Tourism has become a new way of living with the living standard development. This study analyses the tourism destination centrality and spatial patterns of the tourism system using complex network analysis. An analysis of 245 destinations in the South of Thailand has found that the network has a low network density, large average path length and low clustering coefficient. Some a small number of high-degree destinations connect to each other, while most connect to others with a low degree. The network comprises 18 subnetworks that destinations densely connect to others in the same subnetwork but sparsely connect to others in different ones. Destinations play different roles in the network based on which a centrality measure is used, degree, betweenness and closeness centrality. 31 destinations with high hub and authority centrality are the centers playing as hubs of the network. The study’s findings draw implications for the sector.

Agriculture products are one of the main income sources of developing countries. This study analyzes the road transport system (network) to reveal the network’s structural properties and identifies hubs that consolidate agricultural products to export to China via the China-Laos railway. Analysis of 20 provinces in the Northeast of Thailand has found that the network has 340 districts connected by 1,015 transport routes. The network is sparse, in which all districts cannot be connected to each other. The network has low connectivity efficiency but has high intra-connectivity among districts in the same province. In addition, the network has a modularity structure that can develop the communities. Hubs consolidating agricultural products of the region are Na Khu, Kuchinaria, Mueng Chiyaphum, Nam Phong, Na Wa, Mueang Mukdahan, Prasat and Rasi Salai. The findings of the study draw implications for the government, sector and exporters to design and improve their operations and services.

This study analyzes the tourism network and destinations after the COVID-19 pandemic using social network analysis (SNA). Analysis of 789 destinations in Thailand has found that the destinations are connected by 1,1175 tourism routes. The network is a sparse network with a low network density. It seems to have a scale-free property that reflects that most destinations have low connectivity and a small number of destinations have high connectivity. The network has a large average path length and low clustering coefficient. Different roles of destinations are identified based on degree, betweenness and closeness centrality. The findings draw implications for vitalizing the sector.