Spatiotemporal Analysis of Urban Perception Using Multi-Year Street View Images and Deep Learning
Year: 2025
Type: article
Abstract: Spatial perception is essential for understanding residents’ subjective experiences and well-being. However, effective methods for tracking changes in spatial perception over time and space remain limited. This study proposes a novel approach that leverages historical street view imagery to monitor the evolution of urban spatial perception. Using the central urban area of Shanghai as a case study, we applied machine learning techniques to analyze 67,252 street view images from 2013 and 2019, aiming to quantify the spatiotemporal dynamics of urban perception. The results reveal the following: temporally, the average perception scores in 2019 increased by 4.85% compared to 2013; spatially, for every 1.5 km increase in distance from the city center, perception scores increased by an average of 0.0241; among all sampling points, 65.79% experienced an increase in perception, while 34.21% showed a decrease; and in terms of visual elements, natural features such as trees, vegetation, and roads were positively correlated with perception scores, whereas artificial elements like buildings, the sky, sidewalks, walls, and fences were negatively correlated. The analytical framework developed in this study offers a scalable method for measuring and interpreting changes in urban perception and can be extended to other cities. The findings provide valuable time-sensitive insights for urban planners and policymakers, supporting the development of more livable, efficient, and equitable urban environments.
DOI: 10.3390/ijgi14100390
Authors: Wen Zhong, Lei Wang, Xin Han, Zhe Gao
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,292
OpenAlex ID: W4414948079