Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China

Yilei Tao, Ying Wang, Xinyu Wang, Guohang Tian and Shumei Zhang
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Yilei Tao: College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou 450002, China
Ying Wang: College of Architecture and Engineering, Zhengzhou University of Industrial Technology, Zhengzhou 450002, China
Xinyu Wang: Doctoral School of Landscape Architecture and Landscape Ecology, Technology, Hungarian University of Agriculture and Life Sciences, 1114 Budapest, Hungary
Guohang Tian: College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou 450002, China
Shumei Zhang: College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou 450002, China

Land, 2022, vol. 11, issue 3, 1-19

Abstract: Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel’s semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities.

Keywords: human perception; human activity density; visual characteristics; urban planning; streetscape (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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