Extracting Important Routes from Illustration Maps Using Kernel Density Estimation

Abstract

Illustration maps often direct our visual attention to the specific route with geographic symbols and annotation labels associated with important landmarks. This inspires us to evaluate the quality of such maps by analyzing the spatial distribution of visual attention over the map domain. In this paper, we introduce kernel density estimation in order to identify important routes that are implicitly designated by the map designers. Our algorithm begins by composing the density field as a combination of Gaussian kernels centered on the landmarks. The algorithm then allows us to extract an important route on the map as the trajectory of a ball running along the valley of the density field. We conducted a user study where we compared the routes reconstructed from the sequence of landmarks specified by the participants and their originally intended routes, and report some insight into possible aesthetic criteria in illustrating such maps.