Abstract:

To investigate the spatial interaction effect and nodal attractions of cities, a set of inter-city social network location-based check-in data with a time span of one year among 348 municipal cities in China is examined with a PSO (Particle Swarm Optimization) method and the gravity model. Twelve variables related with economic development, industrial structure, population scale and structure, tourism competitiveness and educational level are introduced to further investigate their influences on nodal attractions of cities. The results indicate a distance decay effect which is relatively weaker than in other systems, suggesting that human mobility at the regional level is less sensitive to the change in geographic distance. A close examination of the nodal attractions suggests variables related to the cities’ tourism competitiveness, maturity of development and population scale significantly influence the value of nodal attractions. This article will serve as a stepping-stone for a better future understanding of human travel pattern, check-in behaviors and the real meaning of nodal attractions in some complicated networks.

Key words: location-based, check-in, spatial interaction, gravity model, nodal attraction, PSO

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。

关键词: 地理位置签到, 空间相互作用, 引力模型, 节点吸引力, 优化粒子群法(PSO)