Evaluation and drive mechanism of tourism ecological security based on the DPSIR-DEA model

Ruan, Wen-Qi; Li, Yongquan; Zhang, Shuning; Liu, Chih‐Hsing

doi:10.1016/j.tourman.2019.06.021

Cited by 156 publications

(129 citation statements)

References 75 publications

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“…Upon reviewing mechanism-related studies (e.g. Power, Di Domenico, & Miller, 2019 ; Ruan, Li, Zhang, & Liu, 2019 ; Tsai & Chen, 2010 ), it was concluded that a ‘mechanism’ is a method or a system of organizing different variables to clarify the relationships among them and solve certain problems or achieve a goal. In the current study, ‘mechanism’ refers to the connecting logic between psychological distance and perceived risk.…”

Section: Literature Reviewmentioning

confidence: 99%

Seeing the invisible hand: Underlying effects of COVID-19 on tourists’ behavioral patterns

Zhang

Liu

et al. 2020

Journal of Destination Marketing & Management

139

118

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Section: Literature Reviewmentioning

confidence: 99%

Seeing the invisible hand: Underlying effects of COVID-19 on tourists’ behavioral patterns

Zhang

Liu

et al. 2020

Journal of Destination Marketing & Management

139

118

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“…Tourism is playing an increasingly important role in the development of local economy [1,2]. With the steady growth in living standards, the tourism industry in China is booming in recent years [3,4]. However, the fast-growing tourist flow has sown the seeds of safety incidents, such as overcrowding and stranding in scenic spots.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tourist Flow Based on Deep Belief Network and Echo State Network

Cheng¹,

Zhao²

2019

RIA

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The accuracy of tourist flow prediction is crucial to the sustainable development of tourism industry. However, it is very difficult to forecast the highly nonlinear tourist flow in an accurate manner. The artificial neural network (ANN) has been widely adopted to predict nonlinear time series, but its shallow structure cannot effectively learn the features of high-dimensional tourist flow data. To solve the problem, this paper puts forward a tourist flow prediction model based on deep learning (DL). First, the deep belief network (DBN), with its strong ability to extract nonlinear features, was employed to extract effective features through unsupervised learning of historical tourist flow. Next, the echo state network (ESN) was effectively fused with the DBN. The ESN was placed at the top layer of the tourist flow prediction model, serving as the logic regression layer. Finally, the offline training and prediction effects of the proposed ESN-DBN were verified through experiments on the holiday tourist flow data extracted from a tourist center, and compared with those of two classical prediction models, namely, backpropagation neural network (BPNN) and autoregressive integrated moving average (ARIMA). The results show that the ESN-DBN achieved a mean absolute percentage error was below 12% and a rational computing time; the proposed model also outperformed the two classical models in prediction accuracy. The research results provide an important reference for the forecast of tourist flow and planning of tourism development.

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“…This determines the degree of concentration of the trait, which is studied in accordance with the units of the aggregate or in assessing the uneven distribution. As the theory and practice of analyzing the proportionality of the distribution of economic indicators substantiates the relationship of distribution, in particular resources in the economic space of the tourist system (Ruan et al, 2019), so ensuring the proportionality of the innovation core of meso-and local levels streamlines the consistency of the distribution of innovative resources and results of activities of travel operators and travel agencies. At the same time, modeling of development of the tourist services market on a group basis implies the definition of clusters of innovative potential of tourism enterprises.…”

Section: Methodsologymentioning

confidence: 99%

The Imperatives of the Development of the Tourist Services Market in Spatial Polarization of the Regional Tourist System

Трусова¹,

Insurance²,

Кирилов³

et al. 2020

GTG

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The article deals with the imperatives of the development of the tourist services market, which determines the parameters of the aggregate value of the sub-indices of the meso-and local levels in the spatial polarization of the regional tourist system. The methodology of spatial polarization of the regional tourist system, which provides convergence of the infra-structural space of tourist services, formation of an innovative nucleus, minimization of manifestation of destructive factors, balancing of interests of regions and the country as a whole is substantiated. It has been proposed the scientific-methodical approach to the definition of the target landmarks for the development of the tourist services market, which implements polarized tourist space in terms of quantitative and qualitative parameters, makes it possible to analyze the destructive determinants of the periphery and determinants-producers of the tourist center of activity, evaluation and forecasting of the safe level of the hierarchy of the regional tourist system, determination of clear impulses for the development of the tourist services market and density of interaction of the periphery with the center. Revenues from the sale of tourist services of local level enterprises have been analyzed. A comprehensive approach to the assessment of the concentration of the aggregate value of the security index of the regional tourist system, which is differentiated by the components of the regional security potential and threats, guarantees the development of the tourist services market and modifies the tourist system on the meso-level as a whole.

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Evaluation and drive mechanism of tourism ecological security based on the DPSIR-DEA model

Cited by 156 publications

References 75 publications

Seeing the invisible hand: Underlying effects of COVID-19 on tourists’ behavioral patterns

Seeing the invisible hand: Underlying effects of COVID-19 on tourists’ behavioral patterns

Prediction of Tourist Flow Based on Deep Belief Network and Echo State Network

The Imperatives of the Development of the Tourist Services Market in Spatial Polarization of the Regional Tourist System

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