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-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

SUBMITTER: Ma S 

PROVIDER: S-EPMC9922321 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature