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长沙市游憩-居住功能空间格局及其匹配关系研究 被引量:2

Study on the Pattern Characteristics and Matching Relationship of Recreational and Residential Function Space in Changsha
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摘要 伴随新型城镇化和全面休闲化时代到来,游憩-居住两大功能空间关系研究成为新时期城市地域结构领域的重要关注议题。基于POI地理空间大数据,通过运用Ripley’s K函数、同位区位商、“不一致指数”定量模型和采用全局空间自相关、核密度估计等ArcGIS空间分析方法,探究了长沙市游憩-居住功能空间格局及其匹配关系特征。研究发现:(1)两大功能空间均具有显著的空间正相关性,形态格局差异鲜明且均呈以“空间极核”为导向的集聚模式,各类型功能空间集聚强度表现为“居住空间>文化休闲空间>体育健身空间>商业娱乐空间>自然生态空间”;(2)两大功能空间存在基于“距离-数量”的函数衰减关系,距离居住空间3000 m半径环带是游憩空间集中分布以及居民日常游憩休闲的重要活动范围;(3)居住空间具有“临近”游憩空间布局的指向特征,但反之不显著,两大功能空间呈单向吸引“非对称性”错位临近关系,居住空间临近不同类型游憩空间的指向强度呈“体育健身空间>文化休闲空间>自然生态空间>商业娱乐空间”位序格局;(4)游憩-居住功能空间协调关系具有较强的空间异质性,湘江西岸整体上优于东岸,岳麓区、望城区为游憩-居住协调型,天心区、芙蓉区、雨花区、开福区为游憩滞后居住型,长沙县为游憩超前居住型。本研究通过聚焦新型城镇化和全民休闲时代的城市游憩设施配置及其与居住空间的关系问题,将为丰富传承新背景趋势下的城市公共设施区位理论、城市空间结构理论等提供重要的理论探索方向,并为长沙市及国内同类大都市合理布局城市游憩休闲设施、优化调整城市空间结构和建设休闲宜居城市等提供科学参考。 In an era of rapid urbanization and competitive entertainment,the relationship between recreational and residential function space has become an important issue in the field of urban regional structure.Based on POI geographic big data,by using the quantitative models such as Ripley’s K function,colocation quotient,and inconsistency index,and adopting the ArcGIS spatial analysis methods for global spatial autocorrelation and kernel density estimation,this paper explores the spatial characteristics of recreational and residential function space and their relationships in Changsha.The results show that:(1)The two function spaces have significantly positive correlation at the spatial domain.The spatial patterns of different types of function spaces are significantly different but both present an agglomeration mode of"polar core of space".The agglomeration intensity of all types of function spaces is characterized by"residential space>cultural leisure space>sports and fitness space>commercial entertainment space>natural ecological space";(2)The two function spaces have a function attenuation relationship based on"istance-quantity",and the circle radius of 3000m from the residential space is an important spatial area for the centralized distribution of recreational space and the residents’daily recreation;(3)The residential space has the spatial feature of"approaching"recreational space,but the reverse is not significant.The two function spaces show the"asymmetric"relation of one-way attraction,and the attraction level different types of recreational spaces to residential spaces is in the order of"sports and fitness space>cultural leisure space>natural ecological space>commercial entertainment space";(4)There is strong spatial heterogeneity in the coordinated relation between recreational-residential function spaces.The west bank of Xiangjiang River is superior to the east bank.The Yuelu and Wangcheng Districts belong to recreational-residential coordination types.The Tianxin,Furong,Yuhua,and Kaifu districts are the recreation lagging residence type,while Changsha County is the type of the recreation advancing residence.This study focuses on the allocation of urban recreational facilities and their relationship with residential space in the era of new urbanization and national leisure,it provides theoretical exploration for enriching and inheriting urban public facilities location theory and urban spatial structure theory,and also provides scientific references for Changsha and other similar metropolises to rationally arrange urban recreational facilities,optimize urban spatial structure,and construct leisure livable city.
作者 杨友宝 李琪 韩国圣 马丽君 YANG Youbao;LI Qi;HAN Guosheng;MA Lijun(School of Tourism,Hunan Normal University,Changsha 410081,China;College of Fine Arts,Hubei University of Arts and Science,Xiangyang 441000,China;College of Business,Shandong University,Weihai 264209,China;College of Business,Xiangtan University,Xiangtan 411105,China)
出处 《地球信息科学学报》 CSCD 北大核心 2022年第8期1589-1603,共15页 Journal of Geo-information Science
基金 国家自然科学基金项目(41871123、41971188、42171215) 湖南省教育厅优秀青年基金项目(18B034) 湖南省自然科学基金项目(2021JJ30062)。
关键词 游憩空间 居住空间 格局特征 匹配关系 地理大数据 长沙市 recreational space residential space pattern characteristics matching relationship geographic big data Changsha city
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