Using machine learning to examine associations between the built environment and physical function: A feasibility study

JN Rachele; J Wang; JS Wijnands; H Zhao; R Bentley; M Stevenson

Journal article

Using machine learning to examine associations between the built environment and physical function: A feasibility study

JN Rachele, J Wang, JS Wijnands, H Zhao, R Bentley, M Stevenson

Health and Place | ELSEVIER SCI LTD | Published : 2021

DOI: 10.1016/j.healthplace.2021.102601

Abstract

Linking geospatial neighbourhood design characteristics to health and behavioural data from population-representative cohorts is limited by data availability and difficulty collecting information on environmental characteristics (e.g. greenery, building setbacks, dwelling structure). As an alternative, this study examined the feasibility of Generative Adversarial Networks (GANs) – machine learning – to measure neighbourhood design using ‘street view’ and aerial imagery to explore the relationship between the built environment and physical function. This study included 3102 adults aged 45 years and older clustered in 200 neighbourhoods in 2016 from the How Areas in Brisbane Influence Health a..

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University of Melbourne Researchers

Rebecca Bentley Author

Mark Stevenson Author

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Grants

Awarded by Australian Research Council

Funding Acknowledgements

This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200. The HABITAT study is fun-ded by the National Health and Medical Research Council (NHMRC) (ID 497236, 339718, 1047453) . At the time that this paper was written, JNR was supported by the National Health and Medical Research Council (NHMRC) Centre for Research Excellence in Disability and Health (APP1116385) , and an ARC Discovery Project (DP170101434) . MS is supported by an NHMRC Fellowship (APP1136250) .