Analyzing factors on tourist movement predictability: a study based on social media data

Figures & data

Figure 1. Factors influencing tourist movement predictability (G. Lau and McKercher 2006).

Figure 2. Study area and Sina Weibo dataset. (a) The location of Suzhou on the scale of Country. (b) The location of Suzhou on the scale of Province. (c) The location of sites in Suzhou and (d) The geo-tagged microblogs in Suzhou.

Figure 3. Diagram of trajectory division.

Note: The number in the circles is the date index. The circles indexed as 1,9,12 are the dates when the microblogs posted in the tourist's residence city. The circles indexed as 3,7,15,17,18,19 are the dates when microblogs were not posted. The other circles are the dates when microblogs were posted in the destination city.

Figure 4. LSTM model architecture.

Figure 5. The training loss and testing loss of our model.

Figure 6. Boxplots of Top@1, Top@3, and the MRR for the prediction results of the LSTM model with tourist features.

Figure 7. Boxplots of Top@1, Top@3, and the MRR for the prediction results of the LSTM model with destination features.

Figure 8. Boxplots of Top@1, Top@3, and the MRR for the prediction results of the LSTM model with time features.

Figure 9. Trajectory length distribution.

Figure 10. Distribution of tourists' travel distance (S).

Figure 11. Length of tourist trajectories and the Top@1, Top@3, and MRR of the prediction.

Figure 12. Travel distance (S) of tourists and the Top@1, Top@3, and MRR of the prediction.

Figure 13. Relationship of the distance between sites and MRR at the local level.

Figure 14. Preference of tourist with different features for site types.

Figure 15. Site types in Suzhou.

Table 1. Summary of the impact of factors on tourist movement prediction.

Influential factors		High Predictability	Low Predictability	Inferred cause	Implication	Corresponding findings
Tourist	Travel distance(OD)	Tourist from 0–100 km	Tourist from 1200–3500 km	Long- and short-haul tourists have different motivations	The place of origin can be an important basis for recommendation	Xue and Zhang (2020)
	Number of visits	More than 5 visits	First time	Repeat visitors prefer a fixed vacation and leisure	The sites should provide annual passes for repeat tourists.	Li et al. (2008); Smallwood, Beckley, and Moore (2012)
	Gender	Female	Male	Female tourists prefer commercial sites	For female tourists, provide a higher quality shopping experience	Kotzé et al. (2012)
Destination	Site title	Equivalent level	Equivalent level	The site titles are not particularly helpful in tourist movement prediction
	Site type	Commercial sites	Cultural sites	Commercial sites provide complex functions including dining, accommodation, shopping, etc.	The manager can provide travel plans
	Site location	Urban areas	Rural areas	The aggregation effect of urban area sites	The manager can package tourism products using the aggregation effect
Spatio-temporal	Time of arrival	Evening	Morning	Evening activities are constrained by the residence of the tourist.	Night-travel recommendations may improve the results	W. G. Lau (2007)
	Holiday	Equivalent level	Equivalent level	The sites in Suzhou hold few holiday events	Sites should hold more festival events
	Season	Winter	Autumn	Few sites to visit in the winter	The manager can launch winter exclusive tourism products
	Length of trajectory	Long trajectory for LSTM	Long trajectory for Markov	Deep learning methods are better at discovering the implicit connections between locations	Tourists with longer trips require specially designed prediction models	Solomon et al. (2021); Xu et al. (2022)
	Travel distance (S)	Long distance for LSTM	Long distance for Markov	The Markov model is unsuitable for larger spatial scales	When using model prediction, the spatial scale must be considered	Xia, Zeephongsekul, and Arrowsmith (2009); Xia, Zeephongsekul, and Packer (2011); W. Zheng, Huang, and Li (2017)

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