中国入境旅游经济空间格局的时空动态性

doi:10.13249/j.cnki.sgs.2014.011.1299

摘要/Abstract

摘要：

以中国31个省域作为研究单元,以入境旅游外汇收入作为入境旅游经济的衡量指标,运用ESTDA框架,通过ESDA、LISA时间路径和时空跃迁等方法,从时空耦合的视角对中国2001~2011年省域入境旅游经济空间格局的时空动态性进行分析。研究发现：中国入境旅游经济存在弱正相关,表现为一种弱积聚格局,入境旅游经济空间差异呈现先缩小后扩大的趋势;东部沿海旅游经济发达地区具有更加动态的局部空间结构,而中西部旅游经济欠发达地区具有更加稳定的局部空间结构;湖北省在空间依赖方向上具有最大的波动性,而浙江省在空间依赖方向上具有最大的稳定性;中国入境旅游经济空间格局的演化具有较强的空间整合性,出现协同高增长趋势的省份主要集中在东部地区,而出现协同低增长趋势的省份主要集中在中西部地区;中国入境旅游经济的局部区域结构和空间自相关性非常稳定,具有一定的路径依赖或锁定特征。

关键词: 入境旅游经济, 时空动态性, 探索性空间数据分析, LISA时间路径, 时空跃迁, 空间自相关

Abstract:

The importance of space to many socioeconomic processes has been gaining attentions and recognition. The spatial effect in regional differences studies is now an important theme in the literature. But, both spatial and temporal attributes of data are important, existing approaches focused primarily on one of these attributes. The empirical analysis should take into account that the nature of tourism economic development is both spatial and temporal. Using the methods of exploratory spatial data analysis (ESDA), LISA Time Path, LISA space-time transition measure and STARS (Space-Time Analysis of Regional Systems) software, this article analyzed the space-time dynamics of the inbound tourism economic development in China, measured by the foreign exchange revenue from China's inbound travel, throughout 31 Chinese mainland provinces from 2001 to 2011. Empirical results show that the inter-province inbound tourism economy is weakly auto-related, and the inter-province inbound tourism economic differences are dwindled first and enlarged afterward. By means of LISA time path analyses, this article finds that the local spatial structure of eastern coastal China tends to be more dynamic while the central and western regions are more stable. The longest three provinces of LISA time path are Beijing (3.804), Shanghai (3.625) and Tianjin (2.187) while the shortest three provinces are Shanxi (0.288), Ningxia (0.400) and Gansu (0.402). The maximum and minimum of the tortuosity of LISA time path are Hubei (16.816) and Zhejiang (1.171). Hubei has a most fluctuating local spatial dependence evolution in direction. According to the directional Moran scatter plot, the predominant direction involves downward moves of both a province and its neighbor. Following this are upward moves. Taking these together, there is striking evidence of strong spatial integration in evolution of spatial pattern of inbound tourism economy in China at the provincial level. According to the local Moran's I transition probability matrix and space-time transitions measures, the relative mobility of the local Moran transition matrices is relatively small. The frequency of type 0 transition is 0.987. This means that local inbound tourism economic structure and local spatial autocorrelation in China are very stable.

Key words: inbound tourism economy, space-time dynamics, ESDA, LISA time path, space-time transitions, spatial autocorrelation

中图分类号:

F590

郭永锐, 张捷, 卢韶婧, 吴荣华. 中国入境旅游经济空间格局的时空动态性[J]. 地理科学, 2014, 34(11): 1299-1304.

Yong-rui GUO, Jie ZHANG, Shao-jing LU, Rong-hua WU. Spatio-temporal Change of the Inbound Tourism Economic Development in China[J]. SCIENTIA GEOGRAPHICA SINICA, 2014, 34(11): 1299-1304.

图/表 4

参考文献 33

[1]	Goodchild M F, Anselin L, Appelbaum R P, et al.Toward spatially integrated social science[J]. International Regional Science Review, 2000, 23(2): 139-159.
[2]	Krugman P.The role of geography in development[J]. International Regional Science Review, 1999, 22(2): 142-161.
[3]	Rey S J.Spatial analysis of regional income inequality[C]//Goodchild M et al. Spatially integrated social science: examples in best practice. Oxford: Oxford University Press, 2004:280-299.
[4]	Abreu M, Groot D, Florax R.Space and growth[J]. Région et Développement, 2005, 21:13-40.
[5]	Ye X Y, Carroll M C.Exploratory space-time analysis of local economic development[J]. Applied Geography, 2011,31(3): 1049-1058.
[6]	Rey S J, Ye X.Comparative spatial dynamics of regional systems[C]//Páez J et al. Progress in spatial analysis: theory, computation, and thematic applications. Heidelberg: Springer, 2010.
[7]	Anselin L.Spatial econometrics: Methods and models[M]. Dordrecht: Kluwer, 1988.
[8]	Rey S J, Janikas M V.STARS: Space-time analysis of regional systems[J]. Geographical Analysis, 2006, 38(1): 67-86.
[9]	Yang Y, Wong K K F. Spatial distribution of tourist flows to China's cities[J]. Tourism Geographies, 2013, 15(2): 338-363.
[10]	Zhang Y, Xu J H, Zhuang P J.The spatial relationship of tourist distribution in Chinese cities[J]. Tourism Geographies, 2011, 13(1):75-90.
[11]	Yang X Z, Wang Q.Exploratory space-time analysis of inbound tourism flows to China cities[J]. International Journal of Tourism Research, 2014, 16(3): 303-312.
[12]	Wen J J, Sinha C.The spatial distribution of tourism in China: trends and impacts[J]. Asia Pacific Journal of Tourism Research, 2009, 14(1): 93-104.
[13]	乌铁红,张捷,李文杰,等. 中国入境旅游经济发展水平的空间格局演变及成因——基于入境旅游经济区位熵的分析[J]. 干旱区资源与环境, 2009, 23(5): 189~194.
[14]	任瀚,苗长虹. 中国入境旅游业时空变异机制的定量分析[J].人文地理, 2007, 22(3): 56~59.
[15]	王良健,李晶晶,陈锦龙. 中国省际旅游业发展演进的时空特征[J]. 地理科学进展, 2010, 29(10): 1249~1255.
[16]	赵安周,白凯,卫海燕. 中国入境旅游重心演变与省域空间分异规律[J]. 陕西师范大学学报(自然科学版), 2011, 39(4): 97~102.
[17]	王淑新, 何元庆, 王学定. 中国入境旅游经济的集聚与分散的实证分析[J].中国软科学, 2011,(10):123~130.
[18]	Wang S X, He Y Q, Wang X D, et al.Regional disparity and convergence of China's inbound tourism economy[J]. Chinese Geographical Science, 2011, 21(6):715-722.
[19]	郭金海,韩雪,罗浩,等. 省域入境旅游经济的区域差异及发展模式[J]. 中国人口·资源与环境, 2009,19(5): 131~135.
[20]	宣国富. 中国入境旅游规模结构的省际差异及影响因素[J]. 经济地理, 2012,32(11):156~161.
[21]	陈秀琼,黄福才. 中国入境旅游的区域差异特征分析[J]. 地理学报, 2006,61(12): 1271~1280.
[22]	靳诚,陆玉麒,徐菁. 1995年来中国省际入境旅游相对优势演化的空间特征分析[J]. 人文地理, 2010,25(1): 148~154.
[23]	陆林,余凤龙. 中国旅游经济差异的空间特征分析[J]. 经济地理, 2005, 25(3): 406~410.
[24]	汪德根,陈田. 中国旅游经济区域差异的空间分析[J]. 地理科学, 2011, 31(5): 528~536.
[25]	叶护平,韦燕生. 中国旅游业发展区域差异的系统分析. 华中师范大学学报(自然科学版), 2005, 39(3): 395~398.
[26]	Goodchild M, Glennon A.Representation and computation of geographic dynamics[C]//Hornsby K S, et al. Understanding Dynamics of geographic domains. Boca Raton: CRC Press, 2008, 13-30.
[27]	Cliff A D, Ord J.Spatial processes, models and applications[M]. London: Pion, 1981.
[28]	Anselin L.Local Indicators of Spatial Association—LISA[J]. Geographical Analysis, 1995, 27(2): 93-115.
[29]	Ye X Y.Comparative space time dynamics [D]. Santa Barbara: University of California, Santa Barbara and San Diego State University, 2010.
[30]	Ye X Y, Rey S.A framework for exploratory space-time analysis of economic data[J]. The Annals of Regional Science, 2013, 50(1): 315-339.
[31]	lang="CN">中华人民共和国国家旅游局.中国旅游统计年鉴[M].北京:中国旅游出版社,2002~2012.
[32]	Rey S J, Murray, A T, Anselin L.Visualizing regional income distribution dynamics[J]. Letters in Spatial and Resource Sciences, 2011, 4(1): 81-90.
[33]	Rey S J.Spatial empirics for economic growth and convergence[J]. Geographical Analysis, 2001, 33(3): 195-214.

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