The second dimension of spatial association
نویسندگان
چکیده
A reasonable and adequate understanding of spatial association between geographical variables is the basis statistical inference geocomputation, such as prediction. Most current models for exploring are constructed using data at sample locations. In this study, approaches observations locations defined first dimension (FDA). However, information outside usually missing in models. To address issue, study proposes concept second (SDA), which an approach that extracts samples association. Based on SDA, three SDA models, including SDA-based multivariate linear regression, machine learning (i.e., random forest), geostatistical forest kriging), developed examining predicting distributions trace elements, Cr Cu, a mining region Western Australia. Model accuracy evaluated by comparing with corresponding FDA new R package “SecDim” to conduct Results show have series advantages compared First, prediction can be critically improved FDA, although identical explanatory used modeling Second, effectively indicate multi-scale effects diverse within environment local ranges variables. Third, avoid underestimating high values overestimating low general FDA-based learning, Finally, provide more smooth predictions across space than predicted massive fluctuations ranges. The provides insight into information-based multiple types great potential accurate effective geocomputation fields.
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ژورنال
عنوان ژورنال: International journal of applied earth observation and geoinformation
سال: 2022
ISSN: ['1872-826X', '1569-8432']
DOI: https://doi.org/10.1016/j.jag.2022.102834