Measuring social, environmental and health inequalities using deep learning and street imagery
File(s)
Published version
Author(s)
Suel, Esra
Polak, john
Bennett, james
Ezzati, Majid
Type
Journal Article
Abstract
Cities are home to an increasing majority of the world’s population. Currently, it is difficult to track social, economic, environmental and health outcomes in cities with high spatial and temporal resolution, needed to evaluate policies regarding urban inequalities. We applied a deep learning approach to street images for measuring spatial distributions of income, education, unemployment, housing, living environment, health and crime. Our model predicts different outcomes directly from raw images without extracting intermediate user-defined features. To evaluate the performance of the approach, we first trained neural networks on a subset of images from London using ground truth data at high spatial resolution from official statistics. We then compared how trained networks separated the best-off from worst-off deciles for different outcomes in images not used in training. The best performance was achieved for quality of the living environment and mean income. Allocation was least successful for crime and self-reported health (but not objectively measured health). We also evaluated how networks trained in London predict outcomes three other major cities in the UK: Birmingham, Manchester, and Leeds. The transferability analysis showed that networks trained in London, fine-tuned with only 1% of images in other cities, achieved performances similar to ones from trained on data from target cities themselves. Our findings demonstrate that street imagery has the potential complement traditional survey-based and administrative data sources for high-resolution urban surveillance to measure inequalities and monitor the impacts of policies that aim to address them.
Date Issued
2019-04-18
Date Acceptance
2019-03-18
Citation
Scientific Reports, 2019, 9 (1)
ISSN
2045-2322
Publisher
Nature Publishing Group
Journal / Book Title
Scientific Reports
Volume
9
Issue
1
Copyright Statement
© The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per-mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Sponsor
Medical Research Council (MRC)
Wellcome Trust
Identifier
https://www.nature.com/articles/s41598-019-42036-w
Grant Number
MR/S003983/1
209376/Z/17/Z
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
LIFE EXPECTANCY
NEIGHBORHOOD
DISORDER
FUTURE
VIEW
URBANIZATION
POVERTY
Cities
Crime
Deep Learning
Employment
Health Status Disparities
Housing
Humans
Income
London
Neural Networks, Computer
Satellite Imagery
Social Problems
Socioeconomic Factors
Humans
Cities
Housing
Crime
Social Problems
Socioeconomic Factors
Employment
Income
London
Health Status Disparities
Satellite Imagery
Deep Learning
Neural Networks, Computer
Publication Status
Published
Article Number
6229