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Improving settlement selection for small-scale maps using data enrichment and machine learning

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cris.lastimport.scopus2024-02-12T19:47:18Z
dc.abstract.enAcquiring and formalizing cartographic knowledge still is a challenge, especially when the generalization process concerns small-scale maps. We concentrate on the settlement selection process for small-scale maps, with the aim of rendering it more holistic, and making methodological contributions in four areas. First, we show how written specifications and rules can be validated against the actual published map products, thus pointing to gaps and potential improvements. Second, we use data enrichment based on supplementing information extracted from point-of-interest data in order to assign functional importance to particular settlements. Third, we use machine learning (ML) algorithms to infer additional rules from existing maps, thus making explicit the deep knowledge of cartographers and allowing to extend the cartographic rule set. And fourth, we show how the results of ML can be transformed into human-readable form for potential use in the guidelines of national mapping agencies. We use the case of settlement selection in the small-scale maps published by the Polish national mapping agency (GUGiK). However, we believe that the methods and findings of this paper can be adapted to other environments with minor modifications.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorWeibel, Robert
dc.contributor.authorKarsznia, Izabela
dc.date.accessioned2024-01-25T03:59:03Z
dc.date.available2024-01-25T03:59:03Z
dc.date.copyright2017-01-12
dc.date.issued2018
dc.description.accesstimeBEFORE_PUBLICATION
dc.description.financeNie dotyczy
dc.description.number2
dc.description.versionFINAL_PUBLISHED
dc.description.volume45
dc.identifier.doi10.1080/15230406.2016.1274237
dc.identifier.issn1523-0406
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/109121
dc.identifier.weblinkhttps://www.tandfonline.com/doi/full/10.1080/15230406.2016.1274237
dc.languageeng
dc.pbn.affiliationsocio-economic geography and spatial management
dc.relation.ispartofCartography and Geographic Information Science
dc.relation.pages111-127
dc.rightsCC-BY
dc.sciencecloudnosend
dc.subject.enSettlement selection
dc.subject.ensmall-scale mapping
dc.subject.endata enrichment
dc.subject.enmachine learning
dc.titleImproving settlement selection for small-scale maps using data enrichment and machine learning
dc.typeJournalArticle
dspace.entity.typePublication