地理学报 ›› 2015, Vol. 70 ›› Issue (8): 1215-1228.doi: 10.11821/dlxb201508003

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北京城市休闲商务区的时空分布特征与成因

朱鹤1,2,3(), 刘家明1,2(), 陶慧1,2,3,4, 李玏1,2,3, 王润5   

  1. 1. 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
    2. 中国科学院地理科学与资源研究所,北京 100101
    3. 中国科学院大学,北京 100049
    4. 湖北民族学院经济管理学院,恩施 445000
    5. 北京农学院园林学院,北京 102206
  • 收稿日期:2014-04-03 修回日期:2015-03-05 出版日期:2015-08-20 发布日期:2015-09-24
  • 作者简介:

    作者简介:朱鹤(1989-), 山东济南人, 博士研究生, 中国地理学会会员(S1100010339M), 主要从事城市旅游与旅游规划研究。E-mail: zhuhe12@mails.ucas.ac.cn

  • 基金资助:
    国家自然科学基金项目(41071110)

Temporal-spatial pattern and contributing factors of urban RBDs in Beijing

He ZHU1,2,3(), Jiaming LIU1,2(), Hui TAO1,2,3,4, Le LI1,2,3, Run WANG5   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
    4. Institute of Economics and Management, Hubei Minzu University, Enshi 445000, Hubei, China
    5. Landscape Agriculture School, Beijing University of Agriculture, Beijing 102206, China
  • Received:2014-04-03 Revised:2015-03-05 Online:2015-08-20 Published:2015-09-24
  • Supported by:
    National Natural Science Foundation of China, No.41071110

摘要:

城市休闲商务区(Recreational Business District, RBD)作为城市重要的游憩空间,为外来游客和城市居民提供休闲消费的场所,逐渐受到学界和业界重视。目前大多数研究还停留在概念层面,在分类、空间结构、分布规律、分异成因等方面上还缺乏一定的实证和定量研究。结合前人研究经验,重新定义RBD,并依据城市RBD的特征和属性将其分为大型购物中心、休闲商业街、城市休闲区三类。选取1990年、2000年、2014年三个时间截面对北京各类城市RBD点进行统计,采用基尼系数、空间插值、核密度分析、地理探测器等方法,结合ArcGIS软件,对北京城市RBD的时空分布特征和成因进行分析,得出以下结论:① 时序上看,北京城市RBD的数量和规模不断增加,增速变快,不同类型的RBD出现不同幅度的空间扩张;② 北京城市RBD的空间集聚程度不断加强,不同类型的RBD,存在一定的增幅和增速差异;③ 北京城市RBD的整体空间结构呈“单核聚集—双核发展—网状扩散”的发展模式;④ 城市RBD多选址在交通便利、临近旅游景区、居民和游客密度较高、地价相对较高的地区。⑤ 游客密度对各类型的城市RBD规模均有较大影响;对于不同类型的城市RBD,各因素对其规模的影响也有所不同。

关键词: 城市RBD, 时空格局, 分布特征, 成因, 北京

Abstract:

Urban RBD (Recreational Business District), a place where local residents and tourists go for leisure, tourism and consumption, is widely accepted as an indispensable component of urban recreation system in recent years. However, existing research in urban RBD puts an emphasis on its conceptual aspects (i.e., classification, influence, spatial structure), and empirical and quantitative studies have been largely ignored. Firstly, a summary about urban RBDs’ characteristics from the perspectives of location, scale, users, function, and culture was made. Based on previous literature and RBDs’ characteristics and attributes, this study divides urban RBD into three groups, namely: Large Shopping Center (LSC), Commercial Pedestrian Street (CPS), and Urban Leisure Area (ULA). Quantitative methods, such as Gini Coefficient, Spatial Interpolation, Kernel Density Estimation, and Geographical Detector, were employed to collect and analyse data of three types of urban RBDs in Beijing in 1990, 2000, and 2014, respectively, and the spatial-temporal evolution pattern as well as distribution characteristics of urban RBDs were analyzed with the aid of ArcGIS software. The results show: (1) The total number and scale of urban RBDs in Beijing have been expanding, with urban RBDs increasing by 8.20% and 7.26% per year in 1990-2000, and 2000-2014, respectively; (2) spatial agglomeration of urban RBD in Beijing keeps strengthening, and the trend that all types of urban RBDs in Beijing are spatially agglomerated is continuing; However, there exist some variances in terms of their growth speed and degree; (3) the spatial structure evolution model of urban RBDs in Beijing is as one core concentration—two cores development—multi-core diffusion; (4) According to the statistics from database concerning traffic, resident and tourist density, tourism attractions and land price in Beijing, the results showed that urban RBDs were generally located in areas with low traffic density, tourist attractions, high resident and tourist population density, and relatively high land valuations; (5) tourists density strongly influenced the scale of each urban RBD type, compared with other factors.

Key words: urban RBD, temporal-spatial pattern, distribution characteristics, contributing factor, Beijing