An Alternative Classification Scheme for Uncertain Attribute Mapping.
The reality of uncertain data cannot be ignored. Anytime that spatial data are used to assist planning, decision making, or policy generation, it is likely that error or uncertainty in the data will propagate through processing protocols and analytic techniques, potentially leading to biased or inco...
| Publicado en: | Professional Geographer Vol. 69; no. 4; pp. 604 - 616 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
2017
|
| 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=125148150&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 125148150 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00330124 PGG jtl: Professional Geographer issn: 00330124 maglogo: Y pubinfo: dt: 2017 vid: 69 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 125148150 10.1080/00330124.2017.1288573 ppf: 604 ppct: 12 formats: tig: atl: An Alternative Classification Scheme for Uncertain Attribute Mapping. aug: au: Wei, Ran Grubesic, Tony H. affil: University of Utah Arizona State University su: Information science Social segmentation Decision making Acquisition of data Records management sug: subj: Information science Social segmentation Decision making Other business support services Acquisition of data Records management keyword: choropleth mapping classification uncertainty clasificación incertidumbre mapeo coroplético mapeo coroplético, clasificación, incertidumbre 不确定性 分类 等值区域製图 choropleth mapping classification uncertainty clasificación incertidumbre mapeo coroplético mapeo coroplético, clasificación, incertidumbre 不确定性 分类 等值区域製图 ab: The reality of uncertain data cannot be ignored. Anytime that spatial data are used to assist planning, decision making, or policy generation, it is likely that error or uncertainty in the data will propagate through processing protocols and analytic techniques, potentially leading to biased or incorrect decision making. The ability to directly account for uncertainty in spatial analysis efforts is critically important. This article focuses on addressing data uncertainty in one of the most important and widely used exploratory spatial data analysis (ESDA) techniques--choropleth mapping--and proposes an alternative map classification method for uncertain spatial data. The classification approach maximizes within-class homogeneity under data uncertainty while explicitly integrating spatial characteristics to reduce visual map complexity and to facilitate pattern perception. The method is demonstrated by mapping the 2009 to 2013 American Community Survey estimates of median household income in Salt Lake County, Utah, at the census tract level. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|