Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.
Shuang MaShuangjin LiJunyi ZhangPublished in: Scientific reports (2023)
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.
Keyphrases
- deep learning
- convolutional neural network
- electronic health record
- coronavirus disease
- sars cov
- big data
- healthcare
- mental health
- public health
- high resolution
- magnetic resonance
- minimally invasive
- climate change
- magnetic resonance imaging
- computed tomography
- data analysis
- depressive symptoms
- network analysis
- optical coherence tomography