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高密度城市街道空间疗愈环境营造研究——以澳门老城区街道空间为例

Research on Constructing a Healing Environment for the Street Spaces of a High-Density City: Using Street Spaces in Macao's Old City Area.

机构信息

Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, Taipa 999078, Macao.

School of Arts, Macau Polytechnic Institute, Taipa 999078, Macao.

出版信息

Int J Environ Res Public Health. 2020 Jul 2;17(13):4767. doi: 10.3390/ijerph17134767.

DOI:10.3390/ijerph17134767
PMID:32630722
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7369807/
Abstract

It is commonly recognized that street spaces in high-density cities are able to cause negative impacts in terms of residents' physical and mental health. This research intends to investigate and analyze how residents use street spaces in a high-density city in order to construct a healing environment for these street spaces. The research was conducted in Macao's old town by using spatial syntax methods to define the research areas, and implemented on-site observations that evaluated the age of the residents in the space and the conditions of their usage of the space. The study collected data through expert grading and employed the Analytic Hierarchy Process to calculate the weight of each indicator in order to attain accurate and objective research outcomes. The evaluation results indicate that the current Macao street spaces are poor healing environments. By analyzing the effective factors for constructing a healing environment in these street spaces, so that residents can get more space for healing when they use it, the paper aims to provide a model example for those who are involved with city governance, planning and design.

摘要

人们普遍认为,高密度城市中的街道空间会对居民的身心健康造成负面影响。本研究旨在调查和分析居民如何使用高密度城市中的街道空间,以便为这些街道空间构建一个有益于康复的环境。该研究在澳门老城区进行,使用空间句法方法来定义研究区域,并进行现场观察,评估空间中居民的年龄以及他们使用空间的条件。研究通过专家评分收集数据,并采用层次分析法计算每个指标的权重,以获得准确和客观的研究结果。评估结果表明,目前澳门的街道空间不利于康复。通过分析构建这些街道空间有益于康复环境的有效因素,使居民在使用街道空间时能获得更多有益于康复的空间,本文旨在为参与城市治理、规划和设计的人员提供一个范例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/4a4b531a216b/ijerph-17-04767-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/663254a75d64/ijerph-17-04767-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/b30d7f3b9301/ijerph-17-04767-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/bf1194d201f8/ijerph-17-04767-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/6d79f61755cc/ijerph-17-04767-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/d748e2e6a9f9/ijerph-17-04767-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/ed9c725a77f3/ijerph-17-04767-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/cd344ccda203/ijerph-17-04767-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/a06463d6d827/ijerph-17-04767-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/4a4b531a216b/ijerph-17-04767-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/663254a75d64/ijerph-17-04767-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/b30d7f3b9301/ijerph-17-04767-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/bf1194d201f8/ijerph-17-04767-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/6d79f61755cc/ijerph-17-04767-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/d748e2e6a9f9/ijerph-17-04767-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/ed9c725a77f3/ijerph-17-04767-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/cd344ccda203/ijerph-17-04767-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/a06463d6d827/ijerph-17-04767-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cefc/7369807/4a4b531a216b/ijerph-17-04767-g009.jpg

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本文引用的文献

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Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China.利用深度学习技术研究中国北京街道景观的绿色和蓝色空间及其与老年抑郁症的关系。
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环境设计干预对临床环境中健康状况的治疗影响:一项叙述性综述。
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