Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery
The spatial distribution pattern of jobs and housing plays a vital role in urban planning and traffic construction. However, obtaining the jobs-housing distribution at a fine scale (e.g., the perspective of individual jobs-housing attribute) presents difficulties due to a lack of social media data a...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8018629 |
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| author | Yao Yao Chen Qian Ye Hong Qingfeng Guan Jingmin Chen Liangyang Dai Zhangwei Jiang Xun Liang |
| author_facet | Yao Yao Chen Qian Ye Hong Qingfeng Guan Jingmin Chen Liangyang Dai Zhangwei Jiang Xun Liang |
| author_sort | Yao Yao |
| collection | DOAJ |
| description | The spatial distribution pattern of jobs and housing plays a vital role in urban planning and traffic construction. However, obtaining the jobs-housing distribution at a fine scale (e.g., the perspective of individual jobs-housing attribute) presents difficulties due to a lack of social media data and useful models. With user data acquired from a location-based service provider in China, this study employs a deep bag-of-features network (BagNet) to classify remote-sensing (RS) images into various jobs-housing types. Considering Wuhan, one of the fastest developing cities in China, as a case study area, three jobs-housing types (i.e., only working, only living, and both working and living) at the land-parcel level are obtained. We demonstrate that the multiscale random sampling method can reduce the influence of image noise, increase the utilization of training data, and reduce network overfitting. By altering the network structure and the training strategy, BagNet achieved excellent fitting accuracy for identifying each jobs-housing type (overall accuracy > 0.84 and kappa > 0.8). For the first time, we demonstrate that urban socioeconomic characteristics can be obtained from high-resolution RS images using deep learning techniques. Additionally, we conclude that the total level of mixing within Wuhan is not high at present; however, Wuhan is continuously improving the mixture of jobs and housing. This study has reference value for extracting urban socioeconomic characteristics from RS images and could be used in urban planning as well as government management. |
| format | Article |
| id | doaj-art-fbf695173d00443ebcbddebdcdd1efbf |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-fbf695173d00443ebcbddebdcdd1efbf2025-08-20T03:25:33ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/80186298018629Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing ImageryYao Yao0Chen Qian1Ye Hong2Qingfeng Guan3Jingmin Chen4Liangyang Dai5Zhangwei Jiang6Xun Liang7School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, Hubei, ChinaDepartment of Engineering Systems and Environment, University of Virginia, Charlottesville,, VA 22904, USAInstitute of Cartography and Geoinformation, ETH Zurich, Stefano-Franscini-Platz 5, CH-8093 Zürich, SwitzerlandSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, Hubei, ChinaDepartment of Data Technology and Products, Alibaba Group, Hangzhou 311121, Zhejiang, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, Hubei, ChinaDepartment of Data Technology and Products, Alibaba Group, Hangzhou 311121, Zhejiang, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, Hubei, ChinaThe spatial distribution pattern of jobs and housing plays a vital role in urban planning and traffic construction. However, obtaining the jobs-housing distribution at a fine scale (e.g., the perspective of individual jobs-housing attribute) presents difficulties due to a lack of social media data and useful models. With user data acquired from a location-based service provider in China, this study employs a deep bag-of-features network (BagNet) to classify remote-sensing (RS) images into various jobs-housing types. Considering Wuhan, one of the fastest developing cities in China, as a case study area, three jobs-housing types (i.e., only working, only living, and both working and living) at the land-parcel level are obtained. We demonstrate that the multiscale random sampling method can reduce the influence of image noise, increase the utilization of training data, and reduce network overfitting. By altering the network structure and the training strategy, BagNet achieved excellent fitting accuracy for identifying each jobs-housing type (overall accuracy > 0.84 and kappa > 0.8). For the first time, we demonstrate that urban socioeconomic characteristics can be obtained from high-resolution RS images using deep learning techniques. Additionally, we conclude that the total level of mixing within Wuhan is not high at present; however, Wuhan is continuously improving the mixture of jobs and housing. This study has reference value for extracting urban socioeconomic characteristics from RS images and could be used in urban planning as well as government management.http://dx.doi.org/10.1155/2020/8018629 |
| spellingShingle | Yao Yao Chen Qian Ye Hong Qingfeng Guan Jingmin Chen Liangyang Dai Zhangwei Jiang Xun Liang Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery Complexity |
| title | Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery |
| title_full | Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery |
| title_fullStr | Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery |
| title_full_unstemmed | Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery |
| title_short | Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery |
| title_sort | delineating mixed urban jobs housing patterns at a fine scale by using high spatial resolution remote sensing imagery |
| url | http://dx.doi.org/10.1155/2020/8018629 |
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