Treemap is one of the most well-known and used techniques for data analysis in InfoVis. However, it still presents some challenges concerning data representation, such as small items, a large number of hierarchies limiting the visual space, few options for visual data representation (restricted to size, color, label), and others. Thus, this paper presents an InfoVis tool that allows the analysis of multidimensional data using treemaps with glyphs, once they represent more data visually combined visual variables. Besides that, considering treemap scenarios with small area items, an algorithm was developed to analyze which part of the glyph the application should draw since parts of the glyph can still provide useful information. In this way, the glyphs become adaptative to available space. The application has a multilabel decision tree technique that decides which part of the glyph should appear. Visualization specialists supplied the training data through a system that showed a diverse range of glyphs' representations. The system varied the glyph's size, the number and the value of the visible visual variables and registered the response of specialists in the training data. Finally, this paper presents images using the treemap with adaptive glyphs approach versos treemap+glyphs and showed that the adaptive approach clears information clutter when treemap items are small.

Data Physicalization is the area that investigates how physical objects can encode data through their physical (geometry and materials) and visual features. In the information visualization field, color plays a fundamental role in communicating and encoding data, and it is no different for data physicalization. However, in the real world, color perception can be influenced by the material that composes the data physicalization, the environment’s brightness, and the characteristics of the light-emitting device. Thus, this article presents a process to evaluate and select colors to compose color palettes (categorical, sequential, and divergent) for data physicalizations, considering the perceptual distance between the chosen colors and the brightness of the light-emitting device for evaluating the influence of neighboring colors. Additionally, users perform Information Visualization tasks (identification, comparison, maximums, minimums, etc.) with different color palettes of the industry and literature in a physical 3D bar chart composed of LED strips. The initial results showed better performance by participants using the proposed color scales in Information Visualization tasks than those using traditional digital color scales. Finally, the steps carried out culminated in the proposition of a pipeline for evaluating and creating color scales for data physicalization, considering the features of the light-emitting device and the material used in the data physicalization.

Recent literature reviews point out that active data physicalizations still lack evaluation scenarios. These evaluations mainly focus on the perception of visual and physical characteristics of physicalizations and users' performance of analysis tasks. This paper addresses a proposed accuracy assessment of data physicalization for data representation. The data physicalization used is a physical and dynamic bar chart using 3D printed models (3D bars) and LED strips inside the bars, both controlled by microcontrollers (Arduino), being possible to represent different datasets. The accuracy assessment of this physicalization is based on the height and color of the 3D bars. For this, distance sensors measure the actual height values of the bars, and a computer vision camera/algorithm collects and estimates the RGB value of the bars. From original and measured values datasets, bar charts images are built to perform a similarity comparison between the images. The similarity metric is used as an accuracy metric of the bar heights to represent data and obtained as initial results value higher than 96%. Furthermore, an evaluation scenario of the distance between the reference and measured colors 2000) classifies most of them as having proper perception. Additionally, the accuracy loss was evaluated on data physicalization in a scenario of continuous use (ten configurations without any calibration), and the variation of errors was around 2%.