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Spatial pattern of population mobility among cities in China: Case study of the National Day plus Mid-Autumn Festival based on Tencent migration data

机译:中国城市间人口流动的空间格局:基于腾讯迁移数据的国庆节与中秋节的案例研究

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摘要

Population migration, social check-in, vehicle navigation, and other spatial behavior big data have become vital carriers characterizing users' spatial behavior. "Tencent Migration" big data can real-timely, dynamically, completely and systematically record population flow routs using LBS device. Through gathering residents daily mobility among 299 cities in China during the period of "National Day Mid-Autumn Festival" (NDMAF) vacation (from September 30 to October 8) in 2017 in "Tencent Migration" and defining three periods with "travel period, journey period, return period", this paper is designed to analyze and explore the characteristics and spatial patterns of daily flow mobility cities from the perspective of population daily mobility distribution levels, flow distribution layers network aggregation, spatial patterns and characteristics of the complex structure of the flow network. Results show that "Tencent migration" big data clearly discovers the temporal-spatial pattern of population mobility in China during the period of NDMAF. The net inflow of population showed a diamond shaped with cross frame support in each period, the four nodes of the diamond contain Beijing, Shanghai, Guangzhou and Xi'an. Main mobility assembling centers are distributed in the urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Chengdu-Chongqing, and those centers have strong coherence with those urban hierarchies. Most cities are in a state of "relative equilibrium" in the population flow, and clear hierarchical structure and level distinction can be identified. Spatial patterns of population mobility present obvious core-periphery structures. The Dali-Hegang line exhibits a significant network of spatial differences in terms of boundary divisions. In this context, the spatial distribution of urban network could be summarized as "dense in the East and sparse in the West", and the core linkages of urban network could be characterized as "parallel in the East and series in the West". The whole network exhibits a typical "small world" network characteristic, which shows that China's urban population flow network has high connectivity and accessibility during the period of NDMAF. The network has a distinct "community" structure in the local area, including 2 national communities, 2 regional communities and 3 local-level communities.
机译:人口迁移,社会登记,车辆导航和其他空间行为大数据已成为表征用户空间行为的重要载体。 “腾讯迁移”大数据可以使用LBS设备实时,动态,完整而系统地记录人口流动的溃败。通过收集2017年“国庆中秋”(NDMAF)假期(9月30日至10月8日)期间中国299个城市中居民的日常出行情况,并将三个时期定义为“旅行期”,旅程期,返回期”,本文旨在从人口日流动性分布水平,流动分布层网络聚集,空间格局和复杂结构特征等方面分析和探索日流动性城市的特征和空间格局。流网络。结果表明,“腾讯移民”大数据清楚地发现了NDMAF时期中国人口流动的时空格局。人口的净流入在每个时期都显示出具有交叉框架支撑的菱形,该菱形的四个节点分别是北京,上海,广州和西安。主要的交通聚集中心分布在京津冀,长三角,珠江三角洲和成渝的城市群中,这些中心与这些城市等级制具有很强的一致性。大多数城市在人口流动中处于“相对均衡”状态,因此可以确定清晰的等级结构和等级区分。人口流动的空间格局呈现出明显的核心-外围结构。大理-鹤岗线在边界划分方面表现出明显的空间差异网络。在这种背景下,城市网络的空间分布可以概括为“东方密集而西方稀疏”,而城市网络的核心联系可以描述为“东方平行而西方串联”。整个网络表现出典型的“小世界”网络特征,表明在NDMAF时期,中国城市人口流动网络具有较高的连通性和可访问性。该网络在本地具有独特的“社区”结构,包括2个国家社区,2个区域社区和3个本地社区。

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