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Embodiment in data sculpture: a model of the physical visualization of information
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Source ACM International Conference Proceeding Series; Vol. 349 archive
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts table of contents
Athens, Greece
SESSION: Enhanced visualization and 3D media table of contents
Pages 343-350  
Year of Publication: 2008
ISBN:978-1-60558-248-1
Authors
Jack Zhao  University of Sydney, NSW Australia
Andrew Vande Moere  University of Sydney, NSW Australia
Sponsor
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

Information is becoming pervasive in the contemporary society, and is increasingly saturating the visual senses and the cognitive efforts of the lay masses. As our attention for visual impulses and cognitive effort has become more competitive, new approaches are being pursued to convey information to people in memorable and intuitive ways. With human's inherent proficiency in comprehending the physical affordances present in the real world, some researchers and designers are investigating how meaningful insights can be conveyed by way of "sculpting" data.

This paper proposes a domain model to establish the concept of data sculpture as a data-based physical artifact, possessing both artistic and functional qualities, that aims to augment a nearby audience's understanding of data insights and any socially relevant issues that underlie it. This paper also proposes a model of embodiment to capture and analyze the wide and multi-layered spectrum of existing data sculptures. In this model, the introduced concepts of metaphorical distances are used as a means to measure embodiment in data sculpture. The models provide groundwork for useful design principles for effective information communication of socially relevant, data-driven insights to a large, lay audience using data sculpture.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jack Zhao: colleagues
Andrew Vande Moere: colleagues