Visualizing spatial data is an important tool when working with large volumes of species occurrences. Plotting millions of points on map, however, is computationally expensive and overlaps can lead to occluded visualization.
In an attempt to overcome this, researchers from the University of Marburg in Germany designed a novel visualization algorithm for creating aggregated, non-overlapping circle representation maps of point coordinates. Their approach—Circle Merging Quadtree—is based on an iterative transformation of all points into circles of a certain radius and then merging and (thus expanding) overlapping circles until no circles overlap.
Using GBIF-mediated occurrences of 50 species, varying from hundreds to millions of records, the authors compared the performance and quality of their algorithm to existing methods. While providing similar or better quality than the other tested methods, CMQ proved superior in terms of processing runtime—at up to two orders of magnitude.