Destination attractiveness research has become an important research domain in leisure and tourism economics. But the mobility behaviour of visitors in relation to local public transport access in tourist places is not yet well understood. The present paper seeks to fill this research gap by studying the attractiveness profile of 25 major tourist destination places in the world by means of a ‘big data’ analysis of the drivers of visitors' mobility behaviour and the use of public transport in these tourist places. We introduce the principle of ‘the path of least resistance’ to explain and model the spatial behaviour of visitors in these 25 global destination cities. We combine a spatial hedonic price model with geoscience techniques to better understand the place-based drivers of mobility patterns of tourists. In our empirical analysis, we use an extensive and rich database combining millions of Airbnb listings originating from the Airbnb platform, and complemented with TripAdvisor platform data and OpenStreetMap data. We first estimate the effect of the quality of the Airbnb listings, the surrounding tourist amenities, and the distance to specific urban amenities on the listed Airbnb prices. In a second step of the multilevel modelling procedure, we estimate the differential impact of accessibility to public transport on the quoted Airbnb prices of the tourist accommodations. The findings confirm the validity of our conceptual framework on ‘the path of least resistance’ for the spatial behaviour of tourists in destination places.

目的地吸引力研究已成为休闲旅游经济学的一个重要研究领域。但是，与旅游景点当地公共交通便利相关的游客的流动行为尚未得到很好的理解。本论文旨在通过对游客出行行为的驱动因素和在这些旅游景点中使用公共交通工具的“大数据”分析，研究世界上 25 个主要旅游目的地的吸引力概况，从而填补这一研究空白。 . 我们介绍了“阻力最小路径”的原理' 来解释和建模这 25 个全球目的地城市中游客的空间行为。我们将空间特征价格模型与地球科学技术相结合，以更好地了解游客出行模式的基于地点的驱动因素。在我们的实证分析中，我们使用了一个广泛而丰富的数据库，结合了源自 Airbnb 平台的数百万条 Airbnb 房源，并辅以 TripAdvisor 平台数据和 OpenStreetMap 数据。我们首先估计 Airbnb 房源的质量、周围的旅游设施以及与特定城市设施的距离对列出的 Airbnb 价格的影响。在多级建模程序的第二步中，我们估计公共交通的可达性对旅游住宿的 Airbnb 报价的不同影响。