主管单位:中国科学技术协会
主办单位:中国地理学会
承办单位:华东师范大学

世界地理研究 ›› 2020, Vol. 29 ›› Issue (1): 86-95.DOI: 10.3969/j.issn.1004-9479.2020.01.2018442

• 城市与区域 • 上一篇    下一篇

长江中游城市群城市活力水平空间格局及影响因素

毛炜圣(), 钟业喜()   

  1. 江西师范大学地理与环境学院,南昌 330022
  • 收稿日期:2018-10-01 修回日期:2018-12-31 出版日期:2020-01-20 发布日期:2022-01-22
  • 通讯作者: 钟业喜
  • 作者简介:毛炜圣(1995-),男,硕士研究生,研究方向为经济地理与区域创新,E-mail:maoweisheng@hotmail.com
  • 基金资助:
    国家自然科学基金项目(41561025)

Spatial pattern and influencing factors of urban vitality in the middle reaches of the Yangtze River

Weisheng MAO(), Yexi ZHONG()   

  1. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
  • Received:2018-10-01 Revised:2018-12-31 Online:2020-01-20 Published:2022-01-22
  • Contact: Yexi ZHONG

摘要:

地理“大数据”的出现为研究城市问题提供了新的契机。基于兴趣点(POI)和位置服务(LBS)的地理“大数据”,采用空间计量模型、数理统计分析等方法,尝试探讨了长江中游城市群城市活力水平及其影响因素。结果表明:①长江中游城市群城市活力整体处于较低水平,武汉、南昌和长沙中心地位突出,城市群边缘形成片状活力“洼地”的空间格局;②长江中游城市群城市活力与经济发展水平存在耦合关系,低度耦合城市少,较高度耦合和高度耦合城市较多;③高校资源、经济密度、基础设施水平和信息化程度4个因素是影响长江中游城市群城市活力的主要因素,各因素的影响效应具有空间异质性。

关键词: 城市活力, 地理“大数据”, GWR模型, 长江中游城市群

Abstract:

The emergence of geographic “big data” provides a new opportunity to study urban issues. Based on the geographic “big data” of interest point (POI) and location service (LBS), spatial measurement model and mathematical statistics analysis are used to explore the urban vitality level and its influencing factors in the UAMRYR. The results show that: (1) the urban vitality of the urban agglomerations in the UAMRYR is at a low level, and the centers of Wuhan, Nanchang and Changsha are prominent, and the spatial pattern of flaky vitality is formed at the edge of the urban agglomeration. (2) There is a coupling relationship between urban vitality and economic development level in the UAMRYR, and there are few low-coupling cities. There are more cities with higher degrees of coupling and highly coupled cities.(3)The four factors of university resources, economic density, infrastructure level and informatization degree are the main factors affecting the urban vitality of the UAMRYR. The influence effect of each factor has spatial heterogeneity.

Key words: urban vitality, geographic “big data”, GWR model, UAMRYR