Publication:
A new approach to analysing spatial data using sparse grids

dc.contributor.author Laffan, Shawn en_US
dc.contributor.author Silcock, Howard en_US
dc.contributor.author Nielsen, Ole en_US
dc.contributor.author Hegland, Markus en_US
dc.date.accessioned 2021-11-25T13:09:55Z
dc.date.available 2021-11-25T13:09:55Z
dc.date.issued 2003 en_US
dc.description.abstract We describe in this paper a new data mining approach for the analysis of spatial data for environmental modelling. The sparse grids analysis system models the functional relationship between a set of predictor variables and a response variable by using a combination of easily computable functions defined on grids of varying mesh sizes in attribute space. The approach circumvents the so-called “curse of dimensionality” by using, instead of a costly high-dimensional grid a with a fine mesh size in every dimension, a collection of grids that are coarse along some dimensions but fine along others. Adaptive sparse grid regression and classification methods select combinations of grids that suit a particular data set. One advantage of the sparse grids approach from an environmental analysis perspective is that it uses machine learning approaches, and so can deal with correlated data, as are common in environmental problems. One advantage of the sparse grids approach from an environmental analysis perspective is that it uses machine learning approaches, and so can deal with correlated data, as is commonly the case with geographic data. They also require fewer degrees of freedom than do full grid models, allowing them to be applied to more datasets. The parameters defining the adaptive sparse grids can be used to interpret relationships in terms of scale and resolution. For example, the distribution of mesh points used in the set of lattices describes the complexity of the relationships present. It can be used to understand if the system is responding to fine scale variations (many mesh points used) or to gross patterns (few mesh points used). This is valuable information for environmental modelling. en_US
dc.description.uri http://www.mssanz.org.au/MODSIM03/Volume_02/A13/09_Laffan.pdf en_US
dc.identifier.uri http://hdl.handle.net/1959.4/39303
dc.language English
dc.language.iso EN en_US
dc.publisher Modelling and simulation society of Australia and New Zealand en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/au/ en_US
dc.source Legacy MARC en_US
dc.subject.other Sparse grids. en_US
dc.subject.other Spatial data. en_US
dc.subject.other Analysing spacial data. en_US
dc.subject.other Physical geography. en_US
dc.subject.other Numerical analysis. en_US
dc.title A new approach to analysing spatial data using sparse grids en_US
dc.type Conference Paper en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.accessRights.uri http://purl.org/coar/access_right/c_14cb
unsw.description.notePublic Conference Editor: David A Post en_US
unsw.publisher.place Canberra en_US
unsw.relation.faculty Science
unsw.relation.ispartofconferenceLocation Townsville, Australia en_US
unsw.relation.ispartofconferenceName International Congress on Modelling and Simulation (MODSIM 2003) en_US
unsw.relation.ispartofconferenceProceedingsTitle Integrative modelling of biophysical, social and economic systems for resource management en_US
unsw.relation.ispartofconferenceYear 2003 en_US
unsw.relation.ispartofpagefrompageto 708-711 en_US
unsw.relation.originalPublicationAffiliation Laffan, Shawn, Biological, Earth & Environmental Sciences, Faculty of Science, UNSW en_US
unsw.relation.originalPublicationAffiliation Silcock, Howard en_US
unsw.relation.originalPublicationAffiliation Nielsen, Ole en_US
unsw.relation.originalPublicationAffiliation Hegland, Markus en_US
unsw.relation.school School of Biological, Earth & Environmental Sciences *
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