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

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

Visualisation of Uncertainty in a Geodemographic Classifier

We explore some ideas around quantifying and visualising classification uncertainty within a geodemographic classifier. We demonstrate spatially-constrained small-multiples to show geographical variation, their combination with a Gastner population cartogram projection to normalise with respect to population, explore a fuzziness parameter when producing fuzzy-sets, and look at implications of taking into account this uncertainty when profiling population, finding that this can have significant effects that are worth investigating further

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

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

Tilemaps for Summarising Multivariate Geographical Variation

We discuss the ‘tilemap’ design space which encompasses approaches that use regular arrays of glyphs to depict geographical variation in multivariate data. We particularly focus on the potential for tilemaps to depict geographical variation in rich summary statistics of distributions, separating data out by category, showing associations between variables and studying multivariate outputs of geographically-weighted statistics. We consider the parameters of the design space, some design considerations, examples of its use and how it compares to other approaches. The tilemap design space is intended to help and encourage the use of rich geographical summaries of data where there are multiple variables, particularly for their comparison by location

Geoinformation, Geotechnology, and Geoplanning in the 1990s

Over the last decade, there have been some significant changes in the geographic information available to support those involved in spatial planning and policy-making in different contexts. Moreover, developments have occurred apace in the technology with which to handle geoinformation. This paper provides an overview of trends during the 1990s in data provision, in the technology required to manipulate and analyse spatial information, and in the domain of planning where applications of computer technology in the processing of geodata are prominent. It draws largely on experience in western Europe, and in the UK and the Netherlands in particular, and suggests that there are a number of pressures for a strengthened role for geotechnology in geoplanning in the years ahead