RT Journal Article
T1 User-generated data to predict visitors in environmental areas
A1 Hervés-Pardavila, David
A1 Castro-Atanes, Ana
A1 Loureiro García, María Luz
K1 Flickr
K1 Recreation
K1 Recreational ecosystem services
K1 Tourism
K1 User-generated data
AB The economic valuation of recreational ecosystem services is challenging due to difficulties in obtaining geo-tagged information of users. The objective of this study is to validate crowdsourced and user-generated content in order to predict visitation patterns to 16 national parks in Spain. The results may serve to encourage its utilization in the study of recreational demand in other countries, particularly developing countries, where on-site visitor information may be limited or expensive to gather. The present article employs a negative binomial regression model to evaluate the validity of two sources of data: Flickr and mobile phones. The accuracy of predictions exhibited variation across the 16 parks, indicating that site-specific characteristics, such as the seasonality of visitation patterns, may be of significance. The utilization of mobile phone data for modelling visitors yielded enhanced predictive capacity, as shown by the goodness of fit of the estimated models.
PB Cambridge University Press
SN 1355-770X
YR 2025
FD 2025-11-05
LK https://hdl.handle.net/10347/44025
UL https://hdl.handle.net/10347/44025
LA eng
NO Hervés-Pardavila D, Castro-Atanes A, Loureiro ML. User-generated data to predict visitors in environmental areas. Environment and Development Economics. Published online 2025:1-19. doi:10.1017/S1355770X2510020X
NO The authors would like to thank Programa de Ciencias Mariñas de Galicia for funding part of this research, and the project PID2022-142642OB-I00 from the State Reseach Agency .
DS Minerva
RD 24 abr 2026