地理研究 ›› 2017, Vol. 36 ›› Issue (1): 134-148.doi: 10.11821/dlyj201701011

• 研究论文 • 上一篇    下一篇

上海市典型就业区的通勤特征分析与模式总结——张江、金桥和陆家嘴的案例比较

田金玲(), 王德(), 谢栋灿, 朱玮   

  1. 同济大学建筑与城市规划学院,上海 200092
  • 收稿日期:2016-08-11 修回日期:2016-12-14 出版日期:2017-01-20 发布日期:2017-01-20
  • 作者简介:

    作者简介:田金玲(1991- ),女,湖北钟祥人,硕士,研究方向为城市规划方法与技术。E-mail: 895153443@qq.com

  • 基金资助:
    国家自然科学基金项目(51378363)

Identifying the commuting features and patterns of typical employment areas in Shanghai using cellphone signaling data: A case study in Zhangjiang, Jinqiao and Lujiazui

Jinling TIAN(), De WANG(), Dongcan XIE, Wei ZHU   

  1. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
  • Received:2016-08-11 Revised:2016-12-14 Online:2017-01-20 Published:2017-01-20

摘要:

通勤问题一直是城市研究的重点。由于普查数据缺少工作地信息,长期以来通勤研究依赖问卷调查,信息和通讯技术的发展为该问题的研究提供了新的数据、思路和方法。利用2014年上海市某两周的手机信令数据,以张江高科技园区、金桥经济技术开发区和陆家嘴金融贸易区为例,对其就业与居住、通勤时空间特征及地铁通勤进行分析。结果表明,张江的职住比最低,内部通勤比例最高,居住与就业靠近,是自我平衡类型;金桥园区内部缺乏居住,是单一生产类型;虽然陆家嘴是就业居住高密度聚集区,但就业者的居住地分布在中心城区,是城市互动类型。最后,分析了区位、轨道交通、规模、产业及园区内外用地六个因素对不同模式就业区形成的影响,并提出了相应规划建议。

关键词: 手机信令数据, 就业区, 通勤特征, 上海市

Abstract:

Commuting features which are of great significance in urban Study have long been concerned. Due to the lack of information about working place in Chinese census data, researches on this topic have relied on traditional data such as questionnaire data in a rather long time. As a result, studies on temporal-spatial commuting characteristics are not refined enough yet. The development of information and communication provides new data, concepts and methods for commuting study, which offers an opportunity for a more comprehensive and in-depth understanding of commuting features. Using the cellphone signaling data within two consecutive weeks in 2014 in Shanghai, China, this paper selects three typical employment areas, which are Zhangjiang Hi-tech Park, Jinqiao Economic Development Zone as well as Lujiazui Finance and Trade Zone to analyze the commuting features respectively and comparatively from four aspects: employment and residence, spatial commuting features, temporal commuting features and commuting by subway. The results show that: (1) Zhangjiang is relatively well self-balanced with the shortest commuting distance; employees here live close to their working place and over a half of them live just in the Park. (2) Jinqiao, having few housing supplies inside and poor rail transportation condition, is typical single-employment; many employees here live surrounding the Zone. (3) Lujiazui, with a rather high proportion of employees live in the central city, is city-interactive; although the inner commuting is still a small part and the commuting distance is not very short, employees here can commute quite conveniently in general. How the six main factors, which are location, transportation condition, scale, industry types as well as land use inside and nearby, contribute to different types of employment areas is then discussed. The results can be instructive for the planning and construction of employment areas. Corresponding planning suggestions are put forward for different patterns of employment areas in the end. For self-balanced ones, increasing supporting facilities inside, such as schools, hospitals and shops, rather than houses only may be more attractive for living. Regarding the single-employment ones, the internal and external public transport links should be mainly enhanced. As for those city-interactive, improving the spatial quality and arousing the regional vitality may be the most important.

Key words: cellphone signaling data, employment area, commuting features, Shanghai