At a time of increasing government concern with the economic health of UK town centres and high streets, and with an independent inquiry (led by Mary Portas) on Revitalising the High Street to report by the end of 2011, this paper seeks to make four contributions. First, to inject into an available evidence base, currently notable for its sparseness, new descriptive evidence on the differential performance of a sample of over 250 town centres/high streets in four regions of the UK as those centres adjusted to the shock wave of global economic crisis. Second, to address the task of theorisirtg the nature of the complex adjustments underway by positioning the policy-significant findings provided in the paper within conceptualisations of 'resilienee' in economic systems-particularly those which stress the anticipatory or reactive capacity of systems to minimise the impacts of a destabilising shock and which focus on resilience as a dynamic and evolutionary process. Third, to offer findings from theory-driven statistical modelling of the determinants of the differential resilience or fragility exhibited by that sample of centres. Fourth, to assess what the implications of those findings and a focus on 'adaptive resilience' might mean for the design of policy proposals and instruments aimed at revitalising UK town centres and high streets. Although some of the paper's empirical findings parallel those suggested by specialist commercial researeh companies which have emerged to fill the need to chart the posteconomic crisis malaise of UK retail centres, they also significantly extend available knowledge. In particular, they offer novel insight into the impact of two factors-'diversity' of a centre's preexisting retail structure and 'town-centres-first' policy-compliant 'in-centre' or 'edge-of-centre' corporate-foodstore entry. Although conventionally portrayed as polar opposites within popular debate in terms of attempts to protect and/or enhance the vitality and viability of town centres and high streets, our analysis suggests that this may not be the ease. Indeed, the retail centres in our sample which proved most resilient to the shock wave of global economic crisis were characterised by both diversity and corporate-food-store entry.

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Early attempts to classify shopping activity often took a relatively simple approach, largely driven by the lack of reliable data beyond fascia name and retail outlet counts by centre. There seems to be a consensus amongst contemporary scholars, commercial research consultancies and retailers that more comprehensive classifications would generate better-informed debate on changes in the urban economic landscape, as well as providing the basis for a more effective comparison of retail centres across time and space, particularly given the availability of new data sources and techniques and in the context of the transformational changes presently affecting the retail sector. This paper seeks to demonstrate the interrelationship between supply and demand for retailing services by integrating newly available data sources within a rigorously specified classification methodology. This in turn provides new insight into the multidimensional and dynamic taxonomy of consumption spaces within Great Britain. First, such a contribution is significant in that it moves debate within the literature past simple linear scaling of retail centre function to a more nuanced understanding of multiple functional forms; and second, in that it provides a nationally comparative and dynamic framework through which the evolution of retail structures can be evaluated. Using non-hierarchical clustering techniques, the results are presented in the form of a two-tier classification with 5 distinctive 'coarse' clusters and 15 more detailed and nested subclusters. The paper concludes that more nuanced and dynamic classifications of this kind can help deliver more effective insights into changing role of retailing and consumer services in urban areas across space and through time and will have implications for a variety of stakeholders.

This research introduces a new method for the identification of local retail agglomerations within Great Britain, implementing a modification of the established density based spatial clustering of applications with noise (DBSCAN) method that improves local sensitivity to variable point densities. The variability of retail unit density can be related to both the type and function of retail centers, but also to characteristics such as size and extent of urban areas, population distribution, or property values. The suggested method implements a sparse graph representation of the retail unit locations based on a distance‐constrained k‐nearest neighbor adjacency list that is subsequently decomposed using the Depth First Search algorithm. DBSCAN is iteratively applied to each subgraph to extract the clusters with point density closer to an overall density for each study area. This innovative approach has the advantage of adjusting the radius parameter of DBSCAN at the local scale, thus improving the clustering output. A comparison of the estimated retail clusters against a sample of existing boundaries of retail areas shows that the suggested methodology provides a simple yet accurate and flexible way to automate the process of identifying retail clusters of varying shapes and densities across large areas; and by extension, enables their automated update over time.

Purpose-Twenty-first century online retailing has reshaped the retail landscape. Grocery shopping is emerging as the next fastest growing category in online retailing in the UK, having implications for the channels we use to purchase goods. Using Sainsbury's data, the authors create a bespoke set of grocery click&collect catchments. The resultant catchments allow an investigation of performance within the emerging channel of grocery click&collect. The paper aims to discuss these issues. Design/methodology/approach-The spatial interaction method of "Huff gravity modeling" is applied in a semi-automated approach, used to calculate grocery click&collect catchments for 95 Sainsbury's stores in England. The catchments allow investigation of the spatial variation and particularly rural-urban differences. Store and catchment characteristics are extracted and explored using ordinary least squares regression applied to investigate "demand per day" (a confidentiality transformed revenue value) as a function of competition, performance and geodemographic factors. Findings-The findings show that rural stores exhibit a larger catchment extent for grocery click&collect when compared with urban stores. Linear regression finds store characteristics as having the greatest impact on demand per day, adhering to wider retail competition literature. Conclusions display a need for further investigation (e.g. quantifying loyalty). Originality/value-New insights are contributed at a national level for grocery click&collect, as well as e-commerce, multichannel shopping and retail geography. Areas for further investigation are identified, particularly quantitatively capturing brand loyalty. The research has commercial impact as the catchments are being applied by Sainsbury's to decide the next 100 stores and plan for the next five years of their grocery click&collect offering.