Journal articles: 'Data Analysis and Visualization'

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Keswani, Hrishikesh, Krishit Shah, Hritik Hassani, Moses Gadkar, and Er Manoj Kavedia. "Data Visualization and Analysis of COVID-19 Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (October 31, 2022): 1328–37. http://dx.doi.org/10.22214/ijraset.2022.47179.

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Abstract: During the COVID-19 pandemic, many data visualizations were created to alert the public to the rapidly growing threat. Statistics on the spread of COVID-19 have been displayed on data dashboards, a mechanism for sharing information throughout the pandemic, which has aided in this process. When developing the visuals for COVID-19, the majority of time was spent on the technical aspects of designing and evaluating various visualization methods. Little is understood about the inner workings of visualization production processes due to the complex sociotechnical environments in which they are embedded. However, such ecological data is necessary for identifying the particulars and tendencies of visualization design practices in the wild and generating insights into how artists learn to perceive and approach visualization design on their terms and for their contextual aims. We conducted in-depth interviews with dashboard designers from federal and state health departments, major news media outlets, and other firms that created (often widely used) COVID-19 dashboards to gain insight into the following areas. What kind of problems, disagreements, and conflicts arose during making the COVID-19 dashboard because of the participation of visualization creators? The trajectory of design practices—from genesis to expansion, maintenance, and termination—is determined by the complex interconnections between design goals, design tools and technologies, labour, emerging crisis circumstances, and public participation. We zeroed in on these procedures' tensions between designers and the general public. Conflicts frequently arose due to a chasm between public demands and prevailing policies. They typically centred on the types and amounts of information that should be visualized and how public perceptions shape and are shaped by visualization design. The strategies used to deal with (potential) misinterpretations and misuse of visualizations. Our findings and takeaways offer fresh viewpoints on visualization design by highlighting the bundled activities typically linked with human and nonhuman participation along the entire trajectory of design practice

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Chin, George, Mudita Singhal, Grant Nakamura, Vidhya Gurumoorthi, and Natalie Freeman-Cadoret. "Visual Analysis of Dynamic Data Streams." Information Visualization 8, no. 3 (January 25, 2009): 212–29. http://dx.doi.org/10.1057/ivs.2009.18.

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For scientific data visualizations, real-time data streams present many interesting challenges when compared to static data. Real-time data are dynamic, transient, high-volume and temporal. Effective visualizations need to be able to accommodate dynamic data behavior as well as Abstract and present the data in ways that make sense to and are usable by humans. The Visual Content Analysis of Real-Time Data Streams project at the Pacific Northwest National Laboratory is researching and prototyping dynamic visualization techniques and tools to help facilitate human understanding and comprehension of high-volume, real-time data. The general strategy of the project is to develop and evolve visual contexts that will organize and orient high-volume dynamic data in conceptual and perceptive views. The goal is to allow users to quickly grasp dynamic data in forms that are intuitive and natural without requiring intensive training in the use of specific visualization or analysis tools and methods. Thus far, the project has prototyped five different visualization prototypes that represent and convey dynamic data through human-recognizable contexts and paradigms such as hierarchies, relationships, time and geography. We describe the design considerations and unique features of these dynamic visualization prototypes as well as our findings in the exploration and evaluation of their use.

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Dessiaming, Takdir Zulhaq, Siska Anraeni, and Suwito Pomalingo. "COLLEGE ACADEMIC DATA ANALYSIS USING DATA VISUALIZATION." Jurnal Teknik Informatika (Jutif) 3, no. 5 (October 24, 2022): 1203–12. http://dx.doi.org/10.20884/1.jutif.2022.3.5.310.

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Data is a collection of information that contains a broad picture related to a situation. The amount of data is not necessarily better, because a large data set makes it difficult to convert data into information in a timely manner, especially in analyzing data which produces meaningful and relevant information which ultimately results in quick and appropriate action. Higher education management in Indonesia requires fast and accurate academic reports so that it can facilitate strategic decision making in order to improve the quality of education. This study aims to carry out a comprehensive process of analyzing academic data at universities to display them into interactive data visualizations, so that they can retrieve the information in it and make strategic decisions. The method used is a data visualization technique, which allows users to easily see the insights or insights contained in the data. The results obtained are data that has passed the preprocessing stage, can prepare data before being analyzed and processed to be used to make data visualization, so that the information obtained is more varied. This information can be used as a reference by academic managers to make strategic decisions.

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O'Donoghue, Seán I., Benedetta Frida Baldi, Susan J. Clark, Aaron E. Darling, James M. Hogan, Sandeep Kaur, Lena Maier-Hein, et al. "Visualization of Biomedical Data." Annual Review of Biomedical Data Science 1, no. 1 (July 20, 2018): 275–304. http://dx.doi.org/10.1146/annurev-biodatasci-080917-013424.

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The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.

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Heer, Jeffrey, and Joseph M. Hellerstein. "Data visualization and social data analysis." Proceedings of the VLDB Endowment 2, no. 2 (August 2009): 1656–57. http://dx.doi.org/10.14778/1687553.1687621.

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Cruz, António, Joel P. Arrais, and Penousal Machado. "Interactive and coordinated visualization approaches for biological data analysis." Briefings in Bioinformatics 20, no. 4 (March 26, 2018): 1513–23. http://dx.doi.org/10.1093/bib/bby019.

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AbstractThe field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein–protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.

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Kullman, Kaur, and Don Engel. "Interactive Stereoscopically Perceivable Multidimensional Data Visualizations for Cybersecurity." Journal of Defence & Security Technologies 4, no. 1 (January 2022): 37–52. http://dx.doi.org/10.46713/jdst.004.03.

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Interactive Data Visualizations (IDV) can be useful for cybersecurity subject matter experts (CSMEs) while they are exploring new data or investigating familiar datasets for anomalies, correlating events, etc. For an IDV to be useful to a CSME, interaction with that visualization should be simple and intuitive (free of additional mental tasks) and the visualization’s layout must map to a CSME's understanding. While CSMEs may learn to interpret visualizations created by others, they should be encouraged to visualize their datasets in ways that best reflect their own ways of thinking. Developing their own visual schemes makes optimal use of both the data analysis tools and human visual cognition. In this article, we focus on a currently available interactive stereoscopically perceivable multidimensional data visualization solution, as such tools could provide CSMEs with better perception of their data compared to interpreting IDV on flat media (whether visualized as 2D or 3D structures).

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Kim, Min Jung, and Eun Ryung Hyun. "Analysis of User Empathy Levels Based on Types of Data Visualization." Korea Institute of Design Research Society 8, no. 4 (December 31, 2023): 256–66. http://dx.doi.org/10.46248/kidrs.2023.4.256.

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This study aims to explore techniques for eliciting emotional empathy in data visualization. To achieve this, it utilizes preceding research to derive tools for measuring levels of empathy, and analyzes the impact of different types of data visualization on empathy and charitable behaviors to develop a humanism-based data design strategy. The methodology encompasses both literature review and empirical research, reviewing 11 previous studies to identify the types of data visualization and tools for empathy measurement. For empirical analysis, four types of visualizations were created and subjected to an online survey with 95 Korean adults between April 10 to 15, 2023. The analysis revealed that the type of data visualization significantly influences the viewer's emotional response and charitable actions. Notably, realistic illustration elicited the highest level of empathy, while metaphorical infographics induced moderate levels of empathy and donations. This research provides valuable insights for establishing data visualization strategies grounded in humanism.

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Kresh, J. Yasha, and Arthur D'Adamo. "Cardiovascular data visualization and analysis." Journal of the American College of Cardiology 17, no. 2 (February 1991): A14. http://dx.doi.org/10.1016/0735-1097(91)91023-8.

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Orner, Sylvia. "Data Visualization for Collection Analysis." Pennsylvania Libraries: Research & Practice 11, no. 1 (September 27, 2023): 34–44. http://dx.doi.org/10.5195/palrap.2023.278.

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Due to the increasingly digital nature of library resources and collections, it is sometimes difficult to envision a library’s unified holdings and to understand how they have changed over time. Conducting a collection analysis and applying data visualization techniques can be an excellent way to get a top-down view of the collection as a whole. This article outlines the author’s process for a collection analysis of the Weinberg Memorial Library’s entire catalog of print and electronic resources. It explores the rationale behind some key collection analysis decisions and discusses approaches for data extraction and clean-up as well as visualization using the Tableau software.

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Pandey, Aryamaan. "Comparative Study of Data Visualization Tools in Big Data Analysis for Business Intelligence." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2591–600. http://dx.doi.org/10.22214/ijraset.2022.44400.

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Abstract: In this era of information, the development of figures has radically expanded, where a colossal amount of data is being delivered from various sources. Because of this huge collection, the worth of information turns into a significant component in each perspective. Data communication is very important to any business - be it small, midsize orBrobdingnagian. Businesses need Data Visualizations to identify data trends at a rapid pace, which would otherwise be tedious. Data Visualization is a robust technology capable of presenting a large dataset in a graphical format. Its centrality goes far beyond merely introducing information to clients. It also assists customers in comprehending information and making decisions. It involves the use of charts, graphs, diagrams, and other info-graphics to communicate data. Surprisingly, a plethora of Data Visualization tools and methodologies have evolved in recent years. This paper conducts an assessment of the mostwidely used and distributed visualization tools for massive data sets, presenting a synoptic of the main functional and non-functional characteristics of the surveyed tools

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Zhang, Ying, Karsten Klein, Oliver Deussen, Theodor Gutschlag, and Sabine Storandt. "Robust visualization of trajectory data." it - Information Technology 64, no. 4-5 (August 1, 2022): 181–91. http://dx.doi.org/10.1515/itit-2022-0036.

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Abstract The analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large and complex trajectory data sets are routinely collected these days. While automated analysis methods are available to extract characteristics of trajectories such as statistics on the geometry, movement patterns, and locations that might be associated with important events, human inspection is still required to interpret the results, derive parameters for the analysis, compare trajectories and patterns, and to further interpret the impact factors that influence trajectory shapes and their underlying movement processes. Every step in the acquisition and analysis pipeline might introduce artifacts or alterate trajectory features, which might bias the human interpretation or confound the automated analysis. Thus, visualization methods as well as the visualizations themselves need to take into account the corresponding factors in order to allow sound interpretation without adding or removing important trajectory features or putting a large strain on the analyst. In this paper, we provide an overview of the challenges arising in robust trajectory visualization tasks. We then discuss several methods that contribute to improved visualizations. In particular, we present practical algorithms for simplifying trajectory sets that take semantic and uncertainty information directly into account. Furthermore, we describe a complementary approach that allows to visualize the uncertainty along with the trajectories.

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.S, Harinishri. "COVID-19 DATA ANALYSIS AND DATA VISUALIZATION." International Journal of Engineering Applied Sciences and Technology 5, no. 4 (August 1, 2020): 267–71. http://dx.doi.org/10.33564/ijeast.2020.v05i04.040.

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Kumar, Nitin, and Gaurav . "Data Analysis and Data Visualization using Python." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 1287–91. http://dx.doi.org/10.26438/ijcse/v7i5.12871291.

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Shelly, Mark A. "Exploratory Data Analysis: Data Visualization or Torture?" Infection Control and Hospital Epidemiology 17, no. 9 (September 1996): 605–12. http://dx.doi.org/10.2307/30141948.

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Shelly, Mark A. "Exploratory Data Analysis: Data Visualization or Torture?" Infection Control and Hospital Epidemiology 17, no. 9 (September 1996): 605–12. http://dx.doi.org/10.1086/647397.

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Sharma, Sharad, and Sri Chandra Dronavalli. "Data Analysis and Visualization of Crime Data." Electronic Imaging 36, no. 1 (January 21, 2024): 364–1. http://dx.doi.org/10.2352/ei.2024.36.1.vda-364.

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Jofre, Ana, Steve Szigeti, and Sara Diamond. "Materializing data." DAT Journal 1, no. 2 (December 27, 2016): 2–14. http://dx.doi.org/10.29147/2526-1789.dat.2016v1i2p2-14.

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The visualization of data elucidates trends and patterns in the phenomena that the data represents, and opens accessibility to understanding complicated human and natural processes represented by data sets. Research indicates that interacting with a visualization amplfies cognition and analysis. A single visualization may show only one facet of the data. To examine the data from multiple perspectives, engaged citizens need to be able to construct their own visualizations from a data set. Many tools for data visualization have responded to this need, allowing non-data experts to manipulate and gain insights into their data, but most of these tools are restricted to the computer screen, keyboard, and mouse. Cognition and analysis may be strengthened even more through embodied interaction with data, whether through data sculpture or haptic and tangible interfaces. We present here the rationale for the design of a tool that allows users to probe a data set, through interactions with graspable (tangible) three-dimensional objects, rather than through a keyboard and mouse interaction. We argue that the use of tangibles facilitates understanding abstract concepts, and facilitates many concrete learning scenarios. Another advantage of using tangibles over screen-based tools is that they foster collaboration, which can promote a productive working and learning environment.

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Wang, Lidong. "Big Data and IT Network Data Visualization." International Journal of Mathematical, Engineering and Management Sciences 3, no. 1 (March 1, 2018): 9–16. http://dx.doi.org/10.33889/ijmems.2018.3.1-002.

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Visualization with graphs is popular in the data analysis of Information Technology (IT) networks or computer networks. An IT network is often modelled as a graph with hosts being nodes and traffic being flows on many edges. General visualization methods are introduced in this paper. Applications and technology progress of visualization in IT network analysis and big data in IT network visualization are presented. The challenges of visualization and Big Data analytics in IT network visualization are also discussed. Big Data analytics with High Performance Computing (HPC) techniques, especially Graphics Processing Units (GPUs) helps accelerate IT network analysis and visualization.

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Yang, Bo, Dong Tian, and Guihua Shan. "Tobacco Spatial Data Intelligent Visual Analysis." Electronics 11, no. 7 (March 23, 2022): 995. http://dx.doi.org/10.3390/electronics11070995.

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A multi-module visualization framework is designed and a visual analysis system called TobaccoGeoVis is implemented to analyze tobacco spatial data efficiently. The proposed system provides a visualization technology for overlaying multiple graphics on a map to enrich the form of tobacco spatial data visualization. The system also adopts artificial intelligence algorithms and multi-view linkage interactive methods and provides flexible data-attribute field mapping and graphical parameter configuration to analyze tobacco spatial data. We demonstrated that the system is user-friendly and the applied visualization methods are effective using cases selected from the three sets of data.

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Haimerl, Edgar. "The visualization of dialect data with VDM." Zeitschrift für romanische Philologie 139, no. 4 (December 1, 2023): 991–1002. http://dx.doi.org/10.1515/zrp-2023-0040.

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Abstract Visualization of the results is the final and essential step in dialect data analysis. For geographically dispersed data such as dialect data, colorful maps have long been the standard of visualization. The focus is on polygon maps, which can be used to represent contiguous areas particularly well. The Dialectometric visualization of script data is quite similar to the visualization of data from dialect atlases. For this reason, the VDM application (Visual DialectoMetry) can be used for the analysis of text documents from the DocLing1 corpus with few modifications. After a short tour of the visualizations in VDM and an explanation of the data structure, the focus of this paper is on these modifications and the new features which are currently being added to VDM. This new version of VDM will be better suited for scripta data and will streamline the generation of maps for VDM.

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Dahake, Prof Hemant, and Shahbaz Hasan Anwarul Hasan Sheikh. "BIM Data Analysis and Visualization Workflow." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2806–10. http://dx.doi.org/10.22214/ijraset.2023.52159.

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Abstract: Building Information Modeling (BIM) has emerged as a powerful technology for managing complex construction projects, providing a way to streamline communication, increase collaboration, and improve project outcomes. However, one area where BIM implementation still requires improvement is data analysis. The quality of data provided by BIM software is critical for making informed decisions, optimizing workflows, and improving project outcomes. This research paper comprehensively reviews the latest advancements in BIM data analysis and visualization techniques. The paper discusses the benefits of data analysis in BIM workflows and provides a framework for implementing data analysis techniques. The paper also highlights the latest tools and techniques available for BIM data analysis and visualization and their potential applications in the construction industry. Additionally, the paper presents a case study to illustrate the implementation of BIM data analysis and visualization techniques in a real-world construction project. The findings of this research paper show that data analysis and visualization are essential for successful BIM implementation and for improving project outcomes. The paper concludes with recommendations for future research in this area, highlighting the need for continued exploration of new data analysis and visualization techniques and their applications in the construction industry.

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Akkucuk, Ulas, and Mehmet Nafi Artemel. "Patent Data Visualization." International Journal of Research in Business and Social Science (2147-4478) 5, no. 3 (April 20, 2016): 66–79. http://dx.doi.org/10.20525/ijrbs.v5i3.358.

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The importance given by the governments to building a sound intellectual property infrastructure is increasing in developing countries and especially in Central Asian countries. This infrastructure is continuously improved to live up to a common standard in collaboration with government agencies, educational institutions and international agencies. In this paper, the infrastructure developments that took place in the Central Asian countries is going to be elaborated and furthermore some statistical analyses will be used in order to compare the differences and similarities between the Central Asian republics within themselves and the rest of the world. Patent based statistical data reveal a broad range of information concerning the innovative capability of countries, regions and firms. The number of patents that a country obtains in different technological fields and the change in this number over the years may provide useful information regarding the growth potential of the country and the ability to follow technological advances. For this purpose, patent statistics collected by institutions like World Intellectual Property Organization (WIPO) have been analyzed using statistical techniques. In addition to basic statistics, multidimensional scaling analysis (MDS) has been applied to the data sets.

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S., R., Priyanka V., Shivani S., and Dipanshu Nagpal. "Twitter Data Sentiment Analysis and Visualization." International Journal of Computer Applications 180, no. 20 (February 15, 2018): 14–16. http://dx.doi.org/10.5120/ijca2018916463.

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Gupta, Vijay. "An Analysis of Data Visualization Tools." International Journal of Computer Applications 178, no. 10 (May 15, 2019): 4–7. http://dx.doi.org/10.5120/ijca2019918811.

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Grané, Aurea, Giancarlo Manzi, and Silvia Salini. "Dynamic Mixed Data Analysis and Visualization." Entropy 24, no. 10 (October 1, 2022): 1399. http://dx.doi.org/10.3390/e24101399.

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One of the consequences of the big data revolution is that data are more heterogeneous than ever. A new challenge appears when mixed-type data sets evolve over time and we are interested in the comparison among individuals. In this work, we propose a new protocol that integrates robust distances and visualization techniques for dynamic mixed data. In particular, given a time t∈T={1,2,…,N}, we start by measuring the proximity of n individuals in heterogeneous data by means of a robustified version of Gower’s metric (proposed by the authors in a previous work) yielding to a collection of distance matrices {D(t),∀t∈T}. To monitor the evolution of distances and outlier detection over time, we propose several graphical tools: First, we track the evolution of pairwise distances via line graphs; second, a dynamic box plot is obtained to identify individuals which showed minimum or maximum disparities; third, to visualize individuals that are systematically far from the others and detect potential outliers, we use the proximity plots, which are line graphs based on a proximity function computed on {D(t),∀t∈T}; fourth, the evolution of the inter-distances between individuals is analyzed via dynamic multiple multidimensional scaling maps. These visualization tools were implemented in the Shinny application in R, and the methodology is illustrated on a real data set related to COVID-19 healthcare, policy and restriction measures about the 2020–2021 COVID-19 pandemic across EU Member States.

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M, Mufeeda. "Crime Data Analysis, Visualization and Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. 3 (March 31, 2021): 71–78. http://dx.doi.org/10.22214/ijraset.2021.32800.

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Post, Frits H. "Data Visualization: Featuring Interactive Visual Analysis." Computer Graphics Forum 30, no. 2 (April 2011): xxiii. http://dx.doi.org/10.1111/j.1467-8659.2011.01911.x.

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Cai, Yue, Zeying Song, Guang Sun, Jing Wang, Ziyi Guo, Yi Zuo, Xiaoping Fan, Jianjun Zhang, and Lin Lang. "On Visualization Analysis of Stock Data." Journal on Big Data 1, no. 3 (2019): 135–44. http://dx.doi.org/10.32604/jbd.2019.08274.

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Ivarsson, Carl-Johan. "Visualization for Advanced Big Data Analysis." Genetic Engineering & Biotechnology News 37, no. 2 (January 15, 2017): 32. http://dx.doi.org/10.1089/gen.37.02.17.

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Nuzzo, Regina L. "Histograms: A Useful Data Analysis Visualization." PM&R 11, no. 3 (March 2019): 309–12. http://dx.doi.org/10.1002/pmrj.12145.

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Epifanov, M. A., V. V. Pilyugin, and V. V. Klimov. "Data Analysis using Augmented Reality Visualization." Scientific Visualization 15, no. 5 (December 2023): 89–102. http://dx.doi.org/10.26583/sv.15.5.08.

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In this paper, the authors describe the history of augmented reality and its applicability in scientific visualization and visual analytics. The study explores the benefits of using this technology for analysts studying spatial scenes and presents an advanced online platform for creating augmented reality projects used for educational purposes.

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Dragan, Dinu, Veljko Petrovic, Dusan Gajic, Zarko Zivanov, and Dragan Ivetic. "An empirical study of data visualization techniques in PACS design." Computer Science and Information Systems 16, no. 1 (2019): 247–71. http://dx.doi.org/10.2298/csis180430017d.

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The paper presents an empirical study of multidimensional visualization techniques. The study is motivated by the problem of decision making in PACS (Picture Archiving and Communications System) design. A comprehensive survey of visualizations used in literature is performed and these survey results are then used to produce the final set of considered visualizations: tables (as control), scatterplots, parallel coordinates, and star plots. An electronic testing tool is developed to present visualizations to three sets of experimental subjects in order to determine which visualization technique allows users to make the correct decision in a sample decision making problem based on real-world data. Statistical analysis of the results demonstrates that visualizations show better results in decision support than tables. Further, when number of dimensions is large, 2D parallel coordinates show the best results in accuracy. The contribution of the presented research operates on two levels of abstraction. On the object level, it provides useful data regarding the relative merits of visualization techniques for the considered narrow use-case, which can then be generalized to other similar problem sets. On the meta level above, it contributes an enhanced methodology to the area of empirical visualization evaluation methods.

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Vera-Piazzini, Ofelia, Massimiliano Scarpa, and Fabio Peron. "Building Energy Simulation and Monitoring: A Review of Graphical Data Representation." Energies 16, no. 1 (December 29, 2022): 390. http://dx.doi.org/10.3390/en16010390.

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Data visualization has become relevant in the framework of the evolution of big data analysis. Being able to understand data collected in a dynamic, interactive, and personalized way allows for better decisions to be made when optimizing and improving performance. Although its importance is known, there is a gap in the research regarding its design, choice criteria, and uses in the field of building energy consumption. Therefore, this review discusses the state-of-the-art of visualization techniques used in the field of energy performance, in particular by considering two types of building analysis: simulation and monitoring. Likewise, data visualizations are categorized according to goals, level of detail and target users. Visualization tools published in the scientific literature, as well as those currently used in the IoT platforms and visualization software, were analyzed. This overview can be used as a starting point when choosing the most efficient data visualization for a specific type of building energy analysis.

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Rabetti Giannella, Júlia, and Luiz Velho. "Data visualization in the time of coronavirus." Strategic Design Research Journal 14, no. 1 (April 9, 2021): 275–88. http://dx.doi.org/10.4013/sdrj.2021.141.23.

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Currently, we observe a proliferation of data visualizations about Covid-19 in the media, which makes it a convenient time to study the topic from the perspective of different disciplines, including information design and mathematics. If, on the one hand, the abundance of such pandemic representations would already be a legitimate reason to address the issue, on the other hand, it is not the central motivation of the present discussion. The uniqueness of the epidemiological phenomenon that we are experiencing highlights new aspects regarding the production and use of data visualizations, one of which is its diversification beyond counting and visual representation of events related to the virus spread. In this sense, the article discusses, through the analysis of examples, three different approaches for this type of schematic representation, namely: visualization of hypothetical data, visualizations based on secondary data, and visualization for social criticism and self-reflection. Ultimately, we can argue that design contributes to the production of data visualizations that can help people to understand the causes and implications involved in the new coronavirus and encourage civic responsibility through self-care and the practice of social distancing.

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Hehman, Eric, and Sally Y. Xie. "Doing Better Data Visualization." Advances in Methods and Practices in Psychological Science 4, no. 4 (October 2021): 251524592110453. http://dx.doi.org/10.1177/25152459211045334.

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Methods in data visualization have rapidly advanced over the past decade. Although social scientists regularly need to visualize the results of their analyses, they receive little training in how to best design their visualizations. This tutorial is for individuals whose goal is to communicate patterns in data as clearly as possible to other consumers of science and is designed to be accessible to both experienced and relatively new users of R and ggplot2. In this article, we assume some basic statistical and visualization knowledge and focus on how to visualize rather than what to visualize. We distill the science and wisdom of data-visualization expertise from books, blogs, and online forum discussion threads into recommendations for social scientists looking to convey their results to other scientists. Overarching design philosophies and color decisions are discussed before giving specific examples of code in R for visualizing central tendencies, proportions, and relationships between variables.

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Fu, Bo, Ben Steichen, and Wenlu Zhang. "Towards Adaptive Ontology Visualization — Predicting User Success from Behavioral Data." International Journal of Semantic Computing 13, no. 04 (December 2019): 431–52. http://dx.doi.org/10.1142/s1793351x1940018x.

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Ontology visualization plays an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability studies of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavioral data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment, as opposed to direct input that informs the underlying visualization system. Although findings from usability studies have contributed to the refinement of ontology visualizations as a whole, the visualization techniques themselves remain a one-size-fits-all approach, where all users are presented with the same visualizations and interactive features. By contrast, this paper investigates the feasibility of using behavioral data, such as user gaze and event logs, as real-time indicators of how appropriate or effective a given visualization may be for a specific user at a moment in time, which in turn may be used to inform the adaptation of the visualization to the user on the fly. To this end, we apply established predictive modeling techniques in Machine Learning to predict user success using gaze data and event logs. We present a detailed analysis from a controlled experiment and demonstrate such predictions are not only feasible, but can also be significantly better than a baseline classifier during visualization usage. These predictions can then be used to drive the adaptations of visual systems in providing ad hoc visualizations on a per user basis, which in turn may increase individual user success and performance. Furthermore, we demonstrate the prediction performance using several different feature sets, and report on the results generated from several notable classifiers, where a decision tree-based learning model using a boosting algorithm produced the best overall results.

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Wang, Junpeng, Xiaotong Liu, and Han-Wei Shen. "High-dimensional data analysis with subspace comparison using matrix visualization." Information Visualization 18, no. 1 (October 14, 2017): 94–109. http://dx.doi.org/10.1177/1473871617733996.

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Due to the intricate relationship between different dimensions of high-dimensional data, subspace analysis is often conducted to decompose dimensions and give prominence to certain subsets of dimensions, i.e. subspaces. Exploring and comparing subspaces are important to reveal the underlying features of subspaces, as well as to portray the characteristics of individual dimensions. To date, most of the existing high-dimensional data exploration and analysis approaches rely on dimensionality reduction algorithms (e.g. principal component analysis and multi-dimensional scaling) to project high-dimensional data, or their subspaces, to two-dimensional space and employ scatterplots for visualization. However, the dimensionality reduction algorithms are sometimes difficult to fine-tune and scatterplots are not effective for comparative visualization, making subspace comparison hard to perform. In this article, we aggregate high-dimensional data or their subspaces by computing pair-wise distances between all data items and showing the distances with matrix visualizations to present the original high-dimensional data or subspaces. Our approach enables effective visual comparisons among subspaces, which allows users to further investigate the characteristics of individual dimensions by studying their behaviors in similar subspaces. Through subspace comparisons, we identify dominant, similar, and conforming dimensions in different subspace contexts of synthetic and real-world high-dimensional data sets. Additionally, we present a prototype that integrates parallel coordinates plot and matrix visualization for high-dimensional data exploration and incremental dimensionality analysis, which also allows users to further validate the dimension characterization results derived from the subspace comparisons.

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Abednego, Luciana, and Cecilia Esti Nugraheni. "Forex Data Analysis using Weka." International Journal of Fuzzy Logic Systems 11, no. 1 (January 31, 2021): 23–36. http://dx.doi.org/10.5121/ijfls.2021.11103.

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This paper conducts some experiments with forex trading data. The data being used is from kaggle.com, a website that provides datasets for machine learning and data scientists. The goal of the experiments is to know how to design many parameters in a forex trading robot. Some questions that want to be investigated are: How far the robot must set the stop loss or target profit level from the open position? When is the best time to apply for a forex robot that works only in a trending market? Which one is better: a forex trading robot that waits for a trending market or a robot that works during a sideways market? To answer these questions, some data visualizations are plotted in many types of graphs. The data representations are built using Weka, an open-source machine learning software. The data visualization helps the trader to design the strategy to trade the forex market.

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Kang, Yan, and Jun Wang. "Mapping and analysis using multisource oceanic satellite data and google earth engine." SHS Web of Conferences 145 (2022): 01023. http://dx.doi.org/10.1051/shsconf/202214501023.

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Ocean satellite observation because of its large coverage area and high frequency observation become ever more important data and information source with global climate changing, ocean resources protecting and oceanic engineering projects implementing. Oceanic satellite data characteristically include multi-physical parameters, are the product of multi-level processing and are multi-sourced data. Therefore, oceanic satellite data often have different manifestations making the understanding and use of these data challenging, and there is an urgent need for a flexible platform to share the data and merge different kinds of the data information. Here we use Google Earth Engine that make these data easily understandable and a fully synthesized and comprehensive visualization. Then the key techniques for creating 2D and 3D visualizations of oceanic satellite data using KML and Google Earth are detailed in this paper. As an example, multi-sourced satellite data including horizontal distribution and vertical profiles of Typhoon Morakot in August 2009 are combined on Google Earth with three-dimensional visualization. We have extended this research and developed a web service system based on an oceanic satellite visualization data model to dynamically display different types of the oceanic satellite data on Google Earth.

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Byrne, Lydia, Daniel Angus, and Janet Wiles. "Figurative frames: A critical vocabulary for images in information visualization." Information Visualization 18, no. 1 (August 29, 2017): 45–67. http://dx.doi.org/10.1177/1473871617724212.

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Critical analyses provide information visualization practitioners with insight into the range and suitability of different techniques for visualization. Theory provides the necessary models and vocabulary to deconstruct, explain and classify visualizations, allowing the analysis and comparison of alternate designs, and evaluation of their success. While the critical vocabulary for information visualization in general is well developed, the same cannot be said for ‘hybrid’ information visualizations which combine abstract representation of data with figurative elements such as illustrations. Figurative elements are widely used in information visualization in practice and are increasingly recognized as beneficial for memorability. However, the information encoded by a figurative image and how that information contributes to the overall content of the visualization lacks robust definition within visualization theory. To support critical analysis of hybrid visualization, we provide a model of the information content of a figurative image, which we call the figurative frame model. We use the model to classify hybrid visualizations along two dimensions: information density in the images (defined as the number of features and preserved measurements) and integration of figurative and abstract forms of representation. The new vocabulary for analysing hybrid visualizations reveals how the figurative images expand the expressiveness of information visualization by integrating descriptive and abstract information and allows the formulation of new measures of visualization quality which can be applied to hybrid visualizations.

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Cook, Dianne, Eun-Kyung Lee, and Mahbubul Majumder. "Data Visualization and Statistical Graphics in Big Data Analysis." Annual Review of Statistics and Its Application 3, no. 1 (June 2016): 133–59. http://dx.doi.org/10.1146/annurev-statistics-041715-033420.

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Comparato, M., U. Becciani, A. Costa, B. Larsson, B. Garilli, C. Gheller, and J. Taylor. "Visualization, Exploration, and Data Analysis of Complex Astrophysical Data." Publications of the Astronomical Society of the Pacific 119, no. 858 (August 2007): 898–913. http://dx.doi.org/10.1086/521375.

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Li, S., N. Marsaglia, C. Garth, J. Woodring, J. Clyne, and H. Childs. "Data Reduction Techniques for Simulation, Visualization and Data Analysis." Computer Graphics Forum 37, no. 6 (March 30, 2018): 422–47. http://dx.doi.org/10.1111/cgf.13336.

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Liu, Wei. "Application of Data Visualization and Big Data Analysis in Intelligent Agriculture." Journal of Computing and Information Technology 29, no. 4 (December 16, 2022): 251–63. http://dx.doi.org/10.20532/cit.2021.1005390.

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Intelligent agriculture can renovate agricultural production and management, making agricultural production truly scientific and efficient. The existing data mining technology for agricultural information is powerful and professional. But the technology is not well adapted for intelligent agriculture. Therefore, this paper introduces data visualization and big data analysis into the application scenarios of intelligent agriculture. Firstly, an intelligent agriculture data visualization system was established, and the RadViz data visualization method was detailed for intelligent agriculture. Moreover, the intelligent agriculture data were processed using dimensionality reduction through principal component analysis (PCA) and further optimized through k-means clustering (KMC). Finally, the crop yield was predicted using the multiple regression algorithm and the residual principal component regression algorithm. The crop yield prediction model was proved effective through experiments.

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Stopar, Karmen, Stanislav Trdan, and Tomaž Bartol. "Thrips and natural enemies through text data mining and visualization." Plant Protection Science 57, No. 1 (December 3, 2020): 47–58. http://dx.doi.org/10.17221/34/2020-pps.

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Thrips can cause considerable economic damage. In order to reduce the use of agrochemicals research has also focused on different natural enemies. We used bibliometric mapping and visualization to understand the structure of this field. Articles from Web of Science as well as software Vosviewer were used. Analysis of co-occurrence of terms shows the principal research areas: transmission of viruses, chemical or biological control and new species. A third of articles refer to biological control. Visualizations reveal three major groups of beneficials: entomopathogens, parasitoids, and predators. Recently, attention has shifted mainly to predatory mites as biocontrol agents. Our analysis aims to make such information visually more explanatory with better overview of research directions.

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Wang, Tianjun, Cengceng Wang, Jiangtao Guo, and dildar alim. "Visual Data Analysis Technology Based on Data Center." Journal of Physics: Conference Series 2146, no. 1 (January 1, 2022): 012016. http://dx.doi.org/10.1088/1742-6596/2146/1/012016.

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Abstract Today, people are in an information explosion society, and visualization technology(VT) is an inevitable product of the development of the information society. With the emergence of multimedia products such as computers, networks, and communications, humans are paying more and more attention to data processing. Many countries in the world have already begun research in this area and have achieved remarkable results. VT is a core part of data analysis, also known as information processing and storage technology. It has a very extensive and important application in the field of data management. However, because the key information hidden in the data is often immersed in the massive data, it is necessary to filter the data information efficiently, and the visualization data analysis technology is a crucial part. This article adopts the experimental analysis method, which aims to provide a new method to solve the problems of traditional technology and the challenges that may arise in the future by further understanding the existing visual data analysis technology and development trend. According to the research results, the recognition rate of the optimized color visualization features under different classifiers is higher than that of the original emotional features. It can be seen that visual analysis technology is not limited to data sets with physical meaning, but can also be applied to abstract feature sets such as emotional features.

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Hsieh, I.-Chung, and Yufen Huang. "Sensitivity analysis and visualization for functional data." Journal of Statistical Computation and Simulation 91, no. 8 (February 26, 2021): 1593–615. http://dx.doi.org/10.1080/00949655.2020.1863405.

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ZHANG, Yang, and Chen WANG. "Spatial data visualization based on cluster analysis." Journal of Computer Applications 33, no. 10 (November 11, 2013): 2981–83. http://dx.doi.org/10.3724/sp.j.1087.2013.02981.

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Park, Young-Keun, Sung-Jin Lee, Gi-Doo Lee, Sang-Soo Lim, and In-Won Lee. "Flight data analysis and visualization program development." Journal of the Korean Society for Aeronautical & Space Sciences 42, no. 3 (March 1, 2014): 263–69. http://dx.doi.org/10.5139/jksas.2014.42.3.263.

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Journal articles on the topic 'Data Analysis and Visualization'

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