基于POI的住宿业时空格局演化及与旅游景点的空间关联研究

Spatial and temporal evolution of the accommodation industry and spatial association with tourist spots based on POI

以乌鲁木齐市住宿类POI(2012年、2018年)为研究对象,利用核密度法和标准差椭圆法探究住宿设施时空演变特征,在此基础上从旅游地域系统角度出发,利用分形维数模型和相关数据统计分析方法测度住宿设施与旅游吸引物的空间耦合情况,旨在为城市住宿和旅游空间优化提供科学参考。结果显示:(1)主城区住宿设施总体沿河滩路、阿勒泰路等重要交通路线分布,数量明显增长,米东新区等地有演变为外围集聚中心的趋势。(2)住宿设施总体向东北方向移动,轴向集聚转为离散分布,2012年各类型空间分布方向和范围差异较大,2018年三星级和经济型空间离散化趋势最为明显。(3)普通型住宿设施由孤立核模式演变为带状多核模式,呈现明显的中心集聚―外围扩散趋势;经济型酒店由点状集聚转变为带状集聚。(4)住宿设施与旅游景点的空间耦合性较高,在主要道路缓冲区内具备较强的空间协调性,旅游地域系统处于单中心集聚发展的初级阶段,分形结构有待优化。

The urban An urban tourism regional system consists of tourism service facilities and tourist attractions.Accommodation facilities,which are the part of tourism service facilities,represent regional progress and investment opportunities.Spatial distribution patterns have an important effect on the development of the urban accommodation industry and urban spatial structure.In addition,it is crucial to explore the relationship between accommodation facilities and tourist attractions for regional tourism systems as the spatial coupling and coordination of these two parts determine the structure,function,and level of a tourism regional system.Urumqi,a large city in western China,has seen an increasing number of tourist receptions and resources in recent years.Tourism has become a strategic pillar industry in Urumqi;however,the temporal and spatial variation of accommodation has not been explored and the coupling relationship between the city and the tourism area system has hardly been studied.Therefore,this paper utilizes the kernel density estimation and standard deviational ellipse model to explore the spatial changes of accommodation facilities using Urumqi as a case.Then,the spatial coordination relationship between accommodation facilities and tourist attractions in Urumqi is explored based on fractal dimension theory and spatial data statistics methods.We found that the overall pattern of accommodation facilities in Urumqi changed significantly,increasing from 915 to 3,123 between 2012 and 2018.The accommodation facilities are basically distributed in the axial direction along important transportation lines such as Hetan highway and Altay road.Additionally,the number of accommodation facilities in the Midong New Area increased significantly,forming a concentration there.In 2018,accommodation facilities were shifting to the northeast significantly and axial aggregation model evolved into patterns of increasing trends of discrete distribution,with the most obvious trend being the dispersal of three-star and economic accommodations.The ordinary type of accommodation facilities in the main urban area has transferred from isolated cores to a belt multi-core model.Additionally,budget hotels have changed from point aggregation to a belt aggregation model.The aggregate fractal dimension results show that tourism attractions,with the International Grand Bazaar as the core,have evolved into a self-organizing fractal system.However,the fractal structure of the tourism service facility,which has the Southern Airlines Pearl International Hotel as its core,is at a low level.The integrated aggregation dimension of tourist facilities and tourist attractions is 1.18,demonstrating that the spatial coupling of these two parts is high.However,the fractal structure of tourist attractions and tourist service facilities has not reached an ideal state and must be optimized and adjusted.To further measure the spatial relationship between these two parts,this study establishing a fishnet grid by calculating the number of the two parts in each grid.These results yielded a Pearson correlation coefficient of 0.77,showing a significantly strong correlation.A linear regression equation is obtained by counting the number of the two parts in a five-level buffer of the main road,and the R2 value obtained was 0.926.This demonstrates that the accommodation facilities and tourist attractions in Urumqi’s tourism regional system have strong spatial coupling and coordination,and the relevance of industrial functions determines their proximity in spatial distribution.