Ballparking the Urban Placeness: A Case Study of Analyzing Starbucks Posts on Instagram

Abstract

Placeness or the "sense of a place" plays a vital role in urban design and planning. Research on placeness in the past has been conducted via conventional methods like surveys to reveal essential insights for urban planners and architects. For taking a glimpse into placeness by analyzing common factors across geographies, we choose Instagram posts from Starbucks as a case study, owing to its the-next-door coffee shop psychological construct. We conduct our research by first adopting a flexible ontological framework to organize the concepts governing placeness. Next, we curate a dataset of community generated Instagram posts from Starbucks in three major metropolitan cities of the world: New York, Seoul, and Tokyo. The curated dataset is then automatically enriched with contextual attributes such as activity, visitor demographics, and time via machine learning techniques. We finally analyze and validate the quantitative variations in contextual information with findings from well-accepted cross-cultural case studies. Our results show that placeness mined from Starbucks, a prominent urban third-place, can be reliably utilized to discover surrounding urban placeness.