COVID-19 restrictions have detrimental effects on the population, both socially and economically. However, these restrictions are necessary as they help reduce the spread of the virus. For the public to comply, easily...
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COVID-19 restrictions have detrimental effects on the population, both socially and economically. However, these restrictions are necessary as they help reduce the spread of the virus. For the public to comply, easily comprehensible communication between decision makers and the public is thus crucial. To address this, we propose a novel 3-D visualization of COVID-19 data, which could increase the awareness of COVID-19 trends in the general population. We conducted a user study and compared a conventional 2-D visualization with the proposed method in an immersive environment. Results showed that the our 3-D visualization approach facilitated understanding of the complexity of COVID-19. A majority of participants preferred to see the COVID-19 data with the 3-D method. Moreover, individual results revealed that our method increases the engagement of users with the data. We hope that our method will help governments to improve their communication with the public in the future.
Fire has been a worldwide disaster, and it is important to analyze regional fire risk for better fire prevention. Regional fire risk can be generally analyzed by using data visualization of fire incidents. A dataset o...
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Fire has been a worldwide disaster, and it is important to analyze regional fire risk for better fire prevention. Regional fire risk can be generally analyzed by using data visualization of fire incidents. A dataset of 20 622 fire incidents in Changsha City of China from 2011 to 2017 was processed and filtered as the origin risk points. Then, a generalized risk model (GRM) was established for calculating fire risk values of anywhere in Changsha. By using JavaScript and HTML5, the generalized fire risk graphics were drawn based on mapping different fire risk values with different colors. For visualizing fire incidents, the inner and outer generalization radiuses of the origin risk point were set to be 1 (110 m) and 10 (1100 m), respectively. It was found that the high and medium fire risk areas were mainly distributed in urban areas and market towns, especially in central city. The spatial distribution characteristics of fire risk of different time ranges and different causes were significantly varied. The results can provide guidance of fire prevention for the fire department in Changsha. Other cities' regional fire risk and even other types of disaster risk can also be generally analyzed using GRM.
data visualization is the graphical and pictorial display of data. data visualization and statistical graphics are often conceived of as latest advancement in statistics and relatively moves hand in hand. Process of c...
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With the explosion of Big data, visualizing statistical data became a challenging topic that has involved many research efforts over the last years. Interpreting Big data and efficiently showing information for good u...
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With the explosion of Big data, visualizing statistical data became a challenging topic that has involved many research efforts over the last years. Interpreting Big data and efficiently showing information for good understanding are difficult tasks, especially in healthcare scenarios, where different types of data have to been managed and cross-related. Some models and techniques for health data visualization have been presented in literature. However, they do not satisfy the visualization needs of physicians and medical personnel. In this paper, we present a new graphical tool for the visualization of health data, that can be easily used for monitoring health status of patients remotely. The tool is very user friendly, and allows physician to quickly understand the current status of a person by looking at colored circles. From a technical point of view, the proposed solution adopts the geoJSON standard to classify data into different circles.
Intelligent electrical power grids, widely referred to as smart grids (SGs), rely on digital technology resources, especially communication and measurement devices, becoming a cyber-physical energy system. Massive dat...
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Intelligent electrical power grids, widely referred to as smart grids (SGs), rely on digital technology resources, especially communication and measurement devices, becoming a cyber-physical energy system. Massive data flow between grid elements makes smart grids more vulnerable to cyber-attacks. Power system state estimation (SE)-an essential function of energy management systems-is one of these data integrity attacks' targets. Integrity validation routines can fail when insufficient redundancy levels are reached, and spurious data occur. These levels are associated with critical data, i.e., those whose unavailability makes the grid unobservable. data redundancy is a metric that gives a precarious indication that SE can run. Alternatively, it is more appropriate to quantify this function strength concerning its results' reliability, which can be achieved by criticality analysis (CA). This paper proposes a novel approach to visualize the results of an extensive CA through representative graphs;they facilitate understanding the usefulness of CA. Critical sets of measurements are generalized, and several metrics are proposed to reveal measuring system vulnerabilities, assisting the design of protection schemes to resist cyber-attacks. Simulations attained on the IEEE-30bus system evince significant improvements in the interpretation/use of CA.
With the rapid development of information technology, the traditional industry is changing dramatically. There are big opportunities in the domain Aerospace & Aviation at the same time. As the core software of the...
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The corporate bond market is one of the areas that has witnessed profound changes since the last financial crisis, prompting regulators (and industry participants) to question its resilience under stress. We are build...
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The present project was based on the realization of a descriptive model for preschool teachers and managers, in order to know the results obtained during the confinement as a result of COVID 19, which was characterize...
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Industries and the manufacturing sector are among the contexts that can enjoy great benefits from adopting digital technologies improvement. In this sense, Industry 4.0 has represented a clear revolution, having alrea...
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The efficiency of modern computer graphics allows us to explore collections of space curves simultaneously with "drag-to-rotate" interfaces. This inspires us to replace "scatterplots of points" wit...
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The efficiency of modern computer graphics allows us to explore collections of space curves simultaneously with "drag-to-rotate" interfaces. This inspires us to replace "scatterplots of points" with "scatterplots of curves" to simultaneously visualize relationships across an entire dataset. Since spaces of curves are infinite dimensional, scatterplots of curves avoid the "lossy" nature of scatterplots of points. In particular, if two points are close in a scatterplot of points derived from high-dimensional data, it does not generally follow that the two associated data points are close in the data space. Andrews plots provide scatterplots of curves that perfectly preserve Euclidean distances, but simultaneous visualization of these graphs over an entire dataset produces "visual clutter" because graphs of functions generally overlap in 2D. We mitigate this "visual clutter" issue by constructing computationally inexpensive 3D extensions of Andrews plots. First, we construct optimally smooth 3D Andrews plots by considering linear isometrics from Euclidean data spaces to spaces of planar parametric curves. We rigorously parametrize the linear isometrics that produce (on average) optimally smooth curves over a given dataset. This parameterization of optimal isometrics reveals many degrees of freedom, and (using recent results on generalized Gauss sums) we identify a particular member of this set which admits an asymptotic "tour" property that avoids certain local degeneracies as well. Finally, we construct unit-length 3D curves (filaments) from Bishop frames induced by 3D Andrews plots. We conclude with examples of filament plots for several standard datasets,(1) illustrating how filament plots avoid "visual clutter". (C) 2022 Elsevier Inc. All rights reserved.
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