Geo-text data, which combine geographical locations with textual information (e.g., geo-tagged tweets), are typically visualized using tag maps. Since tags are rich in attribute information, tag maps are an intuitive method of visualizing how attribute domains carried by tags vary across space. However, users may be interested not only in the overall spatial distribution of tags but also in exploring detailed attributes-in-space analyses, such as examining how a subclass of attribute domains is distributed globally or checking whether all attribute subclasses exhibit the same global distribution pattern. To date, the methods for representing tags with visual encoding (e.g., size, color) to extend various attributes-in-space tasks to support exploratory analysis remain unclear. In this work, we extended tag maps to support exploratory analysis by distinguishing space searching into local or global spaces and attribute domains into within or between attribute classes, supporting four types of attributes-in-space tasks: global-within, local-within, global-between, and local-between tasks. We evaluated our exploratory tag map through two case studies: investigating major disaster occurrences from 1981 to 2020 and examining the leading causes of death in 2000 and 2019 for Spain, France, Germany and Italy. We used eye-tracking and a questionnaire to evaluate our exploratory tag map for comparison. Both methods had similar self-reported usability scores in terms of aesthetics, density, layout, and legibility. However, our exploratory tag map was more effective and efficient and had a lower cognitive load.

中文翻译：

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用于 attributes-in-space 任务的探索性标记映射


地理文本数据将地理位置与文本信息（例如，地理标记的推文）相结合，通常使用标签图进行可视化。由于标签具有丰富的属性信息，因此标签映射是一种直观的方法，可以直观地显示标签携带的属性域如何随空间变化。但是，用户可能不仅对标签的整体空间分布感兴趣，还对探索详细的空间属性分析感兴趣，例如检查属性域的子类如何全局分布或检查所有属性子类是否都表现出相同的全局分布模式。迄今为止，用视觉编码（例如大小、颜色）表示标签以扩展各种空间属性任务以支持探索性分析的方法仍不清楚。在这项工作中，我们扩展了标签映射以支持探索性分析，方法是将空间搜索区分为局部或全局空间，并将属性域区分为内部或属性类之间，支持四种类型的空间属性任务：global-within、local-within、global-between和local-between任务。我们通过两个案例研究评估了我们的探索性标签图：调查 1981 年至 2020 年的重大灾害事件，以及调查西班牙、法国、德国和意大利 2000 年和 2019 年的主要死亡原因。我们使用眼动追踪和问卷来评估我们的探索性标签图以进行比较。两种方法在美学、密度、布局和易读性方面都有相似的自我报告的可用性得分。然而，我们的探索性标签图更有效和高效，并且具有较低的认知负荷。