地理学报 ›› 2020, Vol. 75 ›› Issue (7): 1418-1431.doi: 10.11821/dlxb202007007

• 青藏高原及人类活动 • 上一篇    下一篇

青藏高原人口流入流出时空模式研究

王楠1,2(), 王会蒙1,2, 杜云艳1,2(), 易嘉伟1,2, 刘张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:2019-04-02 修回日期:2020-06-28 出版日期:2020-07-25 发布日期:2020-09-25
  • 通讯作者: 杜云艳 E-mail:wangnan171@mails.ucas.ac.cn;duyy@lreis.ac.cn
  • 作者简介:王楠(1994-), 女, 硕士生, 研究方向为时空数据挖掘。E-mail: wangnan171@mails.ucas.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项(XDA20040401);中国科学院战略性先导科技专项(XDA19040501)

Spatiotemporal patterns of in- and out-bound population flows of the Qinghai-Tibet Plateau

WANG Nan1,2(), WANG Huimeng1,2, DU Yunyan1,2(), YI Jiawei1,2, LIU Zhang1,2, TU Wenna1,2   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-04-02 Revised:2020-06-28 Online:2020-07-25 Published:2020-09-25
  • Contact: DU Yunyan E-mail:wangnan171@mails.ucas.ac.cn;duyy@lreis.ac.cn
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20040401);Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19040501)

摘要:

在大数据时代,来自网络的人口迁徙数据,为开展区域间人口流入、流出模式研究提供了强有力的数据支撑。本文基于腾讯日迁徙数据(2015—2018年),借助时间序列、社交网络和时空统计等分析方法,剖析、挖掘了青藏高原人口流入与流出模式,探讨了青藏高原与国内其他城市间的季节性人口流向和人口互动网络特征。结果表明:① 青藏高原人口流动具有显著的周期性特征,年内呈低—中—中高—高的季节性变化规律;2015至2018年,青藏高原在全国城市流动人口互动网络中的地位持续提升,人口流动量提升了8.2%,网络排名提升了24.5%,中值季、中高值季和高值季相对低值季而言,日均人口流动量依次增高14.2%、26.7%、57.8%;② 青藏高原人口流动方向集中在青藏高原NEE-67.5°至SEE-112.5°的45°扇形区间,并且青藏高原与周边省份的省会城市互动频繁;③ 全国大部分城市与青藏高原人口互动强度持续增强,旅游业起到了关键的推动作用;新疆和田和巴音州、重庆、广安、眉山和河南三门峡与青藏高原间呈现中值季—中高值季升高而高值季下降模式,反映了青藏高原的劳务输入存在季节性变化的规律;吐鲁番、东莞和运城出现高值季下降模式则主要是物资供给关系导致的结果。

关键词: 青藏高原, 腾讯迁徙数据, 网络分析, 季节性规律, 时空交互模式

Abstract:

In the big data era, data of population migration on the Internet has exerted great influence on conducting researches on the patterns of inter-regional population inflows and outflows. This study, based on Tencent's daily migration data (2015-2018), analyzed and explored the patterns of population inflows and outflows in the Qinghai-Tibet Plateau by means of methods like time series analysis, social networks analysis and space-time statistical analysis, and discussed the seasonal law of the fluid population between the study area and other cities in China and the network features of population interaction. The results indicated that: (1) The population flow in the Qinghai-Tibet Plateau was featured by seasonal variability, which could be represented as four seasons "Low, Medium, Mid-High, High" during the year. From 2015 to 2018, the Qinghai-Tibet Plateau in the interaction network of national urban floating population witnessed an increasing status. The population flow rate improved by 8.2%, and the ranking on the population mobility network increased by 24.5%. Relative to the Low season, the average daily population movements in the Medium season, Mid-High season and High season increased by 14.2%, 26.7% and 57.8%, respectively. (2) The population flow direction lied in the 45° fan-shaped range from NEE-67.5° to SEE-112.5°, and the Qinghai-Tibet Plateau had strong and frequent interactions with the capitals of the surrounding provinces. (3) Most cities across the country attached importance to a continual increase of the intensity of population interaction with the Qinghai-Tibet Plateau, and tourism had played a dominant role in promoting the changing and development. The Hotan and Bayin prefectures in Xinjiang, Chongqing, Guang'an and Meishan in Sichuan, and Sanmenxia in Henan presented a pattern of seasonal rise in the Medium and Mid-High seasons and a pattern of seasonal fall in the High season, reflecting the seasonality of labor export to the Qinghai-Tibet Plateau. In addition, population flow in cities like Turpan, Dongguan and Yuncheng showed a declining trend in the stage of High season as a result of the goods and materials supply relations.

Key words: Qinghai-Tibet Plateau, Tencent migration data, network analysis, seasonal law, spatiotemporal interaction pattern