Big Data-Driven Dynamic Analysis of Tourist Behavioral Trajectories and Intelligent Service Strategies in Tourist Attractions
17 mar 2025
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Publicado en línea: 17 mar 2025
Recibido: 23 oct 2024
Aceptado: 29 ene 2025
DOI: https://doi.org/10.2478/amns-2025-0291
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© 2025 Guo Hu, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Information description of tourist portraits
| User portrait information | Attribute | Describe |
| Basic information | Gender | The classification variable, 1= "male, 2=" female" |
| Age | Class variable, 1= under 18,2=Between 18 and 25,3=, Between 25 and 35,4=, Between 35 and 60,5= over 60 years old. | |
| Educational background | Grade variables,1= "junior high school and below;2= "high school and secondary school;3="Bachelor’s degree and junior college;4="Graduate student" | |
| Travel characteristics | Pedestrian | The classification variable, 1= friend, 2= family, 3= parents,4= Couple,5= One person |
| Travel cost | Numerical variables, the average daily travel cost of tourists | |
| Cognitive degree | Cognitive level | The numerical variable, 1= "very low," 2 = "low, 3=" general, "4=" high, "5=" very high" |
| Occupation | The classification variable, 0= "no,"1= "yes" | |
| Trajectory information | Tourist attraction | Nominal variable, tourist attractions |
| Playtime | The number of variables, the visitor’s stay in a scenic spot |