Citation

Yuan K, Abe H, Otsuka N, Yasufuku K, Takahashi A. Urban Sci. 2022; 6(3): e44.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/urbansci6030044

PMID

unavailable

Abstract

The COVID-19 pandemic has greatly affected the mobility of individuals everywhere. This has been especially true in China, where many restrictions, including lockdowns, have been widely applied. This paper discusses the impact of the pandemic on walkability, an important factor in promoting urban neighborhoods, in the main urban area of Xi'an, China, one of China's four great ancient capitals. Based on the street view data obtained before and after the pandemic, the paper quantitatively compares changes in specific components of selected streetscapes through a deep learning (DL) street view analysis. The aim is to identify the impact of the pandemic on walkability and determine the elements that influence increased walkability in Xi'an's historical area, using a walkability evaluation model based on a regression analysis involving three factors (streetscape components, walkability check scores, and street connectivity of space syntax for every image). Although Xi'an's urban structure did not change significantly, the pandemic has clearly impacted street vitality, especially in terms of reducing pedestrian flow and commercial value. Based on study results, the street environment has great room for improvement, especially in the city's historical blocks, by reconsidering safety measures to pedestrians and the important role of atmospheric aspects on the streets.

Language: en

Keywords

COVID-19; deep learning; street view; walkability; Xi’an