中国城市间人口流动空间格局的网络分析——以国庆-中秋长假和腾讯迁徙数据为例
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摘要
"腾讯迁徙"大数据基于位置服务,实时、动态、完整、系统地描述了用户日常出行活动的轨迹。通过采集"腾讯迁徙"数据平台中2017年国庆-中秋长假期间国内299个城市之间的逐日人口流动数据,分"出行期、旅途期、返程期"3个时间段,利用复杂网络分析方法,从人口流动集散层级、集散网络体系的分层集聚、人口流动空间格局、网络空间特征等角度分析各时间段城市间的人口流动特征与空间格局。结果表明,腾讯迁徙大数据直观地揭示了国庆-中秋期间中国各地级城市间人口的迁移规律,3个时段人口的净流入均呈现十字形骨架支撑的菱形分布,人流集散中心主要集中在京津冀、长三角、珠三角和成渝四大城市群,与城市等级有较强的一致性。人口流动集散体系呈明显的分层集聚,城市行政级别的高低与人口流动影响力存在一定的正相关关系,大部分城市人口流动处于"相对平衡"状态。人口流动空间格局呈现出明显的核心-边缘结构,大理-鹤岗一线是人口流动强度空间分布的显著分界线,以此线为界,城市网络呈现东密西疏的分布特征和东部并联、西部串联的网络关联特征。人口流动网络总体表现出"小世界"网络特征,局部具有较明显的"社区"结构特征,聚为2个国家级、2个区域级和3个地区级社区。
Population migration, social check-in, vehicle navigation, and other spatial behavior big data have become vital carriers characterizing users' spatial behavior. The big data used in this paper were collected from the locations provided by hundreds of millions intelligent mobile phone users through Location Based Service(LBS) Tencent Migration data platform,and were displayed by means of real-time heat map which indicates user's moving trajectory in China. "Tencent Migration" big data can real-timely, dynamically, completely and systematically record population flow routes 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 pattern with cross frame support in each period, with the four nodes of 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. There is a positive correlation between the level of urban administration and the influence of population flow. 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.
Population migration, social check-in, vehicle navigation, and other spatial behavior big data have become vital carriers characterizing users' spatial behavior. The big data used in this paper were collected from the locations provided by hundreds of millions intelligent mobile phone users through Location Based Service(LBS) Tencent Migration data platform,and were displayed by means of real-time heat map which indicates user's moving trajectory in China. "Tencent Migration" big data can real-timely, dynamically, completely and systematically record population flow routes 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 pattern with cross frame support in each period, with the four nodes of 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. There is a positive correlation between the level of urban administration and the influence of population flow. 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.
引文
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[14]赵梓渝,魏冶,王士君,等.有向加权城市网络的转变中心性与控制力测度:以中国春运人口流动网络为例.地理研究,2017,36(4):647-660.[Zhao Ziyu,Wei Ye,Wang Shijun,et al.Measurement of directed alternative centricity and power of directed weighted urban network:A case of population flow network of China during"Chunyun"period.Geographical Research,2017,36(4):647-660.]
[15]蒋小荣,汪胜兰,杨永春.中国城市人口流动网络研究:基于百度LBS大数据分析.人口与发展,2017,23(1):13-23.[Jiang Xiaorong,Wang Shenglan,Yang Yunchun,et al.Research on China’s urban population mobility network based on Baidu LBS Big Data.Population and Development,2017,23(1):13-23.]
[16]王贤文,王虹茵,李清纯.基于地理位置大数据的京津冀城市群短期人口流动研究.大连理工大学学报(社会科学版),2017,38(2):105-113.[Wang Xianwen,Wang Hongyin,Li Qingchun.Location based big data analysis of the shortterm population flow of Beijing,Tianjin and Hebei Urban Agglomeration.Journal of Dalian University of Technology(Social Sciences),2017,38(2):105-113.]
[17]赵梓渝,魏冶,庞瑞秋,等.基于人口省际流动的中国城市网络转变中心性与控制力研究:兼论递归理论用于城市网络研究的条件性.地理学报,2017,72(6):1032-1048.[Zhao Ziyu,Wei Ye,Pang Ruiqiu,et al.Alter-based centrality and power of Chinese city network using inter-provincial population flow.Acta Geographica Sinica,2017,72(6):1032-1048.]
[18]王珏,陈雯,袁丰.基于社会网络分析的长三角地区人口迁移及演化.地理研究,2014,33(2):385-400.[Wang Jue,Chen Wen,Yuan Feng.Human mobility and evolution based on social network:An empirical analysis of Yangtze River Delta,Geographical Research,2014,33(2):385-400.]
[19]顾朝林,庞海峰.基于重力模型的中国城市体系空间联系与层域划分.地理研究,2008,27(1):1-12.[Gu Chaolin,Pang Haifeng.Study on spatial relations of Chinese urban system:Gravity Model Approach.Geographical Research,2008,27(1):1-12.]
[20]甄峰,王波,陈映雪.基于网络社会空间的中国城市网络特征:以新浪微博为例.地理学报,2012,67(8):1031-1043.[Zhen Feng,Wang Bo,Chen Yingxue.China's city network characteristics based on social network space:An empirical analysis of Sina Micro-blog.Acta Geographica Sinica,2012,67(8):1031-1043.]
[21]Zhu D,Wang D,Hassan S U,et al.Small-world phenomenon of keywords network based on complex network.Scientometrics,2013,97(2):435-442.
[22]Meo P D,Ferrara E,Fiumara G,et al.Fast unfolding of communities in large networks.Journal of Statistical Mechanics Theory&Experiment,2008,(10):155-168.
[23]杨强,李丽,王运动,等.1935-2010年中国人口分布空间格局及其演变特征.地理研究,2016,35(8):1547-1560.[Yang Qiang,Li Li,Wang Yundong,et al.Spatial distribution pattern of population and characteristics of its evolution in China during 1935-2010.Geographical Research,2016,35(8):1547-1560.]
[2]朱宇,林李月.中国人口迁移流动的时间过程及其空间效应研究:回顾与展望.地理科学,2016,36(6):820-828.[Zhu Yu,Lin Liyue.Studies on the temporal processes of migration and their spatial effects in China:Progress and prospect.Scientia Geographica Sinica,2016,36(6):820-828.]
[3]马红旗,陈仲常.我国省际流动人口的特征:基于全国第六次人口普查数据.人口研究,2012,36(6):87-99.[Ma Hongqi,Chen Zhongchang.Patterns of inter-provincial migration in China:Evidence from the sixth population census.Population Research,2012,36(6):87-99.]
[4]国家卫生和计划生育委员会流动人口司.中国流动人口发展报告.北京:中国人口出版社,2017.[Floating Population Division of the National Health and Family Planning Commission.Report on the Development of Floating Population.Beijing:China Population Press,2017.]
[5]赵梓渝,魏冶,庞瑞秋,等.中国春运人口省际流动的时空与结构特征.地理科学进展,2017,36(8):952-964.[Zhao Ziyu,Wei Ye,Pang Ruiqiu,et al.Spatiotemporal and structural characteristics of interprovincial population flow during the 2015 Spring Festival travel rush.Progress in Geography,2017,36(8):952-964.]
[6]邓羽,刘盛和,蔡建明,等.中国省际人口空间格局演化的分析方法与实证.地理学报,2014,69(10):1473-1486.[Deng Yu,Liu Shenghe,Cai Jianming,et al.Spatial pattern and its evolution of Chinese provincial population and empirical study.Acta Geographica Sinica,2014,69(10):1473-1486.]
[7]董上,蒲英霞,马劲松,等.中国省际人口迁移的复杂网络研究.南方人口,2014,29(2):54-61.[Dong Shang,Pu Yingxia,Ma Jinsong,et al.A study of the complex network of China’s interprovincial migration.South China Population,2014,29(2):54-61.]
[8]蒋小荣,汪胜兰.中国地级以上城市人口流动网络研究:基于百度迁徙大数据的分析.中国人口科学,2017,(2):35-46.[Jiang Xiaorong,Wang Shenglan.Research on China's urban population mobility network:Based on Baidu migration big data.Chinese Journal of Population Science,2017,(2):35-46.]
[9]Liu X,Neal Z,Derudder B.Featured graphic:City networks in the United States:A comparison of four models.Environment and Planning A,2012,44(2):255-256.
[10]马学广,李贵才.全球流动空间中的当代世界城市网络理论研究.经济地理,2011,31(10):1630-1637.[Ma Xueguang,Li Guicai.Study on world city network theory within global space of flow.Economic Geography,2011,31(10):1630-1637.]
[11]Beaverstock J V,Smith R G,Taylor P J.World-city network:A new metageography?Annals of the American Association of Geographers,2000,90(1):123-134.
[12]秦萧,甄峰,熊丽芳,等.大数据时代城市时空间行为研究方法.地理科学进展,2013,32(9):1352-1361.[Qin Xiao,Zhen Feng,Xiong Lifang,et al.Methods in urban temporal and spatial behavior research in the Big Data Era.Progress in Geography,2013,32(9):1352-1361.]
[13]刘望保,石恩名.基于ICT的中国城市间人口日常流动空间格局:以百度迁徙为例.地理学报,2016,71(10):1667-1679.[Liu Wangbao,Shi Enming.Spatial pattern of population daily flow among cities based on ICT:A case study of"Baidu Migration".Acta Geographica Sinica,2016,71(10):1667-1679.]
[14]赵梓渝,魏冶,王士君,等.有向加权城市网络的转变中心性与控制力测度:以中国春运人口流动网络为例.地理研究,2017,36(4):647-660.[Zhao Ziyu,Wei Ye,Wang Shijun,et al.Measurement of directed alternative centricity and power of directed weighted urban network:A case of population flow network of China during"Chunyun"period.Geographical Research,2017,36(4):647-660.]
[15]蒋小荣,汪胜兰,杨永春.中国城市人口流动网络研究:基于百度LBS大数据分析.人口与发展,2017,23(1):13-23.[Jiang Xiaorong,Wang Shenglan,Yang Yunchun,et al.Research on China’s urban population mobility network based on Baidu LBS Big Data.Population and Development,2017,23(1):13-23.]
[16]王贤文,王虹茵,李清纯.基于地理位置大数据的京津冀城市群短期人口流动研究.大连理工大学学报(社会科学版),2017,38(2):105-113.[Wang Xianwen,Wang Hongyin,Li Qingchun.Location based big data analysis of the shortterm population flow of Beijing,Tianjin and Hebei Urban Agglomeration.Journal of Dalian University of Technology(Social Sciences),2017,38(2):105-113.]
[17]赵梓渝,魏冶,庞瑞秋,等.基于人口省际流动的中国城市网络转变中心性与控制力研究:兼论递归理论用于城市网络研究的条件性.地理学报,2017,72(6):1032-1048.[Zhao Ziyu,Wei Ye,Pang Ruiqiu,et al.Alter-based centrality and power of Chinese city network using inter-provincial population flow.Acta Geographica Sinica,2017,72(6):1032-1048.]
[18]王珏,陈雯,袁丰.基于社会网络分析的长三角地区人口迁移及演化.地理研究,2014,33(2):385-400.[Wang Jue,Chen Wen,Yuan Feng.Human mobility and evolution based on social network:An empirical analysis of Yangtze River Delta,Geographical Research,2014,33(2):385-400.]
[19]顾朝林,庞海峰.基于重力模型的中国城市体系空间联系与层域划分.地理研究,2008,27(1):1-12.[Gu Chaolin,Pang Haifeng.Study on spatial relations of Chinese urban system:Gravity Model Approach.Geographical Research,2008,27(1):1-12.]
[20]甄峰,王波,陈映雪.基于网络社会空间的中国城市网络特征:以新浪微博为例.地理学报,2012,67(8):1031-1043.[Zhen Feng,Wang Bo,Chen Yingxue.China's city network characteristics based on social network space:An empirical analysis of Sina Micro-blog.Acta Geographica Sinica,2012,67(8):1031-1043.]
[21]Zhu D,Wang D,Hassan S U,et al.Small-world phenomenon of keywords network based on complex network.Scientometrics,2013,97(2):435-442.
[22]Meo P D,Ferrara E,Fiumara G,et al.Fast unfolding of communities in large networks.Journal of Statistical Mechanics Theory&Experiment,2008,(10):155-168.
[23]杨强,李丽,王运动,等.1935-2010年中国人口分布空间格局及其演变特征.地理研究,2016,35(8):1547-1560.[Yang Qiang,Li Li,Wang Yundong,et al.Spatial distribution pattern of population and characteristics of its evolution in China during 1935-2010.Geographical Research,2016,35(8):1547-1560.]