Choropleth Mapping with Uncertainty: A Maximum Likelihood–Based Classification Scheme.
Choropleth mapping provides a powerful way to visualize geographical phenomena with colors, shadings, or patterns. In many real-world applications, geographical data often contain uncertainty. How to incorporate such uncertainty into choropleth mapping is challenging. Although a few existing methods...
| Publicado en: | Annals of the American Association of Geographers Vol. 109; no. 5; pp. 1493 - 1511 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Sep2019
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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=137680296&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 137680296 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: Sep2019 vid: 109 iid: 5 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 137680296 10.1080/24694452.2018.1549971 ppf: 1493 ppct: 18 formats: tig: atl: Choropleth Mapping with Uncertainty: A Maximum Likelihood–Based Classification Scheme. aug: au: Mu, Wangshu Tong, Daoqin affil: School of Geographical Sciences and Urban Planning, Arizona State University su: Income Thematic maps Geodatabases Linear programming Dynamic programming sug: subj: Income Thematic maps Geodatabases Linear programming Dynamic programming keyword: cálculo de máxima probabilidad clasificación de mapas incertidumbre mapeo coroplético cálculo de máxima probabilidad clasificación de mapas incertidumbre mapeo coroplético cálculo de máxima probabilidad clasificación de mapas incertidumbre mapeo coroplético cálculo de máxima probabilidad clasificación de mapas incertidumbre mapeo coroplético ab: Choropleth mapping provides a powerful way to visualize geographical phenomena with colors, shadings, or patterns. In many real-world applications, geographical data often contain uncertainty. How to incorporate such uncertainty into choropleth mapping is challenging. Although a few existing methods attempt to address the uncertainty issue in choropleth mapping, there are limitations to widely applying these methods due to their strong assumption on the distribution of uncertainty and the way in which similarity or dissimilarity is assessed. This article provides a new classification scheme for choropleth maps when data contain uncertainty. Considering that in a choropleth map, units in the same class are assigned with the same color or pattern, this new approach assumes the existence of a representative value for each class. A maximum likelihood estimation–based approach is developed to determine class breaks so that the overall within-class deviation is minimized while considering uncertainty. Different methods—including linear programming, dynamic programming, and an interchange heuristic—are developed to solve the new classification problem. The proposed mapping approach has been applied to map the median household income data from the American Community Survey and simulated disease occurrence data. Test results show the effectiveness of the new approach. The linkage between the new approach and the existing methods is also discussed. Key Words: choropleth mapping, map classification, maximum likelihood estimation, uncertainty. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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