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

doi:10.13209/j.0479-8023.2017.084

摘要/Abstract

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)

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

贺泽亚, 吴必虎, 刘瑜. 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[J]. 北京大学学报自然科学版, 2017, 53(5): 862-872.

Zeya HE, Bihu WU, Yu LIU. Study of Spatial Interaction and Nodal Attractions of Municipal Cities in China from Social Media Check-in Data[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(5): 862-872.

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链接本文: https://xbna.pku.edu.cn/CN/10.13209/j.0479-8023.2017.084

https://xbna.pku.edu.cn/CN/Y2017/V53/I5/862

图/表 9

参考文献 40

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城市	北京	苏州	杭州	南京	广州	香港	嘉兴	无锡	厦门
上海	70261	137847	95936	36741	18033	35742	43273	36550	31652
北京		2558	6050	5308	6621	6080	714	1258	3578
苏州			2928	4574	357	359	1657	7664	353
杭州				1901	1092	717	11088	1204	1050
南京					628	424	411	3343	731
广州						3348	84	182	1198
香港							87	166	388
嘉兴								454	127
无锡									177

城市	北京	苏州	杭州	南京	广州	香港	嘉兴	无锡	厦门
上海	70261	137847	95936	36741	18033	35742	43273	36550	31652
北京		2558	6050	5308	6621	6080	714	1258	3578
苏州			2928	4574	357	359	1657	7664	353
杭州				1901	1092	717	11088	1204	1050
南京					628	424	411	3343	731
广州						3348	84	182	1198
香港							87	166	388
嘉兴								454	127
无锡									177

城市	上海	北京	苏州	杭州	南京	广州	香港	嘉兴	无锡
北京	1067
苏州	85	1028
杭州	165	1123	121
南京	270	898	189	237
广州	1212	1889	1161	1048	1132
香港	1224	1959	1179	1063	1170	119
嘉兴	86	1091	63	79	236	1127	1140
无锡	114	999	34	136	157	1161	1183	92
厦门	821	1722	797	676	846	513	467	745	809

城市	上海	北京	苏州	杭州	南京	广州	香港	嘉兴	无锡
北京	1067
苏州	85	1028
杭州	165	1123	121
南京	270	898	189	237
广州	1212	1889	1161	1048	1132
香港	1224	1959	1179	1063	1170	119
嘉兴	86	1091	63	79	236	1127	1140
无锡	114	999	34	136	157	1161	1183	92
厦门	821	1722	797	676	846	513	467	745	809

变量		解释
因变量	城市节点吸引力	根据PSO算法模拟出的城市节点吸引力P的值
自变量	(a) 人均GDP	2011年各城市常住人口人均GDP
	(b) 人均消费水平	2011年各城市常住人口人均消费水平
	(c) 常住人口规模	2011年各城市常住人口
	(d) 第二产业人口比重	2010年各城市第二产业人口占常住人口比例
	(e) 第三产业人口比重	2010年各城市第三产业人口占常住人口比例
	(f) 劳动人口比重(15~59岁)	2010年各城市劳动人口占常住人口比例
	(g) 成人受教育率	2010年各城市成人受教育率
	(h) 中等教育接受率	2010年各城市中等教育接受率
	(i) 高等教育接受率	2010年各城市高等教育接受率
	(j) 观光旅游竞争力	2011年各城市观光旅游竞争力
	(k) 休闲旅游竞争力	2011年各城市休闲旅游竞争力
	(l) 综合旅游竞争力	2011年各城市综合旅游竞争力