Citation

Wu C, Zheng P, Xu X, Chen S, Wang N, Hu S. Int. J. Environ. Res. Public Health 2020; 17(21): e8167.

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph17218167

PMID

33167348

Abstract

Mental health is the foundation of health and happiness as well as the basis for an individual's meaningful life. The environmental and social health of a city can measure the mental state of people living in a certain areas, and exploring urban dwellers' mental states is an important factor in understanding and better managing cities. New dynamic and granular urban data provide us with a way to determine the environmental factors that affect the mental states of urban dwellers. The characteristics of the maximal information coefficient can identify the linear and nonlinear relationships so that we can fully identify the physical and social environmental factors that affect urban dwellers' mental states and further test these relationships through linear and nonlinear modeling. Taking the Greater London as an example, we used data from the London Datastore to discover the environmental factors that had the highest correlation with urban mental health from 2015 to 2017 and to prove that they had a high nonlinear correlation through neural network modeling. This paper aimed to use a data-driven approach to find environmental factors that had not yet received enough attention and to provide a starting point for research by establishing hypotheses for further exploration of the impact of environmental factors on mental health.

Language: en

Keywords

mental health; city environment; data-driven approach; model comparison; urban data