Do poverty and wealth look the same the world over? A comparative study of 12 cities from five high-income countries using street images.
Do poverty and wealth look the same the world over? A comparative study of 12 cities from five high-income countries using street images.
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DOI：
10.1140/epjds/s13688-023-00394-6
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发表时间：
2023
期刊：
影响因子：
3.6
通讯作者：
Ezzati, Majid
中科院分区：
文献类型：
--
作者：
Suel, Esra;Mueller, Emily;Bennett, James E.;Blakely, Tony;Doyle, Yvonne;Lynch, John;Mackenbach, Joreintje D.;Middel, Ariane;Mizdrak, Anja;Nathvani, Ricky;Brauer, Michael;Ezzati, Majid
城市化和不平等是我们这个时代的两个主要政策主题，在社会和经济不平等特别明显的大城市交织在一起。大规模的街道图像是全市视觉信息的来源，并允许对多个城市进行比较分析。基于深度学习的计算机视觉方法应用于街道图像已经被证明成功地测量了社会经济和环境特征的不平等，然而现有的工作一直是在特定的地理区域内，并且没有考虑不同城市和国家之间的视觉环境的比较。在这项研究中，我们旨在应用现有的方法来了解贫穷和富裕群体是否以及在多大程度上生活在不同城市和国家的视觉相似的社区中。我们使用街道级别的图像和深度学习方法，提出了关于社区相似性的新见解。我们分析了五个高收入国家的12个城市的720万张图片，这些城市拥有8500多万人口：奥克兰(新西兰)、悉尼(澳大利亚)、多伦多和温哥华(加拿大)、亚特兰大、波士顿、芝加哥、洛杉矶、纽约、旧金山和华盛顿特区(美利坚合众国)以及伦敦(英国)。与社区劣势相关的视觉特征对每个城市来说比与富裕相关的视觉特征更明显和独特。例如，从街道图像可以看到，位于市中心附近的高密度贫困社区(例如，在伦敦)在视觉上与以低密度和较低可达性为特征的贫穷郊区社区(例如，在亚特兰大)截然不同。这表明，两个城市之间的差异也是由历史因素、政策和当地地理因素驱动的。我们的结果对基于图像的城市不平等测量也有影响，特别是在使用与目标城市视觉上不同的城市的数据进行训练时。我们表明，对于贫困地区来说，这些更容易出错，特别是在跨城市转移时，这表明需要更多地关注改善方法，以捕捉世界各地城市恶劣环境中的异质性。网上版载有补充材料，可在10.1140/epjds/s13688-023-00394-6查阅。
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. The online version contains supplementary material available at 10.1140/epjds/s13688-023-00394-6.
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影响因子：
6.2
作者：
Doersch, Carl;Singh, Saurabh;Efros, Alexei A.
通讯作者：
Efros, Alexei A.
影响因子：
6.3
作者：
Marmot M;Allen J
通讯作者：
Allen J
DOI：
10.1073/pnas.1700035114
发表时间：
2017-12-12
影响因子：
11.1
作者：
Gebru T;Krause J;Wang Y;Chen D;Deng J;Aiden EL;Fei-Fei L
通讯作者：
Fei-Fei L
影响因子：
168.9
作者：
Bennett, James E.;Li, Guangquan;Ezzati, Majid
通讯作者：
Ezzati, Majid
影响因子：
1.8
作者：
Glaeser, Edward L.;Kominers, Scott Duke;Naik, Nikhil
通讯作者：
Naik, Nikhil