Modeling the perception of green and blue space using spatial exposure measurement methods

The perception of green and blue spaces has been widely recognized for its positive impact on health and can be assessed through surveys that capture individuals’ experiences of their surrounding environment. While such surveys provide data that can be seen as ground truth, their implementation is often constrained by privacy concerns, time limitations, and inefficiencies. To address these challenges, quantitative datasets—such as the Normalized Difference Vegetation Index (NDVI) and land use data—can serve as inputs for spatial measurement methods, including buffer models, street view analyses, and viewshed analyses, to estimate green and blue space exposure. However, existing spatial measurement methods often fail to align with how people perceive green and blue spaces in their environment.

This study aims to address the question: How can green and blue space perception be modeled using spatial exposure measurement methods? To explore this, three spatial measurement approaches are applied: Euclidean buffer models, Streetview analyses, and viewshed analyses. These results are converted into Spearman correlation coefficients. Additionally, survey data collected in the Netherlands, where participants assessed green and blue spaces within their residential surroundings, are also analyzed using Spearman correlations. The correlations derived from spatial measurement methods are compared with those from the survey data to evaluate how well these methods capture perceived green and blue space exposure.

The findings aim to identify which spatial measurement methods best model individuals’ perceptions and offer insights into improving urban planning and policy. By enhancing the alignment between spatial models and human perception, this research contributes to more effective evaluations of green and blue space distribution in the Netherlands and highlights areas that may benefit from additional green and blue infrastructure.