Using kernel density estimation to understand the influence of neighbourhood destinations on BMI

BMJ Open
T KingA M Kavanagh

Abstract

Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs. A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)-Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable. For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were s...Continue Reading

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Citations

Oct 22, 2020·The International Journal of Behavioral Nutrition and Physical Activity·Shin Bin Tan, Mariana Arcaya

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Software Mentioned

ArcGIS
KDE
Spatial Analyst
VicLANES

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