Navigation auf zora.uzh.ch

Search ZORA

ZORA (Zurich Open Repository and Archive)

Migration von ZORA auf die Software DSpace

ZORA will change to a new software on 8th September 2025. Please note: deadline for new submissions is 21th July 2025!

Information & dates for training courses can be found here: Information on Software Migration.

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.

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:17 Jul 2025 01:38
Publisher:Taylor & Francis
ISSN:1523-0406
OA Status:Green
Publisher DOI:https://doi.org/10.1080/15230406.2016.1274237

Metadata Export

Statistics

Citations

Dimensions.ai Metrics
22 citations in Web of Science®
27 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

273 downloads since deposited on 12 Jan 2018
48 downloads since 12 months
Detailed statistics

Authors, Affiliations, Collaborations

Similar Publications