Comparing discrete choice models: some housing market examples

Introduction: Since the mid nineteen seventies there has been strong interest within variolls branches of social science in the adaptation of the discrete choice modeling methodology towards a wide range of research problems. This has required recognition of a wide variety of alternative decision-contexts (Landau et a1. 1982) and behaviour-patterns (Lerman, 1979), and has also raised general issues concerning the variable extent to which individual or subgroup choices may be restricted by spatial and temporal constraints. Further interest has been expressed about the spatial and temporal transferability of alternative discrete choice models (Atherton and Ben-Akiva, 1976: Galbraith and Hensher, 1982). This substantive diversification has been accompanied by a variety of technical and methodological refinements of the multinomiallogit (MNL) and multinomial probit (MNP) models, ranging from new estimation procedures (Hausman and Wise, 1978) to the development of less-restrictive, computationally tractable discrete choice model forms (for example, Williams, 1977: Daly and Zachary, 1978). Faced with both a wider selection of methodological tools and a broader spectrum of substantive enquiry, there exists a clear need for formal comparison procedures which the analyst can call upon to evaluate a given model specification or framework. In this paper, I attempt to review briefly some trends amongst recent housing choice studies which employ discrete choice modeling methods. A new procedure is then presented (Hubert and Golledge, 1981; Halperin et al. 1984) which may be used to compare discrete choice models specified and/or structured in accordance with different a priori hypotheses. It is argued that this method fills a gap between existing discrete choice model comparison-procedures which are inapplicable to 'nonnested' model specifications, that is, to competing discrete choice models which comprise totally different variable specifications and that such procedures can usefully aid selection of the discrete choice model most appropriate to any given decision context

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

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

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

The UK geography of the E-Society: a national classification

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

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

Spatial dependence and heterogeneity in patterns of urban deprivation

Developments in the provision and quality of digital data are creating possibilities for finer resolution spatial and temporal measurement of the properties of socio-economic systems. We suggest that the ?lifestyles? datasets collected by private sector organisations provide one such prospect for better inferring the structure, composition and heterogeneity of urban areas. Clearly, deprivation and hardship are inextricably linked to incomes from earnings and transfer payments. In many countries (e.g. the UK) no small area income measures are collected at all, and this forces reliance upon commercial sources. Yet, the use of such data in academic research is not without considerable problems. In the same spirit as Gordon and Pantazis (1995) we thus think it necessary to retain some linkage to population census data ? but in a way which is much more sensitive to spatial context. A critical issue is thus to understand the scales at which both income, and the variables that are used to predict it, vary (see also Rees, 1998; Harris and Longley, 2002). We address some of these issues in the context of the debate about the intra-urban geography of hardship and social exclusion. Low income fundamentally restricts the abilities of people to participate actively in society (Harris and Longley, 2002), yet reliable, up-to-date income measures at fine spatial scales are rarely available from conventional sources. As a consequence, many indicators of deprivation are reliant upon data sources that are out of date and/or entail use of crude surrogate measures. Some measures bear little clear correspondence with hardship at all. Other widely-used indicators are spatially variable in their operation. The broader issue concerns the scale and extent of ?pockets? of hardship and the scale ranges at which difference is deemed manifests. The problems are further compounded if each of the range of surrogate measures used to specify a concept operates at different scales. Taken together, it remains unclear whether meaningful indicators of social conditions can ever be adequately specified, or whether generalised representations can be sufficiently sensitive to place. Using a case study of Bristol, UK, we compare the patterns of spatial dependence and spatial heterogeneity observed for a small area (?lifestyles?) income measure with those of the census indicators that are commonly used as surrogates for it. This leads to specification of spatial dependence using a spatially autoregressive model, and accommodation of local heterogeneity using geographically weighted regression (GWR). This analysis begins to extend our understanding of the determinants of hardship and poverty in urban areas: urban policy has hitherto used aggregate, outdated or proxy measures of income in a less critical manner; and techniques for measuring spatial dependence and heterogeneity have usually been applied at the regional, rather than intra urban, scales. The consequence is a limited understanding of the geography and dynamics of income variations within urban areas. The advantages and limitations of the data used here are explored in the light of the results of our statistical analysis, and we discuss our results as part of a research agenda for exploring dependence and heterogeneity in spatial distributions.

Family names as indicators of Britain’s changing regional geography

In recent years the geography of surnames has become increasingly researched in genetics, epidemiology, linguistics and geography. Surnames provide a useful data source for the analysis of population structure, migrations, genetic relationships and levels of cultural diffusion and interaction between communities. The Worldnames database (www.publicprofiler.org/worldnames) of 300 million people from 26 countries georeferenced in many cases to the equivalent of UK Postcode level provides a rich source of surname data. This work has focused on the UK component of this dataset, that is the 2001 Enhanced Electoral Role, georeferenced to Output Area level. Exploratory analysis of the distribution of surnames across the UK shows that clear regions exist, such as Cornwall, Central Wales and Scotland, in agreement with anecdotal evidence. This study is concerned with applying a wide range of methods to the UK dataset to test their sensitivity and consistency to surname regions. Methods used thus far are hierarchical and non-hierarchical clustering, barrier algorithms, such as the Monmonier Algorithm, and Multidimensional Scaling. These, to varying degrees, have highlighted the regionality of UK surnames and provide strong foundations to future work and refinement in the UK context. Establishing a firm methodology has enabled comparisons to be made with data from the Great British 1881 census, developing insights into population movements from within and outside Great Britain

Interpreting interpolation: the pattern of interpolation errors in digital surface models derived from laser scanning data

Errors within height models have, in the past, been communicated in terms of global measures ofaccuracy for the model. Such quantification ignores the spatial structure of errors across thesurface, hindering subsequent analysis. This paper demonstrates the importance ofunderstanding the spatial structure of error using, as an example, the creation of a DigitalSurface Model (DSM) from laser scanner data