Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing
By Jingxian Tang, Ying Long. Landscape and Urban Planning, 2019
324 citations recorded by the source corpus.
Abstract Although it is widely known that quality of street space plays a vital role in promoting urban vibrancy, there is still no consensus on how to quantitatively measure it for a large scale. Recent emerging dataset Street View Picture has revealed the possibility to overcome the previous limit, thus bringing forward a research paradigm shift. Taking this advantage, this paper explores a new approach for visual quality evaluation and variation identification of street space for a large area. Hutongs, which typically represent for historical street space in Beijing, are selected for empirical study. In the experimental part, we capture multi-years Tencent Street View Picture covering all the Hutongs, and conduct both physical and perceived visual quality evaluation. The physical visual quality of street space is achieved automatically by combining 3-dimensional composition calculation of greenery, openness, enclosure using machine-learning segmentation method SegNet, and 2-dimensional analysis of street wall continuity and cross-sectional proportion; perceived visual quality of street space is evaluated by stay willingness scoring from five aspects. The variation of quality is evaluated based on the identified physical space variations. The result indicates that visual quality of Hutongs are not satisfied, while some regeneration projects in the historical protection block is better. Most Hutongs are in shortage of visual green, relative more continuous but with low cross-sectional ratio. Hutongs near main road witness an increasing trend of motorization. The difference between physical and perceived quality indicates the feasibility and limitation of the auto-calculation method. In the most recent 3–4 years, less than 2.5% Hutongs are improved, which are mainly slow beautification.
Subfields: Building and Construction, Global and Planetary Change, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/9606a79e73dbffc727ae8733bd9eeea56bbc2cea
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In Building and Construction
Citations in the corpus
- Street view imagery in urban analytics and GIS: A review (2021, background): “The central theme is measuring the perceived quality of streetscape (Li & Long, 2019; Liu et al., 2019; Wu, Peng, Ma, Li, & Rao, 2020; Ye, Zeng, Shen, Zhang, & Lu, 2019), and researchers have been using SVI to measure urban perceptual attributes such as safety and wealth, vibrancy, comfort, and…”
- Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique (2021, unknown): “Based on street view images and deep learning methods, existing studies have used indicators such as green-ness, openness, and enclosure as the expression of visual space to evaluate the perception of street [11,41].”