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《Journal of Northeast Forestry University》 2010-04
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Recreation Behavior of Village Residents in Zhejiang Province

Shi Jiuxi, Wang Xiaoming(Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang 311400, P. R. China); Cheng Feilong(the Forestry Bureau of Yongkang City, Zhejiang Province); Huang Yiqing(the Forestry Bureau of Cixi City, Zhejiang Province); Shen Qiang(the Forestry Bureau of Changxing County, Zhejiang Province)  
Theories of tourism psychology and tourism geography were applied to the study of the recreational behavior of village residents in Zhejiang Province based on household questionnaire survey. Results show that the visit-amount to village parks hase some relationships with residents’ sex and education, and close relationships with their age, occupation, and hobby. The tourists who do not like watching TV have higher visit-amount than those who prefer watching TV, while the group who prefer gathering, playing card, and entertainment has larger visit-amount than the group who do not like these activities. Village residents’ favorite park sceneries are in a decreasing order of big trees, flower and grass, tables and chairs, health exercise appliances, pavilions and porches, water body, fitness equipment, path for body-building, basketball field, and paved ground. The distance from home to a park (O-D pairs) was the most significant factor among spatio-temporal factors related to visit-amount, which would present logarithm attenuation trend when a center park as a visiting destination, or present inverse curve attenuation trend when a small local service-oriented park as a visiting destination. According to the estsblished regression models of the recreation demands for rural residents, it would be propitious to increase visit-amount by improving their living standards and leisure time, developing their extroverted hobbies and making inhabitation area concentrated. The regression models have lower fitting precision and more advantages to explain the causality than the Radial Basis Function (RBF) neural network-based Model, so it is reasonable to use them together.
【Fund】: 国家“十一五”林业科技支撑计划专题(2006BAD03A00-6);; 浙江省科技重大项目(2004C12030)
【CateGory Index】: F592
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