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Numerous systems of musical notation have been developed to address some of the complexities associated with conventional Staff notation, such as translating it into physical movements and memorizing the meaning of its symbols. Surprisingly, there has been little empirical research assessing and comparing the comprehensibility of conventional versus alternative notation methods. In this study, three main features were assessed for 10 different musical notation systems: discriminability (the ease of visually distinguishing pitch or duration changes in notation), iconicity (extent of resemblance between melodies and notation), and complexity. A total of 213 valid responses were collected in an online experiment. Participants completed two tasks, visual discriminability and melody-notation matching. They also provided complexity ratings for different notational systems. Multilevel Bayesian regression models show strong evidence that Figurenotes, Numbered notation, and Piano Roll notation have a relatively high level of discriminability, while Figurenotes, Proportional notation, Staff notation, and Piano Roll notation have a relatively high level of iconicity. Piano Roll notation was rated the least complex musical notation system. Differences in the results across pitch and duration dimensions, age, and musical sophistication were also found. Importantly, we also examined the effects of the different visual variables used by the notational systems (color, position, shape): changes in position have the highest discriminability, iconicity, and the lowest complexity. Qualitative analysis for some open questions also supported Piano Roll notation as being the most favorable musical notation, especially among novices.

In this paper, Harris operator and DoG operator are used to extract visual features from images to ensure that a large number of useful feature points can be extracted in regions with little texture information. After determining the five main visual variables, virtual reality technology is used to conduct a preliminary exploration of the effect of visual variable perception in a three-dimensional model environment and to construct a mapping model of multiple visual variables. Combining the hardware and the visual variable mapping model, the design of the visual aesthetic element visualization system based on VR technology is completed, and the multiple visual variable mapping model is empirically analyzed using simulation analysis. The results show that the average accuracy of the qualitative test is as high as 65.73%, and the average time is as low as 7.02s, indicating that the visualization effect of the multiple visual variable model is obvious and alleviates the inadequacy of the ineffective color visualization scheme to a certain extent. This study analyzes the types and characteristics of visual aesthetic elements in the design industry and then proposes strategies for constructing visual aesthetic elements in graphic design to promote the sustainable development of the design industry.

The development of the Internet era acts on every change brought by spatial design so that the application of virtual reality technology in spatial design solutions begins to emerge. In this paper, firstly, combining the general process of visualization and the characteristics of visual variables, seven visual variables are selected for preliminary 3D visual representation, and their applicability under virtual reality space is analyzed. The HSV color space is also used for visual coding. According to the intuitive nature of visual perception of spatial design, a multiple visual variables mapping model based on VR is proposed. Considering the sequential characteristics of spatial design, PCA-LSTM spatial design classification model is proposed, and virtual space is applied in spatial design by local changes of the top surface, ground surface, and vertical interface. The simulation experiments are conducted by selecting SVM, BP neural network, and LSTM network as the comparison algorithms, and the results show that the accuracy of both the training set and the test set is found to be improved based on the PCA-LSTM classification model for spatial design classification, which is 4.9% and 9.49% higher than the traditional LSTM algorithm, respectively, verifying the PCA-LSTM for spatial design The effectiveness of PCA-LSTM for spatial design classification is verified. This study cleverly uses the design technique of virtual space to better apply virtual space in spatial design.

Software maps provide a general-purpose interactive user interface and information display in software analytics. This paper classifies software maps as a containment-based treemap embedded into a 3D attribute space and introduces respective terminology. It provides a comprehensive overview of advanced visual metaphors and techniques, each suitable for interactive visual analytics tasks. The metaphors and techniques are briefly described, located within a visualization pipeline model, and considered within a software map design space. The general expressiveness and applicability of visual variables are detailed and discussed. Consequent applications and use cases for different software system data and software engineering data are discussed, arguing for the versatile use of software maps in visual software analytics.

The overlap of visual items in data visualization techniques is a known problem aggravated by data volume and available visual space issues. Several methods have been applied to mitigate occlusion in data visualizations, such as random jitter, transparency, layout reconfiguration, focus+context techniques, etc. This paper aims to present a comparative study of the reading of visual variables values with partial overlap. The study focuses on categorical data representations varying the percentage limits of partial overlap and the number of distinct values for each visual variable: hue, lightness, saturation, shape, text, orientation, and texture. A computational application generated random scenarios for a unique visual pattern target to perform location tasks. Each scenario involved presentation of the visual items in a grid layout with 160 elements (10 × 16), each visual variable had from three to five distinct values encoded, and the partial overlap percentages applied, represented by a gray square in the center of each grid element, were 0% (control), 50%, 60%, and 70%. Similar to the preliminary tests, the tests conducted in this study involved 48 participants organized into four groups, with 126 tasks per participant, and the application captured the response and time for each task performed. The results analysis indicated that the hue, lightness, and shape visual variables were robust to high percentages of occlusion and gradual increase in encoded visual values. The text visual variable showed promising results for accuracy, and the resolution time was a little higher than for the last visual variables mentioned. In contrast, the texture visual variable presented lower accuracy to high levels of occlusion and more different visual encoding values. Finally, the orientation and saturation visual variables exhibited the highest error and worst perfomance rates during the tests.

A key cartographic challenge associated with the rise of big data is to show when spatial data observations are missing or to communicate variables that indicate absence. For example, showing where people are tweeting during a disaster might be interesting, but visually identifying where normal signals are missing could in fact highlight the most affected places. Parcel data might be fully present, but attributes of their observations could convey qualities of absence (e.g., abandoned structures). Current geovisualization approaches normally do not show anything at all when data are missing or contain qualities of absence and only in rare cases might use a specific hue to highlight the presence of absence on maps. This work argues that people perceive missingness and absence in a way that is distinct from other spatial data qualities, and we propose a typology of static and dynamic means by which we can draw user attention to the presence of absence. To explore the application of these techniques, I use urban parcel data to visualize patterns of property blight in a Detroit neighborhood. Based on conceptual development and case study application, I propose research challenges to evaluate visual representations of missing and absent information on maps.

In a 3D voxel-based digital urban model - or digital urban twin, the semantics of each cell is typically visualized using RGB values to give a realistic impression. However, due to measurement uncertainties or incompleteness, some voxel occupancy might be less likely than others. This paper suggests computing an occupancy grid map using an inverse sensor model and embodying the obtained occupancy probabilities into the urban digital twin.This article then presents visualization of additional information, in this case the uncertainty in space occupancy, in a 3D voxel-based urban model. To this end, a concept is proposed, in which static visual variables are complemented by dynamics. It suggests to utilize the visual variables transparency, size, and color, supplemented by animation. The main idea is to communicate occupancy uncertainty information and enhance its perception via visual variables while visually inspecting a digital twin in an animation mode, observing the occupancy uncertainty without losing the perception of the urban environment.

Flow maps allow users to perceive not only the location where interactions take place, but also the direction and volume of events. Previous studies have proposed numerous methods to produce flow maps. However, how to evaluate the usability of flow maps has not been well documented. In this study, we combined eye-tracking and questionnaire methods to evaluate the usability of flow maps through comparisons between (a) straight lines and curves and (b) line thicknesses and color gradients. The results show that curved flows are more effective than straight flows. Maps with curved flows have more correct answers, fixations, and percentages of fixations in areas of interest. Furthermore, we find that the curved flows require longer finish times but exhibit smaller times to first fixation than straight flows. In addition, we find that using color gradients to indicate the flow volume is significantly more effective than the application of different line thicknesses, which is mainly reflected by the presence of more correct answers in the color-gradient group. These empirical studies could help improve the usability of flow maps employed to visualize geo-data.

As the development of small-scale thematic cartography continues, there is a growing interest in simple graphic solutions, e.g., in the form of numerical values presented on maps to replace or complement well-established quantitative cartographic methods of presentation. Numbers on maps are used as an independent form of data presentation or function as a supplement to the cartographic presentation, becoming a legend placed directly on the map. Despite the frequent use of numbers on maps, this relatively simple form of presentation has not been extensively empirically evaluated. This article presents the results of an empirical study aimed at comparing the usability of numbers on maps for the presentation of quantitative information to frequently used proportional symbols, for simple map-reading tasks. The study showed that the use of numbers on single-variable and two-variable maps results in a greater number of correct answers and also often an improved response time compared to the use of proportional symbols. Interestingly, the introduction of different sizes of numbers did not significantly affect their usability. Thus, it has been proven that—for some tasks—map users accept this bare-bones version of data presentation, often demonstrating a higher level of preference for it than for proportional symbols.

An understanding of guidance, which means guiding attention, and constancy, meaning that an area can be perceived for what it is despite environmental changes, of the visual variables related to three-dimensional (3D) symbols is essential to ensure rapid and consistent human perception in 3D visualization. Previous studies have focused on the guidance and constancy of visual variables related to two-dimensional (2D) symbols, but these aspects are not well documented for 3D symbols. In this study, we used eye tracking to analyze the visual guidance from shapes, hues and sizes, and the visual constancy that is related to the shape, color saturation and size of 3D symbols in different locations. Thirty-six subjects (24 females and 12 males) participated in the study. The results indicate that hue and shape provide a high level of visual guidance, whereas guidance from size, a variable that predominantly guides attention in 2D visualization, is much more limited in 3D visualization. Additionally, constancy of shape and saturation are perceived with relatively high accuracy, whereas constancy of size is perceived with only low accuracy. These first empirical studies are intended to pave the way for a more comprehensive user understanding of 3D visualization design.