Towards Learning of Spatial Triad from Online Text

The Spatial Triad model provides a framework for studying human interactions and experiences with the environment, which helps to improve human well-being and quality of life. Typical studies that use this framework require time-consuming and expensive surveys. This paper presents a simple yet effective approach to learning what humans feel, think, and see about their surroundings from easily accessible online text descriptions (e.g., descriptions of listings on real estate or travel blogs). The proposed technologies learn meaningful document and locality representations in a unified representation space, capturing important concepts shared among documents within the same locality. The proposed approach outperforms the existing method in finding associated localities in online text and shows exciting insights into locality similarity using the learned representations.