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This study examined geodemographic factors associated with availability of comprehensive intimate partner violence (IPV) screening services in Miami-Dade County, Florida. We geocoded 2014 survey data from 278 health facilities and created a population-normalized density surface of IPV screening comprehensiveness. We used correlation analysis and spatial regression techniques to evaluate census tract-level predictors of the mean normalized comprehensiveness score (NCS) for 505 census tracts in Miami-Dade. The population-adjusted density surface of IPV screening comprehensiveness revealed geographic disparities in the availability of screening services. Using a spatial lag regression model, we observed that race and ethnicity are associated with mean NCS by census tract after controlling for age, median gross rent, and receipt of Social Security benefits. The percentage of White non-Hispanic residents was positively associated with NCS, Black non-Hispanic was negatively associated with NCS, while Hispanic—the majority ethnicity in Miami-Dade—was not associated with NCS. This exploratory study may be the first to put IPV screening comprehensiveness on the map, and provides a starting point for addressing urban disparities in the availability of IPV screening services that are shaped by race, ethnicity, zoning, and socioeconomic status.

The purpose of this study was to use a statistical model to examine the effects of various geodemographic factors on levels of driver injury severity. A driver's geodemographic profile with respect to involvement in a traffic crash consists of variables from multiple hierarchical levels such as drivers nested within crashes and crashes clustered within areas. This structure implies that driver-level observations are correlated rather than independent as assumed in many injury severity models. To capture within-group and between-group correlations in observations, a multilevel mixed-effects ordered logit model was employed. The mixed effect allows some variables to vary by observations (i.e., random parameters). The analysis was based on national traffic crash data in the United Kingdom between 2009 and 2011, consisting of 271,654 drivers from 217,523 traffic crashes occurring across 27,773 census areas. Data on area, social deprivation, census, and land use patterns were collected from multiple sources and integrated with a geographic information system framework. Results indicated that the severity of injuries sustained by urban drivers involved in crashes increased if they traveled to rural areas; the level of driver injury severity also increased if traffic crashes occurred in areas with high car ownership per capita; and drivers from more disadvan-taged areas would sustain, if all else is equal, more severe injuries. Findings from this study would be useful to the UK Department for Transport and local authorities in formulating safety policies aimed at enhancing driver education, training, and licensing programs.

This paper presents a preliminary empirical evaluation of the strategic importance of infusing Twitter social media data into classifications of small areas, as a way of moving beyond the nighttime residential geographies of conventional geodemographic classifications. We attempt an empirically based critique of the merits and drawbacks of the use of social media data, in which the value of high spatial and temporal granularity of revealed activity patterns is contrasted with the paucity of individual attribute information. We apply new and novel methods to enrich the profiles of Twitter users in order to generalize about activity patterns in London, our case-study city. More insidious problems in the use of social media data arise from the as-yet-unknown sources and operation of bias in their user bases. Our contribution is to begin to identify and assess the biases inherent in social media usage in social research, and use these to evaluate their deployment in research applications. Keywords: geodemographics, GIS, social media

Geodemographics is the study of people by where they live, and historically has relied in part on census information to provide essential variables. In 2005 the Office for National Statistics for England and Wales published a geodemographic classification of output areas (OAs; the smallest published areas of enumeration for the UK) of the 2001 UK Census. Uniquely among UK examples of such classifications not only are the classes to which each OA is assigned published for free with no restriction on use, but the difference between each OA and all class centroids are also made available. In this paper these differences (being Euclidian distances in the classification feature space) are used to introduce uncertainty by softening the classification using the methods derived from fuzzy c-means and the possibilistic c-means classifications. The former applies a probabilistic assumption that all fuzzy memberships for a particular OA sum to unity while the latter does not. This assumption (which is the same as that behind the published OA classification) may generate results which are potentially misleading because the fuzzy c-means method forces OAs into classes to which they may have relatively little similarity while minimising the importance of classes to which they may have a larger affinity. If the fuzzy memberships or possibilities were to be integrated with other socioeconomic variables which are also conceived as fuzzy sets for geodemographic applications (decision making for government resource allocation, site location modelling or marketing, for example), then the two results would produce different outcomes. It is suggested that the possibilistic c-means are the more useful although they do lead to the possibly confusing but analytically rich situation where one OA may have minimal affinity with any class, at the same time as others may have strong affinity with many classes.

The continuous flow of resources through the economy is the origin of most of the environmental problems evident today. These problems are directly related to production and introduction of wastes and pollutants into the natural environment. The study investigated the relationship between household waste and geodemographic grouping (GDG) at the municipal level across England. A correlation analysis of the GDG and waste data revealed a positive relationship between waste and proportion of different GDG. A robust regression model generated represents 74% of the variation across the dataset, and it shows that there is a significant relationship between municipal waste and GDG. A higher number of GDG (e.g. 3, 4, 6, 7 and 8) displayed a very strong association with increasing total municipal household waste (TMHW). GDG such as 1, 2 and 10, despite their higher disposable incomes, displayed a weak association with TMHW. Furthermore, GDGs such as 8, 3, 7, 4 and 6 generated more household waste in comparison to GDG. This model provides an understanding that can influence the organisation of waste management programmes and forecasting of municipal waste. It also provides essential input for decisions in structuring promotion of sustainable behaviour and implementing local initiatives to support such activities.

This paper addresses our ability to analyse progression rates into UK Higher Education (HE) using a range of data available at the individual and neighbourhood levels. The then Department for Children, Schools and Families has recently released data which make it possible to profile national patterns of student educational progression from post-compulsory schooling through to university. However, the linked records lack detailed socioeconomic information, and thus a geodemographic classification is used to analyse the flows of students from different sociospatial backgrounds into the HE system. Rates of progression are shown to vary greatly between these groups, and a disaggregation of HE participants by courses of study demonstrates that the abilities of institutions to attract students from different backgrounds will be constrained by the mix of their course offerings.

Geodemographic systems currently provide classifications of small areas based primarily on their demographic and socioeconomic characteristics. This paper presents an innovative use of geodemographic methods in the public sector, in particular to determine people's road injury risk. It uses Stats19 road accident data from the London Metropolitan Police, specifically postcode information relating to the driver and casualty of each road accident from 1999 to 2003. Experian's Mosaic software was used to append each postcode to a geodemographic Mosaic type. The results showed distinct spatial and statistical prevalence of certain groups within society to be more at risk of being in a road accident. This highlights the need for a more sophisticated understanding of the different risks people face as they engage in the road environment.

This paper develops a cross-classification of material deprivation and lack of digital engagement, at a far more spatially disaggregated level than has previously been attempted in the UK. This is achieved by matching the 2004 Index of Multiple Deprivation (IMD) with a unique nation-wide geodemographic classification of ICT usage, aggregated to unit postcodes. 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 the paper investigates the extent to which this outcome may be linked to factors such as lack of confidence, skills or motivation. As with material deprivation, there are distinctive regional and local geographies of digital unengagement and these have important implications for digital policy implementation.

Postapartheid South Africa has been plagued by an increase in crime across all categories. While a significant amount of criminological research has been undertaken in the country, the spatial analysis of crime and offenders, a basic prerequisite for a functional crime management strategy, has not been adequately addressed at a sufficiently fine scale of aggregation. This paper reports on the geodemographic development of offender risk profiles for neighbourhoods in the City of Tshwane Metropolitan Municipality in South Africa. Geodemographics is a relatively new geo-analytical technique that is being increasingly used in policing applications to complement law enforcement techniques and provide further insight into offenders and their offences. Findings of the study indicate that neighbourhoods at a high risk for offender development are amongst the most socially and economically deprived in the municipality and are disproportionately occupied by black Africans. The results highlight a need to reassess the current law enforcement approach to crime reduction in the country and return to the crime prevention initiatives that were part of the National Crime Prevention Strategy of the 1990s.

There is a growing body of research relating to the ways in which digital code contributes to the production of space. In much of this work this issue is approached by first examining particular spaces and then considering the code and its effects on those spaces. In contrast, we explore the production of space from another angle, examining the ways in which an example of code—geodemographic classification—is constructed, and then questioning what it is about the emergent production of space that may feed back recursively into the production of that code.

It is simplistic to think of the impacts of new information and communication technologies (ICTs) in terms of a single, or even small number of, 'digital divides'. As developments in what has been termed the ?e-society? reach wider and more generalisedaudiences, so it becomes appropriate to think of digital media as having wider-ranging but differentiated impacts upon consumer transactions, information gathering and citizen participation. This paper describes the development of a detailed, nationwide household classification based on levels of awareness of different ICTs; levels of use of ICTs; andtheir perceived impacts upon human capital formation and the quality of life. It discusses how geodemographic classification makes it possible to provide context for detailed case studies, and hence identify how policy might best improve both the quality and degree ofsociety?s access to ICTs. The primary focus of the paper is methodological, but it alsoillustrates how the classification may be used to investigate a range of regional and subregional policy issues. This paper illustrates the potential contribution of bespoke classifications to evidence-based policy, and the likely benefits of combining the most appropriate methods, techniques, datasets and practices that are used in the public and private sectors. It is simplistic to think of the impacts of new information and communication technologies (ICTs) in terms of a single, or even small number of, 'digital divides'. As developments in what has been termed the ?e-society? reach wider and more generalisedaudiences, so it becomes appropriate to think of digital media as having wider-rangingbut differentiated impacts upon consumer transactions, information gathering and citizen participation. This paper describes the development of a detailed, nationwide household classification based on levels of awareness of different ICTs; levels of use of ICTs; and their perceived impacts upon human capital formation and the quality of life. It discusses how geodemographic classification makes it possible to provide context for detailed case studies, and hence identify how policy might best improve both the quality and degree of society?s access to ICTs. The primary focus of the paper is methodological, but it also illustrates how the classification may be used to investigate a range of regional and subregional policy issues. This paper illustrates the potential contribution of bespoke classifications to evidence-based policy, and the likely benefits of combining the most appropriate methods, techniques, datasets and practices that are used in the public and private sectors.

Government frequently adopts an area-based approach to the targeting of urban policy initiatives as an indirect way of reaching the individuals that the initiatives are intended to help. The paper develops a method for assessing the success of this spatial targeting. It uses a geodemographic classification system to produce a generalised socioeconomic profile for a particular initiative. This profile can be used to examine the targeting of the initiative in different localities, in order to assess whether targeting has been inefficient (the targeted areas have been defined so that many of the people they contain are in fact not those for whom the initiative is intended) or incomplete (deserving cases have been missed because the initiative's boundaries have been drawn too tightly). The utility of the method is demonstrated by employing the P2 People and Places geodemographic system to assess the targeting of the Sure Start initiative in eight large provincial cities in England.

Classifying consumers according to the type of neighbourhood in which they live isnow standard practice among most of Britain?s successful consumer facingorganisations. In recent years these ?geodemographic? classifications have becomeincreasingly used in public sector applications. Their use has made it possible notjust to gain a clearer understanding of the level of inequalities that exist betweendifferent types of neighbourhood but also to understand which policy interventionsare likely to be most successful in different localities throughout the country.This paper summarises key findings resulting from the appending of the UK Mosaicneighbourhood classification system to the records of the Pupil Level Annual SchoolCensus. The most significant of these findings is that other than the performance ofthe pupil at an earlier key stage test the type of neighbourhood in which a pupil livesis a more reliable predictor of a pupil?s GCSE performance than any otherinformation held about that pupil on the PLASC database.Analysis then shows the extent to which the performance of pupils from anyparticular type of neighbourhood is also incrementally affected by theneighbourhoods from which the other pupils in the school they attend are drawn. Itfinds that whilst a pupil?s exam performance is affected primarily by the socialbackground of people he or she may encounter at home, the social background offellow school pupils is of only marginally lower significance.These findings suggest that so long as pupils? GCSE performances are so stronglyaffected by the type of neighbourhood in which they live, a school?s league positionbears only indirect relationship to the quality of school management and teaching. Abetter measurement of the latter would be a league table system which took intoaccount the geodemographic profile of each school?s pupil intake.The paper concludes with discussion of the relevance of these findings to thesociology of education, to the debate on consumer choice in public services, to thegeneral appropriateness of adjusting public sector performance metrics to take intoaccount the social mix of service users and to parental strategies in the educationalsector in particular. Classifying consumers according to the type of neighbourhood in which they live isnow standard practice among most of Britain?s successful consumer facingorganisations. In recent years these ?geodemographic? classifications have becomeincreasingly used in public sector applications. Their use has made it possible notjust to gain a clearer understanding of the level of inequalities that exist betweendifferent types of neighbourhood but also to understand which policy interventionsare likely to be most successful in different localities throughout the country.This paper summarises key findings resulting from the appending of the UK Mosaicneighbourhood classification system to the records of the Pupil Level Annual SchoolCensus. The most significant of these findings is that other than the performance ofthe pupil at an earlier key stage test the type of neighbourhood in which a pupil livesis a more reliable predictor of a pupil?s GCSE performance than any otherinformation held about that pupil on the PLASC database.Analysis then shows the extent to which the performance of pupils from anyparticular type of neighbourhood is also incrementally affected by theneighbourhoods from which the other pupils in the school they attend are drawn. Itfinds that whilst a pupil?s exam performance is affected primarily by the socialbackground of people he or she may encounter at home, the social background offellow school pupils is of only marginally lower significance.These findings suggest that so long as pupils? GCSE performances are so stronglyaffected by the type of neighbourhood in which they live, a school?s league positionbears only indirect relationship to the quality of school management and teaching. Abetter measurement of the latter would be a league table system which took intoaccount the geodemographic profile of each school?s pupil intake.The paper concludes with discussion of the relevance of these findings to thesociology of education, to the debate on consumer choice in public services, to thegeneral appropriateness of adjusting public sector performance metrics to take intoaccount the social mix of service users and to parental strategies in the educationalsector in particular.

This paper discusses the effect of social background and ethnicity on educational performance in an area with traditionally poor levels of attainment. It begins by examining the variation in school performance for London and specifically east London. It shows how the disadvantaged nature of the area, as measured by such variables as Mosaic group and ethnic heritage, helps to explain the poor results at GCSE. The paper then changes the focus to schools within a seven-borough area of east London. Using the Pupil Level Annual School Census (PLASC) linked to the geodemographic Mosaic codes based on pupils' home postcode, the authors demonstrate that, although ethnicity accounts for some of the variation in performance, this is considerably less than that accounted for by pupil social background. In addition, they show that it is not simply the social background of the individual pupil that affects school performance at GCSE. The proportion of pupils from a given social background plays some role in boosting or diminishing the overall school performance and will influence the performance of individual pupils whatever their background. It is argued that these social background effects together with the school composition effects have a considerable impact on school performance.

This article examines some of the implications for the sociological analysis of social class of the migration of geodemographic classifications of various sorts into software systems designed to ‘sort out’ people and places. It begins by offering an overview of the history and development of geodemographic classifications. It then argues that such classifications are increasingly becoming embedded in ‘soft-ware sorting’ procedures of various sorts, which in turn leads to the prospect of ‘automated spatiality’ becoming a common feature of the contemporary constitution of social class.

Consumers purchase goods from various channels or retail formats, such as grocery stores, drugstores, mass merchandisers, club stores, and convenience stores. To identify the most appropriate channels and to allocate the distribution of products among channels efficiently, managers need a better understanding of consumer behavior with respect to these channels. The authors examine the moderating role of channel-category associations in consumer channel patronage by extending the literature on brand associations to the context of channels, and they estimate a model that links channel-category associations with consumer geodemographics and channel share of volume. The authors first identify the product categories associated with particular channels through a correspondence analysis of a field-intercept survey. They then use the channel-category associations and geodemographic factors to estimate their direct and interactive effects on channel share of volume. The channel-category associations have significant main effects and interaction effects with channel type and geodemographic factors on channel share of volume, and they account for the majority of the explained variance (72%) in channel share of volume. Overall, the findings provide several conceptual and managerial insights into consumer channel perceptions and patronage behavior.

Previous studies by Bourdieu and others have shown a tendency for cultural tastes to serve as status markers that distinguish members of social classes differing in economic capital (money, income, wealth) and cultural capital (family background, education, training). However, questions remain concerning the extent to which such findings—especially those obtained over 20 years ago in France—can be meaningfully generalized to the contemporary United States. The present study explores this issue using a large-scale database that covers a broad range of cultural activities pursued by a comprehensive sample of American geodemographic clusters. The results support the Bourdieusian view by showing clear contrasts in differential cultural preferences among groups varying in income, education, and various demographic characteristics.

School performance tables emphasising aggregate examination scores have become an enduring feature of the educational landscape. These tables are problematic, even flawed, as a guide, given the recognised broad link between pupil performance and the social and economic environment in which they live. There is continued interest in being able to contextualise school examination scores so as better to reflect relative achievement. The inherent spatiality of inequalities lends itself to analysis using geographical information systems (GIS), particularly in the task of creating context from geodemographic and lifestyle data. In this paper I explore a methodology for creating and analysing a contextual index of ambient disadvantage centred on robust normalisation of data and illustrate this by using census variables, pupil numbers, and test scores for 3687 primary schools in the north of England. Relevant census variables are interpolated using ordinary kriging with an element of smoothing so as to simulate, to some extent, the effect of school catchment areas. Key features of using robust normalisation are that variable weights can be tested and the internal level of support for an index, the weighted absolute deviation, can be calculated and mapped. This latter quantity provides a quality measure for an index. The methodology is critically assessed in relation to other recent approaches.

The objectives of this study were to compare the socioeconomic make-up and the dental caries and dental treatment patterns of a general dental practice (GDP) population of five-year-old children with those of the total resident population of five-year-old children in a specific locality, and, secondly, to examine the process of gathering information on oral healthcare needs in primary dental care. The study was set in Halton, North Cheshire. Data were collected retrospectively from the patient records of four GDP ‘sentinel’ practices using a common data abstraction form. The socioeconomic profiles of the GDP population and the 1995/6 NHS child dental health survey population were compared using the Super Profiles geodemographic classification by plotting frequency distributions. The dmft of each population was compared by calculating 95% confidence intervals. The GDP population showed a slight over-representation in the more affluent groupings of the Super Profiles Lifestyle categories and a more dramatic under-representation in two of the more deprived groupings. The confidence intervals for dt and dmft of the GDP data did not include the mean figures produced by the NHS survey, indicating a significant difference at the P

Political redistricting has increasingly been carried out with the aid of geographic information systems (GIS). Concerns about the systems have focused on their misuse, but their critics have failed to see that they raise deeper issues. First, they conceptualize districts as containers in space, where in fact people typically see themselves as belonging in and attached to their neighborhoods. Second, GIS-based geodemographic systems, based on inferred data about individuals and groups, appeal to an inadequate means of theorizing about community and culture. Finally, the availability of these data is increasingly leading to a society in which views are imputed to people who may not believe them, while politicians act as though people have those views, without having good reason to believe that people actually hold them. Together, GIS has been associated with a recasting of the public and the private in ways that render the political much less viable.

Formal models of urban systems have the potential to reveal a lot about the form and functioning of urban settlements, yet much of this potential has still to be realised. In this paper we focus on the extent to which this has reflected the dearth of digital data that are rich, relevant, and disaggregate. Geodemographic classifications have made important and enduring contributions to small-area analysis. Yet, on the one hand, reliance upon census data makes them outdated and irrelevant and, on the other, fragmentation and diversification of social areas in cities has made the 'mosaic metaphor' of small-area analysis untenable. As part of the quest for a new perspective on data modelling, we investigate in this paper the potential of 'lifestyles' data sets for creating richer, more relevant digital models of human activity patterns in cities.

This paper investigates the hypothesis that the level of crime in disadvantaged areas will be influenced by their levels of social cohesion. This issue is examined using two methods for delineating areas of disadvantage (geodemographic classifications and the British government's official deprivation measure, the Index of Local Conditions) and two independent components of social cohesion, one defines the level of 'social control' in an area and the other identifies 'ethnic heterogeneity'. The results suggest that levels of crime are significantly lower than expected in disadvantaged areas with high levels of social cohesion and vice versa. A complementary analysis of Homewatch schemes revealed that such schemes lead to reduced levels of burglary in affluent areas, but appear to have the opposite effect to that desired in more disadvantaged areas.

The 1991 Census contains a wealth of information which has been refined using ACORN, the pioneer geodemographic classification system. Through a sophisticated analysis of postcodes, in combination with an age analysis, the tourist industry can now benefit from important strategic information for marketing use.

One of the key objectives of AT&T consumer long distance marketing is to offer “personalized services” to the 85-million-customer household base. A primary concern is to communicate with customers in a way that is relevant to them. One of the ways AT&T seeks to accomplish this goal is through direct contacts (mail and telemarketing) with the consumer. An example is a direct mail program called the AT&T Opportunity Calling® Program which was launched in 1984 to provide information about AT&T and its offering during the transitional time following AT&T's divestiture of the Bell Operating Companies. This direct mail program sought to provide value to consumers by recognizing the different needs of consumers, and responding accordingly. Specifically, Opportunity Calling rewarded customers for their patronage by providing them with discounts on commonly used goods and services. (Note: The Opportunity Calling Program officially ended on December 31, 1988.) In order to gain a better understanding of the consumer, a marketing research study was undertaken in late 1986 to tailor a segmentation scheme specifically for the Opportunity Calling universe. The study sought to determine significant attitudinal, demographic, behavioral, and geodemographic factors that contribute to participation in a direct mail program like Opportunity Calling.

Data analytics, particularly the current rhetoric around “Big Data”, tend to be presented as new and innovative, emerging ahistorically to revolutionize modern life. In this article, we situate one branch of Big Data analytics, spatial Big Data, through a historical predecessor, geodemographic analysis, to help develop a critical approach to current data analytics. Spatial Big Data promises an epistemic break in marketing, a leap from targeting geodemographic areas to targeting individuals. Yet it inherits characteristics and problems from geodemographics, including a justification through the market, and a process of commodification through the black-boxing of technology. As researchers develop sustained critiques of data analytics and its effects on everyday life, we must so with a grounding in the cultural and historical contexts from which data technologies emerged. This article and others (Barnes and Wilson, 2014) develop a historically situated, critical approach to spatial Big Data. This history illustrates connections to the critical issues of surveillance, redlining, and the production of consumer subjects and geographies. The shared histories and structural logics of spatial Big Data and geodemographics create the space for a continued critique of data analyses’ role in society.