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We present a technique that allows distinguishing between index finger and thumb input on touchscreen phones, achieving an average accuracy of 82.6% in a real-life application with only a single touch. We divide the screen into a virtual grid of 9mm 2 units and use a dedicated set of training data and algorithms for classifying new touches in each screen location. Further, we present correlations between physical and digital touch properties to extend previous work.

Researchers have reported a lack of experience and low graph literacy as significant problems when making visual analytics applications available to a general audience. Therefore, it is fundamental to understand the strengths and weaknesses of different visualizations in the decision-making process. This paper explores the benefits and challenges of an intuitive, a compact, and a detailed visualization for supporting non-expert users in two different decision-making contexts. Using objective and subjective means proposed by earlier work, we determine the benefits and trade-offs of these visualizations for different task complexity levels. We found that while an intuitive visualization can be a good choice for easy level and medium level tasks, hard level tasks are best supported with a richer, yet visually more demanding visualization.

This article presents a first step towards the definition of a visual guide for communicating uncertainty which is to fit into existing visualisation frameworks and toolkits. The first entry in our guide is made by a set of visual variables appropriate for representing areal uncertainty in algorithm mechanics. Such visualisations show users how data points are distributed in the classification space and allow them to understand the "goodness-of-fit" of their data to the algorithm. This is important for Visual Analytics applications, which combine Information Visualisation with information mining techniques in an interactive decision-making process. Model uncertainties stemming from widely spread data points need to be visualised so that the user can make adjustments and improve the analysis. To capitalise on established knowledge and meaning, we explore whether popular visual variables for representing areal uncertainty in the domain of geospatial visualisation may also be effective for representing uncertainty in the visualisation of the mechanics of K-means clustering and Linear Regression algorithms, as both use a spatial distribution of data points. In a study with 500 participants we find that overall the visual means opacity performs best, followed by texture, but that grid and blur may be unsuitable for quantifying uncertainty. The performance of contour lines appears to depend on the algorithm visualisation. Using this study, we extend the validity of a set of domain-specific findings from geospatial visualisation to the visualisation of algorithm mechanics and use these to form the first building blocks of a cross-disciplinary visual guide for representing uncertainty, laying promising foundations for future work.

Operating a website with one hand on a touchscreen mobile phone remains difficult despite advances in hardware and software development. This problem is exacerbated by manufacturers producing phones with larger screens which are more difficult to hold and operate one-handedly. We present a way to enhance one-handed operation of a website using standard client-side web technologies, without the need to redesign the site or to overwrite any CSS styles. It transforms input for form elements, media control and page access on the fly into a thumb-friendly interaction model. Initial user testing of our interface prototype confirms efficiency and learnability, and highlights its usefulness for navigating long pages and finding the desired information more quickly, even between different websites, when operating the device with one hand.