地球信息科学学报

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (1): 97-106.doi: 10.12082/dqxxkx.2019.180262

• 地理大数据时空模式挖掘的方法与应用研究 • 上一篇    下一篇

基于手机数据的北京市城市与近郊交互模式挖掘

彭卉1,3(), 杜云艳1,2,*(), 易嘉伟1,2, 刘张1,2, 王会蒙1,2   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 国网思极神往位置服务（北京）有限公司,北京 102211
  • 收稿日期:2018-05-29 修回日期:2018-07-15 出版日期:2019-01-20 发布日期:2019-01-20
  • 作者简介:

    作者简介：彭 卉（1993-）,女,硕士,主要从事时空数据挖掘与城市计算研究。E-mail:pengh@lreis.ac.cn

  • 基金资助:
    国家自然科学基金重大项目（41590845）;国家重点研发计划项目（2017YFB0503605）

Mining Urban-rural Spatial Interaction Pattern from Mobile Data of Beijing

Hui PENG1,2,3(), Yunyan DU1,*(), Jiawei YI1, Zhang LIU1,2, Huimeng WANG1,2   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. State Grid Shen Wang LBS(Beijing), Company Limited, Beijing 102211, China;
  • Received:2018-05-29 Revised:2018-07-15 Online:2019-01-20 Published:2019-01-20
  • Contact: Yunyan DU
  • Supported by:
    National Natural Science Foundation of China, No.41590845;National Key Research and Development Program of China, No.2017YFB0503605

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摘要：

在城镇化进程中,城市与近郊之间通过职住、货运、游憩等活动产生越来越紧密的交互联系,对于这些交互联系的准确识别和定量刻画,是理解城乡空间关系的重要手段,也能为城市的资源有效配置与合理规划提供重要的现状信息。本文通过对全北京在一日之内的手机信令数据所反映的人群移动轨迹的深入分析,融合城市的POI信息形成顾及人类活动时空信息的空间交互类型推断。以北京市为例,对城市中心与近郊之间远距离的强交互进行定性、定量和定位的探索。本文发现了北京市多尺度下空间交互模式和距离衰减规律,判断了城乡异常交互类型,对比了城乡之间和城市内部的交互模式的异同,以及基于交互类型视角提取了城乡异常交互的空间特征。研究认为,基于手机信令数据,利用停留点提取和高斯核密度估计的空间交互类型推断有效地发现了北京市周末的远距离出行类型特点,提取了其空间交互强度和空间特征,揭示了基于人类活动的北京市周末城乡交互模式。

关键词: 城乡交互, 模式挖掘, 轨迹分析, 信令数据, 北京

Abstract:

During the process of urbanization, the interactions between urban and suburb areas are becoming increasingly close through substantial exchanges of people, products, information etc. To accurately identify and quantify such interactions has become a key to understanding urban-rural relationships and achieving more effective resource allocation and scientific urban planning. Mobile phone data has high user penetration rate and low cost on data collection, making it an important source of for investigating human dynamics. We reconstructed composite user trajectories from the raw mobile phone data of Beijing in a weekend day. After data preprocessings including cleaning missing data, correcting error data, and coordinate conversion are performed, this study inferred the types of spatial interactions by integrating the point of interest (POI) data along the trajectories and the pre-knowledge of human activities, and investigated the spatio-temporal characteristics of the distant and the intensive interactions of different types between urban and rural areas. Through the decomposition of interaction types, complex urban-rural anomaly interaction patterns can be decomposed into 49 basic types. By calculating the ratio of the intensity of one flow type in a certain direction to the intensity of the same type in both directions, the characteristics of urban and rural interaction in Beijing can be obtained. The results showed that the urban-rural interactions among different regions of Beijing exhibit multi-scale patterns and distance attenuation patterns, and unveiled the spatio-temporal patterns of very long-distance interactions with the approaches of the stay-point extraction and the Gaussian kernel density function. It can be seen from the results that the overall pattern of spatial interaction among the internal regions of Beijing presents a trend of decreasing from the center of the city to the periphery. The method proposed in this paper attempts to reflect the distribution difference of public resources and infrastructure between urban and rural areas by mining the spatial interaction of human activities. It reveals the spatial structure and resource allocation characteristics of Beijing, and provides a scientific basis for the urban and rural planning policy formulation.

Key words: urban-rural interaction, pattern discovery, trajectory analysis, mobile signaling data, Beijing