A Classification Method for Choropleth Maps Incorporating Data Reliability Information.
Observations assigned to any two classes in a choropleth map are expected to have attribute values that are different. Their values might not be statistically different, however, if the data are gathered from surveys, such as the American Community Survey, in which estimates have sampling error. Thi...
| Publicado en: | Professional Geographer Vol. 67; no. 1; pp. 72 - 84 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Feb2015
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| 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=100299079&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 100299079 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: Feb2015 vid: 67 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 100299079 10.1080/00330124.2014.888627 ppf: 72 ppct: 12 formats: tig: atl: A Classification Method for Choropleth Maps Incorporating Data Reliability Information. aug: au: Sun, Min Wong, David W. Kronenfeld, Barry J. affil: George Mason University Eastern Illinois University su: Social surveys Psychological typologies Ambivalence Design science Estimation theory sug: subj: Social surveys Psychological typologies Ambivalence Design science Estimation theory keyword: class breaks class separability confidence level legend design. diseño de la leyenda. diseño de la leyenda. nivel de confianza quiebres de clase separabilidad de clase class breaks class separability confidence level legend design. diseño de la leyenda. diseño de la leyenda. nivel de confianza quiebres de clase separabilidad de clase ab: Observations assigned to any two classes in a choropleth map are expected to have attribute values that are different. Their values might not be statistically different, however, if the data are gathered from surveys, such as the American Community Survey, in which estimates have sampling error. This article presents an approach to determine class breaks using the class separability criterion, which refers to the levels of certainty that values in different classes are statistically different from each other. Our procedure determines class breaks that offer the highest levels of separability given the desired number of classes. The separability levels of all class breaks are included in a legend design to show the statistical likelihood that values on two sides of each class break are different. The legend and the associated separability information offer map readers crucial information about the reliability of the spatial patterns that could result from the chosen classification method. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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