Calibrating and validating a simulation model to identify drivers of urban land cover change in the Baltimore, MD Metropolitan Region

Land

Volumn 3, Issue 3, 2014, Pages 1158-1179

  Jantz, Claire ^a   Drzyzga, Scott ^a   Maret, Michael ^b  

^a SHIPPENSBURG UNIVERSITY   (United States)

^b Urban Development Department   (United States)

Author keywords

Baltimore; Cellular automata (CA); Land use change; Maryland; SLEUTH; Validation

Indexed keywords

EID: 84999814424     PISSN: None     EISSN: 2073445X     Source Type: Journal    
DOI: 10.3390/land3031158     Document Type: Article

References (33)

1. Gardner, R.H.; Urban, D.L. Model validation and testing: Past lessons, present concerns, future prospects. In Models in Ecosystem Science; Canham, C.D.; Cole, J.J.; Lauenroth, W.K., Eds.; Princeton University Press: Princeton, NJ, USA, 2003; pp. 184-203
2. Clarke, K.C.; Hoppen, S.; Gaydos, L. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area. Environ. Plan. B 1997, 24, 247-261
3. Jantz, C.A.; Goetz, S.J.; Donato, D.; Claggett, P. Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Comput. Environ. Urban Syst. 2010, 34, 1-16
4. Chaudhuri, G.; Clarke, K.C. The SLEUTH land use change model: A review. Int. J. Environ. Resour. Res. 2013, 1, 88-104
5. Silva, E.A.; Clarke, K.C. Complexity, emergence and cellular urban models: Lessons learned from applying SLEUTH to two Portuguese metropolitan areas. Eur. Plan. Stud. 2005, 13, 93-116
6. Dietzel, C.; Oguz, H.; Hemphill, J.J.; Clarke, K.C.; Gazulis, N. Diffusion and coalescence of the Houston Metropolitan Area: Evidence supporting a new urban theory. Environ. Plan. B 2005, 32, 231-246
7. Goldstein, N.C.; Candau, J.T.; Clarke, K.C. Approaches to simulating the "March of Bricks and Mortar". Comput. Environ. Urban Syst. 2004, 28, 125-147
8. Jantz, C.A.; Goetz, S.J.; Shelley, M.K. 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. Plan. B 2004, 31, 251-271
9. Onsted, J.; Clarke, K.C. The inclusion of differentially assessed lands in urban growth model calibration: A comparison of two approaches using SLEUTH. Int. J. Geogr. Infor. Sci. 2012, 26, 881-898
10. Dietzel, C.; Clarke, K.C. Toward optimal calibration of the SLEUTH land use change model. Trans. GIS 2007, 11, 29-45
11. Guan, Q.; Clarke, K.C. A general-purpose parallel raster processing programming library test application using a geographic cellular automata model. Int. J. Geogr. Infor. Sci. 2010, 24, 695-722
12. Jantz, C.A.; Goetz, S.J. Analysis of scale dependencies in an urban land-use-change model. Int. J. Geogr. Inf. Sci. 2005, 19, 217-241
13. Chaudhuri, G.; Clarke, K.C. Temporal accuracy in urban growth forecasting: A study using the SLEUTH model. Trans. GIS 2014, 18, 302-320
14. Dietzel, C.; Clarke, K. The effect of disaggregating land use categories in cellular automata during model calibration and forecasting. Comput. Environ. Urban Syst. 2006, 30, 78-101
15. Arthur-Hartranft, S.T.; Carlson, T.N.; Clarke, K.C. Satellite and ground-based microclimate and hydrologic analyses coupled with a regional urban growth model. Remote Sens. Environ. 2003, 86, 385-400
16. Ciavola, S.J.; Jantz, C.A.; Reilly, J.; Moglen, G. Forecast changes in runoff quality and quantity in the DelMarVa Peninsula. J. Hydrol. Eng. 2014, 19, 1-9
17. Chen, H.; Pontius, R.G., Jr. Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landsc. Ecol. 2010, 25, 1319-1331
18. Pontius, R.G., Jr.; Peethambaram, S.; Castella, J.-C. Comparison of three maps at multiple resolutions: A case study of land change simulation in Cho Don District, Vietnam. Ann. Assoc. Am. Geogr. 2011, 101, 45-62
19. Pontius R.G., Jr.; Millones, M. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2011, 32, 4407-4429
20. Yang, X.; Lo, C.P. Modelling urban growth and landscape changes in the Atlanta metropolitan area. Int. J. Geogr. Inf. Sci. 2003, 17, 463-488
21. Rafiee, R.; Mahiny, A.S.; Khorasani, N.; Darvishsefat, A.A.; Danekar, A. Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM). Cities 2009, 26, 19-26
22. Project Gigalopolis. Available online: http://www.ncgia.ucsb.edu/projects/gig/ (accessed on 14 September 2014)
23. Jantz, C.A.; Goetz, S.J. Can smart growth save the Chesapeake Bay? Int. J. Green Build. 2007, 2, 41-51
24. Hanlon, B.; Short, J.R.; Vicino, T.J. Cities and Suburbs: New Metropolitan Realities in the U.S.; Routledge: New York, NY, USA, 2010
25. Short, J.R. Liquid City: Megalopolis and the Contemporary Northeast; Resources for the Future: Washington, DC, USA, 2007
26. Maryland Department of Planning. Smart, Green & Growing Planning Guide; Maryland Department of Planning: Baltimore, MD, USA, 2010; p. 60
27. Irani, F.M.; Claggett, P. Chesapeake Bay Watershed Land Cover Data Series; Chesapeake Bay Program: Annapolis, MD, USA, 2010
28. Onsted, J.A.; Chowdhury, R.R. Does zoning matter? A comparative analysis of landscape change in Redland, Florida using a cellular automata. Landsc. Urban Plan. 2014, 121, 1-18
29. Irwin, E.G.; Bockstael, N.E. The evolution of urban sprawl: Evidence of spatial heterogeneity and increasing land fragmentation. Proc. Natl. Acad. Sci. USA 2007, 104, 20672-20677
30. Maryland Department of Natural Resources. Geospatial Data: Data Download. Available online: http://dnrweb.dnr.state.md.us/gis/data/ (accessed 9 September 2014)
31. Cheng, J.; Masser, I. Understanding spatial and temporal processes of urban growth: Cellular automata modelling. Environ. Plan. B 2004, 31, 167-194
32. U.S. Census Bureau. Commuting in the United States: 2009: American Community Survey Reports; U.S. Census Bureau: Washington, DC, USA, 2011; p. 20
33. Chaudhuri, G.; Clarke, K.C. How does land use policy modify urban growth? A case study of the Italo-Slovenian border. J. Land Use Sci. 2012, 8, 443-465