Use Of Data Visualization Research Articles

Data visualization in Indian print media: a comparative study of English and Hindi newspapers

The advancing technology is affecting every aspect of life and journalism is also not untouched by this. Due to digitalization, huge amount of data is being generated and the continuous advancement of computer science has made it possible to extract meaningful information by storing and analysing this huge data. The term “data journalism” has become quite popular over the last decade. Analysing data sets, extracting newsworthy information from it and passing it on to the public is data journalism. Data visualization also has a very important place in this whole process. Data visualization is used to communicate information extracted from the data to the users in a clear, interesting and engaging way. The amount of data-based content has started increasing in the news media as well, so the importance of data visualization has also increased. The use of data visualization improves readers’ reading experience and also helps to better understand the data-based content. This preliminary study focuses on the use of data visualizations by English and Hindi newspapers in India. In this research, a comparative study of various aspects of the use of data visualizations in English and Hindi newspapers has been done. Content analysis with quantitative approach has been employed as the research method. This study reveals that there is a big difference in every aspect of the use of data visualizations in English and Hindi newspapers. English newspaper used data visualizations in a better way than their Hindi counterpart.

Understanding the Role of Visualizations on Decision Making: A Study on Working Memory

The use of data visualization is increasing; however, there is little empirical explanation for how it supports users. Our goal in this paper is to deepen our understanding of the role of interactive visualizations in a particular context of decision making. Specifically, we attempt to understand the role of the working memory system, which is a concept to understand the mechanism of the processing and temporary storage of information in variety of cognitive tasks. We compared two interfaces, SimulSort and its non-visual counterpart Typical Sorting, with a multi-attribute decision-making problem. Because decision outcomes are known to be affected by the limitations of a person’s working memory, we conducted a crowdsourcing-based user study using SimulSort to understand how working memory, especially the phonological loop, can benefit from the using visualizations. We examined the impact on working memory with a well known dual-task methodology by designing a concurrent task to tap into the main decision-making task. The experiment was conducted with a total of 137 participants and an ordered logistic regression using a proportional odds model was applied to analyze the decision quality. The results supported the hypothesis that when using SimulSort, participants required less working memory than they required with Typical Sorting to accomplish the multi-attribute decision-making task even though SimulSort outperformed Typical Sorting in terms of decision quality. We also provide methodologies to conduct working memory studies by implementing an articulatory suppression task on crowdsourcing platforms in which experimenters have less control over the participants.

Quantitative systematic review: Methods used for the in vivo measurement of body composition in pregnancy.

Because of the increased overall prevalence of pre-pregnancy obesity among racial-ethnic groups, we conducted a review of published methods for body composition measurement during pregnancy considering at present there is no consensus on the best practices and type of study design that researchers should use for this purpose. Quantitative systematic review. PubMed, EMBASE, Scopus, Web of Science, and Virtual Library of Health. Search dates from 1997-2016. Search of articles indexed in selected databases from 1997-2016. Studies were published in English, Spanish, and Portuguese. Graphs were carried out using data visualization software. From the 112 included studies, 70 were prospective cohorts, 30 cross-sectional studies, 10 randomized controlled trial, and two retrospective studies. Cross-sectional studies and randomized controlled trial depicted a positive correlation with significant trend. Although several methods for body composition measurement exist, only bioelectrical impedance analysis, displacement plethysmography, and displacement plethysmography show a significant growing trend. Use of data visualization allows understanding various associations among categorical variables, with a graphical display of their multidimensional behaviour. Public and private health-care institution evaluating pregnancy women. Health-care personnel, including nursing professional, dealing with measurements of body composition during pregnancy will find reading this manuscript beneficial.

Data Visualization Classification Using Simple Convolutional Neural Network Model

Data visualization is developed from the need to display a vast quantity of information more transparently. Data visualization often incorporates important information that is not listed anywhere in the document and enables the reader to discover significant data and save it in longer-term memory. On the other hand, Internet search engines have difficulty processing data visualization and connecting visualization and the request submitted by the user. With the use of data visualization, all blind individuals and individuals with impaired vision are left out. This article utilizes machine learning to classify data visualizations into 10 classes. Tested model is trained four times on the dataset which is preprocessed through four stages. Achieved accuracy of 89 % is comparable to other methods’ results. It is showed that image processing can impact results, i.e. increasing or decreasing level of details in image impacts on average classification accuracy significantly.

Use of Visualization Tools to Improve Financial Knowledge: An Experimental Approach

This study examined the use of data visualization to improve financial literacy in adults. Using financial knowledge questions as test items this study used an experimental approach. Poisson regression was conducted on responses from 1,797 participants to an online survey via SurveyMonkey. Approximately one-third of respondents were assigned to a text-only group explaining a financial concept, one-third to a group that received a visualization plus text explanation of the concept, and one-third to a control group with no intervention. The findings suggest visualization of data assist in assimilation of financial knowledge compared to no intervention and to text interventions. The study has implications for financial education programs attempting to implement interventions in order to improve financial knowledge.

Role of absence in academic success: an analysis using visualization tools

Understanding the academic performance of students in colleges is an essential topic in Education research field. Educators, program coordinators and professors are interested in understanding how students are learning specific topics, how specific topics may influence the learning of other topics, how students’ grades/attendances in each course may represent important indicators to measure their performance, among other tasks. The use of data visualization and analytics is expanding in education institutions to perform a variety of tasks related to data processing and gaining into data-informed insights. In this paper, we present a visual analytic tool that combines data visualization and machine learning techniques to perform some visual analysis of students’ data from program courses. Two educational data collections were used to guide the creation of i) predictive models employing a variety of well known machine learning strategies, attempting to predict students’ future grade based on grade and attendance previous semesters and ii) a set interactive layouts that highlight the relationship between grades and attendance, also including additional variables such as gender, parents education level, among others. We performed several experiments, also using these data collections, to evaluate the layouts ability of highlighting interesting patterns, and we obtained promising results, demonstrating that such analysis may help the education experts to understand deficiencies on course structures.

Проектирование инструментов учебной аналитики с использованием визуализации на основе поведенческих, мотивационных и социально-сетевых данных

The article presents a description of the approach to the use of data visualization in various educational Analytics tools when building University courses. In addition to the analysis of educational behavior, socio-psychological approaches, including the theory of expectations and social values, and the social network approach, are separately considered as prospects for analysis. An example of designing training Analytics using modern data analysis and visualization tools is analyzed.

박물관에서 나타나는 데이터시각화 활용 사례 연구

박물관에서 나타나는 데이터시각화 활용 사례 연구

US Centers for Disease Control and Prevention's HEADS UP Branding and Evaluation Process.

For 15 years, the US Centers for Disease Control and Prevention's (CDC) HEADS UP education campaign has focused on increasing awareness of concussion among children to reduce adverse outcomes from this injury. To keep pace with the rapid expansion in media coverage, research, state laws and education efforts on concussion, the CDC embarked on a comprehensive evaluation effort to assess the campaign's brand. The CDC conducted a multi-step process to evaluate HEADS UP. This included surveying partner organisations and focus groups targeting key audiences. The analysis demonstrated that most (70%) partner organisations have used the materials for 4 years or longer and more than half (51%) found the materials very useful or extremely useful. Focus groups with the campaign's target audiences yielded insights and opportunities to refresh the CDC HEADS UP brand. The need for more compelling images, prevention facts and use of eye-catching colours was signaled by all the campaign's target audiences. Updates focused on the use of data visualisation, more emotionally compelling images, and more engaging colours. The CDC also created a new tagline to focus on concussion prevention that encourages children and young people to think about protecting their brains from concussion so they can have a safer future. This paper describes the process used to update the branding of an educational health promotion campaign in order to meet the needs of key audiences. Findings can help others working to keep their educational campaigns relevant and aligned with an expanding and changing public health problems.

Using data visualization for ranking alternatives with partial information and interactive tradeoff elicitation

This paper puts forward the use of data visualization in a new method for solving multiple criteria decision-making problems for ranking of alternatives, based on Flexible and Interactive Tradeoff (FITradeoff) elicitation. This approach uses partial information about the decision maker’s preferences, based on a structured process for eliciting scale constants (or weights) within the scope of multi-attribute value theory. Different from most of the partial information methods present in the literature, our approach is based on the traditional Tradeoff, which is the most axiomatically founded procedure for elicitation of criteria weights. Pairwise dominance concept is incorporated into the mathematical model of FITradeoff in such way that, at each interaction with the decision maker, a partial—or complete—order of the alternatives can be achieved, based on a two-step algorithm proposed. The method is operated by means of a decision support system, which provides graphical visualization of the ranking at each interaction, in order to support the decision-making process with a simpler visualization of dominance relations. The proposed method is applied for solving a practical case of supplier selection in a food industry.

How Operation Fistula uses data visualization to help women with obstetric fistula

Aims: To enable all parts of Operation Fistula to make data-driven decisions in their work and to use data visualisation to simplify complex processes. Moreover, to effectively use data visualisation to educate the public about Obstetric Fistula and other issues of gender inequality.
 Methods: An experienced visual design analyst was hired full time to lead the analytics setup at the organisation and provide training for the team in the use of Tableau Desktop software.
 Results: Using a visual representation of data has spread information about Obstetric Fistula in an entirely new way to new audiences. One of our analyses has received over 10,000 views at this time. The use of data visualisation has created powerful partnerships for the organisation that have allowed Operation Fistula to raise significant funds and improve visibility of the cause. Staff members are now empowered to use data in their everyday work to make data-driven decisions. 
 Conclusions: In a world that is driven by data, it is important that evidence-driven actions are implemented not only in the treatment of obstetric fistula but that we make data central to the everyday operation of our workplaces. Data visualisation is a powerful technique to make unfamiliar topics accessible, gain clarity on complex processes, and garner interest from otherwise unengaged actors. With new developments in commercial tools, data visualisation has become more accessible to every level of the business and is no longer exclusively the realm of data analysts.

Students׳ use of data visualizations in historical reasoning: A think-aloud investigation with elementary, middle, and high school students

Data literacy – the ability to analyze, interpret, evaluate, and use data and data visualizations – has become increasingly important for understanding and communicating information in the discipline of history. In the United States, curricular standards and standardized assessments already reflect this importance, but educators lack a clear picture of how students use data visualizations when reasoning about the past. How do students use data visualizations when reasoning about a historical question? To what degree does using data visualizations enhance students’ historical reasoning? What challenges do students face when attempting to use data visualizations? This study addresses these questions, investigating how fifth-grade, eighth-grade, and high school students use data visualizations from typical school textbooks. Twenty-seven students were interviewed, surveyed, and asked to think aloud about a historical question while using a textbook entry that included a data visualization. Results from qualitative and quantitative analyses highlight the potential for data visualizations to enhance historical reasoning. However, results also indicate that students are unlikely to read data visualizations on their own, underscoring the importance of providing targeted data literacy instruction.

Beyond path diagrams: Enhancing applied structural equation modeling research through data visualization

IntroductionStructural equation modeling (SEM) is a multivariate data analytic technique used in many domains of addictive behaviors research. SEM results are usually summarized and communicated through statistical tables and path diagrams, which emphasize path coefficients and global fit without showing specific quantitative values of data points that underlie the model results. Data visualization methods are often absent in SEM research, which may limit the quality and impact of SEM research by reducing data transparency, obscuring unexpected data anomalies and unmodeled heterogeneity, and inhibiting the communication of SEM research findings to research stakeholders who do not have advanced statistical training in SEM. Methods and resultsIn this report, we show how data visualization methods can address these limitations and improve the quality of SEM-based addictive behaviors research. We first introduce SEM and data visualization methodologies and differentiate data visualizations from model visualizations that are commonly used in SEM, such as path diagrams. We then discuss ways researchers may utilize data visualization in SEM research, including by obtaining estimates of latent variables and by visualizing multivariate relations in two-dimensional figures. R syntax is provided to help others generate data visualizations for several types of effects commonly modeled in SEM, including correlation, regression, moderation, and simple mediation. DiscussionThe techniques outlined here may help spur the use of data visualization in SEM-based addictive behaviors research. Using data visualization in SEM may enhance methodological transparency and improve communication of research findings.

The Use of Data Visualizations for Analyzing Social Trends

This essay posits that now is a particularly propitious time for the development and use of data visualization as a means for communicating abstract baseline information about society’s complex institutions, organizations, and social structures. It reviews recent developments in off- the-shelf visualization software and describes supporting literature and tutorials. Finally, it presents some of the ethical dilemmas and constraints confronting visualization producers.

Understanding Older Adult Use of Data Visualizations as a Resource for Maintaining Health and Wellness

To better understand how older adults use health visualizations and the potential barriers that impact utility, we conducted semistructured interviews with 21 older adults. Within these sessions, we presented participants with two interactive visualizations for exploration. Through an affinity mapping exercise, we extracted five key themes associated with how older adults utilize health visualizations and provide corresponding recommendations as points of consideration for designers developing older adult focused health visualizations. By examining how older adults perceive the utility of health visualizations, we lay the groundwork for design choices that impact eventual use and adoption of systems that generate data for such visualizations.

Data Visualization for Human Rights Advocacy

In a world of ‘Big Data’, data visualization allows the viewer to explore curated data; the creator to quickly convey complex information; and advocates to vividly display their view of a better world. Fields as disparate as journalism, environmental advocacy, and development assistance are taking advantage of these data-filled times. A similar movement can be described for the realm of human rights advocacy—although at a much smaller scale. Human rights advocates have been increasingly using data to better understand rights violations and to communicate their findings and messages to targeted audiences, from the general public to policymakers and judicial bodies. While the use of data and visualization among human rights advocates is becoming more common, innovations are being taken up unevenly, and advocates admit that choices about approaches and techniques are largely based on anecdotal evidence. This article introduces the results of preliminary research into some of these questions that are the product of collaboration between researchers from a school of engineering and a school of law. It provides an initial assessment of the field, presenting the results of a study examining the use of data visualization and other visual features by Amnesty International and Human Rights Watch through content coding and expert interviews. It then offers the findings of two crowdsourced user studies into pressing questions in the visualization field which hold promise for human rights advocates seeking to communicate their messages through data visualization, and concludes by suggesting further areas for research.

Numbers, Pictures, and Politics: Teaching Research Methods Through Data Visualizations

Data visualization is the term used to describe the methods and technologies used to allow the exploration and communication of quantitative information graphically. Data visualization is a rapidly growing and evolving discipline, and visualizations are widely used to cover politics. Yet, while popular and scholarly publications widely use visualizations, the skills necessary for developing analytically powerful and aesthetically compelling graphics are not widely taught, at least not in political science. This article thus focuses on two innovative aspects of teaching research methods through the use of data visualizations. The first aspect concerns what is taught and the second concerns how the relevant material is taught. Unlike a more traditional course that emphasizes probability theory, inferential statistics, and formal hypothesis testing, my “Numbers, Pictures and Politics” (NPP) course spotlights the visualization tools and skills useful for understanding and communicating political and other social phenomena.

Philosophising Data

This paper aims to critically reflect on the processes, agendas and use of Big Data by presenting existing issues and problems in place and consolidating our points of views presented from different angles. This paper also describes current practices of handling Big Data, including considerations of smaller scale data analysis and the use of data visualisation to improve business decisions and prediction of market trends. The paper concludes that alongside any data collection, analysis and visualisation, the ‘researcher' should be fully aware of the limitations of the data, by considering the data from different perspectives, angles and lenses. Not only will this add the validation and validity of the data, but it will also provide a ‘thinking tool' by which to explore the data. Arguably providing the ‘human skill' required in a process apparently destined to be automated by machines and algorithms.

Decision Confidence, Information Usefulness, and Information Seeking Intention in the Presence of Disconfirming Information

The increasing use of data visualization for displaying information in a transdisciplinary environment raises issues about client information seeking in the decision-making processes. Field experts and individuals with high confidence tend to seek less information for their decisions than non-experts and individuals who are less confident. This work fills a gap about the effect of the information display format, visual or nonvisual, upon the client’s decision. In the context of data visualization and confirmation bias, this work provides needed research on computer aided information seeking in a decision-making, informing environment. Guided by the theories of confirmation bias and cognitive fit, we propose and test a model that investigates the impact of apriori decision confidence on seeking more information and information usefulness. Findings indicate that both information presentation type and confirmation bias affect the decision process of the information seeker. Findings also indicate that overall, the higher the confidence, the less likely users will find disconfirming evidence useful.

Encourage Students to Read Through the Use of Data Visualization

Encourage Students to Read Through the Use of Data Visualization