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作 者:王毅[1,2] 陈娱[1,2] 陆玉麒[1,2] 丁正山[1,2] 车冰清[1,2]
机构地区:[1]南京师范大学地理科学学院,南京210023 [2]江苏省地理信息资源开发与利用协同创新中心,南京210023
出 处:《地球信息科学学报》2017年第5期613-624,共12页Journal of Geo-information Science
基 金:国家自然科学基金重点项目(41430635);国家自然科学基金项目(41501122)
摘 要:从科技创新的基础、投入、产出和潜力4方面构建了旅游产业科技创新能力结构模型和综合评价指标体系,利用熵值法、线性加权法以及Arc GIS空间分析工具分析评价了2004、2008、2014年中国旅游产业科技创新能力的总体水平、时空动态演化及驱动因素。结果表明:(1)2004-2014年,中国旅游产业科技创新能力总体上呈不断提高态势,但空间分布极不均衡,存在明显的地区差异,在趋势上基本表现出东西方向递增,南北方向倒"U"型分布态势;(2)中国旅游产业科技创新能力在地理空间上存在着显著而稳定的集聚特征和一定的极化特征,毗邻的区域在旅游科技创新方面存在一定的空间外溢效应;(3)中国旅游产业科技创新能力热点区主要分布在北京、天津和少数东部沿海省份和中部省份,冷点区主要集中在中西部内陆地区的省份;(4)空间残差回归和地理加权回归研究表明,旅游产业基础、空间外溢效应、政策制度因素是驱动中国旅游产业科技创新能力时空变化3个核心因素。Science and technology innovation is not only the core driving force of the development of the tourism industry but also an important part of tourism innovation system. It affects the quality and speed of economic development of the tourism. Science and technology innovation can improve the tourism industrial structure and change the development mode of the tourism industry. This paper builds the structural model and evaluation index system of scientific and technological innovation ability of tourism industry from four respects including base, input, output and potential. Then, we analyzed and evaluated the overall level, space-time dynamics and influencing factors of scientific and technological innovation ability of China in 2004, 2008 and 2014. The results are as follows: from 2004 to 2014, the overall level of scientific and technological innovation ability of China had been growing. But there were obvious regional differences. All the three time sections presented the distributional pattern, namely East-West direction increasing and South-North direction having the inverted"U-shaped'pattern. The spatial autocorrelation analysis showed that there were significant and stable agglomeration features and some polarization features in the scientific and technological innovation ability of China. The type of spatial correlation mainly appeared to "HH" type and "LL" type. Specifically, they were the eastern coastal areas HH cluster and the western inland areas LL cluster. Simultaneously, its spatial structure presented characteristic of lock-in and road dependence. The spatial hot spots of scientific and technological innovation ability of China mainly concentrated in Beijing, Tianjin and a few eastern coastal provinces. And in 2014, Henan, Anhui, Fujian and Guangdong became the spatial hot spots. The spatial cold spots were mainly concentrated in the Midwest hinterland. There were spatial spillover effect of scientific and technological innovation activity among adjacent areas. The spatial error regr
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