Climate is an essential component in the sustainability of tourism cities. Coastal tourism cities face unprecedented challenges under a changing climate. The complexity of the tourism–climate interface predicates the need for tools that can assess the weather and climate accurately. Tourism climate indices have been widely developed to evaluate the temporal and spatial distribution of climate resources, but these indices are not entirely applicable to coastal cities facing air pollution. This study developed a Coastal Tourism Climate Index (CTCI) to assess the tourism climate suitability of Chinese coastal cities. The CTCI was developed to include five variables: thermal comfort, sunshine, precipitation, wind, and air quality. This index was applied and verified in the case of nine coastal tourism cities in China compared to the Holiday Climate Index (HCI: Beach). According to the results, the CTCI is more suitable for coastal tourism climate assessment in China. Finally, corresponding countermeasures are put forward for the balanced and sustainable development of Chinese coastal tourism cities. This study takes the lead in applying big data to the development and validation of tourism climate indices. These findings provide novel insights for the tourism climate assessment of coastal destinations facing air pollution.

This study has revealed spatial-temporal changes in Recreational Business Districts (RBDs) in Beijing and examined the relationship between the location of urban RBDs and traffic conditions, resident and tourist density, scenic spots, and land prices. A more reasonable classification of urban RBDs (LSC, CPS, and ULA) is also proposed. Quantitative methods such as Gini Coefficient, Spatial Interpolation, Kernel Density Estimation, and Geographical Detector were employed to collect and analyze the data from three types of urban RBDs in Beijing in 1990, 2000, and 2014, respectively, and the spatial-temporal patterns as well as the distribution characteristics of urban RBDs were analyzed using ArcGIS software. It was concluded that (1) both the number and scale of urban RBDs in Beijing have been expanding and the trend for all types of urban RBDs in Beijing to be spatially agglomerated is continuing; (2) the spatial-temporal evolution pattern of urban RBDs in Beijing is “single-core agglomeration–dual-core agglomeration–multi-core diffusion”; and (3) urban RBDs were always located in areas with low traffic density, tourist attractions, high resident and tourist population density, and relatively high land valuations; these factors also affect the scale size of RBDs.

The network theory is having an important role in tourism research, which provides a useful lens for understanding the structures and interrelations among tourism destinations. This paper analyzes the structure of China’s inbound tourist flow network (ITFN) in a dynamic view, from three levels: the global, the meso, and the individual, meanwhile the correlation between network structure and tourism performance is verified. The interpretation of ITFN structure has been divided into three meanings: the global-level metrics identify the holistic topology, the meso-level metrics are used to understand collaboration, and the individual-level metrics work for competitiveness assessing. China’s case shows the ITFN has a small-world characteristic but gradually becomes weak, the collaboration clusters become more, and the difference of competitiveness among regions has amplified. Moreover, the small-world nature and individual competitiveness have positive impacts on tourism performance, but the correlation between performance and clustering coefficient is significantly negative.

Although zoning has great potential in coordinating regional development and destination management, it is challenging to derive an optimal zoning method in the historic district. To address this challenge, this paper proposes a zone division method attempting to balance tourists’ and residents’ spatial demands and realizing urban historic area sustainable development. A 3-zones (Tourist active zone, Local community zone, and Buffer zone) plan is put forward based on a case study of the Qianmen area in Beijing, China. Then zone extraction is conducted with two kinds of big data, Points of Interests (POI) and Mobile Phone Signal (MPS). The Thiessen polygon-based method to divide the tourists’ concentrated area and a fishnet-based density method to distinguish the area of residents mainly living are used to help identify borderlines of different zones. A survey map for tourists and residents is used to verify the zone extraction results. Subsequently, zone management strategies are suggested for different zones improvement. This discussion contributes to overcoming the traditional methods’ defects in subarea-scale research with scientifically sound and practical.