Multivariate Visualization Research Articles

State-level differentials in COVID-19 fatality: exploring age and sex disparities in Malaysia’s pandemic experience

IssueCOVID-19 has resulted in mortality worldwide and exposed vulnerabilities in public health systems. Although countries have since transitioned to the endemic phase, it is nonetheless important to identify inequities within populations to improve public health strategies in light of another health crisis.PurposeThis study aims to offer deeper insights into the sex and age differentials in COVID-19 fatality across different states in Malaysia, from the pandemic’s start to the country’s transition to the endemic phase.MethodologyAnalyses utilized data on the number of COVID-19 cases and deaths in Malaysia from January 2020 until April 2022. Case fatality rate (CFR) was computed for each state based on sex and age groups, for four periods (January–December 2020, January–June 2021, July–December 2021, and January–April 2022). Principal component analysis (PCA) uncovered patterns in the data and produced a multivariate visualization of the COVID-19 fatality within Malaysia.FindingsFindings revealed heterogeneity in fatality and identified several vulnerable states, notably in the northern region (Kelantan, Perlis) and on the East Coast (Sabah, Sarawak). A sex disparity was apparent in Kelantan as the CFR among older females was higher than their male counterparts. Sabah exhibited relatively higher CFR among the child population from 2020 to 2021.ImplicationThe findings deepened our understanding of COVID-19 fatality complexity, providing recommendations for Malaysia’s government in preparing for future health crises. Malaysia’s case study offers lessons on the need to address regional disparities and implement targeted strategies to protect vulnerable subgroups.

Designing a sequence of activities to build reasoning about data and visualization

AbstractOur complex world requires multivariate reasoning to make sense of reality. Within this paper, we offer a sequence of activities designed to develop multivariate reasoning by explicitly connecting data and visualization. The activities were designed based on a hypothetical learning trajectory we conjectured for students with limited experience with multivariate visualizations. Drawing from evidence collected using these activities in a series of professional development sessions with in‐service teachers, we find that the activities functioned as intended, and thus we promote these activities for developing students' multivariate reasoning at the secondary and post‐secondary level. We detail specific challenges the teachers faced, and based on these results, offer our reflections and recommendations for curricula and teaching.

Stereotomic and Tectonic Architecture from Structural Point of View: Case of a Single 10-Story Perforated Shell Structure

This paper is part of a research on innovative structural systems techniques in tall buildings and starts from the collaboration between the Department of Scientific Research and the Department of Architecture and Engineering at Polis University. The objective of this paper is to emphasize the structural differences between tectonic and stereotomic architecture by analyzing the different structural behaviors of both languages. The initial hypothesis is that an interaction field can be found where these two languages can be combined to propose a design method for a preliminary design process. The methodology goes through the following steps: the first step, analysis and determination of tectonic and stereotomic modelling approach; the second step, analysis of a single 10 story perforated shell structure by changing the openings ratio from a structural point of view and the third step, interpretation of results stating these two different architectural and structural profiles are intertwined. The instruments used are presented by a multivariate visualization method, an easy-to-use and as a key concept for generating the perforated pattern of the shell structure. They describe a visual and interactive, performance informed tool-using software of Rhino Grasshopper, Karamba 3D plugin, indicating the designer's preferences and concluding with the structural control of the model build. It is expected that the results will introduce a generative design method for architecture grounded on the stereotomic and tectonic modelling approach that can contribute towards the integration of structural rationality into preliminary architectural design process of tall buildings.

Development of a component-based interactive visualization system for the analysis of ocean data

ABSTRACT With the continuous development of various types of fixed marine observation equipment, satellite remote sensing technology and computer simulation technology, modern marine scientific research has entered the era of big data. Interactive ocean visualization has become ubiquitous owing to the use of ocean data in studies of marine disasters, global climate change and fisheries. However, the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves. Therefore, an interactive multi-scale, multivariate visualization system with dynamic expansion potential is needed for analyzing larger volumes of ocean data. In this study, a unified visual data service was constructed, and a component-based interactive visualization structure for multi-dimensional, spatiotemporal ocean data is presented in this paper. Based on this structure, users can easily customize the system to visualize other types of scientific data.

DynamoVis 1.0: an exploratory data visualization software for mapping movement in relation to internal and external factors

BackgroundThis paper introduces DynamoVis version 1.0, an open-source software developed to design, record and export custom animations and multivariate visualizations from movement data, enabling visual exploration and communication of patterns capturing the associations between animals’ movement and its affecting internal and external factors. Proper representation of these dependencies grounded on cartographic principles and intuitive visual forms can facilitate scientific discovery, decision-making, collaborations, and foster understanding of movement.ResultsDynamoVis offers a visualization platform that is accessible and easily usable for scientists and general public without a need for prior experience with data visualization or programming. The intuitive design focuses on a simple interface to apply cartographic techniques, giving ecologists of all backgrounds the power to visualize and communicate complex movement patterns.ConclusionsDynamoVis 1.0 offers a flexible platform to quickly and easily visualize and animate animal tracks to uncover hidden patterns captured in the data, and explore the effects of internal and external factors on their movement path choices and motion capacities. Hence, DynamoVis can be used as a powerful communicative and hypothesis generation tool for scientific discovery and decision-making through visual reasoning. The visual products can be used as a research and pedagogical tool in movement ecology.

Categorical CVA biplots

Techniques to visualise and understand large amounts of data are of paramount importance. In most settings, this data is usually multivariate, which further stresses the need for effective visualisation techniques. Multivariate visualisation techniques such as canonical variate analysis (CVA) biplots allow for simultaneous lower-dimensional visualisation and data classification by incorporating class-specific data. CVA biplots, however, are currently restricted to numerical data. Through combining concepts from both CVA and non-linear principal component analysis (PCA) biplots, a new biplot construction methodology that improves on the traditional CVA biplot by allowing for categorical variables is proposed. This technique, named CVA(Hr), is showcased using the established mushroom data set, which contains a mix of categorical and ordinal variables. This novel method improves upon existing biplot construction in terms of classification accuracy and class separation.

Defining global benchmarks in elective secondary bariatric surgery comprising conversional, revisional and reversal procedures

Abstract Objective Management of poor response and of long-term complications after bariatric surgery (BS) is complex and under-investigated. Indications and types of reoperations vary widely and postoperative complication rates are higher compared to primary BS. Benchmarking uses best performance in a given field as reference point for improvement. Our aim was to define ‘‘best possible’’ outcomes for elective secondary BS. Methods The establishment of benchmarks in secondary BS followed a standardized methodology, based on recommendations of a Delphi consensus panel of experts. This multicenter study analyzed patients undergoing elective secondary BS in 18 high-volume centers on 4 continents from 06/2013 to 05/2019. Twenty-one outcome benchmarks were established in low-risk patients, defined as the 75th percentile of the median outcome values of the centers. Benchmark cases had no: previous laparotomy, diabetes, sleep apnea, cardiopathy, renal insufficiency, inflammatory bowel disease, immunosuppression, history of thromboembolic events, BMI>50kg/m2 or age>65 years. Descriptive statistics, multivariate logistic regression and data visualization were performed using the R software. Results Out of 44’884 elective bariatric procedures performed in the participating centers, 5’328 secondary BS cases were identified. The benchmark cohort included 3143 cases, mainly females (85%), aged 43.8±10 years, 8.4±5.3 years after primary BS, with a body mass index 35.2±7kg/m2. Main indications were insufficient weight loss (43%) and gastro-esophageal reflux disease/dysphagia (25%). 90-days postoperatively, 14.57% of benchmark patients presented ≥1 complication, mortality was 0.06% (n = 2). Significantly higher morbidity was observed in non-benchmark cases (OR 1.36) and after conversional or revisional procedures with gastrointestinal suture/stapling (OR 1.7). Benchmark cutoffs at 90-days postoperatively were ≤5.8% re-intervention and ≤8.8% re-operation rate. At 2-years (IQR 1-3) 15.6% of benchmark patients required a reoperation. Conclusion Secondary BS is safe, although postoperative morbidity exceeds the established benchmarks for primary BS. The excess morbidity is due to an increased risk of gastrointestinal leakage and higher need for intensive care. The considerable rate of tertiary BS warrants expertise and future research to optimize the management of non-success after BS.

A Susceptible Period of Photic Day-Night Rhythm Loss in Common Marmoset Social Behavior Development.

The prevalence of neurodevelopmental psychiatric disorders such as pervasive developmental disorders is rapidly increasing worldwide. Although these developmental disorders are known to be influenced by an individual’s genetic background, the potential biological responses to early life’s environmental exposure to both physical and psychological factors must also be considered. Many studies have acknowledged the influence of shorter time for rest at night and the simultaneous occurrence of various kinds of complications involving developmental disorders. In a prior study, we examined how a common marmoset’s (Callithrix jacchus) psychosocial development was affected when it was reared under constant daylight from birth and then reared individually by humans nursing them under constant light (LL) during their juvenile development stages. The behaviors of these marmosets were compared with those of normal day-night cycle (LD) marmosets using a multivariate analysis based on principal component analysis (PCA). That study found that LL marmosets relatively elicited egg-like calls (Ecall) and side-to-side shakes of the upper body with rapid head rotation through adulthood frequently. Based on the PCA, these behaviors were interpreted as “alert” or “hyperactive” states. However, we did not clarify susceptible periods of the photic rhythm loss experience and the psychological development output. In this study we summarize the following studies in our model animal colonies involving 30 animals (11 female, 19 males) to further explore critical age states of inquiry about each social behavior profiling. We compared social behaviors of three age stages, juvenile, adolescent and young adult equivalent to one another in four LL experience conditions, LL (postnatal day (P) 0 to around 150), Middle (P60–149, 90 days), Late (P150–239, 90 days), and LD (no experience). In the most representative 1st and 2nd principal component scores, the shifting to higher frequency of alert behaviors developed at the adult stage in LL, Middle, then Late in turn. The no LL experience group, LD, generally featured higher frequency of local preference of high position compared to LL experience present groups, in adulthood. This limited model primate study might inspire different developmental age sensitive mechanisms of neuronal network to control socio-emotional functions by utilizing the multivariate visualization method, BOUQUET. This study could potentially contribute to nurturing educational designs for social developmental disorders.

Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. These techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

Identification of key discriminating variables between spinner dolphin (Stenella longirostris) whistle types.

Descriptions of the six different spinner dolphin (Stenella longirostris) whistle types were developed from a random sample of 600 whistles collected across a 2-yr period from a Fijian spinner dolphin population. An exploratory multivariate visualization suggested an inverse relationship between delta and minimum frequency (58.6%) as well as whistle duration (18.1%) as the most discriminating variables in this dataset. All three of these variables were deemed to be significant when considered jointly in a multivariate analysis of variance (MANOVA): delta frequency (F5594 = 27.167, p < 0.0001), minimum frequency (F5594 = 14.889, p < 0.0001), and duration (F5594 = 24.303, p < 0.0001). Significant differences between at least two of the whistle types were found for all five acoustic parameters in univariate analysis of variation (ANOVA) tests. Constant and sine whistles were found to be the most distinctive whistles, whereas upsweep and downsweep whistles were the most similar. The identification of which parameters differ most markedly between whistle types and the relatively high explanatory power of this study's results provide a logical starting point for objective classification of spinner dolphin whistle types using machine learning techniques.

Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models-the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)-produced over the Red Sea domain for the 1998-2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models' responses to various climate factors. Here, we use the models' eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models' connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts.

Multivariate visualization for atmospheric pollution

Multivariate visualization for atmospheric pollution is a challenging research topic. Appropriate algorithms and data structures based on modern graphics hardware are used to obtain high performance. 3D visualization of the atmospheric wind field and pollutant concentrations can easily result in visual perception problems such as occlusion and cluttering even artifacts. To solve the above issues, a K-means clustering technique is used in combination with a similarity metric between streamlines based on an iterative closest point method to cluster the initial streamlines. A small set of streamlines is then selected to represent the prominent structure of the wind field. The proper illumination model and the depth sorting method reduce the inter-occlusion between streamlines and isosurfaces to show much clearer wind field pattern and important features effectively. The atmospheric pollution data set is employed to evaluate the proposed algorithm framework.

Functional and Multivariate Hydrological Data Visualization and outlier Detection of Sukkur Barrage

Functional and Multivariate Hydrological Data Visualization and outlier Detection of Sukkur Barrage

Catalog of 2017 Thunderstorm Ground Enhancement (TGE) events observed on Aragats

The natural electron accelerator in the clouds above Aragats high-altitude research station in Armenia operates continuously in 2017 providing more than 100 Thunderstorm Ground enhancements (TGEs). Most important discovery based on analysis of 2017 data is observation and detailed description of the long-lasting TGEs. We present TGE catalog for 2 broad classes according to presence or absence of the high-energy particles. In the catalog was summarized several key parameters of the TGEs and related meteorological and atmospheric discharge observations. The statistical analysis of the data collected in tables reveals the months when TGEs are more frequent, the daytime when TGEs mostly occurred, the mean distance to lightning flash that terminates TGE and many other interesting relations. Separately was discussed the sharp count rate decline and following removal of high-energy particles from the TGE flux after a lightning flash. ADEI multivariate visualization and statistical analysis platform make analytical work on sophisticated problems rather easy; one can try and test many hypotheses very fast and come to a definite conclusion allowing crosscheck and validation

Multivariate visualization of particle data

In this review paper, we review methods for interactive particle rendering techniques, multi-view particle visualization systems, multivariate visualization techniques, and methods for correlation visualizations. Visualization is vital for gaining insight into particle data. Multivariate particle data are generated to understand different aspects of the underlying physics. The visualization of multivariate particle data is typically performed in multiple linked view systems (multi-view systems) that render particles of interest that are selected by the user interactively with brushing-and-linking. To this end, the non-spatial aspects of particles are explored with multivariate visualization methods, e.g., scatter plots, scatter plot matrix, parallel coordinates, dimensional reduction and radial plots.

Decal-Lenses: Interactive Lenses on Surfaces for Multivariate Visualization.

We present decal-lenses, a new interaction technique that extends the concept of magic lenses to augment and manage multivariate visualizations on arbitrary surfaces. Our object-space lenses follow the surface geometry and allow the user to change the point of view during data exploration while maintaining a spatial reference to positions where one or more lenses were placed. Each lens delimits specific regions of the surface where one or more attributes can be selected or combined. Similar to 2D lenses, the user interacts with our lenses in real-time, switching between different attributes within the lens context. The user can also visualize the surface data representations from the point of view of each lens by using local cameras. To place lenses on surfaces of intricate geometry, such as the human brain, we introduce the concept of support surfaces for designing interaction techniques. Support surfaces provide a way to place and interact with the lenses while avoiding holes and occluded regions during data exploration. We further extend decal-lenses to arbitrary regions using brushing and lassoing operations. We discuss the applicability of our technique and present several examples where our lenses can be useful to create a customized exploration of multivariate data on surfaces.

Illustrative Multivariate Visualization for Geological Modelling

AbstractIn this paper, we present a novel illustrative multivariate visualization for geological modelling to assist geologists and reservoir engineers in visualizing multivariate datasets in superimposed representations, in contrast to the single‐attribute visualizations supported by commercial software. Our approach extends the use of decals from a single surface to 3D irregular grids, using the layering concept to represent multiple attributes. We also build upon prior work to augment the design and implementation of different geological attributes (namely, rock type, porosity, and permeability). More specifically, we propose a new sampling strategy to generate decals for porosity on the geological grid, a hybrid visualization for permeability which combines 2D decals and 3D ellipsoid glyphs, and a perceptually‐based design that allows us to visualize additional attributes (e.g., oil saturation) while avoiding visual interference between layers. Furthermore, our visual design draws from traditional geological illustrations, facilitating the understanding and communication between interdisciplinary teams. An evaluation by domain experts highlights the potential of our approach for geological modelling and interpretation in this complex domain.

Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.

Intraskeletal histovariability, allometric growth patterns, and their functional implications in bird-like dinosaurs

With their elongated forelimbs and variable aerial skills, paravian dinosaurs, a clade also comprising modern birds, are in the hotspot of vertebrate evolutionary research. Inferences on the early evolution of flight largely rely on bone and feather morphology, while osteohistological traits are usually studied to explore life-history characteristics. By sampling and comparing multiple homologous fore- and hind limb elements, we integrate for the first time qualitative and quantitative osteohistological approaches to get insight into the intraskeletal growth dynamics and their functional implications in five paravian dinosaur taxa, Anchiornis, Aurornis, Eosinopteryx, Serikornis, and Jeholornis. Our qualitative assessment implies a considerable diversity in allometric/isometric growth patterns among these paravians. Quantitative analyses show that neither taxa nor homologous elements have characteristic histology, and that ontogenetic stage, element size and the newly introduced relative element precocity only partially explain the diaphyseal histovariability. Still, Jeholornis, the only avialan studied here, is histologically distinct from all other specimens in the multivariate visualizations raising the hypothesis that its bone tissue characteristics may be related to its superior aerial capabilities compared to the non-avialan paravians. Our results warrant further research on the osteohistological correlates of flight and developmental strategies in birds and bird-like dinosaurs.

RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting

RNA-Seq is increasingly being used to measure human RNA expression on a genome-wide scale. Expression profiles can be interrogated to identify and functionally characterize treatment-responsive genes. Ultimately, such controlled studies promise to reveal insights into molecular mechanisms of treatment effects, identify biomarkers, and realize personalized medicine. RNA-Seq Reports (RSEQREP) is a new open-source cloud-enabled framework that allows users to execute start-to-end gene-level RNA-Seq analysis on a preconfigured RSEQREP Amazon Virtual Machine Image (AMI) hosted by AWS or on their own Ubuntu Linux machine via a Docker container or installation script. The framework works with unstranded, stranded, and paired-end sequence FASTQ files stored locally, on Amazon Simple Storage Service (S3), or at the Sequence Read Archive (SRA). RSEQREP automatically executes a series of customizable steps including reference alignment, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially expressed genes, heatmaps, co-expressed gene clusters, enriched pathways, and a series of custom visualizations. The framework outputs a file collection that includes a dynamically generated PDF report using R, knitr, and LaTeX, as well as publication-ready table and figure files. A user-friendly configuration file handles sample metadata entry, processing, analysis, and reporting options. The configuration supports time series RNA-Seq experimental designs with at least one pre- and one post-treatment sample for each subject, as well as multiple treatment groups and specimen types. All RSEQREP analyses components are built using open-source R code and R/Bioconductor packages allowing for further customization. As a use case, we provide RSEQREP results for a trivalent influenza vaccine (TIV) RNA-Seq study that collected 1 pre-TIV and 10 post-TIV vaccination samples (days 1-10) for 5 subjects and two specimen types (peripheral blood mononuclear cells and B-cells).