Gis-based Landscape Appreciation Model Towards the development of GLAM version 3 Sjerp de Vries Landscape Centre, Alterra, Wageningen The Netherlands

Background GLAM is a model that predicts the attractiveness of the countryside to local residents, based solely on physical characteristics of the landscape on which information is available in national GIS- databases Version 2 uses four indicators, each with five levels: Naturalness, Historical distinctiveness, Urbanization, Skyline disturbance. Spatial resolution of the model: 250 x 250 meters (6.25 ha) Predictive validity is reasonable: 47% of variance explained Problem: usability to evaluate policy measures is still low, because small changes often do not lead to different indicator values

Research questions and method Level of spatial detail is quite acceptable, but how to improve the sensitivity of the model to more subtle/smaller changes in the physical appearance of the landscape? Step 1: recalibrate the model based on recently gathered data on landscape appreciation (larger dataset, more areas rated) Step 2: error analysis using recalibrated model –Where do predictions deviate most from actual attractiveness scores? –Is there some structure to be discerned in the direction or size of errors? Spatial clustering Type of landscape

Main results thus far Recalibrated version of GLAM 2 available –Based on study “Beleving naar gebieden”, part of “Belevingswaardenmonitor” –About 300 demarcated areas rated by people living nearby –Explained variance: 38% (lower than in previous validation phase!) Naturalness of the areas as main focus –Adding average rating of naturalness: explained variance 76% –Correlation GIS-indicator with rating of naturalness: r = 0.61 Naturalness indicator performs quite reasonable already –Correlation of rating of naturalness with prediction error: r = 0.57  But very important to improve it further! Preliminary results based on –Over 700 postcode areas with at least 10 participants –Rating of attractiveness of countryside surrounding place of residence –51% of variance in average attractiveness rating explained by GLAM 2

Preliminary conclusion and next steps Feasibility of improved version of GLAM seems high –Already identified clear source of errors: naturalness –Still unused GIS-data on naturalness are likely to be available Next steps –Looking at spatial pattern of errors what is wrong with the present naturalness indicator? –Develop and implement new naturalness indicator –Calibrate new version of GLAM