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Identifying built environmental patterns using cluster analysis and GIS: Relationships with walking, cycling and body mass index in French adults

Hélène Charreire12, Christiane Weber3, Basile Chaix4, Paul Salze3, Romain Casey5, Arnaud Banos6, Dominique Badariotti3, Emmanuelle Kesse-Guyot2, Serge Hercberg2, Chantal Simon5 and Jean-Michel Oppert27*

Author Affiliations

1 Lab-Urba, Urbanism Institute of Paris, University of Paris Est, Créteil, France

2 UREN, INSERM U557/INRA U1125/CNAM/University of Paris 13/CRNH, Ile-de-France, Bobigny, France

3 Image, Ville, Environnement, CNRS ERL730, University of Strasbourg, Strasbourg, France

4 INSERM U707, University Pierre et Marie Curie-Paris 6, UMR-S 707, Paris, France

5 CarMeN, INSERM U1060/INRA U1235/University of Lyon, CRNH Rhône-Alpes, Lyon, France

6 Géographie-Cité, UMR 8504 CNRS, Paris, France

7 Department of Nutrition, Pitié-Salpêtrière Hospital (AP-HP), University Pierre et Marie Curie-Paris 6, CRNH Ile-de-France, Paris, France

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International Journal of Behavioral Nutrition and Physical Activity 2012, 9:59  doi:10.1186/1479-5868-9-59

Published: 23 May 2012



Socio-ecological models suggest that both individual and neighborhood characteristics contribute to facilitating health-enhancing behaviors such as physical activity. Few European studies have explored relationships between local built environmental characteristics, recreational walking and cycling and weight status in adults. The aim of this study was to identify built environmental patterns in a French urban context and to assess associations with recreational walking and cycling behaviors as performed by middle-aged adult residents.


We used a two-step procedure based on cluster analysis to identify built environmental patterns in the region surrounding Paris, France, using measures derived from Geographic Information Systems databases on green spaces, proximity facilities (destinations) and cycle paths. Individual data were obtained from participants in the SU.VI.MAX cohort; 1,309 participants residing in the Ile-de-France in 2007 were included in this analysis. Associations between built environment patterns, leisure walking/cycling data (h/week) and measured weight status were assessed using multinomial logistic regression with adjustment for individual and neighborhood characteristics.


Based on accessibility to green spaces, proximity facilities and availability of cycle paths, seven built environmental patterns were identified. The geographic distribution of built environmental patterns in the Ile-de-France showed that a pattern characterized by poor spatial accessibility to green spaces and proximity facilities and an absence of cycle paths was found only in neighborhoods in the outer suburbs, whereas patterns characterized by better spatial accessibility to green spaces, proximity facilities and cycle paths were more evenly distributed across the region. Compared to the reference pattern (poor accessibility to green areas and facilities, absence of cycle paths), subjects residing in neighborhoods characterized by high accessibility to green areas and local facilities and by a high density of cycle paths were more likely to walk/cycle, after adjustment for individual and neighborhood sociodemographic characteristics (OR = 2.5 95%CI 1.4-4.6). Body mass index did not differ across patterns.


Built environmental patterns were associated with walking and cycling among French adults. These analyses may be useful in determining urban and public health policies aimed at promoting a healthy lifestyle.

Built environment; Urban form; Geographical Information Systems; Cluster analysis; Health-enhancing physical activity; Walking; Cycling; Body Mass Index