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Impacts of Spatial Extend and Site Location on Calibration of Urban Growth Models

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DOI: 10.4236/cus.2015.32008    2,186 Downloads   2,729 Views   Citations

ABSTRACT

During the last decades, cities in sub-saharan Africa have undergone rapid urban growth due to increased population growth and high economic activities. This research explores the impacts of varying modelling settings including spatial extend and its location for the city of Nairobi using a cellular automata (CA) urban growth model (UGM). Our UGM used multi-temporal satellite-based data for classification of urban land-use of 1986, 2000 and 2010, road data, slope data and exclusion layer. Monte-Carlo technique was used for model calibration and Multi Resolution Validation (MRV) technique for validation. Simulation of urban land-use was done up to the year 2030 when Kenya plans to attain Vision 2030. Three spatial grid sizes varying in extent and location were applied in the UGM calibration and validation. Thus, this research explored the impacts of varying spatial extent (grid) and location on urban growth modelling and hence can contribute to an improved sustainable planning and development. This is useful for future planning as the Nairobi grows and expands into the peri-urban areas.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Mubea, K. , Rienow, A. and Menz, G. (2015) Impacts of Spatial Extend and Site Location on Calibration of Urban Growth Models. Current Urban Studies, 3, 82-94. doi: 10.4236/cus.2015.32008.

References

[1] Akin, A., Clarke, K., & Berberoglu, S. (2014). The Impact of Historical Exclusion on the Calibration of the SLEUTH Urban Growth Model. International Journal of Applied Earth Observation and Geoinformation, 27, 156-168. http://dx.doi.org/10.1016/j.jag.2013.10.002
[2] Barredo, J. I., Kasanko, M., McCormick, N., & Lavalle, C. (2003). Modelling Dynamic Spatial Processes: Simulation of Urban Future Scenarios through Cellular Automata. Landscape and Urban Planning, 64, 145-160. http://dx.doi.org/10.1016/S0169-2046(02)00218-9
[3] Clarke, K., Hoppen, S., & Gaydos, L. (1996). Methods and Techniques for Rigorous Calibration of Cellular Automaton Model of Urban Growth. Third International Conference/Workshop on Integrating GIS and Environmental Modeling. Santa Fe: National Center for Geographic Information and Analysis.
[4] Clarke, K., 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. http://dx.doi.org/10.1068/b240247
[5] Goetzke, R., & Judex, M. (2011). Simulation of Urban Land-Use Change in North Rhine-Westphalia (Germany) with the Java-Based Modelling Platform Xulu. In P. Mandl, & A. Koch (Eds.), Modeling and Simulating Urban Processes (pp. 99-116). Munster: LIT-Verlag.
[6] Government of Kenya (2007). Kenya Vision 2030. Nairobi: Ministry of Planning and National Development. http://www.theredddesk.org/sites/default/files/vision_2030_brochure__july_2007.pdf
[7] Han, J., Hayashi, Y., Cao, X., & Imura, H. (2009). Application of an Integrated System Dynamics and Cellular Automata Model for Urban Growth Assessment: A Case Study of Shanghai, China. Landscape and Urban Planning, 91, 133-141. http://dx.doi.org/10.1016/j.landurbplan.2008.12.002
[8] Itami, R. M. (1994). Simulating Spatial Dynamics: Cellular Automata Theory. Landscape and Urban Planning, 30, 27-47. http://dx.doi.org/10.1016/0169-2046(94)90065-5
[9] Jantz, A. J., 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. http://dx.doi.org/10.1016/j.compenvurbsys.2009.08.003
[10] Jantz, C. A., Goetz, S. J., & Shelley, M. K. (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. Environment and Planning B: Planning and Design, 31, 251-271.
http://dx.doi.org/10.1068/b2983
[11] Leão, S., Bishop, I., & Evans, D. (2004). Spatial-Temporal Model for Demand and Allocation of Waste Landfills in Growing Urban Regions. Computers, Environment and Urban Systems, 28, 353-385. http://dx.doi.org/10.1016/S0198-9715(03)00043-7
[12] Lebel, L., Thaitakoo, D., Sangawongse, S., & Huaisai, D. (2007). Views of Chiang Mai: The Contribution of Remote-Sens- ing to Urban Governance and Sustainability. In M. Netzband, W. Stefanov, & C. Redman (Eds.), Applied Remote Sensing for Urban Planning, Governance and Sustainability (pp. 221-247). Berlin: Springer. http://dx.doi.org/10.1007/978-3-540-68009-3_10
[13] Liu, Y. (2008). Modelling Urban Development with Geographical Information Systems and Cellular Automata (1 ed.). Boca Raton, FL: CRC Press. http://dx.doi.org/10.1201/9781420059908
[14] Mubea, K., Goetzke, R., & Menz, G. (2013). Simulating Urban Growth in Nakuru (Kenya) Using Java-Based Modelling Platform XULU. 2013 European Modelling Symposium (EMS), Manchester, 20-22 November 2013, 97-102.
[15] Mubea, K., Goetzke, R., & Menz, G. (2014). Applying Cellular Automata for Simulating and Assessing Urban Growth Scenario Based in Nairobi, Kenya. International Journal of Advanced Computer Science and Applications, 5. http://dx.doi.org/10.14569/IJACSA.2014.050201
[16] Oguz, H., Klein, A. G., & Srinivasan, R. (2007). Using the Sleuth Urban Growth Model to Simulate the Impacts of Future Policy Scenarios on Urban Land Use in the Houston-Galveston-Brazoria CMSA. Research Journal of Social Sciences, 2, 72-82.
[17] Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review. Annals of the Association of American Geographers, 93, 314-337. http://dx.doi.org/10.1111/1467-8306.9302004
[18] Pontius Jr., R. G., Huffaker, D., & Denman, K. (2004). Useful Techniques of Validation for Spatially Explicit Land-Change Models. Ecological Modelling, 179, 445-461.
http://dx.doi.org/10.1016/j.ecolmodel.2004.05.010
[19] Republic of Kenya (1970). Kenya Population Census 1969. Nairobi: Government Printer.
[20] Republic of Kenya (1981). Kenya Population Census 1979. Nairobi: Government Printer.
[21] Republic of Kenya (1994). Kenya Population Census 1989. Nairobi: Government Printer.
[22] Republic of Kenya (2000). Economic Survey 2000. Nairobi: Government Printer.
[23] Republic of Kenya (2010). Economic Survey 2010. Nairobi: Government Printer.
[24] Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Cellular Automata Models for the Simulation of Real-World Urban Processes: A Review and Analysis. Landscape and Urban Planning, 96, 108-122. http://dx.doi.org/10.1016/j.landurbplan.2010.03.001
[25] Silva, E., & Clarke, K. C. (2002). Calibration of the SLEUTH Urban Growth Model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26, 525-552.
http://dx.doi.org/10.1016/S0198-9715(01)00014-X
[26] Sipper, M. (1997). Evolution of Parallel Cellular Machines: The Cellular Programming Approach. Berlin: Springer. http://dx.doi.org/10.1007/3-540-62613-1
[27] Tobler, W. (1979). Cellular Geography. In S. Gale, & G. Olsson (Eds.), Philosophy in Geography (pp. 379-386). Dortrecht: Reidel.
[28] Triantakonstantis, D., & Mountrakis, G. (2012). Urban Growth Prediction: A Review of Computational Models and Human Perceptions. Journal of Geographic Information System, 4, 555-587.
http://dx.doi.org/10.4236/jgis.2012.46060
[29] UN-HABITAT (2005). Regional Urban Sector Profile Study (RUSPS). Nairobi: UN-HABITAT.
[30] Vliet, J., White, R., & Dragicevic, S. (2009). Modeling Urban Growth Using a Variable Grid Cellular Automaton. Computers, Environment and Urban Systems, 33, 35-43.
http://dx.doi.org/10.1016/j.compenvurbsys.2008.06.006
[31] White, R., & Engelen, G. (1993). Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use. Environment and Planning A, 25, 1175-1199.
http://dx.doi.org/10.1068/a251175
[32] Wolfram, S. (1994). Cellular Automata. In A. Wesley, S. Wolfram, & M. A. Reading (Eds.), Cellular Automata and Complexity: Collected Papers. Boulder: Westview Press.

  
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