Measuring Physical Disorder in Urban Street Spaces: A Large-Scale Analysis Using Street View Images and Deep Learning

被引:27
作者
Chen, Jingjia [1 ]
Chen, Long [1 ,2 ]
Li, Yan [1 ]
Zhang, Wenjia [3 ]
Long, Ying [4 ]
机构
[1] Tsinghua Univ, Sch Architecture, Beijing, Peoples R China
[2] Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing, Peoples R China
[3] Peking Univ, Shenzhen Grad Sch, Shenzhen, Peoples R China
[4] Tsinghua Univ, Sch Architecture & Hang Lung Ctr Real Estate, Key Lab Eco Planning & Green Bldg, Minist Educ, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
deep learning; physical disorder; street view image; urban street space; NEIGHBORHOOD DISORDER; PERCEPTIONS; CRIME; FEAR;
D O I
10.1080/24694452.2022.2114417
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
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.
引用
收藏
页码:469 / 487
页数:19
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