Huang Qiuhua, Xia Linyuan, Li Qianxia, Xia Yixiong
Department of Geography Information Science, School of Geography and Tourism, Huizhou University, Huizhou 516007, China.
Department of Remote Sensing and GIS, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China.
Entropy (Basel). 2024 Jul 18;26(7):607. doi: 10.3390/e26070607.
With the development of positioning technology and the widespread application of mobile positioning terminal devices, the acquisition of trajectory data has become increasingly convenient. Furthermore, mining information related to scenic spots and tourists from trajectory data has also become increasingly convenient. This study used the normalization results of information entropy to evaluate the attraction of scenic spots and the experience index of tourists. Tourists and scenic spots were chosen as the probability variables to calculate information entropy, and the probability values of each variable were calculated according to certain methods. There is a certain competitive relationship between scenic spots of the same type. When the distance between various scenic spots is relatively close (less than 8 km), a strong cooperative relationship can be established. Scenic spots with various levels of attraction can generally be classified as follows: cultural heritage, natural landscape, and leisure and entertainment. Scenic spots with higher attraction are usually those with a higher A-level and convenient transportation. A considerable number of tourists do not choose to visit crowded scenic destinations but choose some spots that they are more interested in according to personal preferences and based on access to free travel.
随着定位技术的发展以及移动定位终端设备的广泛应用，轨迹数据的获取变得越来越便捷。此外，从轨迹数据中挖掘与景区和游客相关的信息也变得越来越容易。本研究利用信息熵的归一化结果来评估景区的吸引力和游客的体验指数。将游客和景区作为概率变量来计算信息熵，并根据一定方法计算每个变量的概率值。同一类型的景区之间存在一定的竞争关系。当各个景区之间的距离相对较近（小于8公里）时，可以建立起较强的合作关系。具有不同吸引力水平的景区通常可分类如下：文化遗产、自然景观以及休闲娱乐。吸引力较高的景区通常是A级较高且交通便利的景区。相当一部分游客不会选择去拥挤的热门景区游览，而是根据个人喜好并基于自由出行的便利性选择一些他们更感兴趣的景点。
