Document Type
Article
Publication Date
2026
DOI
10.1080/26395916.2026.2713041
Publication Title
Ecosystems and People
Volume
22
Issue
1
Pages
2713041 (18 pp.)
Abstract
Quantifying the recreational value of protected natural areas is essential for sustainable management, conservation financing, and informed decision-making. Traditional approaches, such as on-site surveys, are often costly, spatially limited, and constrained by regulatory barriers. This study evaluates the potential of crowdsourced user-generated data to jointly assess visitor perceptions and the economic value of nature-based recreation, using Back Bay National Wildlife Refuge (Virginia, USA) as a case study. We integrated georeferenced photographs and textual content from four platforms (Flickr, TripAdvisor, Yelp, and AllTrails) with supplementary survey data to analyze visitation patterns, visitor sentiment, cultural ecosystem services, and recreational value using the travel cost method. Sentiment analysis revealed predominantly positive visitor perceptions, particularly associated with aesthetic appreciation, wildlife encounters, and recreation. Crowdsourced data identified 1,289 visits with known visitor origins, capturing a broader and more geographically diverse population than surveys alone. Travel cost estimates derived from crowdsourced data yielded an average per-trip cost of US$527, compared to US$91 from surveys, resulting in an annual recreational value of US$74.7 million versus US$13.0 million. These findings demonstrate that integrating multiple crowdsourced platforms enhances recreation assessment along two complementary dimensions: the experiential dimension, defined as the subjective quality of visitor experiences captured through sentiment analysis, and the economic dimension, quantified through travel cost modeling to estimate recreational value. This approach expands spatial coverage, improves origin characterization, and offers a scalable, cost-effective complement to traditional methods for valuing nature-based recreation.
- This study improves recreation assessment by capturing both how visitors experience nature and how they value it economically.
- Publicly available data from online platforms show that visitors consistently report positive experiences, especially related to scenic landscapes, wildlife, and outdoor activities.
- Economic estimates based on these data suggest that the value of nature-based recreation may be substantially higher than previously measured using traditional surveys.
- Using multiple digital data sources provides a scalable and cost-effective way for agencies to monitor recreational use and benefits across large areas.
- Management strategies that protect diverse visitors' experiences can help sustain recreational demand and support long-term economic benefits.
Original Publication Citation
Costadone, L., & Zhang, S. (2026). From reviews to value: Harnessing crowdsourced data to capture visitor perceptions and economic benefits of recreation. Ecosystems and People, 22(1), Article 2713041. https://doi.org/10.1080/26395916.2026.2713041
ORCID
0000-0002-8143-9466 (Costadone), 0009-0000-3352-1492 (Zhang)
Repository Citation
Costadone, L., & Zhang, S. (2026). From reviews to value: Harnessing crowdsourced data to capture visitor perceptions and economic benefits of recreation. Ecosystems and People, 22(1), Article 2713041. https://doi.org/10.1080/26395916.2026.2713041
Supplementary Material
Included in
Data Science Commons, Finance and Financial Management Commons, Natural Resources and Conservation Commons, Natural Resources Management and Policy Commons, Recreation, Parks and Tourism Administration Commons
Comments
Data availability statement: Article states: "All code used for CES keyword classification, BERT sentiment analysis, and data visualization is publicly available at https://github.com/lcostado/BERT-Sentiment-Analysis (https://doi.org/10.5281/zenodo.19484918)."
© 2026 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.