Correlation Fractal Dimension of Spatial Structure of Tourist Spots Systems --A Case Study in Nanjing City
旅游景区（点）系统空间结构关联维数分形研究——以南京市景区（点）系统为例

Using the method of correlation dimension, this paper has studied spatial structure of tourist scenic spots systems in Nanjing. The tourist spots systems are in chaos, and their evolution tendency is driven by strange attractors. Strange attractors are featured as non-integer dimension and infinite nested self-similarity structure, those characters make the fractal method a optimum one to study the structure of tourist spots systems. The fractals are non-scaling and self-organization, and the fractals carry natural optimized structure. The structure of tourist spots system has the character of the fractals, it is therefore feasible to apply the fractal methods for the analysis of the structure of the system. According to geographical character and the tourist spots systems in Nanjing, the author has classified the tourist systems into three parts, namely the northern tourist scenic spots systems, the southern tourist scenic spots systems and the middle tourist scenic spots systems, and has measured the value of correlation fractal dimension of each systems, and analyzed and explained the spatial structure of the systems. It is found that the spatial structure of tourist scenic spots systems in Nanjing is fractal, and it behaves the character of Self-affine Fractal, the spots are aggregated in spatial distributing. The spatial structure of the systems follows the role of the Brown Motion during its evolution process. And the spot systems vary from the center to circumference with different rate at different directions. For instance, the values of correlation fractal dimension are decreasing form the center to circumference, and decreasing rate in the northern tourist scenic spots systems is faster than that in the southern, Furthermore, the fractal of the spatial structure of the systems is quasi-symmetry among different hierarchies, and they are approximately self-similar.