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• Spending time in natural (green/blue) environments offers multiple benefits to individuals. • Disparities exist between population groups in frequency of visits to natural environments. • Analysis of the gender gap amongst subgroups highlights further (intersectional) inequalities. • Key population subgroups for green health policies and programmes to target are identified. Spending time outdoors in natural environments is associated with an array of benefits to individuals' health and wellbeing, as well as being implicated in the development of connections to the natural world, attachment to place, and environmental stewardship behaviours. Disparities between population subgroups in relation to frequency of nature contact may, however, shape the extent to which natural environments deliver benefits to marginalised population subgroups. In this paper we examine disparities in contact with nature amongst the adult population of Scotland, UK using cross-sectional data from the Scottish Household Survey (N = 19,441). The analysis proceeds to explore whether the observed 'gender gap' between men and women in nature contact persists across the intersections of population subgroups and geographies. The findings point to inequalities in contact with nature which should be considered in public policy agendas to promote use of natural environments across urban and rural areas. The findings also highlight the value of adopting intersectional perspectives in the consideration of environmental justice issues surrounding access to natural environments. [ABSTRACT FROM AUTHOR]

• This study examines the relationships between perceived landscape aesthetic quality and landscape physical characteristics. • These were explored using a multiscale GWR to identify the spatial scales of these relationships. • The results show that some factors have a globally consistent relationship (Remoteness). • Others exhibited highly localised relationships (Absence of human artefacts, Naturalness, Ruggedness). • The study shows how spatially aware approaches such as MGWR better inform landscape decision making. 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. [ABSTRACT FROM AUTHOR]