Visitor Motivation and Landscape Preferences in Shigaoshan Forest Therapy Base
In order to enhance the utilization rate of recreational resources in the Taiyue Mountains and promote the development of local health industry,the study collects the motives to visit and landscape preference of tourists,and adopts the methods of cluster analysis and K-W test to categorize and analyze the motives.The results show that the tourists'visiting motives can be divided into five categories,i.e.,cultural-motivated,health-motivated,economy-motivated,interaction-motivated,and business-motivated,while the health-motivated tourists have the highest proportion(50.85%),followed by the culturally-motivated tourists(21.46%),and the business-motivated tourists are in the lowest proportion(2.29%).There are significant differences(P＜0.05)in landscape preferences among the five categories of tourists for outside-forest landscapes(autumn landscapes outside pure forest,winter landscapes outside pure forest,autumn landscapes outside mixed forest,and winter landscapes outside mixed forest)and artificial landscapes(traditional architectural landscapes and small-town landscapes).The health-motivated tourists prefer diverse types of landscapes with a high preference(average mean rank at 261.04).The culturally-motivated tourists show a significant preference for traditional architectural landscapes(mean rank at 246.33)with pronounced artificial features and small-town landscapes(mean rank at 250.27).The interaction-motivated tourists prefer spatially open landscapes outside forest(mean rank from 253.89 to 245.63).The business-motivated and the economy-motivated tourists have a generally low preference for health-oriented tourism landscapes(average rank means at 207.45 and 213.41,respectively).The research results indicate that the motivation of tourists to travel is closely related to their landscape preferences,and targeted health-oriented landscapes and services could be offered in line with forest therapy bases'characteristics and tourist preferences.
万方数据
维普
