Based on the research sites of 88 world heritage dependent sites in China, an evaluation system for the coordinated development of inbound tourism flows and World Heritage sites was constructed,and the spatial-temporal characteristics and influencing factors of the two systems from 2000 to 2017 were analyzed by using the coupling coordination model and generalized moment estimation methods.The results show that it has formed a complex system of “inbound tourism flow-world heritage dependent site” in China.The comprehensive development level of the two systems shows a trend of increasing fluctuation, with intermittent fluctuation law.The coupling coordination level of the two systems is generally low and the amplitude is small, presenting a spatial pattern of “large dispersion and small agglomeration”. Coordinated development of the two systems is the result of multi-dimensional factors. Among which, foreign exchange income of inbound tourism is the core power, tourism workers is the dual driving force, sales of social consumer goods is the guiding function, green coverage is not necessary, total storage of public library books is the potential factor, and total passenger volume is the non-sensitive factor.

Spatial pattern and differences of continent-scale inbound tourism are rarely studied.The changing characteristics of spatial patterns of the inbound tourism in Asian countries from 1998 to 2017 are revealed by use of Theil index, Gini coefficient, gravity center, spatial autocorrelation, and some key factors of these patterns are preliminarily discussed. The results show that the spatial difference of inbound tourism in Asian countries can be roughly divided into two stages, that is, fluctuation stage (1998-2004) and continuous decline stage (2005-2017). The high and low developed countries of inbound tourism show a random distribution trend, some countries are always in the high or higher level, while most countries are always in the low or lower level. In recent years, the gravity center of inbound tourism in Asian countries has shifted eastward to the south at an accelerated speed, and a “high value agglomerations of inbound tourism” has basically formed in eastern, southern and east southern Asia. However, there is no spatial autocorrelation in inbound tourism in Asian countries.It is found that FDI, air traffic and national economic level have significant impacts on spatial differences in inbound tourism in Asian countries.

Based on the double-indicator of flow share and flow quality index of China′s inbound tourism flow, the Q-Q model of China′s inbound tourism market structure is proposed, which reflecting the quantity indicator of the market shares on the one hand, and the quality indicator of the flow quality on the other hand, using a combination of the two indicators comprehensively reflecting the quantity and quality status of the market. By combining the study of inbound tourism flow and quality with market structure analysis, the Boston matrix was used to divide the China′s inbound tourism market into “bright-star markets”,“cash-cow markets”,“thin-dog markets” and “problem markets”. Taking the inbound tourism of 31 provinces from 2009 to 2018 as the research object and the Q-Q model of China′s inbound tourism market structure was used to analyze the structure and changes of the China′s provincial inbound tourism market, the results show that the “bright-star markets” of China′s inbound tourism are concentrated in the southeast coastal areas with developed economy and superior transportation locations.The development of China′s inbound tourism market is unbalance,and there are fewer high-quality markets and the overall quality of the markets is low.The structure of China′s provincial inbound tourism market is constantly optimizing.

Based on the relevant data of inbound tourism in 1978-2018, the development pattern of inbound tourism in Shaanxi province in the past 40 years was studied by using structural hole, center degree, first degree and Pearson correlation analysis. The results show that the inbound tourism of Shaanxi province has maintained a high growth rate and exceeded the average level of national development in 2008. Shaanxi province has always been an important destination and transit place for inbound tourism in the western region, and it is the most important gateway and hub for inbound tourism in Northwest China, which plays a great role in driving the radiation of the whole region. There is a strong positive correlation between the development of inbound tourism and tourism resources, the level of economic development, the ability of tourism reception and the ability of tourism dredging.

With BCG Matrix model, the preference of the major inbound tourists generating nations to the US as a tourist destination, the market competition state of the major inbound tourists generating nations in the US inbound tourism market, and the competitive advantage and the development potential of these tourists generating nations, especially to the US from 1990 to 2014 were analyzed. Based on a view of spatial and temporal integration, the dynamic characteristics of the US inbound tourists generating nations were discussed. The results showed that, in temporal the growth and evolution progress of the main inbound tourists generating nations to the US in the last 25 years was complex and fluctuated, and its structure has been constantly optimized. In spatial, the US inbound tourists generating nations expanded from Europe to Asia-Pacific. Moreover, the various objective and subjective factors of the inbound tourism market of the US in that their long-term high and benign operations were revealed. As a result, it aims at reflecting the changes of the world international tourism regional pattern, while starting from a typical international tourism destination. Much more, it also aims at revealing the general development rule of the inbound tourism market, and intends to provide a realistic model for the development of the inbound tourism market of China.

Information and communications technologies (ICTs) have a great impact on tourism and tourists. The use of communication networks, the internet and the internet of things by tourists in the process of tourism will produce a large amount of data with geographical labels. These big data provide the possibility for the study of large-scale instant tourism flows and tourists′ behavior. Based on the research of tourism flow and tourists′ spatio-temporal behavior at home and abroad, this paper discovers that: (1)In the research of the driving forces, massive and unstructured big data provide a new perspective for the study of tourism flow and tourism spatio-temporal behavior, and it has become an important feature in the study of tourism flow and tourists′ spatio-temporal behavior to discover knowledge driven by both data and problems in the future. (2)In the research of methods, the spatial analysis method using geographic information system is adopted to carry out the analysis of the tourism hot spot, the network and the visualization, so that the law and the knowledge can be discovered from the massive data to enhance the understanding of tourists′ behavior and promote the development of tourists′ spatio-temporal behavior theory. (3)In the research of data sources, besides the traditional data, the mobile phone data, the UGC data will become important data sources to study tourists′ spatio-temporal behavior, and different data sources and combination can be chosen according to the actual research situation. (4)In the research of topics, at present, most researches focus on the temporal and spatial distribution of tourists, the discovery of spatial network structure of tourism flow, and so on, while researches on the reasons, processes and mechanisms hidden in the law of tourists′ spatio-temporal behavior are not enough; therefore, causality analysis, process and mechanism research will be the key research directions in the future. (5)The research hotspots in the future may include the systematic study of the whole process of spatio-temporal behavior modeling of tourists based on big data, the research of data standardization and data mining methods, the research of data fusion and assimilation, the research of tourists′ privacy and ethics with big data as the background, and the research of tourists′ spatio-temporal experience, etc.