Quantitative Geography Analysis on Spatial Structure of A-grade Tourist Attractions in China

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1 Jan., 2015 Journal of Resources and Ecology Vol.6 No.1 J. Resour. Ecol (1) DOI: /j.issn x Article Quantitative Geography Analysis on Spatial Structure of A-grade Tourist Attractions in China PAN Jinghu 1 *, LI Junfeng 1 and CONG Yibo 2 1 College of Geography and Environmental Science, Northwest Normal University, Lanzhou , China; 2 College of Earth and Environmental Sciences, Lanzhou University, Lanzhou , China Abstract: Tourist attraction is a very important carrier of tourism activities. A-grade tourist attraction is a national standard of comprehensive evaluation about tourist attractions quality and grade in China. In this paper, spatial structure of 2424 national A-grade tourist attractions are investigated by using GIS and quantitative analysis methods, such as nearest neighbor index (NNI), quadrat analysis, Gini coefficient and hot spot clustering. Spatial accessibility of all A-grade tourist attractions was calculated using cost weighted distance method and ArcGIS software. Service range of each tourist attraction at 4A grade and above in China was delimitated based on the cost allocation method. Results show that China s A-grade tourist attractions present aggregate distribution characteristics on the whole, and cultural attractions aggregate distribution is higher than of natural tourist attractions. Above-4A grade quality attractions aggregate distribution is lower than the Below-4A grade tourist attractions; cultural Above-4A grade are significantly greater than the cultural Below-4A grade tourist attractions. A-grade tourist attractions in China s eight districts are aggregate. The spatial distribution uniformity is low. Affected by the degree of economic development and tourism resources endowment, the service range of Above-4A grade attractions is big in West, small in East, big in North, and small in South. First-order hot spots areas were mainly concentrated in the east side of the line formed by Deqen-Alxa Left Banner. The second-order hot spot areas were composed of 11 regions, while the third-order hot spot areas include Beijing, Tianjin, Central Plains and the Yangtze River Delta. Key words: spatial structure; spatial accessibility; tourist attractions; GIS; China 1 Introduction Tourist attraction is an indispensable carrier of tourist activities, which has an important special status in the tourism activities (Guo et al. 2012). Spatial structure of tourist attractions refers to natural attractions and cultural attractions on the spatial correlation and combinations. It is the complex of node (spots), channel (communication) and domain (region) (Xie and Wu 2008), which is a hotspot of research on tourism spatial structure. Spatial structure of tourist attractions not only includes the combination of distribution pattern, grade and quantity, but also caused the spatial behavior of tourists directly, which has a profound impact on the tourist attractions development speed, scale, efficiency, temporal-spatial arrangement, and the nature, extent, and development strategy of tourism regional spatial competition (Weaver 1998; Andreas 2004; Li and Wu 2012). Researches of tourism spatial structure began in the 1960 s, mainly focusing on the spatial analysis and determination research of tourist source market, while on tourist spatial structure is less. Wilson (1967) proposed statistical theory based on tourist spatial distribution model. Miossec (1976) analyzed the evolution process of the destination tourism by using the spatial dynamic method. Dredge (1999) proposed three kinds of spatial structure model about tourism destinations. Sophie and Romain (2009) come to a conclusion that the accessibility have influence on tourism spatial structure and the economic structure. Chinese scholars have been more concerned about the spatial structure of tourist attractions that are mainly concentrated in: the accessibility and transportation of tourist attractions (Jin et al. 2009; Pan and Cong 2012), spatial distribution Received: Accepted: Foundation: National Natural Science Foundation of China (No , ). * Corresponding author: PAN Jinghu. panjh_nwnu@nwnu.edu.cn.

2 PAN Jinghu, et al.: Quantitative Geography Analysis on Spatial Structure of A-grade Tourist Attractions in China 13 Grade 5A-grade 4A-grade 3A-grade 2A-grade 1A-grade Natural tourist attractions Type Cultural tourist attractions Fig.1 Distribution of A-grade tourist attractions characteristics (Yuan et al. 2010; Ma et al. 2013), the spatial structure evolution model (Mao et al. 2011) and spatial structure optimization (Wang and Li 2010). For methods, most researches use Pattern Analysis (Zhang et al. 2011), Lorentz curve and Gini Coefficient (Wei and Chen 2012), varied coefficient and equilibrium degree coefficient (Jin and Xu 2012), fractal (Cui and Sun 2011), etc. On the whole, the studies on spatial structure of tourist attractions evolved from qualitative analysis to the combination of GIS spatial analysis and mathematical statistics method, from the static description to the dynamic research of tourist attractions distribution, and from the spatial pattern description to the spatial structure optimization study. The study scope focused on the provinces, cities, or urban agglomeration in Eastern and Central China, and nationwide comprehensive research is relatively scarce. The intuitive and effective methods to perform the spatial distribution characters of tourist attractions are insufficient. A-grade tourist attraction is a unique quality grade division and evaluation system issued by China National Tourism Administration, which released firstly in Nowadays, A-grade tourist attraction has become a national standard to evaluate the quality and class of tourist attraction comprehensively. This paper adopt the GIS spatial measurement analysis method, quadrat analysis, nearest neighbor distance index, Gini Coefficient and hot spot detection, to analyze spatial structure and distribution characteristics of A-grade tourist attractions in China, which aims to provide a scientific reference for quality tourism resources, spatial structure optimization and regional joint development of attractions. 2 Data and research method 2.1 Data sources and processing China National Tourism Administration issued A-grade Tourist Attractions Directory on its website ( cnta.gov.cn/). Released on the Directory, we merged the repeating spots for updating. By the end of 2012, there are 2424 A-grade tourist attractions in China (Fig. 1). The number of 5A-grade tourist attractions is 127, 4A-grade tourist attractions is 926, 3A-grade tourist attractions is 443, 2A-grade tourist attractions is 815, and 1A-grade tourist attractions is 113. Regions such as Hong Kong, Macao, and Taiwan present special geographical and social barriers and use inconsistent statistical procedures, were not included in the present study. According to the nature of the tourism resources, we divide tourist attractions into two types, natural and cultural, for the convenience of analysis. There are 1108 natural tourist attractions, which including natural tourist attractions, forest parks, nature reserves, rural village, etc. Cultural tourist attractions total 1316, including historical and cultural sites, ancient architectures and gardens, sports halls, museums, amusement parks, theme parks, resorts, etc. We marked the attractions location by ArcGIS9.3 software. The coordinates of A-grade tourist attractions obtained from Google Maps ( ditu.google.cn/), and the attractions with larger areas take their centroidal coordinates as attractions coordinates. We divided all A grade tourist attractions into two grades: 4A and 5A (abbreviation for Above-4A ) and, below 4A (abbreviation for Below-4A ). The Above-4A grade tourist attractions can fully reflect the degree and protection status of the dominated tourism resources; at the same time, this sub-classification also reflects the current tourism development situation and service ability of a region. Traffic data digitized from 1:4,500,000 Chinese Traffic Map by Chinese borders vector data were obtained from the National Geometrics Center of China at a scale of 1:4,000,000. The graphic data unified projection to Albers equal-area conic projection system by ArcGIS9.3. All data are stored in a geographical database.

3 14 Journal of Resources and Ecology Vol.6 No.1, Point pattern analysis Nearest Neighbor Index (NNI) NNI is result of the average distance of the nearest neighbor points divided by the average distance of the random distribution model, to describe deviation degree of the random distribution (Yan et al. 2009): ( d ) N min i= 1 NNI = 0.5 Sqrt A / N ij N ( ) In Formula (1), min (d ij ) is the distance between point and its nearest neighbor point, N is the total number of tourist attraction, A is total area of the study area Quadrat analysis We use a set of quadrats covered on the study area and make overlay analysis. Then we count the sample number on each quadrat. After statistics the number of samples and its frequency with m points, compared with completely random process to determine the spatial distribution characteristics of point mode. The results generally judged by the Variance Mean Ratio (VMR) (Cox and Lewis 1966): VMR=VAR/Mean (2) In Formula (2), VAR = (1) 2 px ( px) 2 n i i i i, Mean n 1 = Σx/m (Mao and Li 2004). Among them, p i is quadrat frequency with i points, x i is the point number in each quadrat, n is the quadrat number, m is the total points. In a Poisson distribution, VMR = 1. When VMR< 1, the object of study has an obvious uniform distribution trend. When VMR> 1, the object has a tendency to concentrate distribution. The more number of quadrats, the smaller the Mean. VAR is smaller and falling faster than Mean, so VMR value becomes smaller, but will not change the type of distribution. Quadrat size determined by the rules of thumb (Mao and Li 2004), namely the quadrat size should be twice the average area of each point Gini coefficient and distribution uniformity degree Gini Coefficient is used to depict the distribution of spatial elements, which can judge distribution of tourist attractions in the study area. In theory, the Gini coefficient is between 0 and 1, the greater the value is, the higher the concentration is. The following is the calculating formula (Yitzhaki 1983): n P ln P i i i= 1 G = (3) ln N In Formula (3), G is the Gini Coefficient, P i is the number of A-grade tourist attractions in the i partition of the A-grade tourist attractions total number in the proportion. N is the number of partitions. C = 1 G (Qi and Wang 2013), which is distribution uniformity Hot spot clustering Hot spots performed lots of spots get together on the space (Getis and Ord 1992), namely is the area with larger density. This paper adopt the Closest Distance Hierarchical Clustering Method (Lance and Williams 1967), which according to the nearest distance of each tourist attraction i, defining a accumulative unit, limit distance or threshold value, and the minimum number of each accumulative unit; then compared accumulative unit with every bit of the nearest distance. When the nearest distance of one point is less than the limit distance, it will be included in the unit. According to the method, original point data will clustering in several ellipse region, named the first order hot area. Similarly, the first order hot area clustering in several ellipse region, named the second order hot area. The second order hot area should go next and so on. 2.3 Partition of tourist attractions in service scope based on the accessibility The accessibility is defined as the average value of trip distance, starting from tourist attraction to its adjacent areas within a certain period of time (Morris et al. 1978; Geurs and Wee 2004; Wang et al. 2011). This paper uses the cost weighted distance method to calculate the shortest weighted distance. The distance is from each grid to a certain objective grid for raster data (Jin et al. 2009). The orthogonal grid with certain accuracy is used to divide research area into raster images. Attribute value of each raster represents its cost. This paper defines the cost as the degree of time consumption required through the raster. In order to improve computational accuracy as much as possible, this paper selected the raster with an area of 1 km 1 km, and set up the reference of time cost value as the minutes required approximately for traveling 1 km on average. The equation was: cost (time cost) = (1/v) 60 (where v represents setting speed of various types of spatial objects). Different land surface types have different schedule types and travel speeds. According to railway mileages and speed standards at different grades of China, and the Technical Standard of Highway Engineering of the People s Republic of China (JTGB ), the various travelling speeds are set as follows: the road network consists of highway, national trunk highway, provincial highway, ordinary road, and railway, with the average travelling speeds of 120, 90, 80, 60, and 120 km h -1, respectively. Land areas except for roads are supposed isotropic, which meant it is able to travel on the land towards any directions. Traveling methods were restricted, mainly referring to on foot. The cost value can be seen in Table 1. By adding road maps and basic geographic maps into ArcGIS as basic maps, with the help of Google Maps, we have obtained the location of A-grade tourist attractions and established a new point shapefile in ArcGIS (ESRI 2006). Obviously, given that a large number of A-grade tourist

4 PAN Jinghu, et al.: Quantitative Geography Analysis on Spatial Structure of A-grade Tourist Attractions in China 15 Table 1 Velocity and time cost of the spatial objects. Spatial objects Velocity (km h -1 ) Time cost (min) National Provincial Ordinary Railway Highway trunk Land highway road highway attractions, we take 4A grade and above attractions which have the most high quality as the analysis object to explore accessibility. Using ArcGIS Cost Weighted, we have calculated the cost time of 4A grade and above attractions based on cumulative cost, and have then calculated the area of the accessibility of tourist attractions. Based on the accessibility of tourist attractions, we partition each service scope of 4A grade and above attractions. Partition standard is if the cost time of any grid to the tourist attraction i less than the time of the grid to any other attractions, then the grid is service scope of attraction i. And by this analogy, service scope of attractions i is composed of a series of consecutive grids for this domain. In addition, the boundaries of the service scope to two tourist attractions cost the same time. 3 Results and analysis 3.1 Distribution characteristics of tourist attractions Analysis of NNI results With the aid of ArcGIS9.3, we calculated the nearest neighbor distance index (NNI) of tourist attractions based on Formula (1). Results are shown in Table 2. The analysis results proceeded with rectangular and circular correction to eliminate the influence of boundary effects. If, for example, all A-grade tourist attractions are taken, the value of NNI without correction is , with rectangular correction is , and with circular correction is Obviously, the results between correction and without correction are inconsequential. In other words, the boundary effects have little spatial effect to the study of Chinese A-grade tourist attractions distribution, can be ignored. Generally recognized (Li et al. 2008) that NNI 0.5 is aggregate distribution and NNI 1.5 is uniform distribution; 0.5<NNI 0.8 is aggregate - random distribution; 0.8<NNI<1.2 is random distribution; 1.2 NNI <1.5 is random - uniform distribution. As the Table 2 shows, the spatial distribution of all types of A-grade tourist attractions on the whole showed aggregate distribution characteristics. Noteworthy, the Z test value is highly significant. Classification: overall, the aggregate distribution of above-4a attractions is lower than below-4a attractions, while above-4a cultural tourist attractions are obviously more fit the aggregate than below 4A cultural tourist attractions Analysis of Quadrat results There are 2424 A-grade tourist attractions in the database, so on average every km 2 area has one A-grade tourist attraction. According to the Rule of Thumb, each quadrat area should be km 2, so this paper uses the size of km km quadrats to analysis distribution patterns of all A-grade tourist attractions (Fig.2). Overlay analysis was carried out on the quadrats with the national boundary, there are 1009 quadrats across A-grade tourist attractions Fig. 2 Quadrats of A-grade tourist attractions distribution. Table 2 Nearest neighbor distance index of tourist attractions distribution. Type Number of samples Average nearest distance (m) Expected nearest distance (m) NNI Z test value All A-grade Above 4A Below 4A Natural A-grade Above 4A Below 4A Cultural A-grade Above 4A Below 4A

5 16 Journal of Resources and Ecology Vol.6 No.1, 2015 Table 3 VMR of quadrat analysis of A-grade tourist attractions distribution. Type All Natural Cultural A-grade Above 4A Below 4A the country, but lots of them are located on the quadrat boundaries. In order to eliminate the influence of boundary effect, we deleted these quadrats. This left 708 quadrats and 1937 A-grade attractions within quadrats; 487 A-grade attractions are not covered by quadrats. Next, the number of A-grade attractions in each quadrat was counted and quadrat frequency variance and quadrat average frequency calculated. According to Formula (2), we can get the VMR. The quadrat side length of Above-4A grade attractions is km; quadrat side length of Below-4A grade attractions is km. Results shown in Table 3. As Table 3 showed, on the whole, VMR of all A-grade attractions is (slightly greater than 1), so we can put it up as aggregate distribution. In analyzing different grades, we find that the Above-4A grade attractions is closer to the aggregate than the Below-4A grade attractions. Namely: high quality tourism resources tend to fit aggregated distribution. This phenomenon is more evident in cultural A-grade attractions; the VMR of Above-4A grade attractions as high as 5.191, much higher than Below-4A grade tourist attractions. Points of view, on the whole cultural attractions concentrate higher than the natural tourist attractions. But for the Below-4A grade attractions, the distribution of the natural A-grade attractions is more clustered than cultural attractions. The above conclusions are consistent with the NNI results Analysis of Gini Coefficient and Distribution Uniformity Degree According to the eight major comprehensive district theory which is proposed by Zhao and Chen (1999), we analyzed the Gini Coefficient based on statistics the number of the A-grade attractions in 8 districts to determine its distribution uniformity, the concrete results shown in Fig. 3 and Table 4. Fig. 3 and Table 4 show that A-grade tourist attractions Gini Coefficient was 0.888, and the distribution uniformity degree: It shows that A-grade tourist attractions in China s 8 districts are concentrated and the distribution uniformity degree is low. Specifically, the assemble region of A-grade tourist attractions are distributed in the middle and lower reaches of the Yangtze River, the North and Southwest China. Those regions are higher than uniform distribution proportions; the others are lower than the uniform distribution proportion. On the one hand, regional economic development level has a notable influence at the distribution of tourist attractions in so much as the degree of economic development will not only affect the infrastructure and traffic conditions which is tourist attractions rely on, but also tourist market. On the other hand, the regional tourism resources endowment is also an important influence factor. In terms of the assembled distribution of A-grade tourist attractions, the nearby tourist resources and tourist market structure tend to have a certain degree of similarity. In such situations there is the danger All Above 4A Below 4A All natural Natural above 4A Natural below 4A All cultural Cultural above 4A Cultural below 4A C Gini Fig. 3 Gini coefficient and distribution uniformity degree of A-grade tourist attractions. Table 4 Spatial distribution of A-grade tourist attractions. Region All Natural attractions Cultural attractions A-grade Above 4A Below 4A A-grade Above 4A Below 4A A-grade Above 4A Below 4A Northeast China North China Shanxi, Shaanxi and Inner Mongolia Middle and lower reaches of the Yangtze River Southeast China Southeast China Northwest China Qinghai-Tibet Total

6 PAN Jinghu, et al.: Quantitative Geography Analysis on Spatial Structure of A-grade Tourist Attractions in China 17 that this excessive concentration might lead to inter-site competition or trigger internal friction something to be vigorously avoided. We suggest the solution is to carry out effective optimization or integration. From the perspective of the grading and classification of tourist attractions, the difference of Gini Coefficient and distribution uniformity degree in large area is negligible. It is noteworthy that relatively, the Above-4A grade tourist attractions are more concentrated than the Below-4A grade tourist attractions in the 8 districts distribution. 3.2 Hot spot detection We adopt the Closest Distance Hierarchical Clustering Method to detect the hot spot distribution of A-grade tourist attractions in China. The result is shown in Fig. 4. It can be seen directly that: (1) On the whole, the first order hot area of A-grade tourist attractions are distributed across the country, but mainly concentrated in the east side of Deqen County Alxa Left Banner line. There are 11 second order hotspot areas: Beijing-Tianjin, Shanghai-Jiangsu, Zhejiang, Shandong peninsula, Chengdu-Chongqing, central Hubei province, eastern and southern Liaoning province, northern Jiangxi province, southern Anhui province, central Henan province, the junction of Shanxi, Henan and Hebei provinces. Third order hotspot areas consist of Beijing- Tianjin-Hebei, the Central Plains and the Yangtze River Delta. (2) Comparison between the natural and cultural tourist attractions, natural tourist attraction have 4 second order hot spot area, namely, Yangtze River Delta, the junction of Shandong, Henan and Jiangsu, central Jiangsu-North Jiangxi, central Shanxi-northern Henan. Second order hotspot areas of cultural tourist attraction number six: Attraction Natural attraction Cultural attraction Above 4A grade Below 4A grade Natural above 4A grade Cultural above 4A grade Natural below 4A grade Cultural below 4A grade First order Second order Third order Fig. 4 Spatial hotspot detection of tourist attractions distribution.

7 18 Journal of Resources and Ecology Vol.6 No.1, km Accessibility (min) Tourist attractions above-4a grade Service range Fig. 5 Accessibility of tourist attractions. Fig. 6 Service range of tourist attractions above-4a grade. Beijing-Tianjin, the boundary of Shandong and Henan, north Henan-central Shaanxi plain, Yangtze River Delta, Chengdu-Chongqing, the junction of Hubei, Hunan and Jiangxi. Third order hotspot area distribution of natural tourist attraction and all A-grade tourist attraction is roughly the same, while cultural tourist attractions to the westward including most of the Shaanxi, Gansu and Sichuan area. (3) Comparison between the Above-4A grade and Below-4A grade: there are 3 second order hotspot areas of Above-4A grade tourist attractions. The biggest one is from central Jilin to southwestern Henan. The other two pieces are Shandong-Henan and Yangtze River Delta. These areas are core areas of quality tourism resource in China. The second order hot areas of Below-4A attractions are widely dispersed. The distribution of third order hotspot area of Above-4A grade is almost similar to and comparable with all A-grade tourist attractions. The third order hotspot area of Below-4A grade tourist attractions is towards to southwest, viz. the Sichuan Basin. It is worth noting that the distribution of second and third order hotspot areas of Above-4A grade and Below-4A grade tourist attractions form large overlapping areas in the Middle East. There are fewer areas of first order hotspot areas in the western region, reflecting the regional distribution imbalance of A-grade tourist attractions. 3.3 Service range measurement We divided A-grade tourist attractions accessibility into 10 time segments: 15min, 30min, 45min, 1h, 1.5h, 2h, 3h, 5h and 8h, for the standard (Fig. 5). Considering A-grade tourist attractions are numerous, and the high quality of 4A grade and above attractions, we took 4A grade and above tourist attractions as the analysis object to explore accessibility. We divided service range of the A grade and above tourist attractions which are followed the principle of the shortest possible time to tourist attractions (Fig. 6). As a result of our intense analysis of the service range of attractions, we conclude that the service range of Above- 4A grade tourist attractions is greater in the West and South and smaller in the East and North of China. The reason is that the tourist attractions are clustered in the eastern China where there has been more economic development, and central China where there has been better tourism resources endowment. In contrast, to the East, western China suffers from a lack of tourist attractions. There is an inverse relationship between accessibility (Fig. 5) and the service range of regions with poor spatial accessibility. The service range of high quality tourist attractions on the East coast and in North China is small compared with those in other regions with poor spatial accessibility. However, one should note that there is significant difference in the service range between each tourist attraction. The largest service range is Héytgah Folk Cultural Tourism Scenic Spot in Xinjiang, which covers km 2. This is almost equal in size to the combined area of five of China s eastern provinces: Jiangsu, Zhejiang, Shandong, Fujian and Anhui. On the other hand, the minimal service range is Beihai-Jingshan park in Beijing, which covers only 3.82 km 2 ; a times difference. Other examples of service ranges with more than km 2 are: Tashilhunpo Monastery of in Shigatse, Tibet; Kuche Palace in Xinjiang; Meri Snow Mountain in Diqing, Yunnan; Mount Qomolangma National Nature Reserve in Shigatse, Tibet; and Xiangsi Lake in Mongolian Autonomous Prefecture of Bayingolin, Xinjiang.

8 PAN Jinghu, et al.: Quantitative Geography Analysis on Spatial Structure of A-grade Tourist Attractions in China 19 4 Conclusion and discussion In this paper, we introduced the GIS spatial analysis and measurement method of geography and a preliminary analysis of the spatial structure of A-grade tourist attractions in China, providing a new way for tourism spatial structure research. Main conclusions are as follows: (1) China s A-grade tourist attractions present an aggregate distribution characteristics on the whole, and NNI = < 1. Cultural attractions aggregate distribution is higher than of natural tourist attractions; Above-4A grade quality attractions aggregate distribution is lower than the Below-4A grade scenic spot; cultural Above-4A grade are significantly greater than the cultural Below-4A grade scenic spot. (2) China s A-grade tourist attractions in China s eight districts are aggregate. The spatial distribution uniformity is 0.112, which is low; the Gini Coefficient is The difference between the Gini Coefficient and the distribution uniformity is very small in these 8 districts. More than half of the A-grade tourist attractions are concentrated in the middle and lower reaches of the Yangtze River and North China. (3) Affected by the degree of economic development and tourism resources endowment, the service scope of Above- 4A grade attractions is big in West, small in East, big in North, and small in South. This correlates with spatial structure and traffic accessibility. China s A-grade tourist attractions are distributed nonuniformly, while they characterized high concentration degree in individual region. According to the core-periphery theory (Friedman 1966), several core tourism regions are suggested to plan in the high concentration zones. On the one hand, we should drive the development of the tourist attractions at the edge, which relying on the advantage of highly concentrated core region tourist attractions. So we can expand the depth of tourism development and the hinterland, make tourism layout keep balance gradually in regions. On the other hand, many tourist attractions in close geographic proximity face fierce internal competition - which has typically led to the cutthroat operating due to the homogeneity. The government and managers need innovation of competition pattern, implements the linkage development, and makes the heterogeneous tourism products, aims at obtained in the development of more space. Limited to the difficulty of data acquisition, this paper does not consider the effect of air and shipping. Neither does the paper take the uneven distribution of population and economic factors such as GDP into account. 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