Parallel Coordinates Research Articles

Modeling the spatiotemporal heterogeneity of land surface temperature and its relationship with land use land cover using geo-statistical techniques and machine learning algorithms.

Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia's cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High-High (H-H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.

Constructing Explainable Classifiers from the Start—Enabling Human-in-the Loop Machine Learning

Interactive machine learning (IML) enables the incorporation of human expertise because the human participates in the construction of the learned model. Moreover, with human-in-the-loop machine learning (HITL-ML), the human experts drive the learning, and they can steer the learning objective not only for accuracy but perhaps for characterisation and discrimination rules, where separating one class from others is the primary objective. Moreover, this interaction enables humans to explore and gain insights into the dataset as well as validate the learned models. Validation requires transparency and interpretable classifiers. The huge relevance of understandable classification has been recently emphasised for many applications under the banner of explainable artificial intelligence (XAI). We use parallel coordinates to deploy an IML system that enables the visualisation of decision tree classifiers but also the generation of interpretable splits beyond parallel axis splits. Moreover, we show that characterisation and discrimination rules are also well communicated using parallel coordinates. In particular, we report results from the largest usability study of a IML system, confirming the merits of our approach.

A Spatial Temporal Classification Analysis And Visualization Of Tropical Cyclone Tracks In Bay Of Bengal Using GIS

Tropical cyclones (TC) are among the most devastating forms of natural hazards and the east coast of India is more prone to TC landfall causing significant socio-economic impacts. The Bay of Bengal (BoB) which forms the eastern sub basin of North Indian Ocean experiences the seasonally reversing monsoon, depression and TCs. In this study TC best track dataset of NIO basin over the period 1960 - 2016 from the IBTrACKs archive maintained by NOAA are used. In this work Firefly optimization is coupled with FCM for TC tracks classification. The classical FCM uses random initialization of cluster centroid often gets trapped in local optimal problem. The firefly algorithm is applied on the FCM for the cluster centroid computation, in this way improving the efficiency of FCM algorithm. The obtained classes are then projected in the visualization space. Visualizations are generated using the GIS environment to gain insight into the spatial distribution of TC tracks over decades. This study aims to develop a comprehensive assessment of variability in tropical cyclones with respect to ENSO modulated events, inter decadal variability and track sinuosity. In this paper we attempt to convey the cognitive results of comparative visualizations of TC tracks over Arabian Sea and Bay of Bengal sub basin during the strong, very strong El Niño and La Niña events. Finally we use Parallel Coordinate Plot (PCP) a visualization technique to demonstrate the correlation patterns of the TC parameters.

Comparative evaluations of visualization onboarding methods

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the effectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at different levels: (1) Firstly, we explored the effect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding. Particularly, participants commented that for highly familiar visualization types no onboarding is needed. However, for the most unfamiliar visualization type — the parallel coordinates plot — performance improvement can be observed with onboarding. (2) Thus, we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) As videos are a traditional method to support users, we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail. Therefore, for our third study, we gathered data towards users’ experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the effect of task difficulty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of visualization.

Phase prediction and effect of intrinsic residual strain on phase stability in high-entropy alloys with machine learning

The phase formation and stability of high-entropy alloys (HEAs) are crucial to their properties, but the efficient prediction of them remains challenging due to the associated vastness of the composition space. In the present work, we study the formation and stability of solid-solution (SS) phases with a dataset consisting of 656 HEAs using machine learning (ML). We demonstrate the independence of nine physical parameters by self-organizing map (SOM) algorithm and rank them by feature importance methods, discovering that the root mean square residual strain (εrmsm s) is the most critical parameter. Based on the theoretical calculations and parallel coordinates plot (PCP) technique, we find that εrms can be used to quantitatively predict the stability of SS phases. Moreover, we apply the support vector machine, gradient boosting decision tree, multi-layer perceptron, and logistic regression algorithms to predict the formation of SS phases with testing accuracy values of 95.22 %, 94.78 %, 90.87%, and 89.57 %, respectively. Our results provide a perspective on the stability of SS phases from the viewpoint of intrinsic residual strain and show the support vector machine could be a better algorithm to predict the phase formation of HEAs.

Using data visualization to detect patterns in whole-person health data.

Data visualization techniques are useful for examining large multidimensional data sets. In this exploratory data analysis (EDA) study, we applied a visualization pattern detection and testing process to deidentified data to discover patterns in whole-person health for adults 65 and older. Whole-person health examines a person's environmental, psychosocial, and physical health, as well as their health-related behaviors; and assesses their strengths, challenges, and needs. Strengths are defined as assets and capabilities in the face of short-or long-term stressors. We collected data using a mobile application that delivers a comprehensive whole-person assessment using a simplified version of a standardized instrument, the Omaha System. The visualization pattern detection process is iterative, includes various techniques, and requires visualization literacy. The data visualization techniques applied in this analysis included bubble charts, parallel coordinates line graphs, box plots, and alluvial flow diagrams. We discovered six patterns within the visualizations. We formulated and tested six hypotheses based on these six patterns, and all six hypotheses were supported. Adults 65 and older had more strengths than challenges and more challenges than needs (p < 0.001). Strengths and challenges were negatively correlated (p < 0.001). Unexpectedly, a subset of adults 65 and older who had many, but not all, strengths had significantly more needs (p = 0.04). The use of standardized terminology with its inherent data interrelationships was key to discovering patterns in whole-person health. This methodology may be used in future EDA research using new data sets.

Multiobjective optimization and Pareto front visualization techniques applied to normal conducting rf accelerating structures

There has been a renewed interest in applying multiobjective (MO) optimization methods to a number of problems in the physical sciences, including to rf structure design. The results of these optimizations generate large datasets, which makes visualizing the data and selecting individual solutions difficult. Using the generated results, Pareto fronts can be found giving the trade-off between different objectives, allowing one to utilize this key information in design decisions. Although various visualization techniques exist, it can be difficult to know which technique is appropriate and how to apply them successfully to the problem at hand. First, we present the setup and execution of MO optimizations of one standing wave and one traveling wave accelerating cavity, including constraint handling and an algorithm comparison. In order to understand the generated Pareto frontiers, we discuss several visualization techniques, applying them to the problem, and give the benefits and drawbacks of each. We found that the best techniques involve clustering the resulting data first to narrow down the possible choices and then using multidimensional visualization methods such as parallel coordinate plots and decision maps to view the clustered results and select individual solutions. Finally, we give some examples of the application of these methods and the cavities selected based on arbitrary design requirements.

Engineering Poster E5: Digitalisation and automation for the development of modern engineering workflows

Poster E5 Automation, as part of the Energy Industry’s Digital Transformation, is producing significant time savings in modern engineering workflows whilst minimising opportunity for human error. A challenge we face is ensuring these automated workflows are accessible to people in the industry without programming experience. Xodus’ solution to this problem is FLUX. FLUX works as an object-oriented, ‘no-code’, web-based platform which tracks, stores and executes both technical and non-technical workflows. Workflows are first automated in-house by developing scripts, which are then hosted on FLUX allowing them to be easily shared across the business without requiring programming knowledge. A popular use case for FLUX is in the concept engineering phase of a project when performing parametric analyses. Multiple simulation cases (typically thousands) assessing all the different possible combinations of design parameters can be interrogated at once. FLUX can then automatically output an interactive parallel coordinate plot to highlight the impact of each parameter on the overall system. Platforms such as FLUX are an effective solution to increasing accessibility to automation for the industry via ‘no-code’ mechanisms, the ability to interface through an intuitive digital platform where project data is stored, exchanged and analysed through automated calculations is a step forward in engineering workflow efficiency. To access the poster click the link on the right. To read the full paper click here

P-Lite: A study of parallel coordinate plot literacy

Visualization literacy, the ability to interpret and comprehend visual designs, is recognized as an essential skill by the visualization community. We identify and investigate barriers to comprehending parallel coordinates plots (PCPs), one of the advanced graphical representations for the display of multivariate and high-dimensional data. We develop a parallel coordinates literacy test with diverse images generated using popular PCP software tools. The test improves PCP literacy and evaluates the user’s literacy skills. We introduce an interactive educational tool that assists the teaching and learning of parallel coordinates by offering a more active learning experience. Using this pedagogical tool, we aim to advance novice users’ parallel coordinates literacy skills. Based on the hypothesis that an interactive tool that links traditional Cartesian Coordinates with PCPs interactively will enhance PCP literacy further than static slides, we compare the learning experience using traditional slides with our novel software tool and investigate the efficiency of the educational software with an online, crowdsourced user-study. User-study results show that our pedagogical tool positively impacts a user’s PCP comprehension.

Where did my Lines go? Visualizing Missing Data in Parallel Coordinates

AbstractWe evaluate visualization concepts to represent missing values in parallel coordinates. We focus on the trade‐off between the ability to perceive missing values and the concept's impact on common tasks. For this purpose, we identified three missing value representation concepts: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values. For the missing values axis and imputed values concepts, we additionally add downplay and highlight variations. We performed a crowd‐sourced, quantitative user study with 732 participants comparing the concepts and their variations using five real‐world datasets. Based on our findings, we provide suggestions regarding which visual encoding to employ depending on the task at focus.

Level of Detail Exploration of Electronic Transition Ensembles using Hierarchical Clustering

AbstractWe present a pipeline for the interactive visual analysis and exploration of molecular electronic transition ensembles. Each ensemble member is specified by a molecular configuration, the charge transfer between two molecular states, and a set of physical properties. The pipeline is targeted towards theoretical chemists, supporting them in comparing and characterizing electronic transitions by combining automatic and interactive visual analysis. A quantitative feature vector characterizing the electron charge transfer serves as the basis for hierarchical clustering as well as for the visual representations. The interface for the visual exploration consists of four components. A dendrogram provides an overview of the ensemble. It is augmented with a level of detail glyph for each cluster. A scatterplot using dimensionality reduction provides a second visualization, highlighting ensemble outliers. Parallel coordinates show the correlation with physical parameters. A spatial representation of selected ensemble members supports an in‐depth inspection of transitions in a form that is familiar to chemists. All views are linked and can be used to filter and select ensemble members. The usefulness of the pipeline is shown in three different case studies.

The Impacts of Early Architectural Design Decisions on Building Performance

The early architectural design involves the most salient decisions. However, because of the large amount of variance, the decision-making is highly arduous. This article presents a methodology to enable the most effective design variables to be selected within the most effective value range by presenting a method that allows the measurement of output uncertainty depending on the impact of design decisions on outputs. The methodology was tested with different building functions and climate regions using two-phase sensitivity analysis. The values of design variables were generated with quasi-random sampling. They were sorted with factor prioritization. Ineffective variables were eliminated with factor fixing. Advanced global sensitivity analyses were performed for the total effect. Factor mapping was applied with the output weighting. The results were presented with Parallel Coordinate Plot (PCP). The designers can make selections from alternatives with PCP. Finally, the study demonstrated how climate and building functions should be considered for building performance.

Digitalisation and automation for the development of modern engineering workflows

Automation, as part of the Energy Industry’s Digital Transformation, is producing significant time savings in modern engineering workflows whilst minimising opportunity for human error. A challenge we face is ensuring these automated workflows are accessible to people in the industry without programming experience. Xodus’ solution to this problem is FLUX. FLUX works as an object-oriented, ‘no-code’, web-based platform which tracks, stores and executes both technical and non-technical workflows. Workflows are first automated in-house by developing scripts, which are then hosted on FLUX allowing them to be easily shared across the business without requiring programming knowledge. A popular use case for FLUX is in the concept engineering phase of a project when performing parametric analyses. Multiple simulation cases (typically thousands) assessing all the different possible combinations of design parameters can be interrogated at once. FLUX can then automatically output an interactive parallel coordinate plot to highlight the impact of each parameter on the overall system. Platforms such as FLUX are an effective solution to increasing accessibility to automation for the industry via ‘no-code’ mechanisms, the ability to interface through an intuitive digital platform where project data is stored, exchanged and analysed through automated calculations is a step forward in engineering workflow efficiency.

Assessing the Spatiotemporal Heterogeneity of Terrestrial Temperature as a Proxy to Microclimate and Its Relationship With Urban Hydro-Biophysical Parameters

Rapid urban land use and land cover changes have become a major environmental issue because of their ecological effects, including loss of green space and urban heat islands. Effective monitoring and management techniques are required. The Saudi Arabian twin city of Abha-Khamis Mushyet was selected as a case study for this research. As a result, the current study aimed to statistically and spatially investigate the relationship between land surface temperature (LST) and land use land cover based urban biophysical parameters such as normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). This study used random forest (RF) to classify LULC in 1990, 2000, and 2018. We also validated the LULC maps in a novel way. Using mono window algorithm techniques, we extracted LST for three periods. The dynamics of LULC, LST, and biophysical parameters were investigated using standard statistical graphs such as the heat map and the Sankey diagram. The correlation coefficient and the global bivariate Moran’ I approach were used to determine the association between LST and urban biophysical parameters. The relationship was then established in greater detail by categorizing the entire pixel into percentile classes and employing parallel coordinate plots. Finally, the association was built using GeoDA software and a conditional map. The LULC maps revealed a 334.4 percent increase in urban areas between 1990 and 2018. The built-up region is the largest stable LULC, with an 83.6 percent transitional probability matrix between 1990 and 2018. While 17.9%, 21.8%, 12.4%, and 10.5% of agricultural land, scrubland, exposed rocks, and water bodies were converted to built-up areas, respectively. The LST has increased rapidly over time because of LULC changes. The link between LST and urban biophysical parameters revealed that NDBI had a positive relationship, whereas NDWI and NDVI had a negative relationship. As a result, this study could be very important because it could help decision makers figure out how to lessen the effects of urban heat islands because of changes in LULC.

VISUEL - A Web Dynamic Dashboard for DataVisualization

Data visualization aims to explore and analyze data quickly, interactively, and intuitively using visual representations. Faced with the constant growth of data in terms of volume and diversity, visualization techniques must confront the challenge of dealing with increasingly large datasets in terms of representation, interaction, and performance. Therefore, these techniques must be able to effectively convey the characteristics of the information space and inspire discovery.In this article, we present VISUEL, a web dynamic dashboard for data visualization. VISUEL supports multiple coordinated views, integrating visualization techniques such as scatter plots, parallel coordinates, and box plots, and interactive schematic maps to represent information enriched with spatial references.VISUEL is fully interactive, supporting traditional interactions like filtering, selection, brushing and linking, and zooming, among others. It also allows the user to configure the visual representation of their data, by selecting the color and shape of the representations.We illustrate the usefulness of this tool using real-life data related to the wine industry in Argentina. Important aspects of our case study are discovered through the construction and analysis of multiple views.

Spectral optimization for Robin Laplacian on domains admitting parallel coordinates

AbstractIn this paper we deal with spectral optimization for the Robin Laplacian on a family of planar domains admitting parallel coordinates, namely a fixed‐width strip built over a smooth closed curve and the exterior of a convex set with a smooth boundary. We show that if the curve length is kept fixed, the first eigenvalue referring to the fixed‐width strip is for any value of the Robin parameter maximized by a circular annulus. Furthermore, we prove that the second eigenvalue in the exterior of a convex domain Ω corresponding to a negative Robin parameter does not exceed the analogous quantity for the exterior of a disk whose boundary has a curvature larger than or equal to the maximum of that for .

Wearable multi-sensor data fusion approach for human activity recognition using machine learning algorithms

Wearable sensor based human activity recognition (HAR) has a broad range of applications in healthcare, fitness, smart home, and surveillance. In spite of the substantial amount of computational research on HAR, the open challenges in multi-sensor based activity recognition including complex temporal data, identifying discriminative feature vectors from multi-modal data and dimensionality reduction require considerable research attention. Hence, in this paper, using multi-resolution time-frequency analysis of received signal strength (RSS) between the wearable sensors, we present a supervised machine learning (ML) based activity recognition framework. The multi-sensor activity data acquired using the wireless sensor network (WSN) nodes and inertial-sensor embedded in a smartphone are decomposed using the discrete wavelet transform (DWT) and the empirical mode decomposition (EMD) techniques for extracting the prominent feature vector. Using the discriminative statistical features from DWT along with the entropy features from EMD, the four ML classifier models such as support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier (EC), and decision tree (DT) are trained to classify various human activities. The efficacy of the proposed design is assessed on a publicly available dataset from UCI. The experimental results assessed using the confusion matrix and parallel coordinate plot (PCP) substantiate that the proposed ML based HAR framework can achieve a maximum classification accuracy of 99.63% and is superior to several state-of-the-art ML techniques.

Application of stable isotopic and elemental composition combined with random forest algorithm for the botanical classification of Chinese honey

To ensure that honey belongs to a very appreciated botanical class, the classical methodology is melissopalynology analysis to identify and count pollen grains. However, this method is time-consuming and laborious. In this work, four stable isotopes (δ13Ch, δ13Cp, δ18O and δ2H) and twelve elemental contents (Na, Mg, Ca, K, Fe, Cr, Mn, Co, Cu, Sr, Se, Mo) were used to build the dataset, and the Random Forest (RF) algorithm, Support Vector Machines (SVM), Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA) were investigated to classify six varieties of Chinese honey (linden, sunflower, vetch, rape, acacia, and jujube). The results showed the RF algorithm exhibits the highest training accuracy (99.4%) and testing accuracy (96.5%) of the four algorithms. Hence, the RF algorithm was selected to rank the 16 attributes according to their contribution, and δ2H, Sr, δ18O, Mn, Ca, and K were considered the most important factors for identifying six varieties of honey. Furthermore, the results of the RF algorithm were verified by the parallel coordinates plot. This suggests that the RF algorithm provides an effective and accurate approach for classifying Chinese honey according to stable isotopic and elemental composition, which theoretically can be used to classify more types of honey.

A representation of the Dunkl oscillator model on curved spaces: Factorization approach

In this paper, we study the Dunkl oscillator model in a generalization of superintegrable Euclidean Hamiltonian systems to the two-dimensional curved ones with a m:n frequency ratio. This defined model of the two-dimensional curved systems depends on a curvature/deformation parameter of the underlying space involving reflection operators. The curved Hamiltonian Hκ admits the separation of variables in both geodesic parallel and polar coordinates, which generalizes the Cartesian coordinates of the plane. Similar to the behavior of the Euclidean case, which is the κ → 0 limit case of the curved space, the superintegrability of a curved Dunkl oscillator is naturally understood from the factorization approach viewpoint in that setting. Therefore, their associated sets of polynomial constants of motion (symmetries) as well as algebraic relations are obtained for each of them separately. The energy spectrum of the Hamiltonian Hκ and the separated eigenfunctions are algebraically given in terms of hypergeometric functions and in the special limit case of null curvature occur in the Laguerre and Jacobi polynomials. Finally, the overlap coefficients between the two bases of the geodesic parallel and polar coordinates are given by hypergeometric polynomials.

The Investigation of the Transformational Leadership Style Managers and Impact on the Financial Managerial Performance With the Modern Distributed Graph Analysis and Parallel Coordinates Algorithm in the United States

This study introduces the application of parallel coordinates to accounting information and business science, illustrating the utility of our tools to visualize and explore different types of multivariate data. Prior studies have not been able to confirm earlier findings showing the leadership research results with the distributed graph analysis for the accounting or business decision. We offer a novel demonstration of how parallel coordinates provide a practical alternative to current data-driven solutions in the business and accounting toolboxes for visualizing and exploring multivariate data, identifying causal relationships, and communicating modern business science via the advanced interactive, web-based application. Modern exploratory digital analysis of business leadership, job performance, job satisfaction requires specialize tools to identify associations among variables. The results of the modern distributed graph analysis show better understanding a transformational leadership style affects employee satisfaction and performance and how may relate to improving business management and outcomes within the company with Artificial Intelligence Business Solution.