Visual Analytics Using Density Equalizing Geographic Distortion

Abstract

Visualizing large geo-demographical data sets using pixel-based techniques involves mapping the geo-spatial dimensions of a data point to screen coordinates and appropriately encoding its statistical value by color. Analysis of such data is a great challenge. General tasks involve clustering, categorization and searching for patterns of interest for sociological or economic research. Available visual encodings and screen space limitations lead to over-plotting and hiding of patterns and clusters in densely populated areas, while sparsely populated areas waste space and draw the attention away from areas of interest. In the current paper, two new approaches (RadialScale and AngularScale) are introduced to create density-equalized maps, while preserving recognizable features and neighborhoods in the visualization. The approaches apply a multi-scaling technique based on local features of the data described as local minima and maxima of point density. Consequently, scaling is conducted several times around these features, thus leading to more effective distortions. Results are applied and discussed on two applications. Evaluation shows to outperform traditional techniques for homogeneity of distortion and efficient use of space.