Data Visualization Methods Research Articles

The Effect of Governmental Health Measures on Public Behaviour During the COVID-19 Pandemic Outbreak.

The coronavirus disease 2019 (COVID-19) pandemic resulted in radical changes in many aspects of life. To deal with this, each country has implemented continuous health measures from the beginning of the outbreak. Discovering how governmental actions impacted public behaviour during the outbreak stage is the purpose of this study. This study uses a hybrid large-scale data visualisation method to analyse public behaviour (epidemic concerns, self-protection, and mobility trends), using the data provided by multiple authorities. Meanwhile, a content analysis method is used to qualitatively code the health measures of three countries with severe early epidemic outbreaks from different continents, namely China, Italy, and the United States. Eight dimensions are coded to rate the mobility restrictions implemented in the above countries. (1) Governmental measures did not immediately persuade the public to change their behaviours during the COVID-19 epidemic. Instead, the public behaviour proceeded in a three-phase rule, which is typically witnessed in an epidemic outbreak, namely the wait-and-see phase, the surge phase and the slow-release phase. (2) The strictness of the mobility restrictions of the three countries can be ranked as follows: Hubei Province in China (with an average score of 8.5 out of 10), Lombardy in Italy (7.125), and New York State in the United States (5.375). Strict mobility restrictions are more likely to cause a surge of population outflow from the epidemic area in the short term, whereas the effect of mobility restrictions is positively related to the stringency of policies in the long term. The public showed generally lawful behaviour during regional epidemic outbreaks and blockades. Meanwhile public behaviour was deeply affected by the actions of local governments, rather than the global pandemic situation. The contextual differences between the various countries are important factors that influence the effects of the different governments' health measures.

Treemap Layout Based on Monte Carlo Tree Search

<p indent=0mm>Treemaps are a commonly used method for hierarchical data visualization. The previous research shows that three treemap layout algorithms(SQR, STR, SPI) that meet the principle of gradually adding points on four common optimization indicators(average aspect ratio, stability, consistent data, readability) have their own advantages and disadvantages. Both Strip and Spiral algorithms result in relatively large aspect ratio of layouts, and the squarified treemap has some room for optimization in the aspect ratio. Based on Monte Carlo tree search, the corresponding search tree is constructed while laying out the treemap and the upper confidence bound formula is used as the selection criterion for the search path. For the three layout algorithms, the arithmetic average aspect ratio and the newly proposed weighted aspect ratio are improved within acceptable time for users. To show the effectiveness of the algorithm, the evaluation with the constructed single-layer dataset which with 3 different distributions and 7 different scales and multi-layer real dataset is conducted and then the results are compared with the original algorithm. The guidelines for users to select a suitable layout algorithm and set proper parameters are also provided.

Hierarchical visualization of geographical areal data with spatial attribute association

Geographical areal data usually presents hierarchical structures, and its characteristics vary at different scales. At the higher scales, the visualization of geographical areal data is abstract and the detailed features are easily missed. As a difference, more detailed information is presented at the lower scales while the visual perception of global features is easily disturbed due to the overdrawing of visual elements. As the geographical areal data is visualized at a single scale at the same time, it seems impossible to balance the visual perception of both the global features and detailed characteristics. In this paper, we propose a multi-scale geographical areal data visualization method based on spatial attribute association to enhance the visual perception of both the global features and detailed characteristics. Firstly, the geographical areal data is aggregated into hierarchical clusters based on the spatial similarity. Then, the coefficient of variation is applied to estimate the attribute distribution of each cluster in the hierarchy, and a novel geographical areal data visualization scheme is proposed to adaptively present the multi-scale clusters with lower variation coefficients at the same time. In addition, a rich set of visual interfaces and user-friendly interactions are provided enabling users to specify those clusters of interest at different scales and compare multi-scale visualizations with different hierarchies. Finally, we implement a geographical areal data visualization framework, allowing users to visually explore the global features and detailed characteristics at the same time and get deeper insights into the potential features in the geographical areal data. Case studies and quantitative comparisons based on real-world datasets have been conducted to demonstrate the effectiveness of the proposed multi-scale visualization method for in-depth visual exploration of geographical areal data.

Multi-omics integration in the age of million single-cell data.

An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.

HPV DeepSeq: An Ultra-Fast Method of NGS Data Analysis and Visualization Using Automated Workflows and a Customized Papillomavirus Database in CLC Genomics Workbench.

Next-generation sequencing (NGS) has actualized the human papillomavirus (HPV) virome profiling for in-depth investigation of viral evolution and pathogenesis. However, viral computational analysis remains a bottleneck due to semantic discrepancies between computational tools and curated reference genomes. To address this, we developed and tested automated workflows for HPV taxonomic profiling and visualization using a customized papillomavirus database in the CLC Microbial Genomics Module. HPV genomes from Papilloma Virus Episteme were customized and incorporated into CLC “ready-to-use” workflows for stepwise data processing to include: (1) Taxonomic Analysis, (2) Estimate Alpha/Beta Diversities, and (3) Map Reads to Reference. Low-grade (n = 95) and high-grade (n = 60) Pap smears were tested with ensuing collective runtimes: Taxonomic Analysis (36 min); Alpha/Beta Diversities (5 s); Map Reads (45 min). Tabular output conversion to visualizations entailed 1–2 keystrokes. Biodiversity analysis between low- (LSIL) and high-grade squamous intraepithelial lesions (HSIL) revealed loss of species richness and gain of dominance by HPV-16 in HSIL. Integrating clinically relevant, taxonomized HPV reference genomes within automated workflows proved to be an ultra-fast method of virome profiling. The entire process named “HPV DeepSeq” provides a simple, accurate and practical means of NGS data analysis for a broad range of applications in viral research.

A Survey on OD Data Visualization

<p indent=0mm>OD data is a type of data that provides origin and destination positions, and sparsely describes the movement trajectory of objects. Being the research hotspots in data visualization and geospatial analysis, visual analysis of OD data contributes to discovering group movement patterns, exploring spatial anomalies and hidden relationships, and in-depth analysis of various statistics attributes. It is started from the pair-point structure of OD data in geospatial space, and classical OD data visualization methods and principles are elaborated, such as OD matrix, OD map, OD flow, etc. Then from the perspective of spatial-temporal feature exploration, spatial and spatial-temporal joint dimensions, the visual analysis of OD data is performed, while its application scenarios and enhanced significance of feature expression, perception, extraction and presentation are researched. To solve the problem of visual clutter in large-scale OD data visualization, the visual simplification methods such as filtering, aggregation, and sampling are sorted out in the research methods of visual analysis of OD data. Finally, the applications and specific cases of OD data visual analysis technology are investigated in different domains, the challenging problems in the research of OD data visual analysis are summarized, and the trend of its future development is prospected.

Lipidomics of Environmental Microbial Communities. I: Visualization of Component Distributions Using Untargeted Analysis of High-Resolution Mass Spectrometry Data.

Lipids, as one of the main building blocks of cells, can provide valuable information on microorganisms in the environment. Traditionally, gas or liquid chromatography coupled to mass spectrometry (MS) has been used to analyze environmental lipids. The resulting spectra were then processed through individual peak identification and comparison with previously published mass spectra. Here, we present an untargeted analysis of MS1 spectral data generated by ultra-high-pressure liquid chromatography coupled with high-resolution mass spectrometry of environmental microbial communities. Rather than attempting to relate each mass spectrum to a specific compound, we have treated each mass spectrum as a component, which can be clustered together with other components based on similarity in their abundance depth profiles through the water column. We present this untargeted data visualization method on lipids of suspended particles from the water column of the Black Sea, which included >14,000 components. These components form clusters that correspond with distinct microbial communities driven by the highly stratified water column. The clusters include both known and unknown compounds, predominantly lipids, demonstrating the value of this rapid approach to visualize component distributions and identify novel lipid biomarkers.

Mass spectrometry-based methods for the advanced characterization and structural analysis of lignin: A review.

Lignin is currently one of the most promising biologically derived resources, due to its abundance and application in biofuels, materials and conversion to value aromatic chemicals. The need to better characterize and understand this complex biopolymer has led to the development of many different analytical approaches, several of which involve mass spectrometry and subsequent data analysis. This review surveys the most important analytical methods for lignin involving mass spectrometry, first looking at methods involving gas chromatography, liquid chromatography and then continuing with more contemporary methods such as matrix assisted laser desorption ionization and time-of-flight-secondary ion mass spectrometry. Following that will be techniques that directly ionize lignin mixtures-without chromatographic separation-using softer atmospheric ionization techniques that leave the lignin oligomers intact. Finally, ultra-high resolution mass analyzers such as FT-ICR have enabled lignin analysis without major sample preparation and chromatography steps. Concurrent with an increase in the resolution of mass spectrometers, there have been a wealth of complementary data analyses and visualization methods that have allowed researchers to probe deeper into the "lignome" than ever before. These approaches extract trends such as compound series and even important analytical information about lignin substructures without performing lignin degradation either chemically or during MS analysis. These innovative methods are paving the way for a more comprehensive understanding of this important biopolymer, as we seek more sustainable solutions for our human species' energy and materials needs.

Sustainable Development as a New Component of Corporate Social Responsibility

Purpose is to study the essential basis of the concept of sustainable development as a new component of corporate social responsibility.Methods: the research is based on a systematic approach to the understanding of corporate social responsibility. The analysis of the state and trends of sustainable development was carried out using the retrospective method, comparative and economic-statistical analysis, as well as the method of data visualization, which allowed us to form a holistic view of the essence and receptivity of Russian business to the ideas of sustainable development.Results: the article substantiates the importance of socially responsible business practices in terms of the concept of corporate social responsibility. Today, it is considered as an umbrella concept, the central place in which is given to sustainable development. A historical retrospective of the formation of the term "sustainable development" is carried out. The content of ESG factors used to assess the contribution of companies to achieving the triune outcome of sustainable development is revealed. The identified indicators of commitment of board members to sustainable development issues allowed us to state that most companies are taking measures to integrate sustainable development issues into the overall strategy of companies and issue non-financial statements. This has which generally positively affects the interest of the investment community in such companies. The special requirements for the content of non-financial reports are described, and the priority of the sustainable Development Goals for Russian companies is analyzed. The author draws a conclusion about the reasons for the weak involvement of Russian business in the selection and achievement of sustainable development goals and suggests solutions to promote the concept of sustainable development in the Russian business community.Conclusions and Relevance: the close attention paid to the sustainable development agenda by the world community and business proves the relevance of solving a whole range of environmental and social problems. The integration of sustainable development issues into the overall strategy of companies, covering an increasing number of companies, allows for strategic initiatives in the ideology of sustainable development with a tendency to expand the planning horizon. Taking into account the increased attention of the investment community to business projects based on the principles of sustainable development, the management of companies realized the need to issue non-financial statements with the inclusion of data that meet special requirements for the disclosure of ESG topics. In addition, the company's requirements for transparency of companies' activities and for their accountability on the results of their impact on the environmental and social situation are supplemented by new requirements in the field of disclosure of the quality of corporate governance. In these circumstances, the role of the top management of companies in promoting the ideology of sustainable development is undeniable. At the same time, the majority of Russian companies do not yet demonstrate a sufficient understanding of the connection of global environmental and social problems with their business, which is clearly reflected in the choice of sustainable development goals.

Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels.

Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.

Soil Organic Matter Characterization by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR MS): A Critical Review of Sample Preparation, Analysis, and Data Interpretation.

The biogeochemical cycling of soil organic matter (SOM) plays a central role in regulating soil health, water quality, carbon storage, and greenhouse gas emissions. Thus, many studies have been conducted to reveal how anthropogenic and climate variables affect carbon sequestration and nutrient cycling. Among the analytical techniques used to better understand the speciation and transformation of SOM, Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) is the only technique that has sufficient mass resolving power to separate and accurately assign elemental compositions to individual SOM molecules. The global increase in the application of FTICR MS to address SOM complexity has highlighted the many challenges and opportunities associated with SOM sample preparation, FTICR MS analysis, and mass spectral interpretation. Here, we provide a critical review of recent strategies for SOM characterization by FTICR MS with emphasis on SOM sample collection, preparation, analysis, and data interpretation. Data processing and visualization methods are presented with suggested workflows that detail the considerations needed for the application of molecular information derived from FTICR MS. Finally, we highlight current research gaps, biases, and future directions needed to improve our understanding of organic matter chemistry and cycling within terrestrial ecosystems.

Issue Highlights-July 2021.

Issue Highlights-July 2021.

Revealing receiver bias in the communication of mapped biodiversity patterns

Researchers often communicate knowledge about biodiversity, especially information about where species are likely to be found, through maps. However, readers do not necessarily interpret such maps in the way the authors intend. We assessed undergraduate students' interpretations of mapped biodiversity data with a mixed-method approach: a survey instrument was developed using writing and focus groups, then delivered to students enrolled in introductory biology courses at the University of Florida in the United States. Surveyed participants (N = 195) were presented with sets of maps for the Palamedes Swallowtail butterfly, Papilio palamedes, with three data visualization methods: point occurrences, expert-assessed range, and correlative distribution model results (distributional models were shown at high and low resolutions). Map interpretations were assessed by asking participants to rate the likelihood of finding a Palamedes Swallowtail at various point on each map and how confident they were in the information the map presented. They were also asked which map type they would most likely use to find a Palamedes Swallowtail. For distributional model maps, the effect of resolution on interpretation was assessed by asking participants to rate the perceived accuracy of each map, as well as their confidence in the data being presented. Participants most trusted in data provided via point maps compared to range and distributional model maps, and trusted point maps most among the three map types. For distribution maps, participants felt more certain in data presented to them via higher-resolution maps and interpreted them as being more accurate. This preference was especially pronounced for participants studying Science, Technology, Engineering, and Mathematics (STEM) fields compared to their non-STEM peers. Our findings suggest biodiversity researchers need to carefully consider symbol choice and resolution when transmitting information about species distributions.

Recognition of Operating Characteristics of Heavy Trucks Based on the Identification of GPS Trajectory Stay Points

As an emerging data source, feature identification based on GPS trajectory data has become a hot issue in the field of data mining and freight management. A method of trajectory data extraction, classification, and visualization based on stay points was proposed in this paper to analyze the operation characteristics of heavy trucks from the perspectives of intracity transportation, intraprovincial transportation, and interprovincial transportation. The GPS trajectory data of heavy trucks in Sichuan Province in March 2019 were taken as an example to analyze the operation characteristics. The results show that the heavy trucks in Sichuan Province are mainly transported within the province, and the freight efficiency is slightly better than the average level of the national freight trucks in the same period, failing to give full play to the advantages of long transport distance. The manufacturing industry is the main service object of heavy trucks, and the vehicles engaged in transportation within the province are more dependent on logistics enterprises and their ancillary facilities. The north-south longitudinal line and east-west horizontal line are the main interprovincial transport channels, and the provincial and municipal transport is mainly concentrated in some urban trunk lines, ring lines, and express routes. The proposed technical method can describe the operating characteristics of freight trucks from the perspective of microscopic and service market, not only to guide the layout of highway freight yards, logistics parks, and logistics hubs and the determination of service functions but also to provide a reference basis for freight management-related departments and drivers to formulate transportation plans and establish freight information platforms to improve freight efficiency and safety.

Analysis of the fulfillment of legal standards regulating the provision of subsidies for legal entities

The purpose of the article is to develop proposals for adapting the most relevant Annotation. The purpose of the article is to identify discrepancies in regulatory legal documents and develop proposals for systematizing the legal framework governing the provision of subsidies to legal entities. The article analyzes the current regulatory legal documents governing the provision of subsidies to legal entities, in particular, the rules for granting subsidies, their compliance with the requirements provided for by the RF Government Decree № 1492, state programs and subprograms within which subsidies are distributed in specific areas of economic development. The authors are provided with facts confirming the need to correct and clarify legal documents, justify. When writing the article, the methods of comparative analysis, induction and deduction, graphical method of data visualization were used. The practical significance of the article lies in the possibility of clarifying regulatory legal documents in accordance with the proposals formulated in the article.

GgtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data.

We present the ggtreeExtra package for visualizing heterogeneous data with a phylogenetic tree in a circular or rectangular layout (https://www.bioconductor.org/packages/ggtreeExtra). The package supports more data types and visualization methods than other tools. It supports using the grammar of graphics syntax to present data on a tree with richly annotated layers and allows evolutionary statistics inferred by commonly used software to be integrated and visualized with external data. GgtreeExtra is a universal tool for tree data visualization. It extends the applications of the phylogenetic tree in different disciplines by making more domain-specific data to be available to visualize and interpret in the evolutionary context.

Automatic Improvement of Continuous Colormaps in Euclidean Colorspaces

AbstractColormapping is one of the simplest and most widely used data visualization methods within and outside the visualization community. Uniformity, order, discriminative power, and smoothness of continuous colormaps are the most important criteria for evaluating and potentially improving colormaps. We present a local and a global automatic optimization algorithm in Euclidean color spaces for each of these design rules in this work. As a foundation for our optimization algorithms, we used the CCC‐Tool colormap specification (CMS); each algorithm has been implemented in this tool. In addition to synthetic examples that demonstrate each method's effect, we show the outcome of some of the methods applied to a typhoon simulation.

Applications of Smart Glasses in Applied Sciences: A Systematic Review

The aim of this study is to review academic papers on the applications of smart glasses. Among 82 surveyed papers, 57 were selected through filtering. The papers were published from January 2014 to October 2020. Four research questions were set up using the systematic review method, and conclusions were drawn focusing on the research trends by year and application fields; product and operating system; sensors depending on the application purpose; and data visualization, processing, and transfer methods. It was found that the most popular commercial smart glass products are Android-based Google products. In addition, smart glasses are most often used in the healthcare field, particularly for clinical and surgical assistance or for assisting mentally or physically disabled persons. For visual data transfer, 90% of the studies conducted used a camera sensor. Smart glasses have mainly been used to visualize data based on augmented reality, in contrast with the use of mixed reality. The results of this review indicate that research related to smart glasses is steadily increasing, and technological research into the development of smart glasses is being actively conducted.

Phasor-based image segmentation: machine learning clustering techniques.

The phasor approach is a well-established method for data visualization and image analysis in spectral and lifetime fluorescence microscopy. Nevertheless, it is typically applied in a user-dependent manner by manually selecting regions of interest on the phasor space to find distinct regions in the fluorescence images. In this paper we present our work on using machine learning clustering techniques to establish an unsupervised and automatic method that can be used for identifying populations of fluorescent species in spectral and lifetime imaging. We demonstrate our method using both synthetic data, created by sampling photon arrival times and plotting the distributions on the phasor plot, and real live cells samples, by staining cellular organelles with a selection of commercial probes.

Myology of the Reptilia

Sauropsid vertebrates (lepidosaurs, turtles, crocodilians and birds) have evolved a diversity of head shapes and feeding behaviors during their history. A key to understanding this great radiation of reptiles is the physiology of the jaw musculature which powers the feeding apparatus. However we still know little about jaw muscle mechanics within lineages of reptiles or how this complicated musculoskeletal system has evolved to employ a variety of behaviors. New imaging and computational methods are now enabling an extraordinary view into the 3D anatomy and biomechanics of reptiles and other vertebrates. Here we illustrate several approaches to analyzing jaw muscle morphology and architecture using contrast imaging, 3D fiber tracking, biomechanical analysis, and data visualization methods that offer enormous potential for exploring the anatomy, function and evolution of jaw muscles. We first illustrate basic workflow of 3D jaw muscle visualization, morphometrics and interpretation using crocodilian jaw muscle anatomy. Second, we show how homologous jaw muscle bellies evolve among lineages of different reptiles and birds to elicit different functional demands. Third, we show how the 3D architecture of hard-to-dissect, small, deep protractor muscles correlate with different types of cranial kinesis among a sample of lizards and birds. These new imaging and analytical approaches offer incredible potential for the quantification of soft tissue morphology like muscle architecture and their applications to comparative biomechanics, ecomorphology, and evolutionary biology should prove remarkable.