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Scientific interest in how residential patterns affect both people’s subjective sense of safety and their behavior is increasing. The surge of gated communities in the world has changed the way we live to a great extent. Research on the gated development trend in postmodern cities is still limited; therefore, the purpose of this study was to analyz...
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Context 1
... R2 value was used to evaluate the overall explanatory power of the structural model, and the Q2 value and the path coefficient β value were used to evaluate the predictive correlation. These results showed that physical function (β = 0.284, p < 0.001), socioeconomic function (β = 0.35, p < 0.001), behavior function (β = 0.316, p < 0.001), and symbolic function (β = 0.356, p < 0.001) all have a significant impact on the perceptions of gated enclaves and thus support H1a to H1d (Figure 4). In addition, the study found that the relationship between gate control and campus safety (β = 0.584; t-value = 13.793; ...
Context 2
... results showed that physical function (β = 0.284, p < 0.001), socioeconomic function (β = 0.35, p < 0.001), behavior function (β = 0.316, p < 0.001), and symbolic function (β = 0.356, p < 0.001) all have a significant impact on the perceptions of gated enclaves and thus support H1a to H1d (Figure 4). In addition, the study found that the relationship between gate control and campus safety (β = 0.584; t-value = 13.793; ...
Citations
... SVI, as an actual image of the city, is an important data source used for a variety of urban studies such as spatial data infrastructure, greenery, health and well-being, urban morphology, transportation and mobility, walkability, socio-economic studies, real estate, and urban perception (Biljecki and Ito 2021;Siriaraya et al. 2020). It has become a popular method in recent research into both urban environments and psychological perceptions (Li et al. 2022a, b, c). ...
... Specifically, in this research, "connectivity" was different from "walkability" focusing on "sidewalk" (Dai et al. 2021). "Enclosure" was also defined by a combined VEP value of "building", "tree", and "fence" in an SVI, separating university campuses from urban space (Kan et al. 2017;Li et al. 2022a, b, c). In this way, this research has provided new knowledge about the relationships between domain-specific VEPs and PSSs in university landscape design. ...
Various types of streetscapes have been the subject of past research, with university campus planning being identified as one example where a psychological impact has been observed. However, due to the visual complexity of campus streetscapes, little or no clear approach is available to quantitively assess their potential impacts. Furthermore, collecting empirical data about environmental stress levels in urban spaces remains a significant challenge. In response, this chapter presents an “intelligent” approach to estimating the relationship between street view imagery (SVI) properties and perceived stress in seven university campuses in China. Specifically, an automatic, semantic segmentation method is used to measure the visual properties of 6056 SVIs—the visual element proportions (VEPs) of design elements and visual features. Then, a human–machine adversarial model using a random forest is applied to predict the perceived stress scores (PSSs) of SVIs. Through this combination of a computer vision technique and machine learning, this research identifies the various impacts of visual elements on PSSs. The research also tests the significance of three visual features holistically contributing to lower stress levels in campus design. This chapter concludes with a discussion of the findings and a contribution to architectural and urban studies.
