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dc.contributor.authorFrancisco, Eduardo de Rezendeen_NZ
dc.contributor.authorWhigham, Peter Aen_NZ
dc.contributor.authorMoore, Antonien_NZ
dc.date.available2011-04-07T03:02:00Z
dc.date.copyright2007-12-06en_NZ
dc.identifier.citationFrancisco, E. de R., Whigham, P. A., & Moore, A. (2007). Point allocation inside polygons and GWR: an experimental analysis with survey data (pp. 55–64). Presented at the 19th Annual Colloquium of the Spatial Information Research Centre (SIRC 2007: Does Space Matter?).en
dc.identifier.urihttp://hdl.handle.net/10523/722
dc.description.abstractThe aim of this paper is to analyse different alternative implementations for a problem defined as "point allocation inside polygons" for Geographically Weighted Regression (GWR). The problem involves situations where the precise location of each observation is not known - just its district, municipality or region, i.e. a polygon geographical location. However, associated data were available that could potentially allows point placement of observations. These analyses were applied in a Income predicting model based on electricity consumption from a survey for a power distribution company in Sao Paulo, Brazil. Completely spatially random allocation and allocations based on spatial distributions of population (universe) and of the independent variable (electricity consumption) were utilized. Results showing the coefficients of determination (R2) suggest that a more realistic measure of the relationship between these two constructs could be evaluated.en_NZ
dc.format.mimetypeapplication/pdf
dc.relation.urihttp://www.business.otago.ac.nz/sirc/conferences/2007/11_francisco.pdfen_NZ
dc.subjectGWRen_NZ
dc.subjectpoint allocation inside polygonsen_NZ
dc.subjectpoint samplingen_NZ
dc.subjectincomeen_NZ
dc.subjectelectricity consumptionen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titlePoint allocation inside polygons and GWR: an experimental analysis with survey dataen_NZ
dc.typeConference or Workshop Item (Paper)en_NZ
dc.description.versionPublisheden_NZ
otago.date.accession2009-04-21 22:05:31en_NZ
otago.relation.pages55-64en_NZ
otago.openaccessOpen
dc.identifier.eprints810en_NZ
dc.description.refereedNon Peer Revieweden_NZ
otago.school.eprintsSpatial Information Research Centreen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.referencesBack, T., D. Fogel & Z. Michalewicz 1997, Handbook of Evolutionary Computation, Oxford University Press and Institute of Physics Publishing, Bristol, City, . Brunsdon, C., A. S. Fotheringham & M. Charlton (1998) Spatial nonstationarity and autoregressive models. Environment and Planning A, 30: pp. 957-973. Farber, S. & A. Paez (2007) A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations. J. Geograph Syst, 9: pp. 371-396. Jelinski, D. & W. Jianguo (1996) The modifiable areal unit problem and implications for landscape ecology. Landscape Ecology, 11:3, pp. 129-140. Openshaw, S. & P. Taylor 1979, 'A million or so correlation coefficients: three experiments on the modifiable areal unit problem.' in Statistical Applications in the Spatial Sciences, Ed N. Wrigley, Pion, London.. Yao, X. 1999, Evolutionary Computation Theory and Applications, World Scientific Publishing Co. Pty. Ltd., City, pp 360.en_NZ
otago.event.dates6-7 Decemberen_NZ
otago.event.placeDunedin, New Zealanden_NZ
otago.event.typeconferenceen_NZ
otago.event.title19th Annual Colloquium of the Spatial Information Research Centre (SIRC 2007: Does Space Matter?)en_NZ
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