365
Views
13
CrossRef citations to date
0
Altmetric
Original Articles

Analysing performance of SLEUTH model calibration using brute force and genetic algorithm–based methods

&
Pages 256-279 | Received 24 Dec 2017, Accepted 06 Aug 2018, Published online: 29 Nov 2018

References

  • Akin A, Clarke KC, Berberoglu S. 2014. The impact of historical exclusion on the calibration of the SLEUTH urban growth model. Int J Appl Earth Observ Geoinform. 27:156–168.
  • Anurag SA, Pradhan B. 2018. Land use/land cover change modelling: issues and challenges. J Rural Develop. 37(2):413–424.
  • Balling RJ, Taber JT, Brown MR, Day K. 1999. Multi objective urban planning using genetic algorithm. J Urban Plan Develop. 125(2):86–99.
  • Batty M. 2005. Agents, cells, and cities: new representational models for simulating multiscale urban dynamics. Environ Plan A. 37(8):1373–1394.
  • Batty M. 2009. Urban modeling. International encyclopedia of human geography. Oxford, UK: Elsevier.
  • Bhatta B. 2010. Causes and consequences of urban growth and sprawl. In: Analysis of urban growth and sprawl from remote sensing data (pp. 17–36). Springer, Berlin, Heidelberg.
  • Bockstael NE. 1996. Modeling economics and ecology: the importance of a spatial perspective. Am J Agricult Econ. 78(5):1168–1180.
  • Brown DG, Pijanowski BC, Duh JD. 2000. Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. J Environ Manage. 59(4):247–263.
  • Brueckner JK, Helsley RW. 2011. Sprawl and blight. J Urban Economics. 69(2):205–213.
  • Candau JT. 2002. Temporal calibration sensitivity of the SLEUTH urban growth model. Santa Barbara: University of California.
  • Carlson D, Haurie AB, Leizarowitz A. 2012. Infinite horizon optimal control: deterministic and stochastic systems. Berlin: Springer Science & Business Media.
  • Chaudhuri G, Clarke K. 2013. The SLEUTH land use change model: A review. Environ Resources Res. 1(1):88–105.
  • Cheng J, Masser I. 2003. Urban growth pattern modeling: a case study of Wuhan city, PR China. Landsc Urban Plan. 62(4):199–217.
  • Chomitz K, Gray AD. 1995. Roads, lands, markets, and deforestation (Vol. 1444). Washington, DC: World Bank Publications.
  • Clarke KC. 1997. Land transition modeling with deltatrons. Department of Geography. Santa Barbara: University of California.
  • Clarke KC. 2008. Mapping and modeling land use change: an application of the SLEUTH model. In: Landscape Analysis and Visualisation (pp. 353–366). Berlin Heidelberg: Springer.
  • Clarke KC. 2014. Why simulate cities? GeoJournal. 79(2):129–136.
  • Clarke KC. 2017. Land use change modeling with SLEUTH: Improving calibration with a genetic algorithm. In: Geomatic Approaches for Modeling Land Change Scenarios (pp. 139–161). Springer: Cham.
  • Clarke KC, Gaydos LJ. 1998. Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geograph Info Sci. 12(7):699–714.
  • Clarke KC, Gazulis N, Dietzel C, Goldstein NC. 2007. A decade of SLEUTHing: Lessons learned from applications of a cellular automaton land use change model. Classics in IJGIS: twenty Years of the International Journal of Geographical Information Science and Systems. 413–427.
  • Clarke KC, McLafferty SL, Tempalski BJ. 1996. On epidemiology and geographic information systems: a review and discussion of future directions. Emerg Infect Dis. 2(2):85.
  • Clarke-Lauer MD, Clarke KC. 2011. Evolving simulation modeling: Calibrating SLEUTH using a genetic algorithm. In Proceedings of the 11th International Conference on GeoComputation, London, UK (Vol. 2022). July 20–22.
  • Couclelis H. 1997. From cellular automata to urban models: new principles for model development and implementation. Environ Plann B. 24(2):165–174.
  • Dietzel C, Clarke KC. 2007. Toward optimal calibration of the SLEUTH land use change model. Trans GIS. 11(1):29–45.
  • Estoque RC, Murayama Y. 2012. Introducing new measures of accuracy for land-use/cover change modeling. Tsukuba Geoenviron Sci. 8:3–7.
  • Feng Y. 2017. Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules. Int J Geograph Info Sci. 31(6):1198–1219.
  • Feng Y, Liu Y. 2016. Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China. Environ Monit Assess. 188(9):540.
  • Feng Y, Tong X. 2018. Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules. GISci Remote Sensing. 55(5):678–698.
  • Gandhi SI, Suresh VM. 2012. Prediction of urban sprawl in Hyderabad city using spatial model, remote sensing and GIS techniques geography. Int J Sci Res. 1(2):80–82.
  • Gazulis N, Clarke KC. 2006. Exploring the DNA of our regions: Classification of outputs from the SLEUTH model. In: International Conference on Cellular Automata (pp. 462–471). Berlin Heidelberg: Springer.
  • Gimblett, H. R. (Ed.). 2002. Integrating geographic information systems and agent-based modeling techniques for simulating social and ecological processes. New York: Oxford University Press.
  • Goldstein NC. 2004. Brains vs. Brawn: Comparative strategies for the calibration of a cellular automata-based urban growth model.” In GeoDynamics, Edited by: Atkinson, P., Foody, G., Darby, S. and Wu, F. Boca Raton, FL: CRC Press.
  • Goldstein NC, Dietzel C, Clarke KC. 2005. Don’t stop ‘til you get enough–sensitivity testing of Monte Carlo iterations for model calibration. In Proceedings of the 8th International Conference on GeoComputation (pp. 1–3). University of Michigan, United States of America, 31 July – 3 August 2005.
  • Guan Q, Clarke KC. 2010. A general-purpose parallel raster processing programming library test application using a geographic cellular automata model. Int J Geograph Inform Sci. 24(5):695–722.
  • Hatna E, Benenson I. 2012. The Schelling model of ethnic residential dynamics: Beyond the integrated-segregated dichotomy of patterns. JASSS. 15(1):6.
  • Irwin EG, Bockstael NE. 2002. Interacting agents, spatial externalities and the evolution of residential land use patterns. J Econ Geography. 2(1):31–54.
  • 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. J Urban Plann Dev. 142(3):05015015.
  • Jantz CA, Goetz SJ. 2005. Analysis of scale dependencies in an urban land‐use‐change model. Int J Geograp Inform Sci. 19(2):217–241.
  • Jantz CA, Goetz SJ, Donato D, Claggett P. 2010. Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Computers, Environ Urban Syst. 34(1):1–16.
  • Jantz CA, Goetz SJ, Shelley MK. 2004. Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environ Plann B Plann Des. 31(2):251–271.
  • Jat MK, Choudhary M, Saxena A. 2017. Urban growth assessment and prediction using RS, GIS and SLEUTH model for a heterogeneous urban fringe. Egyptian J Remote Sens Space Sci. 20(2):223–241.
  • Jat MK, Garg PK, Khare D. 2008. Modeling of urban growth using spatial analysis techniques: a case study of Ajmer city India. Int J Remote Sens. 29(2):543–567.
  • Kanta Kumar LN, Sawant NG, Kumar S. 2011. Forecasting urban growth based on GIS, RS and SLEUTH model in Pune metropolitan area. Int J Geomatics Geosci. 2(2):568.
  • Li X, Yeh AGO. 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inform Sci. 16(4):323–343.
  • Liu Y, Feng Y, Pontius RG. 2014. Spatially-explicit simulation of urban growth through self-adaptive genetic algorithm and cellular automata modeling. Land. 3(3):719–738.
  • Niazi M, Hussain A. 2011. Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics. 89(2):479–499.
  • Nelson GC, Hellerstein D. 1997. Do roads cause deforestation? Using satellite images in econometric analysis of land use. Am J Agricult Econ. 79(1):80–88.
  • Newburn DA, Berck P. 2011. Growth management policies for exurban and suburban development: theory and an application to Sonoma County, California. Agricult Res Econ. 40:1–18.
  • Onsted J, Clarke KC. 2012. The inclusion of differentially assessed lands in urban growth model calibration: a comparison of two approaches using SLEUTH. Int J Geograph Inform Sci. 26(5):881–898.
  • Pathiranage ISS, Kantakumar LN, Sundaramoorthy S. 2018. Remote sensing data and SLEUTH urban growth model: as decision support tools for urban planning. Chin Geogr Sci. 28(2):274–286.
  • Saxena A, Jat MK. 2018. An integrated approach for natural resources monitoring using geo-informatics and CA. JRD. 37(2):341–354.
  • Saxena A, Jat MK, Choudhary M. 2016. Analysis of Urban Growth using Geospatial Techniques. Int J Earth Sci Eng. 9(6):2855–2861.
  • Schelling TC. 1971. Dynamic models of segregation. J Mathemat Soc. 1(2):143–186.
  • Shan J, Alkheder S, Wang J. 2008. Genetic algorithms for the calibration of cellular automata urban growth modeling. Photogramm Eng Remote Sensing. 74(10):1267–1277.
  • Sietchiping R. 2004. A geographic information systems and cellular automata-based model of informal settlement growth (Doctoral dissertation). Melbourne: The University of Melbourne.
  • Silva EA, Clarke KC. 2002. Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environ Urban Syst. 26(6):525–552.
  • Towe CA, Nickerson CJ, Bockstael N. 2008. An empirical examination of the timing of land conversions in the presence of farmland preservation programs. Am J Agricultural Economics. 90(3):613–626.
  • Van der Werf E, Peterson S. 2009. Modeling linkages between climate policy and land use: an overview. Agricult Econ. 40(5):507–517.
  • Van Meijl JCM, van Tongeren FW. 2001. Multilateral trade liberalisation and developing countries: A North-South perspective on agriculture and processing sectors (No. 6.01. 07). Wageningen, Netherlands: Agricultural Economics Research Institute (LEI).
  • Verburg PH, Neumann K, Nol L. 2011. Challenges in using land use and land cover data for global change studies. Global Change Biol. 17(2):974–989.
  • White R, Engelen G. 1993. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environ Plan A. 25(8):1175–1199.
  • Wolfram S. 1984. Cellular automata as models of complexity. Nature. 311(5985):419–424.
  • Wolfram S. 1986. Theory and applications of cellular automata (Vol. 1, pp. 560). Singapore: World Scientific.
  • Wrenn DH, Irwin EG. 2012. How do land use policies influence fragmentation? An econometric model of land development with spatial simulation. Environ Econ, 3, 82–96.
  • Wu F, Webster CJ. 1998. Simulation of land development through the integration of cellular automata and multi-criteria evaluation. Environ Plann B. 25(1):103–126.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.