基于神经网络法与加权模型的乡村旅游用地竞争力评价研究——以北京市密云区为例

科学评价乡村旅游用地竞争力并识别其障碍因素是优化乡村旅游产业布局的前提和基础。以北京市密云区为例,从资源禀赋特征、区域生态环境和旅游开发条件3个维度选择13项指标,构建了基于神经网络法的旅游用地竞争力评价模型,以揭示密云区乡村旅游用地竞争力的格局。引入障碍因素诊断模型,分析主要乡村旅游用地竞争力类型的限制因素。结果表明:(1)乡村旅游用地低竞争力类以组团状形式,在密云区东南部和密云水库东北集中连片分布;中竞争力类沿“潮河轴带”“白河轴带”“安达木河轴带”和北部山区带状分布;高竞争力类以点状形式,涵盖张家坟村、司马台村、贾峪村、河北村、石城村、圣水头村、石马峪村和龙潭沟村。(2)以中竞争力类区域为例,基于主要障碍因素,将其划分为资源、环境和开发障碍型,并分别提出优化对策。研究成果可为乡村产业用地规划提供参考。

Evaluation of rural tourism land competitiveness based on neural-network method and weighted model: a case study of Miyun District in Beijing,China

Scientific evaluation of the competitiveness of rural tourism land and identification of its obstacles are the premise and basis for optimizing the layout of rural tourism industry, and also an effective way to achieve rural revitalization.In the present study, Miyun District of Beijing was selected as an sample. Thirteen indicators from three dimensions of resource endowment characteristics, regional ecological environment and tourism development conditions were selected to construct a tourism land competitiveness evaluation model based on neural-network method, which would help reveal the pattern of rural tourism land competitiveness in Miyun District. The diagnostic model of obstacle factors was introduced to analyze limiting factors of the competitiveness types of main rural tourism land. The results were shown as follows. (1) Low-competitiveness rural tourism land was distributed in clusters in the south of Miyun District and northeast of Miyun Reservoir. Medium-competitiveness rural tourism land was distributed along Chao River Axis Zone, Bai River Axis Zone, Andamu River Axis Zone and northern mountainous belt, with 176 units in Xin’anzhuang and Fenggezhuang Village and so on. High competitiveness rural tourism land was in the form of dots, covering the villages of Zhangjiafen, Simatai, Jiayu, Hebei, Shicheng, Shengshuitou, Shimayu and Longtangou. (2) The medium competitiveness region was divided into the resource, environment and development obstacle types based on main obstacle factors, and corresponding optimization countermeasures were put forward, respectively. The results could provide references for rural industrial land planning.