地理学报 ›› 2020, Vol. 75 ›› Issue (8): 1585-1602.doi: 10.11821/dlxb202008003

• 人口与城市研究 • 上一篇    下一篇

基于手机信令数据的上海市不同住宅区居民就业空间研究

王德1(), 李丹2, 傅英姿3   

  1. 1.同济大学建筑与城市规划学院,上海 200093
    2.中国城市规划设计研究院上海分院,上海 200335
    3.江苏省城市规划设计研究院,南京 210036
  • 收稿日期:2018-12-21 修回日期:2020-05-13 出版日期:2020-08-25 发布日期:2020-10-25
  • 作者简介:王德(1963-), 男, 教授, 博导, 主要从事城市规划方法论、空间与行为、城市大数据、城市模型领域的教学与研究。E-mail:dewang@tongji.edu.cn
  • 基金资助:
    国家自然科学基金项目(41771170)

Employment space of residential quarters in Shanghai: An exploration based on mobile signaling data

WANG De1(), LI Dan2, FU Yingzi3   

  1. 1. Collage of Architecture and Urban Planning, Tongji University, Shanghai 200093, China
    2. Shanghai Branch, China Academy of Urban Planning & Design, Shanghai 200335, China
    3. Jiangsu Institute of Urban Planning and Design, Nanjing 210036, China
  • Received:2018-12-21 Revised:2020-05-13 Online:2020-08-25 Published:2020-10-25
  • Supported by:
    National Natural Science Foundation of China(41771170)

摘要:

上海市住宅区地域分布广,住房属性、就业环境与交通环境各异,就业活动空间呈现不同的组织与分布模式。利用2014年手机信令数据识别上海市移动手机用户的居住地和工作地,选取253个典型住宅区为分析样本,将样本住宅区居民就业地核密度分布、通勤距离—概率分布等可视化方法结合就业地特征量化因子,以综合归纳上海市住宅区就业空间分布模式,分析影响因子及形成机制。研究揭示了上海市住宅区就业空间的几类典型模式,包括单中心、带状、双中心、多中心或分散,以及模式间的过渡类型。在就业空间影响因子方面,就业中心分布与轨道交通线路是主导因子,影响就业空间模式的整体分布;居住区类型特征是次要因子,导致局部就业空间模式变异。研究结论可为上海市空间结构优化布局、产业空间调整、轨道交通建设及住房建设提供参考。

关键词: 就业空间, 空间模式, 住宅区, 手机信令数据, 上海市

Abstract:

The employment space of a residential quarter is widely affected by factors including housing properties and the neighboring supply of transportation and employments, which forms numerous spatial diagrams. Investigating and understanding the exemplar patterns of the employment space are thus crucial before we make targeted planning policies. Taking Shanghai as a case study and using mobile signaling data, this paper endeavors to extract typical patterns and distinguish the key factors. After the users' home and work locations are inferred, the employment space of 253 residential quarters is characterized in combination of the kernel densities estimation and the probability density analysis of commuting distance. We extracted five spatial patterns, namely, single-nucleated, ribbon-shaped, dual-nucleated, multi-nucleated, and decentralized, and highlighted several transitional patterns between them. Moreover, several factors are considered significant: the accessibility of employment centers and subway stations is a dominant factor that determines the global distribution of the patterns, while housing type is the secondary factor which leads to local variations and transitions. Finally, an integrated portrait covering the whole city area is summarized in terms of how different factors can be combined to explain the employment space of any quarters. We believe that our findings can help make planning policies regarding spatial structure optimization, industrial spatial adjustment, and housing development.

Key words: employment space, spatial pattern, residential quarter, mobile signaling data, Shanghai