Generating Surface Models of Population Using Dasymetric Mapping.
A methodology is developed for creating a surface-based representation of population that mitigates the analytical and cartographic problems due to the arbitrary nature of areal unit partitioning. The relationship between categorical ancillary data and population distribution is evaluated using das...
| Publicado en: | Professional Geographer Vol. 55; no. 1; pp. 31 - 43 |
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| Formato: | Artículo |
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Taylor & Francis Ltd
February 2003
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| 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=513166904&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513166904 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00330124 PGG jtl: Professional Geographer issn: 00330124 maglogo: N pubinfo: dt: February 2003 vid: 55 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 513166904 ppf: 31 ppct: 12 formats: tig: atl: Generating Surface Models of Population Using Dasymetric Mapping. aug: au: Mennis, Jeremy su: Statistics Geography -- Statistical methods Population geography Cartography Methodology sug: subj: Statistics Geography -- Statistical methods Population geography Cartography Methodology keyword: Pennsylvania -- Population geography ab: A methodology is developed for creating a surface-based representation of population that mitigates the analytical and cartographic problems due to the arbitrary nature of areal unit partitioning. The relationship between categorical ancillary data and population distribution is evaluated using dasymetric mapping, areal weighting, and empirical sampling techniques. The methodology is demonstrated by generating a 100-meter-resolution population surface from U.S. Census block group data for the southeast Pennsylvania region, with remote-sensing-derived land-cover data serving as ancillary data in the dasymetric mapping. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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