基于社会网络分析法的中国省际入境旅游经济增长空间关联性

doi:10.13249/j.cnki.sgs.2017.11.012

摘要/Abstract

摘要：

收集1991~2014年有关数据,利用社会网络分析法、格兰杰因果关系检验,揭示中国省际入境旅游经济增长的空间关联特征及影响因素,结果发现：① 入境旅游经济增长空间关联网络的关联度较高,但网络整体紧密度不够。② 经济越发达的地区,个体网络密度越高,空间上呈现出阶梯式特征。③ 担任“发动机”的第二板块所含省市,将入境旅游经济增长“动能”沿发挥经纪人作用的第一板块所含省市,传递给第三、四板块所含省市。④ 入境旅游人数关联、旅游资源分布、经济发展关联、交通便捷度是影响入境旅游经济增长空间关联的重要因素。

关键词: 入境旅游, 经济增长, 空间关联, 社会网络分析法

Abstract:

This article collects data from 1991 to 2014 to analyze the spatial correlation of economic growth of inbound tourism in China by using the method of Granger Causality Test and Social Network Analysis. Then, we reveal its influencing factors based on QAP correlation and QAP regression. The results show that: 1) Spatial correlation of China’s inbound tourism economical development is highly connected. There is no independent development area. It exists multiple superimposed effects, and spillovers have been greater than benefit. However, the network is not compact entirely, and connections are dispersive generally. 2) The province economy is more developed, the higher the density of individual network, and the more spatial associations with other provinces. Moreover, the correlations of some central and western regions have more connections and stronger mediating effects. 3) The network structure can be divided into four plates. The first plate is “agent plate” that plays a role of bridge and the second plate is “main beneficial plates” that acts as an engine in the integrated network. The third and the fourth plate are both belong to “main beneficial plates”. The transfer mechanism of spatial correlation is that the second plate conveys energy from the first plate to the third and the fourth plate. 4) Number of inbound tourists, distribution of tourism resources, economic development and traffic convenience are important factors of the spatial correlation of economic growth in China’s inbound tourism.

Key words: inbound tourism, economic growth, spatial correlation, social network analysis

中图分类号:

马丽君, 龙云. 基于社会网络分析法的中国省际入境旅游经济增长空间关联性[J]. 地理科学, 2017, 37(11): 1705-1711.

Lijun Ma, Yun Long. The Spatial Correlation of Economic Growth of Inbound Tourism in China Based on Social Network Analysis[J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(11): 1705-1711.

图/表 6

表1

块模块分析中4种板块模型"

位置内部的关系比例	位置接受到的关系比例
位置内部的关系比例	≈0	>0
≥ $(g K - 1) / (g - 1)$	双向溢出板块	主受益板块
< $(g K - 1) / (g - 1)$	主溢出板块	经纪人板块

表1

图1

表2

表3

图2

表4

影响因素矩阵的QAP回归分析结果"

变量	非标准化回归系数	标准化回归系数	显著性概率	概率1	概率2
截距	0.174	0.000
$N$	0.069	0.130	0.000	0.000	1.000
$T$	0.063	0.068	0.030	0.030	0.970
$G$	0.052	0.050	0.095	0.095	0.906
$S$	0.064	0.072	0.042	0.042	0.959

表4

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地区	网络规模	网络密度（%）	点入度	点出度	关系总数	度数中心度（%）	中间中心度（%）	接近中心度（%）
北京	11	36.36	8	8	16	36.667	0.804	61.224
天津	11	42.73	10	4	14	36.667	0.643	61.224
河北	11	37.27	5	6	11	36.667	1.051	61.224
山西	15	34.29	10	6	16	50.000	2.591	66.667
内蒙古	15	28.57	7	15	22	50.000	2.854	66.667
辽宁	12	28.79	6	7	13	40.000	1.610	62.500
吉林	18	25.82	14	11	25	60.000	4.199	71.429
黑龙江	11	31.82	3	10	13	36.667	1.557	61.224
上海	13	32.05	5	8	13	43.333	1.459	63.830
江苏	10	30.00	4	6	10	33.333	1.105	60.000
浙江	13	41.03	8	9	17	43.333	1.097	63.830
安徽	8	28.57	7	2	9	26.667	0.653	57.692
福建	12	31.82	7	9	16	40.000	1.335	62.500
江西	7	40.48	6	3	9	23.333	0.244	56.604
山东	8	41.07	6	2	8	26.667	0.349	57.692
河南	12	40.15	8	9	17	40.000	1.028	62.500
湖北	22	30.74	21	9	30	73.333	4.841	78.947
湖南	10	38.89	9	5	14	33.333	0.653	60.000
广东	11	43.64	8	8	16	36.667	0.515	61.224
广西	12	29.55	5	9	14	40.000	1.540	62.500
海南	12	32.58	9	6	15	40.000	1.350	62.500
重庆	21	28.57	18	13	31	70.000	5.170	76.923
四川	21	20.95	4	19	23	70.000	7.206	76.923
贵州	9	40.28	8	5	13	30.000	0.408	58.824
云南	9	29.17	7	3	10	30.000	1.133	58.824
西藏	16	27.50	6	12	18	53.333	3.161	68.182
陕西	12	32.58	9	6	15	40.000	1.527	62.500
甘肃	10	35.56	7	5	12	33.333	0.715	60.000
青海	17	31.62	5	14	19	56.667	3.122	69.767
宁夏	21	22.38	14	16	30	70.000	7.073	76.923
新疆	6	30.00	4	3	7	20.000	0.387	55.556