Measuring Physical Disorder in Urban Street Spaces: A Large-Scale Analysis Using Street View Images and Deep Learning
Measuring Physical Disorder in Urban Street Spaces: A Large-Scale Analysis Using Street View Images and Deep Learning
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DOI：
10.1080/24694452.2022.2114417
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发表时间：
2022-08-26
影响因子：
3.9
通讯作者：
Long, Ying
中科院分区：
文献类型：
--
作者：
Chen, Jingjia;Chen, Long;Long, Ying
身体障碍与经济表现、公共健康和社会稳定的负面后果有关，如财产贬值、精神压力、恐惧和犯罪。数量有限但不断增加的文献认为，城市空间中的身体障碍，特别是在很小范围内识别身体障碍的主题。然而，目前还没有有效和可复制的方法来在小范围内以低成本测量大面积的身体障碍。为了填补这一空白，本文提出了一种利用海量街景图像作为虚拟审计的输入数据，并使用深度学习模型来定量衡量城市街道空间的物理无序性的方法。在中国城市70多万条街道上实施这种方法的结果--据我们所知，这是全球首次尝试对如此大的城市地区的身体紊乱进行量化--验证了该方法的有效性和效率。通过对《中国》这一大规模的实证分析，本文做出了几点理论贡献。首先，我们扩大了以前在美国研究中被忽视的身体障碍的因素。其次，我们发现城市身体障碍呈现三种典型的空间分布模式--散布、扩散和线性集中模式，这为揭示身体障碍的发展趋势和进行空间干预提供了参考。最后，我们对身体障碍与街道特征的回归分析确定了可能影响身体障碍的因素，从而丰富了理论基础。
Physical disorder is associated with negative outcomes in economic performance, public health, and social stability, such as the depreciation of property, mental stress, fear, and crime. A limited but growing body of literature considers physical disorder in urban space, especially the topic of identifying physical disorder at a fine scale. There is currently no effective and replicable way of measuring physical disorder at a fine scale for a large area with low cost, however. To fill the gap, this article proposes an approach that takes advantage of the massive volume of street view images as input data for virtual audits and uses a deep learning model to quantitatively measure the physical disorder of urban street spaces. The results of implementing this approach with more than 700,000 streets in Chinese cities-which, to our knowledge, is the first attempt globally to quantify the physical disorder in such large urban areas-validate the effectiveness and efficiency of the approach. Through this large-scale empirical analysis in China, this article makes several theoretical contributions. First, we expand the factors of physical disorder, which were previously neglected in U.S. studies. Second, we find that urban physical disorder presents three typical spatial distributions-scattered, diffused, and linear concentrated patterns-which provide references for revealing the development trends of physical disorder and making spatial interventions. Finally, our regression analysis between physical disorder and street characteristics identified the factors that could affect physical disorder and thus enriched the theoretical underpinnings.