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Proceedings Paper

Use of Google SketchUp to implement 3D spatio-temporal visualization
Author(s): Linhai Li; Lina Qu; Shen Ying; Dongdong Liang; Zhenlong Hu
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Paper Abstract

Geovisualization is an important means to understand the geographic features and phenomena. Urban space, especially buildings, keeps changing with social development. However, traditional 2D visualization can only represent the plane geometric description, which is unable to support 3D dynamic visualization. Only with 3D dynamic visualization can the buildings' spatial morphology be exhibited temporally, including buildings' creation, expansion, removing, etc. But these buildings' changes are impossible to be studied in traditional 2D and 3D static visualization systems. As a result, it becomes urgent to find an effective solution to implement 3D spatial-temporal visualization of buildings. Inspired by 2D spatial-temporal visualization methods, like snapshot and event-based spatio-temporal data model(ESTDM), we propose a new data model called Spatio-Temporal Page Model(STPM) and implement 3D spatial-temporal visualization in Google SketchUp based on STPM. This paper studies 3D visualization of real estate focusing on its spatio-temporal characteristics. First of all, 3D models are built for every temporal scenario by the Google SketchUp. And every Geo-object is identified by a unique and permanent ObjectID, the linkage of Geo-objects between different time spots. Then, each temporal scenario is represented as page. After having the page series, finally, it is possible to display its spatial-temporal changes and create an animation. Underlying this solution, we have built a prototype system on part of real estate data. It is proven that users are able to understand clearly the real estate's changes from our prototype system. Consequently, we believe our method for 3D spatial-temporal visualization definitely has many merits.

Paper Details

Date Published: 15 October 2009
PDF: 7 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74920Y (15 October 2009); doi: 10.1117/12.837572
Show Author Affiliations
Linhai Li, Wuhan Univ. (China)
Lina Qu, Wuhan Univ. (China)
Shen Ying, Wuhan Univ. (China)
Dongdong Liang, Wuhan Univ. (China)
Anhui Normal Univ. (China)
Zhenlong Hu, Wuhan Univ. (China)
69028 Troops (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining

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