基于社交网络签到数据的城市空间相互作用和节点吸引力研究

发布时间：2018-05-21 20:06

  本文选题：地理位置签到 + 空间相互作用　； 参考：《北京大学学报(自然科学版)》2017年05期

【摘要】：基于社交网络大数据的研究视角,选取全国348个城市之间一年的跨城市社交媒体地理位置签到数据,采取优化粒子群(PSO)方法,使用引力模型,逆向推导该系统中空间相互作用的距离衰减函数以及各城市的节点吸引力。通过引入经济发展水平、产业结构、人口规模和结构、旅游竞争力、教育水平5个方面的12项变量,经过因子分析和回归分析,探究这些变量对节点吸引力的影响作用。结果表明,社交媒体签到系统中的交互流量符合距离衰减的幂律函数,与其他交互系统相比,其距离衰减系数偏小,说明全国尺度的城市间人的移动受距离影响不明显。对全国城市节点吸引力及其排名的进一步分析发现,与旅游竞争力、城市发展成熟度、人口规模这几个维度相关的因子对社交媒体签到系统中的城市节点吸引力有显著的影响。研究结论将为更好地理解人类签到和移动行为,为进一步了解复杂网络系统中节点吸引力的内涵做出一定的理论和实际贡献。
[Abstract]:Based on the research perspective of social network large data, this paper selects the data of cross city social media geographic location between 348 cities of the country for one year, adopts the optimization particle swarm (PSO) method, uses the gravitational model, and deduces the distance attenuation function of the spatial interaction in the system and the attraction of the nodes of each city. There are 12 variables in 5 aspects: exhibition level, industrial structure, population size and structure, tourism competitiveness and education level. Through factor analysis and regression analysis, the influence of these variables on the attraction of nodes is explored. The results show that the interaction flow in the social media signature system meets the power law function of distance attenuation and is associated with other interactive systems. The distance attenuation coefficient is small, which indicates that the distance between urban people is not affected by the distance of the national scale. Further analysis of the attraction and ranking of the national urban nodes finds that the factors related to tourism competitiveness, urban development maturity, and population size are related to the urban nodes in the social media sign system. Gravity has a significant impact. The research conclusions will be a better understanding of human signature and mobile behavior, and make some theoretical and practical contributions to further understanding the connotation of node attraction in complex network systems.
【作者单位】： 北京大学城市与环境学院旅游研究与规划中心;北京大学遥感与地理信息系统研究所;
【基金】：国家自然科学基金(41271151)资助
【分类号】：F299.23;TP18
，

本文编号：1920593