Modeling scenic quality of residential streets using mensurational variables

Abstract

Regression models were developed to predict scenic quality for residential streets in Ann Arbor, Michigan for both Summer and Winter vegetative conditions. Scenic quality was quantified using the Scenic Beauty Estimation method. Only variables that existed in the city's computer data base were used. Variables such as diameter at breast height, basal area, number of trees, and tree species diversity were investigated as to their predictive ability. In addition, the predictive ability of quadratic, power, inverse, and logarithmic transformations of these variables was investigated. The best predictive Summer model used the natural log of the average diameter of street trees and the natural log of the average assessed property value as variables. The best predictive Winter model used the natural log of the average diameter of street trees as its independent variable.