Do Poverty and Wealth Look the Same The World Over? A Comparative Study of 12 Cities From Five High-Income Countries Using Street Images
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
EPJ Data Science
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.
Description
Research Article
Keywords
Street images, Visual similarity, Computer vision, Urban inequalities
Citation
Suel et al. EPJ Data Science (2023) 12:19 https://doi.org/10.1140/epjds/s13688-023-00394-6