Observing the relationship between two or more variables over space and time is essential in many domains. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location; and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only). Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Based on these findings we present a set of design guidelines about geo-temporal visualization techniques for communicating correlation.

中文翻译：

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识别时空相关性的可视化比较。

在许多领域中，观察两个或多个变量在空间和时间上的关系至关重要。例如，针对不同的国家，看一下出生时预期寿命和生育率的演变，就可以大致了解其人口统计数据。选择此类多元数据的视觉表示形式是使分析师能够提取模式和趋势的关键。先前的工作已将地理时空可视化技术与随时间和空间变化的单个主题变量或特定时间点的两个变量进行了比较。但是有效的可视化技术如何有效地传达随时间和空间变化的两个变量之间的相关性，还有待研究。我们报告了一项研究，比较了三种代表不同策略的技术以可视化地时多元数据：要么将给定时间步长的所有位置并置，要么将给定时间步长的所有时间并置；并使用覆盖在地图要素顶部的符号或使用地图要素本身的可视通道对主题属性进行编码。参与者执行了一系列任务，要求他们确定两个变量是否随时间而相关，以及它们的演化方式是否存在。任务在两个维度上的粒度不同：时间（所有时间步长，一个步长的子范围，仅一个步长）和空间（所有位置，子区域中的位置，仅一个位置）。我们的结果表明，可视化的效果很大程度上取决于要执行的任务。