Abstract
This study examined public attitudes concerning the value of outdoor spaces which people use daily. Two successive analyses were performed based on data from common residents and college students in the city of Hangzhou, China. First, citizens registered various items constituting desirable values of residential outdoor spaces through a preliminary questionnaire. The result proposed three general attributes (functional, aesthetic and ecological) and ten specific qualities of residential outdoor spaces. An analytic hierarchy process (AHP) was applied to an interview survey in order to clarify the weights among these attributes and qualities. Second, principal factors were extracted from the ten specific qualities with principal component analysis (PCA) for both the common case and the campus case. In addition, the variations of respondents’ groups were classified with cluster analysis (CA) using the results of the PCA. The results of the AHP application found that the public prefers the functional attribute, rather than the aesthetic attribute. The latter is always viewed as the core value of open spaces in the eyes of architects and designers. Furthermore, comparisons of ten specific qualities showed that the public prefers the open spaces that can be utilized conveniently and easily for group activities, because such spaces keep an active lifestyle of neighborhood communication, which is also seen to protect human-regarding residential environments. Moreover, different groups of respondents diverge largely in terms of gender, age, behavior and preference.
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Shi, Jr., Zhao, Xm., Ge, J. et al. Relationship of public preferences and behavior in residential outdoor spaces using analytic hierarchy process and principal component analysis—a case study of Hangzhou City, China. J. Zhejiang Univ. - Sci. A 7, 1372–1385 (2006). https://doi.org/10.1631/jzus.2006.A1372
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DOI: https://doi.org/10.1631/jzus.2006.A1372
Key words
- Public preference
- Open space
- Analytic hierarchy process (AHP)
- Principal component analysis (PCA)
- Cluster analysis (CA)