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

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.

KEYWORDS:

• Settlement selection
• small-scale mapping
• data enrichment
• machine learning

Acknowledgments

The authors would like to express their gratitude to the Swiss Government SCIEX SCIentific EXchange program, which by supporting the effective scientist’s mobility made possible to conduct this research.

The authors also gratefully acknowledge Professor Wieslaw Ostrowski as well as M.Sc. Ali Soleymani for their contributions to the discussions of this project.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental meterial

The supplemental data for this article can be accessed here.

Notes

1. As of spring 2015, a new version of this database called GGOD has become available. For the purposes of this work, however, this is not relevant.

2. We use a slightly simplified version of the official rules, removing one special case that does not apply to the districts that form the basis of our experiments.

3. https://rapidminer.com/.

Additional information

Funding

This work was supported by the Swiss Government SCIEX SCIentific EXchange program under project number 12.187 and by the University of Zurich.