Spatial Effects of Varying Model Coefficients in Urban Growth Modeling in Nairobi, Kenya
Affiliation(s)
1 Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, Nyeri, Kenya. 2 Remote Sensing Research Group (RSRG), University of Bonn, Bonn, Germany. 3 Centre for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Bonn, Germany.
1 Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, Nyeri, Kenya. 2 Remote Sensing Research Group (RSRG), University of Bonn, Bonn, Germany. 3 Centre for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Bonn, Germany.
ABSTRACT
Urban land-use modeling has gained increased attention as a research topic over the last decade. This has been attributed to advances in remote sensing and computing technology that now can process several models simultaneously at regional and local levels. In this research we implemented a cellular automata (CA) urban growth model (UGM) integrated in the XULU modeling frame-work (eXtendable Unified Land Use Modeling Platform). We used multi-temporal Landsat satellite image sets for 1986, 2000 and 2010 to map urban land-use in Nairobi. We also tested the spatial effects of varying model coefficients. This approach improved model performance and aided in understanding the particular urban land-use system dynamics operating in our Nairobi study area. The UGM was calibrated for Nairobi and predicted development was derived for the city for the year 2030 when Kenya plans to attain Vision 2030. Observed land-use changes in urban areas were compared to the results of UGM modeling for the year 2010. The results indicate that varying the UGM model coefficients simulates urban growth in different directions and magnitudes. This approach is useful to planners and policy makers because the model outputs can identify specific areas within the urban complex which will require infrastructure and amenities in order to realize sustainable development.
Urban land-use modeling has gained increased attention as a research topic over the last decade. This has been attributed to advances in remote sensing and computing technology that now can process several models simultaneously at regional and local levels. In this research we implemented a cellular automata (CA) urban growth model (UGM) integrated in the XULU modeling frame-work (eXtendable Unified Land Use Modeling Platform). We used multi-temporal Landsat satellite image sets for 1986, 2000 and 2010 to map urban land-use in Nairobi. We also tested the spatial effects of varying model coefficients. This approach improved model performance and aided in understanding the particular urban land-use system dynamics operating in our Nairobi study area. The UGM was calibrated for Nairobi and predicted development was derived for the city for the year 2030 when Kenya plans to attain Vision 2030. Observed land-use changes in urban areas were compared to the results of UGM modeling for the year 2010. The results indicate that varying the UGM model coefficients simulates urban growth in different directions and magnitudes. This approach is useful to planners and policy makers because the model outputs can identify specific areas within the urban complex which will require infrastructure and amenities in order to realize sustainable development.
KEYWORDS
Urban Growth Model, Cellular Automata, XULU, Model Coefficients, Prediction, Sustainable Development
Urban Growth Model, Cellular Automata, XULU, Model Coefficients, Prediction, Sustainable Development
Cite this paper
Mubea, K. , Menz, G. (2014) Spatial Effects of Varying Model Coefficients in Urban Growth Modeling in Nairobi, Kenya. Journal of Geographic Information System, 6, 636-652. doi: 10.4236/jgis.2014.66053.
Mubea, K. , Menz, G. (2014) Spatial Effects of Varying Model Coefficients in Urban Growth Modeling in Nairobi, Kenya. Journal of Geographic Information System, 6, 636-652. doi: 10.4236/jgis.2014.66053.
References
[1] Triantakonstantis, D. and 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 [2] UN-HABITAT (2010) State of the World Cities 2010/2011. Bridging the Urban Divide.
http://sustainabledevelopment.un.org/content/documents/11143016_alt.pdf [3] Mundia, C.N. and Aniya, M. (2007) Modeling Urban Growth of Nairobi City Using Cellular Automata and Geographical Information Systems. Geographical Review of Japan, 80, 777-788.
http://dx.doi.org/10.4157/grj.80.777 [4] Mundia, C.N., Aniya, M. and Murayama, Y. (2010) Remote Sensing and GIS Modeling of Spatial Processes of Urban Growth in an African City. A Case Study of Nairobi. In: Mundia, C.N., Kamusoko, C. and Murayama, Y., Eds., Recent Advances in GIS and Remote Sensing Analysis in Sub-Sahara Africa, Nova Science Publishers, New York. [5] Verburg, P. (2006) Simulating Feedbacks in Land Use and Land Cover Change Models. Landscape Ecology, 21, 1171-1183. http://dx.doi.org/10.1007/s10980-006-0029-4 [6] Von Neumann, J. (Burks, A.W., Ed.) (1966) Theory of Self-Reproducing Automata Theory of Self-Reproducing Automata. University of Illinois Press, Champaign. [7] Tobler, W. (1979) Cellular Geography. In: Gale, S. and Olsson, G., Eds., Philosophy in Geography, Reidel, Dortrecht, 379-386. [8] Silva, E. and 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 [9] Jantz, A.J., Goetz, S.J., Donato, D. and 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] Fuglsang, M., Münier, B. and Hansen, H.S. (2013) Modelling Land-Use Effects of Future Urbanization Using Cellular Automata: An Eastern Danish Case. Environmental Modelling & Software, 50, 1-11. http://dx.doi.org/10.1016/j.envsoft.2013.08.003 [11] Mubea, K., Goetzke, R. and Menz, G. (2013) Simulating Urban Growth in Nakuru (Kenya) Using Java-Based Modelling Platform XULU. UKSim-AMSS 7th European Modelling Symposium, Manchester, 20-22 November 2013, 103-108. http://dx.doi.org/10.1109/EMS.2013.18 [12] Dietzel, C. and Clarke, K.C. (2007) Toward Optimal Calibration of the SLEUTH Land Use Change Model. Transactions in GIS, 11, 29-45. http://dx.doi.org/10.1111/j.1467-9671.2007.01031.x [13] Goetzke, R. and Judex, M. (2011) Simulation of Urban Land-Use Change in North Rhine-Westphalia (Germany) with the Java-Based Modelling Plat-Form Xulu. In: Mandl, P. and Koch, A., Eds., Modelling and Simulating Urban Processes, LIT-Verlag, Munster, 99-116. [14] Clarke, K., Hoppen, S. and 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 [15] Clarke, K., Hoppen, S. and Gaydos, L. (1996) Methods and Techniques for Rigorous Calibration of Cellular Automaton Model of Urban Growth. 3rd International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, 21-25 January 1996, Session 7.
http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/program.html [16] Silva, E. and 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 [17] Government of Kenya (2007) Kenya Vision 2030.
http://www.theredddesk.org/sites/default/files/vision_2030_brochure__july_2007.pdf [18] Republic of Kenya (1981) Kenya Population Census 1979. Government Printer, Nairobi. [19] Republic of Kenya (1994) Kenya Population Census 1989. Government Printer, Nairobi. [20] Republic of Kenya (2000) Economic Survey 2000. Government Printer, Nairobi. [21] Republic of Kenya (2010) Economic Survey 2010. Government Printer, Nairobi. [22] UN-HABITAT (2005) Regional Urban Sector Profile Study. UN-HABITAT, Nairobi. [23] Schmitz, M., Bode, T., Thamm, H.P. and Cremers, A.B. (2007) XULU—A Generic JAVA-Based Platform to Simulate Land Use and Land Cover Change. In: Oxley, L. and Kulasiri, D., Eds., MODSIM 2007 International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand, 2645-2649. [24] Menz, G., Judex, M., Orekan, V., Kuhn, A., Heldmann, M. and Thamm, H.P. (2010) Land Use and Land Cover Modeling in Central Benin. In: Spezh, P., Christoph, M. and Dieckkrüger, B., Eds., Impacts of Global Change on the Hydrological Cycle in West and Northwest Africa, Springer, Heidelberg, 70-73. [25] Pontius Jr., R.G., Huffaker, D. and 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 [26] Jantz, C.A., Goetz, S.J. and 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 [27] Liu, Y. (2008) Modelling Urban Development with Geographical Information Systems and Cellular Automata. CRC Press, Boca Raton. [28] Wolfram, S. (1994) Cellular Automata. In: Wesley, A., Wolfram, S. and Reading, M.A., Eds., Cellular Automata and Complexity: Collected Papers, Westview Press, Boulder, 411-436. [29] Caglioni, M., Pelizzoni, M. and Rabino, G.A. (2006) Urban Sprawl: A Case Study for Project Gigalopolis Using SLEUTH Model. In: El Yacoubi, S., Chopard, B. and Bandini, S., Eds., Cellular Automata, Springer Berlin Heidelberg, Heidelberg, 436-445. http://dx.doi.org/10.1007/11861201_51 [30] Kocabas, V. and Dragicevic, S. (2006) Assessing Cellular Automata Model Behaviour Using a Sensitivity Analysis Approach. Computers, Environment and Urban Systems, 30, 921-953.
http://dx.doi.org/10.1016/j.compenvurbsys.2006.01.001 [31] Han, J., Cao, X. and 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 [32] Visser, H. and de Nijs, T. (2006) The Map Comparison Kit. Environmental Modelling and Software, 21, 346-358. http://dx.doi.org/10.1016/j.envsoft.2004.11.013 [33] Anderson, J.R., Hardy, E.E., Roach, J.T. and Witmer, R.E. (1976) A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey, Washington DC. [34] Mubea, K. and Menz, G. (2012) Monitoring Land-Use Change in Nakuru (Kenya) Using Multi-Sensor Satellite Data. Advances in Remote Sensing, 1, 74-84. http://dx.doi.org/10.4236/ars.2012.13008 [35] Congalton, R.G. and Green, K. (1999) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, Florida. [36] Wasike, W.S. (2001) Road Infrastructure Policies in Kenya: Historical Trends and Current Challenges. KIPPRA Working Paper No. 1. The Kenya Institute for Public Policy Research and Analysis (KIPPRA), Infrastructure and Economic Services Division, Nairobi, 1-42. [37] Hagen, A. (2002) Multi-Method Assessment of Map Similarity. In: Ruiz, M., Gould, M. and Ramon, J., Eds., Proceedings of the 5th AGILE Conference on Geographic Information Science, European Commission, Palma, 171-182. [38] Foody, G.M. (2004) Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy. Photogrammetric Engineering and Remote Sensing, 70, 627-633.
http://dx.doi.org/10.14358/PERS.70.5.627
[1] Triantakonstantis, D. and 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 [2] UN-HABITAT (2010) State of the World Cities 2010/2011. Bridging the Urban Divide.
http://sustainabledevelopment.un.org/content/documents/11143016_alt.pdf [3] Mundia, C.N. and Aniya, M. (2007) Modeling Urban Growth of Nairobi City Using Cellular Automata and Geographical Information Systems. Geographical Review of Japan, 80, 777-788.
http://dx.doi.org/10.4157/grj.80.777 [4] Mundia, C.N., Aniya, M. and Murayama, Y. (2010) Remote Sensing and GIS Modeling of Spatial Processes of Urban Growth in an African City. A Case Study of Nairobi. In: Mundia, C.N., Kamusoko, C. and Murayama, Y., Eds., Recent Advances in GIS and Remote Sensing Analysis in Sub-Sahara Africa, Nova Science Publishers, New York. [5] Verburg, P. (2006) Simulating Feedbacks in Land Use and Land Cover Change Models. Landscape Ecology, 21, 1171-1183. http://dx.doi.org/10.1007/s10980-006-0029-4 [6] Von Neumann, J. (Burks, A.W., Ed.) (1966) Theory of Self-Reproducing Automata Theory of Self-Reproducing Automata. University of Illinois Press, Champaign. [7] Tobler, W. (1979) Cellular Geography. In: Gale, S. and Olsson, G., Eds., Philosophy in Geography, Reidel, Dortrecht, 379-386. [8] Silva, E. and 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 [9] Jantz, A.J., Goetz, S.J., Donato, D. and 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] Fuglsang, M., Münier, B. and Hansen, H.S. (2013) Modelling Land-Use Effects of Future Urbanization Using Cellular Automata: An Eastern Danish Case. Environmental Modelling & Software, 50, 1-11. http://dx.doi.org/10.1016/j.envsoft.2013.08.003 [11] Mubea, K., Goetzke, R. and Menz, G. (2013) Simulating Urban Growth in Nakuru (Kenya) Using Java-Based Modelling Platform XULU. UKSim-AMSS 7th European Modelling Symposium, Manchester, 20-22 November 2013, 103-108. http://dx.doi.org/10.1109/EMS.2013.18 [12] Dietzel, C. and Clarke, K.C. (2007) Toward Optimal Calibration of the SLEUTH Land Use Change Model. Transactions in GIS, 11, 29-45. http://dx.doi.org/10.1111/j.1467-9671.2007.01031.x [13] Goetzke, R. and Judex, M. (2011) Simulation of Urban Land-Use Change in North Rhine-Westphalia (Germany) with the Java-Based Modelling Plat-Form Xulu. In: Mandl, P. and Koch, A., Eds., Modelling and Simulating Urban Processes, LIT-Verlag, Munster, 99-116. [14] Clarke, K., Hoppen, S. and 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 [15] Clarke, K., Hoppen, S. and Gaydos, L. (1996) Methods and Techniques for Rigorous Calibration of Cellular Automaton Model of Urban Growth. 3rd International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, 21-25 January 1996, Session 7.
http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/program.html [16] Silva, E. and 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 [17] Government of Kenya (2007) Kenya Vision 2030.
http://www.theredddesk.org/sites/default/files/vision_2030_brochure__july_2007.pdf [18] Republic of Kenya (1981) Kenya Population Census 1979. Government Printer, Nairobi. [19] Republic of Kenya (1994) Kenya Population Census 1989. Government Printer, Nairobi. [20] Republic of Kenya (2000) Economic Survey 2000. Government Printer, Nairobi. [21] Republic of Kenya (2010) Economic Survey 2010. Government Printer, Nairobi. [22] UN-HABITAT (2005) Regional Urban Sector Profile Study. UN-HABITAT, Nairobi. [23] Schmitz, M., Bode, T., Thamm, H.P. and Cremers, A.B. (2007) XULU—A Generic JAVA-Based Platform to Simulate Land Use and Land Cover Change. In: Oxley, L. and Kulasiri, D., Eds., MODSIM 2007 International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand, 2645-2649. [24] Menz, G., Judex, M., Orekan, V., Kuhn, A., Heldmann, M. and Thamm, H.P. (2010) Land Use and Land Cover Modeling in Central Benin. In: Spezh, P., Christoph, M. and Dieckkrüger, B., Eds., Impacts of Global Change on the Hydrological Cycle in West and Northwest Africa, Springer, Heidelberg, 70-73. [25] Pontius Jr., R.G., Huffaker, D. and 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 [26] Jantz, C.A., Goetz, S.J. and 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 [27] Liu, Y. (2008) Modelling Urban Development with Geographical Information Systems and Cellular Automata. CRC Press, Boca Raton. [28] Wolfram, S. (1994) Cellular Automata. In: Wesley, A., Wolfram, S. and Reading, M.A., Eds., Cellular Automata and Complexity: Collected Papers, Westview Press, Boulder, 411-436. [29] Caglioni, M., Pelizzoni, M. and Rabino, G.A. (2006) Urban Sprawl: A Case Study for Project Gigalopolis Using SLEUTH Model. In: El Yacoubi, S., Chopard, B. and Bandini, S., Eds., Cellular Automata, Springer Berlin Heidelberg, Heidelberg, 436-445. http://dx.doi.org/10.1007/11861201_51 [30] Kocabas, V. and Dragicevic, S. (2006) Assessing Cellular Automata Model Behaviour Using a Sensitivity Analysis Approach. Computers, Environment and Urban Systems, 30, 921-953.
http://dx.doi.org/10.1016/j.compenvurbsys.2006.01.001 [31] Han, J., Cao, X. and 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 [32] Visser, H. and de Nijs, T. (2006) The Map Comparison Kit. Environmental Modelling and Software, 21, 346-358. http://dx.doi.org/10.1016/j.envsoft.2004.11.013 [33] Anderson, J.R., Hardy, E.E., Roach, J.T. and Witmer, R.E. (1976) A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey, Washington DC. [34] Mubea, K. and Menz, G. (2012) Monitoring Land-Use Change in Nakuru (Kenya) Using Multi-Sensor Satellite Data. Advances in Remote Sensing, 1, 74-84. http://dx.doi.org/10.4236/ars.2012.13008 [35] Congalton, R.G. and Green, K. (1999) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, Florida. [36] Wasike, W.S. (2001) Road Infrastructure Policies in Kenya: Historical Trends and Current Challenges. KIPPRA Working Paper No. 1. The Kenya Institute for Public Policy Research and Analysis (KIPPRA), Infrastructure and Economic Services Division, Nairobi, 1-42. [37] Hagen, A. (2002) Multi-Method Assessment of Map Similarity. In: Ruiz, M., Gould, M. and Ramon, J., Eds., Proceedings of the 5th AGILE Conference on Geographic Information Science, European Commission, Palma, 171-182. [38] Foody, G.M. (2004) Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy. Photogrammetric Engineering and Remote Sensing, 70, 627-633.
http://dx.doi.org/10.14358/PERS.70.5.627