This study proposes a three-step analytical framework to examine the proximity between Airbnb listings and points of interest (POIs), and to evaluate the spatial dependence between them across dimensions. Grounded in Tobler's first law (TFL) of geography, this framework integrates standard deviational ellipse, average nearest neighbor, and geographically weighted regression methods. The framework is validated using data from 14 U.S. cities of varying sizes, encompassing 122,005 Airbnb listings and 113,169 POIs. Although the results reveal a notable proximity of Airbnb listings to POIs, the degree of spatial dependency varies across cities and property types. Notably, several frequently overlooked POIs, such as schools, post boxes, bakeries, and bicycle rental stations, demonstrate significant relationships with Airbnb listings. This research advances the literature on shared accommodations by elucidating the spatial relationship between Airbnb and POIs, introducing new location factors, and providing a robust analytical framework. The findings provide valuable practical implications for enhancing business and management strategies for Airbnb stakeholders, while balancing the growth of the shared accommodation sector against its effects on local communities.

中文翻译：

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检查共享住宿和兴趣点之间的空间依赖性


本研究提出了一个三步分析框架，用于检查 Airbnb 房源和兴趣点 (POI) 之间的接近程度，并评估它们之间跨维度的空间依赖性。该框架以托布勒地理学第一定律 (TFL) 为基础，集成了标准差椭圆、平均最近邻法和地理加权回归方法。该框架使用来自 14 个不同规模的美国城市的数据进行了验证，其中包括 122,005 个 Airbnb 房源和 113,169 个 POI。尽管结果显示 Airbnb 房源与 POI 显着接近，但空间依赖程度因城市和房产类型而异。值得注意的是，一些经常被忽视的 POI，例如学校、邮政信箱、面包店和自行车租赁站，都显示出与 Airbnb 房源的重要关系。这项研究通过阐明 Airbnb 和 POI 之间的空间关系、引入新的位置因素并提供强大的分析框架，推进了共享住宿方面的文献。研究结果为加强 Airbnb 利益相关者的业务和管理策略提供了宝贵的实际意义，同时平衡共享住宿行业的增长与其对当地社区的影响。