A comprehensive framework for automated identification of cultural ecosystem services

Richard Kovárník, Jitka Janová and David Hampel

Ecosystem Services, 2025, vol. 75, issue C

Abstract: Cultural ecosystem services (CES) represent intangible values, making them inherently challenging to analyze. In this study, we present a framework that combines web scraping, text mining, and statistical analysis to gain deeper insights into how people perceive valuable ecosystems and the benefits they derive from them. A total of 4,760 public reviews were collected from the Google Maps platform using dynamic web scraping techniques. Machine learning-based topic modelling was then applied to identify key themes related to CES in selected national parks and protected areas across the Czech Republic. Finally, we tested the hypothesis that the relative frequency of specific topics varies significantly between locations. The proposed approach proved effective in the automated evaluation of CES and in highlighting the distinctive features of the studied sites.

Keywords: Web scraping; Topic modelling; Correspondence analysis; Cultural ecosystem services; Crowdsourced data; Automation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecoser:v:75:y:2025:i:c:s2212041625000749

DOI: 10.1016/j.ecoser.2025.101770

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