Skip to main content

Advertisement

Log in

Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran

  • Article
  • Published:
Journal of Housing and the Built Environment Aims and scope Submit manuscript

Abstract

Understanding, analysis, monitoring and modeling of urban growth evolution as a major driving force of land use/land cover transformation, especially in developing countries, is of great importance for land managers in the process of sustainable development. Using spatial predictive models and change detection techniques can provide an additional level of knowledge of the causes and impacts of urban growth mechanisms, which finally provide comprehensive insight into urban chronology. Karaj, the capital of Alborz province, has been experiencing a substantial increase in total area of urban environments mainly due to its socioeconomic attractions during the last three decades. The present work aims to reveal how the historical trend of the urban growth can affect its future spatial pattern. For conducting this study, the SLEUTH cellular automata urban growth model was executed via three calibration steps including coarse, fine and final. Relying on the calibrated model, dynamics of the Karaj City were predicted under its historical trend as well as two different scenarios including compact and extensive growth up to year 2040. According to the findings of the present study, while extensive growth option indicates the most consumption of the vacant lands, the compact scenario dictates infill form of the urban growth in addition to saving spaces. Finally, urban growth forecasting based on its historical trend illustrates that total area of the human-constructed elements will be in the middle of other two predictive scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Al-Ahmadi, K., Heppenstall, A. J., Hogg, J., & See, L. (2009). A fuzzy cellular automata urban growth model (FCAUGM) for the City of Riyadh, Saudi Arabia. Part 1: Model structure and validation. Applied Spatial Analysis and Policy, 2, 65–83.

    Article  Google Scholar 

  • Al-ahmadi, K., See, L., Heppenstall, A., & Hogg, J. (2008). Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia. Ecological Complexity, 6, 80–101.

    Article  Google Scholar 

  • Al-shalabi, L., Billa, L., Pradhan, B., Mansor, S., & Al-sharif, A. A. A. (2012). Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: The case of Sana’a metropolitan city, Yemen. Earth Sciences, 70, 425–437.

    Article  Google Scholar 

  • Asgarian, A., Amiri, B. J., & Sakieh, Y. (2014). Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosystems,. doi:10.1007/s11252-014-0387-7.

    Google Scholar 

  • Bagheri, M., Sulaiman, W. N. A., & Vaghefi, N. (2012). Application of geographic information system technique and analytical hierarchy process model for land-use suitability analysis on coastal area. Coastal Conservation, 17, 1–10.

    Article  Google Scholar 

  • Barredo, J. I., Demicheli, L., Lavalle, C., Kasanko, M., & McCormick, N. (2004). Modelling future urban scenarios in developing countries: An application case study in Lagos, Nigeria. Environment and Planning B: Planning and Design, 32, 65–84.

    Article  Google Scholar 

  • Barredo, J. I., Kasanko, N., McCormick, M., & Lavalle, C. (2003). Modelling dynamic spatial process: Simulation of urban future scenarios through cellular automata. Landscape and Urban Planning, 64, 145–160.

    Article  Google Scholar 

  • Bathrellos, G. D., Gaki-Papanastassiou, K., Skilodimou, H. D., Papanastassiou, D., & Chousianitis, K. G. (2012). Potential suitability for urban planning and industry development using natural hazard maps and geological–geomorphological parameters. Environmental Earth Sciences, 66, 537–548.

    Article  Google Scholar 

  • Bathrellos, G. D., Skilodimou, H. D., Kelepertsis, A., Alexakis, D., Chrisanthaki, I., & Archonti, D. (2008). Environmental research of groundwater in the urban and suburban areas of Attica region, Greece. Environmental Geology, 56, 11–18.

    Article  Google Scholar 

  • Batty, M. (1989). Urban modeling and planning: Reflections, retrodictions and prescriptions. In B. Macmillan (Ed.), Remodeling geography (pp. 147–169). Oxford: Basil Blackwell.

    Google Scholar 

  • Batty, M., & Longley, A. (1986). The fractal simulation of urban structure. Environment and Planning A, 18, 1143–1179.

    Article  Google Scholar 

  • Batty, M., & Longley, P. (1994). Fractal cities: A geometry of form and function. London: Academic Press.

    Google Scholar 

  • Batty, M., & Xie, Y. (1994). From cells to cities. Environment and Planning B: Planning and Design, 21, S31–S38.

    Article  Google Scholar 

  • Batty, M., & Xie, Y. (1997). Possible urban automata. Environment and Planning B: Planning and Design, 24, 175–192.

    Article  Google Scholar 

  • Bihamta, N., Soffianian, A., Fakheran, S., & Gholamalifard, M. (2014). Using the SLEUTH Urban Growth Model to Simulate Future Urban Expansion of the Isfahan Metropolitan Area, Iran. Journal of Indian Society of Remote Sensing. doi:10.1007/s12524-014-0402-8.

  • Brown, L. R. (2001). Eco-economy: Building an economy for the earth. New York: W.W. Norton.

    Google Scholar 

  • Candau, J. T. (2002). Temporal calibration sensitivity of the SLEUTH urban growth model. M.Sc Theses. Santa Barbara University, 116 pp.

  • Cecchini, A., & Rinaldi, E. (1999). The multi-cellular automaton: A tool to build more sophisticated models. A theoretical foundation and a practical implementation. In: Rizzi P (Ed.), Computer in urban planning and urban management 6th international conference. Milano, Franco Angeli.

  • Chaudhuri, G., & Clarke, K. C. (2012). How does land use policy modify urban growth? A case study of Italo-Slovenian border. Land Use Science, 8, 443–465.

    Article  Google Scholar 

  • Chaudhuri, G., & Clarke, K. C. (2013). Temporal accuracy in urban growth forecasting: A study using the SLEUTH model. Transactions in GIS, 2, 302–320.

    Google Scholar 

  • Clarke, K. C., & Gaydos, L. J. (1998). Loose-coupling a cellular automata model and GIS: Long-term urban growth prediction for San Francisco and Washing- ton/Baltimore. International Journal of Geographical Information Science, 12, 699–714.

    Article  Google Scholar 

  • Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24, 247–261.

    Article  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.

    Article  Google Scholar 

  • Couclelis, H. (1985). Cellular worlds: A framework for modeling micro-macro dynamics. Environment and Planning A, 17, 585–596.

    Article  Google Scholar 

  • Couclelis, H. (1989). Macrostructure and micro behavior in metropolitan area. Environment and Planning A, 16, 141–154.

    Article  Google Scholar 

  • Dai, F. C., Lee, C. F., & Zhang, X. H. (2001). GIS based geo-environmental evaluation for urban land-use planning: A case study. Engineering Geology, 61, 257–271.

    Article  Google Scholar 

  • Dezhkam, S., Amiri, B. J., Darvishsefat, A. A., & Sakieh, Y. (2014). Simulating the urban growth dimensions and scenario prediction through sleuth model: A case study of Rasht County, Guilan, Iran. GeoJournal, 79, 591–604.

    Article  Google Scholar 

  • Dietzel, C., & Clarke, K. C. (2007). Toward optimal calibration of the SLEUTH land use change model. Transactions in GIS, 11, 29–45.

    Article  Google Scholar 

  • Dietzel, C., Oguz, H., Hemphill, J. J., Clarke, K. C., & Gazulis, N. (2005). Diffusion and coalescence of the Houston Metropolitan Area: Evidence supporting a new urban theory. Environment and Planning B: Planning and Design, 32, 231–246.

    Article  Google Scholar 

  • Echenique, M. (2004). Econometric models of land use and transportation. In D. A. Hensher & K. J. Button (Eds.), Transport geography and spatial systems, handbook 5 of handbook in transport (pp. 185–202). Kidlington: Pergamon/Elsevier Science.

    Google Scholar 

  • Engelen, G., White, R., & Uljee, I. (1997). Integrating constrained cellular automata models, GIS and decision support tools for urban planning and policy making. In H. P. J. Timmermans, & E. F. N. Spon (Eds.), Decision support systems in urban planning, London, pp. 125–155.

  • Feng, H. H., Liu, H. P., & Lü, Y. (2012). Scenario prediction and analysis of urban growth using SLEUTH model. Pedosphere, 22, 206–216.

    Article  Google Scholar 

  • Feng, Y., Liu, Y., Tong, X., Liu, M., & Deng, S. (2011). Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102, 188–196.

    Article  Google Scholar 

  • Gandhi, S. I., & Suresh, V. M. (2012). Prediction of urban sprawl in Hyderabad City using spatial model, remote sensing and GIS techniques geography. International Journal of Scientific Research, ISSN No 2277–8179.

  • Geurs, K. T., & van Wee, B. (2004). Accessibility evaluation of land-use and transport strategies: Review and research directions. Transport Geography, 12, 127–140.

    Article  Google Scholar 

  • Hasani Sangani, M., Amiri, B. J., Alizadeh Shabani, A., Sakieh, Y., & Ashrafi, S. (2014). Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea. Environmental Science and Pollution Research. doi:10.1007/s11356-014-3727-5.

  • He, C., Okada, N., Zhang, Q., Shi, P., & Li, J. (2008). Modelling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape and Urban Planning, 86, 79–91.

    Article  Google Scholar 

  • Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The spatiotemporal form of urban growth: Measurement, analysis and modeling. Remote Sensing of Environment, 86, 286–302.

    Article  Google Scholar 

  • Iranian Statistics Center. (2012). General census of population and housing of Karaj City.

  • Itami, R. M. (1994). Simulating spatial dynamics: Cellular automata theory. Landscape and Urban Planning, 30, 27–47.

    Article  Google Scholar 

  • Jantz, C. A., Goetz, S. J., Donato, D., & Claggett, P. (2010). Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Computers, Environment and Urban Systems, 34, 1–16.

    Article  Google Scholar 

  • Jantz, C. A., Goetz, S. J., & Shelley, M. K. (2003). Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B: Planning and Design, 31, 251–271.

    Article  Google Scholar 

  • Jeong, J., García-Moruno, L., & Hernández-Blanco, J. (2013). A site planning approach for rural buildings into a landscape using a spatial multi-criteria decision analysis methodology. Land Use Policy, 32, 108–118.

    Article  Google Scholar 

  • Jie, L., Wang, Y., & Shua-xia, Y. (2010). Environmental impact assessment of land use planning in Wuhan city based on ecological suitability analysis. Procedia Environmental Sciences, 2, 185–191.

    Article  Google Scholar 

  • Knox, P. L. (1994). Urbanization: Introduction to urban geography (p. 608). New Jersey: Prentice Hall.

    Google Scholar 

  • Leao, S., Bishop, I., & Evans, D. (2004). Simulating urban growth in a developing nation’s region using a CA-based model. Urban Planning and Development, 130, 145–158.

    Article  Google Scholar 

  • Lee, C. (1973). Models in planning. New York: Pergamon Press.

    Google Scholar 

  • Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16, 323–343.

    Article  Google Scholar 

  • Liu, X., & Andersson, C. (2004). Assessing the impact of temporal dynamics on land-use change modeling. Computers, Environment and Urban Systems, 28, 107–124.

    Article  Google Scholar 

  • Liu, X., Li, X., Shi, X., Wu, S., & Liu, T. (2007). Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling, 1, 169–181.

    Google Scholar 

  • Lu, T., Man-chun, L., Yong-xue, L., Wei, W., & Wei, H. (2009). Study of urban expansion simulation on the condition of ecological environment protection: A case study in Dianchi Basin in Kunming. Joint Urban Remote Sensing Event, 2, 1–6.

    Google Scholar 

  • Mahiny, A. S., & Clarke, K. C. (2012). Guiding SLEUTH land-use/land-cover change modeling using multicriteria evaluation: Towards dynamic sustainable land-use planning. Environment and Planning B: Planning and Design, 39, 925–944.

    Article  Google Scholar 

  • Mahiny, A. S., & Clarke, K. C. (2013). Simulating hydrologic impacts of urban growth using SLEUTH, multi criteria evaluation and runoff modeling. Environmental Informatics, 22, 27–38.

    Article  Google Scholar 

  • Mahiny, A. S., & Gholamalifard, M. (2007). Dynamic spatial modeling of urban growth through cellular automata in a GIS environment. International Journal of Environmental Research, 3, 272–279.

    Google Scholar 

  • Maithani, S. (2010). Application of cellular automata and GIS techniques in urban growth modelling: A new perspective. India Journal, 7, 36–49.

    Google Scholar 

  • Makhdum, M. (2007). Fundamental of land use planning. University of Tehran publication, pp. 272.

  • Municipality of the Karaj City. (2012). Comprehensive report on land use planning of the Karaj City.

  • Norman, L. M., Feller, M., & Phillip, G. D. (2009). Forecasting urban growth across the United States-Mexico border. Computers, Environment and Urban Systems, 33, 150–159.

    Article  Google Scholar 

  • Norman, L. M., Feller, M., & Villarreal, M. L. (2012). Developing spatially explicit footprints of plausible land-use scenarios in the Santa Cruz Watershed, Arizona and Sonora. Landscape and Urban Planning, 107, 225–235.

    Article  Google Scholar 

  • Oguz, H., Klein, A. G., & Srinivasan, R. (2007). Using the SLEUTH urban growth model to simulate the impact of future policy scenarios on urban land use in the Houston–Galvestone–Brazoria CMSA. Social Science, 2, 72–82.

    Google Scholar 

  • Onsted, J., & Clarke, K. C. (2013). The inclusion of differentially assessed lands in urban growth model calibration: A comparison of two approaches using SLEUTH. International Journal of Geographical Information Science, 26, 881–898.

    Article  Google Scholar 

  • Pontius, R. G, Jr, & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85, 239–248.

    Article  Google Scholar 

  • Portugali, J. (2000). Self-organization and the city. Berlin: Springer.

    Book  Google Scholar 

  • Pourebrahim, S., Hadipour, M., & Mokhtar, M. B. (2011). Integration of spatial analysis for land use planning in coastal areas; case of Kuala District, Selangor, Malaysia. Landscape and Urban Planning, 101, 84–97.

    Article  Google Scholar 

  • Rafiee, R., Mahiny, A. S., Khorasani, N., Darvishsefat, A. A., & Danekar, A. (2009). Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM). Cities, 26, 19–26.

    Article  Google Scholar 

  • Randolph, J. (2004). Environmental land use planning and management (p. 704). Washington, DC: Island Press.

    Google Scholar 

  • Rienbow, A., & Goetzke, R. (2014). Supporting SLEUTH—Enhancing a cellular automata with support vector machines for urban growth modeling. Computers, Environment and Urban Systems,. doi:10.1016/j.compenvurbsys.2014.05.001.

    Google Scholar 

  • Sakieh, Y. (2013). Urban sustainability analysis through the SLEUTH urban growth model and multi criteria evaluation: A case study of Karaj City. Dissertation, University of Tehran.

  • Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2014). Scenario-based evaluation of urban development sustainability: an integrative modeling approach to compromise between urbanization suitability index and landscape pattern. Environment, Development and Sustainability. doi:10.1007/s10668-014-9609-7.

  • Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata model for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96, 108–122.

    Article  Google Scholar 

  • Sheng, J., Qing, G., Chun-yu, W., Bei, L., Xiao-dong, L., Guang-ming, Z., et al. (2012). Ecological suitability evaluation for urban growth boundary in red soil hilly areas based on fuzzy theory. Central South University, 19, 1364–1369.

    Article  Google Scholar 

  • Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26, 525–552.

    Article  Google Scholar 

  • Silva, E. A., & Clarke, K. C. (2005). Complexity, emergence and cellular urban models: Lessons learned from applying SLEUTH to two Portuguese metropolitan areas. European Planning Studies, 13, 93–115.

    Article  Google Scholar 

  • Singh, A. K. (2003). Modeling Landuse landcover changes using cellular automata in geo-spatial environment. Dissertation, ITC, Netherland. Spatial Analysis (CASA), London. pp. 58.

  • Soares-Filho, B. S., Cerqueira, G. C., & Pennachin, C. L. (2002). DINAMICA—A stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling, 154, 217–235.

    Article  Google Scholar 

  • Stevens, D., Dragicevic, S., & Rothley, K. (2007). iCity: A GISeCA modelling tool for urban planning and decision making. Environmental Modelling and Software, 22, 761–773.

    Article  Google Scholar 

  • Straatman, B., White, R., & Engelen, G. (2004). Towards an automatic calibration procedure for constrained cellular automata. Computers, Environment and Urban Systems, 28, 149–170.

    Article  Google Scholar 

  • Sui, D. Z., & Zeng, H. (2001). Modeling the dynamics of landscape structure in Asia’s emerging Desakota regions: A case study in Shenzhen. Landscape and Urban Planning, 53, 37–62.

    Article  Google Scholar 

  • Sullivan, D. O., & Torrens, P. M. (2000). Cellular models of urban systems, CASA working paper series, paper 22, www.casa.ucl.uk. Accessed August 01, 2010.

  • Syphard, A. D., Clarke, K. C., & Franklin, J. (2005). Using a cellular automaton model to forecast the effects of urban growth on habitat pattern in southern California. Ecological Complexity, 2, 185–203.

    Article  Google Scholar 

  • Tobler, W. R. (1979). Cellular geography. In S. Gales & G. Olson (Eds.), PhD Dissertation, Reidel, Dortrecht, The Netherlands, D. Reidel, pp. 279–386.

  • Varanka, D. (2001). Modeling urban expansion in the Philadelphia Metropolitan Area. http://mcmcweb.er.usgs.gov/phil/modeling.html. Accessed March 26, 2010.

  • Verburg, P. H., Nijs, T. C. M. D., Eck, J. R. V., Visser, H., & Jong, K. D. (2004). A method to analyse neighbourhood characteristics of land use patterns. Public Health, 28, 667–690.

    Google Scholar 

  • Verburg, P. H., & Overmars, K. P. (2009). Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology, 24, 1167–1181.

    Article  Google Scholar 

  • Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. A. (2002). Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management, 30, 391–405.

    Article  Google Scholar 

  • Vliet, J. V., White, R., & Dragicevic, S. (2008). Modeling urban growth using a variable grid cellular automaton. Computers, Environment and Urban Systems, 33, 35–43.

    Article  Google Scholar 

  • Wang, H., He, S., Liu, X., Dai, L., Pan, P., Hong, S., & Zhang, W. (2012). Simulating urban expansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China. Landscape and Urban Planning, 110, 99–112.

    Article  Google Scholar 

  • White, R., & Engelen, G. (1993). Cellular automata and fractal urban form: A cellular modeling approach to the evolution of urban land use patterns. Environment and Planning, A, 25, 1175–1199.

    Article  Google Scholar 

  • White, R., & Engelen, G. (1994). Cellular dynamics and GIS: Modelling spatial complexity. Geographical Systems, 1, 237–253.

    Google Scholar 

  • White, R., & Engelen, G. (1997). Cellular automata as the basis of integrated dynamic regional modeling. Environment and Planning B, 24, 235–246.

    Article  Google Scholar 

  • White, R., Engelen, G., & Uljee, I. (1997). The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design, 24, 323–343.

    Article  Google Scholar 

  • Wu, F. (1996). Changes in the structure of public housing provision in urban China Urban Studies. Urban Studies, 33, 1601–1627.

    Article  Google Scholar 

  • Wu, F. (1998). SimLand: A prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rule. International Journal of Geographical Information Science, 12, 63–82.

    Article  Google Scholar 

  • Wu, X., Hu, Y., He, H. S., Bu, R., Onsted, J., & Xi, F. (2009). Performance evaluation of the SLEUTH model in the Shenyang Metropolitan Area of Northeastern China. Environmental Modeling and Assessment, 14, 221–230.

    Article  Google Scholar 

  • Xi, F., He, H. S., Hu, Y., Bu, R., Chang, Y., Wu, X., et al. (2009). Simulating the impacts of ecological protection policies on urban land use sustainability in Shenyang-Fushun, China. International Journal of Urban Sustainable Development, 1, 111–127.

    Article  Google Scholar 

  • Xi, F., He, H. S., Clarke, K. C., Hu, Y., Wu, X., Liu, M., Shi, T., Geng, Y., & Gao, C. (2012). The potential impacts of sprawl on farmland in Northeast China– a new strategy for rural development. Landscape and Urban Planning, 104, 34–46.

  • Xu, K., Kong, C., Li, J., Zhang, L., & Wu, C. (2011). Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China. Computers & Geosciences, 37, 992–1102.

    Article  Google Scholar 

  • Yang, Q., Li, X., & Shi, X. (2008). Cellular automata for simulation land use changes based on support vector machines. Computers & Geosciences, 34(6), 592–602.

    Article  Google Scholar 

  • Yang, X., & Lo, C. P. (2003). Modeling urban growth and landscape changes in the Atlanta metropolitan area. International Journal of Geographical Information Science, 17, 463–488.

    Article  Google Scholar 

  • Youssef, A. M., Pradhan, B., & Tarabees, E. (2010). Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: Contribution from the analytic hierarchy process. Arabian Journal of Geosciences, 4, 463–473.

    Article  Google Scholar 

  • Yuechen, I., Chunxia, L., Hong, Z., & Xin, G. (2011). Evaluation on the human settlements environment suitability in the Three Gorges Reservoir Area of Chongqing based on RS and GIS. Geographical Sciences, 21, 346–358.

    Article  Google Scholar 

Download references

Acknowledgments

Authors are grateful to Reza Rafiee who kindly associated in all steps of this study and Keith C. Clarke, University of California, Santa-Barbara, who practically answered to the questions on applying the SLEUTH model. The authors wish to thank the anonymous reviewers whose comments and views helped improve this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bahman Jabbarian Amiri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sakieh, Y., Amiri, B.J., Danekar, A. et al. Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. J Hous and the Built Environ 30, 591–611 (2015). https://doi.org/10.1007/s10901-014-9432-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10901-014-9432-3

Keywords

Navigation