Geovisual analytics: human factors

This special issue is the Part II of a two-volume special issue comprising of a series of papers documenting the state-of-the-art research in geovisual analytics. Part I consisted of four papers and focused on design and implementation issues in geovisual analytics. Here, with an additional four papers focused on the human factors in geovisual analytics,we present Part II. Visual analytics aims at combining the strengths of machines and humans (Keim et al. 2010). In the light of this definition, studying human factors is essential in understanding what we can truly achieve using visual analytics and how we can optimize software systems and visualization design to human strengths and limitations (Tory and Möller 2004). In the Part I of the series Special Issue on Geovisual Analytics: Design and Implementation edited by Pettit et al. (2015), Cartwright (2015) collects expert interpretations to build a definition of the word map and distinguish it from other representations, because for tasks such as qualitatively defining and interpreting a concept, we need human input. Similarly, to understand which processes should be left to machines and which to humans; how well geovisual analytics concepts and tools are understood and utilized by their users; what are their limitations; we need to understand human experience with these systems. This special issue puts together research articles in which various components of geovisual analytics environments are evaluated through user-centered approaches. Some take an efficiency and effectiveness (performance) approach, while some others take perceptual and cognitive approach to human factors in visual analytics; offering insights into how humans interact with geospatial products. For example, MacArdle, Tahir, and Bertolotto (2014) study mouse trajectories that we leave behind when we interact with a digital geospatial product. If we have different levels of expertise, do we use the computer mouse differently? If yes, why should this be the case? Such knowledge can be powerful in providing real-time (and effective) help to users when they need it, and can help developing better interaction designs, thus making Digital Earth displays more usable and useful. Tackling another important aspect of human experience with Digital Earth-related visualization environments, Bleisch and Dykes (2014) offer a comparative view of human performance with 2D and 3D displays in a desktop-based virtual reality environment. The study contributes incrementally to our (scarce) knowledge as to whether such realistic 3D representations work for people and if yes how well they work, and for which task. Further in this special issue, Bernabé Poveda and Çöltekin (2014) study a perceptual issue in relation to interpreting 3D forms on satellite images through an online user study, providing us with new knowledge on the well-known terrain reversal effect. This is an illusion that can have undesired consequences in Digital Earth applications and other use of satellite images and shaded relief maps. When shadow is the dominant depth cue in such visualizations and shadows are not where they are supposed to be, the scene interpretation becomes ambiguous or even reversed: we see convex shapes as concave and vice versa. Can we really trust what we see? This paper demonstrates how prevalent this interesting phenomenon is, and briefly discusses the proposed solutions. Last but not least, on another front where 3D visualizations International Journal of Digital Earth, 2015 Vol. 8, No. 8, 595–598, http://dx.doi.org/10.1080/17538947.2015.1047173