PREDICTING VISUAL AESTHETIC PREFERENCES OF LANDSCAPES NEAR HISTORICAL SITES BY FLUENCY THEORY USING SOCIAL MEDIA DATA AND GIS

Authors : Derya GÜLÇİN

Pages : 265-277

Doi:10.32003/igge.811658

View : 23 | Download : 11

Publication Date : 2021-01-26

Article Type : Research Paper

Abstract :There is an interactive relationship between humans and landscapes. Humans inherently assess landscapes by creating spontaneous preferences based on surrounding stimuli. Vision plays a key role in these preferences. Visual preferences are relevant for understanding visual aesthetic liking insert ignore into journalissuearticles values(VAL);, which needs to be evaluated objectively. This study was carried out in Herakleia ad Latmos, comprising Lake Bafa Natural Park and the Latmos-Beşparmak Mountains. The aim of this paper is to predict people’s VAL of historical sites insert ignore into journalissuearticles values(HS); by applying processing fluency theory to social media data. Among fluency theory metrics, four metrics – visual simplicity, visual symmetry, visual contrast, and visual self-similarity, were used to develop an ordinary least squares insert ignore into journalissuearticles values(OLS); regression model. Two primary questions are explored in this study: insert ignore into journalissuearticles values(1); How to quantify spontaneous visits of people near historical sites, and insert ignore into journalissuearticles values(2); how to estimate preferences of people based on distances to HS regardless of landscape types insert ignore into journalissuearticles values(either cultural or natural);. Results show that people mostly visited three HS out of thirteen historical sites between 2004 and 2020: Kapıkırı Island insert ignore into journalissuearticles values(HS 1);, and the ancient cities of Herakleia insert ignore into journalissuearticles values(HS 2); and Latmos insert ignore into journalissuearticles values(HS 3);. According to the findings of the OLS regression model, year insert ignore into journalissuearticles values(t = 8.99, p <.0001);, visual simplicity insert ignore into journalissuearticles values(t = -4.64, p ≤ 0.0001);, and visual contrast insert ignore into journalissuearticles values(t = -2.01, p = 0.04); of the geotagged photos were all statistically significant predictors of VAL. HS 2 had the highest VAL value, followed by HS 1, and HS 3.
Keywords : Visual Aesthetic Liking, GIS, Fluency Theory Metrics, Historical Site, Latmos