地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (9): 1367-1381.doi: 10.12082/dqxxkx.2019.190067

• 专栏:青藏高原城镇化的生态环境影响数据挖掘 • 上一篇    下一篇

基于位置大数据的国庆假期青藏高原人群分布 时空变化模式挖掘

易嘉伟1,2,*(),杜云艳1,2,涂文娜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
  • 收稿日期:2019-02-15 修回日期:2019-06-25 出版日期:2019-09-25 发布日期:2019-09-24
  • 通讯作者: 易嘉伟 E-mail:yijw@lreis.ac.cn
  • 作者简介:易嘉伟(1988-),男,湖南衡阳人,博士生,助理研究员,主要从事时空数据挖掘与应用研究。E-mail: yijw@lreis.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项(A类)资助(XDA19040501);中国科学院战略性先导科技专项课题(XDA20040401);国家重点研发计划项目(2017YFB0503605);国家重点研发计划项目(2017YFC1503003)

Spatiotemporal Pattern of Population Distribution in the Qinghai-Tibet Plateau during the National Day Holidays: Based on Geospatial Big Data Mining

YI Jiawei1,2,*(),DU Yunyan1,2,TU Wenna1,2   

  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
  • Received:2019-02-15 Revised:2019-06-25 Online:2019-09-25 Published:2019-09-24
  • Contact: YI Jiawei E-mail:yijw@lreis.ac.cn
  • Supported by:
    the Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA19040501(XDA19040501);The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20040401);National Key Research and Development Program of China(2017YFB0503605);National Key Research and Development Program of China(2017YFC1503003)

摘要:

人类活动是引起青藏高原生态环境发生改变的重要因素。很多学者对青藏高原史前人类活动和近几十年的人口分布格局与人口流动开展了大量研究,但有关人群时空分布的精细尺度研究相对缺乏。海量的位置大数据为认识高原人群短期的动态变化提供了新途径。本文利用手机定位数据、人口迁徙数据等高时空分辨率的位置大数据,通过时间序列分解方法和基于统计检验的异常判别方法,分析了2017年国庆期间青海与西藏的人群分布时空变化特征,并探讨了假期旅游行为及人口迁徙与变化特征之间的关系。研究结果显示:① 在省级和城市整体尺度上,定位请求量的假期变化在时间上呈现先降后升的“潮汐”变化模式;② 在精细网格尺度上,西宁和拉萨城市及周边地区的人群分布变化在空间上呈现中心跌、周边涨的“离心化”变化模式。国庆假期人们向城市周边热门景点移动聚集的旅游行为和城市之间的人口迁徙都是导致西宁和拉萨周边地区定位请求量上涨的重要潜在原因,而两座高原城市中心定位请求的下跌不仅与人口迁徙有关,还与假期人类日常行为及定位请求频次的变化等因素有关。通过位置大数据挖掘节庆假期人群分布的时空变化,不仅加深了对高原人口分布格局与流动变化的认识,也为高原城镇化与生态保护的精细化管理与决策提供支撑。

关键词: 人群分布, 人口流动, 手机定位数据, 人口迁徙数据, 时间序列分解方法, 青藏高原, 青海, 西藏

Abstract:

Human activities play an important role in transforming the eco-environment of the Qinghai-Tibet Plateau (QTP). Extensive studies have been conducted on the human activities in the prehistoric QTP and on population distribution and migration in the recent decades, yet, most of them rely on limited demographic materials of coarse spatial resolutions. It remains understudied regarding the fine-scale spatiotemporal pattern of human distribution in the QTP. In this context, geospatial big data generated from ubiquitous mobile communication technology and internet provide a great opportunity to investigate the dynamic human distribution at very fine scales. This study took the advantage of the geospatial big data, including the mobile phone location requests (LR) and population migration, and employed time series decomposition and anomaly detection approaches to explore the population distribution changes in the QTP during the 2017 National Day holidays. Results show that, at the provincial and prefectural scales, Qinghai, Tibet, and their provincial cities exhibit a featured "tidal change" pattern that the LR first decreased then increased. Such fluctuation in Qinghai were stronger than that in Tibet, and cities in the same province demonstrated significant differences. At the grid scale, the LR in the surrounding areas of Xining and Lhasa displayed a spatially “decentralized pattern” that the LP dropped in the central areas yet increased in the peripheral. Based on the anomaly detection approach, we found the number of anomaly grids and deviation magnitude increased in Xining, Haidong, Haibei, Hainan, and Huangnan of Qinghai since the holiday. More positive anomalies were observed than the negative ones, and the negative anomalies were concentrated in cities of large population densities such as Xining and Lhasa. Further analysis combining the population migration data reveals that the travel behaviors potentially drove people swarming to the nearby scenic spots and that the massive migration between cities was an important reason for the increase of LR in areas surrounding Xining and Lhasa. The decrease of LR in the central areas of cities could be partly attributed to significant population migration, but the different daily routines and location request frequencies during holidays may also be important reasons. Our findings demonstrate the potential of using geospatial big data to improve our understanding of human distribution and migration, which could further support fine management and decision-making for plateau urbanization and ecological protection.

Key words: population distribution, population migration, smartphone location request, population migration data, time series decomposition, Qinghai-Tibet Plateau, Qinghai, Tibet