Explaining subjective perceptions of public spaces as a function of the built environment: A massive data approach

People's perceptions of the built environment influence the way they use and navigate it. Understanding these perceptions may be useful to inform the design, management and planning process of public spaces. Recently, several studies have used data collected at a massive scale and machine learning methods to quantify these perceptions, showing promising results in terms of predictive performance. Nevertheless, most of these models can be of little help in understanding users' perceptions due to the difficulty associated with identifying the importance of each attribute of landscapes. In this work, we propose a novel approach to quantify perceptions of landscapes through discrete choice models, using semantic segmentations of images of public spaces, generated through machine learning algorithms, as explanatory variables. The proposed models are estimated using the Place Pulse dataset, with over 1.2 million perceptual indicators, and are able to provide useful insights into how users perceive the built environment as a function of its features. The models obtained are used to infer perceptual variables in the city of Santiago, Chile, and show they have a significant correlation with socioeconomic indicators.