3D desktop-based virtual environments provide a means for displaying quantitative data in context.Data that are inherently spatial in three dimensions may benefit from visual exploration and analysis in relation to th...3D desktop-based virtual environments provide a means for displaying quantitative data in context.Data that are inherently spatial in three dimensions may benefit from visual exploration and analysis in relation to the environment in which they were collected and to which they relate.We empirically evaluate how effectively and efficiently such data can be visually analyzed in relation to location and landform in 3D versus 2D visualizations.In two experiments,participants performed visual analysis tasks in 2D and 3D visualizations and reported insights and their confidence in them.The results showed only small differences between the 2D and 3D visualizations in the performance measures that we evaluated:task completion time,confidence,complexity,and insight plausibility.However,we found differences for different datasets and settings suggesting that 3D visualizations or 2D representations,respectively,may be more or less useful for particular datasets and contexts.展开更多
Visualizing big and complex multivariate data is challenging.To address this challenge,we propose flexible visual analytics(FVA)with the aim to mitigate visual complexity and interaction complexity challenges in visua...Visualizing big and complex multivariate data is challenging.To address this challenge,we propose flexible visual analytics(FVA)with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics,while maintaining the strengths of multiple perspectives on the studied data.At the heart of our proposed approach are transitions that fluidly transform data between userrelevant views to offer various perspectives and insights into the data.While smooth display transitions have been already proposed,there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas.As a call to further action,we argue that future research is necessary to develop a conceptual framework for flexible visual analytics.We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them,and consider the display user for whom such depictions are produced and made available for visual analytics.With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.展开更多
文摘3D desktop-based virtual environments provide a means for displaying quantitative data in context.Data that are inherently spatial in three dimensions may benefit from visual exploration and analysis in relation to the environment in which they were collected and to which they relate.We empirically evaluate how effectively and efficiently such data can be visually analyzed in relation to location and landform in 3D versus 2D visualizations.In two experiments,participants performed visual analysis tasks in 2D and 3D visualizations and reported insights and their confidence in them.The results showed only small differences between the 2D and 3D visualizations in the performance measures that we evaluated:task completion time,confidence,complexity,and insight plausibility.However,we found differences for different datasets and settings suggesting that 3D visualizations or 2D representations,respectively,may be more or less useful for particular datasets and contexts.
基金The authors gratefully acknowledge that this work is a result of the Dagstuhl Seminar 19192 on Visual Analytics for Sets over Time and Space(Fabrikant et al.,2019)Dagstuhl seminars are funded by the Leibniz Association,Germany.Sara Irina Fabrikant gratefully acknowledges funding from the European Research Council(ERC),under the GeoViSense Project,Grant number 740426.
文摘Visualizing big and complex multivariate data is challenging.To address this challenge,we propose flexible visual analytics(FVA)with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics,while maintaining the strengths of multiple perspectives on the studied data.At the heart of our proposed approach are transitions that fluidly transform data between userrelevant views to offer various perspectives and insights into the data.While smooth display transitions have been already proposed,there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas.As a call to further action,we argue that future research is necessary to develop a conceptual framework for flexible visual analytics.We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them,and consider the display user for whom such depictions are produced and made available for visual analytics.With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.
