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Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.
Ma, Shuang; Li, Shuangjin; Zhang, Junyi.
  • Ma S; College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China.
  • Li S; Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, 739-8529, Japan.
  • Zhang J; Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, 739-8529, Japan. junyizh24@gmail.com.
Sci Rep ; 13(1): 2447, 2023 02 11.
Article in English | MEDLINE | ID: covidwho-2242956
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Experimental Studies / Observational study Limits: Humans Language: English Journal: Sci Rep Year: 2023 Document Type: Article Affiliation country: S41598-023-29189-5

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Experimental Studies / Observational study Limits: Humans Language: English Journal: Sci Rep Year: 2023 Document Type: Article Affiliation country: S41598-023-29189-5