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Optimizing community green roof spaces in high-density cities: a K-modes clustering algorithm based analysis of resident preferences and spatial configuration

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2024年

作者： Cui, Yi-Tong Zhang, Guang-Zhu Zhejiang Univ Finance & Econ Dongfang Coll Sch Culture Commun & Design Haining Peoples R China Catholic Univ Korea Dept Publ Performing Arts & Culture Songsim Global Campus Bucheon Si South Korea Northeast Forestry Univ Sch Civil Engn & Transportat Harbin 150040 Peoples R China

Green roofs are an important part of the ecological system in high-density cities and a crucial component of community public green spaces. Analyzing preferences of residents during spatial planning can help improve their satisfaction and efficiency in using community rooftop spaces. This study uses K-modes algorithm to perform cluster analysis on questionnaires from 699 residents, and summarizes user profiles with significant characteristics by combing the comprehensive ratings of residents regarding different roof space functions. The study shows that differences in preferences among people are not only related to demographic characteristics but also to their interests and environmental perceptions. For example, low-to-middle-income groups, being price-sensitive, tend to reduce energy consumption expenditure. The highly educated population, driven by social needs, shows a clear preference for activity spaces and sports facilities. The elderly population, emphasizing healthy eating and rural memories, gives higher ratings to agricultural spaces. Finally, this study explores the correlation between certain resident characteristics and their preferences for green roof space functions, and proposes a "1+X" spatial configuration strategy dominated by Landscape Leisure Space, with Ecological Low-carbon Space, Agricultural Production Space, and Activity Gathering Space as options, in order to optimize community green roof spaces guided by resident needs.

关键词： High-density communities green roofs K-modes clustering algorithm user profiles spatial function preference optimize spatial configuration

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