BinSq: Visualizing Geographic Dot Density Patterns with Gridded Maps

Dot maps have become a popular way to visualize discrete geographic data. Yet, beyond showing how the data is spatially distributed, dot maps are often visually cluttered in terms of consistency, overlap and representativeness. Existing clutter reduction techniques like jittering, refinement, distortion and aggregation also address this issue, but do so by arbitrarily displacing dots from their exact location, removing dots from the map, changing the spatial reference of the map, or reducing its level of detail, respectively. We present BinSq, a novel visualization technique to compare variations in dot density patterns without visual clutter. Based on a careful synthesis of existing clutter reduction techniques, BinSq reduces the wide variety of dot density variations on the map to a representative subset of density intervals that are more distinguishable. The subset is derived from a nested binning operation that introduces order and regularity to the map. Thereafter, a dot prioritization operation improves the representativeness of the map by equalizing visible data values to correspond with the actual data. In this paper, we describe the algorithmic implementation of BinSq, explore its parametric design space, and discuss its capabilities in comparison to six existing clutter reduction techniques for dot maps.status: publishe