HeapCraft

Data visualization has been widely utilized in various scenarios in data analytics for business purposes, especially helping novice readers make sense of complex dataset with interactive functions. However, due to an insufficient theoretical understanding of the process of developing interactive functions and visual presentations, interactive data visualization tools often display all available automatic graphing functions in front of users, instead of guiding them to choose a visualization based on their demands. Thus, this paper is intended to construct a process of developing interactive visualization with a specific focus on enabling the interoperation between design and interpretation. Stemmed from organizational semiotics, an abductive process will be portrayed in this paper to interpret the process of developing interactive data visualization. Especially the interactive functions will be employed in an iterative process, where producers can be aware of and respond to readers’ information demands on semantic, pragmatic and social levels.

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual's health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

Nearly one third of the world's population live in coastal areas, and ten of the fifteen most populous cities in the world lie on a coast. Inhabitants of the Low Elevation Coastal Zone (LECZ) — defined as the contiguous area along the coast that is less than 10 meters above sea level — make up 10% of the world's population and 13% of the world's urban population. Sea level rise, coastal inundation and associated shoreline retreat have emerged as one of the primary threats to these populations and the resources located near the coastal fringe. To meet the needs of governments, planners and managers, researchers are continually faced with the challenge of integrating large volumes of complex environmental and spatial-temporal data. Typically the spatial and temporal components of data sets are underutilized because methods for effectively handling these data have not been available. To address these issues, Eonfusion, a 4-Dimensional software solution, is easily incorporated into the geospatial workflow to significantly enhance the ease with which we can now integrate and explore complex spatially and temporally variant data sets. This paper explores case studies along prominent coastal regions in which, as example, models are developed to predict the impact of rising sea levels on low-lying coastal areas focusing on: ✓ Coastal Inundation ✓ Coastal Vulnerability ✓ Property Devaluation The inundation is spatially modeled as a function of time and enables the visualization of sea level rise scenarios to assess the extent and impact. The coastal vulnerability mapping highlights the analytical power of Eonfusion, through the efficient integration of inundation and vulnerability models to demonstrate the universal application of the software to the field of climate change research. The third model fuses the cadastral layer and a simple property valuation model to complete the scenario, thus demonstrating a powerful pathway for the estimation and visualization of the impact of these climate change events. The data for these case studies include: • Sea level rise scenarios from IPCC stage 4 • LID AR Elevation Data • Cadastral Parcels and Value Indicators • Storm Surge Information • Vulnerability Mapping The processing steps required to integrate, analyze and visualize these models are: • Generation of 3D terrain model from LID AR data • Adjustments for any Height Data (AHD) discrepancies • Integrate IPCC sea level rises and storm surges into Sea level rise timeseries • Identification of sinks using Eonfusion API application • Calculate inundation levels and tipping points at which sinks get filled • Fuse Cadastre and value model with 3D surface — value decreases as % of title flooded • Airphoto drape • Set up visualization scene with integrated graph for all scenarios The outcomes from this work include the identification of powerful pathways through the employment of new visualization and spatial-temporal analysis tools for: • Dynamic scenario based modeling for assessing cost and environmental impacts from climate change; • The provision of a mechanism enabling the visualization of the complex spatial and temporal patterns from a wide range of data derived empirically and from models. This enables key stakeholders to rapidly assess scenarios and their likely impacts • Modeling of this type could be used in a number of areas including fire, flood, tsunami, hurricanes, etc

Background: The United States Centers for Disease Prevention and Control (CDC) National Program of Cancer Registries (NPCR) works to disseminate cancer surveillance data to multiple audiences in accessible, discoverable, and usable formats. To this end, CDC released the official federal cancer statistics, U.S. Cancer Statistics (USCS), in a data visualization tool available at www.cdc.gov/cancer/dataviz . We made further enhancements to the online tool since its initial release in 2017. Aim: We describe the process and enhancements made to the USCS data visualization tool's content, graphical displays, and sharing capabilities. Methods: CDC partnered with Agency for Toxic Substances and Disease Registry's Geospatial Research, Analysis, and Services Program to further work initiated by a group of cancer registrars, program planners, epidemiologists, computer programmers, and communication specialists to improve the visual presentation of USCS cancer incidence and mortality data. We conducted usability testing and implemented changes to the Web site's layout and added content, including county-level data, survival data, and prevalence estimates. Results: New features include county statistics, survival, prevalence, Puerto Rico data, and tobacco-, alcohol-, and obesity-related cancers data displays. The tool was also enhanced to better display on mobile devices. Data displays on national and state incidence, mortality and trends are available as maps and bar charts with interpretive text when users scroll over each graphic. Users can customize displays of overall and cancer-specific statistics, download data tables, and share each page via social media. Conclusion: Surveillance data are fundamental to measure progress and target action. CDC's interactive USCS data visualization tool is designed to make cancer data more accessible and usable to multiple users, including the general public, media, policy makers and planners. We will continue to improve the tool's accessibility and usefulness to facilitate the interpretation and sharing of cancer data.

The detection of anomalous events in large multivariate data is sought in many domains. Analysis of data is an important fraud detection procedure in detecting suspicious events and prevent attempts to defraud. While now the data is becoming more complicated and difficult as data scales and complexities increase than ever before, the rich insights within the data may be difficult to identify by traditional means and often remain hidden. People require powerful tools to extract valid conclusions from the data while maintaining trustworthy and interpretable results. Hence, various fraud detection approaches have started to exploit Visual Analytics (VA) techniques to reveal the hidden knowledge in such fraudulent activities. Interactive data visualization tools have substantial potential for making the detection of fraudulent transactions more efficient and effective by allowing the investigator to change the representation of data from text and numeric into graphics and filter out subsets of transactions for further fraud investigation. However, little research to date has directly examined the efficacy of data visualization techniques for fraud detection especially telecommunication fraud. In this paper, we present an overview of several fraud detection solutions that use data visualization techniques to detect fraudulent transactions in the telecommunication domain. The paper concludes by discussing how academic research might proceed in investigating the efficacy of interactive data visualization tools for fraud detection.

Methodological improvements and new inventions in biological experiments, such as DNA chips, microarrays, mass spectrometry, exhaustive two-hybrid screening provides us information about the relations of genes (or its products) comprehensively. Those information are, for example, about physical or genetic interactions of proteins, clusters of genes that are co-expressed, sets of proteins of macromolecular complexes, [1, 2, 3, 4] which can be thought as components of signals in cellular signal transduction pathways. Bottom up modeling by linking up those relations will be a necessary step in systematic reconstruction of the pathways. Unfortunately, however, the actual data obtained from experiments are uncategorized and contain artifacts. Thus, an interactive data visualization tool was developed to study the nature of those interaction data.

This study forms a broader input on ICT and learner integration; it has specific scientific results that not only address access but also go beyond social and economic inequalities that communities face with limitations on social and economic activity that impact and scientific and academic growth of communities. This study aimed to investigate the effects of interactive visualization tools on the mathematical performance of intermediate pupils at Dangguinan Elementary School, Conner, Apayao, Philippines. The problem being studied centers on persistent difficulties in Mathematics performance, particularly learners' struggles in grasping abstract concepts and applying problem-solving skills effectively. Amongst the tools the study investigated included GeoGebra, simulations, and animated presentations could address persistent challenges in Mathematics performance. The research utilized a mixed-methods design, combining both quantitative and qualitative approaches to examine improvements in mathematics learning and explore implementation challenges. The study's mixed-methods approach offers a comprehensive understanding of both the quantitative impact on learning outcomes and the qualitative challenges encountered during implementation. Its findings are crucial for educational stakeholders in developing countries like the Philippines, as they highlight the potential of ICT integration in addressing persistent low math proficiency, while also underscoring the critical need to address infrastructure and digital literacy gaps for successful technology adoption in underserved areas. The study was conducted at Dangguinan Elementary School, Conner, Apayao, during the academic year 2024–2025. Forty-six pupils from Grades 4 to 6 participated in the study. The research applied the DepEd Likert Scale to group learners by their mathematics skills, instead of just using mean scores to assess them. Learners who had 15–17 points out of 20 (equivalent to 71%–85%) received the rating of "Proficient"—signifying they met the expected understanding and skills for their age. By using this method, educators could watch how each learner was improving and decide who might need more help. Rather than presenting average scores, the study used a thorough scale to better show how learning improved when interactive visualizations were used. A pre-test was administered to assess baseline mathematical proficiency. Following this, an instructional intervention was implemented using interactive visualization tools. After the intervention, a post-test was conducted to evaluate performance improvement. Additionally, interviews were carried out to understand the challenges encountered during the intervention. Quantitative results were analyzed using descriptive statistics and a paired sample t-test, while qualitative data were thematically analyzed. The pre-test mean score was 7.6, falling under the "Developing" performance level. Post-test scores improved significantly to a mean of 11.8, aligning with the "Approaching Proficiency" level. Statistical analysis revealed a significant difference between pre-test and post-test results (P = .000). Interviews revealed two major challenges: poor internet connectivity and insufficient orientation in using the tools, both of which affected the learners’ engagement with interactive visualization. To address this, alternative offline versions of instructional materials were provided, ensuring accessibility despite connectivity issues. Additionally, structured training sessions were introduced to familiarize learners with visualization tools before implementation, enhancing their confidence and ability to navigate digital learning environments effectively. The study concludes that interactive visualization tools significantly enhance intermediate pupils' mathematical understanding and performance. The research shows that using these interactive tools greatly improves how intermediate pupils understand and perform in mathematics. Still, overcoming problems with access and training is necessary to use them well. According to the study, upgrading the infrastructure and adding satellite technology for better connectivity, as well as apps that let learners work offline, would help make technology-aided math more effective. In addition, teachers should receive instruction, and learners should participate in orientation to help ICT become an important part of learning mathematics. Schools need support from educational stakeholders towards long-term digital change, helping them select and learn how to use the needed technology.

<p>Strong winds associated with extratropical cyclones are one of the most dangerous natural hazards in Europe. These high winds are mostly connected with five mesoscale dynamical features, namely the warm (conveyor belt) jet (WJ), the cold (conveyor belt) jet (CJ), (post) cold-frontal convective gusts (CFC), strong cold sector winds (CS) and – at least in some storms – the sting jet (SJ). While all these have strong winds in common, the timing, location and some further characteristics tend to differ and hence likely also the forecast errors occurring in association with them.</p><p>Here we present a novel objective identification approach for the features listed above, based on a probabilistic random forest using each feature’s most important characteristics in wind, rainfall, pressure and temperature evolution. However, as CJ and SJ turn out to be difficult to distinguish in surface observations alone, we decided to consider the two features together. This identification can then be used to generate a climatology for Central Europe, to analyse forecast errors specific to individual features, and to ultimately improve forecasts of high wind events through feature-dependent statistical post-processing. To achieve this, we strive to identify the features in irregularly spaced surface observations and in gridded analyses and forecasts in a consistent way, thus making it independent of spatial dependencies and gradients.</p><p>To train the probabilistic random forest, we subjectively identify the four storm features in twelve winter storm cases between 2015 and 2020 in both hourly surface observations and high-resolution reanalyses of the German COSMO model over Europe, using an interactive data analysis and visualisation tool. Results show that mean sea-level pressure (tendency), potential temperature, precipitation amount and wind direction are most important for the distinction between the features. From the random forest we get occurrence probabilities for each feature at every station, which can be converted into areal information using Kriging.</p><p>The results show a satisfactory identification for all features, especially for WJ and CFC. We encounter, however, some difficulties to clearly distinguish the CJ and CS, which are dynamically similar. A climatology is currently being compiled for both surface observations and the reanalyses over a period of around 20 years using the trained probabilistic random forests and further for high-resolution COSMO ensemble forecasts, for which we want to analyse forecast errors and develop feature-dependent postprocessing procedures.</p>

Big data analytics in healthcare would be almost useless, without suitable tools allowing users see" them, and gain insight for their situated decisions. The VVH (Valuable Visualization in Healthcare) workshop focuses on the role of interactive data visualization tools by which people can make sense of healthcare data; these data include sensor data, the messages exchanged in social media, the emails between patients and their doctors, the content of patient records as well as the discussions among different specialists that led to such record content. All these data are used by different types of users, like doctors, nurses, policy makers and common citizens. The VVH workshop aims at contributing on: The assessment of the usability of advanced interactive tools of health-related data visualization; the assessment of the quality of the information and value for insight that these tools make available to their users; the collection of reports of either success stories or failures in the appropriation and use of complex and multidimensional healthcare datasets; the collection of methodological and design-oriented contributions that could share methods, techniques, and heuristics for the design of interactive tools and applications supporting data work, data telling and data interpretation in healthcare.

Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. Visualizations have to be carefully selected in order for them to partake in the behavioral transformation that end users are encouraged to adopt. In this paper, six energy data visualizations are evaluated in a randomized controlled trial fashion to determine the optimal data visualization tool. Conventional visualizations, namely bar, line, and stacked area, are compared against enhanced charts, namely spiral, heatmap, and stacked bar, in terms of effectiveness, aesthetic, understandability, and three analysis questions. The study is conducted through a questionnaire in a mobile application. The application, created through React Native, is circulated to participants in multiple countries, collecting 133 responses. From the received responses, conventional plots scored higher understandability (by 22.74%), effectiveness (by 13.44%), and aesthetic (by 10.54%) when compared with the enhanced visualizations. On the flipside, enhanced plots generated higher correct analysis questions’ responses by 8% compared to the conventional counterparts. From the 133 collected responses, and after applying the unpaired t-test, conventional energy data visualization plots are considered superior in terms of understandability, effectiveness, and aesthetic.

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.

BackgroundConstant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases.ResultsIn this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease.ConclusionsThe KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.

Patients and providers often lack clinical decision tools to enable effective shared decision making. This is especially true in the rapidly changing therapeutic landscape of metastatic kidney cancer. Using the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria, a validated risk prediction tool for patients with metastatic renal cell carcinoma, we created and user-tested a novel interactive visualization for clinical use. An interactive visualization depicting IMDC criteria was created, with the final version including data for more than 4,500 patients. Usability testing was performed with nonmedical lay-users and medical oncology fellow physicians. Subjects used the tool to calculate median survival times based on IMDC criteria. User confidence was surveyed. An iterative user feedback implementation cycle was completed and informed revision of the tool. The tool is available at CloViz-IMDC. Initially, 400 lay-users and 15 physicians completed clinical scenarios and surveys. Cumulative accuracy across scenarios was higher for physicians than lay-users (84% v 74%; P = .03). Eighty-three percent of lay-users and 87% of physicians thought the tool became intuitive with use. Sixty-eight percent of lay-users wanted to use the tool clinically compared with 87% of physicians. After revisions, the updated tool was user-tested with 100 lay-users and 15 physicians. Physicians, but not lay-users, showed significant improvement in accuracy in the updated version of the tool (90% v 67%; P = .008). Seventy-two percent of lay-users and 93% of physicians wanted to use the updated tool in a clinical setting. A graphical method of interacting with a validated nomogram provides prognosis results that can be used by nonmedical lay-users and physicians, and has the potential for expanded use across many clinical conditions.

For many years, students have been taught to visualise data by drawing graphs. Recently, there has been a growing trend to teach statistics, particularly statistical concepts, using interactive and dynamic visualisation tools. Free down-loadable teaching and simulation software designed specifically for schools, and more general data visualisation tools are increasingly being used in New Zealand classrooms. This paper discusses four examples: the use of GenStat for Teaching and Learning Schools and Undergraduate (GTL); Auckland University’s iNZight and VIT (Visual Inference Tools) for teaching bootstrapping and randomisation; the CAST e-books, and the use of data visualisation tools to assist learning concepts in official statistics. All these tools are publically available and several are already being used internationally. First published November 2014 at Statistics Education Research Journal Archives

NASA's Solar System Treks Project (SSTP) online portals provide web-based suites of interactive visualization and analysis tools to enable planetary scientists, mission planners, students, and the general public to access mapped data products from past and current missions for a growing number of planetary bodies. The Solar System Treks portals provide advanced data visualization and analysis capabilities for data returned from a vast number of instruments aboard many past and current missions to a growing number of planetary bodies throughout Solar System. Multiple map projections as well as interactive 3D views are available to optimize visualization of different landforms. A detailed set of analysis tools helps users find and interpret morphological features across diverse landscapes on the surfaces of planets, moons, and asteroids. In some cases, these tools make use of machine learning and artificial intelligence to help users locate, identify, and understand landforms drawn from very large datasets. Having an integrated suite of portals presenting geomorphology across a range of planetary bodies within the Solar System greatly facilitates studies of comparative planetology. The portals are currently being used for site selection and analysis by NASA and its international and commercial partners supporting upcoming missions. Today, 11 web portals in the program are available to the public. This list includes portals for the Moon; the planets Mercury, Venus, and Mars; the asteroids Bennu, Ryugu, Vesta, and Ceres; and the outer moons Titan and Europa. The Icy Moons Trek portal features seven of Saturn’s smaller icy moons. All of the portals are unified under a project home site with supporting content. These web-based portals are free resources and publicly available. This presentation for EGU will detail and share examples of the how the portals can be applied to research in planetary geomorphology.