首页|城郊大型保障房住区的社会空间特征与问题研究——以南京岱山为例

城郊大型保障房住区的社会空间特征与问题研究——以南京岱山为例

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保障房住区是多分布于城市郊区的大型居住空间,因其住宅性质与居住群体的特殊性而得到持续关注.保障房住区居民的社会、经济、文化属性,保障房住区对居民就业、生活、行为模式的影响,以及保障房建设引发的居住空间分异和资源分配差异等城市社会空间问题,渐成为城市社会地理领域的研究热点.针对目前保障房住区社会空间研究中存在的大数据、全样本与多维度、小尺度分析不足的缺憾,论文选择南京最大的保障房片区——岱山为研究对象,以片区内21个居住小区(组团)为空间单元,依托手机用户画像和调查问卷数据,结合社会、空间、行为、感知等多维度要素,开展城市郊区大型保障房住区社会空间特征与问题研究.研究发现,岱山片区表现出弱势群体集聚、服务配套不足、通勤负担较重和社区归属感偏弱等特点;岱山片区整体社会空间相对均质,但公租房集中的小区居民家庭经济条件相对最差,而社区环境和住宅品质较好的小区则居住着部分较高收入的年轻白领;未来发展需要警惕可能存在的贫困延续、社区衰败和社会歧视等风险.基于大小数据融合与细微空间尺度的社会空间研究,可以有效弥补社会经济统计和抽样调查问卷等传统数据源的不足,能更细致刻画、更真实揭示城市社会空间现象与问题.
Social-spatial characteristics and issues of large affordable housing area in urban outskirts:A case study of Daishan,Nanjing City
Urban affordable housing communities are often located in suburbs,and these large communities have increasingly become focal points in urban social geography research due to the unique social,economic,and cultural attributes of their residents,the impact of affordable housing communities on residents'employment,lifestyle,and behavioral patterns,as well as the urban social-spatial issues such as residential spatial differentiation and resource distribution disparities induced by the construction of affordable housing.To address the deficiencies in relevant existing studies,including the lack of big data,comprehensive samples,and multi-dimensional,small-scale analyses,this study selected Daishan,the largest affordable housing area in Nanjing City,as the research object.Using 21 residential communities(groups)within the area as spatial units,and utilizing mobile user profiles and questionnaire survey data,combined with social,spatial,behavioral,and perceptual factors,this study investigated the social-spatial characteristics and issues of large affordable housing districts on urban outskirts.The findings reveal that the Daishan area exhibits characteristics such as aggregation of vulnerable groups,insufficient service facilities,heavy commuting burdens,and weak community belonging.While the social space across the area is relatively homogeneous,residents of communities with high concentration of public rental housing have the poorest economic conditions.Conversely,areas with better environmental and residential quality tend to house high-income young professionals.Future development in the area must pay attention to potential risks such as the perpetuation of poverty,community decline,and social discrimination.By integrating big data and conventional data sources to explore social spaces on a fine-grained spatial scale,this approach effectively compensates for the deficiencies of traditional data sources,such as socioeconomic statistics and sample questionnaire survey.This enables a nuanced exploration of urban social-spatial phenomena,providing detailed and authentic insights into the complexities and issues within social spaces.

social spaceresidential differentiationaffordable housingDaishan area,Nanjing City

宋伟轩、王富平、汪毅、汪徽

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河海大学地理与遥感学院,流域水土过程省高校重点实验室,南京 211100

中国科学院南京地理与湖泊研究所,南京 210008

中国科学院大学,北京 100049

南京市规划设计研究院,南京 210005

南京林业大学风景园林学院,南京 210037

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社会空间 居住分异 保障房 南京岱山

2024

地理科学进展
中国科学院地理科学与资源研究所 中国地理学会

地理科学进展

CSTPCDCSSCI北大核心
影响因子:2.458
ISSN:1007-6301
年,卷(期):2024.43(12)