京津冀城市间人口流动网络研究——基于腾讯位置大数据分析
|
摘要
基于腾讯位置大数据,运用GIS和复杂网络分析等方法,构建京津冀区域城市间的人口流动网络模型,从网络结构和空间格局两个方面测度京津冀区域城市间人口流动的复杂网络特征。结果表明:(1)京津冀城市间人口流动网络符合"小世界"网络特征,具有较高的连通性和运行效率,并且所有城市节点的人口流入和人口流出均呈显著正相关,说明目前京津冀人口可以跨越行政界限自由流动,且保持相对平衡,京津冀协同战略发展已经取得了实质性效果。(2)京津冀城市间人口流动网络具有明显层次性,呈现"北疏南密"流动分布态势和"一大三小"多中心网络空间格局,并非传统认知上"京津双核心"空间格局。研究结果对于引导人口合理流动,促进京津冀区域人口、资源与经济社会的协调发展具有参考价值。
Based on Tencent location big data,GIS and complex network analysis methods were used to construct a population mobility network model among Beijing,Tianjin,and Hebei provinces,and the complex network characteristics of inter-city population mobility in Beijing,Tianjin,and Hebei were measured in terms of network structure and spatial structure. The results show that:(1)At present,the Beijing-Tianjin-Hebei inter-city population mobility network is in line with the"small-world"network characteristics,with high connectivity and operational efficiency,and there is a significant positive correlation between population inflow and outflow of population in all urban nodes. It shows that the current population of Beijing,Tianjin and Hebei can move freely across administrative boundaries and maintain a relatively balanced balance. The development of the Beijing-Tianjin-Hebei coordinated strategy has achieved substantial results.(2)The inter-city population mobility network of Beijing,Tianjin,and Hebei has a clear hierarchy,showing the"Northern-southern-secret"flow distribution pattern and the"one big three small"multi-centered cyberspace pattern,which is not traditionally known as"Beijing-Tianjin Core "spatial pattern. The research results have a reference value for guiding the reasonable flow of population and promoting the coordinated development of population,resources and economic society in the Beijing-Tianjin-Hebei region.
Based on Tencent location big data,GIS and complex network analysis methods were used to construct a population mobility network model among Beijing,Tianjin,and Hebei provinces,and the complex network characteristics of inter-city population mobility in Beijing,Tianjin,and Hebei were measured in terms of network structure and spatial structure. The results show that:(1)At present,the Beijing-Tianjin-Hebei inter-city population mobility network is in line with the"small-world"network characteristics,with high connectivity and operational efficiency,and there is a significant positive correlation between population inflow and outflow of population in all urban nodes. It shows that the current population of Beijing,Tianjin and Hebei can move freely across administrative boundaries and maintain a relatively balanced balance. The development of the Beijing-Tianjin-Hebei coordinated strategy has achieved substantial results.(2)The inter-city population mobility network of Beijing,Tianjin,and Hebei has a clear hierarchy,showing the"Northern-southern-secret"flow distribution pattern and the"one big three small"multi-centered cyberspace pattern,which is not traditionally known as"Beijing-Tianjin Core "spatial pattern. The research results have a reference value for guiding the reasonable flow of population and promoting the coordinated development of population,resources and economic society in the Beijing-Tianjin-Hebei region.
引文
[1]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[J].中国人口科学,2017(2):35-46+127.
[2]董南,杨小唤,蔡红艳.人口数据空间化研究进展[J].地球信息科学学报,2016(10):1295-1304.
[3]刘望保,等.“百度迁徙”下中国城市间的人口流动空间格局[J].华南师范大学学报(自然科学版),2016(5):67-73.
[4]王文刚,孙桂平,等.京津冀地区流动人口家庭化迁移的特征与影响机理[J].中国人口·资源与环境,2017(1).
[5]张耀军,岑俏.中国人口空间流动格局与省际流动影响因素研究[J].人口研究,2014(5):56-73.
[6]刘爱华.京津冀流动人口的空间集聚及其影响因素[J].人口与经济,2017(6):71-78.
[7]陈明星,郭莎莎,等.新型城镇化背景下京津冀城市群流动人口特征与格局[J].地理科学进展,2018(3):363-372.
[8]韩枫.城镇流动人口社会保障参保率的影响因素研究——基于京津冀流动人口动态监测数据的分析[J].人口学刊,2016(1):63-69.
[9] Naaman M,Zhang A X,Google S B,et al. On the Study of Diurnal Urban Routines on Twitter[C]//6th International Aaaiconference on Weblogs and Social Media,2012:4-7.
[10] Bo W,Feng Z,Hao Z,et al. The Dynamic Changes of Urban Space-time Activity and Activity Zoning Based on Check-in Data in Sina Web[J]. Scientia Geographica Sinica,2016(2):151-160.
[11]龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报,2012(10):1339-1352.
[12] Jiansheng WU,Li H,Yu L,et al. Traffic Flow Simulation Based on Call Detail Records[J]. Acta Geographica Sinica,2012(12):1657-1665.
[13] Calabrese F,Colonna M,Lovisolo P,et al. Real-time Urban Monitoring Using Cell Phones:a Case Study in Rome[J]. Ieee Transactions on Intelligent Transportation Systems,2011(1):141-151.
[14] Xing Z Y,Yong H Z. Looking Into the Network Structure of Chinese Urban System From the Perspective of Air Transportation[J]. Geographical Research,2002(3):276-286.
[15]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[J].地理学报,2016(10):1667-1679.
[16]叶强,张俪璇,彭鹏,等.基于百度迁徙数据的长江中游城市群网络特征研究[J].经济地理,2017(8):53-59.
[17] Dj W,Sh S. Collectivedynamics of‘small-world’Networks[C]//Nature,1998:440-442.
[18] Barabási A L,Albert R. Emergence of scaling in random networks[J]. Science,1999(15):509-512.
[19]韩跃,等.基于多标签传播机制的蛋白质相互作用网络功能模块检测[J].模式识别与人工智能,2016(6):548-557.
[20]戢晓峰,刘传颖,李杰梅.旅游业驱动的区域航空网络时空演化特征[J].经济地理,2017(11):205-212.
[21]傅杰,邹艳丽,谢蓉.基于复杂网络理论的电力网络关键线路识别[J].复杂系统与复杂性科学,2017(3):91-96.
[22]张宇星.空间蔓延和连绵的特性与控制[J].新建筑,1995(4):29-31+41.
[23]张耀军.京津冀城市圈人口有序流动及合理分布[J].人口与发展,2015(2):35-40.
[2]董南,杨小唤,蔡红艳.人口数据空间化研究进展[J].地球信息科学学报,2016(10):1295-1304.
[3]刘望保,等.“百度迁徙”下中国城市间的人口流动空间格局[J].华南师范大学学报(自然科学版),2016(5):67-73.
[4]王文刚,孙桂平,等.京津冀地区流动人口家庭化迁移的特征与影响机理[J].中国人口·资源与环境,2017(1).
[5]张耀军,岑俏.中国人口空间流动格局与省际流动影响因素研究[J].人口研究,2014(5):56-73.
[6]刘爱华.京津冀流动人口的空间集聚及其影响因素[J].人口与经济,2017(6):71-78.
[7]陈明星,郭莎莎,等.新型城镇化背景下京津冀城市群流动人口特征与格局[J].地理科学进展,2018(3):363-372.
[8]韩枫.城镇流动人口社会保障参保率的影响因素研究——基于京津冀流动人口动态监测数据的分析[J].人口学刊,2016(1):63-69.
[9] Naaman M,Zhang A X,Google S B,et al. On the Study of Diurnal Urban Routines on Twitter[C]//6th International Aaaiconference on Weblogs and Social Media,2012:4-7.
[10] Bo W,Feng Z,Hao Z,et al. The Dynamic Changes of Urban Space-time Activity and Activity Zoning Based on Check-in Data in Sina Web[J]. Scientia Geographica Sinica,2016(2):151-160.
[11]龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报,2012(10):1339-1352.
[12] Jiansheng WU,Li H,Yu L,et al. Traffic Flow Simulation Based on Call Detail Records[J]. Acta Geographica Sinica,2012(12):1657-1665.
[13] Calabrese F,Colonna M,Lovisolo P,et al. Real-time Urban Monitoring Using Cell Phones:a Case Study in Rome[J]. Ieee Transactions on Intelligent Transportation Systems,2011(1):141-151.
[14] Xing Z Y,Yong H Z. Looking Into the Network Structure of Chinese Urban System From the Perspective of Air Transportation[J]. Geographical Research,2002(3):276-286.
[15]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[J].地理学报,2016(10):1667-1679.
[16]叶强,张俪璇,彭鹏,等.基于百度迁徙数据的长江中游城市群网络特征研究[J].经济地理,2017(8):53-59.
[17] Dj W,Sh S. Collectivedynamics of‘small-world’Networks[C]//Nature,1998:440-442.
[18] Barabási A L,Albert R. Emergence of scaling in random networks[J]. Science,1999(15):509-512.
[19]韩跃,等.基于多标签传播机制的蛋白质相互作用网络功能模块检测[J].模式识别与人工智能,2016(6):548-557.
[20]戢晓峰,刘传颖,李杰梅.旅游业驱动的区域航空网络时空演化特征[J].经济地理,2017(11):205-212.
[21]傅杰,邹艳丽,谢蓉.基于复杂网络理论的电力网络关键线路识别[J].复杂系统与复杂性科学,2017(3):91-96.
[22]张宇星.空间蔓延和连绵的特性与控制[J].新建筑,1995(4):29-31+41.
[23]张耀军.京津冀城市圈人口有序流动及合理分布[J].人口与发展,2015(2):35-40.