Header

UZH-Logo

Maintenance Infos

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


Karsznia, Izabela; Weibel, Robert (2018). Improving settlement selection for small-scale maps using data enrichment and machine learning. Cartography and Geographic Information Science, 45(2):111-127.

Abstract

Acquiring 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.

Abstract

Acquiring 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.

Statistics

Citations

Dimensions.ai Metrics
12 citations in Web of Science®
15 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

210 downloads since deposited on 12 Jan 2018
25 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Physical Sciences > Civil and Structural Engineering
Social Sciences & Humanities > Geography, Planning and Development
Social Sciences & Humanities > Management of Technology and Innovation
Language:English
Date:2018
Deposited On:12 Jan 2018 13:00
Last Modified:26 Jan 2022 15:06
Publisher:Taylor & Francis
ISSN:1523-0406
OA Status:Green
Publisher DOI:https://doi.org/10.1080/15230406.2016.1274237