Skip to main content
SpringerLink
Log in

Land Use Change Modeling with SLEUTH: Improving Calibration with a Genetic Algorithm

  • Chapter
  • First Online:
Geomatic Approaches for Modeling Land Change Scenarios

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

SLEUTH is a cellular automaton computer simulation model that uses historical land use and other data to project growth and land use change into the future. The model has seen over 100 applications worldwide, and has been among the leading cellular automaton (CA) models applied in simulating land use change at many different spatial scales. The model is highly dependent on the use of historical data to derive the behavioral parameters that best capture the structure and dynamics of the location-specific growth history. While several improvements have been made to the model to increase calibration speed, the current brute force calibration technique has proven popular, in spite of it requiring a multi-phase process and hundreds of CPU hours. This chapter reports on the use of a new alternative calibration method, in which the brute force method is replaced with a genetic algorithm (GA). A version of the model code that executes the GA calibration has been written and made public. The GA calibration process populates a “chromosome” with a set of parameter combinations (genes), of which five are required by the model, each with ranges from 0 to 100. These combinations are then used for model calibration runs, and the most successful (as measured by the Optimal SLEUTH metric) are selected for mutation (recombination of their values), while the least successful are replaced with new randomly selected values. Critical values that must be provided are the population size of the chromosome, the number of iterations or generations over which evolution will continue, the evolution mutation rate, and the number of offspring and replacements in each generation. To select suitable default values for these rates, two SLEUTH applications were used at the extremes of the model’s calibration performance success. These were for San Diego, California where the model fit was very strong, and Andijan, Uzbekistan, where the model was most hard pressed to capture the complex growth process. In both cases, full model calibrations were completed using brute force calibration, followed by calibrations using the GA. It was found necessary to hold the GA parameters constant while repeatedly recalibrating the model using different values for the GA settings. In all cases, the GA model performed as well as the brute force method, but used vastly less computation time. There were also subtle but minor differences in the best SLEUTH forecasts that were explored by mapping the differences among results. The optimal values for GA calibration are given and set as the defaults for SLEUTH-GA, a new version of the SLEUTH model.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • An L, Brown D (2008) Survival analysis in land change science: Integrating with GIScience to address temporal complexities. Ann Assoc Am Geogr 98(2):323–344

    Google Scholar 

  • Batty M, Torrens PM (2001) Modeling complexity: the limits to prediction.12th European colloquium on quantitative and theoretical geography. St-Valery-en-Caux, France, 7–11 Sept 2001

    Google Scholar 

  • Benenson I, Torrens P (2004) Geosimulation—object-based modeling of urban systems. Comput Environ Urban Syst 28(1/2):1–8

    Google Scholar 

  • Blecic I, Cecchini A, Trunfio GA (2010) A comparison of evolutionary algorithms for automatic calibration of constrained cellular Automata. In: Taniar D et al (eds) ICCSA 2010, Part I. LNCS, vol 6016. Springer, Berlin, pp 166–181

    Google Scholar 

  • Cao K, Huang B, Li M, Li W (2014) Calibrating a cellular automata model for understanding rural–urban land conversion: a Pareto front-based multiobjective optimization approach. Int J Geogr Inf Sci 28(5):1028–1046. doi:10.1080/13658816.2013.851793

  • Cao K, Wang S, Li X, Chen R (2011) Modeling conversion of rural-urban land use based on cellular automata and genetic algorithm geoinformatics. In: 19th IEEE international conference on, Shanghai 1–5. doi:10.1109/GeoInformatics.2011.5981029

  • Clarke KC, Hoppen S, Gaydos L (1996) Methods and techniques for rigorous calibration of a cellular automaton model of urban growth. proceedings. In: Third international conference/workshop on integrating geographic information systems and environmental modeling, 21–25th Jan 1996, Santa Fe, NM

    Google Scholar 

  • Clarke KC, Hoppen S, Gaydos L (1997) A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environ Plan 24:247–261

    Article  Google Scholar 

  • Clarke KC, Gaydos L (1998) Loose coupling a cellular automaton model and GIS: long-term growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci 12(7):699–714

    Article  Google Scholar 

  • Clarke KC (2003) Geocomputation’s future at the extremes: high performance computing and nanoclients. Parallel Comput 29(10):12811295

    Article  Google Scholar 

  • Clarke KC (2004) The limits of simplicity: toward geocomputational honesty in urban modeling. In: Atkinson P, Foody G, Darby S, Wu F (eds) GeoDynamics. CRC Press, Florida

    Google Scholar 

  • Clarke KC, Gazulis N, Dietzel CK, Goldstein NC (2007) A decade of SLEUTHing: Lessons learned from applications of a cellular automaton land use change model. In: Fisher P (ed) Classics from IJGIS. twenty years of the International Journal of Geographical Information Systems and Science. Taylor and Francis, CRC, Boca Raton, FL, pp 413–425

    Google Scholar 

  • Clarke KC (2008a) Mapping and Modelling Land Use Change: an Application of the SLEUTH Model. In: Pettit C, Cartwright W, Bishop I, Lowell K, Pullar D, Duncan D (eds) Landscape analysis and visualisation: spatial models for natural resource management and planning. Springer, Berlin, pp 353–366

    Chapter  Google Scholar 

  • Clarke KC (2008b) A Decade of Cellular Urban Modeling with SLEUTH: Unresolved Issues and Problems. In: Brail RK, Lincoln RK (eds) Planning support systems for cities and regions rail. Lincoln Institute of Land Policy, Cambridge MA, pp 47–60

    Google Scholar 

  • Clarke KC (2014a) Why simulate cities? GeoJournal 79:129–136. doi:10.1007/s10708-013-9499-5

    Article  Google Scholar 

  • Clarke KC (2014b) Cellular automata and agent-based models. In: Fischer MM, Nijkamp P (eds) Handbook of regional science. Springer, Berlin Heidelberg

    Google Scholar 

  • Clarke-Lauer MD, Clarke KC (2011) Evolving simulation modeling: Calibrating SLEUTH using a genetic algorithm. In: Proceedings of the 11th international conference on geo computation. Univ. College London, London

    Google Scholar 

  • Chaudhuri G, Clarke KC (2013) The SLEUTH land use change model: a review. Int J Environ Resour Res 1(1):88–104

    Google Scholar 

  • Colonna A, Di Stefano V, Lombardo S, Papini L, Rabino GA (1998) Learning urban cellular automata in a real world: the case study of Rome metropolitan area. In: ACRI’98 third conference on cellular automata for research and industry. Trieste, 7–9 Oct 1998. Springer, London, pp 165–218

    Google Scholar 

  • Dietzel C, Clarke KC (2007) Toward optimal calibration of the SLEUTH land use change model. Trans GIS 11(1):29–45

    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. Landsc Urban Plann 102(3):188–196. doi:10.1016/j.landurbplan.2011.04.004

    Article  Google Scholar 

  • Feng Y, Liu Y (2012) Anoptimised cellular automata model based on adaptive genetic algorithm for urban growth simulation. In: Shi W, Yeh A, Leung Y, Zhou C (eds) Advances in spatial data handling and GIS: 14th international symposium on spatial data handling. Springer, Heidelberg, Germany, pp 27–38

    Chapter  Google Scholar 

  • García A, Santé MI, Boullón M, Crecente R (2013) Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain. Int J Geogr Inf Sci 27(8):1593–1611. doi:10.1080/13658816.2012.762454

    Article  Google Scholar 

  • Goldstein NC (2004) Brains vs. Brawn: Comparative strategies for the calibration of a cellular automata-based urban growth model. In: Atkinson P, Foody G, Darby A, Wu F (eds) GeoDynamics. CRC Press, Boca Raton, FL

    Google Scholar 

  • Gong Z, Tang W, Thill JC (2012) Parallelization of ensemble neural networks for spatial land-use modeling. In: Proceedings of the 5th ACM SIGSPATIAL international workshop on location-based social networks. ACM, pp 48–54

    Google Scholar 

  • Guan Q, Wang L, Clarke KC (2005) An Artificial-Neural-Network-based, Constrained CA model for simulating urban growth. Cartogr Geogr Inf Sci 32(4):369–380

    Article  Google Scholar 

  • Guan Z, Wang Y, Zou Y, Yu F, Jia Z, Li Z (2010) Study on urban spatial structure changes of Jiaozuo City based on SLEUTH Model. In: Third international symposium of computer science and computational technology (ISCSCT 2010) 14–15 Aug 2010. Acad Publisher Finland

    Google Scholar 

  • Houet T, Aguejdad R, Doukari O, Battaia G, Clarke K (2016) Description and validation of a ‘non path-dependent’ model for projecting contrasting urban growth futures. Cybergeo: Eur J Geogr. Systèmes, Modélisation, Géostatistiques, document 759 http://cybergeo.revues.org/27397. doi:10.4000/cybergeo.27397

  • Holland JH (1998) Emergence: from chaos to order. Addison-Wesley, Redwood City, CA

    Google Scholar 

  • Hu Z, Lo C (2007) Modeling urban growth in Atlanta using logistic regression. Comput Environ Urban Syst 31(6):667–688

    Article  Google Scholar 

  • Jafarnezhad J, Salmanmahiny A, Sakieh Y (2015) Subjectivity versus objectivity: Comparative study between Brute Force method and Genetic Algorithm for calibrating the SLEUTH urban growth model. Urban Plann Dev. doi:10.1061/(ASCE)UP.1943-5444.0000307

    Google Scholar 

  • Kirtland D, Gaydos L, Clarke KC, DeCola L, Acevedo W, Bell C (1994) An analysis of human-induced land transformations in the San Francisco Bay/Sacramento area. World Resour Rev 6(2):206–217

    Google Scholar 

  • Li X, Lin J, Chen Y, Liu X, Ai B (2013) Calibrating cellular automata based on landscape metrics by using genetic algorithms. Int J Geogr Inf Sci 27(3):594–613. doi:10.1080/13658816.2012.698391

    Article  Google Scholar 

  • Li X, Yeh AGO (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16(4):323–343

    Article  Google Scholar 

  • Li X, Yeh AGO (2004) Data mining of cellular automata’s transition rules. Int J Geogr Inf Sci 18:723–744

    Article  Google Scholar 

  • Liu Y, Phinn SR (2003) Modelling urban development with cellular automata incorporating fuzzy-set approaches. Comput Environ Urban Syst 27:637–658

    Article  Google Scholar 

  • Long Y, Mao Q, Dang A (2009) Beijing urban development model: Urban growth analysis and simulation. Tsinghua Science and Technology 14(6):782–794

    Article  Google Scholar 

  • National Research Council (2014) Advancing land change modeling: opportunities and research requirements. Geographical Sciences Committee. National Academy Press, Washington DC

    Google Scholar 

  • Pijanowski BC, Shellito B, Pithadia S (2002) Using artificial neural networks, geographic information systems and remote sensing to model urban sprawl in coastal watersheds along eastern Lake Michigan. Lakes Reservoirs 7:271–285

    Article  Google Scholar 

  • Pontius RG Jr, Boersma W, Castella J-C, Clarke K, de Nijs T, Dietzel C, Zengqiang D, Fotsing E, Goldstein N, Kok K, Koomen E, Lippitt CD, McConnell W, Sood AM, Pijanowski B, Pithadia S, Sweeney S, Trung TN, Veldkamp AT, Verburg PH (2007) Comparing the input, output, and validation maps for several models of land change. Ann Region Sci 42(1):11–37

    Article  Google Scholar 

  • Rienow A, Goetzke R (2014) Supporting SLEUTH—Enhancing a cellular with support vector machines for urban growth modeling. Comput Environ Urban Syst 49:66–81

    Article  Google Scholar 

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

    Google Scholar 

  • Santé I, García AM, Miranda D, Maseda RC (2010) Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc Urban Plann 96(2):108–122

    Article  Google Scholar 

  • Shan J, Alkheder S, Wang J (2008) Genetic algorithms for the calibration of cellular automata urban growth modeling. Photogramm Eng Remote Sens 74(10):1267–1277

    Article  Google Scholar 

  • Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26(6):525–552. doi:10.1016/S0198-9715(01)00014-X

    Article  Google Scholar 

  • Silva EA, Clarke KC (2005) Complexity, emergence and cellular urban models: Lessons learned from applying SLEUTH to two Portuguese metropolitan areas. Eur Plan Stud 13(1):93–115

    Article  Google Scholar 

  • Syphard AD, Clarke KC, Franklin J, Regan HM, Mcginnis M (2011) Forecasts of habitat loss and fragmentation due to urban growth are sensitive to source of input data. J Environ Manage 92(7):1882–1893

    Article  Google Scholar 

  • Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667

    Article  Google Scholar 

  • Straatman B, White R, Engelen G (2004) Towards an automatic calibration procedure for constrained cellular automata. Comput Environ Urban Syst 28:149–170

    Article  Google Scholar 

  • Torrens PT, O’Sullivan D (2001) Cellular automata and urban simulation: where do we go from here? Environ Plan 28(2):163–168

    Article  Google Scholar 

  • Veerbeek W, Pathirana A, Ashley R, Zevenbergen C (2015) Enhancing the calibration of an urban growth model using a memetic algorithm. Comput Environ Urban Syst 50:53–65

    Article  Google Scholar 

  • Verburg PH, Schot PP, Dijst MJ, Veldkamp A (2004) Land use change modelling: current practice and research priorities. GeoJournal 61:309–324

    Article  Google Scholar 

  • Wu F (1998) SimLand: a prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules. Int J Geogr Inf Sci 12(1):63–82

    Article  Google Scholar 

  • Wu F, Webster CJ (1998) Simulation of land development through the integration of cellular automata and multi-criteria evaluation. Environ Plan 25(1):103–126

    Article  Google Scholar 

  • Wu F (2002) Calibration of stochastic cellular automata: the application to rural-urban land conversions. Int J Geogr Inf Sci 16(8):795–818

    Article  Google Scholar 

  • Wu X, Hu Y, He H, Bu R, Onsted J, Xi F (2009) Performance evaluation of the SLEUTH model in the Shenyang Metropolitan Area of Northeastern China. Environ Model Assess 14(2):221–230

    Article  Google Scholar 

  • Xiang WN, Clarke KC (2003) The use of scenarios in land use planning. Environ Plan 30:885–909

    Article  Google Scholar 

  • Yang QS, Li X (2007) Calibrating urban cellular automata using genetic algorithms. Geogr Res 26(2):229–237

    Google Scholar 

  • Yang Q, Li X, Shi X (2008) Cellular automata for simulating land use changes based on support vector machines. Comput Geosci 34(6):592–560

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, University of California, Santa Barbara, USA

    K. C. Clarke

Authors
  1. K. C. Clarke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. C. Clarke .

Editor information

Editors and Affiliations

  1. Departamento de Análisis Geográfico Regional y Geografía Física, Universidad de Granada, Granada, Spain

    María Teresa Camacho Olmedo

  2. Département de Géographie, Aménagement, Environnement, Université Toulouse Jean Jaurès, Toulouse, Garonne (Haute), France

    Martin Paegelow

  3. Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México (UNAM), Morelia, Michoacán, Mexico

    Jean-François Mas

  4. Department of Geology, Geography and Environmental Sciences, University of Alcalá, Alcalá de Henares, Madrid, Spain

    Francisco Escobar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Clarke, K.C. (2018). Land Use Change Modeling with SLEUTH: Improving Calibration with a Genetic Algorithm. In: Camacho Olmedo, M., Paegelow, M., Mas, JF., Escobar, F. (eds) Geomatic Approaches for Modeling Land Change Scenarios. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-60801-3_8

Download citation

Publish with us

Policies and ethics

Search

Navigation