| 基于微博签到数据的成渝城市群空间结构及其城际人口流动研究 |
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| 引用本文： | 潘碧麟,王江浩,葛咏,马明国.基于微博签到数据的成渝城市群空间结构及其城际人口流动研究[J].地球信息科学,2019,21(1):68-76. |
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| 作者姓名： | 潘碧麟 王江浩 葛咏 马明国 |
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| 作者单位： | 1. 南京大学地理与海洋科学学院, 南京 2100232. 中国科学院地理科学与资源研究所, 北京 1001013. 西南大学遥感大数据应用重庆市工程研究中心, 重庆 400715 |
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| 基金项目： | 重庆市高等教育教学改革研究项目（183171）;重庆市2017年高技术产业重大产业技术研发项目（2017-1231）;中国科学院大学生创新实践训练计划项目 |
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| 摘 要： | 随着区域一体化进程的加快,中国城市群快速地发展起来,城市群城际间的人口流动研究得到了国内外学者的关注。城市群空间结构的研究以地理实体空间分析为主,城际人口流动的研究多使用传统统计数据,而将大数据运用于城市群空构特征,并结合传统的社会经济统计数据对该区域人口流动的影响因素进行分析。研究发现：① 微博签到数据进一步解释了成渝城市群呈现出“双核多中心”的组团特征,成都市和重庆主城构成了“双核”;② 微博人口流动的方向会受到行政区划的影响,微博人口流动的强度呈现出一定的等级差异;③ 微博人口流动的强度与方向同社会经济发展水平呈现出相对一致性,即地区生产总值越高、人口规模越大或交通联系强度越强,则人口流动越强烈。
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| 关 键 词： | 城际人口流动 空间结构 影响因素 大数据 微博签到 成渝城市群 |
| 收稿时间： | 2018-05-10 |
Spatial Structure and Population Flow Analysis in Chengdu-Chongqing Urban Agglomeration based on Weibo Check-in Big Data |
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| Bilin PAN,Jianghao WANG,Yong GE,Mingguo MA.Spatial Structure and Population Flow Analysis in Chengdu-Chongqing Urban Agglomeration based on Weibo Check-in Big Data[J].Geo-information Science,2019,21(1):68-76. |
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| Authors: | Bilin PAN Jianghao WANG Yong GE Mingguo MA |
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| Institution: | 1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China3. Chongqing Engineering Research Center for Remote Sensing Big Data Application, Southwest University, Chongqing 400715,China |
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| Abstract: | With the rapid development of regional integration, nowadays the regional inter-city migration gets the more attention of the scholars at home and abroad. Micro-blog, as one of the most popular application in China, has become a hotspot of research in areas such as sociology and computer. Check-in, as one of Micro-blog's functions, can reflect the flow of inter-city population in real time. We used the crawler program to collect the research samples in the Chengdu-Chongqing urban agglomeration in January 2014. The information includes the Micro-blog's unique ID number, the grid coordinates of Micro-blog sending place, and the city code of the registered place, etc. By running this program, a total of 804204 valid Micro-blog check-in data weare obtained from the Chengdu-Chongqing urban agglomeration. Based on Micro-blog checking areas, this study analyzeds the spatial structure of the Chengdu-Chongqing urban agglomeration. And Wwe combined the micro-blog data with the traditional socioeconomic data, in order to analyze the impact factors of the regional migration. The results indicates that the spatial structure of micro-blog shows the characteristics of "many centers of dual-core" group in this area. There are only two cities whose micro-blog flows are more than 100,000. They are Chengdu and Chongqing, forming athe “dual-core”. The direction of Micro-blog flow is affected by administrative division, and the intensity of Micro-blog flow presents a certain grade difference. The network shows an obvious hierarchy, and it closely correlatesnnects with the actual social-economic area closely, such as GDP, population size and the strength of traffic connection. For Chengdu and Chongqing, its GDP ranksed first and second,1, 2 respectively, with athe population size all of greater than 7.59 million and both as a regional transport hubs, it makes their micro-blogWeibo flows areintensity in ranked 1st and, 2nd, places respectively. Lastly, there are still some differences between Micro-blog's space and the actual geographic space inof Chengdu-Chongqing urban agglomeration. In the background of the national Yangtze River Economic Belt and China's new urbanization, we put the network information into the geographical space. Actually In this paper we discovered the spatial network characteristics of Chengdu-Chongqing urban agglomeration, and then this paper pointeds out the influence of socioeconomic factors on Micro-blog cyberspace flow. Of course, there may still be other factors behind Micro-blog's cyberspace, which need to be explored and analyzed in the future. |
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| Keywords: | inter-city migration spatial structure impact factor big data check-in Chengdu-Chongqing Urban Agglomeration |
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