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Geographical Information System and Multi-criteria Based Cellular Automata Markov Model for Urban Growth and Analysis the Case Study of Bungoma Town Kenya

Received: 9 January 2019     Accepted: 11 February 2019     Published: 11 March 2019
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Abstract

Increase in population and the desire to seek new opportunities has contributed to urban growth, putting pressure on facilities in urban centres. Bungoma town, being the headquarter of Bungoma County has undergone radical changes in its physical form, not only in territorial expansion, but also through internal physical transformation. In the process of urbanization, physical characteristic of the town is gradually changing as cropland (agricultural land), vegetation and wetland has been converted to built-up areas. This new urban fabric needs to be analysed to understand the impact of these changes. The aim of this research was to evaluate the suitability of Bungoma town setting, model its growth and predict the future growth of the town based on land cover changes (1985-2015). Landsat satellite images were classified with five land cover classes followed by change detection. To simulate land cover map for Bungoma town in 2030, Markov Chain model and Cellular Automata Markov (CA-Markov) model were used. It was found that built-up area increased over the study period. The major contributors to this change are cropland, vegetation and wetland land cover types. The CA-Markov model results showed that 52% of the total study area will be converted into built-up area, 19% to cropland, 20% to vegetation, 5% to open spaces and 3% to wetland by 2030. This would have negative implication on food security in the region which is a major source of income for the inhabitants. There is need therefore for proper land use planning in the area. In addition, vertical urban development should be encouraged to control rapid expansion of the town.

Published in American Journal of Environmental Science and Engineering (Volume 3, Issue 1)
DOI 10.11648/j.ajese.20190301.14
Page(s) 22-30
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

CA-Markov, Simulate, Suitability

References
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[15] Musa, S. I., M. Hashim, & M. N. M. Reba. (2017) “A Review of Geospatial-Based Urban GrowthModels and Modelling Initiatives.” Geocarto International 32 (8): 813833. doi: 10.1080/10106049.2016.1213891.
[16] Pontius, R., G., & M., Millones. (2011). “Death to Kappa: Birth of Quantity Disagreement And Allocation Disagreement for Accuracy Assessment.” International Journal of Remote Sensing 32 (15): 4407–4429. doi:10.1080/01431161.2011.552923.
[17] Townroe, S., & A. Callaghan. 2014. “British Container Breeding Mosquitoes: The Impact of Urbanisation and Climate Change on Community Composition and Phenology.” PLOS ONE 9 (4): 1–7. doi: 10.1371/journal.pone.0095325.
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    Morris Barasa Waswa, Andrew Thiaine Imwati. (2019). Geographical Information System and Multi-criteria Based Cellular Automata Markov Model for Urban Growth and Analysis the Case Study of Bungoma Town Kenya. American Journal of Environmental Science and Engineering, 3(1), 22-30. https://doi.org/10.11648/j.ajese.20190301.14

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    ACS Style

    Morris Barasa Waswa; Andrew Thiaine Imwati. Geographical Information System and Multi-criteria Based Cellular Automata Markov Model for Urban Growth and Analysis the Case Study of Bungoma Town Kenya. Am. J. Environ. Sci. Eng. 2019, 3(1), 22-30. doi: 10.11648/j.ajese.20190301.14

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    AMA Style

    Morris Barasa Waswa, Andrew Thiaine Imwati. Geographical Information System and Multi-criteria Based Cellular Automata Markov Model for Urban Growth and Analysis the Case Study of Bungoma Town Kenya. Am J Environ Sci Eng. 2019;3(1):22-30. doi: 10.11648/j.ajese.20190301.14

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  • @article{10.11648/j.ajese.20190301.14,
      author = {Morris Barasa Waswa and Andrew Thiaine Imwati},
      title = {Geographical Information System and Multi-criteria Based Cellular Automata Markov Model for Urban Growth and Analysis the Case Study of Bungoma Town Kenya},
      journal = {American Journal of Environmental Science and Engineering},
      volume = {3},
      number = {1},
      pages = {22-30},
      doi = {10.11648/j.ajese.20190301.14},
      url = {https://doi.org/10.11648/j.ajese.20190301.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20190301.14},
      abstract = {Increase in population and the desire to seek new opportunities has contributed to urban growth, putting pressure on facilities in urban centres. Bungoma town, being the headquarter of Bungoma County has undergone radical changes in its physical form, not only in territorial expansion, but also through internal physical transformation. In the process of urbanization, physical characteristic of the town is gradually changing as cropland (agricultural land), vegetation and wetland has been converted to built-up areas. This new urban fabric needs to be analysed to understand the impact of these changes. The aim of this research was to evaluate the suitability of Bungoma town setting, model its growth and predict the future growth of the town based on land cover changes (1985-2015). Landsat satellite images were classified with five land cover classes followed by change detection. To simulate land cover map for Bungoma town in 2030, Markov Chain model and Cellular Automata Markov (CA-Markov) model were used. It was found that built-up area increased over the study period. The major contributors to this change are cropland, vegetation and wetland land cover types. The CA-Markov model results showed that 52% of the total study area will be converted into built-up area, 19% to cropland, 20% to vegetation, 5% to open spaces and 3% to wetland by 2030. This would have negative implication on food security in the region which is a major source of income for the inhabitants. There is need therefore for proper land use planning in the area. In addition, vertical urban development should be encouraged to control rapid expansion of the town.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Geographical Information System and Multi-criteria Based Cellular Automata Markov Model for Urban Growth and Analysis the Case Study of Bungoma Town Kenya
    AU  - Morris Barasa Waswa
    AU  - Andrew Thiaine Imwati
    Y1  - 2019/03/11
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajese.20190301.14
    DO  - 10.11648/j.ajese.20190301.14
    T2  - American Journal of Environmental Science and Engineering
    JF  - American Journal of Environmental Science and Engineering
    JO  - American Journal of Environmental Science and Engineering
    SP  - 22
    EP  - 30
    PB  - Science Publishing Group
    SN  - 2578-7993
    UR  - https://doi.org/10.11648/j.ajese.20190301.14
    AB  - Increase in population and the desire to seek new opportunities has contributed to urban growth, putting pressure on facilities in urban centres. Bungoma town, being the headquarter of Bungoma County has undergone radical changes in its physical form, not only in territorial expansion, but also through internal physical transformation. In the process of urbanization, physical characteristic of the town is gradually changing as cropland (agricultural land), vegetation and wetland has been converted to built-up areas. This new urban fabric needs to be analysed to understand the impact of these changes. The aim of this research was to evaluate the suitability of Bungoma town setting, model its growth and predict the future growth of the town based on land cover changes (1985-2015). Landsat satellite images were classified with five land cover classes followed by change detection. To simulate land cover map for Bungoma town in 2030, Markov Chain model and Cellular Automata Markov (CA-Markov) model were used. It was found that built-up area increased over the study period. The major contributors to this change are cropland, vegetation and wetland land cover types. The CA-Markov model results showed that 52% of the total study area will be converted into built-up area, 19% to cropland, 20% to vegetation, 5% to open spaces and 3% to wetland by 2030. This would have negative implication on food security in the region which is a major source of income for the inhabitants. There is need therefore for proper land use planning in the area. In addition, vertical urban development should be encouraged to control rapid expansion of the town.
    VL  - 3
    IS  - 1
    ER  - 

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Author Information
  • Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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