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

  • Daming Li
  • Lianbing DengEmail author
  • Zhiming Cai
Original Article


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.


Tourism culture Big data technology DA-HKRVM algorithm Intelligent tourism 



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.


  1. 1.
    Zeng B, He Y (2018) Factors influencing Chinese tourist flow in Japan—a grounded theory approach[J]. Asia Pac J Tour Res 24(1):1–14Google Scholar
  2. 2.
    Zhong S, Zhang J, Li X (2011) A reformulated directional bias of tourist flow[J]. Tour Geogr 13(1):129–147Google Scholar
  3. 3.
    Lew A, Mckercher B (2006) Modeling tourist movements: a local destination analysis[J]. Ann Tour Res 33(2):403–423Google Scholar
  4. 4.
    Lim C (2014) The major determinants of Korean outbound travel to Australia [J]. Math Comput Simul 64(3–4):477–485MathSciNetzbMATHGoogle Scholar
  5. 5.
    Hui TK, Chiching Y (2002) A study in the seasonal variation of Japanese tourist arrivals in Singapore [J]. Tour Manag 23(2):127–131Google Scholar
  6. 6.
    Yi W, Yu C, Yuqi LU et al (2017) Analysis of the space-time dynamics and influencing factors of scientific and technological innovation ability of tourism industry in China [J]. J Geo-Inform Sci 14(7):45–49Google Scholar
  7. 7.
    Shoval N, Isaacson M (2007) Sequence alignment as a method for human activity analysis in space and time [J]. Ann Assoc Am Geogr 97(2):282–297Google Scholar
  8. 8.
    Wang LC, Yan CX, Li W (2017) Spatio-temporal characteristics of tourism flow based on Sina Weibo big data: a case study of Lanzhou City [J]. J Tour 32(5):94–105Google Scholar
  9. 9.
    Silva FBE, Herrera MAM, Rosina K et al (2018) Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources [J]. Tour Manag 68:101–115Google Scholar
  10. 10.
    Cheng Q (2018) Railway passenger flow forecasting model based on wavelet packet and long-term and short-term memory fusion [J]. Comput Syst Appl 27(07):123–128Google Scholar
  11. 11.
    Fukunami M, Yellon DM, Kudoh Y, Maxwell MP, Wyse RK, Hearse DJ (1987) Spatial and temporal characteristics of the transmural distribution of collateral flow and energy metabolism during regional myocardial ischemia in the dog [J]. Can J Cardiol 3(2):94–103Google Scholar
  12. 12.
    Bai M, Di X, Zhang S et al (2013) Spatial–temporal distribution characteristics of water-nitrogen and performance evaluation for basin irrigation with conventional fertilization and fertigation methods [J]. Agric Water Manag 126(8):75–84Google Scholar
  13. 13.
    Lu W, Xiao W (2017) Spatio-temporal distribution pattern of cable car passenger flow in panholidays: a case study of Huangshan scenic area [C]// IEEE Second International Conference on Data Science in CyberspaceGoogle Scholar
  14. 14.
    Chao J, Wei G, Ying L et al (2014) Temporal and spatial distribution characteristics of the effective wind and solar energy in the Bohai Bay coastal area [J]. J Renew Sust Energy 6(4):043133Google Scholar
  15. 15.
    Liu B, Liu X, Yao WU et al (2016) Spatial and temporal distribution characteristics of planktonic crustaceans in Lake Poyang [J]. Acta Ecol Sin 36(24):11–15Google Scholar
  16. 16.
    Dong L, Bai H (2017) Temporal and spatial distribution characteristics of water quality of stagnant river network in Tianjin City, China [J]. Adv Sci Technol Water Resour 37(4):8–18Google Scholar
  17. 17.
    Litaor I, Eshel, Rimmer et al (2008) Hydrogeological characterization of an altered wetland [J]. J Hydrol 349(3–4):333–349Google Scholar
  18. 18.
    Blakey T, Melesse A, Hall M (2015) Supervised classification of benthic reflectance in shallow subtropical waters using a generalized pixel-based classifier across a time series [J]. Remote Sens 7(5):5098–5116Google Scholar
  19. 19.
    Cornelissen S (2005) Tourism impact, distribution and development: the spatial structure of tourism in the Western Cape province of South Africa [J]. Dev South Afr 22(2):163–185MathSciNetGoogle Scholar
  20. 20.
    Wang DG (2008) Spatial structure of tourism resources in the tourism region of Hulun Buir-Aershan [J]. Arid Land Geogr 31(3):456–463Google Scholar
  21. 21.
    Yin Z (2016) GroRec: a group-centric intelligent recommender system integrating social, mobile and big data technologies [J]. IEEE Trans Serv Comput 9(5):786–795Google Scholar
  22. 22.
    Schatz MC (2009) CloudBurst: highly sensitive read mapping with MapReduce [J]. Bioinformatics 25(11):1363–1369Google Scholar
  23. 23.
    Luo Y, Luo S, Guan J et al (2013) A RAMCloud storage system based on HDFS: architecture, implementation and evaluation [J]. J Syst Softw 86(3):744–750MathSciNetGoogle Scholar
  24. 24.
    Zhong Q, Que HK, Chen RM et al (2012) Application of internet of things technology in equipment all life cycle management [J]. Comput Eng 38(5):247–207Google Scholar
  25. 25.
    Zhang D, Li G, Pan Z et al (2015) A new anti-collision algorithm for RFID tag [J]. Int J Commun Syst 27(11):3312–3322Google Scholar
  26. 26.
    Cheng W, Long ZH, Jiang GQ, Wireless IP (2011) Voice communication system based on GPRS network [J]. Comput Eng 37(14):82–81Google Scholar
  27. 27.
    Wang X, Wang Z, Wang TY et al (2013) The design and realization of CNC system test board based on STM32 [J]. Adv Mater Res 694(2):1215–1218Google Scholar
  28. 28.
    Ting LU, Fang J, Qiao Y (2015) HBase-based real-time storage system for traffic stream data [J]. J Comput Appl 35(1):103–107Google Scholar
  29. 29.
    Memishi B, Pérez MS, Antoniu G (2015) Diarchy: an optimized management approach for MapReduce masters [J]. Procedia Comput Sci 51(1):9–18Google Scholar
  30. 30.
    Agrawal R, Gehrke J, Gunopulos D et al (1998) Automatic subspace clustering of high dimensional data for data mining applications [J]. ACM SIGMOD Rec 27(2):94–105Google Scholar
  31. 31.
    Chen MS, Han J, Yu PS (2002) Data mining: an overview from a database perspective [J]. IEEE Trans Knowl Data Eng 8(6):866–883Google Scholar
  32. 32.
    Suvannang N, Preeyanon L, Malik AA et al (2018) Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study [J]. RSC Adv 8(21):11344–11356Google Scholar
  33. 33.
    Peral J, Maté A, Marco M (2017) Application of data mining techniques to identify relevant key performance indicators [J]. Comput Stand Interfaces 50:55–64Google Scholar
  34. 34.
    Jin M, Wang Y, Zeng Y (2018) Application of data mining technology in financial risk analysis [J]. Wirel Pers Commun 102(1):1–15Google Scholar
  35. 35.
    Xu Y, Shen S Q, He Y L, et al (2018) A novel hybrid method integrating ICA-PCA with relevant Vector machine for multivariate process monitoring [J]. IEEE Transactions on Control Systems Technology, PP(99):1–8Google Scholar
  36. 36.
    Zhang C, He Y, Yuan L et al (2018) A multiple heterogeneous kernel RVM approach for analog circuit fault prognostic [J]. Clust Comput 2:1–13Google Scholar
  37. 37.
    Veeramsetty V, Venkaiah C, Kumar DMV (2018) Hybrid genetic dragonfly algorithm based optimal power flow for computing LMP at DG buses for reliability improvement [J]. Energy Syst 9(3):709–757Google Scholar
  38. 38.
    Xu L, Jia H, Lang C, et al (2019) A novel method for multilevel color image segmentation based on Dragonfly Algorithm and Differential Evolution [J]. IEEE Access (99):1–1Google Scholar
  39. 39.
    Mauro AD, Greco M, Grimaldi M (2016) A formal definition of Big Data based on its essential features [J]. Libr Rev 65(3):122–135Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Post-Doctoral Research Center of Zhuhai Da Hengqin Science and Technology Development Co., LtdMacauChina
  2. 2.Institute of Data ScienceCity University of MacauMacauChina

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