基于POI大数据的重庆主城区多中心识别

doi:10.11849/zrzyxb.20170945

自然资源学报 ›› 2018, Vol. 33 ›› Issue (5): 788-800.doi: 10.11849/zrzyxb.20170945

所属专题：地理大数据

基于POI大数据的重庆主城区多中心识别

段亚明¹, 刘勇^2,*, 刘秀华¹, 王红蕾¹

1. 西南大学资源环境学院,三峡库区土地利用-重庆涪陵野外基地,重庆 400715;
2. 重庆大学建设管理与房地产学院,重庆 400045

收稿日期:2017-09-12 修回日期:2018-02-06 出版日期:2018-05-20 发布日期:2018-05-20
通讯作者: *刘勇（1980- ）,男,湖南岳阳人,副教授,主要从事城市地理、城市生态、土地管理。E-mail: ly6505@163.com
作者简介:段亚明（1994- ）,男,河南洛阳人,硕士研究生,研究方向为土地利用与规划。E-mail: 527931407@qq.com
基金资助:
国家自然科学基金项目（41771534）; 西南大学资源环境学院“光炯”创新实验项目(2016013)

Identification of Polycentric Urban Structure of Central Chongqing Using Points of Interest Big Data

DUAN Ya-ming¹, LIU Yong², LIU Xiu-hua¹, WANG Hong-lei¹

1. College of Resources and Environment & Field Base in Fuling, Chongqing, Land Use of Three Georges Reservoir Area, Southwest University, Chongqing 400715, China;
2. School of Construction Management and Real Estate, Chongqing University, Chongqing 400045, China

Received:2017-09-12 Revised:2018-02-06 Online:2018-05-20 Published:2018-05-20
Supported by:
National Natural Science Foundation of China, No. 41771534; The Science and Technology Innovation “Guangjiong” Project of Southwest University, No. 2016013.

摘要/Abstract

摘要： 我国大量城市在其规划中均提出多中心的空间发展战略,但多中心结构演变是否达到规划预期,能否通过有效途径识别多中心结构和功能,成为目前学术界研究的热点。相比于人口、用地、产业等传统调查和识别方法,城市大数据的出现为多中心识别提供了契机,尤其是基于POI（Point of Interest）大数据的分析比传统方法更加准确高效。论文以重庆主城区40余万条POI数据为基础,利用核密度分析、自然断点法和邻近分析等方法,根据整体及不同类型POI数据的空间分布特征与聚集程度,识别城市总体及不同职能的多中心结构及其影响范围。结果表明：重庆作为山地城市,在自然约束和规划引导下,呈现出明显的“多中心、组团式”结构。不同职能类型的城市中心也呈现明显的多中心分布特征。重庆的主副中心在内环以内集聚,但不同中心的发育程度及其空间聚集度差异显著。外围新兴的西永、茶园等副中心发展相对滞后,城市要素集聚功能有待加强。

关键词: POI, 城市空间结构, 多中心, 重庆

Abstract: Many Chinese cities have proposed the strategy of polycentric urban development in their master plans. There are still questions about polycentric urban development to be answered: does the evolution of polycentric urban form conform with the master plans? How to identify and measure the morphological and functional urban forms? Previous research has used traditional data to identify the polycentric form, such as population, land use and industry. Nowadays, the emergence of urban big data provides an opportunity to improve the accuracy of identifying urban form and structure. The new methods based on urban big data such as POIs (Points of Interest) can provide accurate and efficient estimation of polycentric urban structure compared with traditional methods. Using 408 768 POIs data in Chongqing urban area, this paper identified the overall pattern of polycentric structure and the polycentric structures of different functions by considering the spatial distribution and the degree of aggregation of POIs. This paper found that Chongqing as a mountain city, constrained by natural condition and stimulated by urban planning, has formed a typical “polycentric form composed of multiple subcenters and clusters”. The distribution of POIs shows that the subcenters have relatively comprehensive urban functions, such as financing, shopping, dwelling and recreation. The main center and several sub-centers in Chongqing gather within the inner ring road. The sub-centers have significant differences in terms of urban development and spatial aggregation. The peripheral subcenters such as Xiyong and Chayuan have lagged behind other subcenters. Therefore, urban aggregation need to be strengthened in the near future.

Key words: Chongqing, POI, polycentric form, urban spatial structure

中图分类号:

TU984.11⁺3

段亚明, 刘勇, 刘秀华, 王红蕾. 基于POI大数据的重庆主城区多中心识别[J]. 自然资源学报, 2018, 33(5): 788-800.

DUAN Ya-ming, LIU Yong, LIU Xiu-hua, WANG Hong-lei. Identification of Polycentric Urban Structure of Central Chongqing Using Points of Interest Big Data[J]. JOURNAL OF NATURAL RESOURCES, 2018, 33(5): 788-800.

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基于POI大数据的重庆主城区多中心识别

Identification of Polycentric Urban Structure of Central Chongqing Using Points of Interest Big Data

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