Neighbourhood typology based on virtual audit of environmental obesogenic characteristics

T. Feuillet, H. Charreire, C. Roda, M. Ben Rebah, J. D. Mackenbach, S. Compernolle, K. Glonti, H. Bárdos, H. Rutter, I. De Bourdeaudhuij, M. McKee, J. Brug, J. Lakerveld, J. M. Oppert

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Virtual audit (using tools such as Google Street View) can help assess multiple characteristics of the physical environment. This exposure assessment can then be associated with health outcomes such as obesity. Strengths of virtual audit include collection of large amount of data, from various geographical contexts, following standard protocols. Using data from a virtual audit of obesity-related features carried out in five urban European regions, the current study aimed to (i) describe this international virtual audit dataset and (ii) identify neighbourhood patterns that can synthesize the complexity of such data and compare patterns across regions. Data were obtained from 4,486 street segments across urban regions in Belgium, France, Hungary, the Netherlands and the UK. We used multiple factor analysis and hierarchical clustering on principal components to build a typology of neighbourhoods and to identify similar/dissimilar neighbourhoods, regardless of region. Four neighbourhood clusters emerged, which differed in terms of food environment, recreational facilities and active mobility features, i.e. the three indicators derived from factor analysis. Clusters were unequally distributed across urban regions. Neighbourhoods mostly characterized by a high level of outdoor recreational facilities were predominantly located in Greater London, whereas neighbourhoods characterized by high urban density and large amounts of food outlets were mostly located in Paris. Neighbourhoods in the Randstad conurbation, Ghent and Budapest appeared to be very similar, characterized by relatively lower residential densities, greener areas and a very low percentage of streets offering food and recreational facility items. These results provide multidimensional constructs of obesogenic characteristics that may help target at-risk neighbourhoods more effectively than isolated features.
Original languageEnglish
Pages (from-to)19-30
Number of pages12
JournalObesity Reviews
Early online date16 Feb 2016
Publication statusE-pub ahead of print - 16 Feb 2016


  • Cluster analysis SPOTLIGHT obesogenic environment virtual audit


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