Data Visualization Approach Research Articles

Data visualization in the latest convergent media in Ukraine

The subject of the study is data visualization as a means of increasing the effectiveness of the media’s informational impact on readers. In the conditions of global digitalization, there was a general blurring of media information distribution channels, leading to convergent media’s emergence. In Ukraine, there is significant potential for the development of convergent media. Therefore, creating decent and competitive content becomes extremely important for the newest mass media. One of the defining components of quality media content is its comprehensibility, recognizability, and appropriateness of the visual objects used. Successful data visualization makes the content more accessible and understandable for the audience, attracts their attention, and increases the popularity of the materials. Thus, the relevance is determined by the need to define the prospects for the use of infographics in Ukrainian convergent media. That is why the aim of the study is to determine the characteristics of visual elements used in the latest convergent media in Ukraine and identify the advantages and challenges associated with their use. To achieve this goal, content analysis and comparative methods were used. The study was based on a sample of eight leading media in Ukraine: Suspilne, Gromadske, Liga, Ukrainska Pravda, Ukrinform, Radio Svoboda, Dzerkalo Tyzhnia, HB, and Babel, which were selected based on the results of a two-stage monitoring of the Institute of Mass Information. The study’s results indicate the diversity of data visualization approaches in Ukraine’s convergent media. Some media, in particular Suspilne and Hromadske, use photos and videos accompanying the textual information, while others, such as Ukrainska Pravda and Ukrinform, pay more attention to the visual design of materials and use visualization not just as a supplement to the text, but build the visualization as a story. The study also showed a general tendency to use geographic maps to present information, which may be related to the war in the country and the need to visualize the enemy’s actions. The study results can be helpful for media resources and journalists who want to improve the quality of their materials. In addition, the results have scientific value for future studies of trends in data visualization by convergent media.

Examining readmission rates of congestive heart failure patients in the United States between 2010 and 2017: Does length of stay matter?

ABSTRACT Aim We analyze 30-day hospital readmission trends of Congestive Heart Failure (CHF) patients by LOS for different sub-groups, address the potential endogeneity bias in LOS, and examine the influence of patient’s discharge location on readmission risk using nationwide multi-year Big Data. Methods Using the 2010–2017 National Readmissions Database (NRD), we estimate an ordinary least squares (OLS) regression and an instrumental variable (IV) model. We use a data visualization approach to show trends by various characteristics and address the potential endogeneity bias in LOS using the IV model. Results Readmitted CHF patients had longer LOS (5.37 days versus 4.80 days) and more medical comorbidities (4.0 versus 3.6), compared to non-readmitted patients. The readmission rates vary depending on patients’ primary payee with Medicaid/Medicare patients exhibiting the highest readmission rates. A patient’s hospital discharge type and hospital ownership also influence the probability of readmission. Conclusions LOS and CCI provide meaningful information in predicting a 30-day hospital readmission risk, but more importantly longer LOS reduces readmission risk. Studying these factors could provide better insights to all stakeholders and allow the healthcare industry to develop effective strategies to reduce readmission rates while improving patients’ quality of care during their hospitalization.

Learning Design versus Instructional Design: A Bibliometric Study through Data Visualization Approaches

The terms instructional design and learning design have been widely used to describe a discipline concerned with improving the process of teaching and learning. However, though both terms are interchangeably used and share a common vision, both terms are used to encompass different aspects of the learning and teaching. In order to better understand the evolution, map intersections and differences of these terms, and identify emerging themes, using text mining and social network analysis approaches, a triangulated bibliometric study was carried out to analyze a total of 514 publications (326 for instructional design and 157 for learning design) indexed in the Scopus database using text mining and social network analysis. Our first round of analysis revealed four broad themes for instructional design: Theory-driven approaches; technology-informed designs; instructional design for higher education; and assessment and evaluation. A second round of analysis for learning design identified four major themes: Design thinking and user experience-driven approaches; online learning informed designs and online environments; analytical approaches for assessment and evaluation; and engagement-based learning design. The study concludes that while instructional design is about developing, assessing, and evaluating instruction, learning design is more about learner engagement and experience, which can be assessed and enhanced by analytical and technological approaches.

An Exploratory Data Analysis of Breast Cancer Features in South of Libya

Exploratory data analysis is a data visualization approach used to extract knowledge from raw data. This approach can be applied to medical data to improve healthcare providers services. In recent years, breast cancer has become more common in women and requires effective procedures to detect it in the early stage. In this context, breast cancer patients' data were collected from the Sebha oncology center through their routine blood tests. The exploratory data analysis technique is used in this study to better analyze the patients' markers. The analysis aims to discover prominent bio and tumor markers that can assist in determining whether a tumor is benign or malignant. Several statistical and visualizations methods are used. The results show that the most effective markers that may be used as cancer predictors are: Cancer Antigen-15.3, Carcinoma Embryonic Antigen, White Blood Cells, Blood platelets, and Albumin. These findings are consistent with the findings of Sebha oncology center specialists. which may eventually aid in their cancer diagnosis.

Semantic-driven Geospatial Data Visualization Approach for an Agricultural Use Case: Apple-growing in South Tyrol, Italy

Semantic-driven Geospatial Data Visualization Approach for an Agricultural Use Case: Apple-growing in South Tyrol, Italy

Adaptive big data maturity model using latent class analysis for small and medium businesses in Thailand

Big data analytics (BDA) is widely adopted in large enterprises. However, very few small- and medium-sized enterprises (SMEs) have adopted BDA because they lack the relevant knowledge, which makes BDA development expensive and unsuitable. A big data maturity model (BDMM) is a tool for assessing the stage for using big data in a company, and it acts as a guide for improvement. However, most BDMMs are designed for large enterprises using rule-based scoring, which is static over time. Developing a suitable BDMM for SMEs is a challenging task for professionals in terms of acquiring small-scale expertise owing to the lack of case studies for verifying the maturity level. This study proposes a new BDMM for Thai SMEs and a new methodology for developing a dynamic model using latent class analysis (LCA), which explains the behaviour of each latent class and provides non-rule-based scoring. We define four types of capabilities in SMEs: organizational and attitude factors, information technology, technology, and people readiness. Data are collected from 135 SMEs in Thailand. We introduce a methodology for developing multiple building stages of the BDMM. Further, we experiment with several clusters suitable for SMEs using statistic-based and data visualization approaches. The proposed BDMM is validated via a secondary evaluation of 11 firms, nine months after the initial evaluation. Further, we introduce a web-based application for respondents to obtain their firm’s assessment results. The visualization-based result helps the respondents compare their business with other companies at the same or higher maturity level. In summary, SMEs can use the proposed BDMM to plan for continuous self-improvement and thus optimize their business using BDA to maximize its value to the organization.

Deep-Learning Supervised Snapshot Compressive Imaging Enabled by an End-to-End Adaptive Neural Network

Snapshot compressive imaging (SCI) is an advanced approach for single-shot high-dimensional data visualization. Deep learning is popularly used to improve SCI's performance. However, most existing methods are merely used as a replacement for analytical-modeling-based image reconstruction. Moreover, these models cling to the conventional random coded apertures and often presume a linear shearing operation. To overcome these limitations, we develop a new end-to-end convolutional neural network, termed deep high-dimensional adaptive net (D-HAN) that offers multi-faceted supervision to SCI by optimizing the coded aperture, sensing the shearing operation, and reconstructing three-dimensional datacubes. The D-HAN is implemented in two representative SCI systems for ultrahigh-speed imaging and hyperspectral imaging. The D-HAN is envisioned to benefit SCI in system design, image reconstruction, and performance evaluation.

High‐resolution real‐time multipurpose tunnel surface 3D rendering

AbstractTunnel surveys making use of photogrammetric three‐dimensional (3D) tunnel reconstruction reach resolutions in the millimeter range. Classical big data visualization approaches display point clouds only, neglecting this considerable resolution difference between structure and texture. The article suggests a data structure that separates structural and textural resolution by a regular grid on the unwrapped design surface for 3D, combined with a UV mapping technique as regularly used in computer graphics. For real‐time rendering of huge multiscale data sets, the result of photogrammetric commercial‐off‐the‐shelf reconstructions is transformed into a proprietary hierarchical data structure. It facilitates to only load currently relevant parts of the tunnel surface from the hard drive, and only upload and render currently adequate levels‐of‐detail onto the graphics card for seamless exploration of high‐resolution geometric and image 3D tunnel data of arbitrary length. The solution allows for smooth interactive analysis and annotation such as crack identification and mapping, inventory, deformation assessment, and dimensional measurements. Aspects of data generation are addressed and information is given about the data structure, showing examples from entire tunnel 3D representations to demonstrate the smooth behaviour of the real‐time rendering of huge data volumes in various scales on standard graphics hardware.

A scalable approach for smart city data platform: Support of real-time processing and data sharing

The concept of a smart city comes with the need to support a data platform that can gather, process and export the data of millions of sensors, coming from different sources, with information in different formats, in a scalable approach for real-time and historical data visualization, processing, and actuation in the city.This paper proposes a Data Platform for the Aveiro Tech City Living Lab, to gather, process, visualize and actuate on mobility, environmental and network data. The architecture of the platform provides a real open platform that is accessible for third-parties to collect data and to experiment their own solutions, through a secure and open data platform at their disposal. The platform was also designed to be scaled and modified when necessary, being composed by distributed solutions across the existent modules. The results with respect to the amount of data gathered, the ingestion and query performance comparison between the persistence solutions considered, and examples of data, show how this platform can be used to efficiently develop new applications and use both real-time and historical data for future predictions and actuations in a smart city.

Virtual-assessment performance hampered by slow adaptation to tech environment

ABSTRACT The current investigation reflects on the insufficient performance of students in virtual exams in the era of transformation of the physical mode of education to digital one due to the passive adaptation to the technical drive of the virtual environment, the tenuous command of platform’s technical language, especially in mathematics and physics exams, and the anxiety of coping with superfluous difficulties of a non-traditional exam framework. R programming and data visualization approach have been employed to highlight the seriousness of these problems and their role in causing a plausible decline in students’ exam outcome. This study also features some crucial developments that could be undertaken to battle these issues and to enhance the virtual exam performance in the future.

Industrial process data visualization based on a deep enhanced t-distributed stochastic neighbor embedding neural network

PurposeThe purpose of this paper is to propose a approach for data visualization and industrial process monitoring.Design/methodology/approachA deep enhanced t-distributed stochastic neighbor embedding (DESNE) neural network is proposed for data visualization and process monitoring. The DESNE is composed of two deep neural networks: stacked variant auto-encoder (SVAE) and a deep label-guided t-stochastic neighbor embedding (DLSNE) neural network. In the DESNE network, SVAE extracts informative features of the raw data set, and then DLSNE projects the extracted features to a two dimensional graph.FindingsThe proposed DESNE is verified on the Tennessee Eastman process and a real data set of blade icing of wind turbines. The results indicate that DESNE outperforms some visualization methods in process monitoring.Originality/valueThis paper has significant originality. A stacked variant auto-encoder is proposed for feature extraction. The stacked variant auto-encoder can improve the separation among classes. A deep label-guided t-SNE is proposed for visualization. A novel visualization-based process monitoring method is proposed.

VascuViz: a multimodality and multiscale imaging and visualization pipeline for vascular systems biology.

Despite advances in imaging, image-based vascular systems biology has remained challenging because blood vessel data is often available only from a single modality or at a given spatial scale, and cross-modality data are difficult to integrate. Therefore, there is an exigent need for a multimodality pipeline that enables ex vivo vascular imaging with MRI, CT and optical microscopy of the same sample, while permitting imaging with complementary contrast mechanisms from the whole-organ to endothelial cell spatial scales. To achieve this, we developed ‘VascuViz’ – an easy-to-use method for simultaneous 3D imaging and visualization of the vascular microenvironment using MRI, CT and optical microscopy in the same intact, unsectioned tissue. The VascuViz workflow permits multimodal imaging with a single labeling step using commercial reagents and is compatible with diverse tissue types and protocols. VascuViz’s interdisciplinary utility in conjunction with novel data visualization approaches opens up new vistas in image-based vascular systems biology.

BIG URBAN DATA VISUALIZATION APPROACHES WITHIN THE SMART CITY: GIS-BASED OPEN-SOURCE DASHBOARD EXAMPLE

Abstract. The change and rapid development in information and communication technologies in recent years has caused increasing the data sources and the volume of data. With the smart city concept, big and complex data collected from the internet of things (IoT), smart sensors, and wearable technologies has been introduced into our lives. Such big data can provide various opportunities and possibilities in urban management, however, to obtain meaningful information from big volume complex data is challenging. Visual analytics techniques provide an effective way for extracting significant information from big data. With the integration of geographic information systems (GIS) and its ability on geographic analysis, big data produced by the IoT and sensors can be analysed and represented in real time via city dashboards. In this study, literature research about big data visualization approaches within the smart cities is examined. Dashboard examples with the integration of GIS is examined and an open-source GIS-based dashboard example is designed in Apache Superset. The relationship between air quality and traffic density is demonstrated with different visualization methods.

Insights from systematic literature review of supply chain resilience and disruption

PurposeThe study of supply chain disruption (SCD) and supply chain resilience (SCR) remains to be studied deeply in the field of business management. The purpose of this paper is to showcase a framework of SCR strategies to reduce the adverse effects of SCD using systematic literature review and data visualization.Design/methodology/approachUsing a systematic literature review approach, the paper explores the concepts of supply chain disruption and supply chain resilience. Through rigorous systematic screening, authors studied papers on both the concepts and have proposed a framework for the same. The authors also have used data visualization and network diagram approaches for better understanding of the topic.FindingsThe systematic literature review of both the concepts brings out some exciting results which give a new direction to supply chain field. The outcome of this research also outlines numerous future research direction, which will be useful for the research community.Practical implicationsThe numerous strategies of SCR should be implemented by manufacturing as well as a service organization. The framework reported in this research help academician and practitioners to understand SCR and to easily overcome any level of disruption. Supply chain managers must also formulate strategies accordingly and make plans to continually expand the system.Originality/valueThis research is the first such attempt to showcase a formal systematic framework and co-occurrence networks as well as overlay networks of SCR and SCD.

Between Stress and Response: Function and Localization of Mechanosensitive Ca2+ Channels in Herbaceous and Perennial Plants.

Over the past three decades, how plants sense and respond to mechanical stress has become a flourishing field of research. The pivotal role of mechanosensing in organogenesis and acclimation was demonstrated in various plants, and links are emerging between gene regulatory networks and physical forces exerted on tissues. However, how plant cells convert physical signals into chemical signals remains unclear. Numerous studies have focused on the role played by mechanosensitive (MS) calcium ion channels MCA, Piezo and OSCA. To complement these data, we combined data mining and visualization approaches to compare the tissue-specific expression of these genes, taking advantage of recent single-cell RNA-sequencing data obtained in the root apex and the stem of Arabidopsis and the Populus stem. These analyses raise questions about the relationships between the localization of MS channels and the localization of stress and responses. Such tissue-specific expression studies could help to elucidate the functions of MS channels. Finally, we stress the need for a better understanding of such mechanisms in trees, which are facing mechanical challenges of much higher magnitudes and over much longer time scales than herbaceous plants, and we mention practical applications of plant responsiveness to mechanical stress in agriculture and forestry.

The Role of Technology in COVID-19 Pandemic Management and Its Financial Impact

A pandemic caused by the novel coronavirus (COVID-19) is causing an unprecedented situation for the world’s health services. Health, local communities, and government are adversely affected by the COVID-19 pandemic. Moreover, on January 21, 2020, the WHO Emergency Committee declared a global health emergency because of increasing numbers of COVID-19 case notifications from countries overseas. While the pandemic caused grave damage to socioeconomic phenomena and multiple global phenomena such as commodity prices, remittances, trade, tourism, significant job loss, and drastically lower wages, the pandemic has also negatively affected multiple global phenomena. The advent of technology has spurred significant changes in many aspects of our lives and improved the exchange of information, the presentation of data, and the management of medical resources through telemedicine. In this article, we present a mathematical data visualization approach for analyzing pandemic data behaviors, such as exponential growth and deviations using the data related to COVID-19 events. Furthermore, this article will include studies on the implications of the COVID-19 pandemic on finance sector.

A data-scattering-preserving adaptive self-organizing map

We propose a novel improved adaptive version of the Self-Organizing Map (SOM). Our enhancement of SOM adapts to the input data scattering, in this way, preserving the principal input data structure. In order to capture and extract the data scattering from the input dataset, we propose carrying out a preliminary input data clustering. As a result of forming the clusters in the input data space, the inner-cluster variances are calculated. The inner-cluster variances are subsequently employed as the basis for the determination of the neighborhood widths of the SOM’s Best Matching Units (BMUs). The method, introduced in this paper, has been a subject to the empirical evaluation carried out using the three real-world datasets of different size, dimensionality, and data type, representing three various experimental fields. Our proposal has been assessed on the basis of a comparison with seven reference data visualization approaches. The introduced method appeared to be superior over the remaining investigated data visualization techniques, and consequently, its effectiveness, usefulness, and accuracy have been verified and confirmed.

Drawing the scientific landscape of 3D Food Printing. Maps and interpretation of the global information in the first 13 years of detailed experiments, from 2007 to 2020

3D Food Printing is a hot field of research in which many efforts are concentrating to unleash its potential for renewal. To facilitate this process, we have drawn the global scientific landscape in these first 13 years of experiments by using bibliometric and data visualization approaches. We find a total of 170 documents between 2007 and 2020. China and Australia are the most productive countries followed by Italy. On a total of 582 co-authors, not more than 10 researchers are collaborating out of their research group/institution. This is a weakness that urges sharing information and experiences. Also, the networks of the keywords have shown some hidden opportunities. The interrelationship between digital design, microstructure and personalized food need to be reinforced to create unprecedented sensory perceptions and alleviate the mastication and swallowing problems of vulnerable consumers. Also, there is a shortage of papers on the printing of protein-rich inks. Industrial relevanceThe paper critically analyzes the global production of scientific experiments in 3D food printing. The results discovered that the level of collaborative researches is weak while the involvement of the industrial sector, being a crucial process to unleash the great ambitions of personalized food manufacturing, is urgent. What emerged is the need to study parallel deposition methodologies such as selective laser sintering (SLS) or hot-air sintering (HAS) that would open for fast printing and the use of highly stable food powders. Other opportunities to make feasible the application of 3DFP could be the use of protein-rich food inks and to make closer the topics of digital design and personalized consumers requirements.

An exploratory approach for urban data visualization and spatial analysis with a game engine

The extensive use of Information and Communication Technologies and the consequent unprecedented generation of data have radically transformed the way we understand cities today. The vision of smart cities considers technology as an enabling force for the emergence of new forms of intelligence and collaboration, enhancing, thus, the problem-solving capacity of the city. Despite the wide range of applications aiming to improve urban systems and city governance, urban planning processes are rarely informed by online platforms and data generated by them lack comprehensive data visualization approaches. This research introduces an exploratory approach to exploit urban data through interactive visualization and game design, as a way to facilitate the access and understanding of such data. A novel methodology is proposed, leveraging on spatial data as an input source which drives the generation of three-dimensional environments and interactive applications supported by game engines. More specifically, this research builds upon existing tools and methods for geoprocessing and spatial analysis and embeds them in a 3D environment that employs game design elements. Three indicative visualization scenarios are designed, developed, and implemented to showcase the dynamics and flexibility of the proposed methodology, based on the registry of an urban reporting application in Thessaloniki.

Raincloud plots: a multi-platform tool for robust data visualization.

Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired 'inference at a glance' nature of barplots and other similar visualization devices. These "raincloud plots" can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.