Virtual Geodemographics: Repositioning Area Classification for Online and Offline Spaces

Computer mediated communication and the Internet has fundamentally changed how consumers and producers connect and interact across both real space, and has also opened up new opportunities in virtual spaces. This paper describes how technologies capable of locating and sorting networked communities of geographically disparate individuals within virtual communities present a sea change in the conception, representation and analysis of socioeconomic distributions through geodemographic analysis. We argue that through virtual communities, social networks between individuals may subsume the role of neighbourhood areas as the most appropriate units of analysis, and as such, geodemographics needs to be repositioned in order to accommodate social similarities in virtual, as well as geographical, space. We end the paper by proposing a new model for geodemographics which spans both real and virtual geographies

Social Deprivation and Digital Exclusion in England

Issues of digital exclusion are now increasingly considered alongside those of material deprivation when formulating interventions in neighbourhood renewal and other local policy interventions in health, policing and education. In this context, this paper develops a cross classification of material deprivation and lack of digital engagement, at a far more spatially disaggregate level than has previously been attempted. This is achieved my matching the well known 2004 Index of Multiple Deprivation (IMD) with a unique nationwide geodemographic classification of access and use of new information and communications technologies (ICTs), aggregated to the unit postcode scale. This ‘E-Society’ classification makes it possible for the first time to identify small areas that are ‘digitally unengaged’, and our cross classification allows us to focus upon the extent to which the 2004 summary measure of material deprivation in England coincides with such lack of engagement. The results of the cross classification suggest that lack of digital engagement and material deprivation are linked, with high levels of material deprivation generally associated with low levels of engagement with ICTs and vice versa. However, some neighbourhoods are ‘digitally unengaged’ but not materially deprived, and we investigate the extent to which this outcome may be linked to factors such as lack of confidence, skills or motivation. Our analysis suggests that approximately 5.61 million people in England are both materially deprived and digitally unengaged. As with material deprivation, there are distinctive regional and local geographies to digital unengagement that have implications for digital policy implementation

Developing efficient web-based GIS applications

There is an increase in the number of web-based GIS applications over the recent years. This paper describes different mapping technologies, database standards, and web application development standards that are relevant to the development of web-based GIS applications. Different mapping technologies for displaying geo-referenced data are available and can be used in different situations. This paper also explains why Oracle is the system of choice for geospatial applications that need to handle large amounts of data. Wireframing and design patterns have been shown to be useful in making GIS web applications efficient, scalable and usable, and should be an important part of every web-based GIS application. A range of different development technologies are available, and their use in different operating environments has been discussed here in some detail

Geo-temporal Twitter demographics

This paper seeks and uses highly disaggregate social media sources to characterize Greater London in terms of flows of people with modelled individual characteristics, as well as conventional measures of land use morphology and night-time residence. We conduct three analyses. First, we use the Shannon Entropy measure to characterize the geography of information creation across the city. Second, we create a geo-temporal demographic classification of Twitter users in London. Third, we begin to use Twitter data to characterize the links between different locations across the city. We see all three elements as data rich, highly disaggregate geo-temporal analysis of urban form and function, albeit one that pertains to no clearly defined population. Our conclusions reflect upon this severe shortcoming in analysis using social media data, and its implications for progressing our understanding of socio-spatial distributions within cities

An Individual Level Method for Improved Estimation of Ethnic Characteristics

This paper develops an improved method for estimating the ethnicity of individuals based on individual level pairings of given and family names. It builds upon previous research by using a global database of names from c. 1.7 billion living individuals, supplemented by individual level historical census data. In focusing upon Great Britain, these resources enable, respectively, greater precision in estimating probable global origins and better estimation of self-identification amongst long-established family groups such as the Irish Diaspora. We report on geographic issues in adjusting the weighting of groups that are systematically under- or over-predicted using other methods. Our individual level estimates are evaluated using both small area Great Britain census data for 2011 and individual level data for asylum seekers in Canada between 1995 and 2012. Our conclusions assess the value of such estimates in the conduct of social equity audits and in depicting the social mobility outcomes of residential mobility and migration across Great Britain

Uncertainty in the Analysis of Ethnicity Classifications: Issues of Extent and Aggregation of Ethnic Groups

Uncertainty is inherent in the conception and measurement of ethnicity, by both individuals themselves and those who seek to gather evidence of discrimination or inequalities in social and economic outcomes. These issues have received attention in the literature, yet rather little research has been carried out on the uncertainty subsequently created through the analysis of such measurements. We argue that, while general-purpose ethnicity classifications offer a method of standardising results, such groupings are inherently unstable, both in their upward aggregation and in their downward granulation. As such, the results of ethnicity analysis may possess no validity independent of the ethnicity classes upon which it is based. While this conclusion is intuitive, it nevertheless seems to pass unnoticed in the interpretation of research conducted in public policy applications such as education, health and residential segregation. In this paper we use examples based on the standard Census classification of ethnicity, alongside new rich ethnicity datasets from the education domain, in order to evaluate the sensitivity of results to the particular aggregation that is chosen. We use a case study to empirically illustrate the far-reaching consequences of this commonly overlooked source of uncertainty

The stability of geodemographic cluster assignments over an intercensal period

A geodemographic classification provides a set of categorical summaries of the built and socio-economic characteristics of small geographic areas. Many classifications, including that developed in this paper, are created entirely from data extracted from a single decennial census of population. Such classifications are often criticised as becoming less useful over time because of the changing composition of small geographic areas. This paper presents a methodology for exploring the veracity of this assertion, by examining changes in UK census-based geodemographic indicators over time, as well as a substantive interpretation of the overall results. We present an innovative methodology that classifies both 2001 and 2011 census data inputs utilising a unified geography and set of attributes to create a classification that spans both census periods. Through this classification, we examine the temporal stability of the clusters and whether other secondary data sources and internal measures might usefully indicate local uncertainties in such a classification during an intercensal period

Delineating Europe\u27s Cultural Regions: Population Structure and Surname Clustering

Surnames (family names) show distinctive geographical patterning and in many disciplines remain an underutilized source of information about population origins, migration and identity. This paper investigates the geographical structure of surnames, using a unique individual level database assembled from registers and telephone directories from 16 European countries. We develop a novel combination of methods for exhaustively analyzing this multinational data set, based upon the Lasker Distance, consensus clustering and multidimensional scaling. Our analysis is both data rich and computationally intensive, entailing as it does the aggregation, clustering and mapping of 8 million surnames collected from 152 million individuals. The resulting regionalization has applications in developing our understanding of the social and cultural complexion of Europe, and offers potential insights into the long and short-term dynamics of migration and residential mobility. The research also contributes a range of methodological insights for future studies concerning spatial clustering of surnames and population data more widely. In short, this paper further demonstrates the value of surnames in multinational population studies and also the increasing sophistication of techniques available to analyze them