Multi dimensional score finds hidden attractions in mountain areas
A Multi-Dimensional Score for Detecting Lesser-Known Points of Interest in Mountain Regions
Social and Information Networks
Summary
Tourists often visit only the most famous spots in mountain regions, causing crowding and environmental stress. The authors created a new way to spot lesser-known places that deserve more visitors by looking at different factors like how well-known or accessible they are. They combined information from free data sources to build this score and showed it works with an online demo. This approach could help spread tourism more evenly and protect popular sites.
What this means in practice
- •For tourism planners: Detect less crowded tourist spots in mountain regions to redistribute visitor flow and reduce environmental strain.
- •For regional marketing teams: Identify and promote lesser-known attractions using a multi-dimensional score based on open data to enrich tourism offerings.
Authors
Adriana Pompejano, Sébastien Laborie, Maxime Masson, Christian Sallaberry, Philippe Roose
Abstract
Overtourism concentrates visitor demand on few iconic sites, while other points of interest (POIs) in the same area often remain known only to a narrower public. This imbalance is especially critical in mountain environments, where limited infrastructure amplifies environmental pressures. Discovering lesser-known POIs is relevant for redistributing tourism demand spatially, valuing underrepresented heritage, and temporally, easing high seasonality pressures on popular POIs. Across the literature, definitions of lesser-known POIs or hidden gems remain scattered, and existing scoring approaches remain incomplete, relying on narrow indicators (e.g., number of photos, reviews, likes) which fail to characterize mountain POIs holistically. These approaches are further constrained by their reliance on commercial proprietary sources, or by questionnaires that are subject to memory bias. To address these gaps, we introduce a novel multi-dimensional metric, the Obscurity Score, that groups fragmented literature into four key dimensions (International Awareness, Attractiveness, Promotion, and Accessibility) to systematically detect and characterize lesser-known POIs and enable their promotion as a first step toward mitigating overtourism. To operationalize this score, we develop a generic pipeline mapping open data sources onto these four dimensions. Preliminary results from a web-based prototype demonstrate the practical relevance of the proposed approach.