An evaluation-guided approach for effective data visualization on tablets

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Prompt diagnosis of abnormally shaped wave forms in ECG signal is an important component in the early diagnosis of cardiac arrhythmias, improving the quality of life of patients. Meanwhile, detection models for Premature Ventricular Contractions (PVC) are widely investigated, a less studied problem is data analysis and visualization. In this paper, we propose an approach for PVC detection and data visualization by exploiting the intrinsic geometry of the high-dimensional data using manifold learning and Support Vector Machines (SVM). ISOMAP forms a neighborhood-preserving projection which allows to uncover the low-dimensional manifold and is used here as a pre-processing step. Then by incorporating training labels the method is capable of recognizing PVC patterns with comparable accuracy of kernel learning machines.

Background: A key strength of population sciences and epidemiology is the breadth and depth of the data these studies, especially prospective cohorts, collect over time. However, publications from and websites of these studies tend to present collapsed or aggregate data in traditional tables, often underestimating the true complexity of information in those data. One goal of the NIH Strategic Plan for Data Science is to “support the development and dissemination of advanced data management, analytics, and visualization tools.” We recently developed a robust, innovative, and scalable data visualization environment for the California Teachers Study (CTS), a prospective cancer epidemiology cohort. Methods: We identified the need to eliminate the practice of storing CTS data in a collection of disparate data silos and constructed a data warehouse to compile two decades of study data, eliminating additional harmonization steps. We then developed a data commons, a remotely accessible environment where cohort data could be utilized by our researchers and study collaborators. This allowed questionnaire-based data, cancer registry information, and hospitalization and mortality data from our study participants to be easily accessed in a secure environment. Serving as a single unified source of information, the data warehouse, coupled with the industry-leading data visualization tool Tableau, was configured using the following key epidemiologic cohort activities: reports, cohort selection, calculated values, and exploring and transforming data variables for analysis. Results: With the ability to sort and filter on specific covariates we can define subsets of populations for cohort selection and endpoint-specific analysis. Using interactive reports with drags and drops, and creating calculated variables within Tableau, gives us the ability to visualize time between event data, such as time from study entry to a cancer diagnosis, multiple cancer diagnoses, hospitalizations, or mortality without requiring programmatic back-end coding. Conclusion: The creation of a data warehouse and data commons has given the CTS the ability to visualize information about the demographics and health-related patterns of our study participants, leading to faster answers to complex questions about our data. The interactive data visualizations that we have created allow users the ability to identify new patterns easily, and drill down into the data for a more detailed look. Defining analytic populations through cohort selection and adding geospatial data to our data warehouse has already resulted in the discovery of new patterns that had been unidentified before—simply by providing the same information but utilizing a more accessible and innovative approach to presenting it. As epidemiology uses and shares more of its data, it’s essential to help the widest possible community understand those data in detail. Data visualization can provide specific benefits to key, essential population sciences research activities. Citation Format: Nadia T. Chung, James V. Lacey Jr., Emma Spielfogel, Paul Hughes, Sandeep Chandra, Elena Martinez, Jennifer Benbow. Opportunities for and benefits of incorporating data visualization in population sciences [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A01.

Data visualization and data mining are transdisciplinary tools for predictive and descriptive analytics. This presentation shows the abundance of data visualization tools currently available that are applicable for multi-disciplinary data. Some examples of visualization are presented as applied for multi-disciplines. Results of applying the most commonly used data visualization tools of Tableau [1] and Power BI [2] are presented as preliminary outputs of a funded Seed Money Grant that applies data for transmissible diseases for humans and also plant pathology. Tableau Software, LLC is an American interactive data visualization software company focused on business intelligence and can be used across all disciplines. Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations.[1] Power BI (Business Intelligence) is a suite of business analytics tools developed by Microsoft that is widely used across various industries for business reporting, data analysis, and decision-making. [2] Future directions of this research are discussed that include using other data mining and visualization methods such as image processing techniques for applications to data for plant pathology and human diseases.

The article describes the modern escalation of “big” and open data; situations where sociologist needs new competences such as coding and programming are analyzed. The author puts attention to two key research strategies when dealing with big data: self-localization and dipping into data. The first strategy refers to the researcher’s focus on traditional sociological analysis restricted by small sample size; the second one implies maximal use of the growing open source data. The challenges a researcher has to face when dealing with big data are as follows: (1) data extraction; (2) data processing and improvements; (3) data visualization. This is why a researcher should master coding and programming. Sociologists with humanitarian education should choose R statistical environment and Python programming language becasue they have simple syntax and allow solving complicated data processing, analysis and visualization tasks. The article provides examples of scripts as a demonstration of the possibilities of the R and Python programming languages.

<p>Over the last few years, the amount of large and complex data in the public domain has increased enormously and new challenges arose in the representation, analysis and visualization of such data. Considering the number of space missions that provided and will provide remote sensing data, there is still the need of a system that can be dispatched in several remote repositories and being accessible from a single client of commodity hardware.</p><p>To tackle this challenge, at the DLR Institute for Software Technology we have defined a dual backend frontend system, enabling the interactive analysis and visualization of large-scale remote sensing data. The basis for all visualization and interaction approaches is CosmoScout VR, a visualization tool developed internally at DLR, and publicly available on Github, that allows the visualization of complex planetary data and large simulation data in real-time. The dual component of this system is based on an MPI framework, called Viracocha, that enables the analysis of large data remotely, and allows the efficient network usage about sending compact and partial results for interactive visualization in CosmoScout as soon as they are computed.</p><p>A node-based interface is defined within the visualization tool, and this lets a domain expert to easily define customized pipelines for processing and visualizing the remote data. Each “node” of this interface is either linked with a feature extraction module, defined in Viracocha, or to a rendering module defined directly in CosmoScout. Being this interface completely customizable by a user, multiple pipelines can be defined over the same dataset to enhance even more the visualization feedback for analysis purposes.</p><p>Being an ongoing project, on top of these tools, as a novel strategy in EO data processing and visualization, we plan to define and implement strategies based on Topological Data Analysis (TDA). TDA is an emerging set of technique for processing the data considering its topological features. These include both the geometric information associated to a point, as well all the non-geometric scalar values, like temperature and pressure, to name a few, that can be captured during a monitoring mission. One of the major theories behind TDA is Discrete Morse Theory, that, given a scalar value, is used to define a gradient on such function, extract the critical points, identify the region-of-influence of each critical point, and so on. This strategy is parameter free and enables a domain scientist to process large datasets without a prior knowledge of it.</p><p>An interesting research question, that it will be investigated during this project is the correlation of changes of critical points at different time steps, and the identification of deformation (or changes) across time in the original dataset.</p>

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.

SummaryPathway analysis is widely used in genomics and omics research, but the data visualization has been highly limited in function, pathway coverage and data format. Here, we develop SBGNview a comprehensive R package to address these needs. By adopting the standard SBGN format, SBGNview greatly extend the coverage of pathway-based analysis and data visualization to essentially all major pathway databases beyond KEGG, including 5200 reference pathways and over 3000 species. In addition, SBGNview substantially extends or exceeds current tools (esp. Pathview) in both design and function, including standard input format (SBGN), high-quality output graphics (SVG format) convenient for both interpretation and further update, and flexible and open-end workflow for iterative editing and interactive visualization (Highlighter module). In addition to pathway analysis and data visualization, SBGNview provides essential infrastructure for SBGN data manipulation and processing.Availability and implementationThe data underlying this article are available as part of the SBGNview package is available on both GitHub and Bioconductor: https://github.com/datapplab/SBGNview, https://bioconductor.org/packages/SBGNview.Supplementary informationSupplementary data are available at Bioinformatics online.

This paper proposes a dynamic analytical processing (DAP) visualization tool based on the Bubble-Wall Plot. It can be handily used to develop visual warning systems for visualizing the dynamic analytical processes of hazard data. Comparative analysis and case study methods are used in this research. Based on a literature review of Q1 publications since 2017, 23 types of data visualization approaches/tools are identified, including seven anomaly data visualization tools. This study presents three significant findings by comparing existing data visualization approaches. The primary finding is that no single visualization tool can fully satisfy industry requirements. This finding motivates academics to develop new DAP visualization tools. The second finding is that there are different views of Line Charts and various perspectives on Scatter Plots. The other one is that different researchers may perceive an existing data visualization tool differently, such as arguments between Scatter Plots and Line Charts and diverse opinions about Parallel Coordinate Plots and Scatter Plots. Users' awareness rises when they choose data visualization tools that satisfy their requirements. By conducting a comparative analysis based on five categories (Style, Value, Change, Correlation, and Others) with 26 subcategories of metric features, results show that this new tool can effectively solve the limitations of existing visualization tools as it appears to have three remarkable characteristics: the simplest cartographic tool, the most straightforward visual result, and the most intuitive tool. Furthermore, this paper illustrates how the Bubble-Wall Plot can be effectively applied to develop a warning system for presenting dynamic analytical processes of hazard data in the coal mine. Lastly, this paper provides two recommendations, one implication, six research limitations, and eleven further study topics.

In this study, an experiment is conducted to measure the performance in speed and accuracy of interactive visualizations. A platform for interactive data visualizations was implemented using Django, D3, and Angular. Using this platform, a questionnaire was designed to measure a difference in performance between interactive and noninteractive data visualizations. In this questionnaire consisting of 12 questions, participants were given tasks in which they had to identify trends or patterns. Other tasks were directed at comparing and selecting algorithms with a certain outcome based on visualizations. All tasks were performed on high content screening data sets with the help of visualizations. The difference in time to carry out tasks and accuracy of performance was measured between a group viewing interactive visualizations and a group viewing noninteractive visualizations. The study shows a significant advantage in time and accuracy in the group that used interactive visualizations over the group that used noninteractive visualizations. In tasks comparing results of different algorithms, a significant decrease in time was observed in using interactive visualizations over noninteractive visualizations.

This article addresses the urgency of improving hydrological monitoring and effective water resource management, which is particularly crucial for flood prevention and rapid emergency response.The problem lies in the need to develop a tool for efficient collection, processing, and visualization of water level data in the rivers of the Lviv region. The research objective is to develop and implement an interactive web application based on ArcGIS for visualizing and analyzing water level dynamics. This tool is designed to display the location of hydrological posts, provide access to their current status information, and analyze time series of hydrological data. The key feature of this approach is the integration and visualization of data from both automated and manual (gauge) posts, significantly expanding the cover-age of hydrological information.Based on the stated objectives, the geoinformation approach has been implemented for collecting and visualizing hydro-logical post data in the Lviv region using ArcGIS tools. The data source for this system comprises observations provided by the Basin Water Resources Management of the Western Bug and Sian Rivers. As a result, the interactive ArcGIS monitoring dash-board has been developed. It visualizes spatial and temporal water level data, integrating a web map with tabular time series data. This ensures automatic synchronization between selecting a post on the map and displaying dynamic charts and analytical tables. The web application facilitates comprehensive analysis of hydrological data.The proposed solution significantly enhances hydrological monitoring and supports managerial decision-making regarding flood prevention and emergency response. Its practical value lies in its application for water resource management and flood risk assessment, including the visualization of indicators and access to operational information for specialists and au-thorities. Future research will focus on real-time data updates, integration with meteorological indicators, and development of models for forecasting water levels and potential flood zones.

Purpose The purpose of this study is to investigate the essential analytical skills required by accounting students to succeed in a rapidly evolving data-driven job market. Specifically, the study explores the role of data analytics, visualisation and software proficiency (e.g. Excel, Power BI) in the contemporary Australian accounting industry. The scope extends to providing recommendations for academia, professional bodies and employers on how accounting education can better align with the dynamic needs of the business environment by integrating these skills into curricula and professional development programmes. Design/methodology/approach Data was collected from Australian job listings incorporating accounting and data analytics tasks from popular job advertisement websites such as Seek, Indeed and Monster. Using the keyword “Data Analyst”, information was gathered on skills demanded, technology proficiency, industry sectors, professional qualifications and salary expectations. The data set comprised 73 observations across various job requirements and characteristics variables. Findings Critical analytical skills sought by accounting professionals in the current job market include extracting insights from big data, solving complex business problems, effective communication and data visualisation. Proficiency in tools like Microsoft Excel, SQL and Power BI is paramount. Originality/value This study enriches the literature by providing comprehensive insights into the contemporary demands for accounting analytics skills. It highlights the importance of integrating technology into accounting education and the varying skill requirements across industries. Additionally, it offers implications for professional qualifications and underscores the necessity for continuous monitoring and adaptation of accounting curricula to meet evolving industry needs.

One of the biggest growth areas in e-mental health resources has been the development and use of mobile mental health apps for smartphones and tablet devices. Such apps are being downloaded at increasing rates, but there have been questions about their efficacy and the research methodologies used to examine this. A review of the major app marketplaces, the Apple App Store and Google Play store, was conducted to locate apps claiming to offer a therapeutic treatment for depression and/or anxiety, and have research evidence for their effectiveness, according to their app store descriptions. App store descriptions were also analyzed to determine whether the app had been developed with mental health expert input; whether they had been developed in association with a government body, academic institution, or medical facility; and, whether or not they were free to download. Overall, 3.41% of apps had research to justify their claims of effectiveness, with the majority of that research undertaken by those involved in the development of the app. Other results indicated that 30.38% of shortlisted apps claimed to have expert development input; 20.48% had an affiliation with a government body, academic institution, or medical facility; and, 74.06% were free to download. Future research must consider other methodologies that may facilitate more research being completed on a greater number of apps, and future development needs to incorporate greater levels of input by mental health experts. Ways in which app stores could play a key role in encouraging more scientific research into the effectiveness of the mental health apps they sell are discussed.

Event-related potential and oscillatory cortical activities of artistic methodology in information visualization design in human–computer interface

Several resources and tools have been developed for carrying out health inequality analysis, including the preparation of disaggregated data and calculation of summary measures of health inequality. There is also a body of literature that addresses best practices for the preparation of commonly used graphs and maps. There is, however, little guidance for the selection and design of appropriate data visuals for corresponding health inequality data and assessments. This paper aims to fill this gap by describing the types of graphs and maps most frequently used for reporting health inequality analyses and recommending how they can be effectively applied to distinct reporting scenarios. For a range of different data visual types, we assess how they can be designed for effectiveness, attending to the principles of accuracy, utility and efficiency. We assess the suitability of different data visuals for reporting the latest status of inequality, change in inequality over time, inequality assessments across settings and impact of eliminating inequality, considering how different data visualization approaches may be needed for reporting disaggregated data versus summary measures of inequality. Effective visuals can enhance the impact of health inequality reporting and support evidence-informed action to advance health equity.