Guo Diansheng, Chen Jin, MacEachren Alan M, Liao Ke
Department of Geography, University of South Carolina, Columbia 29208, USA.
IEEE Trans Vis Comput Graph. 2006 Nov-Dec;12(6):1461-74. doi: 10.1109/TVCG.2006.84.
The research reported here integrates computational, visual, and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial, and temporal dimensions via clustering, sorting, and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display, and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and, through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 Contest data set, which contains time-varying, geographically referenced, and multivariate data for technology companies in the US.
本文所报告的研究整合了计算、视觉和制图方法，以开发一种地理视觉分析方法，用于探索和理解时空模式及多变量模式。所开发的方法和工具可帮助分析师通过聚类、排序和可视化来研究跨多变量、空间和时间维度的复杂模式。具体而言，该方法涉及自组织映射、平行坐标图、几种形式的可重排矩阵（包括几种排序方法）、地理小多重显示以及二维制图颜色设计方法。这些方法之间的耦合利用了它们各自的优势，便于对难以通过其他方式发现的模式进行视觉探索。我们开发的可视化系统支持对复杂模式的概览，并通过各种交互方式，使用户能够专注于特定模式并查看详细视图。我们通过将该系统应用于IEEE InfoVis 2005竞赛数据集来进行演示，该数据集包含美国科技公司的随时间变化、地理参考和多变量数据。
