1. Delineating urban job-housing patterns at a parcel scale with street view imagery.
- Author
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Yao, Yao, Zhang, Jiaqi, Qian, Chen, Wang, Yu, Ren, Shuliang, Yuan, Zehao, and Guan, Qingfeng
- Subjects
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URBAN growth , *DEEP learning , *URBAN planning , *URBAN studies , *NEIGHBORHOODS , *STREET children - Abstract
Empirical data are limited to decipher where people live and work in large cities; however, neighborhood information, such as street view image, is rich and abundant. We construct a ResNet-50-based social detection model to explore the potential relationship between street view images and job-housing attributes. The method extracts street view images of a neighborhood in all eight directions to predict land parcels' job-housing attributes and uses an entropy index to measure the degree of job-housing mixture in Shenzhen as an example. The social-detection model performs well with a low RMSE (0.1094) in identifying job-housing patterns. The eight-direction neighborhood method shows the best support for sufficient neighborhood information from street view images (RMSE = 0.1135) compared with other neighborhood methods. This study demonstrates the feasibility of using street-view images and deep learning to characterize job-housing attributes consistent with findings from urban studies with socioeconomic data; for example, the research finding concurs that Shenzhen has many high job-housing mixtures with very few areas designated for jobs or residences. The proposed method, when applied regularly, can help monitor spatial dynamics of urban job-housing patterns to inform city planning and development. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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