to uneven distributions of geographical variables. Spatial interpolation methods that utilize spatial heterogeneity are sensitive to the way in which spatial heterogeneity is characterized. This study developed a Generalized Heterogeneity Model (GHM) for characterizing local and stratified heterogeneity within variables and to improve interpolation accuracy. GHM first divides a study area into multiple spatial strata according to the sample values and locations of a variable. Then, GHM estimates simultaneously the spatial variations of the variable within and between the spatial strata. Finally, GHM interpolates unbiased estimates and uncertainty at unsampled locations. We demonstrated the GHM by predicting the spatial distributions of marine chlorophyll in Townsville, Queensland, Australia. Results show that GHM improved both the overall interpolation accuracy across the study area and along strata boundaries compared with previous interpolation models. GHM also avoided bull's eye patterns and abrupt changes along strata boundaries. In future studies, GHM has the potential to be integrated with machine learning and advanced algorithms to improve spatial prediction accuracy for studies in broader fields.
ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2022.2147530