Spatial and deep learning analyses of urban recovery from the impacts of COVID-19
Abstract This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-s...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29189-5 |
| _version_ | 1828035283080183808 |
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| author | Shuang Ma Shuangjin Li Junyi Zhang |
| author_facet | Shuang Ma Shuangjin Li Junyi Zhang |
| author_sort | Shuang Ma |
| collection | DOAJ |
| description | Abstract This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs. |
| first_indexed | 2024-04-10T15:44:34Z |
| format | Article |
| id | doaj.art-c3c2f0a57f814039a448afbc97b4ad96 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T15:44:34Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c3c2f0a57f814039a448afbc97b4ad962023-02-12T12:12:15ZengNature PortfolioScientific Reports2045-23222023-02-0113111410.1038/s41598-023-29189-5Spatial and deep learning analyses of urban recovery from the impacts of COVID-19Shuang Ma0Shuangjin Li1Junyi Zhang2College of Civil Engineering and Architecture, Zhejiang UniversityGraduate School of Advanced Science and Engineering, Hiroshima UniversityGraduate School of Advanced Science and Engineering, Hiroshima UniversityAbstract This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs.https://doi.org/10.1038/s41598-023-29189-5 |
| spellingShingle | Shuang Ma Shuangjin Li Junyi Zhang Spatial and deep learning analyses of urban recovery from the impacts of COVID-19 Scientific Reports |
| title | Spatial and deep learning analyses of urban recovery from the impacts of COVID-19 |
| title_full | Spatial and deep learning analyses of urban recovery from the impacts of COVID-19 |
| title_fullStr | Spatial and deep learning analyses of urban recovery from the impacts of COVID-19 |
| title_full_unstemmed | Spatial and deep learning analyses of urban recovery from the impacts of COVID-19 |
| title_short | Spatial and deep learning analyses of urban recovery from the impacts of COVID-19 |
| title_sort | spatial and deep learning analyses of urban recovery from the impacts of covid 19 |
| url | https://doi.org/10.1038/s41598-023-29189-5 |
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