Heterogeneity Study of the Visual Features Based on Geographically Weighted Principal Components Analysis Applied to an Urban Community

Yong Liu, Shutong Yang and Shijun Wang
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Yong Liu: Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China
Shutong Yang: Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China
Shijun Wang: Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China

Sustainability, 2021, vol. 13, issue 23, 1-15

Abstract: Communities in urban space are the most basic living units. Community visual features directly reflect the local living quality and influence the perception of residents and visitors. The evaluation of the community visual features is of great significance to the space design under the guidance of urban landscape recognition and urban space perception. Based on the street view image data, this paper analyzes the composition of visual features in the community space scale by using the geographically weighted principal components analysis. GWPCA can not only reflect the global characteristics, but also analyze the local components, thus describing the visual features of the community in a comprehensive manner. The results show that: (1) community visual features have significant spatial heterogeneity at different statistical scales, and the spatial heterogeneity of community visual features can provide a basis for urban landscape planning and design; (2) the combination mode of dominant visual elements can reflect different community landscapes. The analysis of this paper further illustrates the effectiveness and application prospect of street view images in identifying the landscape composition mode of urban space from the medium-micro perspective. This conclusion is helpful for planners to learn the dominant visual features of the community through street view images, and, further, use the classification of elements of street view images to guide the planning and design of cityscape.

Keywords: street view image; community visual spatial features; geographical weighted principal components analysis; GWPCA (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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