Untangling Rhetoric, Pathos, and Aesthetics in Data Visualization.

Focus on software

With the development of computer science, the era of big data has arrived. Facing the new era and new challenges, the traditional analytical methods of problems in various fields have been unable to meet the needs. Data visualization is a rapidly developing discipline, it has significant advantages in analyzing problems, so data visualization shines in the era of big data. As people are very concerned about the fields of energy and environment, we choose to conduct data visualization studies in two areas, energy and the environment. According to the different characteristics of data in different fields, we propose targeted data visualization processes and design data visualization solutions. For energy data, we follow the process of data processing, visualization design, and data visualization. Based on the principle of high efficiency and intuitiveness, we add timeline and a combination of various charts to our design, and finally show a dynamic effect. We also propose a multi-dimensional visual mapping visualization scheme. The scheme can refine and enrich the visual results. For environmental data, we follow the process of goal analysis, data processing, visualization and analysis, the work shows the importance of visualization in information analysis and decision-making.

Data visualization and communication are important components in disaster response management. Data management should be a basic part of emergency preparation in the same way as prepositioning essential supplies. For this preparation to be effective, well-conceived data structures and data collection systems must be in place before disasters happen, and required hardware should be designed to operate in contingency environments. However, due to challenges in disaster complexities and data management, there is still a pressing need for improvement. This paper identifies key principles to assist practitioners and software developers in designing and implementing data collection and reporting systems that can be used for data visualization during a disaster response. The authors reviewed existing literature on data and disaster management and incorporated their personal experiences as first responders with the US Army Corps of Engineers Hurricane Maria response mission to develop principles for improving data management and visualization during a disaster response. These principles are illustrated by two case studies from the Task Force Power and Operation Blue Roof mission efforts in Puerto Rico during 2017-2018. Suggested principles include considering data management as part of disaster preparedness, having flexible data tools resilient to unprecedented disaster outcomes, eg, interruption of telecommunications networks, and using diverse graphics and tools that are appropriate to their communication purpose and audience.

Data visualization refers to studies that schematically represent abstracted data visually while simultaneously including their own features and factors. Data visualization's primary goal is to use graphical tools to communicate information in a more understandable and efficient manner. In order to come up with good results, data analysis is required. Decisions can be made using the final result. In order to help the human brain better understand and inspect the data, which is to comprehend the outcomes of their processing, data visualizat ion entails turning the data into a visual context. The fastest human cognitive pathway is the visual canal. Because of this, more individuals prefer to view data than hear or read it. Fast data processing, analysis, and display are typically priorities in everyday life in a time of overproduction of digital data. Visual data analysis and processing tools are constantly updated as a result of constant contact between managers, users and developers on the one hand, and on the other, on the other hand. The essential points and the possible effects of data visualization tools and approaches in diverse fields are summarized in the chapter's conclusion. The purpose of this chapter is to present a thorough review of data visualization methods and techniques and to emphasize their significance in many disciplines for efficient data analysis and communication

Today businesses are becoming more productive and their return on investment (ROI) is increasing with the development of new technologies like data science, artificial intelligence and data analytics. In today's trend organizations are dealing with big data and these data can drive the whole organization in many ways. The process of doing data analysis and extracting meaningful insight is known as data science. Most business organizations are taking data driven models to ease their work and for making intelligent business decisions. The life cycle of a data science involves so many steps like understanding the business, data collection, analysis and data modelling etc., and to achieve these steps various new technologies and methods are available. Firstly, the process of data collection has been significantly augmented by artificial intelligence, allowing businesses to gather vast amounts of structured and unstructured data efficiently. This rich pool of data serves as the foundation upon which strategic decisions are made. By leveraging advanced data collection methods, organizations gain invaluable insights into market trends, customer behaviour, and operational patterns, empowering them to make informed, data-driven decisions. Secondly, data analysis, a core element of data science, plays a pivotal role in extracting meaningful insights from the collected data. Through sophisticated analytical techniques, businesses can uncover hidden patterns, correlations, and trends within the data. This deep understanding of the data not only facilitates efficient problem-solving but also enables the identification of opportunities for innovation and growth. Informed by data analysis, businesses can optimize processes, identify cost-saving measures, and enhance overall operational efficiency. Lastly, data visualization techniques such as real-time visualization and augmented analytics empower organizations to transform complex data sets into easily understandable visual representations. Real-time visualization provides businesses with up-to-the-minute insights, enabling them to respond promptly to market changes and emerging trends. Augmented analytics, on the other hand, leverages machine learning algorithms to automate data analysis and present actionable insights in an intuitive manner, further accelerating the decision-making process. In this study the recent trends in data science like artificial intelligence for data collection, augmented analytics and predictive analysis for data analysis and data democratization & real time visualization techniques for data visualization are discussed in detail. This study also presents the tools, key challenges and applications of these recent methods in brief.

Chapter 7 - Data Visualization and Communication

In recent years, there is an emerging direction that leverages information theory to solve many challenging problems in scientific data analysis and visualization. In this article, we review the key concepts in information theory, discuss how the principles of information theory can be useful for visualization, and provide specific examples to draw connections between data communication and data visualization in terms of how information can be measured quantitatively. As the amount of digital data available to us increases at an astounding speed, the goal of this article is to introduce the interested readers to this new direction of data analysis research, and to inspire them to identify new applications and seek solutions using information theory.

ABSTRACTData visualizations are now commonplace in the public media. The ability to interpret and create such visualizations, as a form of data literacy, is increasingly important for democratic participation. Yet, the cross-disciplinary knowledge and skills needed to produce and use data visualizations and to develop data literacy are not fluidly integrated into traditional K–12 subject areas. In this article, we nuance and complicate the push for data literacy in STEM reform efforts targeting youth of color. We explore a curricular reform project that integrated explicit attention to issues pertaining to the collection, analysis, interpretation, representation, visualization, and communication of data in an introductory computer science class. While the study of data in this unit emphasized viewing and approaching data in context, neither the teacher nor the students were supported in negotiating the racialized context of data that emerged in classroom discussions. To better understand these dynamics, we detail the construct of racial literacy and develop an interpretative framework of racial-ideological micro-contestations. Through an in-depth analysis of a classroom interaction using this framework, we explore how contestations about race can emerge when data visualizations from the public media are incorporated into STEM learning precisely because the contexts of data are often racialized. We argue that access to learning about data visualization, without a deep interrogation of race and power, can be counterproductive and that efforts to develop authentic data literacy require the concomitant development of racial literacy.

In response to an increased demand for data, staff from the Regional Epidemiology and Surveillance Unit of the Department of Health Metro Manila developed an internal dashboard to automate the aggregation and visualization of COVID-19 data from multiple sources, thereby decreasing the time taken to complete the process by approximately 35minutes, or more than half the duration of the original workflow. Time, financial, and human resource constraints were overcome with staff commitment and communication, and the approach was scaled to develop dashboards for other surveillance systems. Dashboards are useful for aggregation and visualization of data at the subnational level, but they are not replacements for critical review and effective communication of data. Further studies on dashboard development will help establish a framework and promote a new standard for public health organizations at the subnational level.

L’article évalue d’abord l’importance du textile dans le devenir des deux héros, Jacob et Marianne, et examine le pouvoir de séduction que leur offrent leurs nouveaux habits, sur eux-mêmes ou sur autrui. Une fois devenus narrateurs, c’est par l’écriture qu’ils tentent de créer une connivence avec leurs destinataires et de les influencer en leur faveur, en ornant, tissant ou brodant le canevas de leur existence. L’article analyse donc les principaux procédés rhétoriques utilisés par les deux narrateurs pour séduire le lecteur : l’affirmation de la spontanéité de l’écriture, le tissage du lien avec la narrataire, l’appel à l’émotion, l’autojustification, la mise en valeur de soi, la réécriture romanesque du passé, l’humour et l’autodérision.

Thrasymachos of Chalcedon was a sophist and orator. His main area of interest was rhetoric, and he was especially known for his emphasis on the importance of the appeal to emotions. We also have fragments of two orations, the first probably linked to the post‐411 constitutional debate in Athens, the second referring to the political situation in Thessaly. In Plato's Republic he defends the view of justice as the interest of the stronger.

The quality evaluation is essential to creating effective data visualization designs. The data visualization research community has produced many quality metrics for evaluating data visualization. However, these quality metrics are rarely integrated into popular data visualization tools. As a result, most data visualization creators are either not aware of these quality metrics or do not know how to apply these metrics to the visualization creation process. In this paper, we propose a novel quality evaluation method that integrates quality metrics into popular data visualization programming tools. Our main contribution is a code-space quality evaluation method, different from the traditional image-space or data-space quality evaluation method. Using our method, a visualization programmer passes a coded data visualization design to a quality evaluation function that generates warnings, comments, and design recommendations. This allows users to integrate quality checks into the design process.

Qi is an ancient concept of life energy in various cultures and it represents continuous ineffable dialogues between the human heart with the body. The goal of Qi Visualizer is to translate the ineffable dialogues into a collective data visualization of pulse spectrograms via audience participation using smartphone photoplethysmography. Informed by our earlier studies, we believe spectrogram is the most effective visual language to invoke broader perception of Qi across cultures and disciplines. In our design process, we extend our notion of audience as constituents of a system for collective expression in this novel Qi Visualizer interactive installation. Specifically, we consider audience members as objects and their responses as processes in a system to guide our visualization strategies. Significance of this work is two fold. In terms of data visualization, we streamline data acquisition and visualization processes into an immediate interactive experience in Qi Visualizer installation. In terms of interactive art, the significance may be conceived as a new process shifting tactile-textual translations in a pulse examination to imaging-visual transformations of the pulse spectrograms visualization via audience participation. Similar to voiceprint, a heart rate harmonic signature in the spectrogram is seen as an artistic interpretation of Qi in this installation.

PurposeThis study aims to examine the use of data visualization as a tool to support practitioner-led organizational learning within the emergency services. The authors investigate how data visualization can support visual communication and the analysis of emergency response data to promote system improvement.Design/methodology/approachThe authors investigate if communication data, presented as node-link diagrams, can be understood and evaluated by firefighters. Objective understanding of the communication network is measured quantitatively, while subject judgement of the emergency response system is measured qualitatively and compared to prior system evaluation outcomes. The authors compare different data visualization layouts and assess their value in supporting practitioner evaluation of emergency response systems.FindingsThe authors find that while firefighters are largely unfamiliar with their use, data visualizations function as a tool for visual communication and analysis. The authors identify the importance of visualization design and the difficulty in representing characteristics of a dynamic network within static diagrams. The authors also find some correlation between layout design and how respondents interpret visual data.Originality/valueResults demonstrate the value of data visualization to support practitioner-led organizational learning and suggest future work to support the development of emergency response management.

Data-driven innovation to capture user-experience product design: An empirical study for notebook visual aesthetics design