Research Article | | Peer-Reviewed

Use of Geo-Information Technologies in Predicting Urban Growth Trends; An Integrated Simulation Approach: The Case Study of Limuru Central Ward

Received: 22 August 2024     Accepted: 26 November 2024     Published: 13 December 2024
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Abstract

Urban areas exhibit different growth patterns spanning from linear development, transit-oriented development, concentric zonal development to multi-nuclei development patterns. In the world we live in today, main urban areas present themselves as Central Business Districts (CBDs), that double up as mixed use commercial and residential areas, which serve most of the population who live in and around them. Ideally, the CBD sites – for most cities around the world, were identified in advance, making it easier for the local authorities to demarcate and plan for sustainable development. Most, if not all jobs, are in these urban areas, making these employment areas urban growth hotspots. Changes in economic processes and evolution of transport networks are the foundation of urban growth and expansion, in that, there is a shift from functional specialization of the CBD to economic specialization of the surrounding urban areas, as in the case of Rhine Main Region in Germany. In Kenya, most of the known urban areas, like Limuru Town, emerged as traditional markets in the 1900’s and grew to modern urban areas and municipalities. Urban growth in Limuru was propelled by the existence of modern infrastructure, reduced land rates, presence of government facilities, security, water and employment from the nearby tea farms and factories. However, urban growth has been accompanied by rapid land use changes and sporicidal growth of informal settlements. As a result, urban areas growing in Limuru Central Ward, are deprived of basic infrastructure, public purpose facilities, land use harmonization and spatial synergies. This study therefore attempts to explore the use of GIS and Remote sensing technologies in observing past and present urban growth trends, that pave the way for predicting sustainable urban planning. The findings from this study are expected to contribute to the knowledge of simulating how urban centers can be planned in the present to cater for the future needs of the growing population. Predicting urban growth trends introduces more practical ways of spatial planning and policy development in developing countries, through spatial analysis and modelling using GIS and Remote Sensing technologies.

Published in Urban and Regional Planning (Volume 9, Issue 4)
DOI 10.11648/j.urp.20240904.13
Page(s) 146-161
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), 2024. Published by Science Publishing Group

Keywords

Urban Areas, Simulation, Prediction, Urban Development

References
[1] Al-Darwish, Y., Ayad, H., Taha, D., & Saadallah, D. (2018). Predicting the future urban growth and it’s impacts on the surrounding environment using urban simulation models: Case study of Ibb city – Yemen. Alexandria Engineering Journal, 57(4), 2887–2895.
[2] Badiane, A., Yachan, A., Tebbal, F., Augustinus, C., Halfani, M., Kiwala, L., Moreno, E., Tuts, R., Gebede, G., & Mboup, G. (2014). Participatory Slum Upgrading Programme in the African, Caribbean and Pacific Countries.
[3] BAKER, B. H., MITCHELL, J. G., & WILLIAMS, L. A. J. (1988). Stratigraphy, geochronology and volcano-tectonic evolution of the Kedong–Naivasha–Kinangop region, Gregory Rift Valley, Kenya. Journal of the Geological Society, 145(1), 107–116.
[4] Clark Labs. (2018). IDRISI GIS Analysis.
[5] CREAF. (2016). GIS and remote sensing methodologies and applications | CREAF. http://www.creaf.cat/earth-observation/gis-and-remote-sensing-methodologies-and-applications
[6] Department of the Interior U. S. Geological Survey (USGS). (2018). Landsat Surface Temperatute (ST) Product Guide.
[7] Dinda, S., Das Chatterjee, N., & Ghosh, S. (2021). An integrated simulation approach to the assessment of urban growth pattern and loss in urban green space in Kolkata, India: A GIS-based analysis. Ecological Indicators, 121, 107178.
[8] FAO. (n.d.). Geographical information systems and remote sensing in inland fisheries and aquaculture. Retrieved 9 August 2021, from
[9] Growe, A. (2012). Emerging polycentric city-regions in Germany. Regionalisation of economic activities in metropolitan regions. Erdkunde, 66(4), 295–311.
[10] Güneralp, B., Lwasa, S., Masundire, H., Parnell, S., & Seto, K. C. (2018). Urbanization in Africa: Challenges and opportunities for conservation. Environmental Research Letters, 13(1), 015002.
[11] Hawkins, R., Ang, J., Crispi, G., Siegel, Y., Abdullahi, S., Ambwere, S., & Mcgill, R. (2018). Urban Planning for city leaders a handbook for Kenya Urban Planning for City Leaders: A Handbook for Kenya UN-Habitat Support to Sustainable Urban Development in Kenya. In ISBN.
[12] Independent Electoral and Boundaries Commission (IEBC). (2012). THE REVISED PRELIMINARY REPORT OF THE PROPOSED BOUNDARIES OF CONSTITUENCIES AND WARDS VOLUME 1.
[13] Interim Independent Boundaries Review Commission (IIBRC). (2010). The Report of the Interim Independent Boundaries Review Commission (IIBRC) Delimitation of Constituencies and Recommendations on Local Authority Electoral Units and Administrative Boundaries for Districts and Other Units.
[14] JICA. (2018). Republic of Kenya Nairobi City County Government REPUBLIC OF KENYA THE PROJECT ON DETAILED PLANNING OF INTEGRATED TRANSPORT SYSTEM AND LOOP LINE IN THE NAIROBI URBAN CORE FINAL REPORT.
[15] Landsat Missions (USGS). (n.d.). Landsat Known Issues | U.S. Geological Survey. Retrieved 4 December 2022, from
[16] Majeed, F. A., & Abaas, Z. R. (2023). Applications of ecological theory in the urban environment. AIP Conference Proceedings, 2651.
[17] Mundia, C. N., & Aniya, M. (2007). Modeling and predicting urban growth of Nairobi City using cellular automata with geographical information systems. Geographical Review of Japan, 80(12), 279–290.
[18] Nairobi Urban Study Group. (1973). Nairobi- Metropolitan Growth Strategy: Volume 1- Main Report.
[19] Nong, D. H., Lepczyk, C. A., Miura, T., & Fox, J. M. (2018). Quantifying urban growth patterns in Hanoi using landscape expansion modes and time series spatial metrics. PLoS ONE, 13(5).
[20] Repo1t, R., & Vitian, K. (1975). Appraisal of a Not for Public Use FILE COPY Document of the International Bank for Reconstruction and Development International Development Association.
[21] Saeidi, S., Mirkarimi, S. H., Mohammadzadeh, M., Salmanmahiny, A., & Arrowsmith, C. (2017). Designing an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes. 33(12), 1381–1397.
[22] Saggerson, E. R. (1991). GEOLOGY OF THE NAIROBI AREA.
[23] Survey of Kenya. (n.d.). Kenya Counties Map. Retrieved 12 January 2023, from
[24] Tredinnick, K. (2014). Urban Growth Models of Urban Development.
[25] UN. (2018). World Urbanization Prospects The 2018 Revision.
[26] UN HABITAT. (2017). Country Programme for Ethiopia 2016-2020.
[27] Wang, R., Hou, H., Murayama, Y., & Derdouri, A. (2020). Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China. Remote Sensing, 12(3), 440.
[28] Yen, B. T. H., Feng, C. M., & Lee, T. C. (2023). Transit-oriented development strategy in Taiwan: An application of land value capture. Asian Transport Studies, 9, 100094.
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  • APA Style

    Gichuki, I. N., Imwati, A. T. (2024). Use of Geo-Information Technologies in Predicting Urban Growth Trends; An Integrated Simulation Approach: The Case Study of Limuru Central Ward. Urban and Regional Planning, 9(4), 146-161. https://doi.org/10.11648/j.urp.20240904.13

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    ACS Style

    Gichuki, I. N.; Imwati, A. T. Use of Geo-Information Technologies in Predicting Urban Growth Trends; An Integrated Simulation Approach: The Case Study of Limuru Central Ward. Urban Reg. Plan. 2024, 9(4), 146-161. doi: 10.11648/j.urp.20240904.13

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    AMA Style

    Gichuki IN, Imwati AT. Use of Geo-Information Technologies in Predicting Urban Growth Trends; An Integrated Simulation Approach: The Case Study of Limuru Central Ward. Urban Reg Plan. 2024;9(4):146-161. doi: 10.11648/j.urp.20240904.13

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  • @article{10.11648/j.urp.20240904.13,
      author = {Ivy Njeri Gichuki and Andrew Thiaine Imwati},
      title = {Use of Geo-Information Technologies in Predicting Urban Growth Trends; An Integrated Simulation Approach: The Case Study of Limuru Central Ward
    },
      journal = {Urban and Regional Planning},
      volume = {9},
      number = {4},
      pages = {146-161},
      doi = {10.11648/j.urp.20240904.13},
      url = {https://doi.org/10.11648/j.urp.20240904.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.urp.20240904.13},
      abstract = {Urban areas exhibit different growth patterns spanning from linear development, transit-oriented development, concentric zonal development to multi-nuclei development patterns. In the world we live in today, main urban areas present themselves as Central Business Districts (CBDs), that double up as mixed use commercial and residential areas, which serve most of the population who live in and around them. Ideally, the CBD sites – for most cities around the world, were identified in advance, making it easier for the local authorities to demarcate and plan for sustainable development. Most, if not all jobs, are in these urban areas, making these employment areas urban growth hotspots. Changes in economic processes and evolution of transport networks are the foundation of urban growth and expansion, in that, there is a shift from functional specialization of the CBD to economic specialization of the surrounding urban areas, as in the case of Rhine Main Region in Germany. In Kenya, most of the known urban areas, like Limuru Town, emerged as traditional markets in the 1900’s and grew to modern urban areas and municipalities. Urban growth in Limuru was propelled by the existence of modern infrastructure, reduced land rates, presence of government facilities, security, water and employment from the nearby tea farms and factories. However, urban growth has been accompanied by rapid land use changes and sporicidal growth of informal settlements. As a result, urban areas growing in Limuru Central Ward, are deprived of basic infrastructure, public purpose facilities, land use harmonization and spatial synergies. This study therefore attempts to explore the use of GIS and Remote sensing technologies in observing past and present urban growth trends, that pave the way for predicting sustainable urban planning. The findings from this study are expected to contribute to the knowledge of simulating how urban centers can be planned in the present to cater for the future needs of the growing population. Predicting urban growth trends introduces more practical ways of spatial planning and policy development in developing countries, through spatial analysis and modelling using GIS and Remote Sensing technologies.
    },
     year = {2024}
    }
    

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    AB  - Urban areas exhibit different growth patterns spanning from linear development, transit-oriented development, concentric zonal development to multi-nuclei development patterns. In the world we live in today, main urban areas present themselves as Central Business Districts (CBDs), that double up as mixed use commercial and residential areas, which serve most of the population who live in and around them. Ideally, the CBD sites – for most cities around the world, were identified in advance, making it easier for the local authorities to demarcate and plan for sustainable development. Most, if not all jobs, are in these urban areas, making these employment areas urban growth hotspots. Changes in economic processes and evolution of transport networks are the foundation of urban growth and expansion, in that, there is a shift from functional specialization of the CBD to economic specialization of the surrounding urban areas, as in the case of Rhine Main Region in Germany. In Kenya, most of the known urban areas, like Limuru Town, emerged as traditional markets in the 1900’s and grew to modern urban areas and municipalities. Urban growth in Limuru was propelled by the existence of modern infrastructure, reduced land rates, presence of government facilities, security, water and employment from the nearby tea farms and factories. However, urban growth has been accompanied by rapid land use changes and sporicidal growth of informal settlements. As a result, urban areas growing in Limuru Central Ward, are deprived of basic infrastructure, public purpose facilities, land use harmonization and spatial synergies. This study therefore attempts to explore the use of GIS and Remote sensing technologies in observing past and present urban growth trends, that pave the way for predicting sustainable urban planning. The findings from this study are expected to contribute to the knowledge of simulating how urban centers can be planned in the present to cater for the future needs of the growing population. Predicting urban growth trends introduces more practical ways of spatial planning and policy development in developing countries, through spatial analysis and modelling using GIS and Remote Sensing technologies.
    
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Author Information
  • Department, of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Kiambu, Kenya

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

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