Using geographically weighted models to explore how crowdsourced landscape perceptions relate to landscape physical characteristics

Abstract

This study explores how formal measures of landscape wildness (i.e. absence of human artefacts, perceived naturalness of land cover, remoteness from mechanised access, and ruggedness of the terrain) correlate with crowdsourced measures of landscape aesthetic quality as captured in Scenic-Or-Not data for Great Britain. It evaluates multiple linear regression (MLR) and two spatially varying coefficients models: geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MLR provided a baseline model in an analysis of national data, exhibiting the presence of spatially autocorrelated residuals and suggesting that geographically weighted models may be appropriate. A standard GWR was found to exacerbate local collinearity between covariates, both overfitting and underfitting the model with highly varied and localised results. This was due to its single one-size-fits-all bandwidth and the assumption that all relationships between the target and predictor variables operate over the same spatial scale. MGWR relaxes this assumption by determining parameter-specific bandwidths, mitigating the local collinearity issues found in a standard GWR and resulting in more spatially stable and consistent coefficient estimates. The findings also indicated that the relationship between some covariates (such as remoteness) and perceived landscape quality varied little spatially, while clear gradients were found for other covariates. For example, naturalness was stronger in the north and west, ruggedness was stronger in the south and east, and the absence of human artefacts was weaker in Scotland and the north than in England and the south. Overall, the study showed that MGWR is more sensitive than GWR to the spatial heterogeneity in the statistical relationships between landscape factors and public perceptions. These findings provide nuanced understandings of how these relationships vary spatially, underscoring the value of such approaches in landscape scale analyses to support policy and planning. The discussion section of this paper considers the MGWR as the default geographically weighted model, assessing the potential for the use of crowdsourced data in landscape studies. In so doing, it illustrates how such approaches could be used to explore both subjective and objective landscape evaluations.