Nadia Boukhelifa

Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

We report on results of a series of user studies on the perception of four visual variables that are commonly used in the literature to depict uncertainty. To the best of our knowledge, we provide the first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives. In addition to blur, dashing and grayscale, we investigate the use of `sketchiness as a visual variable because it conveys visual impreciseness that may be associated with data quality. Inspired by work in non-photorealistic rendering and by the features of hand-drawn lines, we generate line trajectories that resemble hand-drawn strokes of various levels of proficiency-ranging from child to adult strokes-where the amount of perturbations in the line corresponds to the level of uncertainty in the data. Our results show that sketchiness is a viable alternative for the visualization of uncertainty in lines and is as intuitive as blur; although people subjectively prefer dashing style over blur, grayscale and sketchiness. We discuss advantages and limitations of each technique and conclude with design considerations on how to deploy these visual variables to effectively depict various levels of uncertainty for line marks.

Sketchy Rendering for Information Visualization

We present and evaluate a framework for constructing sketchy style information visualizations that mimic data graphics drawn by hand. We provide an alternative renderer for the Processing graphics environment that redefines core drawing primitives including line, polygon and ellipse rendering. These primitives allow higher-level graphical features such as bar charts, line charts, treemaps and node-link diagrams to be drawn in a sketchy style with a specified degree of sketchiness. The framework is designed to be easily integrated into existing visualization implementations with minimal programming modification or design effort. We show examples of use for statistical graphics, conveying spatial imprecision and for enhancing aesthetic and narrative qualities of visualization. We evaluate user perception of sketchiness of areal features through a series of stimulus-response tests in order to assess users ability to place sketchiness on a ratio scale, and to estimate area. Results suggest relative area judgment is compromised by sketchy rendering and that its influence is dependent on the shape being rendered. They show that degree of sketchiness may be judged on an ordinal scale but that its judgement varies strongly between individuals. We evaluate higher-level impacts of sketchiness through user testing of scenarios that encourage user engagement with data visualization and willingness to critique visualization design. Results suggest that where a visualization is clearly sketchy, engagement may be increased and that attitudes to participating in visualization annotation are more positive. The results of our work have implications for effective information visualization design that go beyond the traditional role of sketching as a tool for prototyping or its use for an indication of general uncertainty.

Comparison of Three-Dimensional Planning-Assisted and Conventional Acetabular Cup Positioning in Total Hip Arthroplasty: A Randomized Controlled Trial.

BACKGROUNDnMalpositioning of the acetabular cup during total hip arthroplasty increases the risk of dislocation, edge-loading, squeaking, early wear, and loosening. We hypothesized that the use of three-dimensional (3-D) visualization tools to identify the planned cup position relative to the acetabular edge intraoperatively would increase the accuracy of cup orientation. The purpose of this study was to compare 3-D planning-assisted implantation and freehand insertion of the acetabular cup.nnnMETHODSnThis was a prospective randomized controlled study of two groups of twenty-eight patients each. In the first group, cup positioning was guided by 3-D views of the cup within the acetabulum obtained during 3-D preoperative planning. In the control group, the cup was placed freehand. All of the patients were operated on by the same surgeon, through a minimally invasive direct anterior approach with the patient in the supine position. Cup anteversion and abduction angles were measured on 3-D computed tomography (CT) reconstructions. The main evaluation criterion was the percentage of outliers according to the Lewinnek safe zone.nnnRESULTSnOperative time did not differ between the two groups. The cup anteversion was more accurate in the 3-D planning group (mean difference from the planned angle [and standard deviation], -2.7° ± 5.4°) compared with the freehand-placement group (6.6° ± 9.5°). According to the Lewinnek safe zone, overall, the percentage of outliers was lower in the 3-D planning group (21%; six patients) than in the control group (46%; thirteen patients). According to the Callanan safe zone, the percentage of outliers was also lower in the 3-D planning group (25% versus 64%). Although cup abduction was also restored with greater accuracy in the 3-D planning group, on the basis of the Lewinnek safe zone, the percentage of abduction outliers was comparable between groups, with fewer high-abduction values, but more low-abduction values, in the 3-D planning group.nnnCONCLUSIONSnPreoperative 3-D planning increased the accuracy of anteversion restoration and reduced the percentage of outliers without increasing the operative time. In this study, the same advantage could not be demonstrated for abduction.nnnLEVEL OF EVIDENCEnTherapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.

Evolutionary visual exploration: evaluation with expert users

We present an Evolutionary Visual Exploration (EVE) system that combines visual analytics with stochastic optimisation to aid the exploration of multidimensional datasets characterised by a large number of possible views or projections. Starting from dimensions whose values are automatically calculated by a PCA, an interactive evolutionary algorithm progressively builds (or evolves) non‐trivial viewpoints in the form of linear and non‐linear dimension combinations, to help users discover new interesting views and relationships in their data. The criteria for evolving new dimensions is not known a priori and are partially specified by the user via an interactive interface: (i) The user selects views with meaningful or interesting visual patterns and provides a satisfaction score. (ii) The system calibrates a fitness function (optimised by the evolutionary algorithm) to take into account the user input, and then calculates new views. Our method leverages automatic tools to detect interesting visual features and human interpretation to derive meaning, validate the findings and guide the exploration without having to grasp advanced statistical concepts. To validate our method, we built a prototype tool (EvoGraphDice) as an extension of an existing scatterplot matrix inspection tool, and conducted an observational study with five domain experts. Our results show that EvoGraphDice can help users quantify qualitative hypotheses and try out different scenarios to dynamically transform their data. Importantly, it allowed our experts to think laterally, better formulate their research questions and build new hypotheses for further investigation.

Supporting Historical Research Through User-Centered Visual Analytics

In this paper we describe the development and evaluation of a visual analytics tool to support historical research. Historians continuously gather data related to their scholarly research from archival visits and background search. Organising and making sense of all this data can be challenging as many historians continue to rely on analog or basic digital tools. We built an integrated note-taking environment for historians which unifies a set of func-tionalities we identified as important for historical research including editing, tagging, searching, sharing and visualization. Our approach was to involve users from the initial stage of brainstorming and requirement analysis through to design, implementation and evaluation. We report on the process and results of our work, and conclude by reflecting on our own experience in conducting user-centered visual analytics design for digital humanities.

Evolutionary visual exploration: Evaluation of an iec framework for guided visual search

We evaluate and analyse a framework for evolutionary visual exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional data sets toward two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this article, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clearly predefined task, which examines how users leverage the system and how the system evolves to match their needs. While we previously showed that using EVE, domain experts were able to formulate interesting hypotheses and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating interactive evolutionary computation (IEC) techniques based on user study methodologies.

A Mixed Approach for the Evaluation of a Guided Exploratory Visualization System

We summarise and reflect upon our experience in evaluating a guided exploratory visualization system. Our system guides users in their exploration of multidimensional datasets to pertinent views of their data, where the notion of pertinence is defined by automatic indicators, such as the amount of visual patterns in the view, and subjective user feedback obtained during their interaction with the tool. To evaluate this type of system, we argue for deploying a collection of validation methods that are: user-centered, observing the utility and effectiveness of the system for the end-user; and algorithm-centered, analysing the computational behaviour of the system. We report on observations and lessons learnt from working with expert users both for the design and the evaluation of our system.

Visualizing Heterogeneous Utility Data: A Case for Aesthetic Design

A map visually enfolds selected messages to a target audience. To achieve this effectively, a clear understanding of ‘the message, the user and the purpose’ of the map needs to be translated into successful design choices covering content, typography, style and layout. Aesthetics not only inform the local design space over which rules for visual mappings are defined, but they also offer global heuristics to ensure overall visual excellence. In the world of underground utilities where companies use maps to communicate the location of their buried services, personal, internal and sector depiction standards and guidelines have a strong influence on visual design. When the scope of a map, defined by its ‘message, user and purpose’ is overlooked, conflicts arise such as between the need for realism and schematisation. In this paper we examine the role aesthetics play in the context of the utility-sector work-flow. We discuss conflicts that arise when the scope of a map’s use is not carefully considered. We give details of a case study where we have attempted to reconcile a conflict between accuracy and clarity through a clutter aesthetic. Central to this research is the observed link between data, task and aesthetics; and the question of to what extent can aesthetics be designed and incorporated algorithmically.