Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1080/13658816.2021.1895170 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE |
Keywords | DocType | Volume |
Urban spatial structure, job-housing, street view images, deep learning, socioeconomic characteristics | Journal | 35 |
Issue | ISSN | Citations |
10 | 1365-8816 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Yao | 1 | 74 | 5.97 |
| Jiaqi Zhang | 2 | 0 | 0.34 |
| Chen Qian | 3 | 0 | 0.34 |
| Yu Wang | 4 | 0 | 0.34 |
| Shuliang Ren | 5 | 2 | 1.42 |
| Zehao Yuan | 6 | 0 | 0.34 |
| Qingfeng Guan | 7 | 16 | 8.64 |