Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Coordinating views for data visualisation and algorithmic profiling

A number of researchers have designed visualisation systems that consist of multiple components, through which data and interaction commands flow. Such multistage (hybrid) models can be used to reduce algorithmic complexity, and to open up intermediate stages of algorithms for inspection and steering. In this paper, we present work on aiding the developer and the user of such algorithms through the application of interactive visualisation techniques. We present a set of tools designed to profile the performance of other visualisation components, and provide further functionality for the exploration of high dimensional data sets. Case studies are provided, illustrating the application of the profiling modules to a number of data sets. Through this work we are exploring ways in which techniques traditionally used to prepare for visualisation runs, and to retrospectively analyse them, can find new uses within the context of a multi-component visualisation system

Blending aggregation and selection: Adapting parallel coordinates for the visualization of large datasets

Many of the traditional data visualization techniques, which proved to be supportive for exploratory analysis of datasets of moderate sizes, fail to fulfil their function when applied to large datasets. There are two approaches to coping with large amounts of data: data selection, when only a portion of data is displayed, and data aggregation, i.e. grouping data items and considering the groups instead of the original data. None of these approaches alone suits the needs of exploratory data analysis, which requires consideration of data on all levels: overall (considering a dataset as a whole), intermediate (viewing and comparing collective characteristics of arbitrary data subsets, or classes), and elementary (accessing individual data items). Therefore, it is necessary to combine these approaches, i.e. build a tool showing the whole set and arbitrarily defined subsets (object classes) in an aggregated way and superimposing this with a representation of arbitrarily selected individual data items. We have achieved such a combination of approaches by modifying the technique of parallel coordinate plot. These modifications are described and analysed in the paper

Doctor of Philosophy

dissertationCorrelation is a powerful relationship measure used in many fields to estimate trends and make forecasts. When the data are complex, large, and high dimensional, correlation identification is challenging. Several visualization methods have been proposed to solve these problems, but they all have limitations in accuracy, speed, or scalability. In this dissertation, we propose a methodology that provides new visual designs that show details when possible and aggregates when necessary, along with robust interactive mechanisms that together enable quick identification and investigation of meaningful relationships in large and high-dimensional data. We propose four techniques using this methodology. Depending on data size and dimensionality, the most appropriate visualization technique can be provided to optimize the analysis performance. First, to improve correlation identification tasks between two dimensions, we propose a new correlation task-specific visualization method called correlation coordinate plot (CCP). CCP transforms data into a powerful coordinate system for estimating the direction and strength of correlations among dimensions. Next, we propose three visualization designs to optimize correlation identification tasks in large and multidimensional data. The first is snowflake visualization (Snowflake), a focus+context layout for exploring all pairwise correlations. The next proposed design is a new interactive design for representing and exploring data relationships in parallel coordinate plots (PCPs) for large data, called data scalable parallel coordinate plots (DSPCP). Finally, we propose a novel technique for storing and accessing the multiway dependencies through visualization (MultiDepViz). We evaluate these approaches by using various use cases, compare them to prior work, and generate user studies to demonstrate how our proposed approaches help users explore correlation in large data efficiently. Our results confirmed that CCP/Snowflake, DSPCP, and MultiDepViz methods outperform some current visualization techniques such as scatterplots (SCPs), PCPs, SCP matrix, Corrgram, Angular Histogram, and UntangleMap in both accuracy and timing. Finally, these approaches are applied in real-world applications such as a debugging tool, large-scale code performance data, and large-scale climate data