Unclassed Animated Choropleth Maps

Abstract

The potential of unclassed animated choropleth maps as a solution to false patterns of geographic change arising from data classification is investigated. Old concerns about unclassed choropleth maps may be mitigated through map interactivity that offers four advantages over traditional data legends, and previous insights from testing static choropleth maps do not necessarily translate to animated cartography. Data from user testing revealed unclassed animated choropleth maps neither help nor hurt the ability of map readers to understand patterns of geographic change. However, the unclassed map (1) appeared 'less jumpy' to participants and was perceived to run at a slower pace (despite running at the same number of frames per second), and (2) subtle geographic shifts (e.g., seasonal unemployment cycles) were more readily noticed on the unclassed maps. Preliminary results also suggest classed data emphasise stability over time – while their unclassed counterparts improve our ability to see changes. This paper also outlines animated simultaneous contrast as a new perceptual issue in the creation of animated choropleth maps.