Space, time and visual analytics

Abstract

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Give full correspondence details here Permanent (Received 28 April 2010; final version received …)Visual analytics aims to combine the strengths of human and electronic data processing. Visualization, whereby humans and computers cooperate through graphics, is the means through which this is achieved. Seamless and sophisticated synergies are required for analyzing spatio-temporal data and solving spatio-temporal problems. In modern society, spatio-temporal analysis is not solely the business of professional analysts. Many citizens need or would be interested in undertaking analysis of information in time and space. Researchers should find approaches to deal with the complexities of the current data and problems and find ways to make analytical tools accessible and usable for the broad community of potential users to support spatio-temporal thinking and contribute to solving a large range of problems.

How does GIScience support spatio-temporal information search in the humanities?

“…One key aspect of GeoVA (Andrienko et al, 2010) is dealing with massive spatio-temporal data sets, as illustrated in Luo, Yin, Di, Hardisty, and MacEachren (2014). These authors explore complex geo-social relationships in an international trade network using traditional network visualization techniques, and additionally present them to users in a dynamic and interactive GeoSocialApp.…”

Effects of Spatial Distribution on Change Detection in Animated Choropleth Maps

“…Perceptual-cognitive approaches to cartography have improved the effectiveness of map designs over the past years (MacEachren, 1995;Harrower, 2007). However, the design of dynamic maps, interactive maps, and threedimensional displays still need more guidelines and empirical evidence to prove its effectiveness (Slocum et al, 2004;Andrienko et al, 2010). In response to this necessity, recent research projects on animated maps have conducted various experiments using survey-based experiments and eye tracking experiments, with the adoption of methods and theories from psychology and vision studies (Montello, 2002;Andrienko et al, 2010).…”

“…However, the design of dynamic maps, interactive maps, and threedimensional displays still need more guidelines and empirical evidence to prove its effectiveness (Slocum et al, 2004;Andrienko et al, 2010). In response to this necessity, recent research projects on animated maps have conducted various experiments using survey-based experiments and eye tracking experiments, with the adoption of methods and theories from psychology and vision studies (Montello, 2002;Andrienko et al, 2010). 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).…”

Core or periphery? The effects of country-of-origin agglomerations on the within-country expansion of MNEs

“…We apply geo-visualization techniques (Andrienko et al, 2010;Pavlovskaya, 2006;Wang, Zhang, Zhang, & Zhang, 2014) from geographic information science (GIS) to identify Japanese MNE agglomerations in China based on a panel of geocoded subnational investment data. Prior research has generally used fixed administrative units (such as provinces and states) to identify agglomerations of foreign direct investment (FDI).…”