珠江三角洲城际间人口流动倾向空间特征——基于网络关注度数据的时空演化
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摘要
通过构建城际人口流动倾向度模型并结合社会网络分析法,利用网络用户搜索行为数据对珠三角城市群城际间人口流动倾向路径时空演变特征、流动倾向强度空间差异进行分析。结果显示:珠三角区域核心城市的城际间人口流动倾向相对强度在区域内处于最高水平。中心城市与副中心城市城际间人口流动倾向路径不断巩固。人们体现在网络空间的城际间流动迁移倾向与现实空间的流动迁移行为表现出的路径依赖效应具有一致性。2011―2015年,深圳一直是广州、东莞和惠州的流动首选城市,广州一直是佛山的流动首选城市。2011和2015年珠三角各市在点度中心度位次结果保持稳定,广州一直高居第1位,深圳位居次席。
This study analyzed the spatio-temporal evolution of intercity population flow propensity paths and the spatial difference in the population flow propensity intensity in the Pearl River Delta region. Internet search engine data were selected, and they were calculated by relevant evaluation models and analyzed with social network analysis method. With the rapid development of the information and communication technologies, information flow has been playing a crucial role in influencing the intercity population flow propensity. This study selected "recruitment in city j", "map of city j" and "real estate in city j"(in Chinese) as three keywords to inquire the search index data which could reflect common demands people had before they made decisions to move to a new place. Mental propensities of people who would move could be uncovered well by these search indexes online. Thus, the intercity population flow propensity model based on Internet attention degree data was built up to evaluate the dynamic trends of population flow propensity among cities in urban agglomeration of the Pearl River Delta. The social network analysis method was used. It was found that the centrality degree ranking kept stable, Guangzhou, Shenzhen, Foshan and Dongguan ranked from the first to the fourth place in the region, that was in line with the realistic population flow features in 2011 and 2015. Therefore, the analysis method used in this study could reflect well the potential population flow trends. The total relative intensity of population inflow propensity had been introduced to the radiation model and the modified population flow radiation model was used to analyze the dynamic features of intercity population flow intensity, and the intercity population attraction index was established to evaluate the ranking of the capability of attracting population to move in. Results showed that Guangzhou and Shenzhen ranked the first two places and Dongguan and Foshan took the third and the fourth places followed by Huizhou, Zhongshan and Jiangmen, and Zhaoqing and Zhuhai ranked the last two places in the Pearl River Delta urban agglomeration. As for the spatial distribution of the intercity population flow propensity, people in the west bank cities of the Pearl River estuary had been attracted to move to Shenzhen in the east bank as the population flow propensity paths were strengthened gradually. The relative population flow propensity intensity results of Guangzhou-Foshan and Shenzhen-Dongguan were at a high level. Furthermore, Guangzhou and Shenzhen were in the first grade followed by Dongguan and Foshan in the second grade with respect to the total relative population flow propensity intensity. As the distributional difference of the total relative population flow propensity intensity showed, cities along the bank of the Pearl River estuary attracted more population flow propensity, and the spatial distribution showed an "n" form along the estuary. Moreover, the equalization of basic public employment service and more job opportunities should be created in the Pearl River Delta region.
This study analyzed the spatio-temporal evolution of intercity population flow propensity paths and the spatial difference in the population flow propensity intensity in the Pearl River Delta region. Internet search engine data were selected, and they were calculated by relevant evaluation models and analyzed with social network analysis method. With the rapid development of the information and communication technologies, information flow has been playing a crucial role in influencing the intercity population flow propensity. This study selected "recruitment in city j", "map of city j" and "real estate in city j"(in Chinese) as three keywords to inquire the search index data which could reflect common demands people had before they made decisions to move to a new place. Mental propensities of people who would move could be uncovered well by these search indexes online. Thus, the intercity population flow propensity model based on Internet attention degree data was built up to evaluate the dynamic trends of population flow propensity among cities in urban agglomeration of the Pearl River Delta. The social network analysis method was used. It was found that the centrality degree ranking kept stable, Guangzhou, Shenzhen, Foshan and Dongguan ranked from the first to the fourth place in the region, that was in line with the realistic population flow features in 2011 and 2015. Therefore, the analysis method used in this study could reflect well the potential population flow trends. The total relative intensity of population inflow propensity had been introduced to the radiation model and the modified population flow radiation model was used to analyze the dynamic features of intercity population flow intensity, and the intercity population attraction index was established to evaluate the ranking of the capability of attracting population to move in. Results showed that Guangzhou and Shenzhen ranked the first two places and Dongguan and Foshan took the third and the fourth places followed by Huizhou, Zhongshan and Jiangmen, and Zhaoqing and Zhuhai ranked the last two places in the Pearl River Delta urban agglomeration. As for the spatial distribution of the intercity population flow propensity, people in the west bank cities of the Pearl River estuary had been attracted to move to Shenzhen in the east bank as the population flow propensity paths were strengthened gradually. The relative population flow propensity intensity results of Guangzhou-Foshan and Shenzhen-Dongguan were at a high level. Furthermore, Guangzhou and Shenzhen were in the first grade followed by Dongguan and Foshan in the second grade with respect to the total relative population flow propensity intensity. As the distributional difference of the total relative population flow propensity intensity showed, cities along the bank of the Pearl River estuary attracted more population flow propensity, and the spatial distribution showed an "n" form along the estuary. Moreover, the equalization of basic public employment service and more job opportunities should be created in the Pearl River Delta region.
引文
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常晓猛,乐阳,李清泉,陈碧宇,萧世伦,涂伟,2014.利用位置的虚拟社交网络地理骨干网提取.武汉大学学报(信息科学版),39(6):706-710.[CHANG Xiaomeng,LE Yang,LI Qingquan,CHEN Biyu,XIAO Shilun,TU Wei,2014.Extracting the geographic backbone of location-based social network.Geomatics and Information Science of Wuhan University,39(6):706-710.]
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程振宇,杜惠英,吕廷杰,2012.社交网络下网络互动对购买意愿的影响因素研究.北京邮电大学学报(社会科学版),14(6):26-31.[CHENG Zhenyu,DU Huiying,LV Tingjie,2012.Study on influence of online interaction on purchase intention in online social network.Journal of Beijing University of Posts and Telecommunications(Social Sciences Edition),14(6):26-31.]
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方滨兴,贾焰,韩毅,2015.社交网络分析核心科学问题、研究现状及未来展望.中国科学院院刊,30(2):187-199.[FANG Binxing,JIA Yan,HAN Yi,2015.Social network analysis-Key research problems,related work,and future prospects.Bulletin of Chinese Academy of Sciences,30(2):187-199.]
顾雯娟,欧向军,叶磊,杨宝宝,2015.基于要素流的长三角城市群空间布局.热带地理,35(6):833-841.[GU Wenjuan,OU Xiangjun,YE Lei,YANG Baobao,2015.Spatial structure of urban agglomeration in the Yangtze River Delta based on the analysis of element flow.Tropical Geography,35(6):833-841.]
何玉梅,2014.社交网络对消费者购买影响分析--基于社交关系的信任购买模型研究.信息系统学报,(1):47-56.[HE Yumei,2014.Power of intimacy:A model between social relationship,trust and online purchase.China Journal of Information Systems,(1):47-56.]
匡耀求,黄宁生,李国敏,2014a.基于土地功能定位的区域人口容量估算模型研究.广东社会科学,(5):13-21.[KUANG Yaoqiu,HUANG Ningsheng,LI Guomin,2014a.Research on regional population capacity evaluation model based on land functional orientation.Social Sciences in Guangdong,(5):13-21.]
匡耀求,姚志远,黄宁生,李国敏,2014b.中国珠三角盆地和日本关东盆地平地人口密度对比研究.热带地理,34(2):217-224.[KUANG Yaoqiu,YAO Zhiyuan,HUANG Ningsheng,LIGuomin,2014b.Comparative study of population density in flat area between the Pearl River basin in China and Kanto basin in Japan.Tropical Geography,34(2):217-224.]
赖茂生,屈鹏,2008.网络用户信息获取语言使用行为研究.现代图书情报技术,(6):16-23.[LAI Maosheng,QU Peng,2008.Study on the users’language behavior to access the web.New Technology of Library and Information Service,(6):16-23.]
李长风,2014.基于社交网络位置数据的区域流动空间特征研究--以长三角城市群为例.上海城市规划,(5):44-50.[LI Changfeng,2014.Study on the regional space of flows based on location data from social network:A case study of city group of Yangtze River Delta.Shanghai Urban Planning Review,(5):44-50.]
李红锦,李胜会,2013.人口迁移承接与珠三角城市经济社会结构演变的耦合.经济地理,33(8):46-51.[LI Hongjin,LI Shenghui,2013.Study on the coupled about population migration to undertake and the evolution of the Pearl River Delta city economic and social structure.Economic Geography,33(8):46-51.]
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