Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, Hunan, China.
Faculty of Geomatics, East China University of Technology, Nanchang 330013, Jiangxi, China.
Comput Intell Neurosci. 2022 Mar 2;2022:9559170. doi: 10.1155/2022/9559170. eCollection 2022.
Tourism and transportation generally have an inseparable association. However, there are still many limitations in the existing research on it. For example, most scholars only adopt one single model method, which fails to consider geospatial elements. Moreover, some researchers simply use socioeconomic data for analysis and research and ignore the solid spatial characteristics between tourism and transportation, which leads to deviations in the results. To solve these problems, this article proposed a spatiotemporal association model by comprehensively using coupling coordination degree, gravity center model, and spatial coincidence degree. Based on the tourism economic and attraction spatial data, and the transportation and its network spatial data, the association between tourism and transportation can be revealed by the proposed model. This study conducted a quantitative analysis on the tourism and transportation industry in Jiangxi Province, China, from 2005 to 2019, and the results show that: (1) the coupling coordination degree of tourism and transportation increases year by year; (2) the change in gravity center of tourism and transportation is subtle. The mean value of spatial overlap is 80.33 km, while the mean value of inter-annual variation consistency is 0.56; (3) the spatial coincidence degree of tourism and transportation in Jiangxi Province indicates a steady upward trend and reaches 0.78 in 2019; and (4) based on the evolution trend in the coupling coordination degree, gravity center coupling model, and spatial coincidence degree of tourism and transportation, it can be seen that the slopes of their trend functions are similar and consistent-the slopes are 0.0239, 0.0253, and 0.0319, respectively-and the standard deviation of the slopes of the three is only 0.000018.
旅游与交通一般具有不可分割的联系。然而，目前关于这方面的研究仍存在许多局限性。例如，大多数学者仅采用单一的模型方法，未能考虑地理空间要素。此外，一些研究人员在分析和研究时仅使用社会经济数据，忽略了旅游与交通之间坚实的空间特征，导致结果出现偏差。为了解决这些问题，本文综合运用耦合协调度、重心模型和空间吻合度，提出了一个时空关联模型。基于旅游经济和吸引力空间数据以及交通及其网络空间数据，该模型可以揭示旅游与交通之间的关联。本文对中国江西省 2005 年至 2019 年的旅游和交通产业进行了定量分析，结果表明：（1）旅游与交通的耦合协调度逐年增加；（2）旅游和交通重心的变化很细微。空间重叠的平均值为 80.33 公里，而年际变化一致性的平均值为 0.56；（3）江西省旅游与交通的空间吻合度呈稳步上升趋势，2019 年达到 0.78；（4）根据旅游与交通的耦合协调度、重心耦合模型和空间吻合度的演变趋势，可以看出它们的趋势函数斜率相似且一致——斜率分别为 0.0239、0.0253 和 0.0319，而这三个斜率的标准差仅为 0.000018。
