Spatio-Temporal Analysis of Tourist Flows in AlUla Governorate (2021–2023) Using Geographic Information Systems (GIS)
Abstract
This study includes an analysis of the spatial and temporal variations in tourism flows in AlUla Governorate, one of the most prominent heritage destinations in the Kingdom of Saudi Arabia, during the period (2021-2023), relying on geostatistical-spatial analysis in a geographic information systems (GIS) environment. This study has gained particular importance in the Kingdom's drive to diversify tourism revenue sources within Vision 2030. A quantitative approach was applied, relying on actual data on tourist numbers and tourist attractions, while using the ordinary kriging technique to estimate tourism rates in locations lacking direct data. The study relied on a representative sample that included various tourist sites in the governorate. The study results revealed clear seasonal variation, with winter representing the peak tourism season, accounting for 41.9% of the total visitor numbers, compared to a sluggish summer, which accounts for no more than 11.9%. The predictive models also demonstrated high statistical accuracy, with average root mean square error (RMSE) values ranging from 8.73% to 17.35%, and correlation coefficients (r) above 0.96, reflecting the models' reliability in representing reality. These indicators suggest they can be relied upon in future planning for the region's tourism sector. The study also revealed a clear spatial clustering of tourist attractions, particularly geomorphological ones, while cultural and entertainment attractions were characterized by a dispersed distribution. The study concludes that understanding the spatio-temporal distribution of tourism in AlUla Governorate represents a critical planning tool for guiding infrastructure investments, distributing tourism activities, and promoting sustainability in the management of heritage and natural resources. The study recommends adopting development strategies based on accurate spatial analysis, which contribute to reducing disparity and achieving a more balanced distribution of tourism activity.
References
2. الزهراني، أ. (2023). السياحة في رؤية المملكة 2030: تحديات وتطلعات. مجلة السياحة والتنمية، 10(2)، 123–140.
3. السراني، م.، وآل زينة، ه. (2022). بناء نموذج خرائطي لتحديد المواقع السياحية في محافظة العلا باستخدام نظم المعلومات الجغرافية. مجلة دراسات الخليج العربي، 18(3)، 205–233.
4. الأحمري، م.، وورغي، م. (2020). تحليل التوزيع المكاني للمواقع الأثرية في منطقة الباحة. مجلة أبحاث السياحة والآثار، 12(4)، 99–125.
5. زويد، ع. ع. (2022). التحليل المكاني للمواقع السياحية في قضاء المناذرة باستخدام نظم المعلومات الجغرافية. جامعة الفرات الأوسط التقنية. منشور على ResearchGate:
https://www.researchgate.net/publication/366582434.
6. القحطاني، ح. ر. (2023). التحليل المكاني للوجهات السياحية في مدينة الرياض باستخدام نظم المعلومات الجغرافية. مجلة مركز البحوث الجغرافية والكارتوجرافية، 37(1)، 171–201.
https://mkgc.journals.ekb.eg/
7. وزارة السياحة. (2022). التقرير الإحصائي السنوي للسياحة في المملكة العربية السعودية. الرياض: وزارة السياحة السعودية.
8. الهيئة العامة للسياحة والتراث الوطني. (2017). تقرير عن جهود الهيئة بمحافظة العلا. النسخة الرابعة النهائية.
9. الهيئة العليا للسياحة. (دون تاريخ). خطة تنمية السياحة المستدامة: المخرج 2.06، خطة العمل الخاصة بالتراث الثقافي (الورقة 2.06.03.04). المملكة العربية السعودية: مسودة الخطة الإدارية للعلا ومحافظة الوجه.
10. الهيئة الملكية لمحافظة العلا. (2022). الدليل الرسمي لزوّار العلا: تجربة العلا. تم الاسترجاع من https://experiencealula.com
11. Altassan, A. (2023). Sustainability of heritage villages through eco-tourism investment (Case study: Al-Khabra Village, Saudi Arabia). Sustainability, 15, 7172. https://doi.org/10.3390/su15187172
12. Bishop, I. D., & Gimblett, H. R. (2000). Modelling tourist movement: A framework to enhance understanding of tourist behaviour. Tourism Geographies, 2(1), 69–87. https://doi.org/10.1080/146166800363466.
13. Cameron, A. C., & Windmeijer, F. A. G. (1997). An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics, 77(2), 329–342. https://doi.org/10.1016/S0304-4076(96)01818-0
14. Cressie, N. (1990). The origins of Kriging. Mathematical Geology, 22(3), 239-252. https://doi.org/10.1007/BF00889887.
15. Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.
16. Fang, W., Deng, H., & Wang, Y. (2023). A Kriging-based model for interpolating tourist flow using mobile signaling data. Journal of Tourism Studies, 34(2), 120-135.
17. Fadhil, S. M., & Ikhwan, A. (2024). Implementation of the algorithm in a web-based geographic information system for mapping tourist attractions in the Toba area. Jurnal Inovtek Polbeng Seri Informatika, 9(2), 988–1000. https://doi.org/10.35314/jawetw85.
18. Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications.
19. Frost, J. (2019). Regression analysis: An intuitive guide for using and interpreting linear models. Statistics By Jim Publishing.
20. Guo, Y., & Liu, M. (2021). Classification and spatial distribution characteristics of tourist attractions in Shanxi Province based on POI data. Scientia Geographica Sinica, 41, 1246–1255.
21. Hengl, T., Heuvelink, G. B., & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120(1-2), 75-93. https://doi.org/10.1016/j.geoderma.2003.08.018.
22. Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral sciences (5th ed.). Houghton Mifflin.
23. Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics. Oxford University Press.
24. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer. https://doi.org/10.1007/978-1-4614-7138-7
25. Journel, A. G. (1983). Nonparametric estimation of spatial distributions. Mathematical Geology, 15(3), 445-468. https://doi.org/10.1007/BF01031292.
26. Kalaitzakis, M., Tzannatos, E., & Zacharias, I. (2022). Technological integration for real-time management of visitor flows in urban environments. Tourism Management, 88, 104400.
27. Koenig, N., & Bischoff, E. E. (2005). Seasonal price variation in accommodation: A contribution to the discussion on seasonality. Journal of Travel Research, 44(2), 163–173. https://doi.org/10.1177/0047287505278991.
28. Lee, C. K., Han, S. Y., & Lockyer, T. (2008). Estimating the environmental impacts of tourism development: A combined approach. Tourism Management, 29(1), 128–138. https://doi.org/10.1016/j.tourman.2007.03.006.
29. Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. https://doi.org/10.2113/gsecongeo.58.8.1246.
30. Moore, D. S., McCabe, G. P., & Craig, B. A. (2014). Introduction to the practice of statistics (8th ed.). W. H. Freeman.
31. Myers, D. E. (1982). Matrix formulation of co-kriging. Mathematical Geology, 14(3), 249-257. https://doi.org/10.1007/BF01032291.
32. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). Wiley.
33. Nistor, C., Nistor, T., & Csapó, J. (2020). GIS-based kernel analysis for mapping international tourism flows. Journal of Spatial Analysis, 32(3), 67-82.
34. Peng, H., Zhang, H., & Lu, L. (2016). Spatial distribution and influencing factors of cross-border tourist flows in China. Tourism Management, 55, 23–33. https://doi.org/10.1016/j.tourman.2016.01.005
35. Sirror, H. (2024). Lessons learned from the past: Tracing sustainable strategies in the architecture of Al-Ula heritage village. Sustainability, 16(13), 5463. https://doi.org/10.3390/su16135463
36. Stupariu, M. S., Josan, I., Cenan, C. G., & Rujescu, C. (2023). The geostatistical dimension of tourist flows generated by international arrivals in Romania. Geographia Technica, 18(1), 141–151. https://doi.org/10.21163/GT_2023.181.13.
37. UNWTO. (2023). World Tourism Barometer and Statistical Annex, Volume 21. World Tourism Organization. https://www.unwto.org/statistics
38. UNESCO. (2021). Cultural Tourism and Heritage Report. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org
39. Wang, R., Wang, D., & Chun, S. (2016). Spatial structure analysis of tourist attraction in Lanzhou based on GIS. Proceedings of EMCS-16, 1185–1188. https://doi.org/10.2991/EMCS-16.2016.292.
40. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (2nd ed.). Wiley.
41. Yu, W. H., & Ai, T. H. (2015). Network kernel density estimation for the analysis of facility POI hotspots. Acta Geodaetica et Cartographica Sinica, 44(1), 82–90.
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