Data scale as cartography: a semi-automatic approach for thematic web map creation

ABSTRACT

Open government promises increased transparency by providing its citizens datasets about city processes. Open data portals have been emerging all over the world as mines of open geographic datasets. Thematic web maps are key to understanding these open geographic datasets. Current thematic web maps are created by programmers and/or cartographers, and thus are not designed to be easily reused with new geographic datasets. As a result, they pose several challenges to non-experts wanting to adapt them to new scenarios. This article introduces a semi-automatic approach for the creation of thematic web maps by and for users with no prior training in cartography. The approach relies on the mapping between Stevens’ data types and Bertin’s visual variables, to suggest (meaningful) thematic map visualizations for a given input geographic dataset. It was implemented as a web prototype in AngularJS and evaluated with 19 participants. Results from the user study suggest that despite facing a few challenges in accurately identifying Stevens’ data types, participants managed to successfully create web maps and correctly answer spatial questions. The prototype and insights gathered from the user study are relevant to making cartographic products more accessible to a broader population, and open geographic data more usable in the context of an open government.

KEYWORDS:

• Geographic visualization
• thematic cartography
• web maps
• adaptive maps
• visual variables
• open government
• human computation

Acknowledgments

We thank the editor and the anonymous reviewers for their very constructive feedback.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the European Commission through the GEO-C project (http://www.geo-c.eu/); H2020 Marie Skłodowska-Curie Actions [H2020-MSCA-ITN-2014, Grant Agreement Number: 64233].