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转型期北京社区公共空间对邻里交往的影响机理:本地居民与移民的对比分析 被引量:17

The Influence of Public Spaces on Neighborhood Social Interaction in Transitional Urban Beijing:Comparing Local Residents and Migrants
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摘要 利用2017年北京市36个小区的居民问卷调查数据,并基于POI数据测量小区尺度上步行道、公园、交通站点、商业设施、学校5类公共空间与设施的可达性,采用结构方程模型考察社区公共空间对本地居民、外来移民邻里交往的不同影响作用和机理。研究发现,在城市转型与居住分异的背景下:典型公共空间(步行道、公园)在促进邻里交往的作用上不如交通站点、商业设施等准公共空间设施;相较于外来移民,本地居民的邻里交往水平更容易受到社区公共空间的影响。 In the context of large-scale urban transformation, Chinese cities have faced great challenges of declining neighborly social interaction, social trust, and neighborhood social cohesion, due to the dismantling of the danwei system, residential suburbanization, and housing marketization. A large volume of literature has investigated the patterns and determinant of neighborhood social interaction and social cohesion, including comparing different experiences between local and migrant residents in Chinese cities. Urban geographers have mainly focused on the effects of neighborhood types, individual socio-demographic characteristics, neighborly social interaction in the context of urban spatial restructuring and housing marketization. Instead, planning scholars and practitioners have advocated the positive contributions of neighborhood-scale public spaces and facilities to promoting neighborhood social cohesion, although empirical studies along this line of inquiry have yielded only mixed findings. This research uses a large-scale questionnaire survey conducted in 36 neighborhoods in Beijing, China, to investigate whether proximity to public spaces and facilities promotes neighborly interaction, and whether such effects may differ between local residents and migrants. We utilize geo-coded POI dataset to capture proximity to 5 different types of neighborhood-scale public spaces and facilities, including sidewalks, parks, transit stations, commercial facilities, schools. Descriptive statistics reveal that the frequency of interaction with neighbors is higher among local residents than migrants, though both groups limit their neighborly interactions to superficial forms. We further adopt structural equation models to account for residential sorting, and find that: 1) quasi-public spaces(e.g. shops, transit stations) are more effective in promoting neighborly interaction, while most typical public spaces have no significant effect(e.g. sidewalks,parks);2) contrary to the conventional wisdom, a higher density of sidewalks in a neighborhood has a negative influence on neighborly social interaction;3) compared with local residents, migrants’ neighborly interaction is less subject to the influence of neighborhood built environment but more subject to the effects of individual socio-demographic characteristics. This research contributes to the ongoing debate of the role of public spaces in promoting neighborly interaction in the international literature with empirical evidence from the transitional urban context of China. It also broadens the scope of knowledge on the determinants of neighborhood social cohesion in Chinese cities with an emphasis on the role of neighborhood-scale built environment such as public spaces and facilities. Our findings offer policy implications for the planning practice for inclusive cities. We argue that simply building public open spaces may not be sufficient for promoting neighborly social interaction. Instead, planning physical spaces need to be combined with social programs aimed at bringing people into the spaces in order to enable meaningful encounters within the public spaces and thereby enhancing social cohesion.
作者 刘志林 王晓梦 马静 Liu Zhilin;Wang Xiaomeng;Ma Jing(School of Public Policy and Management,Tsinghua University,Beijing 100084,China;Hang Lung Center for Real Estate at Tsinghua University,Beijing 100084,China;Faculty of Geographical Science,Beijing formal University,Beijing 100875,China)
出处 《地理科学》 CSSCI CSCD 北大核心 2020年第1期69-78,共10页 Scientia Geographica Sinica
基金 国家自然科学基金项目(41571153)资助。
关键词 公共空间 邻里交往 社区融合 结构方程模型 北京 public spaces neighborly interaction neighborhood social cohesion structure equation models Beijing
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