School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN 47907, USA.
IEEE Trans Vis Comput Graph. 2013 Sep;19(9):1438-54. doi: 10.1109/TVCG.2013.66.
We present Bristle Maps, a novel method for the aggregation, abstraction, and stylization of spatiotemporal data that enables multiattribute visualization, exploration, and analysis. This visualization technique supports the display of multidimensional data by providing users with a multiparameter encoding scheme within a single visual encoding paradigm. Given a set of geographically located spatiotemporal events, we approximate the data as a continuous function using kernel density estimation. The density estimation encodes the probability that an event will occur within the space over a given temporal aggregation. These probability values, for one or more set of events, are then encoded into a bristle map. A bristle map consists of a series of straight lines that extend from, and are connected to, linear map elements such as roads, train, subway lines, and so on. These lines vary in length, density, color, orientation, and transparencyâcreating the multivariate attribute encoding scheme where event magnitude, change, and uncertainty can be mapped as various bristle parameters. This approach increases the amount of information displayed in a single plot and allows for unique designs for various information schemes. We show the application of our bristle map encoding scheme using categorical spatiotemporal police reports. Our examples demonstrate the use of our technique for visualizing data magnitude, variable comparisons, and a variety of multivariate attribute combinations. To evaluate the effectiveness of our bristle map, we have conducted quantitative and qualitative evaluations in which we compare our bristle map to conventional geovisualization techniques. Our results show that bristle maps are competitive in completion time and accuracy of tasks with various levels of complexity.
我们提出了 Bristle Maps，这是一种新颖的时空数据聚合、抽象和风格化方法，可实现多属性可视化、探索和分析。这种可视化技术通过在单个视觉编码范例中为用户提供多维数据的多参数编码方案，支持多维数据的显示。给定一组地理位置上的时空事件，我们使用核密度估计将数据近似为连续函数。密度估计编码了在给定时间聚合内事件在空间中发生的概率。然后，将这些概率值（对于一个或多个事件集）编码到 Bristle Maps 中。Bristle Maps 由一系列从线性地图元素（如道路、火车、地铁线路等）延伸并连接的直线组成。这些线的长度、密度、颜色、方向和透明度各不相同，创建了多变量属性编码方案，其中事件的大小、变化和不确定性可以映射为各种 Bristle 参数。这种方法增加了单个图中显示的信息量，并允许为各种信息方案设计独特的设计。我们展示了我们的 Bristle Maps 编码方案在分类时空警察报告中的应用。我们的示例演示了我们的技术用于可视化数据大小、变量比较以及各种多变量属性组合的用途。为了评估我们的 Bristle Maps 的有效性，我们进行了定量和定性评估，将我们的 Bristle Maps 与传统的地理可视化技术进行了比较。我们的结果表明，Bristle Maps 在完成时间和各种复杂程度任务的准确性方面具有竞争力。
