Chen Jin, Maceachren Alan M, Guo Diansheng
Jin Chen and Alan M. MacEachren, GeoVISTA Center and Department of Geography, Pennsylvania State University, 302 Walker Building, University Park, Pennsylvania16802. Email:<
Cartogr Geogr Inf Sci. 2008 Jan 1;35(1):33-50. doi: 10.1559/152304008783475689.
While many data sets carry geographic and temporal references, our ability to analyze these datasets lags behind our ability to collect them because of the challenges posed by both data complexity and tool scalability issues. This study develops a visual analytics approach that leverages human expertise with visual, computational, and cartographic methods to support the application of visual analytics to relatively large spatio-temporal, multivariate data sets. We develop and apply a variety of methods for data clustering, pattern searching, information visualization, and synthesis. By combining both human and machine strengths, this approach has a better chance to discover novel, relevant, and potentially useful information that is difficult to detect by any of the methods used in isolation. We demonstrate the effectiveness of the approach by applying the Visual Inquiry Toolkit we developed to analyze a data set containing geographically referenced, time-varying and multivariate data for U.S. technology industries.
虽然许多数据集都带有地理和时间参考信息，但由于数据复杂性和工具可扩展性问题带来的挑战，我们分析这些数据集的能力落后于收集它们的能力。本研究开发了一种可视化分析方法，该方法利用人类专业知识结合视觉、计算和制图方法，以支持将可视化分析应用于相对较大的时空多变量数据集。我们开发并应用了多种数据聚类、模式搜索、信息可视化和综合的方法。通过结合人类和机器的优势，这种方法更有机会发现单独使用任何一种方法都难以检测到的新颖、相关且可能有用的信息。我们通过应用我们开发的视觉探究工具包来分析一个包含美国科技行业地理参考、随时间变化的多变量数据集，证明了该方法的有效性。
