The Landscape of Tranquility in Sweden: Lessons for Urban Design from Crowdsourced Data and Deep Learning

Abstract

Tranquility is typically associated with low noise levels and remote natural areas. Various methods for preserving potentially tranquil places have been proposed, although these typically involve setting aside places with low noise levels located in remote areas. To gain the benefits of tranquility in accessible urban areas, we need to identify the characteristics of tranquil spaces. This study focuses on the landscape-based, visual aspects of the phenomena. We investigated the role of visual context using a nationwide dataset of crowdsourced photographs from Sweden. Text mining identified personal perception and accompanying photographs identified the physical features. The photographs were characterized by time period and landscape conditions using computer vision technology. We found that waterbodies consistently enhanced tranquil views, while grass, flowers, and other dense vegetation were generally not well connected. Trees were positively correlated during daylight hours but had a negative impact at night. Dynamic objects such as people and vehicles were negatively associated, potentially due to aural considerations. Their effect was less significant during hours when noise would generally be less of a factor. This study provides insights for future research and design practices aimed at promoting tranquil experiences in urban environments and demonstrates the potential for crowdsourced data to help understand the qualities of built environments as perceived by the public.