Environment and Planning A: Economy and Space

In the paper we address the way Japanese housebuilders have developed innovative approaches to supplying highly customised housing. We draw on notions of path dependency to explore the evolution of Japan's large-scale industrialised housing suppliers, the way they have differentiated themselves from local suppliers, and their possible future evolution. We first outline how structural conditions influence the business strategies adopted by housebuilders in general. We then consider the evolution of the Japanese mass customised housebuilding industry, the innovations it has introduced across its production system, and how these have been shaped by the structural conditions within which the industry operates. Finally, we draw conclusions on the role of path dependency in shaping Japan's mass customised housing sector.

Assessments of environmental and territorial justice are similar in that both assess whether empirical relations between the spatial arrangement of undesirable hazards (or desirable public goods and services) and sociodemographic groups are consistent with notions of social justice, evaluating the spatial distribution of benefits and burdens (outcome equity) and the process that produces observed differences (process equity). Using proximity to major highways in New York City as a case study, we review methodological issues pertinent to both fields and discuss choice and computation of exposure measures, but focus primarily on measures of inequity. We present inequity measures computed from the empirically estimated joint distribution of exposure and demographics and compare them with traditional measures such as linear regression, logistic regression, and Theil's entropy index. We find that measures computed from the full joint distribution provide more unified, transparent, and intuitive operational definitions of inequity and show how the approach can be used to structure siting and decommissioning decisions.

As British society has become increasingly multiethnic and multicultural, debate has grown regarding the advantages and disadvantages of ethnically segregated schools, with regard both to educational achievement and to multicultural accommodation. Compared with issues regarding the class composition of schools, however, little work has been done on the degree of ethnic segregation in schools, let alone its impact. The authors use a recently developed classification procedure to identify the degree of ethnic segregation in England's secondary schools in 2001, using a database that gives every student's ethnic identity. It is shown that, although there is considerable segregation for members of ethnic minority groups in London and a small number of other urban areas, elsewhere there is much more exposure of members of those groups to White students; White students are much more segregated, however. In general, the level of segregation in schools is greater than the residential segregation of the various ethnic groups.

The authors explore the composition of service-class households in rural, suburban, and inner-city residential environments in terms of the occupations of household members. Focusing on single-adult and two-adult households, the authors use the Office for National Statistics (ONS) Longitudinal Study (LS) to examine the occupations of LS members and their partners. They find differences in the likelihood that service-class households are single-person households (with inner London standing apart in this regard). Findings contradict dominant messages in the literature that rural areas have fewer women with paid work and fewer with service-class jobs. Most evidently, provincial inner cities stand apart in these terms, with the position regarding paid work for adult couples in rural areas being more similar to paid work for adult couples in London than in suburban or provincial inner-city environments.

In this paper, two forms of local regression are employed in the analysis of relations between out-commuting distance and other socioeconomic variables in Northern Ireland. The two regression approaches used are moving window regression (MWR) and geographically weighted regression (GWR). For the first approach different window sizes are applied and changes in results assessed. For the second approach, a Gaussian kernel is used and its bandwidth varied. Seven independent variables are utilised, although a single variable (deprivation) provides the main analytical focus. Differences in results obtained with use of the two approaches are discussed. The relationship between window size or bandwidth size and observed spatial patterning is discussed and elucidated. The results support previous work that indicated severe limitations in using global regressions to examine relationships between socioeconomic variables. Also, the utility of comparing results obtained from MWR and GWR is assessed and the benefits of both approaches are outlined.

This paper reports the results of an application of the Aurora model to estimate the utility functions underlying activity–duration decisions in daily activity–travel patterns. Multidimensional sequence alignment is used to derive segments for activity–travel diary data, collected in the Amsterdam–Utrecht corridor in the Netherlands. The profiles of the resulting segments are derived from a descriptive analysis of sociodemographic variables. A tailored genetic algorithm is then used to estimate the parameter of an asymmetrical utility function for each of the resulting segments. The results suggest that the utility of activity duration varies between activities and between segments, and hence sociodemographics.

Data processing for the spatial analysis of small-area social, demographic, and economic data often requires the combination of data spatially aggregated to two or more incompatible zone systems in a region, such as a set of enumeration districts that changes over time. Such situations can be addressed by areal interpolation—the transfer of data between zonal systems according to spatial algorithms. The authors test a technique of areal interpolation using geographic information systems (GIS) that employs a digital map layer representing streets and roads to derive varying density weights for small areas within aggregation zones. The technique reduces errors in estimation compared with estimates derived using the commonly applied area-weighting technique, with its assumption of uniform density. The street-weighting technique is much easier to use than other interpolation techniques that have also been shown to reduce error compared with area-based weighting.

Administrative data sources are increasingly being used for spatial analysis and policy formation. For example, ‘welfare to work’ programs have stimulated demand for spatial mismatch studies in which ES-202 employment files are used. The increased resolution gained by geocoding the address records in administrative files can be of enormous research value when the process under study resolves over small distances. Yet the resulting point-referenced data are problematic for inferential analysis. In particular, administrative data typically represent a sample of convenience, thus posing serious validity problems for statistical inference. The authors propose a robust estimation method for spatial pattern inference based on spatially censored data. The performance of the estimator is explored with the aid of simulated data and is also demonstrated with ES-202 data from North Carolina.