[2I1-OS-1a] OS-1

[2I1-OS-1a-01] Assessing the diversity in cities using reviews

〇Shun Kishimoto¹, Hiroki Nakajima¹, Ichiro Sakata¹, Kimitaka Asatani¹ (1. The University of Tokyo)

Keywords:computational social science, complex networks, Urban Informatics

The large number of establishments in a city creates opportunities for social interaction. Using Points of Interest (POIs) for restaurants in New York, London and Tokyo and review data on these POIs, we estimated the diversity of encounters mediated by POIs in each city. Previous studies have estimated the diversity of encounters based on single factors such as the income proportion and ethnicity of each visitor, overlooking the various characteristics of individual visitors. This study assesses individual experiences based on the visit history of POIs and defines a diversity measure, Unpredictability, which is more reflective of individual preferences and experiences; it considers how far people have come from POIs that are distantly related, based on the embeddedness of the POIs' co-visit networks. Using this, we found that diversity is heavily influenced by three general factors: 'location', 'price' and 'shop type'. The existence of an income bias in the choice of these factors revealed that higher-income consumers enjoyed higher encounter diversity. Differences by income exist not only in the choice of price and location, where income factors are significant, but also in the choice of shop type.