全方位緑視率を用いた緑地環境に対する印象評価推定モデル作成と検証

Abstract

In recent years, there have been a number of social initiatives related to improving the environment in city landscapes. Green space is becoming a tool to enhance the comfort of city space. "The Basic Plan for Green of Kyoto City" is one such example, where the ratio of visible green space is being used as a tool to improve the city environment. Many studies are being carried out to support this initiative and this study is one of them. The purpose of this study was to: 1) present a method to measure the location/angle specific ratio of green spaces in the omnidirectional visibility rate using a three-dimensional model of the target location, 2) create a perception deduction model based on Self-organizing Maps and 72 variables of visible green space in omnidirectional visibility rate, and 3) statistical verification of the accuracy of the perception deduction model. There are 72 categories of green space in the omnidirectional visibility rate. These categories are based on the location- and angle specific ratio of these spaces. Six of these categories were used for the location specific measurement, namely, "tall trees", "medium trees", "shrubs", " implantable ", "ground cover", and "others". Twelve angle specific measurements for every fifteen degrees were used and eight perception estimation parameters were selected. The perception estimation parameters included: “many or less", "satisfied or not satisfied", "pleasant", "serene", "covered (wrapped)", "close by or far", " surrounded by", "refreshing” and “widely spread". In this paper, we present results from the "ratio of visible green space in the omnidirectional visibility rate map”, the “self-organizing map" and the "perception estimation value map”. During the verification of the perception estimation model (the primary objective of this study), we compared the estimated perception values with the survey based observed values associated with a location of green space that was not included in the model creation. When we compared them statistically, we confirmed a significant correlation (n=32, p<0.05) between the estimated values and observed values (Pearson's correlation). We noted that the strength of the correlation was moderate but significant (correlation coefficient values around 0.6), with when we used the lower significance level (p<0.001). Taking into account effect size from psychological statistics, the average difference between the estimated and observed values of perception can be considered small for the parameters "many or less" "satisfied or not satisfied", "pleasant", "serene", "covered (wrapped)", "close by or far" and" surrounded by". However, the average difference was moderate for “Refreshing” and “Widely spread” and a significant difference between observed and estimated perception values was noted for these parameters in a paired t-test. Consequently, this perception deduction model is able to predict low and high values of “Refreshing” and “Widely spread", however, we need to be aware of the one degree difference, which happens to be the width of the confidence interval and may affect the estimated values.