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Open AccessArticle

Why Shape Matters—On the Inherent Qualities of Geometric Shapes for Cartographic Representations

Research Division Cartography, Department of Geodesy and Geoinformation, Vienna University of Technology, 1040 Vienna, Austria
ISPRS Int. J. Geo-Inf. 2019, 8(5), 217;
Received: 15 November 2018 / Revised: 23 April 2019 / Accepted: 2 May 2019 / Published: 8 May 2019
(This article belongs to the Special Issue Human-Centered Geovisual Analytics and Visuospatial Display Design)
All human communication involves the use of signs. By following a mutually shared set of signs and rules, meaning can be conveyed from one entity to another. Cartographic semiology provides such a theoretical framework, suggesting how to apply visual variables with respect to thematic content. However, semiotics does not address how the choice and composition of such visual variables may lead to different connotations, interpretations, or judgments. The research herein aimed to identify perceived similarities between geometric shape symbols as well as strategies and processes underlying these similarity judgments. Based on a user study with 38 participants, the (dis)similarities of a set of 12 basic geometric shapes (e.g., circle, triangle, square) were examined. Findings from cluster analysis revealed a three-cluster configuration, while multidimensional scaling further quantified the proximities between the geometric shapes in a two-dimensional space. Qualitative and quantitative content analyses identified four strategies underlying the participants’ similarity judgments, namely visual, affective, associative, and behavioral strategies. With the findings combined, this research provides a differentiated perspective on shape proximities, cognitive relations, and the processes involved. View Full-Text
Keywords: visual communication; cartography; semiotics; geometric shape; thematic maps visual communication; cartography; semiotics; geometric shape; thematic maps
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Klettner, S. Why Shape Matters—On the Inherent Qualities of Geometric Shapes for Cartographic Representations. ISPRS Int. J. Geo-Inf. 2019, 8, 217.

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