Quantitative data graphics in 3D desktop-based virtual environments – an evaluation

3D desktop-based virtual environments provide a means for displaying quantitative data in context. Data that is 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 data sets and settings suggesting that 3D visualizations, or 2D representations respectively, may be more or less useful for particular data sets and contexts

Graphical aids to the estimation and discrimination of uncertain numerical data

This research investigates the performance of graphical dot arrays designed to make discrimination of relative numerosity as effortless as possible at the same time as making absolute (quantitative) numerosity estimation as effortful as possible. Comparing regular, random, and hybrid (randomized regular) configurations of dots, the results indicate that both random and hybrid configurations reduce absolute numerosity estimation precision, when compared with regular dots arrays. However, discrimination of relative numerosity is significantly more accurate for hybrid dot arrays than for random dot arrays. Similarly, human subjects report significantly lower levels of subjective confidence in judgments when using hybrid dot configurations as compared with regular configurations; and significantly higher levels of subjective confidence as compared with random configurations. These results indicate that data graphics based on the hybrid, randomized-regular configurations of dots are well-suited to applications that require decisions to be based on numerical data in which the absolute quantities are less certain than the relative values. Examples of such applications include decision-making based on the outputs of empirically- based mathematical models, such as health-related policy decisions using data from predictive epidemiological models

Mining candidate causal relationships in movement patterns

This is an Accepted Manuscript of an article published by Taylor & Francis in the International Journal of Geographical Information Science on 01 October 2013, available online: http://wwww.tandfonline.com/10.1080/13658816.2013.841167In many applications, the environmental context for, and drivers of movement patterns are just as important as the patterns themselves. This paper adapts standard data mining techniques, combined with a foundational ontology of causation, with the objective of helping domain experts identify candidate causal relationships between movement patterns and their environmental context. In addition to data about movement and its dynamic environmental context, our approach requires as input definitions of the states and events of interest. The technique outputs causal and causal-like relationships of potential interest, along with associated measures of support and confidence. As a validation of our approach, the analysis is applied to real data about fish movement in the Murray River in Australia. The results demonstrate the technique is capable of identifying statistically significant patterns of movement indicative of causal and causal-like relationships. 1365-8816Australian Research Council Discovery Projec

Special issue introduction: Approaching spatial uncertainty visualization to support reasoning and decision making

While research on uncertainty and decision-making has a long history across several disciplines, recent technological developments compel researchers to rethink how to best address and advance the understanding of how humans reason and make decisions under spatial uncertainty. This introduction presents a visual summary graphic to provide an overview of each article in this special issue. Upon viewing these visual summaries, the reader will find that each of these articles covers different topics in the uncertainty visualization domain, offering complementary research in this field. Extending this body of research and finding new ways to explore how these visualizations may help or hinder the analytical and reasoning process of humans continues to be a necessary step towards designing more effective uncertainty visualizations to support reasoning and decision-making

Extracting causal rules from spatio-temporal data

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23374-1_2This paper is concerned with the problem of detecting causality in spatiotemporal data. In contrast to most previous work on causality, we adopt a logical rather than a probabilistic approach. By defining the logical form of the desired causal rules, the algorithm developed in this paper searches for instances of rules of that form that explain as fully as possible the observations found in a data set. Experiments with synthetic data, where the underlying causal rules are known, show that in many cases the algorithm is able to retrieve close approximations to the rules that generated the data. However, experiments with real data concerning the movement of fish in a large Australian river system reveal significant practical limitations, primarily as a consequence of the coarse granularity of such movement data. In response, instead of focusing on strict causation (where an environmental event initiates a movement event), further experiments focused on perpetuation (where environmental conditions are the drivers of ongoing processes of movement). After retasking to search for a different logical form of rules compatible with perpetuation, our algorithm was able to identify perpetuation rules that explain a significant proportion of the fish movements. For example, approximately one fifth of the detected long-range movements of fish over a period of six years were accounted for by 26 rules taking account of variations in water-level alone.EPSRCAustralian Research Council (ARC) under the Discovery Projects Schem

Toward flexible visual analytics augmented through smooth display transitions

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 user-relevant 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