Faceted Views of Varying Emphasis (FaVVEs): a framework for visualising multi-perspective small multiples

Many datasets have multiple perspectives – for example space, time and description – and often analysts are required to study these multiple perspectives concurrently. This concurrent analysis becomes difficult when data are grouped and split into small multiples for comparison. A design challenge is thus to provide representations that enable multiple perspectives, split into small multiples, to be viewed simultaneously in ways that neither clutter nor overload. We present a design framework that allows us to do this. We claim that multi-perspective comparison across small multiples may be possible by superimposing perspectives on one another rather than juxtaposing those perspectives side-by-side. This approach defies conventional wisdom and likely results in visual and informational clutter. For this reason we propose designs at three levels of abstraction for each perspective. By flexibly varying the abstraction level, certain perspectives can be brought into, or out of, focus. We evaluate our framework through laboratory-style user tests. We find that superimposing, rather than juxtaposing, perspective views has little effect on performance of a low-level comparison task. We reflect on the user study and its design to further identify analysis situations for which our framework may be desirable. Although the user study findings were insufficiently discriminating, we believe our framework opens up a new design space for multi-perspective visual analysis

Real geographies and virtual landscapes: exploring the influence on place and space on mortality Lexis surfaces using shaded contour maps

This paper describes how shaded contour plots, applied to mortality data from the Human Mortality Database, can be used to compare between nations, and start to tease out some of the ways that place and space matters. A number of shaded contour plots are presented, in order to describe the age, period and cohort effects which are apparent within them. They show variations between different subpopulations within the same nation, over time, and between nations. In illustrating these intra- and international variations in the patterns, we hope to encourage the development of hypotheses about the influence of such factors on mortality rates. We conclude with a brief discussion about how such hypotheses might be developed into statistical models, allowing for more rigourous testing of hypotheses and projection across time, place and space

A visual analytics approach for visualisation and knowledge discovery from time-varying personal life data

A thesis submitted to the University of Bedfordshire, in ful filment of the requirements for the degree of Doctor of PhilosophyToday, the importance of big data from lifestyles and work activities has been the focus of much research. At the same time, advances in modern sensor technologies have enabled self-logging of a signi cant number of daily activities and movements. Lifestyle logging produces a wide variety of personal data along the lifespan of individuals, including locations, movements, travel distance, step counts and the like, and can be useful in many areas such as healthcare, personal life management, memory recall, and socialisation. However, the amount of obtainable personal life logging data has enormously increased and stands in need of effective processing, analysis, and visualisation to provide hidden insights owing to the lack of semantic information (particularly in spatiotemporal data), complexity, large volume of trivial records, and absence of effective information visualisation on a large scale. Meanwhile, new technologies such as visual analytics have emerged with great potential in data mining and visualisation to overcome the challenges in handling such data and to support individuals in many aspects of their life. Thus, this thesis contemplates the importance of scalability and conducts a comprehensive investigation into visual analytics and its impact on the process of knowledge discovery from the European Commission project MyHealthAvatar at the Centre for Visualisation and Data Analytics by actively involving individuals in order to establish a credible reasoning and effectual interactive visualisation of such multivariate data with particular focus on lifestyle and personal events. To this end, this work widely reviews the foremost existing work on data mining (with the particular focus on semantic enrichment and ranking), data visualisation (of time-oriented, personal, and spatiotemporal data), and methodical evaluations of such approaches. Subsequently, a novel automated place annotation is introduced with multilevel probabilistic latent semantic analysis to automatically attach relevant information to the collected personal spatiotemporal data with low or no semantic information in order to address the inadequate information, which is essential for the process of knowledge discovery. Correspondingly, a multi-signi ficance event ranking model is introduced by involving a number of factors as well as individuals' preferences, which can influence the result within the process of analysis towards credible and high-quality knowledge discovery. The data mining models are assessed in terms of accurateness and performance. The results showed that both models are highly capable of enriching the raw data and providing significant events based on user preferences. An interactive visualisation is also designed and implemented including a set of novel visual components signifi cantly based upon human perception and attentiveness to visualise the extracted knowledge. Each visual component is evaluated iteratively based on usability and perceptibility in order to enhance the visualisation towards reaching the goal of this thesis. Lastly, three integrated visual analytics tools (platforms) are designed and implemented in order to demonstrate how the data mining models and interactive visualisation can be exploited to support different aspects of personal life, such as lifestyle, life pattern, and memory recall (reminiscence). The result of the evaluation for the three integrated visual analytics tools showed that this visual analytics approach can deliver a remarkable experience in gaining knowledge and supporting the users' life in certain aspects

Visualising the uncertain in heritage collections : understanding, exploring and representing uncertainty in the First World War British Unit War Diaries

This paper argues that cultural heritage data is inherently ambiguous and may involve different types and levels of uncertainty. Using a variety of examples based on The National Archives (UK)’s Unit War Diaries collection unveiling stories of the British Army and its units on the Western Front in the First World War, we discuss the ways in which visualisation can help us approach heritage collections as data, enabling their visual representation in a constructive and informed way. It also aims to open up the discussion about the theoretical and methodological challenges that uncertainty, which is often hidden, can bring to the understanding of ambiguous heritage data. In brief, we discuss ways in which uncertainty appears in cultural heritage collections, either as something innate in the collections or resulting from the data extraction and narrative construction process. We identify three main types of uncertainty: inaccuracy, incompleteness and ambiguity, with the latter then subdivided into inconsistency, imprecision and non-specificity. Distinguishing, considering and quantifying these different types of uncertainty can help understand the level of confidence that we can have in narratives, source data and the extraction process. This can then enhance the discoverability of cultural heritage collections that involve high levels of uncertainty. In this way, we suggest that cultural heritage organisations should strategically focus on improving the understandability and discoverability of their digital collections by exposing and embracing uncertainty in cultural heritage collections and by innovating in its visual presentation to researchers and the public