Spatial modeling of Cultural Ecosystem Services from social media data: Systematic review of operability, opportunities, limitations and ways forward

This systematic review explores the use of social media data to spatially model Cultural Ecosystem Services (CES), which are non-material benefits provided by ecosystems that support human well-being. Based on a search of 510 scientific articles in Web of Science and Scopus, we carefully selected and analyzed those that focused on spatial modeling of CES using social media data. We aimed to (a) identify the diversity of CES assessed, (b) analyse the social media platforms used as data sources, (c) evaluate the modelling frameworks employed, and (d) summarise the predictor variables included in these models.

We found that the most studied CES were those related to physical and psychological experiences (62%; especially recreation) and the main predictor variables were the presence of natural elements (52%), land use and land cover maps, and topographic variables, often weighted by applying distance-based metrics (24%). Twenty-four social media data sources were identified but Flickr was by far the most widely used one (40%). MaxEnt (37%) and Random Forest (16%) were the most commonly used modeling tools. The most commonly used metrics to assess model performance were AUC-ROC, AIC, and R2 values.

While the use of social media offers an opportunity to study CES and provide cost-effective and scalable insights, this article discusses some limitations and considerations raised from the literature review to be taken into account when using this type of data. These include the quality and representativeness of social media data, the importance of a clear definition of CES, a proper labeling of social media data, and an appropriate selection of spatial modeling techniques.

Future research should address these limitations and considerations by integrating different data sources and refining methodologies to improve the accuracy and applicability of CES models. This review provides a comprehensive overview of current practices and highlights areas for further investigation in the spatial modeling of CES using social media data.