Modeling Earth Systems and Environment

, Volume 4, Issue 1, pp 13–25 | Cite as

Modeling the role of topography on the potential of tourism climate in Iran

  • Mahmoud Ahmadi
  • Mohammad Baaghide
  • AbbasAli Dadashi Roudbari
  • Mehdi Asadi
Original Article
  • 28 Downloads

Abstract

Investigating climatic features of any place can be of considerable contribution to tourism managers and planners in determining and assigning areas for particular kinds of tourism activities. To investigate the role of elevation levels (topography) on Iran tourism climate, meteorological data from 144 synoptic stations were gathered during the (1980–2010) period. Data include: daily mean and daily maximum temperature, daily mean and daily minimum humidity, monthly total precipitation, sunshine hours, and wind speed. Evaluating monthly tourism climate condition was done by using Miskofski Index (TCI); altitudinal diversity of the stations being taken into account, monthly correlations between the differences of climate tourism indexes and elevations were computed using Pearson correlation model at the significant level p value < 0.05.Then comparing the ordinary least square with geographically weighted regression, the best model was selected. Thereafter, considering the result of geographically weighted regression as the best model and using Digital Elevation Model of Iran, different zones for TCI scores were recognized. Categorizing stations was done on the basis of TCI scores, using hierarchical cluster method. The findings showed that elevation has a positive role in increasing TCI scores, only in warm half of the year. The coordinating role of elevations appears in May, so the high index scores are concentrated at high elevation levels of 800 m and more in June, July, August, September, and October. Results of clustering revealed three tourism zones: Winter, Autumn-Spring, and Spring-Summer zones. In general, 6 tourism regions and 12 areas were distinguished.

Keywords

Tourism Climate Index Geographically Weighted Regression (GWR) Cluster method Global Moran’s I Iran 

Notes

Acknowledgements

The authors are grateful to the Islamic Republic of Iran Meteorological Organization (IRIMO) for providing the requisite meteorological data. They wish to express their gratitude to the anonymous reviewers whose suggestions and remarks have greatly helped to improve the quality of the manuscript.

References

  1. Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27(2):93–115.  https://doi.org/10.1111/j.1538-4632.1995.tb00338.x CrossRefGoogle Scholar
  2. Anselin L, Sridharan S, Gholston S (2007) Using exploratory spatial data analysis to leverage social indicator databases: the discovery of interesting patterns. Soc Indic Res 82(2):287–309.  https://doi.org/10.1007/s11205-006-9034-x CrossRefGoogle Scholar
  3. ASHRAE, (2010) Thermal environment conditions for human occupancy. ANSI/ASHRAE Standards, pp 55–2010Google Scholar
  4. Balyani S, Khosravi Y, Ghadami F, Naghavi M, Bayat A (2017) Modeling the spatial structure of annual temperature in Iran. Model Earth Syst Environ 1–13.  https://doi.org/10.1007/s40808-017-0319-7
  5. Bivand R, Brunstad R (2005) Further explorations of interactions between agricultural policy and regional growth in Western Europe: approaches to nonstationarity in spatial econometrics. 45th Congress of the European Regional Science Association, Amsterdam, pp 3–27 August, 2005Google Scholar
  6. Blazejczyk K, Epstein Y, Jendritzky G, Staiger H, Tinz B (2012) Comparison of UTCI to selected thermal indices. Int J Biometeorol 56(3):515–535.  https://doi.org/10.1007/s00484-011-0453-2 CrossRefGoogle Scholar
  7. Charlton M, Fotheringham S, Brunsdon C (2009) Geographically weighted regression. White paper. National Centre for Geocomputation. National University of Ireland MaynoothGoogle Scholar
  8. Chen Y (2013) New approaches for calculating Moran’s index of spatial autocorrelation. PLoS One 8(7):e68336.  https://doi.org/10.1371/journal.pone.0068336 CrossRefGoogle Scholar
  9. Clarke FW (2012) The Architect’s role in urban regeneration, economic development, and sustainability. http://pcparch.com/firm/bibliography/essays/the-architect-s-role-in-urban-regeneration-economic-development-and-sustainability. Accessed 24 June 2016
  10. Diggle P J  (2003) Statistical Analysis of Spatial Point Patterns. Arnold, London, second edition.Google Scholar
  11. Endler C, Matzarakis A (2010) Assessment of climate for tourism purposes in Germany. Berichte des Meteorologischen Instituts der Albert-Ludwigs-Universität Freiburg, p 380Google Scholar
  12. Fotheringham AS, Charlton ME, Brunsdon C (2001) spatial variations in school performance: a local analysis using geographically weighted regression. Geogr Environ Model 5(1):43–66.  https://doi.org/10.1080/13615930120032617 CrossRefGoogle Scholar
  13. Fotheringham AS, Crespo R, Yao J (2015) Geographical and temporal weighted regression (GTWR). Geogr Anal 47(4):431–452.  https://doi.org/10.1111/gean.12071 CrossRefGoogle Scholar
  14. Geary RC (1954) the contiguity ratio and statistical mapping. Statistician 5(3): 115–146.  https://doi.org/10.2307/2986645 Google Scholar
  15. Ghalhari GF, Roudbari AD (2016) An investigation on thermal patterns in Iran based on spatial autocorrelation. Theor Appl Climatol 1–12.  https://doi.org/10.1007/s00704-016-2015-3
  16. Griffith D A (1987) Spatial autocorrelation. A Primer (Washington, DC, Association of American Geographers).Google Scholar
  17. Ghalhari GF, Roudbari AD, Asadi M (2016) Identifying the spatial and temporal distribution characteristics of precipitation in Iran. Arab J Geosci 9(12):595.  https://doi.org/10.1007/s12517-016-2606-4 CrossRefGoogle Scholar
  18. Hall CM (2010) Tourism and biodiversity: more significant than climate change? J Heritage Tour 5(4):253–266.  https://doi.org/10.1080/1743873X.2010.517843 CrossRefGoogle Scholar
  19. Hamilton JM, Lau MA (2005) The role of climate information in tourist destination choice decision making. Tourism and global environmental change: ecological, economic, social and political interrelationships, p 229Google Scholar
  20. Hurvich CM, Simonoff JS, Tsai CL (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J J R Stat Soc Series B Stat Methodol 60(2):271–293.  https://doi.org/10.1111/1467-9868.00125 CrossRefGoogle Scholar
  21. Krivoruchko K (2011) Spatial statistical data analysis for GIS users. Esri Press, Redlands, p 928Google Scholar
  22. Lin TP, Matzarakis A (2008) Tourism climate and thermal comfort in Sun Moon Lake, Taiwan. Int J Biometeorol 52(4):281–290.  https://doi.org/10.1007/s00484-007-0122-7 CrossRefGoogle Scholar
  23. Lin TP, Matzarakis A (2011) Tourism climate information based on human thermal perception in Taiwan and Eastern China. Tour Manag 32(3):492–500.  https://doi.org/10.1016/j.tourman.2010.03.017 CrossRefGoogle Scholar
  24. Martín MBG (2005) Weather, climate and tourism a geographical perspective. Ann Tour Res 32(3):571–591.  https://doi.org/10.1016/j.annals.2004.08.004 CrossRefGoogle Scholar
  25. Matzarakis A (2006) Weather-and climate-related information for tourism. Tour Hosp Plan Dev 3(2):99–115CrossRefGoogle Scholar
  26. Matzarakis A, De Freitas CR, Scott D (2004) Advances in tourism climatology. Meteorologisches Institut der Universität FreiburgGoogle Scholar
  27. Mennis J (2006) mapping the results of geographically weighted regression. Cartogr J 43(2):171–179.  https://doi.org/10.1179/000870406X114658 CrossRefGoogle Scholar
  28. Mieczkowski Z (1985) the tourism climatic index: a method of evaluating world climates for tourism. Can Geogr 29(3):220–233.  https://doi.org/10.1111/j.1541-0064.1985.tb00365.x CrossRefGoogle Scholar
  29. Mondal B, Das DN, Dolui G (2015) Modeling spatial variation of explanatory factors of urban expansion of Kolkata: a geographically weighted regression approach. Model Earth Syst Environ 1(4):29.  https://doi.org/10.1007/s40808-015-0026-1 CrossRefGoogle Scholar
  30. Moran PA (1948) The interpretation of statistical maps. J R Stat Soc Series B Stat Methodol 10(2):243–251Google Scholar
  31. Nakaya T (2014) GWR4 user manual. WWW Document. http://www.St-andrews.Ac.uk/geoinformatics/wp-content/uploads/GWR4manual_201311.Pdf. Accessed 4 Nov 2013
  32. Perch-Nielsen SL, Amelung B, Knutti R (2010) Future climate resources for tourism in Europe based on the daily Tourism Climatic Index. Clim Change 103(3–4):363–381.  https://doi.org/10.1007/s10584-009-9772-2 CrossRefGoogle Scholar
  33. Rutty M, Scott D, Johnson P, Pons M, Steiger R, Vilella M (2017) Using ski industry response to climatic variability to assess climate change risk: An analogue study in Eastern Canada. Tour Manag 58:196–204.  https://doi.org/10.1016/j.tourman.2016.10.020 CrossRefGoogle Scholar
  34. Schliephack J, Dickinson JE (2017) Tourists’ representations of coastal managed realignment as a climate change adaptation strategy. Tour Manag 59:182–192.  https://doi.org/10.1016/j.tourman.2016.08.004 CrossRefGoogle Scholar
  35. Scott N (2011) Tourism policy: a strategic review. Goodfellow Publishers, OxfordGoogle Scholar
  36. Scott LM, Janikas MV (2010) Spatial statistics in ArcGIS. Handbook of applied spatial analysis. Springer, Berlin, pp 27–41CrossRefGoogle Scholar
  37. Scott D, McBoyle G (2001) Using a ‘tourism climate index’to examine the implications of climate change for climate as a tourism resource. In: Proceedings of the first international workshop on climate, tourism and recreation (pp 69–88). Freiburg: International Society of Biometeorology.’ Scott D. (2011), why sustainable tourism must address climate change, J Sust Tourism 19:17–34Google Scholar
  38. Scott D, Rutty M, Amelung B, Tang M (2016) An inter-comparison of the holiday climate index (HCI) and the tourism climate index (TCI) in Europe. Atmosphere (Basel) 7(6):80.  https://doi.org/10.3390/atmos7060080 CrossRefGoogle Scholar
  39. Telfer DJ, Sharpley R (2015)Tourism and development in the developing world. RoutledgeGoogle Scholar
  40. UNWTO (2008) Home page: World Tourism Organization: http://www.unwto.org/index.php. Accessed 22 Aug 08
  41. Wikipedia (2017) Thermal comfort. https://en.wikipedia.org/wiki/Thermal_comfort
  42. WTO (1998) Tourism–2020 Vision: Influences, Directional Flows and Key Influences. World Tourism Organization, MadridGoogle Scholar
  43. Yazdanpanah H, Barghi H, Esmaili A (2016) Effect of climate change impact on tourism: A study on climate comfort of Zayandehroud River route from 2014 to 2039. Tour Manag Persp 17:82–89.  https://doi.org/10.1016/j.tmp.2015.12.002 Google Scholar
  44. Yu ZK, Sun GN, Luo ZW, Feng Q (2015) An analysis of climate comfort degree and tourism potential power of cities in Northern China in Summer to the North of 40 N. Nat Resour J 2:015Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mahmoud Ahmadi
    • 1
  • Mohammad Baaghide
    • 2
  • AbbasAli Dadashi Roudbari
    • 3
  • Mehdi Asadi
    • 4
  1. 1.Faculty of Earth SciencesShahid Beheshti UniversityTehranIran
  2. 2.Climatology DepartmentHakim Sabzevari UniversitySabzevarIran
  3. 3.Urban ClimatologyShahid Beheshti UniversityTehranIran
  4. 4.Agricultural ClimatologyHakim Sabzevari UniversitySabzevarIran

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