Restorative potential, fascination, and extent for designed digital landscape models

Abstract

Few studies have investigated whether manipulating objective measures related to fascination and extent affect respondents’ ratings of restorative potential (RP) and estimations of fascination and extent. The following study addresses this need. We manipulated or measured variables in 27 color digital landscape model views. Tree height represented a measure of fascination. Three measures related to extent: The number of organized plant groups represented coherence. Shannon’s Information Entropy bit values represented plant species complexity. The visible view polygon area in each model represented scope. We included 65 respondents’ RP ratings for the digital model views in analyses, along with estimations of fascination (n=48), extent (n=43), coherence (n=38), complexity (n=44), and scope (n=35). Collinearity diagnostics indicated dependency between respondents’ estimations of fascination, extent, and complexity, and between estimations of coherence and scope. A strong, inverse correlation occurred between respondents’ RP ratings and the view polygon area. Repeated measures ANOVA test results suggest that respondents’ RP ratings increased as mean designed tree height increased. RP ratings for model views depicting scattered and formally arranged plants were significantly higher than views of clustered plants. Moreover, the decrease in RP ratings between scattered and clustered scenes was greater when plants represented three bits of entropy instead of two. Chief among the implications stemming from this study is that increasingly taller trees and groundcover plants may have increasingly greater restorative potential.