In the "era of sharing economy 2.0", China's B&Bs industry enjoys rapid development as a new business format, boosting regional economy to reach a new height. This paper selects the Yangtze River Delta region, China's largest economic zone, as the research area, and takes exploratory spatial data analysis (ESDA) and GIS to study the agglomeration characteristics and evolutionary trajectories of B&Bs spatial patterns amid 2013-2017. The results indicate that: (1) B&Bs in the Yangtze River Delta region exhibit balanced development. (2) From the perspective of global spatial autocorrelation analysis, B&Bs spatial distribution features significant spatial autocorrelation, with spatial agglomeration degree on the rise. (3) From the perspective of local spatial autocorrelation analysis, it gradually shows a gradual decreasing pattern from the east to the west, forming a significant high concentration of eastern cities and a low concentration of western cities. By comparing the results of the 3 years, Huzhou and Shanghai showed a significant high concentration from 2013 to 2015, and neighboring cities such as Suzhou, Jiaxing and Hangzhou have shown a high approaching trend, indicating that in recent years, the development awareness of the industry is converging. (4) The four factors: endowment of tourism resources, transportation convenience, policy conditions and residents' living standards exert important influences on B&Bs spatial distribution.
According to China’s tourism statistics, the inbound tourism market is composed of eight types of travel motivations (sightseeing, leisure, business meeting (business-m), visiting relatives and friends (visiting-rf), shopping, religious worship (religious-w), culture and sports (culture-s), and health care (health-c)), and the spatial distribution of each type of travel motivation is significantly different. Four inbound sub-markets (foreigners, Hong Kong, Macao, and Taiwan) were selected as our research object. Through empirical analysis of the variable elasticity of eight different inbound motive market segments, we found that the sensitivities (elasticity) of the influencing factors (traffic conditions (traffic-c), destination image (destination-i), industry structure (industry-s), infrastructure, consumer price index (CPI), resource endowment (resource-e), and dressing index (ICL)) are different. Therefore, investment options in the target market can have differential treatment based on the rate of marginal return on investment. In accordance with the characteristics of different market segments, we suggest more feasible development paths and countermeasures, providing a decision-making basis for the accurate development of the inbound tourism market.
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