EPJ Data Sci. (2023) 12: 19
https://doi.org/10.1140/epjds/s13688-023-00394-6

Regular Article

Do poverty and wealth look the same the world over? A comparative study of 12 cities from five high-income countries using street images

¹ Centre for Advanced Spatial Analysis (CASA), University College London, London, UK
² Department of Epidemiology and Biostatistics, Imperial College London, London, UK
³ MRC Center for Environment and Health, Imperial College London, London, UK
⁴ Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
⁵ Public Health England, London, UK
⁶ School of Public Health, University of Adelaide, Adelaide, Australia
⁷ Bristol Medical School Population Health Sciences, University of Bristol, Bristol, UK
⁸ Department of Epidemiology and Data Science, Amsterdam UMC–Vrije Universiteit, Amsterdam, The Netherlands
⁹ School of Arts, Media and Engineering, Arizona State University, Tempe, USA
¹⁰ Department of Public Health, University of Otago, Wellington, New Zealand
¹¹ School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
¹² School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA
¹³ Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
¹⁴ Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
¹⁵ Regional Institute for Population Studies, University of Ghana, Accra, Ghana

^a e.suel@ucl.ac.uk

Received: 5 December 2022
Accepted: 23 May 2023
Published online: 7 June 2023

Abstract

Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world.