Dynamic Visualisation of Many-Objective Populations

This is the author accepted manuscript. The final version is available from the Operational Research SocietyThere has been an increase in research activity recently regarding the visualisation of many-objective populations. Two of the main drivers for this have been (i) to aid decision makers in comparing and selecting designs returned from a many-objective optimisation run, and (ii) to help in the selection of solutions in interactive optimisation. In both of these situations there is often a dynamic element – populations evolving over time change their relative relationships, and the quality comparison measure itself can be altered, redefining member relations. Here we illustrate how a number of existing visualisations from various domains may be applied to many-objective populations to aid the understanding of population relations using the d3 package. d3 is inherently dynamic, and will automatically respond to any changes in the base document underpinning the visualisation, allowing the visualisation package to 'bolt-on' to any other program that can produce or update the underlying file

État des lieux des représentations dynamiques des temporalités des territoires

Le temps et ses caractéristiques ont toujours fait l’objet de grandes attentions pour comprendre les dynamiques des territoires. Aujourd’hui, que ce soit à cause des nouvelles capacités d’observation en temps réel, de l’accumulation des séries de données au cours du temps, ou à cause de la multiplication des rythmes, les temporalités à prendre en compte pour comprendre les dynamiques territoriales se multiplient et leurs imbrications se complexifient. Interroger les rythmes, les vitesses, les cycles de ces dynamiques, ou mettre en relation temporelle des phénomènes spatiaux tels que les évènements catastrophiques passés devient plus que jamais un enjeu pour comprendre et décider.Les jeux de méthodes mobilisables aujourd’hui pour représenter les temporalités des territoires sont en plein renouvellement, et imposent désormais bien souvent de franchir les fractures disciplinaires traditionnelles entre échelles, entre outils, entre formalismes. Les domaines d’applications potentiellement concernés, comme celui du développement durable des territoires, sont autant de domaines susceptibles de nourrir les questions associées à l’exploration des temporalités des territoires. Le projet "Représentation dynamique des temporalités des territoires" se veut un état des lieux de différents développements et solutions pour analyser et rendre compte des temporalités des territoires. Cet état des lieux est à entrées multiples, interrogeant à la fois des choix amont (modélisation) et des choix proprement liés à la question de la représentation. Le projet débouche sur un ensemble de résultats dont certains sont mis en ligne sur le site: http://www.map.cnrs.fr/jyb/puca/- Une grille de lecture de la collection d'applications analysée (voir onglet "47 applications"), grille où sont combinés des indicateurs généraux sur par exmeple le type de service rendu ou le type de dynamique spatiale analysée, et des indicateurs plus spécifiques au traitement des dimensions spatiales et temporelles. Cette grille est mise en place sur 47 applications identifiées et analysées,- Des visualisations récapitulatives conçues comme outils d'analyse comparative de la collection,- Une bibliographie structurée en relation avec la grille de lecture

Visualization Techniques for the Analysis of Neurophysiological Data

In order to understand the diverse and complex functions of the Human brain, the temporal relationships of vast quantities of multi-dimensional spike train data must be analysed. A number of statistical methods already exist to analyse these relationships. However, as a result of expansions in recording capability hundreds of spike trains must now be analysed simultaneously. In addition to the requirements for new statistical analysis methods, the need for more efficient data representation is paramount. The computer science field of Information Visualization is specifically aimed at producing effective representations of large and complex datasets. This thesis is based on the assumption that data analysis can be significantly improved by the application of Information Visualization principles and techniques. This thesis discusses the discipline of Information Visualization, within the wider context of visualization. It also presents some introductory neurophysiology focusing on the analysis of multidimensional spike train data and software currently available to support this problem. Following this, the Toolbox developed to support the analysis of these datasets is presented. Subsequently, three case studies using the Toolbox are described. The first case study was conducted on a known dataset in order to gain experience of using these methods. The second and third case studies were conducted on blind datasets and both of these yielded compelling results

Understanding Optimisation Processes with Biologically-Inspired Visualisations

Evolutionary algorithms (EAs) constitute a branch of artificial intelligence utilised to evolve solutions to solve optimisation problems abound in industry and research. EAs often generate many solutions and visualisation has been a primary strategy to display EA solutions, given that visualisation is a multi-domain well-evaluated medium to comprehend extensive data. The endeavour of visualising solutions is inherent with challenges resulting from high dimensional phenomenons and the large number of solutions to display. Recently, scholars have produced methods to mitigate some of these known issues when illustrating solutions. However, one key consideration is that displaying the final subset of solutions exclusively (rather than the whole population) discards most of the informativeness of the search, creating inadequate insight into the black-box EA. There is an unequivocal knowledge gap and requirement for methods which can visualise the whole population of solutions from an optimiser and subjugate the high-dimensional problems and scaling issues to create interpretability of the EA search process. Furthermore, a requirement for explainability in evolutionary computing has been demanded by the evolutionary computing community, which could take the form of visualisations, to support EA comprehension much like the support explainable artificial intelligence has brought to artificial intelligence. In this thesis, we report novel visualisation methods that can be used to visualise large and high-dimensional optimiser populations with the aim of creating greater interpretability during a search. We consider the nascent intersection of visualisation and explainability in evolutionary computing. The potential high informativeness of a visualisation method from an early chapter of this work forms an effective platform to develop an explainability visualisation method, namely the population dynamics plot, to attempt to inject explainability into the inner workings of the search process. We further support the visualisation of populations using machine learning to construct models which can capture the characteristics of an EA search and develop intelligent visualisations which use artificial intelligence to potentially enhance and support visualisation for a more informative search process. The methods developed in this thesis are evaluated both quantitatively and qualitatively. We use multi-feature benchmark problems to show the method’s ability to reveal specific problem characteristics such as disconnected fronts, local optima and bias, as well as potentially creating a better understanding of the problem landscape and optimiser search for evaluating and comparing algorithm performance (we show the visualisation method to be more insightful than conventional metrics like hypervolume alone). One of the most insightful methods developed in this thesis can produce a visualisation requiring less than 1% of the time and memory necessary to produce a visualisation of the same objective space solutions using existing methods. This allows for greater scalability and the use in short compile time applications such as online visualisations. Predicated by an existing visualisation method in this thesis, we then develop and apply an explainability method to a real-world problem and evaluate it to show the method to be highly effective at explaining the search via solutions in the objective spaces, solution lineage and solution variation operators to compactly comprehend, evaluate and communicate the search of an optimiser, although we note the explainability properties are only evaluated against the author’s ability and could be evaluated further in future work with a usability study. The work is then supported by the development of intelligent visualisation models that may allow one to predict solutions in optima (importantly local optima) in unseen problems by using a machine learning model. The results are effective, with some models able to predict and visualise solution optima with a balanced F1 accuracy metric of 96%. The results of this thesis provide a suite of visualisations which aims to provide greater informativeness of the search and scalability than previously existing literature. The work develops one of the first explainability methods aiming to create greater insight into the search space, solution lineage and reproductive operators. The work applies machine learning to potentially enhance EA understanding via visualisation. These models could also be used for a number of applications outside visualisation. Ultimately, the work provides novel methods for all EA stakeholders which aims to support understanding, evaluation and communication of EA processes with visualisation

Visualisation for household energy analysis: techniques for exploring multiple variables across scale and geography

The visualisation of large volumes of data can provide rich and meaningful representations that enable users to gain insights quickly and efficiently. Household energy consumer characteristics are explored in this thesis using innovative interactive visualisation techniques. Initial research with energy analysts, from a major UK utility company, investigates visual possibilities and opportunities for future (smart home) energy analytics and explicitly uses creativity techniques for information visualisation requirements gathering. The results, along with exploratory visual analysis combining geodemographic groups and energy consumption, identifes a need for profiling consumers by typical traits. While energy consumption has been a popular topic of research in recent years, there is still limited understanding of the relationship between energy consumption and measurable characteristics of the general population. An investigation of the process of creating an energy-based geodemographic classification led to the proposal and design of a new theoretical framework for visually comparing multivariate data across scale and geography; a necessary step when selecting reliable variables for running clustering algorithms, such as during the geodemographic classification creation process. The framework for including geography and scale in multivariate comparison forms the major contribution of this thesis. This framework is demonstrated and justified through the building of an interactive visualisation prototype, using input variables deemed relevant for consideration for energy-based geodemographic classification. Important transitions in the framework are highlighted in the proposed design, which uses both statistical and spatial representations. The utility of the framework is validated in the context of energy-based geodemographic variable selection where the multivariate geography of the UK is explored. The sensitivities of varying scale and geography { through varying resolution, extent and the calculation of locally weighted summary statistics { are investigated in context and are shown to be important elements to consider during the variable selection process. The broader applicability of the framework is demonstrated through two further scenarios where multivariate visualisation across scale and geography is shown to be important. The research provides a framework and viable solutions through which geographical visual parameter space analysis (gvPSA) can be undertaken. It uses a design science approach that results in a series of artifacts that open up new visualisation possibilities. This project covers a wide topic where the breadth of research options is extensive and many possibilities for continued research are identified

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Trends and forecasts in cause-specific mortality

Mortality forecasting models are typically limited in that they pertain only to national death rates, predict only all-cause mortality, or do not capture and utilize the correlation among diseases. I have developed a novel Bayesian hierarchical model that jointly forecasts cause- specific death rates for geographic subunits. I examined the model’s effectiveness by applying it to United States vital statistics data from 1982 to 2011 that I prepared using a new cause of death reassignment algorithm. I found that the model not only generated coherent forecasts for mutually exclusive causes of death, but it also exhibited lower out-of-sample error than alternative commonly-used models for forecasting mortality. I then used the model to produce forecasts of US cause-specific mortality through 2025 and analysed the resulting trends. I found that total death rates in the US were likely to continue their decline, but at a slower rate of improvement than has been observed for the past several decades. While death rates due to major causes of death like ischaemic heart disease, stroke, and lung cancer were projected to continue trending downward, increases in causes such as unintentional injuries and mental and neurological conditions offset many of these gains. These findings suggest that the US health system will need to adapt to a changing cause composition of disease burden as its population ages in the coming decade. Forecasting research should continue to consider how to best incorporate and balance the many dimensions of mortality when producing projections.Open Acces

Visualizing Set Relations and Cardinalities Using Venn and Euler Diagrams

In medicine, genetics, criminology and various other areas, Venn and Euler diagrams are used to visualize data set relations and their cardinalities. The data sets are represented by closed curves and the data set relationships are depicted by the overlaps between these curves. Both the sets and their intersections are easily visible as the closed curves are preattentively processed and form common regions that have a strong perceptual grouping effect. Besides set relations such as intersection, containment and disjointness, the cardinality of the sets and their intersections can also be depicted in the same diagram (referred to as area-proportional) through the size of the curves and their overlaps. Size is a preattentive feature and so similarities, differences and trends are easily identified. Thus, such diagrams facilitate data analysis and reasoning about the sets. However, drawing these diagrams manually is difficult, often impossible, and current automatic drawing methods do not always produce appropriate diagrams. This dissertation presents novel automatic drawing methods for different types of Euler diagrams and a user study of how such diagrams can help probabilistic judgement. The main drawing algorithms are: eulerForce, which uses a force-directed approach to lay out Euler diagrams; eulerAPE, which draws area-proportional Venn diagrams with ellipses. The user study evaluated the effectiveness of area- proportional Euler diagrams, glyph representations, Euler diagrams with glyphs and text+visualization formats for Bayesian reasoning, and a method eulerGlyphs was devised to automatically and accurately draw the assessed visualizations for any Bayesian problem. Additionally, analytic algorithms that instantaneously compute the overlapping areas of three general intersecting ellipses are provided, together with an evaluation of the effectiveness of ellipses in drawing accurate area-proportional Venn diagrams for 3-set data and the characteristics of the data that can be depicted accurately with ellipses