Angular Histograms: Frequency-Based Visualizations for Large, High Dimensional Data

Wan

et al. 2023

Sci Rep

Air quality is a significant environmental issue among the Chinese people and even the global population, and it affects both human health and the Earth’s long-term sustainability. In this study, we proposed a multiperspective, high-dimensional spatiotemporal data visualization and interactive analysis method, and we studied and analyzed the relationship between the air quality and several influencing factors, including meteorology, population, and economics. Six visualization methods were integrated in this study, each specifically designed and improved for visualization analysis purposes. To reveal the spatiotemporal distribution and potential impact of the air quality, we designed a comprehensive coupled visual interactive analysis approach visually express both high-dimensional and spatiotemporal attributes, reveal the overall situation and explain the relationship between attributes. We clarified the current spatiotemporal distribution, development trends, and influencing factors of the air quality in China through interactive visual analysis of a 25-dimensional dataset involving 31 Chinese provinces. We also verified the correctness and effectiveness of relevant policies and demonstrated the advantages of our method.

Section: Methodsmentioning

confidence: 99%

High-dimensional spatiotemporal visual analysis of the air quality in China

Wan

et al. 2023

Sci Rep

“…To enhance PCP, Bok et al added color-stacked histograms to parallel coordinate axes [24], enabling users to visually inspect the relationships between attributes even when the attribute axes are separated by a large distance. An accessible approach that enables users to study clustering, linear correlations, and outliers in large datasets without running on the overdraw and clutter issues of the original PCP is a histogram attached to the PCP axis [25] that depicts both the density of the polylines and their slopes. In the geo-coordinated parallel coordinates (GCPC) [26] method, box plots are attached above the parallel axes to illustrate the relationships between the paired coordinate axes.…”

Section: Related Workmentioning

confidence: 99%

Visually Enhanced Parallel Coordinates Plot With Two-Dimensional Kernel Density Scatter Plots

Liu,

Wan,

Wang

et al. 2023

IEEE Access

Parallel coordinates plots are popular tools for high-dimensional data visualization. To alleviate the difficulties caused by the inherent defects that arise when the dimensions are increased, this study attached two-dimensional kernel density scatter plots to parallel coordinates plot, which integrates the Cartesian and parallel coordinate systems, to combine their benefits and conveniently explore the relationships between paired attributes. The collaborative design combining the two visual types was realized through the corelative axis swap interaction method, which allows users to freely exchange parallel coordinate axes while also updating the associated kernel density scatter plots based on the Cartesian coordinate system. In this study, we obtained six cases of real datasets and performed a field trial study to assess the usefulness of our method in assisting users in evaluating correlations between paired attributes. The results show that our method is more useful for analyzing the relationships between paired attributes.INDEX TERMS parallel coordinates plot (PCP), scatter plot, two-dimensional kernel density contour plot, visual analysis, interactive analysis

“…Instead, we considered showing data distribution in each axis because it helps users understand clusters in each group (e.g., normal vs. abnormal). To represent data distributions in each variable, angular histograms [51] were generated to illustrate the density and slopes of underlying polylines overlaid onto the parallel coordinates. Angular histogram is a technique that presents the frequency distribution of underlying data within each parallel axis by measuring the frequency of the data and the directional information of polylines in each axis.…”

Section: Visual Analysismentioning

confidence: 99%

“…Therefore, no unique statistical approach can be applied to determine an appropriate bin size for handling all variables in the parallel coordinates visualization. Instead, a user-driven approach is commonly utilized to determine the bin size of histograms in parallel coordinates [51,53,54]. To generate density distributions, the angular histogram utilizes a user-defined binning approach to determine the denseness of each distribution.…”

Section: Visual Analysismentioning

confidence: 99%

Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization

Ji,

Jayarathna,

Perrotti

et al. 2023

Applied Sciences

Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities by integrating feature extraction, machine learning, and visual analysis based on EEG signals collected from individuals with neurological and mental disorders. The classification performance of four feature approaches—EEG frequency band, raw data, power spectral density, and wavelet transform—is assessed using machine learning techniques to evaluate their capability to differentiate neurological disabilities in short EEG segmentations (one second and two seconds). In detail, the classification analysis is conducted under two conditions: single-channel-based classification and region-based classification. While a clear demarcation between normal (healthy) and abnormal (neurological disabilities) EEG metrics may not be evident, their similarities and distinctions are observed through visualization, employing wavelet features. Notably, the frontal brain region (frontal lobe) emerges as a crucial area for distinguishing abnormalities among different brain regions. Also, the integration of wavelet features and visual analysis proves effective in identifying and understanding neurological disabilities.