TY - JOUR
T1 - Designing healthy communities
T2 - Creating evidence on metrics for built environment features associated with walkable neighbourhood activity centres
AU - Gunn, Lucy Dubrelle
AU - Mavoa, Suzanne
AU - Boulangé, Claire
AU - Hooper, Paula
AU - Kavanagh, Anne
AU - Giles-Corti, Billie
N1 - Funding Information:
The Victorian Department of Economic Development, Jobs, Transport, and Resources is gratefully acknowledged for administering and providing the VISTA data. The research team is grateful for the assistance of GIS specialist Rebecca Roberts who produced the spatial map of Melbourne. We are grateful for the comments provided by the anonymous reviewers. Funding An NHMRC Senior Principal Research Fellow Award supports BGC (#1107672); and, LDG and PH are supported by the Centre for Research Excellence in Healthy, Liveable Communities (#1061404). SM is supported by The Australian Prevention Partnership Centre (#9100001). CB is supported by The University of Melbourne International Research Scholarship (MIRS) and the North & West Metropolitan Region Victorian Department of Health, and the NHMRC CRE in Healthy Liveable Communities (#1061404).
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/12/4
Y1 - 2017/12/4
N2 - Background: Evidence-based metrics are needed to inform urban policy to create healthy walkable communities. Most active living research has developed metrics of the environment around residential addresses, ignoring other important walking locations. Therefore, this study examined: metrics for built environment features surrounding local shopping centres, (known in Melbourne, Australia as neighbourhood activity centres (NACs) which are typically anchored by a supermarket); the association between NACs and transport walking; and, policy compliance for supermarket provision. Methods: In this observational study, cluster analysis was used to categorize 534 NACs in Melbourne, Australia by their built environment features. The NACS were linked to eligible Victorian Integrated Survey of Travel Activity 2009-2010 (VISTA) survey participants (n=19,984). Adjusted multilevel logistic regressions estimated associations between each cluster typology and two outcomes of daily walking: any transport walking; and, any 'neighbourhood' transport walking. Distance between residential dwellings and closest NAC was assessed to evaluate compliance with local planning policy on supermarket locations. Results: Metrics for 19 built environment features were estimated and three NAC clusters associated with walkability were identified. NACs with significantly higher street connectivity (mean:161, SD:20), destination diversity (mean:16, SD:0.4); and net residential density (mean:77, SD:65) were interpreted as being 'highly walkable' when compared with 'low walkable' NACs, which had lower street connectivity (mean:57, SD:15); destination diversity (mean:11, SD:3); and net residential density (mean:10, SD:3). The odds of any daily transport walking was 5.85 times higher (95% CI: 4.22, 8.11), and for any 'neighborhood' transport walking 8.66 (95% CI: 5.89, 12.72) times higher, for residents whose closest NAC was highly walkable compared with those living near low walkable NACs. Only highly walkable NACs met the policy requirement that residents live within 1km of a local supermarket. Conclusions: Built environment features surrounding NACs must reach certain levels to encourage walking and deliver walkable communities. Research and metrics about the type and quantity of built environment features around both walking trip origins and destinations is needed to inform urban planning policies and urban design guidelines.
AB - Background: Evidence-based metrics are needed to inform urban policy to create healthy walkable communities. Most active living research has developed metrics of the environment around residential addresses, ignoring other important walking locations. Therefore, this study examined: metrics for built environment features surrounding local shopping centres, (known in Melbourne, Australia as neighbourhood activity centres (NACs) which are typically anchored by a supermarket); the association between NACs and transport walking; and, policy compliance for supermarket provision. Methods: In this observational study, cluster analysis was used to categorize 534 NACs in Melbourne, Australia by their built environment features. The NACS were linked to eligible Victorian Integrated Survey of Travel Activity 2009-2010 (VISTA) survey participants (n=19,984). Adjusted multilevel logistic regressions estimated associations between each cluster typology and two outcomes of daily walking: any transport walking; and, any 'neighbourhood' transport walking. Distance between residential dwellings and closest NAC was assessed to evaluate compliance with local planning policy on supermarket locations. Results: Metrics for 19 built environment features were estimated and three NAC clusters associated with walkability were identified. NACs with significantly higher street connectivity (mean:161, SD:20), destination diversity (mean:16, SD:0.4); and net residential density (mean:77, SD:65) were interpreted as being 'highly walkable' when compared with 'low walkable' NACs, which had lower street connectivity (mean:57, SD:15); destination diversity (mean:11, SD:3); and net residential density (mean:10, SD:3). The odds of any daily transport walking was 5.85 times higher (95% CI: 4.22, 8.11), and for any 'neighborhood' transport walking 8.66 (95% CI: 5.89, 12.72) times higher, for residents whose closest NAC was highly walkable compared with those living near low walkable NACs. Only highly walkable NACs met the policy requirement that residents live within 1km of a local supermarket. Conclusions: Built environment features surrounding NACs must reach certain levels to encourage walking and deliver walkable communities. Research and metrics about the type and quantity of built environment features around both walking trip origins and destinations is needed to inform urban planning policies and urban design guidelines.
KW - Built environment
KW - Cluster analysis
KW - Geographic information systems
KW - Land use mix
KW - Neighbourhood activity/town centre
KW - Planning policy
KW - Transport walking
KW - Urban design
UR - http://www.scopus.com/inward/record.url?scp=85037612474&partnerID=8YFLogxK
U2 - 10.1186/s12966-017-0621-9
DO - 10.1186/s12966-017-0621-9
M3 - Article
C2 - 29202849
AN - SCOPUS:85037612474
SN - 1479-5868
VL - 14
SP - 1
EP - 12
JO - International Journal of Behavioral Nutrition and Physical Activity
JF - International Journal of Behavioral Nutrition and Physical Activity
IS - 1
M1 - 164
ER -