Issues of Change Detection in Animated Choropleth Maps

Abstract

One primary utility of animated maps is their ability to depict change over time and space; unfortunately, recent research suggests that humans frequently fail to perceive changes within dynamic graphics. However, different types of dynamic graphics include different manifestations of change. For example, an animated proportional-symbol map possesses different change properties than an animated choropleth map. This article examines issues of change on animated choropleth maps. We identify relevant limitations of the human visual system that pertain to animated map reading, including change blindness and foveal versus peripheral attention, and introduce methods to quantify the magnitude of change that separates individual scenes within choropleth animations. These methods are useful for measuring and describing changes that confront users of animated choropleth maps. We also characterize the transitional behaviours of enumeration units and discuss the influences of data classification and other cartographic controls on change within animated choropleth maps. RésuméUne des principales utilités des cartes animées est qu'elles permettent de suivre les changements en fonction du temps et de l'espace. Malheureusement, selon de récentes recherches, il arrive souvent que les humains ne perçoivent pas ces changements dans les graphiques dynamiques. De plus, chaque type de graphique dynamique inclut des manifestations différentes de l'évolution. Par exemple, les propriétés d'une carte animée avec symboles proportionnels sont différentes de celles d'une carte choroplèthe animée. Dans l'article, on examine les problèmes liés au changement dans les cartes choroplèthes animées. On définit les limitations pertinentes des systèmes visuels humains qui sont associées à la lecture de cartes animées, y compris la cécité au changement et l'attention fovéale/périphérique, et on présente des méthodes pour quantifier la magnitude du changement qui sépare des scènes individuelles dans des animations choroplèthes. Ces méthodes sont utiles pour mesurer et décrire les changements auxquels se heurtent les utilisateurs de cartes choroplèthes animées. On caractérise aussi les comportements transitionnels des unités de dénombrement et on parle des effets de la classification des données et d'autres mesures cartographiques sur les changements apportés aux cartes choroplèthes animées.

Effects of Spatial Distribution on Change Detection in Animated Choropleth Maps

“…This paper focuses on unsolved issues of change detection in animated choropleth maps as an extension of Goldsberry andBattersby (2009), andFish s (2010) studies. Goldsberry and Battersby (2009) …”

“…In other words, in order to calculate the sum total, clustered units require less time and efforts, but dispersed or random units require more time and effort, on account of spatial contiguity effect. Finally, changes in peripheral enumeration units may be less likely to be noticed, due to limited foveation distance (Goldsberry and Battersby, 2009). …”

“…According to Goldsberry and Battersby (2009) The simplest method for quantifying MOC is the basic magnitude of change (BMOC) across the map, which is equal to the sum of all values of off-diagonal elements in the array. In other words, BMOC is the total number of enumeration units that shift during a transition, and normalized BMOC is the proportion of shifting units with respect to the total number of enumeration units, ranging from 0 (no shifting units) to 1 (no persisting units).…”

“…These empirical studies have addressed a wide range of research topics, including comparisons of static small-multiple maps and animated maps (Kossoulakou and Kraak, 1992;Slocum et al, 2004;Griffin et al, 2006), comparisons between visual variables effectiveness in animated maps or interactive maps (Cinnamon, 2009;Çöltekin et al, 2009;Garlandini and Fabrikant, 2009;Hegarty et al, 2010), and the impact of interactivity (Mayer, 2001;Mayer and Chandler, 2001) and dynamic variables such as rate of change and abrupt or smooth transitions (Goldsberry and Battersby, 2009;Fish, 2010). Some studies have concluded that dynamic animated maps that are too complex leads to difficulties in conveying information (Bétrancourt and Tversky, 2000;Morrison and Tversky, 2001;Goldsberry and Battersby, 2009), because of human cognitive limits such as split attention, retroactive inhibition, and cognitive overload (Harrower, 2007). Therefore, it is essential to consider how dynamic variables affect the acquisition of map information.…”

“…In order to investigate the effect of spatial distribution on gross change detection, we have (1) specified the level of gross change detection in a similar way to Goldsberry and Battersby (2009), (2) designed a cognitive psychological experiment that allowed us to examine effects of spatial distributions of change , MOC , and duration on gross change detection, (3) produced experimental materials by introducing the change-characterization arrays and global Moran s I, in order to control MOC and spatial distribution of change in our visual stimuli, and (4) finally conducted an actual experiment.…”