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京津冀地区日常人口流动特征分析——基于百度迁徙大数据

Analysis of Daily Population Flow Characteristics in Beijing-Tianjin-Hebei——Based on the Big Data of Baidu Migration
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摘要 以京津冀地区的流动人口为研究对象,基于人口普查资料、年鉴数据与百度迁徙大数据,对京津冀地区流动人口的内部、外部网络结构特征进行分析。研究结果显示,河北省人口以省内流动为主,北京市、天津市则以人口跨省流动为主。京津冀内部形成“一主(北京市)三次(廊坊市、天津市、保定市)”的核心网络结构,优势流多为北京市向外指出。京津冀内外联系网络表现出强烈的地域邻近性,有着很强的空间不均衡性,整体表现为东密西疏的态势。京津冀三地对外联系度与优势流排名前五的城市在同一省份,只有贡献度稍有差别。研究结果可为京津冀地区流动人口管理与区域发展提供理论与现实参考。 The study takes the floating population in Beijing-Tianjin-Hebei region,and analyzes the internal and external network structure characteristics of the migrant population in Beijing-Tianjin-Hebei based on demographic census materials,yearbook data and Baidu migration data through social network analysis.The results of the research show that Hebei Province is dominated by intra-provincial floating population,while Beijing and Tianjin are dominated by inter-provincial floating population.Beijing-Tianjin-Hebei region has formed a core network structure of“one main(Beijing)and three times(Langfang,Tianjin,Baoding)”,and the advantage flow is mostly pointed out by Beijing.The internal and external communication network of Beijing-Tianjin-Hebei shows strong geographical proximity,with strong spatial imbalance,and the overall manifestation is a state of“eastern density and western sparsity”.Beijing-Tianjin-Hebei region has the same degree of external relations and the top five provinces in the dominant stream,but the degree of contribution is slightly different.The research results can provide theoretical and practical reference for the management of migrant population and regional development in the Beijing-Tianjin-Hebei region.
作者 吴金辉 闫顺玺 梁丽芳 Wu Jinhui;Yan Shunxi;Liang Lifang(School of Mining Engineering,North China University of Science and Technology,Tangshan 063200,China;Hebei Provincial Natural Resources Archives,Shijiazhuang 050000,China)
出处 《黑龙江科学》 2024年第7期6-10,共5页 Heilongjiang Science
基金 河北省高等学校自然科学研究基金项目(QN:2020125)。
关键词 京津冀 流动人口 联系强度 网络层级 Beijing-Tianjin-Hebei Floating population Connection strength Network hierarchy
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