Temporal Data Visualization Research Articles

Visual Analytic for Traffic Impact Assessment

This study strives to promote the state of traffic impact assessment through high-end visual analytics by incorporating spatial and temporal data visualization to enhance traffic management. Based on a dataset on traffic flow at three major intersections, we married data cleaning, integration, and transformation to set out for a detailed visual analysis. Thus, the critical materials comprise the traffic count in multiple lanes, vehicle types, and saturation flow rates to understand the road network's capacity. They essentially explored the traffic volume variations daily and hourly and pattern identification using heat maps, parallel coordinate charts, and bar plots. Thus, the findings expose the remarkable traffic volume and pattern differences by distinguishing peak and off-peak hours on weekdays and weekends. The level of service at each junction was determined by the volume-to-capacity ratio, identifying potential congested areas. As such, this work points to the importance of further improvements to visual analytic techniques to accurately predict traffic patterns and evaluate traffic management strategies effectively. Predictive models based on visual analytic findings can pave the way for proactive traffic control and congestion mitigation, making urban traffic management more efficient and safer. The current study provides a scaffold for additional exploration of the above-detailed methods and their penal outcomes in urban development planning and policy provision in terms of developing sustainable traffic control strategies and real-time decision-making improvements.

Multivariate Data Exploration Through Coordinated Views

Many fields of study still face the challenges inherent to the analysis of complex multidimensional datasets, such as the field of computational biology, whose research of infectious diseases must contend with large protein-protein interaction networks with thousands of genes that vary in expression values over time. In this paper, we explore the visualization of multivariate data through CroP, a data visualization tool with a coordinated multiple views framework where users can adapt the workspace to different problems through flexible panels. In particular, we focus on the visualization of relational and temporal data, the latter being represented through layouts that distort timelines to represent the fluctuations of values across complex datasets, creating visualizations that highlight significant events and patterns. Moreover, CroP provides various layouts and functionalities to not only highlight relationships between different variables, but also dig-down into discovered patterns in order to better understand their sources and their effects. These methods are demonstrated through multiple experiments with diverse multivariate datasets, with a focus on gene expression time-series datasets. In addition to a discussion of our results, we also validate CroP through model and interface tests performed with participants from both the fields of information visualization and computational biology.

Digital Humanities in Academic Library: Creating Narratives through Spatial Data

A growing trend in digital humanities has been digital scholarship and the emphasis on usage of digital tools and methods in research, teaching, and publishing. Academic libraries as laboratories for humanities scholars (Christine Borgman) need to keep up with these changes. Digital humanities (DH) have become an important area of librarianship and demand for librarians with skills and expertise in metadata creation and control, digital repository management, information organization, intellectual property rights management, research data preservation, and visualization has been on the rise. This paper focuses on the English-language literature presenting visualization of spatial and temporal data in DH projects through rich narratives involving text, interactive maps, and map-enabled spatial data services. Using the examples of digital mapping projects developed and implemented in a large academic library, the article shows a case of big academic library on how spatial and temporal data visualization can be employed to tell stories, promote, and inform about cartographic resources of relevance to DH scholars, and provide self-help tools to empower users.

The DiscoverFramework freeware toolkit for multivariate spatio-temporal environmental data visualization and evaluation

The freeware DiscoverFramework provides new tools to build spatial and temporal data visualization applications accessible to stakeholders, policy makers, scientists, and educators. By focusing on environmental data and supporting applications accessible via laptops, tablets, and cell phones, the DiscoverFramework can be used to increase public awareness and inspire responsible use of complex environmental systems upon which human society depends. DiscoverFramework enables computer-savvy domain scientists to develop interactive applications using “Elements” and workflows defined to make visualization easy and address common problems such as spatio-temporal scales and user engagement. Two applications are used to demonstrate DiscoverFramework: DiscoverWater and DiscoverHABs. DiscoverWater uses Map, Chart, and Text Elements to relate streamflow changes to groundwater withdrawals. DiscoverHABs uses the Scenario Element to aid stakeholders, such as resource managers and users, struggling to identify when and where harmful algal blooms (HABs) are likely given that causal relations in these systems remain poorly understood.

Priming Effect of Colour on Aiding the Attentional Reorientation in Sequential Presentations of Temporal Data Visualization: Evidence From Eye-Tracking

Abstract In the present study, we focus on the priming effect of colour on mitigating the attentional reorientation cost, which is led by re-constructing the frame of reference for attention shift and visual search in sequential presentations of temporal data visualization. The study involves two experiments using complementary recordings of behavioural performance and eye-tracking events. Two aspects of colour primes are highlighted: the prime validity and the colour perceptual accessibility. A task paradigm integrating the feature search and keeping-track task was adopted in our experiments. In Experiment 1 (with a group of 16 participants), we confirmed the colour priming effect by comparing the priming condition to the neutral baseline. Furthermore, global colours that are with high perceptual accessibility generated more evident priming effects than local colours. However, more interferences in misguiding the attention to task-irrelevant regions were found when the global primes were invalid. In Experiment 2 (with another group of 15 participants), we verify the finding in Experiment 1 that global colours produced more pronounced priming effects in alleviating the attentional reorientation cost by comparing two groups of real-world visualizations with either global or local colours as the prime. Large saccades were initiated much earlier, and the search efficiency got improved when provided with global colours. We conjecture that the facilitatory effect from global colours may stem from its benefit on the pre-attentive processing of the search field. The research findings provide evidence for utilizing colours as the primes in mitigating the attentional reorientation cost and accelerating visual search in sequential presentations.

Visual Drift Detection for Event Sequence Data of Business Processes.

Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this article, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data.

TimeSets: Temporal Sensemaking in Intelligence Analysis.

TimeSets is a temporal data visualization technique designed to reveal insights into event sets, such as all the events linked to one person or organization. In this article, we describe two TimeSets-based visual analytics tools for intelligence analysis. In the first case, TimeSets is integrated with other visual analytics tools to support open-source intelligence analysis with Twitter data, particularly the challenge of finding the right questions to ask. The second case uses TimeSets in a participatory design process with analysts that aims to meet their requirements of uncertainty analysis involving fake news. Lessons learned are potentially beneficial to other application domains.

Visualizing and labeling dense multi-sensor earth observation time series: The EO Time Series Viewer

Multi-spectral spaceborne sensors with different spatial resolutions produce Earth observation (EO) time series (TS) with global coverage. The interactive visualization and interpretation of TS is essential to better understand changes in land-use and land-cover and to extract reference information for model calibration and validation. However, available software tools are often limited to specific sensors or optimized for application-specific visualizations. To overcome these limitations, we developed the EO Time Series Viewer, a free and open source QGIS plugin for user-friendly visualization, interpretation and labeling of multi-sensor TS data. The EO Time Series Viewer (i) combines advantages of spatial, spectral and temporal data visualization concepts that are so far not available in a single tool, (ii) provides maximum flexibility in terms of supported data formats, (iii) minimizes the user-interactions required to load and visualize multi-sensor TS data and (iv) speeds-up labeling of TS data based on enhanced GIS vector tools and formats.

Research on the Construction of Information Visualization-Dynamic Chart

According to the current development direction in the field of information visualization, the dynamic chart is studied as the expression form of time series data, and the visualization model between data and people is established. By means of cognitive psychology, statistical analysis and eye tracking, this paper studies and analyzes the attention, memory and thinking of users in each stage of visual thinking activities at visual terminals, and explores the specific use environment, methods and standards of dynamic charts. Furthermore, it provides an effective reference for the research of information visualization in temporal data visualization and visualization effect evaluation.

Popup-Plots: Warping Temporal Data Visualization.

Temporal data visualization is used to analyze dependent variables that vary over time, with time being an independent variable. Visualizing temporal data is inherently difficult, due to the many aspects that need to be communicated to the users (e.g., time and variable changes). This is an important topic in visualization, and a wide range of visualization techniques dealing with different tasks have already been designed. In this paper we propose popup-plots, a novel concept where the common interaction of 3D rotation is used to navigate through the data. This allows the users to view the data from different perspectives without having to learn and adapt to new interaction concepts. Popup-plots are therefore a novel method for visualizing and interacting with dependent variables over time. We extend 2D plots with the temporal information by bending the space according to the time. The bending is calculated based on a spherical coordinates approach, which is continuously influenced by the viewing direction towards the plot. Hence, the plot can be viewed from various angles with seamless transitions in between, offering the possibility to analyze different aspects of the represented data. As the current viewing direction is inherently depicted by the shape of the data, the users are able to deduce which part of the data is currently viewed. The temporal information is encoded into the visualization itself, resembling annual rings of a tree. We demonstrate our method by applying it to data from two different domains, comprising measurements at spatial positions over time, and we also evaluated the usability of our solution.

STRAD Wheel: Web-Based Library for Visualizing Temporal Data.

Recent advances in web development, including the introduction of HTML5, have opened a door for visualization researchers and developers to quickly access larger audiences worldwide. Open source libraries for the creation of interactive visualizations are becoming more specialized but also modular, which makes them easy to incorporate in domain-specific applications. In this context, the authors developed STRAD (Spatio-Temporal-Radar) Wheel, a web-based library that focuses on the visualization and interactive query of temporal data in a compact view with multiple temporal granularities. This article includes two application examples in urban planning to help illustrate the proposed visualization's use in practice.

AnaFe: Visual Analytics of Image-derived Temporal Features-Focusing on the Spleen.

We present a novel visualization framework, AnaFe, targeted at observing changes in the spleen over time through multiple image-derived features. Accurate monitoring of progressive changes is crucial for diseases that result in enlargement of the organ. Our system is comprised of multiple linked views combining visualization of temporal 3D organ data, related measurements, and features. Thus it enables the observation of progression and allows for simultaneous comparison within and between the subjects. AnaFe offers insights into the overall distribution of robustly extracted and reproducible quantitative imaging features and their changes within the population, and also enables detailed analysis of individual cases. It performs similarity comparison of temporal series of one subject to all other series in both sick and healthy groups. We demonstrate our system through two use case scenarios on a population of 189 spleen datasets from 68 subjects with various conditions observed over time.

Embedding Spatio-Temporal Information into Maps by Route-Zooming.

Analysis and exploration of spatio-temporal data such as traffic flow and vehicle trajectories have become important in urban planning and management. In this paper, we present a novel visualization technique called route-zooming that can embed spatio-temporal information into a map seamlessly for occlusion-free visualization of both spatial and temporal data. The proposed technique can broaden a selected route in a map by deforming the overall road network. We formulate the problem of route-zooming as a nonlinear least squares optimization problem by defining an energy function that ensures the route is broadened successfully on demand while the distortion caused to the road network is minimized. The spatio-temporal information can then be embedded into the route to reveal both spatial and temporal patterns without occluding the spatial context information. The route-zooming technique is applied in two instantiations including an interactive metro map for city tourism and illustrative maps to highlight information on the broadened roads to prove its applicability. We demonstrate the usability of our spatio-temporal visualization approach with case studies on real traffic flow data. We also study various design choices in our method, including the encoding of the time direction and choices of temporal display, and conduct a comprehensive user study to validate our embedded visualization design.

Temporal learning analytics visualizations for increasing awareness during assessment

Visual representations of student-generated trace data during learning activities help both students and instructors interpret them intuitively and perceive hidden aspects of these data quickly. In this paper, we elaborate on the visualization of temporal trace data during assessment. The goals of the study were twofold: a) to depict students’ engagement in the assessment procedure in terms of time spent and temporal factors associated with learning-specific characteristics, and b) to explore the factors that influence the teachers’ Behavioural Intention to use the proposed system as an information system and their perceptions of the effectiveness and acceptance of our approach. The proposed visualizations have been explored in a study with 32 Secondary Education teachers. We adopted a design-based research methodology and employed a survey instrument – based on the Learning Analytics Acceptance Model (LAAM) – in order to measure the expected impact of the proposed visualizations. The analysis of the findings indicates that a) temporal factors can be used for visualizing students’ behaviour during assessment, and b) the visualization of the temporal dimension of students’ behaviour increases teachers’ awareness of students’ progress, possible misconceptions (e.g., guessing the correct answer) and task difficulty.

VisRuption: Intuitive and Efficient Visualization of Temporal Airline Disruption Data

AbstractThe operation of an airline is a very complex task and disruptions to the planned operation can occur on very short notice. Already a small disruption like a delay of some minutes can cost the airline a tremendous amount of money. Hence, it is crucial to proactively control all operations of the airline and efficiently prioritize and handle disruptions. Due to the complex setting and the need for ad hoc decisions this task can only be carried out by human operation controllers. In the field of airline operations control there exists already a vast variety of different software in productive use. We analyze the different approaches from two of the market leaders and identify problematic design choices. We take into account this analysis and develop a set of rules for an intuitive visualization of airline disruption data. Finally, we introduce our tool for visualizing such data which complies to these rules. The visualization enables the user to gain a fast overview over the current problem situation and to intuitively prioritize different problems and problem hierarchies. The efficiency of the design is evaluated with the help of a user study which shows that the new system significantly outperforms the current state of the art.

The History of Temporal Data Visualization and a Proposed Event Centric Timeline Visualization Model

Temporal data management has an ancient history. From the earliest days people are using timeline (a way of displaying a list of events in chronological order) to record their transaction data in a log file or table and often those files or tables are used by researchers to understand the events or trends of the transaction. The first attempt to illustrate chronological events graphically was made in 1765 and presently timeline is used ubiquitously. This paper attempts to explain the history and present state of timeline visualization and proposes a timeline visualization model that provides a new perspective on the existing models. We showed the development of timeline visualization of temporal data over decades, evaluated different techniques, connected and presented them in sequential order to justify the importance of our proposed technique. Our study is structured in three parts: First we discussed different methods of management and visualization of temporal data, then we showed the history and current state of timeline visualization and finally we proposed a timeline visualization technique. 1 General Terms Data Visualization, Timeline.

Temporal Data Mining and Visualization for Treatment Outcome Prediction in HIV Patients

Patients living with HIV are eligible for treatment when their CD4 count is less than 350 cells/mm3. The patients receive antiretroviral treatment (ART) which they need to take every day for the rest of their life. To maintain treatment effcacy, it is necessary to avoid the event of treatment failure. In order to assist physicians monitoring HIV patients this paper propose temporal data mining to predict treatment outcome by providing visual representation of prediction results. Temporal abstraction is used to classify time series data into discrete categories, each represented typically with a symbol. Artificial neural networks are used in this study where the problem of unbalanced data size occurs during the learning process. Two under-sampling techniques are proposed. With the nearest samples to cluster center technique, accuracy is achieved at levels higher than 85% and the discovered patterns correspond with real world diagnosis where viral load is the primary feature to predict treatment outcome.