Interactive Visualization Techniques Research Articles

Interactive tools for explaining multidimensional projections for high-dimensional tabular data

We present a set of interactive visual analysis techniques aiming at explaining data patterns in multidimensional projections. Our novel techniques include a global value-based encoding that highlights point groups having outlier values in any dimension as well as several local tools that provide details on the statistics of all dimensions for a user-selected projection area. Our techniques generically apply to any projection algorithm and scale computationally well to hundreds of thousands of points and hundreds of dimensions. We describe a user study that shows that our visual tools can be quickly learned and applied by users to obtain non-trivial insights in real-world multidimensional datasets. We also show how our techniques can help understanding a real-world dataset containing quantitative, ordinal, and categorical attributes.

Place-bound planning support systems for deliberation: Affording better communication and comprehension

Despite planning support systems (PSS) becoming increasingly useful for citizen participation processes, the effects of such systems’ material and spatial setup on citizen participation processes still need to be studied. PSS have long been equated to software- and data-based technologies, and only little attention has been put on place-bound PSS that prescribe onsite face-to-face collaboration. As closing the ‘implementation gap’ requires extensive conceptualisation, description, and critical analysis of different ideal types, workings, and use cases of PSS, this study researches this understudied place-bound type of PSS. More precisely, this study uses empirical material from Haifa’s 3 S Lab to contribute to closing the implementation gap by identifying place-bound PSS – an under-studied type of PSS – as useful for deliberative decision-making – an overlooked implementation context. This research advances the conceptualisation of PSS by discussing place-bound PSS and their hypothesised utility, practical setup, and empirically tested benefits for deliberative citizen participation. We find that the benefits of place-bound PSS for planning lie in deliberative affordances that ease the communication and comprehension deficiencies that often plague deliberative citizen participation processes. As place-bound PSS, the 3 S Lab provides an immersive shared space that improves communication, while its interactive visualisation techniques afford improved comprehension of complex urban issues.

MLtox, online phototoxicity prediction webpage

Phototoxicity, sometimes in the literature referred to as photo-irritation, is a chemically induced reaction requiring light. While it is generally accepted that phototoxicity testing can be performed in the majority of cases in vitro (i.e. without the use of experimental animals), these tests may sometimes provide contradictory predictions. Understanding the mechanisms of initiating events based on the molecule's structure and its ability to reach the excited state and consequently generate ROS enables the creation of predictive QSAR for this adverse outcome. The ability to predict the phototoxicity potential via a QSAR model is beneficial in reducing the number of mechanical in vitro/in chemico tests needed to demonstrate absence of phototoxicity and it is very helpful in the overall safety assessment process.The QSAR prediction model presented here focused on developing a robust platform freely available on the web via the link http://mltox.online to provide interpretable predictions of the phototoxicity of tested molecules. Great attention was devoted to interpretability and explainability of the prediction results. The web application allows the user to input a chemical by CAS number, SMILES code or trivial name. The user can choose between simple prediction or advanced tools options. These extended tools include the artificial intelligence explainability of model prediction using XSMILES (interactive visualization technique to support the interpretation of SMILES) and SHAP values (impact each element on the prediction). The comprehensive tools in question allow the user to explore the properties of phototoxic substances and to understand the prediction outcomes better.

The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models

Abstract. Atmospheric fronts are a widely used conceptual model in meteorology, most encountered as two-dimensional (2-D) front lines on surface analysis charts. The three-dimensional (3-D) dynamical structure of fronts has been studied in the literature by means of “standard” 2-D maps and cross-sections and is commonly sketched in 3-D illustrations of idealized weather systems in atmospheric science textbooks. However, only recently has the feasibility of the objective detection and visual analysis of 3-D frontal structures and their dynamics within numerical weather prediction (NWP) data been proposed, and such approaches are not yet widely known in the atmospheric science community. In this article, we investigate the benefit of objective 3-D front detection for case studies of extra-tropical cyclones and for comparison of frontal structures between different NWP models. We build on a recent gradient-based detection approach, combined with modern 3-D interactive visual analysis techniques, and adapt it to handle data from state-of-the-art NWP models including those run at convection-permitting kilometre-scale resolution. The parameters of the detection method (including data smoothing and threshold parameters) are evaluated to yield physically meaningful structures. We illustrate the benefit of the method by presenting two case studies of frontal dynamics within mid-latitude cyclones. Examples include joint interactive visual analysis of 3-D fronts and warm conveyor belt (WCB) trajectories, as well as identification of the 3-D frontal structures characterizing the different stages of a Shapiro–Keyser cyclogenesis event. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment the surface charts by providing additional pertinent information in the vertical dimension. A second application illustrates the relation between convection and 3-D cold-front structure by comparing data from simulations with parameterized and explicit convection. Finally, we consider “secondary fronts” that commonly appear in UK Met Office surface analysis charts. Examination of a case study shows that for this event the secondary front is not a temperature-dominated but a humidity-dominated feature. We argue that the presented approach has great potential to be beneficial for more complex studies of atmospheric dynamics and for operational weather forecasting.

Online data service for geologic formations (Lexicons) of China, India, Vietnam and Thailand with one‐click visualizations onto East Asia plate reconstructions

AbstractPaleogeography is the merger of sediment and volcanic facies, depositional settings, tectonic plate movements, topography, climate patterns and ecosystems through time. The construction of paleogeographic maps on tectonic plate reconstruction models requires a team effort to compile databases, data sharing standards and map projection methods. Two goals of the Paleogeography Working Group of the International Union of Geological Sciences (IUGS) program for Deep‐Time Digital Earth (DDE) are: (1) to interlink online national lexicons for all sedimentary and volcanic formations, and develop online ones for nations that currently lack these; (2) to target specific regions and intervals for testing/showcasing paleogeography output from the merger of these databases. Following those goals, we developed and applied new cloud‐based lexicon data services and interactive visualization techniques to regions in East Asia. This has been a successful collaboration among computer engineers and plate modellers and has involved stratigraphers in India (ONGC), China (Chengdu Univ. Tech., and Chinese Acad. Geol. Sci.), United States (Paleomap Project, and Purdue Univ.), Australia (GPlates visualization team) and Vietnam (Vietnam Nat. Univ.). Independent online lexicons with map‐based and stratigraphy‐based user interfaces have been developed (as of the date of this submission in March 2022) for all Proterozoic to Quaternary formations on the Indian Plate (over 800) and Vietnam (over 200), the majority of the Devonian through Neogene of China (ca. 2000) and partially for Thailand. A multi‐database search system returns all geologic formations of a desired geologic time from these four independent databases. With one click, users can plot the regional extent of one or of all of those regional formations on different plate reconstruction models of that desired age, and these polygons are filled with the lithologic facies pattern.

It's about Time: Analytical Time Periodization

AbstractThis paper presents a novel approach to the problem of time periodization, which involves dividing the time span of a complex dynamic phenomenon into periods that enclose different relatively stable states or development trends. The challenge lies in finding such a division of the time that takes into account diverse behaviours of multiple components of the phenomenon while being simple and easy to interpret. Despite the importance of this problem, it has not received sufficient attention in the fields of visual analytics and data science. We use a real‐world example from aviation and an additional usage scenario on analysing mobility trends during the COVID‐19 pandemic to develop and test an analytical workflow that combines computational and interactive visual techniques. We highlight the differences between the two cases and show how they affect the use of different techniques. Through our investigation of possible variations in the time periodization problem, we discuss the potential of our approach to be used in various applications. Our contributions include defining and investigating an earlier neglected problem type, developing a practical and reproducible approach to solving problems of this type, and uncovering potential for formalization and development of computational methods.

Visual analytics for diagnosing spatial interpolation models

AbstractThe development and widespread use of statistical learning models have brought the need for tools that help analysts diagnose, build, and refine those models. In this work, in particular, we focus on interpolation models, which spatially predict the value of a variable based on the values of its neighborhood. Investigating these results spatially or comparing them with other models at different levels of granularity is still a challenge for the analysts trying to understand and refine their models. To deal with that, we propose a visual analytics model‐agnostic tool for facilitating the comparison and refinement of spatial models at different levels of granularity using interactive visualization techniques. The tool was built in collaboration with specialists who used it to diagnose and improve a spatial model for predicting residential real estate prices.

XSMILES: interactive visualization for molecules, SMILES and XAI attribution scores

BackgroundExplainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a molecule to reveal their influence over a predicted property. For this purpose, some XAI techniques calculate attribution scores associated with tokens of SMILES strings or with atoms of a molecule. While an association of a score with an atom can be directly visually represented on a molecule diagram, scores computed for SMILES non-atom tokens cannot. For instance, a substring [N+] contains 3 non-atom tokens, i.e., [, +, and ], and their attributions, depending on the model, are not necessarily revealing an influence of the nitrogen atom over the predicted property; for that reason, it is not possible to represent the scores on a molecule diagram. Moreover, SMILES’s notation is complex, foregrounding the need for techniques to facilitate the analysis of explanations associated with their tokens.ResultsWe propose XSMILES, an interactive visualization technique, to explore explainable artificial intelligence attributions scores and support the interpretation of SMILES. Users can input any type of score attributed to atom and non-atom tokens and visualize them on top of a 2D molecule diagram coordinated with a bar chart that represents a SMILES string. We demonstrate how attributions calculated for SMILES strings can be evaluated and better interpreted through interactivity with two use cases.ConclusionsData scientists can use XSMILES to understand their models’ behavior and compare multiple modeling approaches. The tool provides a set of parameters to adapt the visualization to users’ needs and it can be integrated into different platforms. We believe XSMILES can support data scientists to develop, improve, and communicate their models by making it easier to identify patterns and compare attributions through interactive exploratory visualization.

FINDING YOURSELF: PERSONALIZED MEDICINE, DATA SCIENCE, AND INTERACTIVE VISUALIZATIONS

Abstract Personalized medicine is care that is tailored to an individual patient. In contrast, randomized trials are designed to report evidence of benefits and harms “on average”. The average trial participant though, is often healthier than older patients seen in clinical practice. A variety of methods have been proposed which offer improvements to traditional (but questionable) practices of one-subgroup-at-a-time examinations of treatment effect heterogeneity, but translating evidence from these advancements has received less attention. Data Science initiatives have allowed broader data sharing, data harmonization and data synthesizing approaches, as well as an enormous maturing of interactive visualization techniques. Motivated by the SPRINT study and using advanced approaches of marginalized standardization and the Predictive Approaches to Treatment effect Heterogeneity (PATH) statement, we show how researchers, regulators, clinicians and patients can "find themselves" on the treatment effect continuum and be better informed of potential individualized evidence through interactive visualizations.

Effectiveness of a Visual Interactive Game on Oral Hygiene Knowledge, Practices, and Clinical Parameters among Adolescents: A Randomized Controlled Trial.

This study aimed to evaluate the effect of a novel interactive game-based visual performance technique (IGVP) and conventional oral health educational (OHE) talk on plaque control, gingival health, and oral hygiene knowledge and practices in 12-15-year-old schoolchildren. A single-blinded randomized controlled trial was undertaken in a private primary school in Belagavi, Karnataka, India. A total of 100 children aged 12-15 years were randomly assigned to either a conventional OHE talk (control group, n = 50) or the IGVP technique (test group, n = 50), using a computer-generated table of random numbers. A self-designed, pre-validated closed-ended questionnaire was collected from both groups, followed by clinical examination carried out using gingival and plaque index, pre- and post-intervention, at three months follow-up. There was a significant reduction in the mean gingival score and plaque score in the test group after intervention, indicating a 58.7% and 63.4% reduction, when compared to the control group, which had a 2.8% and 0.7% reduction, respectively (p < 0.001). The test group showed a significant increase in the percentage of knowledge gained (22.4%), compared to control group (7.8%). The IGVP technique proved to be more effective than a conventional OHE talk in the reduction of the plaque score, gingival score, and in the improvement of the knowledge of oral hygiene maintenance and its application.

Using set visualisation to find and explain patterns of missing values: a case study with NHS hospital episode statistics data

ObjectivesMissing data is the most common data quality issue in electronic health records (EHRs). Missing data checks implemented in common analytical software are typically limited to counting the number of...

Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

AbstractEpidemiologists use individual‐based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex ‘infection maps’ of time‐varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time‐varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns.

Automatic assessment of adverse drug reaction reports with interactive visual exploration

A large number of adverse drug reaction (ADR) reports are collected yearly through the spontaneous report system (SRS). However, experienced experts from ADR monitoring centers (ADR experts, hereafter) reviewed only a few reports based on current policies. Moreover, the causality assessment of ADR reports was conducted according to the official approach based on the WHO-UMC system, a knowledge- and labor-intensive task that highly relies on an individual’s expertise. Our objective is to devise a method to automatically assess ADR reports and support the efficient exploration of ADRs interactively. Our method could improve the capability to assess and explore a large volume of ADR reports and aid reporters in self-improvement. We proposed a workflow for assisting the assessment of ADR reports by combining an automatic assessment prediction model and a human-centered interactive visualization method. Our automatic causality assessment model (ACA model)—an ordinal logistic regression model—automatically assesses ADR reports under the current causality category. Based on the results of the ACA model, we designed a warning signal to indicate the degree of the anomaly of ADR reports. An interactive visualization technique was used for exploring and examining reports extended by automatic assessment of the ACA model and the warning signal. We applied our method to the SRS report dataset of the year 2019, collected in Guangdong province, China. Our method is evaluated by comparing automatic assessments by the ACA model to ADR reports labeled by ADR experts, i.e., the ground truth results from the multinomial logistic regression and the decision tree. The ACA model achieves an accuracy of 85.99%, a multiclass macro-averaged area under the curve (AUC) of 0.9572, while the multinomial logistics regression and decision tree yield 80.82%, 0.8603, and 85.39%, 0.9440, respectively, on the testing set. The new warning signal is able to assist ADR experts to quickly focus on reports of interest with our interactive visualzation tool. Reports of interest that are selected with high scores of the warning signal are analyzed in details by an ADR expert. The usefulness of the overall method is further evaluated through the interactive analysis of the data by ADR expert. Our ACA model achieves good performance and is superior to the multinomial logistics and the decision tree. The warning signal we designed allows efficient filtering of the full ADR reports down to much fewer reports showing anomalies. The usefulness of our interactive visualization is demonstrated by examples of unusual reports that are quickly identified. Our overall method could potentially improve the capability of analyzing ADR reports and reduce human labor and the chance of missing critical reports.

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017-2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, web-based tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended.

Spatiotemporal Variation of Land Use Pattern and Land Cover Changes in Gidan Kwano Watershed Area in North Central Nigeria

Land Management Systems (LMS) are institutional frameworks complex by the tasks they must achieve, national, cultural, political, and judicial settings and technology. The urbanisation process in Nigeria has increased since the 1960s because of the crude oil boom era and other government-backed industrial initiatives and investments. This study employed the use of both primary and secondary sources of data. Preliminary data comprise methods of land use, types of agricultural activities carried out and the process of working on the land by the farmers within a 10 km radius. The secondary data involved the interactive digital and visual techniques of the Geographical Information system for five seasons, with each having a ten-year interval span within 1975 to 2015. Statistical analysis was carried out with SPSS 2013 and XLSTAT 2015. Five land use and land cover types were observed within the Gidan Kwano watershed, which includes wetlands (WL), water bodies (WB), bare grounds (BG), vegetation (VG), and settlements (SL). The most prevalent landform in the study area during the 1975 period was the vegetative area which was 50% of the total landmass. Thus, the vegetation (VG) covered half of the Gidan Kwano watershed. However, the vegetative area decreased substantially during the study period of 1975 to 2015. It was observed that the vegetation (VG) within the study area had the highest percentage of coverage, of 34%. During the study period, a significant decrease was observed in the WL, WB and VG areas. It was also concluded that due to the built-up places, the infiltration of surface runoff from rainwater would be drastically reduced as most of the sections are paved for construction activities while a section of the study area is covered with rock outcrops and farmlands.

Інтерпретація еволюції в рамках популяризації науки у природничих музеях

The paper presents a brief review of the scientific and educational role of natural history museums, particular-ly in the field of interpretation of organic evolution. The scientific and social value of collections that are stored at natural history museums are highlighted. The history of interpretation and popularization of basic ideas of the theory of evolution are considered, as well as the perception of evolution by the society and vari-ous categories of museum visitors. Analysed are the main tools of interpretation available in natural history museums as well as basic principles of display of palaeontological specimens, particularly in dioramas as pe-culiar museum tools for interpretation. The main approaches to the interpretation of evolution are considered in the context of selection of interpretation methods and tools and in regard to the specifics of various groups of museum visitors. Minimum professional skills of interpreters who take part in exhibiting and educational ac-tivities of natural history museums are discussed. The possibilities of interpretation of evolution are shown on the example of the palaeontological exhibition of the National Museum of Natural History at the National Academy of Sciences of Ukraine (Kyiv, Ukraine). A brief description of the palaeontological display is given, particularly on the general principles of its structure and on examples of exhibited specimens that demonstrate the evolution of various groups of vertebrates (e.g. fishes and mammals). Dioramas of the palaeontological exhibition are presented, which are especially valuable visual tools of interpretation of evolution in the muse-um. The main issues of further existence and development of natural history museums are discussed in the context of their role as powerful research and educational centres, maintaining a high level of attendance by visitors, and involvement of the public in the activities of these museums in times of high competition of lei-sure offers by various entertaining facilities. The need for the modernization of earlier exhibitions using mod-ern interactive tools and visualization techniques in underlined in order to increase public interest and expand the pool of visitors.

Combining 2D and 3D Visualization with Visual Analytics in the Environmental Domain

The application potential of Visual Analytics (VA), with its supporting interactive 2D and 3D visualization techniques, in the environmental domain is unparalleled. Such advanced systems may enable an in-depth interactive exploration of multifaceted geospatial and temporal changes in very large and complex datasets. This is facilitated by a unique synergy of modules for simulation, analysis, and visualization, offering instantaneous visual feedback of transformative changes in the underlying data. However, even if the resulting knowledge holds great potential for supporting decision-making in the environmental domain, the consideration of such techniques still have to find their way to daily practice. To advance these developments, we demonstrate four case studies that portray different opportunities in data visualization and VA in the context of climate research and natural disaster management. Firstly, we focus on 2D data visualization and explorative analysis for climate change detection and urban microclimate development through a comprehensive time series analysis. Secondly, we focus on the combination of 2D and 3D representations and investigations for flood and storm water management through comprehensive flood and heavy rain simulations. These examples are by no means exhaustive, but serve to demonstrate how a VA framework may apply to practical research.

Visual Analysis of Large Multivariate Scattered Data using Clustering and Probabilistic Summaries.

Rapidly growing data sizes of scientific simulations pose significant challenges for interactive visualization and analysis techniques. In this work, we propose a compact probabilistic representation to interactively visualize large scattered datasets. In contrast to previous approaches that represent blocks of volumetric data using probability distributions, we model clusters of arbitrarily structured multivariate data. In detail, we discuss how to efficiently represent and store a high-dimensional distribution for each cluster. We observe that it suffices to consider low-dimensional marginal distributions for two or three data dimensions at a time to employ common visual analysis techniques. Based on this observation, we represent high-dimensional distributions by combinations of low-dimensional Gaussian mixture models. We discuss the application of common interactive visual analysis techniques to this representation. In particular, we investigate several frequency-based views, such as density plots in 1D and 2D, density-based parallel coordinates, and a time histogram. We visualize the uncertainty introduced by the representation, discuss a level-of-detail mechanism, and explicitly visualize outliers. Furthermore, we propose a spatial visualization by splatting anisotropic 3D Gaussians for which we derive a closed-form solution. Lastly, we describe the application of brushing and linking to this clustered representation. Our evaluation on several large, real-world datasets demonstrates the scaling of our approach.

The Effectiveness of Interactive Visualization Techniques for Time Navigation of Dynamic Graphs on Large Displays

Dynamic networks can be challenging to analyze visually, especially if they span a large time range during which new nodes and edges can appear and disappear. Although it is straightforward to provide interfaces for visualization that represent multiple states of the network (i.e., multiple timeslices) either simultaneously (e.g., through small multiples) or interactively (e.g., through interactive animation), these interfaces might not support tasks in which disjoint timeslices need to be compared. Since these tasks are key for understanding the dynamic aspects of the network, understanding which interactive visualizations best support these tasks is important. We present the results of a series of laboratory experiments comparing two traditional approaches (small multiples and interactive animation), with a more recent approach based on interactive timeslicing. The tasks were performed on a large display through a touch interface. Participants completed 24 trials of three tasks with all techniques. The results show that interactive timeslicing brings benefit when comparing distant points in time, but less benefits when analyzing contiguous intervals of time.

Need for Interactive Data Visualization in Public Health Practice: Examples from India.

The world is full of data which is increasing by leaps and bounds. In health care, big data is becoming common with increased electronic health data accumulation and/or accessibility to public data previously held under lock and key. At the same time, health data visualization applications have become popular over recent years. Against this background, a review was done to summarize the application of data visualization in public health & the challenges faced. Peer-reviewed original research articles and review articles searched in Google Scholar and Pubmed databases that were indexed in the last ten years period, using the keywords “Big data” or “data visualization” or “Interactive visualization techniques.” Other related information in books, blogs, and published documents were searched in Google search engine using the same keywords. Contents from the downloaded documents were presented and discussed under three headings viz. (a) the visualizations that are still current and how they have evolved further, (b) tools or methods that can be used by end-users to make their own modifications, (c) the platforms to disseminate them. Usage of different plots in public health is explained with suitable examples using the data from public health datasets. From the discussion it can be understood that when big data is visualized well, it can identify implementation gaps and disparities and accelerate implementation strategies to reach the population groups in most need for interventions. As health administrator may come from diverse specialties, robust training and career development for big data in public health is the need of the hour.