TY - JOUR
T1 - Neighbourhood typology based on virtual audit of environmental obesogenic characteristics
AU - Feuillet, T.
AU - Charreire, H.
AU - Roda, C.
AU - Ben Rebah, M.
AU - Mackenbach, J. D.
AU - Compernolle, S.
AU - Glonti, K.
AU - Bárdos, H.
AU - Rutter, H.
AU - de Bourdeaudhuij, I.
AU - Mckee, M.
AU - Brug, J.
AU - Lakerveld, J.
AU - Oppert, J. M.
PY - 2016/2/1
Y1 - 2016/2/1
N2 - 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.
AB - 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.
KW - Cluster analysis
KW - SPOTLIGHT
KW - obesogenic environment
KW - virtual audit
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84958548935&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/26879110
U2 - https://doi.org/10.1111/obr.12378
DO - https://doi.org/10.1111/obr.12378
M3 - Article
C2 - 26879110
SN - 1467-7881
VL - 17
SP - 19
EP - 30
JO - Obesity Reviews
JF - Obesity Reviews
ER -