A predominance map expresses the predominant data category for each geographical entity and colors are used to differentiate a small number of data categories. In tag maps, many data categories are present in the form of different tags, but related tag map approaches do not account for predominance, as tags are either displaced from their respective geographical locations or visual clutter occurs. We propose predominance tag maps, a layout algorithm that accounts for predominance for arbitrary aggregation granularities. The algorithm is able to utilize the font sizes of the tags as visual variable and it is further configurable to implement aggregation strategies beyond visualizing predominance. We introduce various measures to evaluate numerically the qualitative aspects of tag maps regarding local predominance, global features, and layout stability and we comparatively analyze our method to the tag map approach by Thom et al. [1] on the basis of real world data sets.
Historical maps tell rich stories, and they are also valuable data objects. They vary greatly in size, type, and context, as well as the kinds and density of information they contain. Historical maps are indeed objects that invite close reading, interpretation, and debate. Whereas a variety of environments exist for the annotation, manipulation, and representation of digital maps, or map-derived data, workflows in the spatial digital humanities can be complex and those environments are not often well integrated. In this article, we describe a prototype named ‘MapFolder’ for studying maps, its algorithms for calculating the areal distortion, its visual design for communicating that distortion, along with a scholarly workflow. We blend annotation practices common in the spatial humanities with the workflows of georeferencing in order to be able to visualize how historic cartographic documents compare with the geospatial representations we are familiar with today. The case studies we use to demonstrate ‘MapFolder’ are maps of the medieval period, a body of maps that are less often studied algorithmically and that are usually avoided in typical workflows of georeferencing. MapFolder is by no means a prototype designed to work exclusively with medieval maps, but since maps of this period are only partially geographic in their design, they offer a particularly fruitful opportunity to rethink the algorithmic manipulation of historical depictions of the world. Working with this complex data from the humanities allows us, as well, to propose the use of visualization for critical, comparative spatial analysis in pre-modern studies and beyond.
When point clouds are labeled in information visualization applications, sophisticated guidelines as in cartography do not yet exist. Existing naive strategies may mislead as to which points belong to which label. To inform improved strategies, we studied factors influencing this phenomenon. We derived a class of labeled point cloud representations from existing applications and we defined different models predicting how humans interpret such complex representations, focusing on their geometric properties. We conducted an empirical study, in which participants had to relate dots to labels in order to evaluate how well our models predict. Our results indicate that presence of point clusters, label size, and angle to the label have an effect on participants' judgment as well as that the distance measure types considered perform differently discouraging the use of label centers as reference points.
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