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Robust quantitative descriptions of the social and physical characteristics of urban contexts are essential for assessing the impacts of urban environments on other, potentially dependent variables. Common methodologies used for that purpose, however, are either coarse or suffer from biasing effects. At the social level, the use of indicators encoded into pre-defined areal units, makes results prone to the Modifiable Areal Unit Problem. At the physical level, the adopted morphological indicators are usually highly aggregated descriptors of urban form. Moreover, there is a lack of explicit methodologies for the purposive sampling of urban contexts with specific combinations of social and physical characteristics, which—we argue—may be more effective than probabilistic sampling, when exploring phenomena as elusive as the effects of urban contextual factors. This article presents a set of GIS-based methods for addressing these issues, based on: a) local indicators of spatial association; b) detailed quantitative morphological descriptions, coupled with unsupervised classification techniques; and c) purposive sampling strategies carried out on the data generated by the proposed context characterization methods (a and b). The methods are illustrated through the characterization of the urban contexts of the 77 state-sector secondary schools in Liverpool, but are generalizable across all categories of urban objects and are independent of the geographical context of implementation.

This article points out a number of key failures of existing practice for building design and then promotes a new approach based upon advances made in the modelling of complex systems and systems engineering. This new approach involves the identification of building functions and modelling techniques to evaluate the performance of a building with respect to a range of criteria in different domains (e.g. spatial, structural, social, environmental, cognitive, organizational, and operational). A systems approach to building design has received limited acceptance to date but can be used to highlight inherent hierarchies and interdependencies between building subsystems, which have traditionally been viewed as being independent. There is currently little agreement as to how the design of different building subsystems should be best integrated in order to satisfy a large range of diverse building functions. Treating the building as a complex system, whereby the interaction of entities produces emergent behaviour that can be evaluated with respect to such building functions, can help to optimize building performance by considering how a change in one domain affects performance in another. The integrated approach described here increases potential added value through technological and information interoperability.

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