A comparative study on the population flow network between Chinese cities based on different transportation modes

doi:10.3969/j.issn.1004-9479.2023.07.2021377

Abstract

Abstract:

This paper establishes a population flow network based on Tencent's migration population flow big data, and uses social network analysis and GIS spatial analysis methods to compare and study the population flow network between cities in different transportation modes in China. The results show that the urban population inflow and outflow of different modes of transportation are relatively balanced, both railway and aviation population flow have obvious hierarchical characteristics; the average population flow distances of roads, railways and aviation are 251 km, 581 km and 1 294 km respectively. The main flow areas for highways and railways are within 400 and 1000 km, respectively, while the peak region for aviation population flow is within 800~1 800 km. The flow of highways exhibits provincial network flow characteristics, while railways form a spatial structure with high-value aggregation areas in the Beijing-Tianjin-Hebei, Yangtze River Delta, Chengdu-Chongqing, and Pearl River Delta regions and a "bow and arrow" network flow feature. Aviation flow shows the characteristics of polarized space and the multi-core network pattern of "two cores and six poles". The population mobility network is characterized by scale-free networks and small-world networks. The integrated transportation system can greatly improve the operational efficiency of the population flow network, realizing the maximization of population flow efficiency.

Key words: population mobility network, transportation mode, Tencent migration, spatial pattern, SNA

摘要：

基于腾讯迁徙人口流动大数据构建人口流动网络，采用社会网络分析和GIS空间分析，对不同交通方式的城市间人口流动网络进行比较研究。结果表明：不同交通方式的城市人口流入与流出呈相对均衡的状态，铁路和航空人口流动呈明显的等级化特征；公路、铁路和航空的人口流动差异化的距离衰减特征明显，400和1 000 km以内分别为公路、铁路的主要流动区域，航空人口流动的峰值区为800~1 800 km；公路流动呈现省域性网络流动特征，铁路流动形成京津冀、长三角、成渝和珠三角为高值集聚区的空间结构和“弓箭型”网络特征，航空流动表现出两极分化空间特征和“两核六极”的多核心网络格局；人口流动网络呈现无标度网络和小世界网络特征，综合网络表明综合交通运输体系能极大提升人口流动网络的运行效率，实现人口流动效率的最大化。

关键词: 人口流动网络, 交通方式, 腾讯迁徙, 空间格局, 社会网络分析

Weidong GUO, Yexi ZHONG, Wei LI. A comparative study on the population flow network between Chinese cities based on different transportation modes[J]. World Regional Studies, 2023, 32(7): 102-112.

郭卫东, 钟业喜, 李炜. 基于不同交通方式的中国城市间人口流动网络比较研究[J]. 世界地理研究, 2023, 32(7): 102-112.

Figures/Tables 6

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等级	铁路	航空
全国中心城市	北京、上海、成都、深圳、广州、重庆	北京、上海
区域中心城市	西安、沈阳、郑州、昆明、兰州、长沙、杭州、武汉、南京、哈尔滨、苏州、贵阳、天津、长春、乌鲁木齐、青岛、东莞、济南	成都、重庆、广州、深圳、昆明、杭州、西安、南京、郑州、武汉
地方中心城市	南宁、厦门、酒泉、合肥、佛山、石家庄、南昌、哈密、大连、咸阳、赣州、西宁、徐州、金华、桂林、太原、福州、无锡、宁波、呼和浩特、银川、吐鲁番、绵阳、临沂、株洲、柳州、玉林、泉州、拉萨、中山、惠州、榆林、襄阳、衡阳、潍坊、保定、镇江、渭南、温州、锦州、吉安、九江、晋中、大理、宝鸡、曲靖、常州、鄂尔多斯、张掖、绥化、淄博、海西州、德阳、上饶、四平	长沙、天津、兰州、乌鲁木齐、哈尔滨、青岛、济南、贵阳、沈阳、石家庄、厦门、海口
一般城市	济宁等其余251个城市	呼和浩特等其余172个城市

等级	铁路	航空
全国中心城市	北京、上海、成都、深圳、广州、重庆	北京、上海
区域中心城市	西安、沈阳、郑州、昆明、兰州、长沙、杭州、武汉、南京、哈尔滨、苏州、贵阳、天津、长春、乌鲁木齐、青岛、东莞、济南	成都、重庆、广州、深圳、昆明、杭州、西安、南京、郑州、武汉
地方中心城市	南宁、厦门、酒泉、合肥、佛山、石家庄、南昌、哈密、大连、咸阳、赣州、西宁、徐州、金华、桂林、太原、福州、无锡、宁波、呼和浩特、银川、吐鲁番、绵阳、临沂、株洲、柳州、玉林、泉州、拉萨、中山、惠州、榆林、襄阳、衡阳、潍坊、保定、镇江、渭南、温州、锦州、吉安、九江、晋中、大理、宝鸡、曲靖、常州、鄂尔多斯、张掖、绥化、淄博、海西州、德阳、上饶、四平	长沙、天津、兰州、乌鲁木齐、哈尔滨、青岛、济南、贵阳、沈阳、石家庄、厦门、海口
一般城市	济宁等其余251个城市	呼和浩特等其余172个城市

指标	公路网络	公路随机网络	铁路网络	铁路随机网络	航空网络	航空随机网络
平均路径长度	4.376	2.218	2.198	2.402	1.905	2.306
聚集系数	0.549	0.051	0.431	0.051	0.523	0.049

指标	公路网络	公路随机网络	铁路网络	铁路随机网络	航空网络	航空随机网络
平均路径长度	4.376	2.218	2.198	2.402	1.905	2.306
聚集系数	0.549	0.051	0.431	0.051	0.523	0.049

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