A Linear-Time Algorithm for the Aggregation and Visualization of Big Spatial Point Data
Christian Beilschmidt, Thomas Fober, Michael Mattig, Bernhard Seeger · 2017
The visualization of spatial data becomes increasingly important in science, business and many other domains. In geography, data often corresponds to a large number of point observations that should be displayed on a constrained screen with limited resolution. This causes, however, a loss of information due to an overloaded and occluded visualization. In this paper we present a new visualization algorithm that avoids this problem by aggregating point data into a set of non-overlapping circles that capture all important information. Our algorithm based on a quadtree computes the circles in linear time with respect to the number of points.