地理研究 ›› 2021, Vol. 40 ›› Issue (2): 477-494.doi: 10.11821/dlyj020200074

• 研究论文 • 上一篇    下一篇

基于多源数据的城市功能区精细化研究——以北京为例

杨振山1,2(), 苏锦华3, 杨航1,2, 赵永宏4   

  1. 1.中国科学院地理科学与资源研究所 区域可持续发展分析与模拟实验室,北京 100101
    2.中国科学院大学资源与环境学院,北京 100049
    3.中国人民大学统计学院,北京 100872
    4.西安外国语大学旅游学院 人文地理研究所,西安,710128
  • 收稿日期:2020-02-06 接受日期:2020-09-02 出版日期:2021-02-10 发布日期:2021-04-10
  • 作者简介:杨振山(1979-),新疆博乐人,博士,研究员,博士生导师,主要从事城市与区域可持续发展研究。 E-mail: yangzs@igsnrr.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项(A类XDA19040402);国家自然科学基金重点项目(41530751);中国科学院青年创新促进研究会会员项目(Y201815);中国科学院地理科学与资源研究所可桢杰出青年学者计划(2016RC101)

ng urban functional areas based on multi-source data: A case study of Beijing

YANG Zhenshan1,2(), SU Jinhua3, YANG Hang1,2, ZHAO Yonghong4   

  1. 1. Key Lab of Regional Sustainable Development of Modelling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Statistics, Renmin University of China, Beijing 100872, China
    4. School of Tourism & Research Institute of Human Geography, Xi'an International Studies University, Xi'an 710128, China
  • Received:2020-02-06 Accepted:2020-09-02 Online:2021-02-10 Published:2021-04-10

摘要:

城市功能分区研究在时空尺度上不断细化,多源数据融合有利于推动城市功能分区研究的精细化发展。本文对比国内外城市功能区研究中对多种新型地理数据的内涵挖掘和应用,通过融合北京市2017年14400个栅格区域的手机信令数据和2016年高德地 图380975条兴趣点(POI)数据,量化区域功能使用强度的日夜差异和内部功能混杂程度,完成区域主导功能类型判定及功能混合度评价,并对北京城市功能区划分结果进行分析与验证,主要结论:① 北京的日间活跃区域面积是夜间活跃区域面积的3倍,其中餐饮、生活等服务设施的夜间使用强度更高,金融、旅游、公共服务设施的日间使用强度更高;② 北京市面积占比最大的三类功能区是旅游(28.2%)、居住(12.1%)、交通(11.4%),面积占比最小为金融功能区(2.8%),在空间上呈现出旅游、金融、公共功能区聚集,其他功能区具有离散分布的特征;③ 居住、餐饮、生活等功能服务存在较强的依赖性,而旅游、企业功能服务存在较强的排他性,北京中心城区内除旅游功能区外均存在不同程度的复合功能特征,高度功能混合区约占研究区域的24.6%。功能区划分结果对于北京城市规划具有较强的现实意义,也为今后深入研究城市功能区提供了有效方法和新的视角。

关键词: 城市功能区, 多源数据, 人口热度, 混合功能区, 北京

Abstract:

It has been a trend for the study of urban functional areas to employ multi-source at a finer tempo and/or spatial scale to contribute a niche understanding of the structure and content of the area. Drawing on advancement of technological development of new type and new sources of geo-coded data, the paper proposes a set of indicators to understand urban functional areas, including the intensity of the use of the area, the difference in the use between day and night, and the degree of multifunctionality. The empirical research is taken in Beijing and these indicators are derived by integrating the mobile phone signaling data of 14400 grid areas in the urban area in 2017 and 380975 points of interest (POI) data from Gaode map in 2016. The main research conclusions are as follows. (1) Every standard area in this paper has a square of 250 m×250 m. The number of Beijing's daytime active areas is three times that of nighttime active areas. The nighttime use intensity of restaurant, living, and other service facilities is higher, and the daytime use intensity of financial, tourism, and public service facilities is higher. (2) Tourism (28.2%), residence (12.1%), transportation (11.4%) are the three types of functional areas with the largest proportion of Beijing area, and the smallest area proportion is financial function district (2.8%), showing that the spatial characteristics of tourism, finance, and public functional areas gather, and other functional areas' distribution presents scattered characteristics. (3) Residential, restaurants, living, and other functional services are strongly dependent on each other, while tourism and enterprise functional services show strong exclusivity to other functional types. Except for the tourism functional areas, there appears an obviously different functional mixing pattern in the central urban area of Beijing. In this paper, the area with the highest function mixing degree (greater than 0.98) is defined as the highly mixed functional area, which accounted for 24.6% of the study area. The result of the functional area divisions has a strong practical significance for Beijing urban planning, and also provides an effective method and a richer perspective for the future in-depth study of urban functional areas.

Key words: urban functional area, multi-source data, population density, mixed functional area, Beijing