Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods

Geovisualization of household energy consumption characteristics

A vast amount of quantitative data is available within the energy sector, however, there is limited understanding of the relationships between neighbourhoods, demographic characteristics and domestic energy consumption habits. We report upon research that will combine datasets relating to energy consumption, saving and loss with geodemographics to enable better understanding of energy user types. A novel interactive interface is planned to evaluate the performance of these energy-based classifications. The research aims to help local governments and the energy industry in targeting households and populations for new energy saving schemes and in improving efforts to promote sustainable energy consumption. Energy based neighbourhood classifications will also promote consumption awareness amongst domestic users. This poster describes the research methodology, data sources and visualization requirements

Special issue introduction: Approaching spatial uncertainty visualization to support reasoning and decision making

While research on uncertainty and decision-making has a long history across several disciplines, recent technological developments compel researchers to rethink how to best address and advance the understanding of how humans reason and make decisions under spatial uncertainty. This introduction presents a visual summary graphic to provide an overview of each article in this special issue. Upon viewing these visual summaries, the reader will find that each of these articles covers different topics in the uncertainty visualization domain, offering complementary research in this field. Extending this body of research and finding new ways to explore how these visualizations may help or hinder the analytical and reasoning process of humans continues to be a necessary step towards designing more effective uncertainty visualizations to support reasoning and decision-making

Visual analysis design to support research into movement and use of space in Tallinn: A case study

We designed and applied interactive visualisation to help an urban study group investigate how suburban residents in the Tallinn Metropolitan Area (Estonia) use space in the city. We used mobile phone positioning data collected from suburban residents together with their socio-economic characteristics. Land-use data provided geo-context that helped characterise visited locations by suburban residents. Our interactive visualisation design was informed by a set of research questions framed as identification, localisation and comparison tasks. The resulting prototype offers five linked and coordinated views of spatial, temporal, socio-economic characteristics and land-use aspects of data. Brushing, sorting and filtering provide visual means to identify similarities between individuals and facilitate the identification, localisation and comparison of patterns of use of urban space. The urban study group was able to use the prototype to explore their data and address their research questions in a more flexible way than previously possible. Initial feedback was positive. The prototype was found to support the research and facilitate the discovery of patterns and relations among groups of participants and their movements

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

The applications of loyalty card data for social science

Large-scale consumer datasets have become increasingly abundant in recent years and many have turned their attention to harnessing these for insights within the social sciences. Whilst commercial organisations have been quick to recognise the benefits of these data as a source of competitive advantage, their emergence has been met with contention in research due to the epistemological, methodological and ethical challenges they present. These issues have seldom been addressed, primarily due to these data being hard to obtain outside of the commercial settings in which they are often generated. This thesis presents an exploration of a unique loyalty card dataset obtained from one of the most prominent UK high street retailers, and thus an opportunity to study the dynamics, potentialities and limitations when applying such data in a research context. The predominant aims of this work were to firstly, address issues of uncertainty surrounding novel consumer datasets by quantifying their inherent representation and data quality issues and secondly, to explore the extent to which we may enrich our current knowledge of spatiotemporal population processes through the analysis of consumer activity patterns. Our current understanding of such dynamics has been limited by the data-scarce era, yet loyalty card data provide individual level, georeferenced population data that are high in velocity. This provided a framework for understanding more detailed interactions between people and places, and what these might indicate for both consumption behaviours and wider societal phenomena. This work endeavoured to provide a substantive contribution to the integration of consumer datasets in social science research, by outlining pragmatic steps to ensure novel data sources can be fit for purpose, and to population geography research, by exploring the extent to which we may utilise spatiotemporal consumption activities to make broad inferences about the general population

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

Community engagement and its role in fire prevention in a West Midlands neighbourhood

In this thesis I investigate inequality in the way in which fires are distributed through society, in particular exploring the role played in this by community engagement and the relationship between public service providers and the communities that they serve. The thesis begins with an extensive, quantitative investigation of the distribution of accidental dwelling fires in the West Midlands. By analysing service data from the West Midlands Fire Service, together with a range of socio-economic and demographic data, I establish that there is considerable inequality in the way in which fire is distributed, with economic status, ethnic make-up and household structure in an area all being predictive of rates of fire. Conceptualising this inequality as an inequality in the delivery of fire prevention work, I then focus in on one socially disadvantaged area with high rates of fire. In the second part of the thesis I use an intensive, interpretivist approach to explore perceptions of, and attitudes towards, public services, and whether these hamper the ability to deliver effective fire prevention initiatives. Residents rarely thought about the fire service directly, with fire not perceived as a priority. However, the fire service was often associated with other services in people’s minds, and I found a number of factors that disinclined people from interacting with public services in general. These include disillusionment, a sense of feeling judged, a fear of adverse consequences and a lack of awareness of the services available. Building on these findings I argue that for engagement to take place community members must feel that there is a space available for dialogue that is safe, comfortable and rewarding. In an area characterised by multiple, heterogeneous communities, many different spaces will be needed to ensure dialogue with the widest range of people. The work both updates knowledge of inequality in the distribution of fire and contributes to understanding of the way in which access to public services can be restricted by the taken-for-granted assumptions of service providers