Associations between the quality of street space and the attributes of the built environment using large volumes of street view pictures

Abstract

In this study, we focus on the quality of street space which has attracted high attentions. We discover associations between the quality of street space and built environment attributes through an ordered logistic model using massive street view pictures (SVPs) and data on street location, form, function and attributes. Before ascertain which built environment factors influence the quality of street space, we checked the concordance of the experts’ scores, as well as correlations between different dimensions through Kappa analysis and drew the distribution map of street space quality. We found that the value of intersection over union is 85.61% for scoring the street space quality by different people. The spatial quality of more than 75% streets are in the middle level with no obvious polarisation observed in the central area of Qingdao. In addition, for street quality index, all variables are statistically significant. The sequence is as follows: near-line rate > D/H ratio > slope > length of street > distance to administrative center > POIs diversity. The D/H ratio, near-line rate, slope length of street, distance to administrative center and POIs diversity have various associations on every dimension of street quality. They can prove useful for drafting more appropriate policy measures aimed at improving street quality.

The impact of geometric and land use elements on the perceived pleasantness of urban layouts

“…The opportunity to show images from cities all around the world and the diversified participant pool, with people of different backgrounds and cultures, reduces biases and strengthens the universality of the study. The use of images for surveys related to urban planning is well documented in the literature (Candeia et al, 2017; D’Acci, 2019; Hofmann et al, 2012; Li et al, 2021; Rijswijk et al, 2016; Sussman et al, 2021; Weinberger et al, 2021; Wergles and Muhar, 2009). The decision to show only 25 pictures was made to keep completion time to a maximum of 10 min, a time frame recommended by Galesic and Bosnjak (2009) and Revilla and Ochoa (2017).…”

“…Examples are Li et al (2021), who studied the quality of street space using logit models, street views, and expert validation, and Ye et al (2019), who used machine learning techniques to evaluate the visual quality of streets. Of the quantitative studies, only Calafiore (2020) and Li et al (2021) used field data to obtain a pleasantness indicator, respectively, a beauty index and a street quality index. Quantitative work exists on the impact of isolated geometric elements on pleasantness (Asgarzadeh et al, 2012; D’Acci, 2014; Lee, 2021; Wang et al, 2021), but none of these works have evaluated the combined landscape at the neighborhood scale.…”

Leveraging Street Level Imagery for Urban Planning

“…Now widely accessible through APIs (Application Programming Interfaces), these sources of street level imagery are providing a wealth of photographic data that can be used for research purposes, e.g., to estimate the socioeconomic characteristics of cities (Gebru et al, 2017). We have also seen increasing numbers of papers being published in Environment and Planning B that use street view imagery to explore different aspects of cities, for example, to assess the quality of urban spaces (Li et al, 2021;Ye et al, 2019), and we expect this trend to continue.…”

Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

“…SVI data is commonly provided by commercial services such as Google Street View (GSV), and crowdsourced platforms such as Mapillary and KartaView. SVI has enabled and enhanced a wide spectrum of applications in urban-related topics including spatial data infrastructure, public health, urban greenery, transportation, mobility, perception, socioeconomics, and so on (Branson et al, 2018;Cheng et al, 2018;Zhang et al, 2019a;Pelizari et al, 2021;Li et al, 2021;Yao et al, 2021;Inoue et al, 2022;Qiu et al, 2022;Hosseini et al, 2022;Byun and Kim, 2022;Guan et al, 2022).…”