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

Abstract

Objectives: 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. Study design and setting: A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Methods: Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 40..