Tool For Exploratory Analysis Research Articles

Automatic Bayesian Density Analysis

Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for exploratory data analysis are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference. As a result, supervision from statisticians is usually needed to find the right model for the data. However, since domain experts are not necessarily also experts in statistics, we propose Automatic Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible at large. Specifically, ABDA allows for automatic and efficient missing value estimation, statistical data type and likelihood discovery, anomaly detection and dependency structure mining, on top of providing accurate density estimation. Extensive empirical evidence shows that ABDA is a suitable tool for automatic exploratory analysis of mixed continuous and discrete tabular data.

Fitting spatial max-mixture processes with unknown extremal dependence class: an exploratory analysis tool

A flexible model called the max-mixture model has been introduced for modeling situations where the extremal dependence structure type may vary with distance. In this paper, we propose a novel estimation procedure for spatial max-mixture model parameters. Our procedure is based on the madogram, a dependence measure used in geostatistics to describe spatial structures. A nonlinear least squares minimization procedure is applied to obtain the estimators for extremal dependence functions. A simulation study shows that the proposed procedure works well for these models. In an analysis of monthly maxima of daily rainfall data collected over the East of Australia, we implement the proposed estimation procedure for diagnostic and confirmatory purposes.

Factors influencing online shopping behavior of university students

This article is focused on examining the factors that influence the online shopping behaviour of university students. A very less studies, have been carried out which explicitly addressed the impact of gender, locality, year of study and stream of students on the factors influencing online shopping. Using exploratory factor analysis and non-parametric testing tools, the present study examines gender, locality, stream, and year of study differences across the four factors. The empirical study demonstrate that among four factors some have significant differences with the factors named as security & description, convenience, captivating and promotion. The results also show that stream of students have no significant difference with four factors.

Exploratory Modeling and Analysis for Transportation: An Approach and Support Tool - TMIP-EMAT

Traditionally, travel forecasting models have been used to provide single point predictions. That is, a single future scenario is developed and the model is applied to that scenario. This approach, however, ignores the deep uncertainty that exists in future land use, demographic, and transportation systems inputs, not to mention the uncertainty that exists in the model itself. More importantly, transportation policy decisions made on the basis of such model outputs may be misguided and ineffective. This paper demonstrates and motivates the use of travel forecasting models in an exploratory manner that accounts for the inherent uncertainties of the future. Specifically, this paper describes the user workflow for a new planning and modeling tool: the Travel Model Improvement Program Exploratory Modeling and Analysis Tool (TMIP-EMAT) that has been developed to facilitate the use of exploratory techniques with travel forecasting models. Examples from the proof of concept deployment using the Greater Buffalo-Niagara Regional Transportation Council regional travel demand model are included. The goal of the longer term study is to provide TMIP-EMAT for state and regional transportation planning agencies to assess how technological innovations will affect traffic and transit demand on major corridors 20 to 30 years down the road. The tool will illuminate interactions between transportation supply and demand on urban surface transportation systems (especially at the corridor level) through exploratory modeling and simulation, and facilitate insights into potential, possible, plausible, probable or preferred futures.

Analysis of Single-Cell RNA-seq Data by Clustering Approaches

Background: The recently developed single-cell RNA sequencing (scRNA-seq) has attracted a great amount of attention due to its capability to interrogate expression of individual cells, which is superior to traditional bulk cell sequencing that can only measure mean gene expression of a population of cells. scRNA-seq has been successfully applied in finding new cell subtypes. New computational challenges exist in the analysis of scRNA-seq data. Objective: We provide an overview of the features of different similarity calculation and clustering methods, in order to facilitate users to select methods that are suitable for their scRNA-seq. We would also like to show that feature selection methods are important to improve clustering performance. Results: We first described similarity measurement methods, followed by reviewing some new clustering methods, as well as their algorithmic details. This analysis revealed several new questions, including how to automatically estimate the number of clustering categories, how to discover novel subpopulation, and how to search for new marker genes by using feature selection methods. Conclusion: Without prior knowledge about the number of cell types, clustering or semisupervised learning methods are important tools for exploratory analysis of scRNA-seq data.</P>

Establishing the relationship between urban land-cover configuration and night time land-surface temperature using spatial regression

ABSTRACT Studies suggest that urban form can influence microclimate regulation. Remote sensing studies have contributed to these findings through analysis of high-resolution land cover maps, landscape ecology metrics, and thermal imagery. Collectively, these have been referred to as land cover configuration studies. There are three objectives to this study. The first is to assess the relationship between nighttime land surface temperatures (LST) and land cover configuration and composition. The second objective is to outline a comprehensive methodology that includes ordinary least squares (OLS), spatial regression, variable selection, and multicollinearity analysis. Our last objective is to test three hypotheses about the relationship between LST and land cover, which can briefly be described as: 1) the importance of land-use regimes in modeling LST from land cover composition and configuration variables; 2) the strength of the correlation between LST and roads, buildings, and vegetation; and 3) the improved quality of models using landscape metrics in modeling the relationship between LST and land cover. Based on 16 different models (8 OLS, 8 spatial regression) we could confirm the above hypotheses, but we found that the configuration of buildings, roads, and vegetation have a complex relationship with LST. Our interpretation of this complexity, combined with the strength of composition variables, is that parsimonious models, for now, are more useful to urban planners because they are more generalizable. Finally, spatial regression models of land cover configuration and LST demonstrated an improvement over non-spatial linear models (OLS). Spatial regression models reduced heteroskedasticity and clusters of residuals, and tempered coefficients, suggesting that the OLS models could be biased. OLS models were still found to be a valuable tool for exploratory analysis.

Performance comparison of linear and non-linear feature selection methods for the analysis of large survey datasets.

Large survey databases for aging-related analysis are often examined to discover key factors that affect a dependent variable of interest. Typically, this analysis is performed with methods assuming linear dependencies between variables. Such assumptions however do not hold in many cases, wherein data are linked by way of non-linear dependencies. This in turn requires applications of analytic methods, which are more accurate in identifying potentially non-linear dependencies. Here, we objectively compared the feature selection performance of several frequently-used linear selection methods and three non-linear selection methods in the context of large survey data. These methods were assessed using both synthetic and real-world datasets, wherein relationships between the features and dependent variables were known in advance. In contrast to linear methods, we found that the non-linear methods offered better overall feature selection performance than linear methods in all usage conditions. Moreover, the performance of the non-linear methods was more stable, being unaffected by the inclusion or exclusion of variables from the datasets. These properties make non-linear feature selection methods a potentially preferable tool for both hypothesis-driven and exploratory analyses for aging-related datasets.

Spatially varying relationships between land ownership and land development at the urban fringe: A case study of Shenzhen, China

The conversion of rural land to urban land, known as land urbanization or urban expansion, often follows two tracks in China: 1) the government becomes the owner of the land through land expropriation and performs primary land development and land transfer; 2) villagers maintain their ownership of the land collectively and convert their non-construction land into construction land by themselves. The dual land ownership system is a unique factor which influences urban expansion in China. However, this influence may be inconsistently distributed over space. Local variations in land use regulations, government controls, and stakeholder participation could all lead to varied relationships between land ownership and land development. This research aims to answer two main questions, 1) whether or not the influence of the dual land ownership system on land development is consistent over space (spatial stationarity), and 2) how such relationships vary over space. Capitalizing on the land parcel data collected in Longhua district in Shenzhen in the years 1996 and 2010, this research utilizes a geographically weighted logistic regression (GWLR) modelling approach to test and visualize spatial non-stationarity in the relationship between land ownership and land development. The study focuses on Shenzhen, China, because it has experienced extremely rapid urbanization and because of the notable dual land system there. The results demonstrate that land ownership (collectively-owned vs. government-owned) has statistically significant relationships to land development and that such relationships vary over space. There are areas where land ownership profoundly influences land development, whereas in other areas the influences are limited. GWLR proved to be a useful exploratory spatial analysis tool which may lead to new insights on the interconnections between dual land ownership and urbanization.

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.

Drawing and studying on histogram

Statistical graphics has three important features -intuition, vivid and lively. It is an important tool in exploratory analysis. Histogram is an important histogram. People can use its intuitionistic images to show the distribution of numbers to get the regularity of data distribution. Histogram is widely used in shape matching, imagine retrieval, feature matching and visual tracking etc. It can be helpful for the subsequent data analysis as well. This paper discusses the mathematical principle, practical principle and the operation method of histogram. Also histograms have been drawn in this article based on data of the Old Faithful Geyser of California. R-project is used to draw the histogram to display the eruption time of Old Faithful. The default histogram of R-project, the probability density compound histogram and multi-interval histogram have been drawn by using R-program and at the meantime, the R-project program and the three imagine results are supplied and the author did the comparative analysis of them.

Governance guidance for an eRegistry for maternal and child health: lessons from the occupied Palestinian territory

BackgroundThe eRegistry for maternal and child health is an electronic registry system developed for the collection, analysis, retrieval, storage, and dissemination of information on health determinants and outcomes for women and children. The purpose of the eRegistry is to improve health-care provision and public health surveillance in the occupied Palestinian territory. Given the highly sensitive nature of reproductive health information, however, careful attention to ethical and legal considerations is essential. The aim of this study was to map the legal, regulatory, and ethical landscape using a situation analysis tool tailored for the Palestinian context and to identify gaps, opportunities, and actions necessary to ensure an ethical and lawful framework for an eRegistry. MethodsWe used an exploratory situation analysis tool (SAT) to map the current legislative, regulatory, and governance environment in the occupied Palestinian territory. The primary objective was to assess, document, and analyse the legal and ethical landscape. Specific focal topics addressed by the SAT included identifying relevant legislation (eg, civil registration, data privacy, health registries, public health law), existing governance structures, accountability, and oversight bodies (ie, ethical review committees, data protection authorities), and data quality and security policies. The Palestinian National Institute of Public Health (PNIPH) completed the SAT and held workshops for relevant stakeholders, including staff from the ministry of health's legal affairs department, PNIPH staff, and other legal experts. No ethical approval was required because data were collected from public documents. FindingsThe findings from the SAT reflect the current situation in the occupied Palestinian territory with respect to existing laws, accountability and oversight bodies, and data collection protocols and policies concerning data privacy and security. Existing Palestinian legislation relevant to health registries includes Article 4 of the General Statistics Law (2000), which honours data confidentiality and the privacy of individuals. The Penal Law number 16 of 1960 addresses the disclosure of confidential information, whereas Article 355 specifically states that disclosure of official confidential information to unauthorised persons is unlawful and could result in imprisonment. Furthermore, the Public Health Law (2004) addresses general maternal and child health issues in Articles 4 and 5. We investigated relevant oversight and accountability bodies (eg, ethical review board) that might be relevant to an electronic maternal and child health registry. Finally, in a review of existing data privacy protocols and policies, we noted that few formal protocols (ie, privacy manual) or policies regulate data collection and access practices. InterpretationSpecific areas within the legal and regulatory framework of the occupied Palestinian territory could be strengthened. The eRegistry Governance Guidance Toolkit, predicated on the SAT content, outlines responsible data stewardship practices and provides assistance in developing protocols and policies in lieu of specific laws by providing a framework to ensure the protection of women and children's privacy. Effective governance that protects women and children's privacy and confidentiality is essential for maintaining public trust and ensuring the long-term success of an eRegistry. FundingEuropean Research Council and the Norwegian Agency for Development Cooperation (Norad).

EcoGenetics: An R package for the management and exploratory analysis of spatial data in landscape genetics.

The integration of ecology and genetics has become established in recent decades, in hand with the development of new technologies, whose implementation is allowing an improvement of the tools used for data analysis. In a landscape genetics context, integrative management of population information from different sources can make spatial studies involving phenotypic, genotypic and environmental data simpler, more accessible and faster. Tools for exploratory analysis of autocorrelation can help to uncover the spatial genetic structure of populations and generate appropriate hypotheses in searching for possible causes and consequences of their spatial processes. This study presents EcoGenetics, an R package with tools for multisource management and exploratory analysis in landscape genetics.

The Role of Behavioural Factors in the Investment Decisions of Household Investors

The process of investment requires investors to take various types of decisions and the quality of those decisions determines the outcomes of the investment process. Standard finance theories and behavioural finance theories present different views on investment decision making based on the concept of rationality. Behavioural finance theories indicate that investors fail to perform in a completely rational manner when making investment decisions due to various biases. The objective of the study is to identify the behavioural finance based factors influence the investment decisions of household investors in the Northern Province of Sri Lanka. The necessary data for the study were collected from 1810 household investors in the Northern Province of Sri Lanka and the sample respondents were selected under Proportionate stratified random sampling method. The analytical tools of exploratory factor analysis and confirmatory factor analysis were used to analyze the data. The current study concluded that Representativeness bias, Overconfidence bias, Availability Bias, Loss Aversion bias, Regret Aversion bias and Herding influence the investment decisions of household investors.

Application of independent component analysis to multitemporal InSAR data with volcanic case studies

AbstractA challenge in the analysis of multitemporal interferometric synthetic aperture radar (InSAR) data is distinguishing and separating volcanic, tectonic, and anthropogenic displacements from each other and from atmospheric or orbital noise. Independent component analysis (ICA) is a method for decomposing a mixed signal based on the assumption that the component sources are non‐Gaussian and statistically independent. ICA has potential as a tool for exploratory analysis of InSAR data, and in particular for testing whether geophysical signals are related or independent. This article presents tests of the applicability of ICA to InSAR by using synthetic data and application to Sentinel‐1A archive images from two contrasting examples of volcano deformation. Coeruptive subsidence associated with the April 2015 eruption of Calbuco (Chile) was identified in spatial patterns found by maximizing both spatial and temporal independence. Spatial patterns and rates of lava subsidence were retrieved by using ICA analysis of interferograms from Parícutin lava fields (México) and found to be consistent with previous observations. I demonstrate that ICA is an appropriate method for the analysis of volcanic signals in the presence of atmospheric noise and propose a strategy for the reliable identification of geophysical displacements by using cluster analysis of the spatial patterns of independent components. This approach allows the detection of geophysical processes on a range of scales and provides a test of signal independence where multiple displacement sources are active.

Exploratory spatio-temporal analysis of linked statistical data

Publishing and sharing open government data in Linked Data format provides many opportunities in terms of data aggregation/integration and creation of information mashups. Statistical data, that contains various performance indicators and their evolution through time, is an example of data that can be used as the foundation for policy prediction, planning and adjustments, and can be re-used in different applications. However, due to Linked Data being relatively a new field, currently there is a lack of tools that enable efficient exploration and analysis of linked geospatial statistical datasets. Therefore, ESTA-LD (Exploratory Spatio-Temporal Analysis) tool was developed to address some of the Linked statistical Data management issues, such as crossing the statistical and the geographical dimensions, producing statistical maps, visualizing different measures, and comparing statistical indicators of different regions through time. This paper discusses the modeling approach that was adopted so that the published data conform to the established standards for representing statistical, spatial and temporal data in Linked Data format. The main contribution is related to the delivery of state-of-the-art open-source tools for retrieving, quality assessment, exploration and analysis of statistical Linked Data that is made available through a SPARQL endpoint.

A model for geographically distributed combat interactions of swarming naval and air forces

AbstractThis article describes the Distributed Interaction Campaign Model (DICM), an exploratory campaign analysis tool and asset allocation decision‐aid for managing geographically distributed and swarming naval and air forces. The model is capable of fast operation, while accounting for uncertainty in an opponent's plan. It is intended for use by commanders and analysts who have limited time for model runs, or a finite budget. The model is purpose‐built for the Pentagon's Office of Net Assessment, and supports analysis of the following questions: What happens when swarms of geographically distributed naval and air forces engage each other and what are the key elements of the opponents’ force to attack? Are there changes to force structure that make a force more effective, and what impacts will disruptions in enemy command and control and wide‐area surveillance have? Which insights are to be gained by fast exploratory mathematical/computational campaign analysis to augment and replace expensive and time‐consuming simulations? An illustrative example of model use is described in a simple test scenario. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 562–576, 2016

SEQualyzer: interactive tool for quality control and exploratory analysis of high-throughput RNA structural profiling data.

To serve numerous functional roles, RNA must fold into specific structures. Determining these structures is thus of paramount importance. The recent advent of high-throughput sequencing-based structure profiling experiments has provided important insights into RNA structure and widened the scope of RNA studies. However, as a broad range of approaches continues to emerge, a universal framework is needed to quantitatively ensure consistent and high-quality data. We present SEQualyzer, a visual and interactive application that makes it easy and efficient to gauge data quality, screen for transcripts with high-quality information and identify discordant replicates in structure profiling experiments. Our methods rely on features common to a wide range of protocols and can serve as standards for quality control and analyses. SEQualyzer is written in R, is platform-independent, and is freely available at http://bme.ucdavis.edu/aviranlab/SEQualyzer. saviran@ucdavis.edu Supplementary data are available at Bioinformatics online.

Study on the Effects of the Korean IT Consultants Training on the Job Satisfaction and the Employee Turnover

Objectives: This study examines the effect of the education and training performed by the IT consulting firms in Korea on the job satisfaction, the organizational commitment, the turnover intention and the management performance of the consultants engaging in IT consulting firms. Methods/Statistical Analysis: In this study, the reliabilities of the exploratory factor analysis and research tool were analyzed using SPSS Windows 12.0 and Cronbach's Alpha (α >= 0.7) were used to evaluate the reliabilities and the validity of the measuring tools was verified by conducting Confirmatory Factor analysis using AMOS 18.0 for the factors extracted from Exploratory Factor Analysis. The causal relationship is often used to clarify correlation between cause and effect. In this study, the causal relationship among education and training, job satisfaction, turnover intention, job commitment and management performance were analyzed using covariance structure model. Findings: The results indicate that education and training influence on job satisfaction, turnover intention, and management performance. As the characteristics of consulting which is the representative occupation in the knowledge-based industries, it requires more specific knowledge, and the job is performed based on such knowledge, and thus has an effect on the performance of the corporations. Improvements/Applications: This study may further improved by considering other efficiency factors related with training and job satisfaction.

Community viral load of HIV in Brazil, 2007 - 2011: potential impact of highly active antiretroviral therapy (HAART) in reducing new infections.

To estimate the human immunodeficiency virus (HIV) viral load in the Brazilian population and to assess the potential impact of highly active antiretroviral therapy (HAART) in reducing new infections to build evidences and to gather information to support health policies. Spatial analysis and modeling tools were used to describe the existing patterns of the viral load density, using the Kernel method. Data on viral load and treatment were retrieved from the databases Laboratory Tests Control System (SISCEL), which contains information on the individual's history of viral load, and Medication Logistics Control System (SICLOM), which controls the dispensing of drugs used for antiretroviral therapy. It was observed that the community viral load (CVL) decreased progressively from 2007 to 2011, accompanied by a decrease of more than 32% in the mean CVL (CVLM) - 22,900 copies/mL in 2007 versus 15,418 copies/mL in 2011. During this period, there was a reduction of CVLM in all regions of Brazil, although North and Northeast showed, respectively, CVLM 1.7 and 1.5 times higher than that in the Southeast region. A comparison between the individuals who underwent and who did not undergo HAART showed an increase of up to 3.9 times in 2011 in the viral load among those who did not undergo the therapy. The approach presented in this study indicates the existence of clusters with high concentrations. The use of Kernel in the identification of clusters proved to be a good tool for exploratory analysis, enabling the risk identification in certain geographic areas without the usual political and administrative divisions.

A Multi-facetted Visual Analytics Tool for Exploratory Analysis of Human Brain and Function Datasets.

Brain research typically requires large amounts of data from different sources, and often of different nature. The use of different software tools adapted to the nature of each data source can make research work cumbersome and time consuming. It follows that data is not often used to its fullest potential thus limiting exploratory analysis. This paper presents an ancillary software tool called BRAVIZ that integrates interactive visualization with real-time statistical analyses, facilitating access to multi-facetted neuroscience data and automating many cumbersome and error-prone tasks required to explore such data. Rather than relying on abstract numerical indicators, BRAVIZ emphasizes brain images as the main object of the analysis process of individuals or groups. BRAVIZ facilitates exploration of trends or relationships to gain an integrated view of the phenomena studied, thus motivating discovery of new hypotheses. A case study is presented that incorporates brain structure and function outcomes together with different types of clinical data.