Quality Measures for Visual Point Clustering in Geospatial Mapping

Visualizing large amounts of point data in a way that resembles the density of the distribution is a complex problem if the size of the drawing area is constrained. Naively drawing points on top of each other leads to occlusion and therefore a loss of information. An intuitive approach is combining close points as clusters that resemble their size as well as their geographic location. However, traditional clustering algorithms are not designed for visual clusterings rather than minimizing an error function independent of a graphical representation. This paper introduces measures for the quality of circle representations based on clustering outputs. Our experimental evaluation revealed that all methods had weaknesses regarding at least one of these criteria.