Context The roles of landscape variables with regard to the recreational services provided by nature parks have been widely studied. However, the potential scale effects of the relationships of landscape features and attributes to categorized nature experiences have not been adequately studied from an experimental perspective. Objectives This article demonstrates multiscale geographically weighted regression (MGWR) as a new method to quantify the relationship between experiences and landscape variables and aims to answer the following questions: 1) Which dimensions of landscape experiences can be interpreted from geocoded social media data, and what landscape variables are associated with speci c dimensions of experience? 2) At what spatial scale and relative magnitude can landscape variables mediate landscape experiences? Methods Social media data (Flickr photos) from Amager Nature Park were categorized into different dimensions of landscape experience. Estimated parameter surfaces resulting from the MGWR were generated to show the patterns of the relationship between the landscape variables and the categorized experiences. Results All considered landscape variables were identi ed as relating to certain landscape experiences (nature, animals, scenery, engagement, and culture). Scale effects were observed in all relationships. This highlights the realities of context-and place-speci c relationships and the limited applicability of simple approaches that assume relationships to be spatially stationary. Conclusions The spatial effect of landscape variables on landscape experiences was clari ed and demonstrated to be important for understanding the spatial patterns of landscape experiences. The demonstrated modelling method may be used to further the study of the value of natural landscapes to human wellbeing.

Context The roles of landscape variables with regard to the recreational services provided by nature parks have been widely studied. However, the potential scale effects of the relationships of landscape features and attributes to categorized nature experiences have not been adequately studied from an experimental perspective. Objectives This article demonstrates multiscale geographically weighted regression (MGWR) as a new method to quantify the relationship between experiences and landscape variables and aims to answer the following questions: 1) Which dimensions of landscape experiences can be interpreted from geocoded social media data, and what landscape variables are associated with specific dimensions of experience? 2) At what spatial scale and relative magnitude can landscape variables mediate landscape experiences? Methods Social media data (Flickr photos) from Amager Nature Park were categorized into different dimensions of landscape experience. Estimated parameter surfaces resulting from the MGWR were generated to show the patterns of the relationship between the landscape variables and the categorized experiences. Results All considered landscape variables were identified as relating to certain landscape experiences (nature, animals, scenery, engagement, and culture). Scale effects were observed in all relationships. This highlights the realities of context- and place-specific relationships and the limited applicability of simple approaches that assume relationships to be spatially stationary. Conclusions The spatial effect of landscape variables on landscape experiences was clarified and demonstrated to be important for understanding the spatial patterns of landscape experiences. The demonstrated modelling method may be used to further the study of the value of natural landscapes to human wellbeing.

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