Modeling The Interactions Of Tourism Demand Drivers In Iran Based On The Geodetector Model

Document Type : Research Paper

Authors

1 Department of Geography , Yazd University, Yazd, Iran

2 Department of Geography, Yazd University, Yazd, Iran

10.22059/jhgr.2026.390055.1008791

Abstract

ABSTRACT
Tourism is one of the most important global industries, playing a fundamental role in economic and social development. Tourism demand not only reflects tourists’ willingness to visit a destination but also mirrors economic trends and social changes at national and regional levels. Identifying and analyzing the driving factors of tourism demand is a key step towards effective planning and sustainable development of the industry. This study aims to model and analyze the interactions among factors affecting tourism demand in Iran during the year 2023.  This study employs a quantitative, descriptive-analytical approach. Data were collected from documentary sources, and the Geo Detector method, novel in domestic research, was applied to meet the study’s objectives. The results indicate that the interaction of variables such as accommodation infrastructure, transportation routes, economic factors, and historical attractions influences tourism demand. Among these, the density of accommodations and historical attractions showed the highest synergy with an interaction coefficient of 0.79. Additionally, investment and infrastructure accessibility were found to be highly significant, with notable interactions between investment rates and density of historical attractions (0.72) and between highways and accommodations (0.69).  The findings of this study can guide policymakers and planners in optimizing resource allocation and formulating tourism development strategies. By adopting a comprehensive and coordinated approach, sustainable growth in tourism demand can be achieved.
Extended Abstract
Introduction
Tourism, a socio-economic phenomenon with deep historical roots, plays a crucial role in cultural interactions and the economic development of societies. In today's globalized world, tourism has emerged as a key strategy for economic expansion, the promotion of cross-cultural understanding, job creation, the revitalization of local businesses, and the enhancement of per capita income. Tourism demand, a reflection of tourists' desires and expectations, is influenced by various economic (e.g., per capita income and service pricing), environmental (e.g., air quality and access to natural resources), and infrastructural factors (e.g., transportation and amenity availability). In Iran, despite its vast tourism potential, challenges such as the uneven distribution of tourists and infrastructural deficiencies hinder the optimal utilization of these resources. The present research aims to identify the factors affecting tourism demand in Iran, employing the spatial analysis method of Geo Detector to assess each factor's contribution to spatial variations in demand. The ultimate goal is to provide practical solutions for improving tourism management and more equitably distributing resources by considering the interplay of diverse influencing elements.
 
Methodology
This descriptive-analytical study examines the factors influencing tourism demand across 31 provinces in Iran during 2023, utilizing the Geo Detector method. Data were collected through documentary methods, encompassing indicators such as investment rate, population density, GDP, density of historical and natural attractions, density of museums, UNESCO World Heritage sites, national intangible heritage sites, tourism destination quality, railway density, highways, and the density of accommodations and hotels. Geo Detector, as an advanced spatial statistical analysis model, is employed to investigate the relationships between spatial and dependent variables and to determine the extent of each spatial variable's impact on changes in the dependent variable. This method enables the analysis of various spatial factors' influence (such as tourism facilities, natural and cultural attractions, local economy, and tourism policies) on the growth of the tourism industry and the development of improvement strategies, the allocation of tourism resources to different regions, and the prediction of future changes in the tourism sector. The fundamental assumption in Geo Detector is that if an independent variable directly affects the dependent variable in space, the spatial distribution of the dependent variable should converge with the spatial distribution of the independent variable. In this study, factor detection and interaction detection sub-modules are used to measure interactions between tourism demand factors, and the Q coefficient measures the influence of independent variable X on dependent variable Y in spatial heterogeneity. To optimize spatial analysis, the optimization algorithm for partitioning parameters of geographic representative variables is used.
 
Results and discussion
Employing the Geo Detector method, the analysis of factors influencing tourism demand in Iran reveals varying impacts of independent variables on attracting tourists. The density of transportation routes and accommodations emerges as the most significant determinant, underscoring the importance of ease of access and suitable lodging infrastructure. Economic investment and tourism quality also play a notable role in augmenting demand, while the density of natural attractions and museums exhibits a moderate influence. Population and nationally and globally registered sites demonstrate the least impact, potentially stemming from deficiencies in the management and marketing of these attractions. The analysis of factor interactions further indicates that the strongest non-linear interaction exists between the density of accommodations and historical attractions, highlighting the importance of the connection between lodging infrastructure and historical sites. Other robust interactions are also observed between the density of accommodations and globally registered sites, and between investment density and historical attractions.
 
 
 
Conclusion
Tourism, as one of the most important and prosperous global industries, has a significant impact on the development of countries, and this has led to the role of tourism as a key indicator for measuring the economic and social development of societies. In the meantime, tourism demand not only indicates the level of interest and desire of tourists to a destination, but also provides valuable information about economic trends and social changes at the regional and national levels.
The results of the analyses conducted using the Geo Detector method indicate that tourism demand in Iran is influenced by a complex interaction of various factors, including accommodation infrastructure, transportation networks, economic investments, and historical and natural attractions. The most significant findings emphasize the importance of the density of accommodations and hotels, which play a crucial role in attracting tourists, especially when located near historical and cultural attractions. Furthermore, transportation networks and infrastructure significantly impact the facilitation of access to various destinations and tourist attractions. Additionally, economic investments, particularly in sectors related to infrastructure development and tourism services, play an important role in tourism demand. Regarding natural and historical attractions, while their impact on tourism demand is relatively lower than other factors, enhancing infrastructure and improving the protection and promotion of these attractions can increase their effect on attracting tourists. Overall, the findings suggest that the complex interactions of these factors shape tourism demand in Iran, and to increase tourism demand, a coordinated and synergistic approach is needed to address these factors effectively.
 
Funding
There is no funding support.
 
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.

Keywords

Main Subjects

References

  1. Afifi, R., Rezghi Shirsavar, H., Valikhani, M., & Ebrahimpour, A. (2022). Identifying Factors Affecting Domestic Tourism Demand in the Geographical Area of Tehran with Demetel Approach and Theme Analysis. Geography (Regional Planning), 12(48), 416-431. doi: 10.22034/jgeoq.2022.319821.3468  [In Persian].
  2. Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: Lessons from tourism and hospitality. Journal of Service Management, 30(4), 484-506.
  3. Chen, S., & Li, X. (2021). Exploring spatial determinants of tourism demand using geodetector: Evidence from urban areas. Tourism Management Perspectives, 39, 100830. https://doi.org/10.1016/j.tmp.2021.100830
  4. Daviran, E. (2025). The Rhythm of Consumption Space in the Tourism of the Urban Peripheral Mountains: A case study Gavazang and Amand Zanjan Mountains. Human Geography Research, 57(1), 67-86. doi: 10.22059/jhgr.2024.369222.1008652. [In Persian].
  5. Dwyer, L., Forsyth, P., & Dwyer, W. (2020). The role of tourism in the economy: A global perspective. Tourism Economics, 26(4), 649-664.
  6.  Ebrahimzadeh, R., & Amini, S. (2021). Tourism diversity and geographical impacts in Iran. Iranian    Journal of Geographical Studies. [In Persian].
  7. Farzin, M. R., Pourjahan, S., Ghanbari, R., & Mohajer, B. (2022). Investigating the Determinants of Iran's Inbound Tourism Demand: Panel Data Analysis. Tourism and Leisure Time, 7(14), 85-100. doi: 10.22133/tlj.2023.383222.1073 [In Persian].
  8. Gallarza, M. G., Arteaga, F., & Gil-Saura, I. (2019). Customer value in tourism and hospitality: Broadening dimensions and stretching the value-satisfaction-loyalty chain. Tourism Management Perspectives, 31, 254-268.
  9. Gao, J., Lin, B., & Zhang, C. (2021). Simulation and projection of tourism-related carbon emissions: A STIRPAT model and scenario analysis. Journal of Cleaner Production, 280, 124391.
  10. Gholizadeh, A., & Mohammadi, S. (2021). Assessing spatial heterogeneity in tourism demand: A case study from Iran. Tourism Geographies, 23(3), 371–389. https://doi.org/10.1080/14616688.2020.1822219
  11. Hall, C. M. (2009). Tourism: Rethinking the social science of mobility. Routledge.
  12. Hjalager, A. M., & Madsen, A. B. (2018). Sustainable tourism culture and tourists’ loyalty: A comparative analysis. GeoJournal. https://link.springer.com/article/10.1007/s10708-018-9901-2
  13. Huang, X., Wang, Y., & Zheng, L. (2020). The impact of transportation infrastructure on tourism development. Journal of Sustainable Tourism, 28(6), 839-858.
  14.  Karami, A., Ehsani, M., & Nouri, S. (2022). Regional disparities and tourism infrastructure in Iran.    Journal of Tourism Studies, 19(1), 22-39. [In Persian].
  15. Liu, P., Zhang, H., Zhang, J., Sun, Y., & Qiu, M. (2019). Spatial-temporal response patterns of tourist flow under impulse pre-trip information search: From online to arrival. Tourism Management, 73, 105–114.
  16. Ma, X., Yang, Z., & Zheng, J. (2024). Analysis of spatial patterns and driving factors of provincial tourism demand in China. Scientific Reports.
  17. Mazzarol, T., & Soutar, G. N. (2002). "Push-pull" factors influencing international student destination choice. International Journal of Educational Management, 16(2), 82-90.
  18. Mododirkhodi, M., & Naghiloo, H. (2023). Explaining Effective Factors on Tourism Demand in Zanjan City. Journal of Urban Ecology Researches, 14(3), 165-184. doi: 10.30473/grup.2020.45070.2313 [In Persian].
  19. Mohammadi, F., Safari, K., & Bagheri garbollagh, H. (2024). Examining the Importance of Tourists' Satisfaction in the Provision of Tourism Services and Effective Factors (Case Study: Hotels in Semnan Province). Human Geography Research, 56(3), 73-89. doi: 10.22059/jhgr.2023.305040.1008133 [In Persian].
  20. Nakhaei Moghadam, M. A., Kafashpor, A., & Noami, A. (2024). Explaining the role of factors affecting the demand for religious tourism. Regional Planning, 13(52), 193-208. doi: 10.30495/jzpm.2022.29762.4041 [In Persian].
  21. Nguyen, T. T. H., Nguyen, T. T. T., & Nguyen, T. T. (2023). An evaluation of factors affecting tourist satisfaction with service quality: Case study of Sam Mountain National Tourist Area in An Giang Province, Vietnam. Semantic Scholar.
  22. Pompili, T., Pisati, M., & Lorenzini, E. (2019). Determinants of international tourist choices in Italian provinces: A joint demand–supply approach with spatial effects. Papers in Regional Science, 98, 2251–2273.
  23. Qiu, H., & Zhao, X. (2022). Environmental determinants of tourism demand: A spatial econometric approach. Journal of Travel Research, 61(2), 345–360. https://doi.org/10.1177/00472875211057267
  24.  Rahimi, R., Ghasemi, M., & Sharifi, H. (2023). Natural and cultural attractions and their role in    tourism demand in Iran. Iranian Journal of Tourism Research, 12(2), 45-62. [In Persian].
  25. Rahnama A, Khaksar Astaneh H, Dadras Moghaddam A. Spatial Analysis of Domestic Tourism Demand in Iran. IUESA 2022; 10 (38):69-86
    [In Persian].
  26. Ramezani, A., & Shirazi, H. (2021). The effects of tourism on host community quality of life: A case study of rural areas in Iran. Iranian Journal of Tourism Studies, 9(1), 85-95. [In Persian].
  27. Sari, D. P., Sihombing, S. O., & Sari, D. P. (2023). Role and determinants of domestic tourism demand in Indonesia. Semantic Scholar.
  28. Seetaram, N., Forsyth, P., & Dwyer, L. (2020). Measuring price elasticities of demand for outbound tourism using competitiveness indices. Annals of Tourism Research, 83, 102944.
  29. Segovia, H. I. S. (2021). El turismo místico, espacios mágicos: potencialidades de la ciudad de Yaguarón, Paraguay. Revista científica en ciencias sociales, 3(2), 122-128.
  30. Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, 312–321. https://doi.org/10.1016/j.jbusres.2020.05.028
  31. Song, H., Qiu, R., & Park, J. (2019). A review of research on tourism demand forecasting. Annals of Tourism Research, 75, 338–362.
  32. Song, Y., Wang, J., Ge, Y., & Xu, C. (2020). An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data. GIScience & Remote Sensing, 57, 593–610.
  33. Tiwari, P., & Zhao, Z. (2022). Technological progress and tourism demand: A multi-faceted analysis. GeoJournal. https://link.springer.com/article/10.1007/s10708-021-10545-4
  34. Tóth, G., Kincses, Á., & Nagy, Z. (2023). Gravity model in tourism: An augmented approach with network analysis. Tourism Management, 95, 104687.
  35. UNWTO. (2022). World Tourism Organization report.
  36. Van Hove, N. (2005). The economics of tourism destination. USA: Elsevier.
  37. Wang, J. F., Zhang, T. L., & Fu, B. J. (2016). A measure of spatial stratified heterogeneity. Ecological Indicators, 67, 250-256.
  38. Wang, J., Zhang, T. L., & Fu, B. J. (2010). Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science, 24, 107–127.
  39. Wang, J., Zhang, Y., & Li, X. (2021). GeoDetector for tourism demand analysis. Tourism Management, 82, 104-119.
  40. Wang, L., Chen, J., & Zhang, H. (2020). Spatiotemporal analysis of tourism demand using geodetector: Evidence from China. Annals of Tourism Research, 85, 102883. https://doi.org/10.1016/j.annals.2020.102883
  41. Yang, Y., & Zhang, H. (2019). Spatial-temporal forecasting of tourism demand. Annals of Tourism Research, 75, 106–119.
  42. Zandi, E., & Yaghoub zadeh, R. (2021). Hybrid Modeling for Forecasting of Domestic Cultural Tourism Demand in Tehran. Strategic Studies of Culture, 1(2), 59-82. doi: 10.22083/scsj.2021.142061 [In Persian].
  43. Zhang, Y., Liu, Q., & Gao, P. (2020). Exploring driving forces of tourism flows: A geodetector approach. Tourism Management, 78, 104007. https://doi.org/10.1016/j.tourman.2019.104007
  44. Zhang, Z., Liu, X., & Chen, H. (2023). Spatial analysis of tourism demand using GeoDetector. Annals of Tourism Research, 95, 103471.
  45. Zhou, T., Liu, H., & Wang, Y. (2023). Tourism spatial analysis with Local Moran’s I: Application in Chinese tourism destinations. Tourism Planning & Development, 20(3), 318-335. https://doi.org/10.1080/21568316.2022.2065579
  46. Ziaabadi, M., Jalai Esfandabadi, S. A., & Jalalabadi, L. (2025). Examining the Factors Influencing Tourism Demand in Iran with Emphasis on the Exchange Rate Gap (Markov Switching Approach - Vector Error Correction). Majlis and Rahbord, 31(120), 335-365. doi: 10.22034/mr.2023.5698.5379  [In Persian].
Human Geography Research
Volume 57, Issue 4
December 2026
Pages 143-156

Files

Share

How to cite

Statistics