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Patterns of public use in 273 natural treatment systems worldwide are investigated by means of geotagged data from two popular photo-sharing websites, using spatial analysis and regression techniques. Standardized Major Axis (SMA) regression is found to perform better than other univariate calibration models in terms of goodness of fit with reported visitation frequencies and predictive accuracy, and is used to predict visitation rates in 139 systems that are associated with at least one geotagged photograph. High visitation rates are found in free-water surface (FWS) constructed wetlands and mixed pond-constructed wetlands systems, as well as systems treating surface water or stormwater runoff. Geographic Information System (GIS) techniques are used to map hot and cold spots of public use in two highly visited systems. Binomial logit regression reveals that the probability to be associated with at least one geotagged photograph is a function of system size, system type, and influent water quality. The findings are discussed in terms of their implications for the evaluation of public use in multifunctional ecologically engineered systems as well as the applicability of the proposed methodology to other natural and man-made ecosystems. [ABSTRACT FROM AUTHOR]

Geolocated social media data counts are increasingly used as proxy for number of visits in natural areas, including their spatial and temporal distribution. This paper synthesizes the empirical evidence concerning the correlation of social media data counts and visits through multi-level meta-analytical models. Analysis of 355 correlations from 41 studies reveals a strong correlation for annual number of visits over multiple sites (pooled Pearson's r = 0.73) and for monthly visits in a single site (pooled Pearson's r = 0.84). Using data from multiple social media sources improves the correlation. Mixed results are obtained with regard to the effect of social media penetration rate and designation as national park on the correlation. Future studies should focus on broadening the scope of investigation to middle and low-income countries, developing a systematic approach toward the use of covariates, and comparing the results from social media data to those from other emerging monitoring techniques. • 355 correlations between social media data counts and visits are analyzed. • Strong correlation for multi-site annual visits and monthly visits is found. • Using data from multiple social media sources improves correlation. • Covariates may increase correlation but a systematic approach is missing. • A broader empirical basis is needed to fully appreciate the technique's potential. [ABSTRACT FROM AUTHOR]

Social media data are increasingly utilised as a low-cost alternative to visitor surveys in characterising nature-based recreation. However, the information available on individual users is limited and typically does not include provenance, restricting the potential applications and impact of the data. Here we investigate a methodology to estimate social media visitors' home locations at various spatial scales and apply it to the entire network of national parks in Germany. We compare predicted visitor provenance to representative onsite survey data and explore group-specific spatial and temporal patterns of recreation as characterised by users' geotagged photographs. Results show that photograph metadata can be used to assign home locations with accuracies between 62 and 89% depending on spatial scale implemented. Said social media-based predictions are reasonably well representative of the surveyed visitor structure in German national parks with Flickr visitor-days composed of 19% local, 62% non-local German and 19% international visits. • Photographs metadata can be used to assign home locations of Flickr users. • Home locations are predicted with accuracies between 62 and 89% depending on scale. • Flickr visits are 19% local, 62% non-local German and 19% international. • Flickr photographs can characterize seasonal, weekly and daily visitation patterns. • International visitors take more photographs per visit than local visitors. [ABSTRACT FROM AUTHOR]