COMPARING TRADITIONAL METHODS FOR SELECTING CLASS INTERVALS ON CHOROPLETH MAPS*.
The most common goat when classing data for choropleth maps is to create homogeneous classes which contain similar data values. None of the four traditional data classing methods examined here (quartile, equal interval, standard deviation, and natural breaks) consistently generalized the experimenta...
| Published in: | Professional Geographer Vol. 38; no. 1; pp. 62 - 68 |
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| Format: | Article |
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Taylor & Francis Ltd
Feb86
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=15546786&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 15546786 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00330124 PGG jtl: Professional Geographer issn: 00330124 maglogo: Y pubinfo: dt: Feb86 vid: 38 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 15546786 10.1111/j.0033-0124.1986.00062.x ppf: 62 ppct: 6 formats: tig: atl: COMPARING TRADITIONAL METHODS FOR SELECTING CLASS INTERVALS ON CHOROPLETH MAPS*. aug: au: Smith, Richard M. affil: University of Arkansas su: Geography -- Statistical methods Historical maps Data analysis Standard deviations Analysis of variance Distribution (Probability theory) Geographical research sug: subj: Geography -- Statistical methods Historical maps Data analysis Standard deviations Analysis of variance Distribution (Probability theory) Geographical research keyword: class intervals classing accuracy optimization classing ab: The most common goat when classing data for choropleth maps is to create homogeneous classes which contain similar data values. None of the four traditional data classing methods examined here (quartile, equal interval, standard deviation, and natural breaks) consistently generalized the experimental data sets into homogeneous classes. These methods were most accurate for data sets with specific distributional characteristics, but none classed all of any type of distribution accurately. Only the optimization method produced reliable and accurate results for all of the experimental data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 1986 holdings: @attributes: islocal: N |
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