LAUSR

Abstract Knowledge on spatio-temporal changes of inbound tourism flow is important for destination economy, cultural communication and city image. This paper proposes a novel research framework for the spatio-temporal distribution and changes of inbound tourism flow by, first, using R-HDBSCAN clustering algorithm to extract tourism area of interest (AOI), second, by utilizing several key indicators adopted from the complex network theory literature to study the structure of inbound tourism flow with a case study example from Shanghai, China. The results show, first, that tourism in Shanghai is highly concentrated on the most popular AOI clustered in the city center relatively close to each other and, second, that, the inbound tourism flow network of Shanghai has small-world characteristics, while the distribution of its AOI (nodes) and tourist routes (edges) has general power law features, which has been influenced by the World Expo.