Seeing through their eyes: Revealing recreationists’ landscape preferences through viewshed analysis and machine learning

Planning for outdoor recreation requires knowledge about the needs and preferences of recreationists. While previous research has mainly relied on stated preferences, recent advances in spatial data collection and analysis have enabled the assessments of actual usage patterns. In this study, we explored how landscape characteristics interact with the attributes of recreationists to determine their area choice for recreation. Using a public participation GIS (PPGIS) approach we asked residents of a Swedish city in the boreal region to draw typical recreational routes and identify favourite places for recreation on a digital online map (1389 routes, 385 individuals). We employed a novel methodology, where LiDAR data was used to calculate what was visible along all routes and at favourite places (viewsheds) in order to more realistically capture the landscape that each recreationist had experienced. Using machine learning modelling, we compared landscape characteristics of experienced areas with areas available to each recreationist. Our novel approach yielded accurate models that revealed that water environments, recreational infrastructure and deciduous forests increased the probability of choosing an area for recreation, while urban environments, noise, forest clearcuts and young forests had the opposite effect. Characteristics of the recreationists such as age, gender, level of education, or of the activity, such as type of activity performed, did not meaningfully influence area choice. Our findings suggest that it is possible to improve the conditions for recreation by developing recreational infrastructure, maintaining recreation opportunities close to waters, and adapting forest management in areas important for recreation.