Data Embroidery with Black-and-White Textures

We investigated data embroidery with black-and-white textures, identifying challenges in the use of textures for machine embroidery based on our own experience. Data embroidery, as a method of physically representing data, offers a unique way to integrate personal data into one's everyday fabric-based objects. Owing to their monochromatic characteristics, black-and-white textures promise to be easy to employ in machine embroidery. We experimented with different textured visualizations designed by experts and, in this paper, we detail our workflow and evaluate the performance and suitability of different textures. We then conducted a survey on vegetable preferences within a family and created a canvas bag as a case study, featuring the embroidered family data to show how embroidered data can be used in practice

Design Characterization for Black-and-White Textures in Visualization

We investigate the use of 2D black-and-white textures for the visualization of categorical data and contribute a summary of texture attributes, and the results of three experiments that elicited design strategies as well as aesthetic and effectiveness measures. Black-and-white textures are useful, for instance, as a visual channel for categorical data on low-color displays, in 2D/3D print, to achieve the aesthetic of historic visualizations, or to retain the color hue channel for other visual mappings. We specifically study how to use what we call geometric and iconic textures. Geometric textures use patterns of repeated abstract geometric shapes, while iconic textures use repeated icons that may stand for data categories. We parameterized both types of textures and developed a tool for designers to create textures on simple charts by adjusting texture parameters. 30 visualization experts used our tool and designed 66 textured bar charts, pie charts, and maps. We then had 150 participants rate these designs for aesthetics. Finally, with the top-rated geometric and iconic textures, our perceptual assessment experiment with 150 participants revealed that textured charts perform about equally well as non-textured charts, and that there are some differences depending on the type of chart

Micro Visualizations: Design and Analysis of Visualizations for Small Display Spaces

The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.Le sujet de cette habilitation est l'étude de très petites visualisations de données, les micro visualisations, dans des contextes d'affichage qui ne peuvent consacrer qu'un espace de rendu minimal aux représentations de données. Depuis plusieurs années, avec mes collaborateurs, j'étudie la perception humaine, l'interaction et l'analyse conduite avec des micro visualisations dans de multiples contextes.Dans ce document, je rassemble trois de mes axes de recherche liés aux micro visualisations~: les glyphes de données, où ma recherche s'est concentrée sur l'étude de la perception de micro visualisations dans un context \textit{small-multiple}, les \textit{word-scale visualizations}, où ma recherche s'est concentrée sur les petites visualisations intégrées dans les documents textuels, et les petites visualisations de données mobiles pour les montres connectées. Je considère ces types de petites visualisations sous le terme générique de ``micro visualisations.'' Les micro visualisations sont utiles dans de multiples contextes de visualisation et j'ai travaillé à une meilleure compréhension de la complexité des conceptions et utilisations des micro visualisations. Je définirai ici le terme de micro visualisation, je résumerai mes propres recherches et celles d'autres chercheurs, ainsi que les directives de conception, et j'esquisserai plusieurs espaces de conception pour différents types de micro visualisations, sur la base de certains des travaux auxquels j'ai participé depuis mon doctorat

Pairgrams: Understanding Collaborative Analysis Behavior with Visualization

Proceedings of the CHI Workshop on Analytic Provenance: Process + Interaction+ Insight.International audienceWe report on our work towards understanding analytic rea soning processes in face-to-face collaborative analysis using visualization techniques. How analysts reason is an active topic of research and in our community we know even less about how a group forms an understanding, insight, and reasons about data. We report on our effort in capturing the richness of reasoning activities through mixed-method approaches and show how Pairgrams-a visualization of interactions with an analytics workspace by pairs of participants-helped us to understand collaborative analysis and reasonin

From the Individual to the Group: Integrating Asynchronous Collaboration with Co-located Work

International audienceA large amount of data analysis work is conducted by individuals interspersed with formally arranged or spontaneous face-to-face meetings. Visual analytics tools provide no easy solution to bridge the gap between such individual and face-to-face work situations. They are typically either designed to work well for individuals or for teams but do not support to be used interchangeably in both synchronous and asynchronous work settings. In order to make collaboration effortless and worth undertaking, however, individuals have to be able to fluidly switch in and out of synchronous collaboration with others, to bring their own data, its visual representations, as well as all data modifications and annotations to a shared meeting where both data and representations can not only be presented but also interacted with, modified, and further analyzed together with others

Sharing Information from Personal Digital Notes using Word-Scale Visualizations

International audienceWe describe how small visualizations embedded in text (word-scale visualizations) can be used to share information from and in per- sonal notes. From our previous research, we learned that people see many opportunities for sharing personal notes, for example among a small social group. Yet, people reported that they were hesitant to share raw notes due to the notes’ often disorganized structure, haphazard writing style, or due to the fact that notes may contain a number of unrelated or irrelevant pieces of information. In this paper, we discuss how word-scale visualizations can be used in a collaborative personal visualization setting—to show abstracted information from a shared set of notes in the context of personal note- taking. In particular, we discuss potential kinds of data about notes that can be shared and motivate why sharing them may be helpful. Additionally, we provide two examples that illustrate the challenges and implications of using word-scale visualizations to share data in notes. The first example describes how notes in a shared notebook can be combined with private notes. The second example shows how data from public sources can be mixed with private comments to support sharing between notes on a common topic

Visualisation localisée en mouvement dans les jeux vidéo pour différents types de données

International audienceWe contribute an analysis of situated visualizations in motion in video games for different types of data, with a focus on quantitative and categorical data representations. Video games convey a lot of data to players, to help them succeed in the game. These visualizations frequently move across the screen due to camera changes or because the game elements themselves move. Our ultimate goal is to understand how motion factors affect visualization readability in video games and subsequently the players' performance in the game. We started our work by surveying the characteristics of how motion currently influences which kind of data representations in video games. We conducted a systematic review of 160 visualizations in motion in video games and extracted patterns and considerations regarding was what, and how visualizations currently exhibit motion factors in video games