Novel insights on intensity and typology of direct human-nature interactions in protected areas through passive crowdsourcing. (November 2020)

• Record Type: Journal Article
• Authors: Ghermandi, Andrea; Sinclair, Michael; Fichtman, Edna; Gish, Moshe
• Is Part Of: Global environmental change. Volume 65(2020)
• Publication Date: 2020-11
• Subjects: Cultural ecosystem services -- Heritage tourism -- Israel -- Nature-based recreation -- Protected areas -- Social media; Environmental policy -- Periodicals; Human ecology -- Periodicals; Nature -- Effect of human beings on -- Periodicals; Environment -- Periodicals; Environnement -- Politique gouvernementale -- Périodiques; Écologie humaine -- Périodiques; Homme -- Influence sur la nature -- Périodiques; Environmental policy; Human ecology; Nature -- Effect of human beings on; Periodicals; Electronic journals; 333.7
• DOI: 10.1016/j.gloenvcha.2020.102189
• Languages: English
• ISSNs: 0959-3780

Abstract

Highlights: We characterize direct human-nature interactions with geotagged SNS data. We investigate visitation in 568 protected areas in Israel and West Bank. Combining multiple SNS sources improves fit with observed data and predictive power. Photo content analysis reveals visitors' interests and differences among SNSs. Photogenic sites and inland waters are over- and under-represented in SNS data. Abstract: Recent advances in geotagging, sharing and automatically analyzing online content from Social Networking Sites (SNS) offer unprecedented opportunities for the analysis of human-nature interactions. Previous studies in this field, however, offer limited insights regarding the benefits of automated content analysis especially at large scales, biases arising from the selection of SNS sources, and the predictive power of visitation models based on SNS data. We explore quantitative and qualitative aspects related to intensity, interests and sentiments associated with on-site experiences in 568 protected areas in Israel and the Palestinian Authority. We analyze counts and content of >100, 000 photographs and tweets from four different SNSs, calibrate visitation models and predict visitation in unmonitored sites, cluster sites based on the typology of human-nature interactions reflected in online photographs, and characterize the polarity of sentiments associated with experiences in individual sites and clusters thereof. We find benefits in combining data from multiple sources and controlling for biases related to sites' photogenicity and type of human-nature interactions. Our results suggest that current best estimates of visitation in unmonitored sites underestimate by 39% the actual number of visits. We discuss how the techniques and findings in this study are applicable in the broader context of the management and conservation of sites of environmental or cultural interest.