Exploring intercity regional similarity using worldwide location-based social network data (demo paper)

Zipei Fan, Guixu Lin, Wei Yuan, Ryosuke Shibasaki, Pengpeng E, Xuan Song

Abstract: Finding out similar regions between cities is important to a variety of real-world applications, such as point-of-interest recommendations, site selection, and travel guidance. With the help of the increasing number of location-based social network users, we can measure the intercity similarity from a new perspective of spatiotemporal characteristics of human mobility. In this paper, we developed an interactive intercity regional similarity explorer (IRSE) that 1) visualizes regional spatiotemporal human mobility features, 2) searches similar region candidates in the target city, and 3) explores the regional similarity from different views in a quantitative and illustrative way. In this paper, we show how our system can be useful in exploring regional similarity across cities in the world by use cases, which will interest various users from different countries in the demonstration session. Demo available at: https://bit.ly/3OjwnGu