Sparse grids: a new predictive modelling method for the analysis of geographic data Laffan, Shawn en_US Nielsen, Ole en_US Silcock, Howard en_US Hegland, Markus en_US 2021-11-25T13:09:44Z 2021-11-25T13:09:44Z 2005 en_US
dc.description.abstract We introduce in this paper a new predictive modelling method to analyse Geographic data known as sparse grids. The sparse grids method has been developed for data-mining applications. It is a machine-learning approach to data analysis and has great applicability to the analysis and understanding of geographic data and processes. Sparse gi grids are a subset of grid-based predictive modelling approaches. The advantages they have over other grid-based methods are that they use fewer parameters and are less susceptible to the curse of dimensionality. These mean that they can be applied to many geographic problems and are readily adapted to the analysis of geographically local samples. We demonstrate the utility of the sparse grids system using a large and spatially extensive data set of regolith samples from Weipa, Australia. We apply both global and local analyses to find relationships between the regolith data and a set of geomorphometric, hydrologic and spectral variables. The results of the global analyses are much better than those generated using an artificial neural network, and the local analysis results are better than those generated using moving window regression for the same analysis window size. The sparse grids system provides a potentially powerful tool for the analysis and understanding of geographic processes and relationships. en_US
dc.identifier.issn 1365-8816 en_US
dc.language English
dc.language.iso EN en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri en_US
dc.source Legacy MARC en_US
dc.subject.other spatial analysis en_US
dc.subject.other geographic data en_US
dc.subject.other predictive modelling en_US
dc.subject.other sparse grids en_US
dc.subject.other bauxite en_US
dc.title Sparse grids: a new predictive modelling method for the analysis of geographic data en_US
dc.type Journal Article en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.identifier.doiPublisher en_US
unsw.relation.faculty Science
unsw.relation.ispartofissue 3 en_US
unsw.relation.ispartofjournal International Journal of Geographical Information Science en_US
unsw.relation.ispartofpagefrompageto 267-292 en_US
unsw.relation.ispartofvolume 19 en_US
unsw.relation.originalPublicationAffiliation Laffan, Shawn, Biological, Earth & Environmental Sciences, Faculty of Science, UNSW en_US
unsw.relation.originalPublicationAffiliation Nielsen, Ole en_US
unsw.relation.originalPublicationAffiliation Silcock, Howard en_US
unsw.relation.originalPublicationAffiliation Hegland, Markus en_US School of Biological, Earth & Environmental Sciences *
Resource type