Skip to main content
Canadian Journal of Public Health = Revue Canadienne de Santé Publique logoLink to Canadian Journal of Public Health = Revue Canadienne de Santé Publique
. 2011 May 1;102(3):176–179. doi: 10.1007/BF03404890

Neighbourhood Environmental Correlates of Perceived Park Proximity in Montreal

Spencer Moore 15,25,, Yan Kestens 25,35
PMCID: PMC6973885  PMID: 21714315

Abstract

Objectives

Perceived proximity to recreational settings has been shown to be associated with increased physical activity levels. We examined individual socio-demographic and environmental correlates of perceived park proximity in Montreal to assess targets for ecological interventions to improve physical activity.

Methods

A stratified clustered sampling design was used to collect data on perceived park proximity from 864 adults residing in 300 Montreal census tracts. Perceived park proximity was measured by asking participants if they perceived a park as within walking distance of their home. Objective measures of park proximity and park density were constructed using geographic information systems (GIS). Canada Census data provided information on census tract population density and median income levels. Multilevel logistic regression was used to examine the likelihood of not perceiving a park as proximate.

Results

Older adults were more likely to perceive a park as not proximate to their home (OR: 1.04; 95% CI: 1.02–1.07). Perceived park proximity varied across Montreal neighbourhoods with an interclass correlation coefficient of 16.10%. Objective distance to the closest park (OR: 1.45; 95% CI: 1.10–1.92) was associated with adults’ subjective perceptions of park proximity. Residents of neighbourhoods with higher population density (OR: 0.92; 95% CI: 0.87–0.97) and higher average income (OR: 0.45; 95% CI: 0.24–0.87) were less likely to view a park as outside walking distance to their residence.

Conclusion

Regardless of the actual distance to the park, neighbourhood environmental factors are associated with people’s perceptions of having a park within walking distance of their homes.

Key words: Spatial behavior, urban health, residence characteristics, socioeconomic factors, environment

References

  • 1.Berrigan D, McKinnon RA. Built environment and health. Prev Med. 2008;47(3):239–40. doi: 10.1016/j.ypmed.2008.07.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Corbett SJ. Public health and regulation of the built environment. N S W Public Health Bull. 2008;19(11–12):212–14. doi: 10.1071/NB08048. [DOI] [PubMed] [Google Scholar]
  • 3.Michael YL, Yen IH. Invited commentary: Built environment and obesity among older adults - Can neighborhood-level policy interventions make a difference. Am J Epidemiol. 2009;169(4):409–12. doi: 10.1093/aje/kwn394. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Kaczynski AT, Henderson KA. Parks and recreation settings and active living: A review of associations with physical activity function and intensity. J Phys Act Health. 2008;5(4):619–32. doi: 10.1123/jpah.5.4.619. [DOI] [PubMed] [Google Scholar]
  • 5.Orsega-Smith E, Mowen AJ, Payne L, Godbey G. The interaction of stress and park use on psycho-physiological health in older adults. J Leisure Research. 2004;36(2):232–56. doi: 10.1080/00222216.2004.11950021. [DOI] [Google Scholar]
  • 6.Cohen DA, McKenzie TL, Sehgal A, Williamson S, Golinelli D, Lurie N. Contribution of public parks to physical activity. Am J Public Health. 2007;97(3):509–14. doi: 10.2105/AJPH.2005.072447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Bedimo-Rung AL, Mowen AJ, Cohen DA. The significance of parks to physical activity and public health: A conceptual model. Am J Prev Med. 2005;28(2):159–68. doi: 10.1016/j.amepre.2004.10.024. [DOI] [PubMed] [Google Scholar]
  • 8.Li F, Fisher KJ, Brownson RC, Bosworth M. Multilevel modeling of built environment characteristics related to neighbourhood walking activity in older adults. J Epidemiol Community Health. 2005;59(7):558–64. doi: 10.1136/jech.2004.028399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hoehner C, Ramirez L, Elliott M, Handy S, Brownson R. Perceived and objective environmental measures and physical activity among urban adults. Am J Prev Med. 2005;28(2S2):105–16. doi: 10.1016/j.amepre.2004.10.023. [DOI] [PubMed] [Google Scholar]
  • 10.Scott M, Evenson K, Cohen D, Cox C. Comparing perceived and objectively measured access to recreational facilities as predictors of physical activity in adolescent girls. J Urban Health. 2007;84:346–58. doi: 10.1007/s11524-007-9179-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Guest AM, Lee BA. How urbanites define their neighborhoods. Popul Environ. 1984;71:32–56. doi: 10.1007/BF01257471. [DOI] [Google Scholar]
  • 12.Sastry N, Pebley AR, Zonta M. Neighborhood definitions and the spatial dimensions of daily life in Los Angeles. RAND Labor and Population Program Working Paper DRU-2400/8 LAFANS. Santa Monica, CA: RAND Corporation; 2002. [Google Scholar]
  • 13.Daniel M, Kestens Y. MEGAPHONE (®1046898): Montreal epidemiological and geographic analysis of population health outcomes and neighbourhood effects. Montréal, QC: Centre de recherche du Centre hospitalier de l’Université de Montréal; 2007. [Google Scholar]
  • 14.Cooper HL, Bossak B, Tempalski B, Jarlais D, Friedman S. Geographic approaches to quantifying the risk environment: Drug-related law enforcement and access to syringe exchange programmes. Int J Drug Policy. 2009;20(3):217–26. doi: 10.1016/j.drugpo.2008.08.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Gatrell AC, Bailey TC, Diggle P, Rowlingson B. Spatial point pattern analysis and its application in geographical epidemiology. Tran Inst Br Geogr. 1996;21:256–74. doi: 10.2307/622936. [DOI] [Google Scholar]
  • 16.Maroko AR, Maantay JA, Sohler N, Grady K, Arno P. The complexities of measuring access to parks and physical activity sites in New York City: A quantitative and qualitative approach. Int J Health Geogr. 2009;8:34. doi: 10.1186/1476-072X-8-34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Brundson C. Estimating probability surfaces for geographical point data: An adaptive kernel algorithm. Computers & Geosciences. 1995;21(7):877–94. doi: 10.1016/0098-3004(95)00020-9. [DOI] [Google Scholar]
  • 18.American Association for Public Opinion Research. Standard definitions: Final dispositions of case codes and outcome rates for surveys. 2008. [Google Scholar]
  • 19.Snijders T, Bosker R. Multilevel Analysis. London: Sage Publications; 1999. [Google Scholar]
  • 20.Smith TW. Developing nonresponse standards. In: Grove RG, Dillman DA, Eltinge JL, Little RJ, editors. Survey Nonresponse. New York: Wiley; 2002. [Google Scholar]
  • 21.Curtin R, Presser S, Singer E. Changes in telephone nonresponse over the past quarter century. Public Opin Q. 2005;69:87–98. doi: 10.1093/poq/nfi002. [DOI] [Google Scholar]
  • 22.Groves R. Nonresponse rates and nonresponse bias in household surveys. Public Opin Q. 2006;70:646–75. doi: 10.1093/poq/nfl033. [DOI] [Google Scholar]

Articles from Canadian Journal of Public Health = Revue Canadienne de Santé Publique are provided here courtesy of Springer

RESOURCES