地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1320-1329.doi: 10.12082/dqxxkx.2020.190717

• 大数据与城市管理 • 上一篇    下一篇

基于累计机会可达性的北京城市公共服务设施复合功能识别

湛东升1,*(), 谢春鑫1, 张文忠2, 丁亮3, 许婧雪2, 甄茂成4   

  1. 1. 浙江工业大学管理学院,杭州 310023
    2. 中国科学院地理科学与资源研究所 中国科学院区域可持续发展分析与模拟实验室,北京 100101
    3. 浙江工业大学建筑工程学院,杭州 310023
    4. 北京城市系统工程研究中心,北京 100044
  • 收稿日期:2019-11-25 修回日期:2020-03-02 出版日期:2020-06-25 发布日期:2020-08-25
  • 通讯作者: 湛东升 E-mail:zhands@126.com
  • 作者简介:湛东升(1987— ),男,安徽寿县人,副教授,主要从事城市与区域发展研究。
  • 基金资助:
    教育部人文社会科学研究青年基金项目(20YJCZH221);国家自然科学基金项目(41871170);中国科学院区域可持续发展分析与模拟重点实验室开放基金项目

Identifying Mixed Functions of Urban Public Service Facilities in Beijing by Cumulative Opportunity Accessibility Method

ZHAN Dongsheng1,*(), XIE Chunxin1, ZHANG Wenzhong2, DING Liang3, XU Jingxue2, ZHEN Maocheng4   

  1. 1. School of Management, Zhejiang University of Technology, Hangzhou 310023, China
    2. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    3. College of Civil Engineering and Architecture, Zhejiang University of Technology, Hangzhou 310023, China
    4. Beijing Research Center of Urban System Engineering, Beijing 100044, China
  • Received:2019-11-25 Revised:2020-03-02 Online:2020-06-25 Published:2020-08-25
  • Contact: ZHAN Dongsheng E-mail:zhands@126.com
  • Supported by:
    Humanities and Social Sciences Research Program of the Ministry of Education(20YJCZH221);National Natural Science Foundation of China(41871170);Supported by Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences

摘要:

城市时空大数据技术的快速发展和应用,为城市功能区识别提供了新的数据基础和技术手段,但专门关于城市公共服务设施复合功能的研究还相对较少。基于北京市9大类公共服务设施的空间点数据,综合考虑不同类型公共服务设施等级和品质特征,采用累计机会方法对1 km×1 km格网尺度的北京城市公共服务设施可达性进行了综合评价,在此基础上重点分析了北京城市公共服务设施复合功能特征与影响因素。研究表明:① 北京城市公共服务设施累计机会空间分布存在明显的中心集聚特征,但不同类型公共服务设施的空间分布模式和覆盖范围却有所区别;② 北京城市公共服务设施功能区可以划分为单一功能、单一化的复合功能、2种复合功能、3种复合功能和均衡化的复合功能等5大类型;③ 人口密度、距市中心距离、土地价格和经营性为主设施的累计机会可达性是影响北京城市公共服务设施复合功能的重要因素。研究结论对进一步细化城市功能区研究和促进北京城市公共服务设施空间结构优化具有科学启示作用。

关键词: 大数据, 城市功能区, 公共服务设施, 复合功能, 累计机会, 可达性, 影响因素, 北京

Abstract:

Rapid development and application of urban space-time big data have provided a new data environment and technical means for identifying urban functional areas. However, the literature regarding mixed urban functional areas detection in the field of urban public service facilities is still lacking. Using spatial point-level data of nine categories of urban public service facilities in Beijing with consideration of their rank and quality, this paper employed cumulative opportunity method to measure urban public service facilities' accessibility in Beijing at 1 km×1 km grid scale, and further emphatically analyzed the mixed functions and influencing factors of urban public service facilities. The results show that the spatial distribution of urban public service facilities' cumulative opportunity accessibility in Beijing presented a similar characteristic of central agglomeration. While the specific spatial patterns and coverage areas of urban public service facilities varied by their categories. In addition, functional areas of urban public service facilities in Beijing were divided into the five types: single functional areas, mixed functional areas with single facility oriented, mixed functional areas with two types of oriented facilities, mixed functional areas with three types of oriented facilities, and balanced mixed functional areas. Finally, population density, distance to the city center, land price, and cumulative opportunity accessibility of several commercial-oriented facilities were important factors affecting the existence of mixed function of urban public service facilities in Beijing. Our findings provide insights for both urban functional studies and spatial optimization of urban public service facilities in Beijing.

Key words: big data, urban functional area, public service facilities, mixed functions, cumulative opportunity, accessibility, influencing factors, Beijing