热带地理 ›› 2020, Vol. 40 ›› Issue (2): 217-228.doi: 10.13284/j.cnki.rddl.003224

• “地理空间智能技术及应用”专题 • 上一篇    下一篇

基于出租车轨迹数据的城市空间结构变化研究——以深圳市为例

庄浩铭, 刘小平()   

  1. 中山大学 地理科学与规划学院,广州 510275
  • 收稿日期:2019-10-28 修回日期:2020-03-02 出版日期:2020-03-10 发布日期:2020-05-15
  • 通讯作者: 刘小平 E-mail:liuxp3@mail.sysu.edu.cn
  • 作者简介:庄浩铭(1994–),男,广东汕头人,博士研究生,主要研究方向为地理大数据挖掘,(E-mail) csuhaoming@163.com。
  • 基金资助:
    国家自然科学基金(41030495)

Identifying Changes in Urban Spatial Structure Using Taxi Trajectory Data:
A Case Study in Shenzhen

Zhuang Haoming, Liu Xiaoping()   

  1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Received:2019-10-28 Revised:2020-03-02 Online:2020-03-10 Published:2020-05-15
  • Contact: Liu Xiaoping E-mail:liuxp3@mail.sysu.edu.cn

摘要:

在地理大数据大量涌现的背景下,利用深圳市2009年5月和2016年9月2期出租车轨迹大数据,通过构建2层空间网络,并使用Infomap社区发现算法,发现网络中的全局空间社区和局部空间社区。通过对比空间社区的变化,并结合遥感数据和规划政策等,探讨深圳市长时期多层空间结构的动态变化,揭示了城市基础设施建设以及规划政策制定等对城市空间结构的影响。结果表明:全局尺度下,城市的空间结构趋向于紧凑式发展,如深圳市郊区的小空间社区合并成了5个大空间社区,这与深圳2020年总体规划有较大的关联。局部尺度下,城市的空间结构趋向于多中心式发展,如围绕深圳高铁北站和南山中心的2个空间社区分裂成了多个小空间社区,这与重大基础设施建设和经济发展有较大的关联。

关键词: 出租车轨迹, 社区发现, 空间结构, 深圳市

Abstract:

The concept of urban spatial structure refers to the inherent structure formed by interactions between people and places. Studying urban spatial structure (especially its dynamic characteristics) is of considerable significance for understanding and managing cities. This study characterized the long-term dynamics of an interaction-based urban spatial structure using a large-scale taxi trajectory dataset from Shenzhen, China, for May 2009 and September 2016. Spatial networks were built to model intra-city spatial interactions at different times in order to extract the dynamic spatial structure. Due to the differences between the global spatial structure and the local spatial structure of the city, two-level hierarchical spatial networks were built by separating long- and short-distance trips. The cut-off point for hierarchical partitioning was set as 5 km in both 2009 and 2016 by comparing the coefficient of determination (R 2) for the fitted probability distribution functions of the trip distances. Furthermore, the Infomap community detection algorithm was applied to detect global and local spatial communities in the network. By comparing changes in spatial communities and combining remote sensing images with planning policies, this study characterized dynamic changes in Shenzhen’s long-term multi-scale spatial structure and revealed the impacts of infrastructure construction and planning policy on the urban spatial structure. The results showed that the spatial structure of Shenzhen underwent dramatic changes from 2009 to 2016. 1) On a global scale, urban spatial structures tend to be compact. For example, numerous small spatial communities in the suburbs of Shenzhen have merged into five large spatial communities the same as planned functional clusters, which has relevance to the Shenzhen 2020 master plan. 2) The spatial form of communities has also undergone significant changes on a global scale. For example, the shape of the community connecting the Shenzhen urban area and Shenzhen airport has changed from “|” to “U”. This is related to the opening of the Guangshen Yangjian Expressway and the expansion of the Shenzhen airport, reflecting the impact of major transportation infrastructure development on the urban spatial structure. 3) On a local scale, urban spatial structures tend to develop in a polycentric manner. For example, in Shenzhen, the two spatial communities surrounding the North High-speed Rail Station and the Nanshan Center were split into multiple spatially small communities, indicating a strong relationship with major infrastructure construction and economic development. This study verified the effectiveness of Shenzhen’s administrative division adjustment, 2020 master plan, high-speed railway station, airport expansion, and arterial expressway construction from 2009 to 2016. The results provide a valuable reference for planning implementation assessment and impact assessment of infrastructure construction, providing support for urban traffic management.

Key words: Taxi trajectory, community detection algorithm, spatial structure, Shenzhen

中图分类号: 

  • P208