Predicting urban growth of Arriyadh city, capital of the Kingdom of Saudi Arabia, using Markov cellular automata in TerrSet geospatial system

Abstract

This study aimed to predict urban expansion in Arriyadh city over a 30-year period, from 2017 to 2047. In the target year 2047, several changes are expected to occur in Arriyadh. The predicting was based on the revealed outputs of urban change within the last 30-year period, from 1987 to 2017. This study aimed to clarify the extent of urban expansion that would occur in the future to compare the expected future expansion with the present expansion in the city by using spatial dimension software. Several different software programs with advanced geographic information system (GIS) and remote sensing techniques were used: for example, ArcGIS, ERDAS, and Markov cellular automata in TerrSet. Using the US Geological Survey (USGS) site, we uploaded imagery (LANDSAT) for the study period from 1987 to 2017 and used the data and information from this period as a basis for drawing conclusions for predicting the urban area in 2047; we then carried out the analysis and adjusted it, such as for classification, editing, mosaic, geometric correction, and entry of geographical factors that we saw as having an impact on the future urban growth of the city. From the analysis and results drawn, the dimension of the physical changes during the 2017–2047 study period in the urban area is expected to be around 38%. The study also makes recommends using the expansion of urban studies as a tool to support decisionmakers in managing cities and to build databases to include demographic, economic, and environmental data. All this information and techniques can help in carrying out urban growth prediction accurately and clearly.

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Abbreviations

GIS:

Geographic information system

USGS:

United State Geological Survey

References

  1. Aayasrh TM (2015) Introduction to urban planning, concepts, theory and practice, chapter one. Dar Al-Hamed, Jordan, pp 2–7

    Google Scholar 

  2. Achmad A, Hasyim S, Dahlan B, Aulia D (2015) Modeling of urban growth in tsunami-prone city using logistic regression: analysis of Banda Aceh, Indonesia. Appl Geogr 62:237–246. https://doi.org/10.1016/j.apgeog.2015.05.001

    Article  Google Scholar 

  3. Aguejdad R, Houet T (2008) Modeling of urban sprawl using the land change modeler on a French metropolitan area (Rennes): foresee the unpredictable. In: Symposium “Spatial Landscape Modelling: From Dynamic Approaches to Functional Evaluations” Toulouse, June 3-5, 2008

  4. Al Sadhan AN (2012) What did the city do with its inhabitants? The effects of urban expansion on Saudi cities Dar Al-Entshar. ISBN, 978–614-404-230-4

  5. Al Tuwaijri HA, Alotaibi MH, Almudlaj AM, Almalki FM (2018) Urban extension of the city of Riyadh (1987-2017) using remote sensing and GIS techniques. Journal of Architecture and Planning 30(2), Riyadh, KSA

  6. Alqurashi AF, Kumar L (2014) Land use and land cover change detection in the Saudi Arabian desert cities of Makkah and Al-Taif using satellite data. Advanced Remote Sensing 3:106–119

    Article  Google Scholar 

  7. Alsharif AAA, Pradhan B (2014) Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain. Arab J Geosci 7:4291–4301. https://doi.org/10.1007/s12517-013-1119-7

    Article  Google Scholar 

  8. Arriyadh Municipality (2017) Accessed 11 Nov 2017. https://www.alriyadh.gov.sa/en/riyadh/popudev/

  9. Berry BJ, Okulicz-Kozaryn A (2012) The city size distribution debate: resolution for US urban regions and megalopolitan areas. Cities 29:s17–s23

    Article  Google Scholar 

  10. Boundless (2016) The process of urbanization. Accessed 22 May 2018. https://courses.lumenlearning.com/boundless-sociology/

  11. Chang J (2007) Stochastic processes. Overseas India Press, New Delhi

    Google Scholar 

  12. Cheng J, Masser I (2003) Urban growth pattern modeling: a case study of Wuhan city, PR China. Landsc Urban Plan 62(4):199–217

    Article  Google Scholar 

  13. Eastman JR (2006) IDRISI Andes tutorial. Clark Labs, Worcester

  14. Fawaz M (1980) Introduction to organization of the city: initial modern city planning. Institute of Arab Development, Arriyadh, pp 41–49

    Google Scholar 

  15. Frenkel A, Ashkenazi M (2008) The integrated sprawl index: measuring the urban landscape in Israel. Ann Reg Sci 42(1):99–121. https://doi.org/10.1007/s00168-007-0137-3

    Article  Google Scholar 

  16. Hathout S (2002) The use of (GIS) for monitoring and predicting urban growth in east and West St. Paul, Winnipeg, Manitoba, Canada. J Environ Manag 66(3):229–238

    Article  Google Scholar 

  17. Henríquez C, Azocar G, Romero H (2006) Monitoring and modeling the urban growth of two mid-sized Chilean cities. Habitat Int 30(4):945–964. https://doi.org/10.1016/j.habitatint.2005.05.002

    Article  Google Scholar 

  18. Jokar Arsanjani J, Helbich M, Kainz W, Darvishi A (2013) Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion—the case of Tehran. Int J Appl Earth Obs Geoinf 21:265–275

    Article  Google Scholar 

  19. Klaus DG (2016) BPMSG AHP Online System: AHP priority calculator, Retrieved (4-May 2017) from: http://bpmsg.com/academic/ahp_calc.php. Accessed 8 Nov 2017

  20. Laidley T (2015) Measuring sprawl: a new index, recent trends, and future research. Urban Aff Rev 52(1):66–97. https://doi.org/10.1177/1078087414568812

    Article  Google Scholar 

  21. Newbold KB, Scott D (2013) Migration, commuting distance, and urban sustainability in Ontario’s greater Golden horseshoe: implications of the greenbelt and places to grow legislation. Can Geogr 57(4):474–487. https://doi.org/10.1111/j.1541-0064.2013.12044.x

    Article  Google Scholar 

  22. Ogle J, Delparte D, Sanger H (2017) Quantifying the sustainability of urban growth and form through time: an algorithmic analysis of a city’s development. Appl Geogr 88:1–14. https://doi.org/10.1016/j.apgeog.2017.08.016

    Article  Google Scholar 

  23. Patino JE, Duque JC (2013) A review of regional science applications of satellite remote sensing in urban settings. Comput Environ Urban Syst 37:1–17

    Article  Google Scholar 

  24. Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill International Book Co., New York

    Google Scholar 

  25. Shbeir A, Qudaih M (2017) Prediction of land using cellular automata model and quantifying its impacts on surface runoff and groundwater recharge. University College of Applied Sciences, Palestine, Palestine

    Google Scholar 

  26. Sivakumar V (2014) Urban mapping and growth prediction using remote sensing and GIS techniques, Pune, India. ISPRS: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8:967–970. https://doi.org/10.5194/isprsarchives-XL-8-967-2014

    Article  Google Scholar 

  27. Sundara Kumar K, Udaya Bhaskar P, Padmakumari K (2016) Application of Markov chain and cellular automata model for prediction of urban transitions, International conference on Electrical Electronics and Optimization Techniques (ICEEOT-2016), IEEE-Explore, pp 4007–4012

  28. Sussman R (1996) Implementing municipal GIS: human behavior and the decision making process. Comput Environ Urban Syst 20(3):213–223

    Article  Google Scholar 

  29. The General Authority for Statistics at Kingdom of Saudi Arabia (2015) https://www.stats.gov.sa/en/page/114. Accessed 12 Nov 2017

  30. The Geological Survey Body (2017) Https://www.usgs.gov. Accessed 12 Oct 2017

  31. The Ministry of Municipal and Rural Affairs (2017) https://www.momra.gov.sa/. Accessed 28 Oct 2017

  32. The Supreme Commission for the Development of Arriyadh, the Site of Arriyadh City (2017) http://www.arArriyadh.com. Accessed 13 Oct 2017

  33. United Nations Statistics Division (2007) Demographic Yearbook: table 6. Accessed May 15, 2017

  34. Václavík T, Rogan J (2009) Identifying trends in land use/land cover changes in the context of post-socialist transformation in Central Europe: a case study of the greater Olomouc region, Czech Republic. GIScience and Remote Sensing 46(1):54–76

    Article  Google Scholar 

  35. Vliet J, White R, Dragicevic S (2009) Modeling urban growth using a variable grid cellular automaton. Comput Environ Urban Syst 33(1):35–43

    Article  Google Scholar 

  36. Zhao C, Jensen J, Zhan B (2017) Comparison of urban growth and the influencing factors of two border cities: Laredo in the US and Nuevo Laredo in Mexico. Appl Geogr 79:223–234. https://doi.org/10.1016/j.apgeog.2016.12.017

    Article  Google Scholar 

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Funding

This study is financially supported by the Deanship of Scientific Research at King Saud University through Research Project No. R6-17-01-04.

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Correspondence to Hamad Ahmed Altuwaijri.

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Altuwaijri, H.A., Alotaibi, M.H., Almudlaj, A.M. et al. Predicting urban growth of Arriyadh city, capital of the Kingdom of Saudi Arabia, using Markov cellular automata in TerrSet geospatial system. Arab J Geosci 12, 135 (2019). https://doi.org/10.1007/s12517-019-4261-z

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Keywords

  • Arriyadh city
  • Prediction
  • Geographic information systems (GIS)
  • Remote sensing
  • Urban growth