IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0137402.html
   My bibliography  Save this article

The Use of Kernel Density Estimation to Examine Associations between Neighborhood Destination Intensity and Walking and Physical Activity

Author

Listed:
  • Tania L King
  • Lukar E Thornton
  • Rebecca J Bentley
  • Anne M Kavanagh

Abstract

Background: Local destinations have previously been shown to be associated with higher levels of both physical activity and walking, but little is known about how the distribution of destinations is related to activity. Kernel density estimation is a spatial analysis technique that accounts for the location of features relative to each other. Using kernel density estimation, this study sought to investigate whether individuals who live near destinations (shops and service facilities) that are more intensely distributed rather than dispersed: 1) have higher odds of being sufficiently active; 2) engage in more frequent walking for transport and recreation. Methods: The sample consisted of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Destinations within these areas were geocoded and kernel density estimates of destination intensity were created using kernels of 400m (meters), 800m and 1200m. Using multilevel logistic regression, the association between destination intensity (classified in quintiles Q1(least)—Q5(most)) and likelihood of: 1) being sufficiently active (compared to insufficiently active); 2) walking≥4/week (at least 4 times per week, compared to walking less), was estimated in models that were adjusted for potential confounders. Results: For all kernel distances, there was a significantly greater likelihood of walking≥4/week, among respondents living in areas of greatest destinations intensity compared to areas with least destination intensity: 400m (Q4 OR 1.41 95%CI 1.02–1.96; Q5 OR 1.49 95%CI 1.06–2.09), 800m (Q4 OR 1.55, 95%CI 1.09–2.21; Q5, OR 1.71, 95%CI 1.18–2.48) and 1200m (Q4, OR 1.7, 95%CI 1.18–2.45; Q5, OR 1.86 95%CI 1.28–2.71). There was also evidence of associations between destination intensity and sufficient physical activity, however these associations were markedly attenuated when walking was included in the models. Conclusions: This study, conducted within urban Melbourne, found that those who lived in areas of greater destination intensity walked more frequently, and showed higher odds of being sufficiently physically active–an effect that was largely explained by levels of walking. The results suggest that increasing the intensity of destinations in areas where they are more dispersed; and or planning neighborhoods with greater destination intensity, may increase residents’ likelihood of being sufficiently active for health.

Suggested Citation

  • Tania L King & Lukar E Thornton & Rebecca J Bentley & Anne M Kavanagh, 2015. "The Use of Kernel Density Estimation to Examine Associations between Neighborhood Destination Intensity and Walking and Physical Activity," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-16, September.
  • Handle: RePEc:plo:pone00:0137402
    DOI: 10.1371/journal.pone.0137402
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137402
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0137402&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0137402?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chaix, Basile & Merlo, Juan & Evans, David & Leal, Cinira & Havard, Sabrina, 2009. "Neighbourhoods in eco-epidemiologic research: Delimiting personal exposure areas. A response to Riva, Gauvin, Apparicio and Brodeur," Social Science & Medicine, Elsevier, vol. 69(9), pages 1306-1310, November.
    2. Millward, Hugh & Spinney, Jamie & Scott, Darren, 2013. "Active-transport walking behavior: destinations, durations, distances," Journal of Transport Geography, Elsevier, vol. 28(C), pages 101-110.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suzanne Mavoa & Nasser Bagheri & Mohammad Javad Koohsari & Andrew T. Kaczynski & Karen E. Lamb & Koichiro Oka & David O’Sullivan & Karen Witten, 2019. "How Do Neighbourhood Definitions Influence the Associations between Built Environment and Physical Activity?," IJERPH, MDPI, vol. 16(9), pages 1-16, April.
    2. J Padmaka Silva & Ankur Singh & Brian Oldenburg & Wasantha Gunathunga & A M A A P Alagiyawanna & Suzanne Mavoa, 2021. "Associations between residential greenness and self-reported heart disease in Sri Lankan men: A cross-sectional study," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-12, May.
    3. Eric T. H. Chan & Tim Schwanen & David Banister, 2021. "The role of perceived environment, neighbourhood characteristics, and attitudes in walking behaviour: evidence from a rapidly developing city in China," Transportation, Springer, vol. 48(1), pages 431-454, February.
    4. Aurélie Mercier & Stéphanie Souche‐Le Corvec & Nicolas Ovtracht, 2021. "Measure of accessibility to postal services in France: A potential spatial accessibility approach applied in an urban region," Papers in Regional Science, Wiley Blackwell, vol. 100(1), pages 227-249, February.
    5. Hamidreza Rabiei‐Dastjerdi & Stephen A. Matthews, 2021. "Who gets what, where, and how much? Composite index of spatial inequality for small areas in Tehran," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 191-205, February.
    6. O'Driscoll, Conor & Crowley, Frank & Doran, Justin & McCarthy, Nóirín, 2022. "Retail sprawl and CO2 emissions: Retail centres in Irish cities," Journal of Transport Geography, Elsevier, vol. 102(C).
    7. Li, Aoyong & Huang, Yizhe & Axhausen, Kay W., 2020. "An approach to imputing destination activities for inclusion in measures of bicycle accessibility," Journal of Transport Geography, Elsevier, vol. 82(C).
    8. Neatt, Kevin & Millward, Hugh & Spinney, Jamie, 2017. "Neighborhood walking densities: A multivariate analysis in Halifax, Canada," Journal of Transport Geography, Elsevier, vol. 61(C), pages 9-16.
    9. Paul Czioska & Ronny Kutadinata & Aleksandar Trifunović & Stephan Winter & Monika Sester & Bernhard Friedrich, 2019. "Real-world meeting points for shared demand-responsive transportation systems," Public Transport, Springer, vol. 11(2), pages 341-377, August.
    10. Carroll, Suzanne J. & Paquet, Catherine & Howard, Natasha J. & Coffee, Neil T. & Taylor, Anne W. & Niyonsenga, Theo & Daniel, Mark, 2016. "Local descriptive norms for overweight/obesity and physical inactivity, features of the built environment, and 10-year change in glycosylated haemoglobin in an Australian population-based biomedical c," Social Science & Medicine, Elsevier, vol. 166(C), pages 233-243.
    11. Dustin T. Duncan & DeMarc A. Hickson & William C. Goedel & Denton Callander & Brandon Brooks & Yen-Tyng Chen & Hillary Hanson & Rebecca Eavou & Aditya S. Khanna & Basile Chaix & Seann D. Regan & Darre, 2019. "The Social Context of HIV Prevention and Care among Black Men Who Have Sex with Men in Three U.S. Cities: The Neighborhoods and Networks (N2) Cohort Study," IJERPH, MDPI, vol. 16(11), pages 1-24, May.
    12. Chen, Yu & Liu, Gengyuan & Yan, Ningyu & Yang, Qing & Gao, He & Su, Liya & Santagata, Remo, 2023. "Comprehensive evaluation of urban greenspace ecological values marketability through the spatial relationship between housing price and ecosystem services," Ecological Modelling, Elsevier, vol. 484(C).
    13. Easton, Sue & Ferrari, Ed, 2015. "Children's travel to school—the interaction of individual, neighbourhood and school factors," Transport Policy, Elsevier, vol. 44(C), pages 9-18.
    14. Perchoux, Camille & Kestens, Yan & Thomas, Frédérique & Hulst, Andraea Van & Thierry, Benoit & Chaix, Basile, 2014. "Assessing patterns of spatial behavior in health studies: Their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study)," Social Science & Medicine, Elsevier, vol. 119(C), pages 64-73.
    15. Grimm, Michael & Treibich, Carole, 2016. "Why do some motorbike riders wear a helmet and others don’t? Evidence from Delhi, India," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 318-336.
    16. Eizaguirre-Iribar, Arritokieta & Etxepare Igiñiz, Lauren & Hernández-Minguillón, Rufino Javier, 2016. "A multilevel approach of non-motorised accessibility in disused railway systems: The case-study of the Vasco-Navarro railway," Journal of Transport Geography, Elsevier, vol. 57(C), pages 35-43.
    17. Mona Jabbari & Fernando Fonseca & Rui Ramos, 2018. "Combining multi-criteria and space syntax analysis to assess a pedestrian network: the case of Oporto," Journal of Urban Design, Taylor & Francis Journals, vol. 23(1), pages 23-41, January.
    18. Vallée, Julie & Cadot, Emmanuelle & Roustit, Christelle & Parizot, Isabelle & Chauvin, Pierre, 2011. "The role of daily mobility in mental health inequalities: The interactive influence of activity space and neighbourhood of residence on depression," Social Science & Medicine, Elsevier, vol. 73(8), pages 1133-1144.
    19. Pablo Sáinz-Ruiz & José Ramón Martínez-Riera, 2022. "Community Assets for Health Model and Assessment Scale: A Delphi-Based Analysis and Expert Validation," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    20. Hatamzadeh, Yaser, 2021. "Working commuters’ tendency toward a travel pattern with potentially more walking: Examining the relative influence of personal and environmental measures," Research in Transportation Economics, Elsevier, vol. 86(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0137402. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.