中国4A级及以上旅游景点（区）空间可达性测度

doi:10.13249/j.cnki.sgs.2012.011.1321

Abstract

Abstract:

Transit route system is link between tourist destination and tourist market, so the good transit system will provide an important theoretical support for optimizing the distribution of scenic spots for planning-making. Scenic spot is a very important carrier of tourism activities. The study of the spatial structure of tourism is receiving increasing attention but methodology so far has used qualitative rather than quantitative methods. The criterion of A-grade scenic spot is a tourist ranking classifiable system in China, consisting of almost all the most popular and important tourist destinations in China. Based on an investigation on 1 063 tourist scenic spots with National AAAA grade (4A for short) and using GIS and some quantitative analysis methods, the spatial structure of scenic spots is investigated, with their characteristics and distribution for different strategies being discussed. Based on the matrix raster data, this article calculates the spatial accessibility of all counties in China using cost weighted distance method and ArcGIS as platforms. Then it discusses the spatial differences of county accessibility of scenic spots by ESDA (Exploratory Spatial Data Analysis). The results show that general scenic spots exhibit an aggregated distribution. Considering the accessibility, it can be found that the human scenic spots are more centralized. The average accessibility is about 60.5 min, and the area where the accessibility of scenic spots within 120 min reaches 63.29%, while the area where the accessibility within 30 min accounts for 19.84% and the longest time needs 595 min which is located at central Tibetan Plateau. The values of the average accessibility of natural scenic spots and human scenic spots are 67.7 and 63.01 min, respectively. And then, the distribution of the accessibility coincides with traffic line. At county level, the estimated values of Moran’s I are all positive using the analysis of spatial association. All the test results indicate that scenic spots and adjacent areas show the strong positive correlation. The distribution of hot spots regarding the accessibility shows a obvious zonal distribution pattern of hot spots-sub-hotspots-sub-cold spots-cold spots from east to west, which the hot spots are in the eastern and southern China, Central Liaoning, Chengdu-Chongqing, Kunming-Guiyang and Hainan. Cold spots are distributed in the border zone of Tibet, Xinjiang and Qinghai Province.

Key words: spatial accessibility, scenic spots, spatial structure, GIS, China

CLC Number:

F592.99

Jing-hu PAN, Yi-bo CONG. Spatial Accessibility of Scenic Spot at 4A Level and Above in China[J].SCIENTIA GEOGRAPHICA SINICA, 2012, 32(11): 1321-1327.

Figures/Tables 7

Table 1

Fig.1

Fig.2

Fig.3

Fig.4

Table 2

Fig.5

References 24

[14]	Linneker B, Spence N.Road transport infrastructure and regional economic development:the regional development effects of the M25 London orbital motorway[J].Journal of Transport Geography,1996,4(2):77-92.
[15]	Goodall B.Dictionary of Human Geography[M].London:Penguin,1987.
[16]	Kwan M P, Murray A T.Recent advances in accessibility research: representation,methodology and applications[J].Geographical Systems,2003,5:129-138.
[17]	Vickerman R.Location, accessibility and regional development:the appraisal of trans-European networks[J].Transport Policy,1996,2(4):225-234.
[18]	曹小曙,薛德升,阎小培.中国干线公路网连接的城市通达性[J].地理学报,2005,60(6):903~910.
[19]	尹海伟,孔繁花,宗跃光.城市绿地可达性与公平性评价[J].生态学报,2008,28(7):3375~3383.
[20]	王丽,邓羽,刘盛和,等.基于改进场模型的城市影响范围动态演变——以中国中部地区为例[J].地理学报,2011,66(2): 189~198.
[21]	钟业喜,刘影,赖格英.江西省红色旅游景区可达性分析及空间结构优化研究[J].江西师范大学学报(自然科学版),2011,35(2): 208~212.
[22]	芦仲进. 中国交通全图[M].北京:地图出版社,2011.
[23]	朱杰,管卫华,蒋志欣,等.江苏省城市经济影响区格局变化[J].地理学报,2007,62(10):1023~1033.
[1]	李琦,孙根年,韩亚芬.中国省际入境旅游资源吸引力与区位可达性的矩阵分析[J].干旱区资源与环境,2008,22(5):101~106.
[2]	靳诚,陆玉麒,范黎丽.基于公路网络的长江三角洲旅游景点可达性格局研究[J].自然资源学报,2010,25(2):258~269.
[3]	靳诚,陆玉麒,张莉,等.基于路网结构的旅游景点可达性分析——以南京市区为例[J].地理研究,2009,28(1):246~258.
[4]	崔大树,孙杨.基于分形维数的湖州旅游景区系统空间结构优化研究[J].地理科学,2011,31(3):337~343.
[5]	卞显红. 长江三角洲国家AAAA级旅游区空间结构[J].经济地理,2007,27(1):157~160.
[6]	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.
[7]	史春云,张捷,尤海梅,等.四川省旅游区域核心——边缘空间格局演变[J].地理学报,2007,62(6):631~639.
[8]	朱竑,陈晓亮.中国A级旅游景区空间分布结构研究[J].地理科学,2008,28(5):607~615.
[9]	张永平,吴健生,黄秀兰,等.海峡西岸经济区旅游景区(点)空间结构分析[J].资源科学,2011,33(9):1799~1805.
[10]	吴必虎,唐子颖. 旅游吸引物空间结构分析——以中国首批国家4A级旅游区(点)为例[J].人文地理,2003,18(1):1~5.
[11]	王振波,徐建刚,朱传耿,等.中国县域可达性区域划分及其与人口分布的关系[J].地理学报,2010,65(4):416~426.
[12]	Hansen W G.How accessibility shapes land-use[J]. Journal of the American Institute of Planners,1959,25:73-76.
[13]	Geurs K T, Bertvan W.Accessibility evaluation of land-use and transport strategies: review and research directions[J].Journal of Transport Geography,2004,12:127-140.
[24]	杨兴柱,顾朝林,王群,等. 南京市内部旅游客流空间测度与模拟[J].地理科学,2011,31(7):802~809.

道路等级	铁路	高速公路	国道	省道	县道
速度（km/h）	100	120	80	60	40
时间成本（min）	0.6	0.5	0.75	1	1.5

分量	所有景点	自然景点	人文景点
Moran′s I	0.7170	0.7660	0.7080
P值	-0.0004	-0.0004	-0.0004
Z值	9.4810	11.2080	8.7740

Spatial Accessibility of Scenic Spot at 4A Level and Above in China

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 24

Related Articles 15

Metrics

Comments

Recommended 0

[1]	Zhang Ziang, Bao Jigang. Effects of Multiple Distances on Inbound and Outbound Tourism Flows in China: A Configuration-based Perspective [J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(1): 13-21.
[2]	Yang Ruozi, Xing Pei, Du Wupeng, Dang Bing, Xuan Chunyi, Xiong Feilin. Climatic Characteristics of Precipitation in North China from 1961 to 2017 [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(9): 1573-1583.
[3]	Zhuang Delin, Wang Pengpeng, Xu Jilan, Zhang Di. Spatial Structure Evolution of China’s Venture Capital City Network: Based on the Analysis Perspective of Four Investment Stages [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(8): 1256-1265.
[4]	Liu Yungang, Liu Xuanyu, Zhang Zhengsheng. The Territorial Structure in South China Sea from Fishermen's Perspective [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1062-1071.
[5]	Qiu Fangdao, Zhang Chunli, Guo Mengmeng, Zheng Ziyan, Shan Yongbing. Heterogeneity of the Coupling Relationship Between the Innovation Capacity and Industrial Transformation of Renewable Resource-based Cities in China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1092-1103.
[6]	Zhao Ruyu, Xu Jun. Spatial Agglomeration and Unbalanced Development of Elements in Northeast China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1104-1113.
[7]	Wang Ying, Liu Hang, Chen Xiaohong, Song Yunting. Evolution Characteristics and Division of Regional Types of Correlation Coupling of Urban-Rural Systems: A Case Study of the Three Provinces of Northeast China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1150-1159.
[8]	Ma Li, Tian Huazheng, Kang Lei, Qi Wei. Assessment of the Social and Economic Support Capacity for the Comprehensive Revitalization of Northeast China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 863-873.
[9]	Xia Han, Zhang Wanshun, Peng Hong, Li Lin, Huang Panpan, Xia Jingjing. Evaluation of Ecological Functions of Urbanized Areas Based on the Plan for Major Functional Areas in China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 882-889.
[10]	Liu Jian, Yang Qingshan, Zhang Yu, Liu Jie, Jiang Xiaojun. Spatial Patterns Evolution and Classification of Population Aging in the Three Provinces of Northeast China Based on the County Scale [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 918-927.
[11]	Deng Qinghua, Xue Desheng, Gong Jianzhou. Influencing Factors of Online Shopping Frequency of Residents and Spatial Differences of These Factors in Guangzhou City [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 928-938.
[12]	Zheng Longfei, Gu Weinan, Long Fenjie, Zhang Su. The Urban Network Structure and Influential Mechanism of Guizhou Province from the Perspective of Various Flows [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 939-947.
[13]	Zhang Hua, Xu Cungang, Wang Hao. Response of Vegetation Change to Meteorological Drought in Northwest China from 2001 to 2018 [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 1029-1038.
[14]	Luo Xiaolong, Cao Shujun, Gu Zongni. The Returning-migrant-induced Urbanization: The New Path of Urbanization in Middle China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(5): 685-690.
[15]	Xue Bing, Xiao Xiao, Li Jingzhong, Xie Xiao. Analysis of Spatial Economic Structure of Northeast China Cities Based on Points of Interest Big Data [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(5): 691-700.