3 August 2007 Establishing a neurocognition-based taxonomy of graphical variables for attention-guiding geovisualisation
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Proceedings Volume 6751, Geoinformatics 2007: Cartographic Theory and Models; 675109 (2007); doi: 10.1117/12.759483
Event: Geoinformatics 2007, 2007, Nanjing, China
Abstract
It is a delicate task to design suitable geovisualisations that allow users an efficient visual processing of geographic information. In digital era, such a design task is confronted with a three-fold challenge: the ever growing amount of geospatial data at various granularity levels, the diversified applications and the continuously expanding range of display sizes. A geovisualisation system that strives for a high usability must satisfy the crucial prerequisite of immediately directing the user's gaze to the location of relevant geographic information and of easy decidability of the underlying semantic meanings. To this end, the cognitive skill of visual attention contributes to mnemonic and executive processes. Attention is indispensable for the visual selection. It facilitates the relevant information retrieval, processing and storage. On the basis of neurocognitive visual information processing, the paper addresses the interdisciplinary approach of attention-guiding design of geovisualisations with the intention to establish a taxonomy of scientifically testable variables. The authors try to relate attention-guiding attributes with graphical variables that cartographers apply to encode geographic information. The work is driven by the motivation to enhance the efficiency of geovisualisations and to enable a more precise neurocognition-based evaluation of geovisualisations.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
O. Swienty, M. Zhang, T. Reichenbacher, L. Meng, "Establishing a neurocognition-based taxonomy of graphical variables for attention-guiding geovisualisation", Proc. SPIE 6751, Geoinformatics 2007: Cartographic Theory and Models, 675109 (3 August 2007); doi: 10.1117/12.759483; https://doi.org/10.1117/12.759483
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KEYWORDS
Visualization

Information visualization

Taxonomy

Data processing

Visual analytics

Eye

Visual information processing

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