Visual Analysis of Reactionary Train Delay from an Agent Based Model

We design and apply interactive visualisation to help understand reactionary (knock-on) delay in trains using a set of ‘Monte-Carlo’ style Agent Based Model runs, and to help train operating companies design interventions. Our interactive graphics indicate the locations where primary delay occurs, where reactionary delay occurs, the types of primary delays, how these vary across the runs and the mechanisms of delay. We continue to work with the train industry to refine these methods. Interactive visualisation still has unexplored potential to help make Agent Based Models more explainable

Locally-varying explanations behind the United Kingdom’s vote to the Leave the European Union

Explanations behind area-based (Local Authority-level) voting preference in the 2016 referendum on membership of the European Union are explored using aggregate-level data. Developing local models, special attention is paid to whether variables explain the vote equally well across the country. Variables describing the post-industrial and economic ‘successfulness’ of Local Authorities most strongly discriminate variation in the vote. To a lesser extent this is the case for variables linked to ‘metropolitan’ and ‘big city’ contexts, which assist the Remain vote, those that distinguish more traditional and ‘nativist’ val- ues, assisting Leave, and those loosely describing material outcomes, again reinforcing Leave. Whilst variables describing economic competitiveness co-vary with voting pref- erence equally well across the country, the importance of secondary variables – those dis- tinguishing metropolitan settings, values and outcomes – does vary by region. For certain variables and in certain areas, the direction of effect on voting preference reverses. For ex- ample, whilst levels of European Union migration mostly assist the Remain vote, in parts of the country the opposite effect is observed

Supporting crime analysis through visual design

We describe and discuss a visual analysis prototype to support volume crime analysis, a form of exploratory data analysis that aims to identify and describe patterns of criminality using historical and recent crime reports. Analysis requirements are relatively familiar: analysts wish to identify, define and compare sets of crime reports across multiple attributes (space, time and description). A challenge particular to the domain, identified through workshops with Police analysts in Belgium and the UK, is in developing exploratory data analysis software that offers some sophistication in data selection, aggregation and comparison, but with interaction techniques and representations that can be easily understood, navigated and communicated. In light of ongoing discussion with Police analysts, we propose four visual design and interaction maxims that relate to this challenge and discuss an early visual analysis prototype that we hope conforms to these maxims

Visualizing Multiple Variables Across Scale and Geography

Comparing multiple variables to select those that effectively characterize complex entities is important in a wide variety of domains – geodemographics for example. Identifying variables that correlate is a common practice to remove redundancy, but correlation varies across space, with scale and over time, and the frequently used global statistics hide potentially important differentiating local variation. For more comprehensive and robust insights into multivariate relations, these local correlations need to be assessed through various means of defining locality. We explore the geography of this issue, and use novel interactive visualization to identify interdependencies in multivariate data sets to support geographically informed multivariate analysis. We offer terminology for considering scale and locality, visual techniques for establishing the effects of scale on correlation and a theoretical framework through which variation in geographic correlation with scale and locality are addressed explicitly. Prototype software demonstrates how these contributions act together. These techniques enable multiple variables and their geographic characteristics to be considered concurrently as we extend visual parameter space analysis (vPSA) to the spatial domain. We find variable correlations to be sensitive to scale and geography to varying degrees in the context of energy-based geodemographics. This sensitivity depends upon the calculation of locality as well as the geographical and statistical structure of the variable

Supporting Theoretically-grounded Model Building in the Social Sciences through Interactive Visualisation

The primary purpose for which statistical models are employed in the social sciences is to understand and explain phenomena occurring in the world around us. In order to be scientifically valid and actionable, the construction of such models need to be strongly informed by theory. To accomplish this, there is a need for methodologies that can enable scientists to utilise their domain knowledge effectively even in the absence of strong a priori hypotheses or whilst dealing with complex datasets containing hundreds of variables and leading to large numbers of potential models. In this paper, we describe enhanced model building processes in which we use interactive visualisations as the underlying mechanism to facilitate the construction and documentation of theory-driven models. We report our observations from a collaborative project involving social and computer scientists, and identify key roles for visualisation to support model building within the context of social science. We describe a suite of techniques to facilitate the exploration of statistical summaries of input variables, to compare the quality of alternative models, and to keep track of the model-building process. We demonstrate how these techniques operate in coordination to allow social scientists to efficiently generate models that are tightly underpinned by domain specific theory

Attribute Signatures: Dynamic Visual Summaries for Analyzing Multivariate Geographical Data

The visual analysis of geographically referenced datasets with a large number of attributes is challenging due to the fact that the characteristics of the attributes are highly dependent upon the locations at which they are focussed, and the scale and time at which they are measured. Specialized interactive visual methods are required to help analysts in understanding the characteristics of the attributes when these multiple aspects are considered concurrently. Here, we develop attribute signatures -- interactively crafted graphics that show the geographic variability of statistics of attributes through which the extent of dependency between the attributes and geography can be visually explored. We compute a number of statistical measures, which can also account for variations in time and scale, and use them as a basis for our visualizations. We then employ different graphical configurations to show and compare both continuous and discrete variation of location and scale. Our methods allow variation in multiple statistical summaries of multiple attributes to be considered concurrently and geographically, as evidenced by examples in which the census geography of London and the wider UK are explored

Developing and applying a user-centered model for the design and implementation of information visualization tools

The objective of this paper is to show how approaches for user-centered information visualization design and development are being applied in the context of healthcare where users are not familiar with information visualization techniques. We base our design methods on user-centered frameworks in which 'prototyping' plays an important role in the process. We modify existing approaches to involve prototyping at an early stage of the process as the problem domain is assessed. We believe this to be essential, as it increases users' awareness of what information visualization techniques can offer them and that it enables users to participate more effectively in later stages of the design and development process. This also acts as a stimulus for engagement. The problem domain analysis stage of a pilot study using this approach is presented, in which techniques are being collaboratively developed with domain users from a healthcare institution. Our results suggest that this approach has engaged users, who are subsequently able to apply generic information visualization concepts to their domains and as a result are better equipped to take part in the subsequent collaborative design and development process

Small Multiples with Gaps

Small multiples enable comparison by providing different views of a single data set in a dense and aligned manner. A common frame defines each view, which varies based upon values of a conditioning variable. An increasingly popular use of this technique is to project two-dimensional locations into a gridded space (e.g. grid maps), using the underlying distribution both as the conditioning variable and to determine the grid layout. Using whitespace in this layout has the potential to carry information, especially in a geographic context. Yet, the effects of doing so on the spatial properties of the original units are not understood. We explore the design space offered by such small multiples with gaps. We do so by constructing a comprehensive suite of metrics that capture properties of the layout used to arrange the small multiples for comparison (e.g. compactness and alignment) and the preservation of the original data (e.g. distance, topology and shape). We study these metrics in geographic data sets with varying properties and numbers of gaps. We use simulated annealing to optimize for each metric and measure the effects on the others. To explore these effects systematically, we take a new approach, developing a system to visualize this design space using a set of interactive matrices. We find that adding small amounts of whitespace to small multiple arrays improves some of the characteristics of 2D layouts, such as shape, distance and direction. This comes at the cost of other metrics, such as the retention of topology. Effects vary according to the input maps, with degree of variation in size of input regions found to be a factor. Optima exist for particular metrics in many cases, but at different amounts of whitespace for different maps. We suggest multiple metrics be used in optimized layouts, finding topology to be a primary factor in existing manually-crafted solutions, followed by a trade-off between shape and displacement. But the rich range of possible optimized layouts leads us to challenge single-solution thinking; we suggest to consider alternative optimized layouts for small multiples with gaps. Key to our work is the systematic, quantified and visual approach to exploring design spaces when facing a trade-off between many competing criteria—an approach likely to be of value to the analysis of other design spaces