Use of a tree-structured hierarchical model for estimation of location and uncertainty in multivariate spatial data

作者：Chen Liyuan; Brown Steven D^*

来源：Journal of Chemometrics, 2014, 28(6): 523-538.

DOI：10.1002/cem.2611

• tree-structured hierarchical modeling
• Bayes classifier
• variogram analysis
• spatial statistics
• geolocation prediction
• uncertainty
• surface water
• partial least squares regression
• prediction interval

Analysis and modeling of spatial data are of considerable interest in many applications. However, the prediction of geographical features from a set of chemical measurements on a set of geographically distinct samples has never been explored. We report a new, tree-structured hierarchical model for the estimation of geographical location of spatially distributed samples from their chemical measurements. The tree-structured hierarchical modeling used in this study involves a set of geographic regions stored in a hierarchical tree structure, with each nonterminal node representing a classifier and each terminal node representing a regression model. Once the tree-structured model is constructed, given a sample with only chemical measurements available, the predicted regional location of the sample is gradually restricted as it is passed through a series of classification steps. The geographic location of the sample can be predicted using a regression model within the terminal subregion. We show that the tree-structured modeling approach provides reasonable estimates of geographical region and geographic location for surface water samples taken across the entire USA. Further, the location uncertainty, an estimate of a probability that a test sample could be located within a pre-estimated, joint prediction interval that is much smaller than the terminal subregion, can also be assessed.

• 出版日期2014-6