Continental-scale quantification of landscape values using social media data

How to cite this study

Van Zanten, B.T., Van Berkel, D.B., Meentemeyer, R.K., Smith, J.W., Tieskens, K.F. and Verburg, P.H. 2016. Continental-scale quantification of landscape values using social media data. Proceedings of the National Academy of Sciences 113(46):12974-12979.

Overview

This study compares three social media platforms (Panoramio, Flickr, and Instagram) and their applicability for mapping and quantifying landscape values at a continental scale through multiple models. The authors assume that landscape values increase as posts about the areas increase, interpreting spatial concentrations of posts as indicators of higher landscape values derived from aesthetic enjoyment and outdoor use. Landscape values are similar across all three platforms, with variables of accessibility, population density, income, mountainous terrain, and water proximity explaining variations in the data across platforms. In all three estimations, hills and mountains are the strongest predictors of aesthetic and outdoor recreation landscape values.

Relevance

This study is relevant to those interested in using social media data to evaluate landscape values and preferences at a large scale. A unique aspect of this study is the continental-scale analysis of Europe. However, this scale cannot capture local factors that could influence the aesthetic and recreational preferences of the natural environment. There are notable differences between the types of platforms: Panoramio users primarily upload landscape images, Flickr uploads tend to be more diverse and include politics and culturally relevant news events, and Instagram also tends to be diverse with text and hashtags providing more data on each post.

Location

This study is a continental-scale study of countries in the European Union.

Trail Type

This study covers all land areas of countries in the European Union, representing a broad range of landscapes, cultures, and economies.

Purpose

The purpose of this study was to evaluate how geolocated social media data can be used to value landscapes in terms of aesthetic enjoyment and outdoor recreation. According to the authors, at the time of publication, there were no other empirical comparisons of different social media platforms’ abilities to quantify and visualize landscape values. This study was funded by the European Commission 7th Framework Programme through the Operational Potential of Ecosystem Research Applications project.

Findings

• There is a high level of spatial agreement of the geolocated photos from Panoramio, Flickr, and Instagram especially in mountainous areas, including the Alps, Pyrenees, the Scottish Highlands, and the Apennines. 
• An absence of photos is prevalent in the agricultural lands of Spain and France, sparsely populated areas in Nordic and Baltic countries (Sweden, Finland, Estonia, and Latvia), and large parts of Bulgaria and Romania.
• The Tatra Mountain range in Slovakia, Czech Republic, and Poland and mountainous areas in Romania, Bulgaria, and Greece are areas where there are significant differences in posting levels in the different platforms. This is likely caused by user preferences in photo-sharing applications across Europe. 
• Instagram and Panoramio are used widely across Europe, whereas Flickr contributions are largely confined to Central and Western Europe. Flickr data has the largest amount of variance across European countries while Instagram data was more equally distributed across countries.
• In all three estimations, hills and mountains are the strongest predictors of aesthetic and outdoor recreation landscape values. Distance to a water body is a strong predictor in the Instagram model, whereas in the Flickr model, the hedges and tree lines have a strong positive effect on the concentration of aesthetic and outdoor recreation values. 
• For all three platforms, predicted locations with high values depict similar landscape characteristics associated with aesthetic and outdoor recreation values. Higher predicted values are found for mountainous areas, areas near water bodies, and areas near population centers.