Modeling the Spatial Distribution of Accommodations in Yazd city

Document Type : Research Article

Authors

Department of Geography, Yazd University, Yazd, Iran

10.22059/jut.2024.370576.1178

Abstract

A B S T R A C T
Accommodations are the most important representatives of tourism in cities,and their location greatly affects a tourist's choice. In studying the geographical distribution of accommodations as a spatial phenomenon, attention should be paid to their spatial characteristics. Spatial modeling is a suitable statistical approach to studying the patterns and distribution of accommodation. In this research, spatial variable relationships between the spatial distribution of accommodations and the factors that affect them in Yazd city have been investigated. In terms of purpose, the research is practical and based on spatial data analysis methods. The research data includes the number and geographical location of accommodations and explanatory variables, including land use, road network, attractions, and population. Using exploratory analysis methods, the spatial patterns were identified, and then, using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR), the effective factors in forming patterns were analyzed. The results of the ANN index test show that the spatial pattern is clustered. Also, the findings showed that attractions, road networks, green spaces, taxi and bus stations, commercial centers, governmental and non-governmental offices, and residential areas were among the most important effective factors. The GWPR model performs better in estimating the effects of independent variables, and the effect of independent variables is not the same in different spatial units. Urban and tourism planners can provide plans for the development and optimization of tourism in Yazd city according to the spatial pattern of accommodations and the factors that affect their spatial distribution
Extended Abstract
Introduction
 Facilities and equipment related to tourism services play an important role in attracting domestic and foreign tourists. Accommodations are very important as the daily origin and destination of tourists in the city. Meanwhile, urban accommodations play a central role among the accommodation centers of the tourism sector, and how they are distributed can significantly impact the provision of services and the attraction of tourism. Since it is almost impossible to move accommodation again, selecting the correct location for a new accommodation is very important. In tourism, selecting a destination mainly relies on effective strategies to attract tourists and promote success amid fierce competition. The location of the accommodation can significantly influence the decision and choice of a tourist to stay. Therefore, the selection of accommodation location and the related determinants require deep analysis. In this research, this problem has been tested to what extent factors such as land area, green space use, administrative and institutional use, residential use, commercial use, taxi and bus station density, attraction density, population density, and road network density, the ability to explain the spatial pattern of tourist accommodations in Yazd city. In this research, spatial distribution models of accommodations in Yazd city are discovered and investigated by examining the spatial variations of dependent and independent variables using geographic weighted regression (GWR) models, as well as spatially diverse relationships between determining factors that are of high importance. This research aims not to search for public relations but to investigate the spatial variable relationships between the spatial distribution of accommodations and a set of factors in Yazd.
 
Methodology
In terms of purpose, the research is applied using the exploratory-causal method. The statistical population of the research is all city blocks in Yazd city.
 
According to the nature and data type, a 500 x 500 meters grid was created. This gridding type is a conventional method for aggregating data and avoiding modifiable areal units problems(MAUP).  Research data based on independent and dependent variables include the number and location of accommodations in Yazd city and explanatory variables compiled based on the latest data in Yazd Municipality. Then, a spatial database was created using ArcGIS software.
The independent variables are 1) land-use; a) institutional and administrative, b) commercial, c) residential and d) green space 2) road network 3) attractions; 4) Population.
The methodology of this research is based on spatial analysis methods as:
1) Exploratory Spatial Data Analysis (ESDA);
2) Spatial Data Analysis (CSDA).
Global Poisson regression (GPR) and geographically weighted Poisson (GWPR) are used to model the spatial pattern of accommodations in Yazd.
 
Results and discussion
The results of the analysis show that the percentage of residential use, the variables of population density, and the density of bus and taxi stations have a positive and significant relationship with the distribution of accommodations. This means that with the increase of these variables, the distribution patterns of accommodations move towards concentration and clustering. In addition, the variables of percentage of institutional and administrative use, percentage of commercial use, percentage of green space use, density of road network, and density of tourist attractions have no significant relationship with the distribution of accommodations. These results show that factors such as the use of buildings, green spaces, and road network density do not affect the distribution of accommodations and do not play an important role in determining the spatial pattern of accommodations in Yazd city. However, based on the results of local regression, the mentioned relationships are not true in all regions. However, they vary from one region to another, as well as the intensity of the coefficients and their direction variation.
 
Conclusion
In this research, the complex relationships between spatial determinants and the distribution of accommodations in the urban environment of Yazd were discussed. Based on the average test results of the nearest neighbor, the distribution of accommodations in Yazd is clustered. This clustering pattern of accommodations can depend on tourist attractions, proper accessibility to urban services, land price, population density, etc. Considering this cluster distribution of accommodations in Yazd, it can be assumed that proper planning for organizing tourism services and managing demand in this city, considering each cluster separately, may significantly improve travelers' experience and promote the tourism industry.  The findings of this research have important policy implications for urban management. Understanding the spatial determinants in selecting the location of accommodations can impact strategic decisions for land use planning, zoning regulations, and infrastructure development. For future research in this field, it is suggested that specific determinants, such as environmental factors and emerging technologies, be dealt with more deeply when selecting the location of accommodations. In addition, comparative studies in other cities can provide valuable insight into the generalizability of the findings.
 
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

References

  1. Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources. Tourism Management, 68, 101-115. https://doi.org/10.1016/j.tourman.2018.02.020
  2. Anselin, L. (1999). Spatial data analysis with SpaceStat and ArcView workbook. Urbana-Champaign: University of Illinois, 3.
  3. Ashworth, G. J., & Tunbridge, J. E. (2004). Whose tourist-historic city? Localizing the global and globalizing the local. A companion to tourism, 210. https://doi.org/10.1002/9780470752272.ch17
  4. Aziz، S., Ngui, R., Lim, Y. A. L., Sholehah, I., Nur Farhana, J., Azizan, A. S., & Wan Yusoff, W. S. (2012). Spatial pattern of 2009 dengue distribution in Kuala Lumpur using GIS application. Trop Biomed, 29(1), 113-120.
  5. Batista e Silva, F., Herrera, M. M., Rosina, K., Barranco, R. R., Freire, S., & Schiavina, M. (2018).
  6. Chang, V., Liu, L., Xu, Q., Li, T., & Hsu, C. H. (2023). An improved model for sentiment analysis on luxury hotel review. Expert Systems, 40(2), e12580. https://doi.org/10.1111/exsy.12580
  7. Chu, R.K.S. & Tat, C. (2000). An important-performance analysis of hotel selection factors in the Hong Kong hotel industry: a comparison of business and leisure travelers. Tourism Management. 21, 363-377. https://doi.org/10.1016/S0261-5177(99)00070-9
  8. Chung, W., & Kalnins, A. (2001). Agglomeration effects and performance: a test of the Texas loading industry. Strategic Management Journal, 22, 969-988. https://doi.org/10.1002/smj.178
  9. Clark, P. J., & Evans, F. C. (1954). Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology, 35(4), 445-453.
  10. Egan, D. J., Chen, W., & Zhang, Y. (2006). The intra-urban location of hotels in the Chinese cities of Beijing, Shanghai & Shenzhen. China Tourism Research, 2(4), 516–530.
  11. Fang, L., Li, H., & Li, M. (2019). Does hotel location tell a true story? Evidence from geographically weighted regression analysis of hotels in Hong Kong. Tourism Management, 72, 78-91. https://doi.org/10.1002/smj.178
  12. Freedman, M., & Kosová, R. (2014). Agency and compensation: Evidence from the hotel industry. Journal of Law, Economics, and Organization, 30(1), 72–103. https://doi.org/10.1093/jleo/ews027
  13. Hadayeghi, A., Shalaby, A., & Persaud, B. (2010). Development of planning-level transportation safety models using full Bayesian semiparametric additive techniques. Journal of Transportation Safety & Security, 2(1), 45-68. https://doi.org/10.1080/19439961003687328
  14. Hesam, M., & Aghaeizadeh, E. (2020). Analysis of Spatial Distribution of Residential Centers in Urban Tourism Destinations Case Study: Rasht city. Journal of Urban Tourism, 6(4), 1-16. doi: 10.22059/jut.2020.287496.702[In Persian].
  15. Jafari Mehrabadi, M., & rokhsari, H. (2019). Spatial Analysis and Locating Hotels in Urban Structure (Case Study: Tabriz City). Urban Structure and Function Studies, 5(17), 26-46. https://doi: 10.22080/shahr.1970.2109[In Persian].
  16. Karagöz, D., Aktaş, S., & Kantar, Y. (2022). Spatial analysis of the relationship between tourist attractions and tourist flows in Turkey. European Journal of Tourism Research, 31, 3102-3102. https://doi.org/10.54055/ejtr.v31i.2745
  17. Kauhl, B., Heil، J., Hoebe, C. J., Schweikart, J., Krafft, T., & Dukers-Muijrers، N. H. (2015). The spatial distribution of hepatitis C virus infections and associated determinants—an application of a geographically weighted poisson regression for evidence-based screening interventions in hotspots. PloS one, 10(9), e0135656. https://doi.org/10.1371/journal.pone.0135656
  18. keyamehr, R., piri, I., bazmavard, S., & azari, M. (2017). Analysis of the spatial distribution of hotels in tourist destinations, case study: the city of Kermanshah. Journal of Urban Tourism, 3(1), 43-57. doi: 10.22059/jut.2017.61394[In Persian].
  19. Lee, Y. J. A., Jang, S., & Kim, J. (2020). Tourism clusters and peer-to-peer accommodation. Annals of Tourism Research, 83, 102960. https://doi.org/10.1016/j.annals.2020.102960
  20. Li, M., Fang, L., Huang, X., & Goh، C. (2015). A spatial–temporal analysis of hotels in urban tourism destination. International Journal of Hospitality Management, 45, 34–43. https://doi.org/10.1016/j.ijhm.2014.11.005
  21. Liu, X., & Chen, W. (2015, October). Analysis on the Spatial Pattern Evolution of Chinese Provincial Environment-Energy Efficiency Based on ESDA-GIS. In 4th International Conference on Information Technology and Management Innovation (pp. 151-157). Atlantis Press.
  22. Luo, H., & Yang, Y. (2016). Intra-metropolitan location choice of star-rated and non-rated budget hotels: The role of agglomeration economies. International Journal of Hospitality Management, 59, 72–83. https://doi.org/10.1016/j.ijhm.2016.09.007
  23. Maheswaran, R., Haining, R., Maheswaran, R., & Craglia, M. (2004). Basic issues in geographical analysis. CRC Press, Boca Raton, Florida.
  24. Marco-Lajara, B., Zaragoza-Sáez, P. d. C., Claver-Cortés, E., Úbeda-García, M., & GarcíaLillo, F. (2017). Tourist districts and internationalization of hotel firms. Tourism Management, 61, pp 451–464. https://doi.org/10.1016/j.tourman.2017.03.015
  25. McNeill, D. (2008). The hotel and the city. Progress in Human Geography, 32(3), 383-398. https://doi.org/10.1177/0309132508089096
  26. Nasiri Hendeh khaleh, E. (2018). Spatial inequalities in the distribution of urban services with spatial justice approach using VIKOR model. Geograohical Planing of Space, 82(28), 133-154. https://10.22080/SHAHR.1970.2109 [In Persian].
  27. Oppermann, M., & Din, K., & Amri, S.Z., (1996). Urban hotel location and evolution in a developing country: the case of Kuala Lumpur, Malaysia. Tourism Recreation Research, 21(1), 55-63. https://doi.org/10.1080/02508281.1996.11014763
  28. Rigall-I-Torrent, R., & Fluvià, M. (2011). Managing tourism products and destinations embedding public good components: a hedonic approach. Tourism Management, 32(2), 244-255. https://doi.org/10.1016/j.tourman.2009.12.009
  29. Shamai, A., & Mosivand, J. (2011). Classification of cities of Isfahan province in view point of tourism infrastructure by using Topsis and AHP models. Urban-Regional Studies And Research (University of Isfahan), 3(10), 5-10. [In Persian].
  30. Shoval, N., & Cohen-Hattab K., (2001). Urban Hotel Development Patterns in the Face of Political Shifts. Annual of Tourism Research, 28(4), 908-925.https://doi.org/10.1016/S0160-7383(00)00083-9
  31. Silva, F. B., Barranco, R., Proietti, P., Pigaiani, C., & Lavalle, C. (2021). A new European regional tourism typology based on hotel location patterns and geographical criteria. Annals of Tourism research, 89, 103077. https://doi.org/10.1016/j.annals.2020.103077
  32. Tsaur, S.-H., & Tzeng, G.-H. (1996). Multiattribute decision making analysis for customer preference of tourist hotels. Journal of Travel & Tourism Marketing, 4(4), 55–69. https://doi.org/10.1300/J073v04n04_04
  33. Tyas، S. W., & Puspitasari, L. A. (2023). Geographically weighted generalized poisson regression model with the best kernel function in the case of the number of postpartum maternal mortality in east java. MethodsX, 10, 102002. https://doi.org/10.1016/j.mex.2023.102002
  34. Urtasun, A., & Isabel, G., (2006). Hotel Location in Tourism Cities Madrid 1936-1998. Annals of Tourism Research, 22(2), 382-402. https://doi.org/10.1016/j.annals.2005.12.008
  35. Varesi, H., & Rezaei, M. (2013). Spatial analysis and site selection residential centers in the historical cities: a case study: Shiraz hotels. Environmental based territorial planning(Amayesh), 5(19), 1-26. [In Persian].
  36. Wall, G., Doug, D., & Joanne, H. (1985) "Point pattern analyses of accomodation in Toronto." Annals of Tourism Research 12(4), 603-618. https://doi.org/10.1016/0160-7383(85)90080-5
  37. Weaver, D. B. (1993). Ecotourism in the small island Caribbean. geojournal, 31, 457-465.
  38. Yang, Y., Kevin, K.F., Wong, T. W. (2012). How do hotels choose their location? Evidence from hotels in Beijing. International Journal of Hospitality Management. https://doi.org/10.1016/j.ijhm.2011.09.003
  39. Yang, Y., Luo, H., & Law R., (2014). Theoretical, empirical, and operational models in hotel location research. International Journal of Hospitality Management, 36, 209-220. https://doi.org/ 10.1016/j.ijhm.2013.09.004
  40. Yang, Y., Luo, H., & Law, R.  (2014). Theoretical, empirical and operational models in hotel location research. Int. J. Hosp. Manag, 36, 209–220. https://doi.org/10.1016/j.ijhm.2013.09.004
  41. Yang, Y., Wong, K. K. F., & Wang, T. (2012). How do hotels choose their location? Evidence from hotels in beijing. International Journal of Hospitality Management, 31(3), 675–685. https://doi.org/10.1016/j.ijhm.2011.09.003
  42. Zhang, H., Liu, Y., Chen, F., Mi, B., Zeng, L., & Pei, L. (2021). The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis. BMC Infectious Diseases, 21(1), 428. https://doi.org/10.21203/rs.3.rs-90877/v1
urban tourism
Volume 11, Issue 3
September 2024
Pages 71-88

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