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Statistical analysis of tourist flow in tourist spots based on big data platform and DA-HKRVM algorithms

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Abstract

In the context of economic globalization, the rapid transmission of network information, people’s attention to tourism culture perspective has changed significantly; new and popular tourism projects are more favored. In order to avoid large-scale crowding and waste of resources in tourist attractions, it is a hot topic in the field of tourism to study the spatial and temporal distribution of tourist flows. By analyzing the spatial and temporal distribution characteristics of tourist flow in scenic spots, this paper constructs a big data platform based on tourist flow information, and proposes a data mining technology based on the DA-HKRVM algorithm to predict the tourist flow in the dimension of spatial and temporal distribution. By feeding the forecast results back to the staff of scenic spots in real time, the scale of passenger flow distribution can be effectively controlled, the purpose of balanced distribution of tourism resources can be achieved, and the development of intelligent tourism can be further promoted. The simulation result shows that the spatial-temporal distribution model of tourist flow based on data mining has good adaptability and accuracy in application. It shows that the method proposed in this paper can reduce the negative impact caused by the uneven spatial and temporal distribution of tourism flow, and can provide theoretical guidance for the efficient development of tourism economy.

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Acknowledgements

The research is supported by project funded by the China Postdoctoral Science Foundation; project funded by the National Key R&D Program of China: International cooperation between governments in scientific and technological innovation (No. YS2017YFGH002008): Horizon 2020•Urban Inclusive and Innovative Nature; and project funded by the project of FDCT: The forecasting of the flood region of Macao through big data association analysis and hydrodynamic model.

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Authors and Affiliations

  1. The Post-Doctoral Research Center of Zhuhai Da Hengqin Science and Technology Development Co., Ltd, Macau, 519031, China

    Daming Li & Lianbing Deng

  2. Institute of Data Science, City University of Macau, Macau, 999078, China

    Daming Li & Zhiming Cai

Authors
  1. Daming Li
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  2. Lianbing Deng
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  3. Zhiming Cai
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Correspondence to Lianbing Deng.

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Li, D., Deng, L. & Cai, Z. Statistical analysis of tourist flow in tourist spots based on big data platform and DA-HKRVM algorithms. Pers Ubiquit Comput 24, 87–101 (2020). https://doi.org/10.1007/s00779-019-01341-x

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