Accommodation services oriented to different tourist segments usually have different price determinants. Thus, in multi-facet destinations such as large regions or cities, it should be possible to find and describe the underlying types of tourism in the destination by using a price determinant analysis. In this paper, a methodology based on stepwise geographically weighted regression (GWR) is developed, using a k-means clustering algorithm to determine the different types of tourism existing in a large geographical area. The method is applied to the island of Gran Canaria (Canary Islands, Spain), using a database of more than 2000 peer-to-peer accommodation units spread over the geography of the island. As a result, it was possible to identify and classify eight different clusters of types of tourism within this geographical area. This methodology can be used in other geographical areas to identify the different types of tourism developed in them.

面向不同旅游群体的住宿服务通常具有不同的价格决定因素。因此，在大区域或城市等多方面的目的地中，应该可以通过使用价格决定因素分析来发现和描述目的地的潜在旅游类型。在本文中，开发了一种基于逐步地理加权回归 (GWR) 的方法，使用k-means聚类算法来确定大地理区域中存在的不同类型的旅游。该方法应用于大加那利岛（西班牙加那利群岛），使用的数据库包含分布在该岛地理上的 2000 多个点对点住宿单元。因此，可以在该地理区域内识别和分类八个不同的旅游类型集群。这种方法可用于其他地理区域，以确定在这些区域开发的不同类型的旅游业。