GIS-assisted mapping of landscape suitability for nearby recreation

Green space around settlements is increasingly important for recreation. However recreation managers have limited spatially explicit data on recreation potential around cities, and representative field data are expensive to gather. To support the identification of hot spots for nearby recreation we developed a GIS model based on a representative survey (N = 1622). The model was tested for Swiss towns with 10,000–100,000 inhabitants. Respondents indicated (1) outdoor activities, (2) time spent, (3) type of transportation used, (4) preference for given landscape properties, and (5) preferred locations on maps with a cell size of 1 km2. Generalized linear models were applied to link people's declared presence/absence in the 1 km2 cells to “objective” landscape properties of the same cells. The models explain 41–65% (adj. D2) of the variance in the data. Many of the “objective” landscape characteristics found to significantly influence nearby recreation in the model match with “subjective” preferences, i.e. distance to residence, open water, forests, summits with overview and avoidance of major roads. Old people are “subjectively” more sensitive to landscape characteristics than young people, and indeed they visited locations with more distinct landscape properties. In contrast, persons reaching their locations by foot are “subjectively” more sensitive to landscape characteristics than mobile people (access by car, bicycle), but they go to close-by locations with less distinct landscape characteristics. As most people reach their locations by foot, we conclude that measures to improve green space should be concentrated within 5–10 min walking or biking distance in order to be effective.

► Novel research framework and practical tool for mapping hotspots for nearby recreation areas around settlements.
► Landscape suitability analysis with a unique dataset featuring (a) nearby recreation preferences of people and (b) their declared geographical presence.
► Allows inferences about people's landscape-related recreation desires and whether these desires are fulfilled on their visited locations.
► Allows practical planning recommendations.