Evaluating the Effect of Timeline Shape on Visualization Task Performance

Timelines are commonly represented on a horizontal line, which is not necessarily the most effective way to visualize temporal event sequences. However, few experiments have evaluated how timeline shape influences task performance. We present the design and results of a controlled experiment run on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape affects task completion time, correctness, and user preference. We tested 12 combinations of 4 shapes -- horizontal line, vertical line, circle, and spiral -- and 3 data types -- recurrent, non-recurrent, and mixed event sequences. We found good evidence that timeline shape meaningfully affects user task completion time but not correctness and that users have a strong shape preference. Building on our results, we present design guidelines for creating effective timeline visualizations based on user task and data types. A free copy of this paper, the evaluation stimuli and data, and code are available at https://osf.io/qr5yu/Comment: 12 pages, 5 figure

Visualização de padrões temporais cíclicos em estudos de fenologia

Orientadores: Ricardo da Silva Torres, Leonor Patrícia Cerdeira MorellatoTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Em diversas aplicações, grandes volumes de dados multidimensionais têm sido gerados continuamente ao longo do tempo. Uma abordagem adequada para lidar com estas coleções consiste no uso de métodos de visualização de informação, a partir dos quais padrões de interesse podem ser identificados, possibilitando o entendimento de fenômenos temporais complexos. De fato, em diversos domínios, o desenvolvimento de ferramentas adequadas para apoiar análises complexas, por exemplo, aquelas baseadas na identificação de padrões de mudanças ou correlações existentes entre múltiplas variáveis ao longo do tempo é de suma importância. Em estudos de fenologia, por exemplo, especialistas observam as mudanças que ocorrem ao longo da vida de plantas e animais e investigam qual é a relação entre essas mudanças com variáveis ambientais. Neste cenário, especialistas em fenologia cada vez mais precisam de ferramentas para, adequadamente, visualizar séries temporais longas, com muitas variáveis e de diferentes tipos (por exemplo, texto e imagem), assim como identificar padrões temporais cíclicos. Embora diversas abordagens tenham sido propostas para visualizar dados que variam ao longo do tempo, muitas não são apropriadas ou aplicáveis para dados de fenologia, porque não são capazes de: (i) lidar com séries temporais longas, com muitas variáveis de diferentes tipos de dados e de uma ou mais dimensões; e (ii) permitir a identificação de padrões temporais cíclicos e drivers ambientais associados. Este trabalho aborda essas questões a partir da proposta de duas novas abordagens para apoiar a análise e visualização de dados temporais multidimensionais. Nossa primeira proposta combina estruturas visuais radiais com ritmos visuais. As estruturas radiais são usadas para fornecer informação contextual sobre fenômenos cíclicos, enquanto que o ritmo visual é usado para sumarizar séries temporais longas em representações compactas. Nós desenvolvemos, avaliamos e validamos nossa proposta com especialistas em fenologia em tarefas relacionadas à visualização de dados de observação direta da fenologia de plantas em nível tanto de indivíduos quanto de espécies. Nós também validamos a proposta usando dados temporais relacionados a imagens obtidas de sistemas de monitoramento de vegetação próxima à superfície. Nossa segunda abordagem é uma nova representação baseada em imagem, chamada Change Frequency Heatmap (CFH), usada para codificar mudanças temporais de dados numéricos multivariados. O método calcula histogramas de padrões de mudanças observados em sucessivos instantes de tempo. Nós validamos o uso do CFH a partir da criação de uma ferramenta de caracterização de mudanças no ciclo de vida de plantas de múltiplos indivíduos e espécies ao longo do tempo. Nós demonstramos o potencial do CFH para ajudar na identificação visual de padrões de mudanças temporais complexas, especialmente na identificação de variações entre indivíduos em estudos relacionados à fenologia de plantasAbstract: In several applications, large volumes of multidimensional data have been generated continuously over time. One suitable approach for handling those collections in a meaningful way consists in the use of information visualization methods, based on which patterns of interest can be identified, triggering the understanding of complex temporal phenomena. In fact, in several domains, the development of appropriate tools for supporting complex analysis based, for example, on the identification of change patterns in temporal data or existing correlations, over time, among multiple variables, is of paramount importance. In phenology studies, for instance, phenologists observe changes in the development of plants and animals throughout their lives and investigate what is the relationship between these changes with environmental changes. Therefore, phenologists increasingly need tools for visualizing appropriately long-term series with many variables of different data types, as well as for identifying cyclical temporal patterns. Although several approaches have been proposed to visualize data varying over time, most of them are not appropriate or applicable to phenology data, because they are not able: (i) to handle long-term series with many variables of different data types and one or more dimensions and (ii) to support the identification of cyclical temporal patterns and associated environmental drivers. This work addresses these shortcomings by presenting two new approaches to support the analysis and visualization of multidimensional temporal data. Our first proposal to visualize phenological data combines radial visual structures along with visual rhythms. Radial visual structures are used to provide contextual insights regarding cyclical phenomena, while the visual rhythm encoding is used to summarize long-term time series into compact representations. We developed, evaluated, and validated our proposal with phenology experts using plant phenology direct observational data both at individuals and species levels. Also we validated the proposal using image-related temporal data obtained from near-surface vegetation monitoring systems. Our second approach is a novel image-based representation, named Change Frequency Heatmap (CFH), used to encode temporal changes of multivariate numerical data. The method computes histograms of change patterns observed at successive timestamps. We validated the use of CFHs through the creation of a temporal change characterization tool to support complex plant phenology analysis, concerning the characterization of plant life cycle changes of multiple individuals and species over time. We demonstrated the potential of CFH to support visual identification of complex temporal change patterns, especially to decipher interindividual variations in plant phenologyDoutoradoCiência da ComputaçãoDoutora em Ciência da Computação162312/2015-62013/501550-0CNPQCAPESFAPES

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

Effective Visualization Approaches For Ultra-High Dimensional Datasets

Multivariate informational data, which are abstract as well as complex, are becoming increasingly common in many areas such as scientific, medical, social, business, and so on. Displaying and analyzing large amounts of multivariate data with more than three variables of different types is quite challenging. Visualization of such multivariate data suffers from a high degree of clutter when the numbers of dimensions/variables and data observations become too large. We propose multiple approaches to effectively visualize large datasets of ultrahigh number of dimensions by generalizing two standard multivariate visualization methods, namely star plot and parallel coordinates plot. We refine three variants of the star plot, which include overlapped star plot, shifted origin plot, and multilevel star plot by embedding distribution plots, displaying dataset in groups, and supporting adjustable positioning of the star axes. We introduce a bifocal parallel coordinates plot (BPCP) based on the focus + context approach. BPCP splits vertically the overall rendering area into the focus and context regions. The focus area maps a few selected dimensions of interest at sufficiently wide spacing. The remaining dimensions are represented in the context area in a compact way to retain useful information and provide the data continuity. The focus display can be further enriched with various options, such as axes overlays, scatterplot, and nested PCPs. In order to accommodate an arbitrarily large number of dimensions, the context display supports the multi-level stacked view. Finally, we present two innovative ways of enhancing parallel coordinates axes to better understand all variables and their interrelationships in high-dimensional datasets. Histogram and circle/ellipse plots based on uniform and non-uniform frequency/density mappings are adopted to visualize distributions of numerical and categorical data values. Color-mapped axis stripes are designed in the parallel coordinates layout so that correlations can be fully realized in the same display plot irrespective of axes locations. These colors are also propagated to histograms as stacked bars and categorical values as pie charts to further facilitate data exploration. By using the datasets consisting of 25 to 130 variables of different data types we have demonstrated effectiveness of the proposed multivariate visualization enhancements

Parallel Hierarchies: Interactive Visualization of Multidimensional Hierarchical Aggregates

Exploring multi-dimensional hierarchical data is a long-standing problem present in a wide range of fields such as bioinformatics, software systems, social sciences and business intelligence. While each hierarchical dimension within these data structures can be explored in isolation, critical information lies in the relationships between dimensions. Existing approaches can either simultaneously visualize multiple non-hierarchical dimensions, or only one or two hierarchical dimensions. Yet, the challenge of visualizing multi-dimensional hierarchical data remains open. To address this problem, we developed a novel data visualization approach -- Parallel Hierarchies -- that we demonstrate on a real-life SAP SE product called SAP Product Lifecycle Costing. The starting point of the research is a thorough customer-driven requirement engineering phase including an iterative design process. To avoid restricting ourselves to a domain-specific solution, we abstract the data and tasks gathered from users, and demonstrate the approach generality by applying Parallel Hierarchies to datasets from bioinformatics and social sciences. Moreover, we report on a qualitative user study conducted in an industrial scenario with 15 experts from 9 different companies. As a result of this co-innovation experience, several SAP customers requested a product feature out of our solution. Moreover, Parallel Hierarchies integration as a standard diagram type into SAP Analytics Cloud platform is in progress. This thesis further introduces different uncertainty representation methods applicable to Parallel Hierarchies and in general to flow diagrams. We also present a visual comparison taxonomy for time-series of hierarchically structured data with one or multiple dimensions. Moreover, we propose several visual solutions for comparing hierarchies employing flow diagrams. Finally, after presenting two application examples of Parallel Hierarchies on industrial datasets, we detail two validation methods to examine the effectiveness of the visualization solution. Particularly, we introduce a novel design validation table to assess the perceptual aspects of eight different visualization solutions including Parallel Hierarchies.:1 Introduction 1.1 Motivation and Problem Statement 1.2 Research Goals 1.3 Outline and Contributions 2 Foundations of Visualization 2.1 Information Visualization 2.1.1 Terms and Definition 2.1.2 What: Data Structures 2.1.3 Why: Visualization Tasks 2.1.4 How: Visualization Techniques 2.1.5 How: Interaction Techniques 2.2 Visual Perception 2.2.1 Visual Variables 2.2.2 Attributes of Preattentive and Attentive Processing 2.2.3 Gestalt Principles 2.3 Flow Diagrams 2.3.1 Classifications of Flow Diagrams 2.3.2 Main Visual Features 2.4 Summary 3 Related Work 3.1 Cross-tabulating Hierarchical Categories 3.1.1 Visualizing Categorical Aggregates of Item Sets 3.1.2 Hierarchical Visualization of Categorical Aggregates 3.1.3 Visualizing Item Sets and Their Hierarchical Properties 3.1.4 Hierarchical Visualization of Categorical Set Aggregates 3.2 Uncertainty Visualization 3.2.1 Uncertainty Taxonomies 3.2.2 Uncertainty in Flow Diagrams 3.3 Time-Series Data Visualization 3.3.1 Time & Data 3.3.2 User Tasks 3.3.3 Visual Representation 3.4 Summary ii Contents 4 Requirement Engineering Phase 4.1 Introduction 4.2 Environment 4.2.1 The Product 4.2.2 The Customers and Development Methodology 4.2.3 Lessons Learned 4.3 Visualization Requirements for Product Costing 4.3.1 Current Visualization Practice 4.3.2 Visualization Tasks 4.3.3 Data Structure and Size 4.3.4 Early Visualization Prototypes 4.3.5 Challenges and Lessons Learned 4.4 Data and Task Abstraction 4.4.1 Data Abstraction 4.4.2 Task Abstraction 4.5 Summary and Outlook 5 Parallel Hierarchies 5.1 Introduction 5.2 The Parallel Hierarchies Technique 5.2.1 The Individual Axis: Showing Hierarchical Categories 5.2.2 Two Interlinked Axes: Showing Pairwise Frequencies 5.2.3 Multiple Linked Axes: Propagating Frequencies 5.2.4 Fine-tuning Parallel Hierarchies through Reordering 5.3 Design Choices 5.4 Applying Parallel Hierarchies 5.4.1 US Census Data 5.4.2 Yeast Gene Ontology Annotations 5.5 Evaluation 5.5.1 Setup of the Evaluation 5.5.2 Procedure of the Evaluation 5.5.3 Results from the Evaluation 5.5.4 Validity of the Evaluation 5.6 Summary and Outlook 6 Visualizing Uncertainty in Flow Diagrams 6.1 Introduction 6.2 Uncertainty in Product Costing 6.2.1 Background 6.2.2 Main Causes of Bad Quality in Costing Data 6.3 Visualization Concepts 6.4 Uncertainty Visualization using Ribbons 6.4.1 Selected Visualization Techniques 6.4.2 Study Design and Procedure 6.4.3 Results 6.4.4 Discussion 6.5 Revised Visualization Approach using Ribbons 6.5.1 Application to Sankey Diagram 6.5.2 Application to Parallel Sets 6.5.3 Application to Parallel Hierarchies 6.6 Uncertainty Visualization using Nodes 6.6.1 Visual Design of Nodes 6.6.2 Expert Evaluation 6.7 Summary and Outlook 7 Visual Comparison Task 7.1 Introduction 7.2 Comparing Two One-dimensional Time Steps 7.2.1 Problem Statement 7.2.2 Visualization Design 7.3 Comparing Two N-dimensional Time Steps 7.4 Comparing Several One-dimensional Time Steps 7.5 Summary and Outlook 8 Parallel Hierarchies in Practice 8.1 Application to Plausibility Check Task 8.1.1 Plausibility Check Process 8.1.2 Visual Exploration of Machine Learning Results 8.2 Integration into SAP Analytics Cloud 8.2.1 SAP Analytics Cloud 8.2.2 Ocean to Table Project 8.3 Summary and Outlook 9 Validation 9.1 Introduction 9.2 Nested Model Validation Approach 9.3 Perceptual Validation of Visualization Techniques 9.3.1 Design Validation Table 9.3.2 Discussion 9.4 Summary and Outlook 10 Conclusion and Outlook 10.1 Summary of Findings 10.2 Discussion 10.3 Outlook A Questionnaires of the Evaluation B Survey of the Quality of Product Costing Data C Questionnaire of Current Practice Bibliograph

MyHealthAvatar and CARRE: case studies of interactive visualisation for Internet-enabled sensor-assisted health monitoring and risk analysis

With the progress of wearable sensor technologies, more wearable health sensors have been made available on the market, which enables not only people to monitor their health and lifestyle in a continuous way but also doctors to utilise them to make better diagnoses. Continuous measurement from a variety of wearable sensors implies that a huge amount of data needs to be collected, stored, processed and presented, which cannot be achieved by traditional data processing methods. Visualisation is designed to promote knowledge discovery and utilisation via mature visual paradigms with well-designed user interactions and has become indispensable in data analysis. In this paper we introduce the role of visualisation in wearable sensor-assisted health analysis platforms by case studies of two projects funded by the European Commission: MyHealthAvatar and CARRE. The former focuses on health sensor data collection and lifestyle tracking while the latter aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The roles of visualisation components including timeline, parallel coordinates, map, node-link diagrams, Sankey diagrams, etc. are introduced and discussed

Evaluating different visualization designs for multivariate personal health data

Over the past decade, self-tracking devices and apps have been increasingly used for personal data collection, particularly in the monitoring of health and physical activity. Data visualization is crucial to helping people understand and make sense of collected data. The literature review carried out on this topic revealed several studies which focus on data visualization, personal informatics systems and people’s self-tracking practice and their requirements. However, in terms of personal health data, there is a significant limitation in addressing the knowledge gap between these areas and the visualizations supported by self-tracking apps/dashboards, which people frequently use when viewing and exploring their health data. The literature review is followed by a user study and a visualization review, which explores the methods used to represent personal health data on popular self-tracking devices, apps and dashboards in order to understand the main limitations of various designs. The work goes on to address two main challenges: the need for combining multiple personal health variables to support a better representation of an individual’s health status, which could help in understanding the relationships between different health data; and to find a more suitable layout that is related to the periodic and temporal nature of personal health data. Therefore, this thesis focuses on the design and evaluation of visualizations to address these challenges by considering different design aspects of the visual layout, methods and visual encoding, while taking into account the multivariate nature of the data. With respect to the visual layout, the design proposes a novel Radial layout that utilizes an analogue clock metaphor to provide cognitive support by representing the data on an hourly basis through a clock-type display. The Radial representation is effective for data that naturally exhibit a periodic pattern. It also supports understanding of patterns with either a circular or seasonal behaviour. The developed visualizations’ evaluation process implements controlled lab-experiments. The evaluation follows a two-step method, starting with a preliminary study that leads to the design of the main evaluation study, which includes quantitative and qualitative measures of participants’ performance and preferences in the designs. The main contributions of the thesis are: (1) A thorough overview of the visualization methods provided by companion apps, dashboards and embedded displays of popular self-tracking devices. It also discusses their limitations and strengths, providing a taxonomy for the applied visualizations. (2) Suggestions and recommendations for addressing the challenges highlighted in personal health visualizations on apps and dashboards. (3) The design and implementation of multiple visualization alternatives to combine multivariate personal health data using various charts, methods and layouts. (4) Insights regarding personal health visualization gained from the results and in-depth analysis of data resulting from a controlled lab experiment. This experiment evaluated the proposed visualizations with respect to the participants’ performance regarding visual tasks related to real datasets. (5) Insights on people’s preferences on the visualizations and a structured qualitative analysis of their feedback, which demonstrated the effects of each implemented visual element. In addition to structuring design guidelines for this specific data type, the results of the study prove that the traditional Linear layout either outperforms or is comparable to the proposed Radial layout. The study also shows how the applied visual encoding and the visualization method influence the performance according to specific tasks and under different data densities. It provides plausible explanations for the significant differences in the observed performance and preferences patterns, which inform future visualization designs for personal health data

The State of the Art in Cartograms

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges

Using information visualization to support the self-management of type 2 diabetes mellitus

The globally increasing number of individuals suffering from Type 2 Diabetes Mellitus (T2DM), a completely preventable incurable disease of the pancreas, highlights the need for an effective tool for users to understand the relationship between their behaviours and the effect that those behaviours can have on their blood glucose levels (BGLs). There are few Information Visualisation (IV) tools available that can be used to reduce the cognition required to understand correlations between behaviour and BGLs. Existing tools require time-consuming, lengthy inputs and provide simple visualisations that do not show correlations. This leads to ineffective self-management of T2DM. Information Visualisation (IV) techniques can be used to support effective self-management of T2DM and reduce the cognition required to interpret DM data. Suitable IV techniques were identified and used to visualize T2DM data to aid in the self-management of the disease. Temporal charts, i.e. The Bar, Pie and Line Chart as well as heat maps, were selected as the most appropriate IV techniques to visualize T2DM data as they support time-series data well. A prototype, MedicMetric was created as an IV tool for visualizing T2DM data. MedicMetric incorporated three designed charts, namely the Change Rate Line View, the Radial Progress View, and the Annotated Line View. The Change Rate Line View and Annotated Line View both used line IV techniques, while the Radial Progress View made use of the bar IV technique. The Change Rate Line View performed the worst overall. A usability evaluation was conducted to compare these techniques and to determine which technique is most suitable for visualizing T2DM data. The results leaned significantly in favour of the Annotated Line View. This view is most similar to the line charts typically used in other IV tools. For this reason, the MedicMetric app was briefly compared to the MySygr and Diabetes:M application. In effectiveness and efficiency, MedicMetric and MySugr obtained almost identical results. However, participants indicated that MedicMetric supported their tasks using the Visual Information Seeking Mantra (VISM) the best overall, with 100% of participants stating that they would prefer to use the MedicMetric application. Several usability problems were identified with the IV techniques and they were addressed shortly after the study was complete. Overall participants were most satisfied with the Annotated Line View.Thesis (MSc) -- Faculty of Science, Computing Sciences, 202