Wang Di, Qian Haizhong
Institute of Geospatial Information, Information Engineering University, Zhengzhou, China.
PLoS One. 2025 May 15;20(5):e0323310. doi: 10.1371/journal.pone.0323310. eCollection 2025.
Scenic area attractiveness is a core factor in urban tourism development. Developments in social media and multi-source spatiotemporal data provide a basis for studying complex tourist behaviors, overcoming the limitations of traditional interview survey data. This study combines point of interest (POI), mobile signaling, and microblog check-in data to analyze scenic area popularity in Dali using kernel density analysis, hotspot analysis, and gravity models. It also uses ROST-CM6 to perform sentiment analysis on microblog check-in and text data to obtain tourist satisfaction, and combines the popularity and satisfaction to assess scenic area attractiveness. Additionally, GeoDetector is used to examine the impact of subjective human factors, objective factors of the attractions themselves, and the number of POI facilities around the attractions on the scenic area attractiveness in Dali. We obtained several key findings. First, the distribution of scenic areas in Dali City showed a two-center, multi-point pattern, including two core scenic areas (i.e., Dali Ancient City and Xizhou Ancient Town) and numerous scattered areas. Second, the majority of scenic areas in Dali City were more active in the daytime than at night, whereas Dali Ancient City was most active at night. Tourists in Dali City mostly came from Yunnan Province, neighboring provinces, and economically developed coastal regions. Third, a text-based sentiment analysis revealed numerous high-frequency adjectives reflecting positive sentiment, indicating high scenic area satisfaction. Fourth, the number of internal POIs had the greatest effects on scenic area popularity and attractiveness. Specifically, the more POIs, the more popular and attractive the scenic area. The interactive decision-making power of various factors was greater than the decision-making power of individual factors. These findings provide insight into the determinants of scenic area satisfaction, providing a basis for the development of urban tourism.
景区吸引力是城市旅游发展的核心因素。社交媒体和多源时空数据的发展为研究复杂的游客行为提供了基础,克服了传统访谈调查数据的局限性。本研究结合兴趣点(POI)、移动信号和微博签到数据,运用核密度分析、热点分析和引力模型分析大理景区的人气。还利用ROST-CM6对微博签到和文本数据进行情感分析,以获取游客满意度,并结合人气和满意度来评估景区吸引力。此外,运用地理探测器考察人为主观因素、景区自身客观因素以及景区周边POI设施数量对大理景区吸引力的影响。我们得出了几个关键发现。首先,大理市景区分布呈现双中心、多点格局,包括两个核心景区(即大理古城和喜洲古镇)以及众多分散区域。其次,大理市多数景区白天比晚上更活跃,而大理古城晚上最活跃。大理市游客大多来自云南省、周边省份以及经济发达的沿海地区。第三,基于文本的情感分析揭示了众多反映积极情绪的高频形容词,表明景区满意度较高。第四,内部POI数量对景区人气和吸引力影响最大。具体而言,POI越多,景区越受欢迎且吸引力越大。各因素的交互决策力大于单个因素的决策力。这些发现深入了解了景区满意度的决定因素,为城市旅游发展提供了依据。