Semantic Visual Variables for Augmented Geovisualization

ABSTRACT

The human–cyber–physical world produces a considerable volume of multi-modal spatio-temporal data, thus leading to information overload. Visual variables are used to transform information into visual forms that are perceived by the powerful human vision system. However, previous studies of visual variables focused on methods of ‘drawing information’ without considering ‘intelligence’ derived from balancing ‘importance’ and ‘unimportance’. This paper proposes semantic visual variables to support an augmented geovisualization that aims to avoid exposing users to unnecessary information by highlighting goal-oriented content over redundant details. In this work, we first give definitions of several concepts and then design a semiotic model for depicting the mechanisms of augmented geovisualization. We also provide an in-depth discussion of semantic visual variables based on a hierarchical organization of the original visual variables, and we analyse the critical influencing factors that affect the choice of visualization forms and visual variables. Finally, a typical application is used to illustrate the relevance of this study.

KEYWORDS:

• Semantic visual variables
• augmented geovisualization
• focus and context
• degree of interest
• level of detail
• degree of abstraction

Notes on the contributor

Yun Li holds a BSc (2011) in Civil Engineering from the School of Civil Engineering and an MSc (2015) in Cartography and Geographic Information Systems from the Faculty of Geomatics, Lanzhou Jiaotong University. He is currently (2015-present) a PhD student studying Surveying and Mapping Science at the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University in Chengdu, China. His research is focused on cartography, geovisualization and mixed reality applications in GIS. He is now involved in China’s major national project, “Pan-spatial information system and intelligent facility management” and focusing on the study of geovisualization.

ORCID

Yun Li http://orcid.org/0000-0001-7620-5983

Junxiao Zhang http://orcid.org/0000-0001-7592-7300

Additional information

Funding

This paper was supported by the National Key Research and Development Program of China (Grant No. 2016YFB0502303), the Smart Guangzhou Spatio-temporal Information Cloud Platform Construction (Grant No. GZIT2016-A5-147).