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The citizenization of the floating population is a primary task of the people-centered new urbanization. Taking the Yangtze River Delta urban agglomeration as an example, this paper analyzes the spatial distribution and influencing factors of the long-term residence intention of the urban floating population using a multilevel logistic regression method. The findings were as follows. (1) The proportion of the floating population with long-term residence intention was 38.09% in the Yangtze River Delta urban agglomeration. Cities with the highest and higher proportions of long-term residence intention were mainly concentrated in Anhui Province in the west of the urban agglomeration and the Shanghai-Nanjing development axis, while the floating population that migrated to Zhejiang Province generally had medium and low residence intention. The spatial difference of the long-term residence intention of the floating population was different from that of the floating population size. As for the types of floating population, for most cities in the coastal provinces, their floating populations were comprised mainly of inter-provincial migrants. Shanghai and Zhejiang Province, especially, were the main destinations of inter-provincial migrants. Anhui was a typical area of population outflow, and the floating population staying there was made up mainly of intra-provincial migrants. (2) The rent-to-income ratio had a significant negative effect on the long-term residence intention of the floating population. Among the personal and family factors, high education, social and psychological identity, and affordable housing were strong motivating factors, but working in the labor-intensive sector had a strong negative effect on long-term residence intention. Among the factors of the destination cities, megacities strongly influenced the long-term residence intention of the floating population. (3) The housing rent was positively related to the long-term residence intention in the Yangtze River Delta, while the average income of residents in the city did not promote the long-term residence intention of the floating population. Migrant age was negatively correlated with their long-term residence intention. These are new findings of the study. (4) The influencing factors of the long-term residence intention of the floating population varied according to the city size. Age and inter-provincial migration were the limiting factors for long-term residence intention in general cities, but they were not significant in megacities. The long-term residence intention in megacities was more related to high education and employment type. This indicates that megacities are more inclusive of inter-provincial migrants and friendly to various ages; migrants' residence intention in megacities depends more on personal qualities and employability. Besides, the average income level of the destination city only had a negative effect on the residence intention in general cities.

The space–time adaptive processing (STAP) technique can effectively suppress the ground clutter faced by the airborne radar during its downward-looking operation and thus can significantly improve the detection performance of moving targets. However, the optimal STAP requires a large number of independent identically distributed (i.i.d) samples to accurately estimate the clutter plus noise covariance matrix (CNCM), which limits its application in practice. In this paper, we fully consider the heterogeneity of clutter in real-world environments and propose a sparse Bayesian learning-based reduced-dimension STAP method that achieves suboptimal clutter suppression performance using only a single sample. First, the sparse Bayesian learning (SBL) algorithm is used to estimate the CNCM using a single training sample. Second, a novel angular Doppler channel selection algorithm is proposed with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR). Finally, the reduced-dimension STAP filter is constructed using the selected channels. Simulation results show that the proposed algorithm can achieve suboptimal clutter suppression performance in extremely heterogeneous clutter environments where only one training sample can be used. [ABSTRACT FROM AUTHOR]

Non‐sidelooking airborne radar encounters significant non‐stationary and heterogeneous clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based space‐time adaptive processing (SR‐STAP) methods can achieve good clutter suppression performance with limited samples. Nonetheless, grid‐based SR‐STAP algorithms encounter off‐grid effects in non‐sidelooking arrays, which can severely degrade the clutter suppression performance. In this study, the authors propose a novel gridless SR‐STAP method in the continuous spatial‐temporal domain to address the issue of off‐grid effects. Inspired by the fact that sparse Bayesian learning (SBL) framework implicitly performs a structured covariance matrix estimation, the authors reparameterise its cost function to directly estimate the block‐Toeplitz structured matrix from the measurements in a gridless manner. Since the proposed cost function is non‐convex, we utilise a majorisation‐minimisation‐based iterative procedure to estimate the clutter covariance matrix. Finally, using the standard concept of semidefinite programming, the authors derive a convex gridless implementation of the SBL cost function for uniformly sampled radar systems. Extensive simulation experiments demonstrate the exceptional clutter suppression and target detection performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and reliable performance. Its implementation in large-scale radar systems is however hindered by the overwhelming computational load and slow convergence speed. This paper aims to address these drawbacks by proposing an improved iterative reweighted sparse Bayesian learning algorithm based on expansion-compression variance-components (ExCoV-IIR-MSBL). Firstly, a modified Bayesian probabilistic model for SR-STAP is introduced. Exploiting the intrinsic sparsity prior of the clutter, we divide the space-time coefficients into two parts: the significant part with nontrivial coefficients and the irrelevant part with small or zero coefficients. Meanwhile, we only assign independent hyperparameters to the coefficients in the significant part, while the remaining coefficients share a common hyperparameter. Then the generalized maximum likelihood (GML) criterion is adopted to classify the coefficients, ensuring both accuracy and efficiency. Hence, the parameter space in Bayesian inference will be significantly reduced, and the computational efficiency can be considerably promoted. Both theoretical analysis and numerical experiments validate that the proposed algorithm achieves superior performance with considerably improved computational efficiency in sample shortage scenarios. [ABSTRACT FROM AUTHOR]

The toll-free highway policy implemented for the first time in China during 2012 has greatly stimulated people's enthusiasm for travel. This policy is promoting the holiday tourism market, especially the self- driving tour market, but this development has at the same time also brought many negative effects, including overcrowded highways, increased levels of traffic accidents, and a decline in the quality of tourism travel. Based on an extensive literature review we developed a questionnaire to seek information on 17 perceptual factors which reflect the influence of the highway free policy, 2 factors which reflect the degree of support for the policy, and 4 factors which reflect the desire to travel again. From this investigation, we construct a hypothetical structural equation model of city residents' perception of the support for the highway free policy and their travel behavior. This model is based on exploratory factor analysis (EFA) and longitudinal studies of the changes in the correlation between resident perceptions and their support for the policy after the Spring Festival and the National Day in 2013, and the level of subsequent travel behavior, with confirmatory factor analysis (CFA) of the survey data. The results show that: (1) travel quality has the strongest correlation with policy support, and this correlation improved in both the Spring Festival and National Day in 2013; (2) public service capacity and the perceptions of hidden dangers are important factors influencing the level of support. In our study the influence of public service capacity-which includes rescue system of high speed road, information release of traffic and road condition and traffic warning mechanism, decreased slightly and the influence of hidden dangers increased; (3) the correlation between travel cost and support is weakened by this policy. However, the correlation between perceptions of fairness and policy support increased, but not significantly; (4) as the intermediary variable between resident perceptions and further travel decisions, support for this policy has an increased mediating effect; and (5) compared with the Spring Festival, the mediating effect of support which influenced tourist behavior became more obvious after National Day. Our study of the changes in city residents' support for the highway toll-free policy and their travel behavior will help to perfect the policy. The required changes are: (1) to improve the travel quality from the aspects of scenic area capacity control, tourist dispersion, stronger supervision of the industry, improvements in supporting facilities, and strengthening environmental protection consciousness; (2) the government should improve information distribution channels and strengthen the number and quality of emergency rescue teams; and (3) the vigorous development of the smarter tourism system to control tourist capacity. This is needed first to publish real time tourist and traffic flow information at main destinations and attractions and on access roads through the official WeChat and MicroBlog social media, and second, to use a variety of methods of ticket purchasing to monitor tourist flows effectively and reduce waiting times; (4) to strengthen the construction of the self- drive tourism support system. On one hand, cities and scenic areas should accelerate the construction of a self-drive tourism system. On the other hand, the government should encourage the development and standardization in the car rental industry; (5) to establish tourism security rescue centers to enhance the speed and timeliness of traffic vehicle breakdown rescue and medical relief, and pay more attention to the spread of high-speed travel safety knowledge to enhance travel security; (6) to perfect supporting policies for the highway toll- free policy, such as implementing the paid vacation system to ensure that workers have more discretionary time to travel in addition to"Golden Week"; and (7) through the passenger bus toll-free policy reduce bus ticket prices to expand the scope of the toll-free policy objective. [ABSTRACT FROM AUTHOR]

This paper compares volumes, flows, and spatial patterns of traffic before and after the opening of the high-speed rail (HSR) link between Beijing and Shanghai. Evidence emerges of a significant change in the shape of isochrones within the region. Under the influence, the temporal and spatial distance between the source region and tourist destination is greatly reduced. Equally, multiple contact modes are more apparent and the overall structure of tourism flow network is closer after the opening of the HSR service. As can be seen from the above, the HSR has a significant impact on regional tourism traffic accessibility and then has a significant on the temporal and spatial distribution of regional tourism resource. The tourism flow will respond positively to the “space compression” effect. [ABSTRACT FROM PUBLISHER]

The influence of research papers is an important indicator to objectively and scientifically reflect its academic value and impact level. Influence has significant correlation with the citation frequency, download frequency and journal impact factor. Evaluating the academic value and influence of Chinese tourism literature can be scientifically and effectively achieved by using a literature influence evaluation model including citation frequency, download frequency and the journal impact factor. This paper refers to a general literature influence evaluation model and uses the analytic hierarchy process (AHP) and the Delphi method to obtain the evaluation factor weights. The model is then modified and applied to evaluate the influence of tourism literature. The paper ranks articles with the key word 'tourism' in descending order by cited frequency. Literature cited over 100 times is taken as a highly cited article, and 410 highly cited tourist articles published in the last 30 years in China are chosen as the research object. The paper calculates the influence of these highly cited tourism articles and classifies them into four levels. The four levels (in decreasing order: highest, high, medium and low) reflect the calculated value of influence, analyzed temporal and spatial distribution, the distribution of research areas and the influential character of the core authors' research group. The study found that (1) relevant highly cited tourism literature shows a four stage distribution. The peak citation period was 1998-2005; strongly influential literature was published earlier in the study period and articles of less strong and weak influence more frequently appear after 2001. Geographically, the study region is divided into four: very high levels of literature, high levels of literature, medium levels of literature, and low levels of literature. High levels of economic or tourism development and a strong research team are important driving forces for uniting highly cited tourism literature. Within China, Beijing and Guangdong have more literature in the four influential levels thanother provinces. (2) Each of the four levels of literature influence focus on the fields of theory and methods, tourism development and planning, tourism environment and eco-tourism, and the development and evaluation of tourism resources. Although Chinese tourism research focuses on the same areas of concern that developed countries in Europe and the United States do, it is important to further improve the influence of Chinese tourism literature. (3) A core group of Chinese high impact tourism research authors has not yet formed. Generally, having a core group of authors in a specific area leads to a high rate of core journal citations, which has more influence on high quality thesis. The study found some problems unconducive to the overall development of the tourism discipline including the low core author cross reference rate, and the high rate of self citation that reflect a lack of knowledge flow between core authors. Therefore, it is important to cultivate key researchers to foster the future development of Chinese tourism research to ensure that the tourism field has a strong and highly influential core author group. There is a need to encourage the academic exchange of ideas between the core authors and to promote the overall development of the tourism discipline. [ABSTRACT FROM AUTHOR]

Tourist flow is a concept within tourism geography that examines spatial attributes. It is the focal point of the tourism system, and reflects the travel pattern of tourists within a region. Transportation is the most significant factor influencing the time and space distribution of tourist flow. The high-speed railway (HSR) is an important milestone in today's "transport revolution". It is the most effective way of providing rapid transport for large numbers of passengers in large channels. The development of HSR is now a global phenomenon and is a notable factor influencing tourist flow. High-speed rail has speed, safety and efficiency advantages that produce a "Time-space compression" effect. This will allow the tourist travel radius to gradually increase, having a knock-on effect on tourism destination choice, and a long-term impact on the regional tourism spatial pattern. This paper examines nine main stations on the Beijing-Shanghai HSR through field and social network analysis to compare tourist flow time-space distribution pre- and post-HSR. Tourist travel blogs uploaded to the Chinese Web 2. 0 self-help website "Youdodo Travel Network" were used to identify the tourist destination choice, tourism spatial behavior and travel time. This study found that Beijing, Shanghai and Nanjing were strengthened as tourist origins and destinations, demonstrating a HSR tourism flow "Matthew effect" ( accumulated advantage). HSR prompts being close to the important tourism destinations and tourism competition in inferior HSR. The accumulation field and degree centrality coefficients were relatively small, and decreased after HSR implementation, demonstrating a tourist flow accumulation "aisle effect". HSR prompts the "urban integration effects" of tourist flow between two cities located in relatively close proximity to each other. Geographical distances from Beijing to Tianjin, Shanghai to Nanjing, and Shanghai to Suzhou are relatively small and there was a strong social network of tourist flow between these cities prior to the arrival of HSR. Since the implementation of HSR, travel flows increased and the social network upgraded to the highest position, demonstrating HSR "urban integration effects". HSR enhanced the accessibility to regions that had a locational disadvantage in respect to their tourism resources and hence enhanced the attraction of these places and dramatically decreasing their dependencies on their core city. The HSR "time-space compression effect" significantly shortened travel times. From the time distribution of tourist flow, the HSR "time-space compression effect" made it possible to visit far-distance destinations over a three-day period, and medium-distance destinations over a weekend, reducing the medium-distance journeys under taken during the China's "golden week". HSR both relieved the burden of travel times and improved the quality of travel. Prior to HSR, long-distance tourists tended to travel during the "golden week" and summer or winter vacations. After the HSR opened, short-stay holidays and weekend travel increased. HSR realized a tourist shift and achieved the target of "cutting the peak to fill the valley". Limited by data, this paper only analyzed the time and space distribution characteristics of tourist flows by train and HSR. Future research will expand the perspective of air, train, car and high-speed rail transportation system, and will consider the surrounding areas influenced by HSR. [ABSTRACT FROM AUTHOR]