Examining non-linear associations between population density and waist-hip ratio: An application of gradient boosting decision trees

Chun Yin, Jason Cao, Bindong Sun

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Many studies have explored the relationship between population density and obesity, but there is no consensus, particularly in dense Chinese cities. This study applied gradient boosting decision trees to 2014 national survey data to examine the non-linear or threshold effects of population density at both local and regional levels on waist-hip ratio (WHR), controlling for other built environment elements and socio-demographics. Built environment elements collectively have a stronger predictive power than socio-demographics (56.6% vs. 43.4%). Within a certain range, regional population density is negatively associated with WHR, but its marginal effect diminishes beyond the upper threshold. Local population density has a U-shaped relationship with WHR. These results suggest that urban planners can alleviate the risk of obesity through population densification, but over-densification tends to be inefficient, and sometimes counterproductive.

Original languageEnglish (US)
Article number102899
JournalCities
Volume107
DOIs
StatePublished - Dec 2020

Keywords

  • China Labor-force Dynamics Survey
  • Compact development
  • Machine learning
  • Obesity
  • Threshold effects

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