Scenic area attractiveness.

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业发展的核心驱动要素。社交媒体与多源时空数据的发展为复杂游客行为研究提供了坚实支撑，同时克服了传统访谈调研数据的局限性。本研究结合兴趣点（Point of Interest，POI）、手机信令与微博签到数据，采用核密度分析、热点分析以及重力模型对大理市的景区热度展开分析；此外，本研究借助ROST-CM6工具对微博签到数据与文本数据进行情感分析，以获取游客满意度，并结合景区热度与满意度对景区吸引力开展综合评估。同时，本研究还利用地理探测器（GeoDetector），探究了主观人文因素、景区自身客观条件以及景区周边POI设施数量对大理市景区吸引力的影响机制。本研究取得多项关键结论：其一，大理市景区空间分布呈现"双核心、多节点"格局，包含大理古城与喜洲古镇两大核心景区，以及大量分散分布的景区点位；其二，大理市多数景区日间活跃度高于夜间，但大理古城的夜间活跃度达到峰值，到访大理的游客主要来自云南省内、周边省份以及经济发达的沿海地区；其三，基于文本的情感分析结果显示，大量高频形容词均体现出积极情感倾向，表明游客对景区的满意度较高；其四，景区内部POI数量对景区热度与吸引力的影响最为显著，具体表现为景区内POI数量越多，其热度与吸引力便越高；此外，各影响因素的交互解释力均高于单因素的单独解释力。上述研究结论揭示了景区满意度的影响机制，可为城市旅游业的发展提供理论支撑与实践参考。