Semiparametric filtering of spatial autocorrelation: the eigenvector approach.
A study was conducted to demonstrate the feasibility, flexibility, and simplicity of the eigenvector spatial filtering approach embedded in a semiparametric statistical framework. Data on cancer mortality for the 508 State Economic Areas in the U.S. were analyzed. Findings suggested that this fram...
| Publicado en: | Environment & Planning A Vol. 39; no. 5; pp. 1193 - 1222 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Pion Limited
May 2007
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511323278&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511323278 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0308518X EPL jtl: Environment & Planning A issn: 0308518X maglogo: N pubinfo: dt: May 2007 vid: 39 iid: 5 pid: 1065 pub: Pion Limited artinfo: ui: 511323278 10.1068/a37378 ppf: 1193 ppct: 29 formats: tig: atl: Semiparametric filtering of spatial autocorrelation: the eigenvector approach. aug: au: Tiefelsdorf, Michael Griffith, Daniel A. su: Geography -- Methodology Geography -- Statistical methods Spatial analysis (Statistics) Regression analysis sug: subj: Geography -- Methodology Geography -- Statistical methods Spatial analysis (Statistics) Regression analysis ab: A study was conducted to demonstrate the feasibility, flexibility, and simplicity of the eigenvector spatial filtering approach embedded in a semiparametric statistical framework. Data on cancer mortality for the 508 State Economic Areas in the U.S. were analyzed. Findings suggested that this framework allows visualizing the logical components of spatial processes, deals well with model misspecification, and can be used to perform spatial predictions and in-depth residual analysis. Findings indicated that the search strategy of minimizing the residual spatial autocorrelation offers an intuitively appealing objective function that results in suitable and more parsimonious models for the stochastic spatial signal. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|