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This paper investigates the spatial distribution of inbound and domestic tourist flows to cities in China and their growth rates using exploratory spatial data analysis. This method is a set of GIS spatial statistical techniques that are useful in describing and visualizing the spatial distribution, detecting patterns of hot-spots, and suggesting spatial regimes. The global Moran's I statistics for inbound and domestic tourist flows reveal strong positive and significant spatial autocorrelation. Furthermore, the Moran significance maps indicate four significant inbound tourism hot-spot areas in 1999 and 2006 (the Beijing-Tianjin cluster, the Yangtze River Delta cluster, the Fujian coast cluster and the Pearl River Delta cluster), and five significant domestic tourism hot-spot areas in 2002 and 2006 (with the addition of the Chengdu cluster). Based on the results, we show that tourism flows are polarized into clusters and remain very stable over time. As has been seen in other countries, hot-spots tend to be in insular and coastal areas, and associated with cities that are higher in China's urban hierarchy. Implications are drawn, such as priority of resource allocations for hot-spot area and the utilization of spillover effects from hot-spots. [ABSTRACT FROM PUBLISHER]

Improving the understanding of temporal and spatial behaviour of tourists is important for enhancing sustainable management of tourism. In recent years, with the emergence of photo-sharing Internet communities, many photographers have posted their photographs on the Internet and made them freely accessible. Generally attached to these shared photos is information about where and when the photos were taken, providing clues to establish the temporal and spatial distribution of the photographers. In this paper, we described a photography-based approach to analyse Chinese tourists' temporal–spatial behaviour identified from 1308 photos taken in the Old Town of Lijiang. We estimated tourists' temporal variation, length of stay, daily average number of tourists, individual movement traces and tourist hotspots. The results suggest that the photo-based methods have obvious advantages. We believe that this wealth of publicly available data sources with spatio-temporal information will provide a new perspective in analysing tourist flow distribution and tourist hotspots and will be helpful to plan tourism resources. [ABSTRACT FROM AUTHOR]