Using decision-tree methodologies to explore determinants of health and wellbeing outcomes at the local authority scale: the case study of London

Abstract

Urban health, including physical health and wellbeing, has gained global a ention as cities prioritize policies that promote healthier living. This study answers one central question: what are the health determinants resulting in diff erent health outcomes throughout a city? It seeks to identify critical determinants of underperforming health and wellbeing status from variables related to urban health. Using a case study based in London, this study applies a decision-tree model approach to identify determinants from three dimensions, i.e. environment, socioeconomics, and urban planning and design, to obtain the relative importance of these health and wellbeing determinants. This study bridges the gap between cutting-edge data science methods and urban health research, providing urban planners and decision-makers with data-driven evidence and a new tool to shape a healthier city.

Haifeng Niu

Research Associate

I am a Research Associate at the Lab of Interdisciplinary Spatial Analysis, Department of Land Economy, University of Cambridge, currently leading spatial analysis work in the European Union’s Horizon 2020-funded project Emotional Cities(WP4). My expertise includes urban big data mining, spatial data science, geo-visualisation, and urban sensing and modelling. I have a strong interest in how the intersection of machine learning & AI and urban big data better supports urban planning, policy-making and smart management.