"Traditionally, estimates of the number of people in small areas (the smallest geographical units for which data are available) have been disaggregated only by age and sex. More recently, much research effort has been directed towards developing some form of enhanced small-area population estimation, in which the population in a small area is disaggregated not only by age and sex, but also by a wide range of additional economic and social characteristics. Solutions to this problem currently include account-based demographic models, often used by local authorities."

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There is a growing interest from a wide variety of sources in information pertaining to the characteristics of residents of small geographical areas together with their associated activity patterns. Reliance on the use of conventional aggregate data sources combined with the British Government's reluctance to make available microdata in the form of a public-use data set has restricted the type of questions analysts have been able to ask. The application of a methodology for generating synthetic microdata from a number of different aggregate sources is reported. The resultant information system can be used in a flexible manner to produce distributions not currently available from aggregate sources. Additionally, the microdata form direct inputs into microsimulation models. The application described has been undertaken with Leeds Metropolitan District as the system of interest and a wide range of outputs is produced to illustrate the method.

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This paper explores the growth of e-commerce in British grocery retailing and examines the spatial variations in e-commerce usage. The main data source is a large commercial consumer survey (Acxiom's Research Opinion Data) rarely used by academics to date. Using these data in combination with census data, the paper addresses a number of key questions. After outlining key trends in the dataset on e-commerce usage (by product and over time) the first research question is: How do e-commerce purchases vary by geodemographic group? To answer this question, we explore e-commerce usage by age, sex and social class. The second key question is: Does e-commerce usage vary by type of geographical region? Thus, we explore variations in usage for urban and rural areas. The dynamics of urban-rural diffusion are also addressed hereby examining, in addition, the spread of broadband use across Britain. The last question is: To what degree do e-commerce sales vary by access to physical stores? This is addressed by examining consumers' home locations in relation to geographical accessibility. The results show that age and income are crucial demographic discriminators of e-commerce usage, as is rural location versus urban, and distance from physical stores.

The aim of this article is to explore the main issues involved with operationalizing entropy-maximizing models in a retail business context. We draw on our experiences of work undertaken with a variety of international blue-chip clients. First, we examine issues relating to demand estimation, focusing on practical considerations involved in different types of application. These issues include choice of demand estimation methodology, dealing with nonresidential flows and demand elasticity. Second, we investigate the supply-side or attractiveness component in detail, including a review of how such factors as brand preference, brand loyalty, and the so-called network effect can be addressed within a spatial interaction modeling framework. We then explore the distance decay variable in more detail before examining more specific measures of model performance. Historically in the literature, goodness of fit has been used as a measure of model performance, but we shall argue that goodness of forecast is the metric by which model performance should be measured. The difficulties of trying to achieve this are outlined and some examples discussed.

Traditionally, researchers have used elaborate regression models to simulate the retail petrol market. Such models are limited in their ability to model individual behaviour and geographical influences. Heppenstall et al presented a novel agent-based framework for modelling individual petrol stations as agents and integrated important additional system behaviour through the use of established methodologies such as spatial interaction models. The parameters for this model were initially determined by the use of real data analysis and experimentation. This paper explores the parameterisation and verification of the model through data analysis and by use of a genetic algorithm (GA). The results show that a GA can be used to produce not just an optimised match, but results that match those derived by expert analysis through rational exploration. This may suggest that despite the apparent nonlinear and complex nature of the system, there are a limited number of optimal or near optimal behaviours given its constraints, and that both user-driven and GA solutions converge on them.

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