An exploratory tag map for attributes-in-space tasks

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.