Mapping indicators of cultural ecosystem services use in urban green spaces based on text classification of geosocial media data

Abstract

In our increasingly urbanized world, the cultural ecosystem services (CES) provided by urban nature play a crucial role in enabling and maintaining the well-being of urban dwellers. Despite the increased number of studies leveraging geosocial media data for more efficient and socio-cultural-oriented CES assessment, the high complexity and costs associated with existing methods such as manual or automated image classification hinder their application in urban planning and ecosystems management. This study introduces a novel method that draws on the semantic similarity between word2vec word embeddings to classify large volumes of geosocial media textual metadata and quantify indicators of CES use. We demonstrated the applicability of our approach by quantifying spatial patterns of aesthetic appreciation and wildlife recreation in the green spaces of the city of Dresden based on the classification of >50,000 geotagged Instagram and Flickr posts. Moreover, we analyzed and mapped semantic patterns embedded in geosocial media and gained essential insights that can contribute toward a context-dependent assessment of CES use, which in turn can help inform decision making for more sustainable planning and management of urban ecosystems. The performance evaluation of the classification proves the validity of the proposed unsupervised text classification approach as a practical, reliable, and more efficient alternative to laborious and expensive annotation efforts required by manual or supervised classification methods.