Delineating urban job-housing patterns at a parcel scale with street view imagery
Yao Yao, Jiaqi Zhang, Qian Chen, Yu Wang, Shuliang Ren, Zehao Yuan, Qingfeng Guan
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.