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dc.contributor.authorLi, Terryen_US
dc.contributor.authorCorcoran, Jonathanen_US
dc.contributor.authorPullar, Daviden_US
dc.contributor.authorRobson, Alistairen_US
dc.contributor.authorStimson, Roberten_US
dc.date.accessioned2017-05-03T15:01:28Z
dc.date.available2017-05-03T15:01:28Z
dc.date.issued2009en_US
dc.date.modified2010-07-07T07:39:54Z
dc.identifier.issn1874463Xen_US
dc.identifier.doi10.1007/s12061-008-9015-3en_AU
dc.identifier.urihttp://hdl.handle.net/10072/29325
dc.description.abstractIn this paper we present a new methodology by which regional employment forecasts can be spatially disaggregated to smaller administrative units. We develop a statistical model for disaggregating spatial data based upon related employment determinants (for example, the proximity of an area to a shopping centre), demonstrating there is a degree of spatial dependence and spatial heterogeneity in relationships. Applying an advanced statistical procedure, Geographically Weighted Regression (GWR), to account for these spatial effects this method utilises the locally fitted relationships to estimate employment numbers at the smaller geography whilst being constrained by the regional forecast. Results demonstrate that our GWR method generates superior estimates over a global regression model for spatially disaggregating regional employment forecasts.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom147en_US
dc.relation.ispartofpageto175en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalApplied Spatial Analysis and Policyen_US
dc.relation.ispartofvolume2en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchEconomic Models and Forecastingen_US
dc.subject.fieldofresearchcode140303en_US
dc.titleA Geographically Weighted Regression Method to Spatially Disaggregate Regional Employment Forecasts for South East Queenslanden_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Environmenten_US
gro.date.issued2009
gro.hasfulltextNo Full Text


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