Multi-dimensional urban environmental exposures and inequality for mental health

Abstract

This study presents a reproducible methodology for creating spatial indicators using open-source data from OpenStreetMap and the Google Earth Engine, using Python automation, with a focus on applicability in Low- and Middle-Income Countries (LMICs) due to the problem of data scarcity within LMICs as opposed to High Income Countries (HICs). The research leverages the diverse landscape of Greater Manchester to assess the impact of both urban and natural environments on mental health. The study employed the K-means clustering algorithm and Principal Component Analysis to effectively assess the heterogeneity of the spatial indicators, with an eight-cluster model emerging as the most suitable. Despite the limitations associated with the use of K-means in general and for geospatial data, the model displayed a logical cluster structure based on the extracted variables. The study further explored the relationships between the environment and mental health, revealing patterns of clusters being more favorable or adverse for mental health. Notably, densely urbanized areas with accessible amenities and urban green spaces were associated with lower levels of depression prevalence and daily antidepressant rate. The study concludes that increased remoteness from densely urbanized areas appears to reduce mental health. However, the conclusions are primarily based on descriptive statistics of the variables connected to the clusters, and future studies could employ inferential statistics to explore causality. The study contributes to the understanding of mental health-environment relationships, with implications for urban planning and public mental health policies, both for HICs and LMICs.