Visual Exploratory Data Analysis Research Articles

Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases

Slope stability prediction research is a complex non-linear system problem. In carrying out slope stability prediction work, it often encounters low accuracy of prediction models and blind data preprocessing. Based on 77 field cases, 5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability. These indicators include slope angle, slope height, internal friction angle, cohesion and unit weight of rock and soil. Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods, namely principal components analysis (PCA), Kernel PCA, factor analysis (FA), independent component analysis (ICA), non-negative matrix factorization (NMF) and t -SNE (stochastic neighbor embedding). Combined with classic machine learning methods, 7 prediction models for slope stability are established and their reliabilities are examined by random cross validation. Besides, the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method. The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability. Random forest (RF), support vector machine (SVM) and k-nearest neighbour (KNN) achieve the best prediction accuracy, which is higher than 90%. The decision tree (DT) has better accuracy which is 86%. The most important factor influencing slope stability is slope height, while unit weight of rock and soil is the least significant. RF and SVM models have the best accuracy and superiority in slope stability prediction. The results provide a new approach toward slope stability prediction in geotechnical engineering.

Systematic Assessment of COVID-19 Pandemic in Bangladesh: Effectiveness of Preparedness in the First Wave.

Background: To develop an effective countermeasure and determine our susceptibilities to the outbreak of COVID-19 is challenging for a densely populated developing country like Bangladesh and a systematic review of the disease on a continuous basis is necessary.Methods: Publicly available and globally acclaimed datasets (4 March 2020–30 September 2020) from IEDCR, Bangladesh, JHU, and ECDC database are used for this study. Visual exploratory data analysis is used and we fitted a polynomial model for the number of deaths. A comparison of Bangladesh scenario over different time points as well as with global perspectives is made.Results: In Bangladesh, the number of active cases had decreased, after reaching a peak, with a constant pattern of death rate at from July to the end of September, 2020. Seventy-one percent of the cases and 77% of the deceased were males. People aged between 21 and 40 years were most vulnerable to the coronavirus and most of the fatalities (51.49%) were in the 60+ population. A strong positive correlation (0.93) between the number of tests and confirmed cases and a constant incidence rate (around 21%) from June 1 to August 31, 2020 was observed. The case fatality ratio was between 1 and 2. The number of cases and the number of deaths in Bangladesh were much lower compared to other countries.Conclusions: This study will help to understand the patterns of spread and transition in Bangladesh, possible measures, effectiveness of the preparedness, implementation gaps, and their consequences to gather vital information and prevent future pandemics.

Exploring Epidemiological Behavior of Novel Coronavirus (COVID-19) Outbreak in Bangladesh.

Globally, there is an obvious concern about the fact that the evolving 2019-nCoV coronavirus is a worldwide public health threat. The appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China at the end of 2019 triggered a major global epidemic, which is now a major community health issue. As of August 13, 2020, according to the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh has reported 269,095 confirmed cases between 8 March and 13 August 2020, with > 1.30% of mortality rate and > 57% of recovery rate. COVID-19 outbreak is evolving so rapidly in Bangladesh; therefore, the availability of epidemiological data and its sensible analysis are essential to direct strategies for situational awareness and intervention. This article presents an exploratory data analysis approach to collect and analyze COVID-19 data on epidemiological outbreaks based on the first publicly available COVID-19 Daily Dataset of Bangladesh. Various publicly open data sources on the outbreak of COVID-19 provided by the IEDCR, World Health Organization (WHO), Directorate General of Health Services (DGHS), and Ministry of Health and Family Welfare (MHFW) of Bangladesh have been used in this research. Visual exploratory data analysis (V-EDA) techniques have been followed in this research to understand the epidemiological characteristics of COVID-19 outbreak in different districts of Bangladesh between 8 March 2020 and 13 August 2020 and these findings were compared with those of other countries. In all, this is extremely important to promptly spread information to understand the risks of this pandemic and begin containment activities in the country.

Fourier Opacity Optimization for Scalable Exploration.

Over the past decades, scientific visualization became a fundamental aspect of modern scientific data analysis. Across all data-intensive research fields, ranging from structural biology to cosmology, data sizes increase rapidly. Dealing with the growing large-scale data is one of the top research challenges of this century. For the visual exploratory data analysis, interactivity, a view-dependent visibility optimization and frame coherence are indispensable. In this work, we extend the recent decoupled opacity optimization framework to enable a navigation without occlusion of important features through large geometric data. By expressing the accumulation of importance and optical depth in Fourier basis, the computation, evaluation and rendering of optimized transparent geometry become not only order-independent, but also operate within a fixed memory bound. We study the quality of our Fourier approximation in terms of accuracy, memory requirements and efficiency for both the opacity computation, as well as the order-independent compositing. We apply the method to different point, line and surface data sets originating from various research fields, including meteorology, health science, astrophysics and organic chemistry.

Data Mining Framework to Analyze the Evolution of Computational Thinking Skills in Game Building Workshops

Computational thinking has become a required capability in the student learning process, and digital games as a teaching approach have presented promising educational results in the development of this competence. However, properly evaluating the effectiveness and, consequently, student progress in a course using games is still a challenge. One of the most widely implemented ways of evaluation is with an automated analysis of the code developed in the classes during the construction of digital games. Nevertheless, this topic has not yet been explored in aspects such as incremental learning, the model and teaching environment and the influences of acquiring skills and competencies of computational thinking. Motivated by this knowledge gap, this paper introduces a framework proposal to analyze the evolution of computational thinking skills in digital games classes. The framework is based on a data mining technique that aims to facilitate the discovery process of the patterns and behaviors that lead to the acquisition of computational thinking skills, by analyzing clusters with an unsupervised neural network of self-organizing maps (SOM) for this purpose. The framework is composed of a collection of processes and practices structured in data collection, data preprocessing, data analysis, and data visualization. A case study, using Scratch, was executed to validate this approach. The results point to the viability of the framework, highlighting the use of the visual exploratory data analysis, through the SOM maps, as an efficient tool to observe the acquisition of computational thinking skills by the student in an incremental course.

A comprehensive review of tools for exploratory analysis of tabular industrial datasets

Exploratory data analysis plays a major role in obtaining insights from data. Over the last two decades, researchers have proposed several visual data exploration tools that can assist with each step of the analysis process. Nevertheless, in recent years, data analysis requirements have changed significantly. With constantly increasing size and types of data to be analyzed, scalability and analysis duration are now among the primary concerns of researchers. Moreover, in order to minimize the analysis cost, businesses are in need of data analysis tools that can be used with limited analytical knowledge. To address these challenges, traditional data exploration tools have evolved within the last few years. In this paper, with an in-depth analysis of an industrial tabular dataset, we identify a set of additional exploratory requirements for large datasets. Later, we present a comprehensive survey of the recent advancements in the emerging field of exploratory data analysis. We investigate 50 academic and non-academic visual data exploration tools with respect to their utility in the six fundamental steps of the exploratory data analysis process. We also examine the extent to which these modern data exploration tools fulfill the additional requirements for analyzing large datasets. Finally, we identify and present a set of research opportunities in the field of visual exploratory data analysis.

Using Visual Exploratory Data Analysis to Facilitate Collaboration and Hypothesis Generation in Cross-Disciplinary Research

Massive open data resources are changing the way that people do science. To make use of those data resources, data science methods and technology can be leveraged by stakeholders of various disciplines. The objective of this paper is to present our experience of using visual exploratory data analysis as a method to facilitate collaboration and hypothesis generation in geoscience research. The research team consisted of both geoscientists and computer scientists. A use case-driven, iterative approach was applied to create a collaborative and communicative environment. Through several rounds of use case analysis and technological development, a data visualization pilot system was created for studying the co-relationships between chemical elements and mineral species. The exploratory data analyses conducted in those use case studies led to several research hypotheses for future work. This research illustrates the usefulness of exploratory data analysis for hypothesis generation in a data science process. Although the presented project is in geoscience, the discussed method and experience can also be translated into other disciplines.

Evolutionary optimization of a multiscale descriptor for leaf shape analysis

Shape analysis and recognition play an important role in the design of robust and reliable computer vision systems. Such systems rely on feature extraction to provide meaningful information and representation of shapes and images. However, accurate feature extraction is not a trivial task since it may depend on parameter adjustment, application domain and the shape data set itself. Indeed, there is a demand for computational tools to understand and support parameter adjustment and therefore unfold shape description representation, since manual parameter choices may not be suitable for real applications. Our major contribution is the definition of an evolutionary optimization methodology that fully supports parameter adjustment of a multiscale shape descriptor for feature extraction and representation of leaf shapes in a high dimensional space. Here, intelligent evolutionary optimization methods search for parameters that best fit the normalized multiscale bending energy descriptor for leaf shape retrieval and classification. The simulated annealing, differential evolution and particle swarm optimization methods optimize an objective function, which is based on the silhouette measure, to achieve the set of optimal parameters. Our methodology improves leaf shape characterization and recognition due to the intrinsic shape differences which are embedded in the set of optimized parameters. Experiments were conducted on public benchmark data sets with the normalized multiscale bending energy and inner-distance shape context descriptors. The visual exploratory data analysis techniques showed that the proposed methodology minimized the total within-cluster variance and thus, improved the leaf shape clustering. Moreover, supervised and unsupervised classification experiments with plant leaves accomplished high Precision and Recall rates as well as Bulls-eye scores with the optimized parameters.

Visualizing robust geographically weighted parameter estimates

This article extends earlier work on visualizing parameter estimates for spatial analysis. Robust parameters are those that are resistant to outliers, and so are order-based rather than moment-based in their derivation. The principles of geographical weighting of descriptive statistics are reviewed and applied to the development and computation of geographically weighted quantile graphs. The principles are also applied to the development and mapping of robust geographically weighted regression models. The robust statistics and parameters developed in this article are especially useful for visual exploratory data analysis because they provide local information in two contexts: with respect to spatial proximity and with respect to proximity in the quantiles of the variable of interest. The suitability of the results for visualization in maps and quantile plots is demonstrated.

Exploring improvement trajectories with dynamic process cost modelling: a case from the steel industry

Improvement trajectories are sequential managed chains of improvement initiatives required to handle changes in competition and market. This paper presents a five-step framework, based on dynamic process cost modelling, which was developed during a four-year research project at a major stainless steel producer, to support the selection of an improvement trajectory based on strategic requirements to combine high product diversity with cost reduction. The framework aims to develop insight into what manufacturing capabilities are required to reach the strategic goals by combining system dynamics simulation with process cost modelling and visual exploratory data analysis in an iterative modelling procedure. The applicability of the five-step framework is demonstrated through a case study from the steel industry, in which a goal driven analysis is used to assess process requirements based on performance and market considerations.

Detecting Novel Option Model Pricing Misspecification Using Visual Exploratory Data Analysis

Options are amongst the most heavily transacted financial instruments in the world. Nevertheless, even the most sophisticated and accurate option pricing models are known to contain misspecifications. In this work, novel misspecifications are discovered using visual exploratory data analysis. These discoveries may help academics improve pricing models and practitioners better trade and hedge their option positions.

A field quantification of coastal dune perennial plants as indicators of surface stability, erosion or deposition

AbstractDune plants both modify the wind field around them and are impacted by various stress factors, among them sand erosion and sand deposition. As coastal dunes are being either stabilized or remobilized, in response to the changes in the rates of sand and dune movement, the vegetation cover and composition are expected to vary reflecting the differences in the sedimentary conditions. In this field study 315 quadrats of 100 m2, in which the perennial plant species were sampled, were analysed with respect to annual rates of sand erosion and deposition that were measured using erosion pins. A visual exploratory data analysis was introduced, based on selective filtering of samples according to their vegetation cover. This method, combined with established statistical tools, enabled the authors to uncover the inclination and indicative power of nine perennial dune plants to either a stabilized or a mobile environment, and to establish whether they are more prevalent in places undergoing sand erosion or sand burial. Two species were found to be clear indicators of a stabilized environment, Stipagrostis lanata, and Retama raetam. Of the species indicating a mobile environment, only one may be stated as a clear indicator of sand erosion: Silene succulenta, with Cyperus macrorrhizus coming close to being an indicator of a less mobile erosive environment. The best indicator species for sand burial was found to be, as expected, Ammophila arenaria, with Artemisia monosperma also indicating high rates of sand mobility especially when its relative cover is higher than 80%. Such information can be used to monitor natural processes of dune stabilization or reactivation, or to assess the success of a management plan that aims at stabilizing a dune, or at remobilizing it by removing vegetation.

Visual exploration of large relational data sets through 3d projections and footprint splatting

This paper discusses 3D visualization and interactive exploration of large relational data sets through the integration of several well-chosen multidimensional data visualization techniques and for the purpose of visual data mining and exploratory data analysis. The basic idea is to combine the techniques of grand tour, direct volume rendering, and data aggregation in databases to deal with both the high dimensionality of data and a large number of relational records. Each technique has been enhanced or modified for this application. Specifically, positions of data clusters are used to decide the path of a grand tour. This cluster-guided tour makes intercluster-distance-preserving projections in which data clusters are displayed as separate as possible. A tetrahedral mapping method applied to cluster centroids helps in choosing interesting cluster-guided projections. Multidimensional footprint splatting is used to directly render large relational data sets. This approach abandons the rendering techniques that enhance 3D realism and focuses on how to efficiently produce real-time explanatory images that give comprehensive insights into global features such as data clusters and holes. Examples are given where the techniques are applied to large (more than a million records) relational data sets.

Scatterplots and scenes: visualisation techniques for exploratory spatial analysis

A collection of geographic data from a particular region contains many explicit and implicit relationships which are difficult to display and communicate without resorting to statistical summarisation or fragmentation into themes. This paper addresses the problems of exploring interconnections between layers of data using state-of-the-art visualisation techniques and is based on the premise that visual exploratory data analysis is a useful tool for providing insight into the complex and subtle relationships that occur in geography. The tools and techniques described extend beyond the current capacity of commercial geographic information systems in terms of (a) the flexibility of the scene description, (b) the volume of data (particularly the number of distinct layers or themes that are viewable concurrently) and (c) the facilities by which the user may study relationships. The techniques proposed are justified from a psychometric standpoint and some important limitations with their use are described. As well as the pictures shown in the paper, further results are given in the form of high resolution colour images, VRML scenes and video clips which may be downloaded from an accompanying Web site.