Natural spaces can provide psychological benefits to individuals, but population-level epidemiologic studies have produced conflicting results. Refining current exposure-assessment methods is necessary to advance our understanding of population health and to guide the design of health-promoting urban forms.Objectives:
The aim of this study was to develop a comprehensive Natural Space Index that robustly models potential exposure based on the presence, form, accessibility, and quality of multiple forms of greenspace (e.g., parks and street trees) and bluespace (e.g., oceans and lakes).Material and methods:
The index was developed for greater Vancouver, Canada. Greenness presence was derived from remote sensing (NDVI/EVI); forms were extracted from municipal and private databases; and accessibility was based on restrictions such as private ownership. Quality appraisals were conducted for 200 randomly sampled parks using the Public Open Space Desktop Appraisal Tool (POSDAT). Integrating these measures in GIS, exposure was assessed for 60,242 postal codes using 100- to 1,600-m buffers based on hypothesized pathways to mental health. A single index was then derived using principal component analysis (PCA).Results:
Comparing NDVI with alternate approaches for assessing natural space resulted in widely divergent results, with quintile rankings shifting for 22–88% of postal codes, depending on the measure. Overall park quality was fairly low (mean of 15 on a scale of 0–45), with no significant difference seen by neighborhood-level household income. The final PCA identified three main sets of variables, with the first two components explaining 68% of the total variance. The first component was dominated by the percentages of public and private greenspace and bluespace and public greenspace within 250 m, while the second component was driven by lack of access to bluespace within 1 km.Conclusions:
Many current approaches to modeling natural space may misclassify exposures and have limited specificity. The Natural Space Index represents a novel approach at a regional scale with application to urban planning and policy-making.