Using treemaps for variable selection in spatio-temporal visualisation

We demonstrate and reflect upon the use of enhanced treemaps that incorporate spatial and temporal ordering for exploring a large multivariate spatio-temporal data set. The resulting data-dense views summarise and simultaneously present hundreds of space-, time-, and variable-constrained subsets of a large multivariate data set in a structure that facilitates their meaningful comparison and supports visual analysis. Interactive techniques allow localised patterns to be explored and subsets of interest selected and compared with the spatial aggregate. Spatial variation is considered through interactive raster maps and high-resolution local road maps. The techniques are developed in the context of 42.2 million records of vehicular activity in a 98 km(2) area of central London and informally evaluated through a design used in the exploratory visualisation of this data set. The main advantages of our technique are the means to simultaneously display hundreds of summaries of the data and to interactively browse hundreds of variable combinations with ordering and symbolism that are consistent and appropriate for space- and time- based variables. These capabilities are difficult to achieve in the case of spatio-temporal data with categorical attributes using existing geovisualisation methods. We acknowledge limitations in the treemap representation but enhance the cognitive plausibility of this popular layout through our two-dimensional ordering algorithm and interactions. Patterns that are expected (e.g. more traffic in central London), interesting (e.g. the spatial and temporal distribution of particular vehicle types) and anomalous (e.g. low speeds on particular road sections) are detected at various scales and locations using the approach. In many cases, anomalies identify biases that may have implications for future use of the data set for analyses and applications. Ordered treemaps appear to have potential as interactive interfaces for variable selection in spatio-temporal visualisation. Information Visualization (2008) 7, 210-224. doi: 10.1057/palgrave.ivs.950018

LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar

Visualisation of Origins, Destinations and Flows with OD Maps

We present a new technique for the visual exploration of origins (O) and destinations (D) arranged in geographic space. Previous attempts to map the flows between origins and destinations have suffered from problems of occlusion usually requiring some form of generalisation, such as aggregation or flow density estimation before they can be visualized. This can lead to loss of detail or the introduction of arbitrary artefacts in the visual representation. Here, we propose mapping OD vectors as cells rather than lines, comparable with the process of constructing OD matrices, but unlike the OD matrix, we preserve the spatial layout of all origin and destination locations by constructing a gridded two‐level spatial treemap. The result is a set of spatially ordered small multiples upon which any arbitrary geographic data may be projected. Using a hash grid spatial data structure, we explore the characteristics of the technique through a software prototype that allows interactive query and visualisation of 105‐106 simulated and recorded OD vectors. The technique is illustrated using US county to county migration and commuting statistics

Visualizing the dynamics of London's bicycle hire scheme

Visualizing flows between origins and destinations can be straightforward when dealing with small numbers of journeys or simple geographies. Representing flows as lines embedded in geographic space has commonly been used to map transport flows, especially when geographic patterns are important as they are when characterising cities or managing transportation. However, for larger numbers of flows, this approach requires careful design to avoid problems of occlusion, salience bias and information overload. Driven by the requirements identified by users and managers of the London Bicycle Hire scheme we present three methods of representation of bicycle hire use and travel patterns. Flow maps with curved flow symbols are used to show overviews in flow structures. Gridded views of docking station location that preserve geographic relationships are used to explore docking station status over space and time in a graphically efficient manner. Origin-Destination maps that visualise the OD matrix directly while maintaining geographic context are used to provide visual details on demand. We use these approaches to identify changes in travel behaviour over space and time, to aid station rebalancing and to provide a framework for incorporating travel modelling and simulation

Visual analytics for supply network management: system design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip

Evaluating Trace Aggregation Through Entropy Measures for Optimal Performance Visualization of Large Distributed Systems

Large-scale distributed high-performance applications are involving an ever-increasing number of threads to explore the extreme concurrency of today's systems. The performance analysis through visualization techniques usually su ers severe semantic limitations due, from one side, to the size of parallel applications, from another side, to the challenges to visualize large-scale traces. Most of performance visualization tools rely therefore on data aggregation in order to be able to scale. Even if this technique is frequently used, to the best of our knowledge, there has not been any real attempt to evaluate the quality of aggregated data for visualization. This paper presents an approach which lls this gap. We propose to build optimized macroscopic visualizations using measures inherited from information theory, and in particular the Kullback-Leibler divergence. These measures are used to estimate the complexity reduced and the information lost during any given data aggregation. We rst illustrate the applicability of our approach by exploiting these two measures in the analysis of work stealing traces using squari ed treemaps. We then report the e ective scalability of our approach by visualizing known anomalies in a synthetic trace le with the behavior of one million processes, with encouraging results

Visualization of dynamic multidimensional and hierarchical datasets

When it comes to tools and techniques designed to help understanding complex abstract data, visualization methods play a prominent role. They enable human operators to lever age their pattern finding, outlier detection, and questioning abilities to visually reason about a given dataset. Many methods exist that create suitable and useful visual represen tations of static abstract, non-spatial, data. However, for temporal abstract, non-spatial, datasets, in which the data changes and evolves through time, far fewer visualization tech niques exist. This thesis focuses on the particular cases of temporal hierarchical data representation via dynamic treemaps, and temporal high-dimensional data visualization via dynamic projec tions. We tackle the joint question of how to extend projections and treemaps to stably, accurately, and scalably handle temporal multivariate and hierarchical data. The literature for static visualization techniques is rich and the state-of-the-art methods have proven to be valuable tools in data analysis. Their temporal/dynamic counterparts, however, are not as well studied, and, until recently, there were few hierarchical and high-dimensional methods that explicitly took into consideration the temporal aspect of the data. In addi tion, there are few or no metrics to assess the quality of these temporal mappings, and even fewer comprehensive benchmarks to compare these methods. This thesis addresses the abovementioned shortcomings. For both dynamic treemaps and dynamic projections, we propose ways to accurately measure temporal stability; we eval uate existing methods considering the tradeoff between stability and visual quality; and we propose new methods that strike a better balance between stability and visual quality than existing state-of-the-art techniques. We demonstrate our methods with a wide range of real-world data, including an application of our new dynamic projection methods to support the analysis and classification of hyperkinetic movement disorder data.Quando se trata de ferramentas e técnicas projetadas para ajudar na compreensão dados abstratos complexos, métodos de visualização desempenham um papel proeminente. Eles permitem que os operadores humanos alavanquem suas habilidades de descoberta de padrões, detecção de valores discrepantes, e questionamento visual para a raciocinar sobre um determinado conjunto de dados. Existem muitos métodos que criam representações visuais adequadas e úteis de para dados estáticos, abstratos, e não-espaciais. No entanto, para dados temporais, abstratos, e não-espaciais, isto é, dados que mudam e evoluem no tempo, existem poucas técnicas apropriadas. Esta tese concentra-se nos casos específicos de representação temporal de dados hierárquicos por meio de treemaps dinâmicos, e visualização temporal de dados de alta dimen sionalidade via projeções dinâmicas. Nós abordar a questão conjunta de como estender projeções e treemaps de forma estável, precisa e escalável para lidar com conjuntos de dados hierárquico-temporais e multivariado-temporais. Em ambos os casos, a literatura para técnicas estáticas é rica e os métodos estado da arte provam ser ferramentas valiosas em análise de dados. Suas contrapartes temporais/dinâmicas, no entanto, não são tão bem estudadas e, até recentemente, existiam poucos métodos hierárquicos e de alta dimensão que explicitamente levavam em consideração o aspecto temporal dos dados. Além disso, existiam poucas métricas para avaliar a qualidade desses mapeamentos visuais temporais, e ainda menos benchmarks abrangentes para comparação esses métodos. Esta tese aborda as deficiências acima mencionadas para treemaps dinâmicos e projeções dinâmicas. Propomos maneiras de medir com precisão a estabilidade temporal; avalia mos os métodos existentes, considerando o compromisso entre estabilidade e qualidade visual; e propomos novos métodos que atingem um melhor equilíbrio entre estabilidade e a qualidade visual do que as técnicas estado da arte atuais. Demonstramos nossos mé todos com uma ampla gama de dados do mundo real, incluindo uma aplicação de nossos novos métodos de projeção dinâmica para apoiar a análise e classificação dos dados de transtorno de movimentos

Visualization of dynamic multidimensional and hierarchical datasets

When it comes to tools and techniques designed to help understanding complex abstract data, visualization methods play a prominent role. They enable human operators to lever age their pattern finding, outlier detection, and questioning abilities to visually reason about a given dataset. Many methods exist that create suitable and useful visual represen tations of static abstract, non-spatial, data. However, for temporal abstract, non-spatial, datasets, in which the data changes and evolves through time, far fewer visualization tech niques exist. This thesis focuses on the particular cases of temporal hierarchical data representation via dynamic treemaps, and temporal high-dimensional data visualization via dynamic projec tions. We tackle the joint question of how to extend projections and treemaps to stably, accurately, and scalably handle temporal multivariate and hierarchical data. The literature for static visualization techniques is rich and the state-of-the-art methods have proven to be valuable tools in data analysis. Their temporal/dynamic counterparts, however, are not as well studied, and, until recently, there were few hierarchical and high-dimensional methods that explicitly took into consideration the temporal aspect of the data. In addi tion, there are few or no metrics to assess the quality of these temporal mappings, and even fewer comprehensive benchmarks to compare these methods. This thesis addresses the abovementioned shortcomings. For both dynamic treemaps and dynamic projections, we propose ways to accurately measure temporal stability; we eval uate existing methods considering the tradeoff between stability and visual quality; and we propose new methods that strike a better balance between stability and visual quality than existing state-of-the-art techniques. We demonstrate our methods with a wide range of real-world data, including an application of our new dynamic projection methods to support the analysis and classification of hyperkinetic movement disorder data.Quando se trata de ferramentas e técnicas projetadas para ajudar na compreensão dados abstratos complexos, métodos de visualização desempenham um papel proeminente. Eles permitem que os operadores humanos alavanquem suas habilidades de descoberta de padrões, detecção de valores discrepantes, e questionamento visual para a raciocinar sobre um determinado conjunto de dados. Existem muitos métodos que criam representações visuais adequadas e úteis de para dados estáticos, abstratos, e não-espaciais. No entanto, para dados temporais, abstratos, e não-espaciais, isto é, dados que mudam e evoluem no tempo, existem poucas técnicas apropriadas. Esta tese concentra-se nos casos específicos de representação temporal de dados hierárquicos por meio de treemaps dinâmicos, e visualização temporal de dados de alta dimen sionalidade via projeções dinâmicas. Nós abordar a questão conjunta de como estender projeções e treemaps de forma estável, precisa e escalável para lidar com conjuntos de dados hierárquico-temporais e multivariado-temporais. Em ambos os casos, a literatura para técnicas estáticas é rica e os métodos estado da arte provam ser ferramentas valiosas em análise de dados. Suas contrapartes temporais/dinâmicas, no entanto, não são tão bem estudadas e, até recentemente, existiam poucos métodos hierárquicos e de alta dimensão que explicitamente levavam em consideração o aspecto temporal dos dados. Além disso, existiam poucas métricas para avaliar a qualidade desses mapeamentos visuais temporais, e ainda menos benchmarks abrangentes para comparação esses métodos. Esta tese aborda as deficiências acima mencionadas para treemaps dinâmicos e projeções dinâmicas. Propomos maneiras de medir com precisão a estabilidade temporal; avalia mos os métodos existentes, considerando o compromisso entre estabilidade e qualidade visual; e propomos novos métodos que atingem um melhor equilíbrio entre estabilidade e a qualidade visual do que as técnicas estado da arte atuais. Demonstramos nossos mé todos com uma ampla gama de dados do mundo real, incluindo uma aplicação de nossos novos métodos de projeção dinâmica para apoiar a análise e classificação dos dados de transtorno de movimentos