Modifying a Geodemographic Classification of the e-Society using public feedback

The e-Society geodemographic classification (Longley et al., 2008) categories neighbourhoods based on their engagement with new information communication technologies. This classification was launched online in 2006, and allowed users to both view and comment on the accuracy of their assigned neighbourhood Type. This paper utilises the user generated feedback on the accuracy of the e-Society classification and through external validation calculates their accuracy. The pilot methodology developed in this paper is scalable and could be repeated for any classification. We believe that this methodology gives the recipients of these classification procedures a voice that their concerns of classification accuracy can be heard

Creating Open Source Geodemographic Classifications for Higher Education Applications

This paper explores the use of geodemographic classifications to investigate the social, economic and spatial dimensions of participation in higher education. Education is a public service that confers very significant and tangible benefits upon receiving individuals: as such, we argue that understanding the geodemography of educational opportunity requires an application-specific classification, that exploits under-used educational data sources. We develop a classification for the UK higher education sector, and apply it to the Gospel Oak area of London. We discuss the wider merits of sector specific applications of geodemographics, with particular reference to issues of public service provision

Collaborative Mapping of London Using Google Maps: The LondonProfiler

This paper begins by reviewing the ways in which the innovation of Google Maps has transformed our ability to reference and view geographically referenced data. We describe the ways in which the GMap Creator tool developed under the ESRC National Centre for E Social Science programme enables users to ‘mashup’ thematic choropleth maps using the Google API. We illustrate the application of GMap Creator using the example of www.londonprofiler.org, which makes it possible to view a range of health, education and other socioeconomic datasets against a backcloth of Google Maps data. Our conclusions address the ways in which Google Map mashups developed using GMap Creator facilitate online exploratory cartographic visualisation in a range of areas of policy concern

A neighbourhood Output Area Classification from the 2021 and 2022 UK censuses

UK-wide multivariate neighbourhood classifications have been built using small area population data following every census since 1971, and have been built using Output Area geographies since 2001. Policy makers in both the public and private sectors find such taxonomies, typically arranged into hierarchies of Supergroups, Groups and Subgroups, useful across a wide range of applications in business and service planning. Recent and forthcoming releases of small area census statistics pose new methodological challenges. For example, the 2022 Scottish Census was carried out a year after those in other UK nations, and some of the variables now collected across different jurisdictions do not bear direct comparison with one another. Here we develop a methodology to accommodate these issues alongside the more established procedures of variable selection, standardisation, transformation, class definition and labelling

Public Domain GIS, Mapping & Imaging Using Web-based Services

In this paper, we outline a series of related applications and a web service designed to enable non-expert users to develop online visualizations which are essentially map-based. In the last five years, public domain GIS (geographic information systems) software for map display and beyond has become available for non-expert users in the public domain, the best examples being the various products from Google such as Google Maps and Google Earth. We have devised various software to enable non-experts to take appropriate map data in standard formats and to transform them so that can be displayed by these software in a one stop action. The first system is called GMapCreator and we show how the software can be used to produce any number of map layers which can be overlaid on Google Maps, can be combined and toggled in combination, and whose transparency can be varied for a myriad of presentation purposes. We then evolve this into a form called ImageCutter which takes any large image and puts this into a Google Map so that the zoom and pan features of the software can be exploited. These software are now available through a site we call MapTube which is a server pointing to various maps created by GMapCreator which is a rudimentary archive of virtual map resources. Finally, we sketch how these software are being moved into 3D using the capabilities of Google Earth and Second Life to display geographic imagery

Using loyalty card records and machine learning to understand how self-medication purchasing behaviours vary seasonally in England, 2012–2014

This paper examines objective purchasing information for inherently seasonal self-medication product groups using transaction-level loyalty card records. Predictive models are applied to predict future monthly self-medication purchasing. Analyses are undertaken at the lower super output area level, allowing the exploration of ~300 retail, social, demographic and environmental predictors of purchasing. The study uses a tree ensemble predictive algorithm, applying XGBoost using one year of historical training data to predict future purchase patterns. The study compares static and dynamic retraining approaches. Feature importance rank comparison and accumulated local effects plots are used to ascertain insights of the influence of different features. Clear purchasing seasonality is observed for both outcomes, reflecting the climatic drivers of the associated minor ailments. Although dynamic models perform best, where previous year behaviour differs greatly, predictions had higher error rates. Important features are consistent across models (e.g. previous sales, temperature, seasonality). Feature importance ranking had the greatest difference where seasons changed. Accumulated local effects plots highlight specific ranges of predictors influencing self-medication purchasing. Loyalty card records offer promise for monitoring the prevalence of minor ailments and reveal insights about the seasonality and drivers of over-the-counter medicine purchasing in England

Developing an Individual-level Geodemographic Classification

Geodemographics is a spatially explicit classification of socio-economic data, which can be used to describe and analyse individuals by where they live. Geodemographic information is used by the public sector for planning and resource allocation but it also has considerable use within commercial sector applications. Early geodemographic systems, such as the UK’s ACORN (A Classification of Residential Neighbourhoods), used only area-based census data, but more recent systems have added supplementary layers of information, e.g. credit details and survey data, to provide better discrimination between classes. Although much more data has now become available, geodemographic systems are still fundamentally built from area-based census information. This is partly because privacy laws require release of census data at an aggregate level but mostly because much of the research remains proprietary. Household level classifications do exist but they are often based on regressions between area and household data sets. This paper presents a different approach for creating a geodemographic classification at the individual level using only census data. A generic framework is presented, which classifies data from the UK Census Small Area Microdata and then allocates the resulting clusters to a synthetic population created via microsimulation. The framework is then applied to the creation of an individual-based system for the city of Leeds, demonstrated using data from the 2001 census, and is further validated using individual and household survey data from the British Household Panel Survey

<i>C-elegans</i> model identifies genetic modifiers of alpha-synuclein inclusion formation during aging

Inclusions in the brain containing alpha-synuclein are the pathological hallmark of Parkinson's disease, but how these inclusions are formed and how this links to disease is poorly understood. We have developed a &lt;i&gt;C-elegans&lt;/i&gt; model that makes it possible to monitor, in living animals, the formation of alpha-synuclein inclusions. In worms of old age, inclusions contain aggregated alpha-synuclein, resembling a critical pathological feature. We used genome-wide RNA interference to identify processes involved in inclusion formation, and identified 80 genes that, when knocked down, resulted in a premature increase in the number of inclusions. Quality control and vesicle-trafficking genes expressed in the ER/Golgi complex and vesicular compartments were overrepresented, indicating a specific role for these processes in alpha-synuclein inclusion formation. Suppressors include aging-associated genes, such as sir-2.1/SIRT1 and lagr-1/LASS2. Altogether, our data suggest a link between alpha-synuclein inclusion formation and cellular aging, likely through an endomembrane-related mechanism. The processes and genes identified here present a framework for further study of the disease mechanism and provide candidate susceptibility genes and drug targets for Parkinson's disease and other alpha-synuclein related disorders