Share Email Print
cover

Proceedings Paper

Visualization analysis of multivariate spatial-temporal data of the Red Army Long March in China
Author(s): Ding Ma; Zhimin Ma; Lumin Meng; Xia Li
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Recently, the visualization of spatial-temporal data in historic events is emphasized by more and more people. To provide an efficient and effective approach to meet this requirement is the duty of Geo-data modeling researchers. The aim of the paper is to ground on a new perspective to visualize the multivariate spatial-temporal data of the Red Army Long March, which is one of the most important events of the Chinese modem history. This research focuses on the extraction of relevant information from a 3-dimensional trajectory, which captures object locations in geographic space at specified temporal intervals. However, existing visualization methods cannot deal with the multivariate spatial-temporal data effectively. Thus there is a potential chance to represent and analyze this kind of data in the case study. The thesis combines two visualization methods, the Space-Time-Cube for spatial temporal data and Parallel Coordinates Plots (PCPs) for multivariable data, to develop conceptual GIS database model that facilitates the exploration and analysis of multivariate spatial-temporal data sets in the combination with 3D Space-Time-Path and 2D graphics. The designed model is supported by the geo-visualization environment and integrates diverse sets of multivariate spatial-temporal data and built-up the dynamic process and relationships. It is concluded that this way of geo-visualization can effectively manipulate a large amount of distributed data, realize the high efficient transmission of quantitative and qualitative information and also provide a new research mode in the field of the History of CPC and military affairs.

Paper Details

Date Published: 15 October 2009
PDF: 10 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74920X (15 October 2009); doi: 10.1117/12.838550
Show Author Affiliations
Ding Ma, Xi'an Univ. of Science and Technology (China)
Zhimin Ma, Chang'an Univ. (China)
Lumin Meng, Xi'an Univ. of Science and Technology (China)
Xia Li, Chang'an Univ. (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

© SPIE. Terms of Use
Back to Top