Computer vision uncovers predictors of physical urban change
Computer vision uncovers predictors of physical urban change
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DOI：
10.1073/pnas.1619003114
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发表时间：
2017-07-18
影响因子：
11.1
通讯作者：
Hidalgo, Cesar A.
中科院分区：
文献类型：
--
作者：
Naik, Nikhil;Kominers, Scott Duke;Hidalgo, Cesar A.
哪些社区的环境得到了改善？在本文中，我们引入了一种计算机视觉方法，从时间序列街道级图像中测量社区物理外观的变化。我们将美国五个城市的外观变化与经济和人口数据联系起来，发现了预测社区改善的三个因素。首先，受过大学教育的成年人密集居住的社区更有可能经历身体上的改善——这一观察结果与将人力资本与地方成功联系起来的经济学文献是一致的。其次，平均而言，初始外观更好的社区会经历更大的积极改善——这一观察结果与城市变化的“小费”理论相一致。第三，社区改善与中央商务区和其他有吸引力的社区的物理接近度呈正相关——这一观察结果与城市社会学的“入侵”理论相一致。总之，我们的研究结果为城市变化的三个经典理论提供了支持，并说明了使用计算机视觉方法和街道级图像来理解城市物理动态的价值。
Which neighborhoods experience physical improvements? In this paper, weintroduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements-an observation that is compatible with the economic literature linking human capital and local success. Second, neighborhoods with better initial appearances experience, on average, larger positive improvements-an observation that is consistent with "tipping" theories of urban change. Third, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods-an observation that is consistent with the "invasion" theories of urban sociology. Together, our results provide support for three classical theories of urban change and illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities.