Understanding urban growth modeling in Africa: Dynamics, drivers, and challenges

Korah, Andrews; Koch, Jennifer A.M.; Wimberly, Michael C.

doi:10.1016/j.cities.2023.104734

Cited by 4 publications

(2 citation statements)

References 118 publications

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“…Urban sprawl is considered a phenomenon that deals with the expansion of auto-oriented development which has a considerable impact on the surrounding ecosystem [3]. As metropolitan areas continue to expand, urban expansion has emerged as a significant issue with far-reaching consequences for sustainable development, the preservation of the environment, and the overall standard of life [4][5][6]. Recognizing and precisely forecasting the land use changes resulting from urban sprawl is essential for successful urban planning and policymaking [7].…”

Section: Introductionmentioning

confidence: 99%

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

2024

Eart & Envi Scie Res & Rev

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With accelerated global urbanization, the expansion of cities has become a pressing concern for urban planners and researchers. The growth and dynamics of urban encroachment are closely tied to changes in land use, especially in urbanized areas. This study seeks to evaluate the accuracy of the CA-Markov (Cellular Automata) model in predicting land use/land cover changes (LULCC) in the Kurunegala district, Sri Lanka as a result of urban expansion. The investigation employs secondary data, including 2007, 2012, 2017, and 2022 Landsat 05, 07, 08, and 09 images respectively. Using software applications such as ArcGIS 10.8, IDRISI 17.0, and MS-Excel 2019, diverse techniques including supervised classification, Markovian transition estimation, and CAMarkov chain analysis, this study attempts to analyze the Urban Growth Modeling for 2027 and 2037 using CA-Markov Chain Multi-criteria Analysis (MCA) under Markov-Markovian Transition Estimator. The study used 32 spatial variables for determining the LULCC. As per the derived results, from 2022 to 2027, urban areas have increased quite markedly. The vegetation cover area has reduced and the areas of water bodies have increased spatially. From 2027 to 2037, the urban area increment is 72.552%. Also, vegetation cover and water body distribution have reduced by 8.051% and 39.91% respectively.

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Section: Introductionmentioning

confidence: 99%

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

2024

Eart & Envi Scie Res & Rev

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“…Urban sprawl is considered a phenomenon that deals with the expansion of auto-oriented development which has a considerable impact on the surrounding ecosystem (Deep & Saklani, 2014). As metropolitan areas continue to expand, urban expansion has emerged as a significant issue with far-reaching consequences for sustainable development (Mallick et al, 2021), the preservation of the environment (Araby, 2002), and the overall standard of life (Korah et al, 2024). Recognizing and precisely forecasting the land use changes resulting from urban sprawl is essential for successful urban planning and policymaking (Mosammam et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

Hasan

2024

Preprint

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With accelerated global urbanization, the expansion of cities has become a pressing concern for urban planners and researchers. The growth and dynamics of urban encroachment are closely tied to changes in land use, especially in urbanized areas. This study seeks to evaluate the accuracy of the CA-Markov (Cellular Automata) model in predicting land use/land cover changes (LULCC) in the Kurunegala district, Sri Lanka as a result of urban expansion. The investigation employs secondary data, including 2007, 2012, 2017, and 2022 Landsat 05, 07, 08, and 09 images respectively. Using software applications such as ArcGIS 10.8, IDRISI 17.0, and MS-Excel 2019, diverse techniques including supervised classification, Markovian transition estimation, and CA-Markov chain analysis, this study attempts to analyze the Urban Growth Modeling for 2027 and 2037 using CA-Markov Chain Multi-criteria Analysis (MCA) under Markov-Markovian Transition Estimator. The study used 32 spatial variables for determining the LULCC. As per the derived results, from 2022 to 2027, urban areas have increased quite markedly. The vegetation cover area has reduced and the areas of water bodies have increased spatially. From 2027 to 2037, the urban area increment is 72.552%. Also, vegetation cover and water body distribution have reduced by 8.051% and 39.91% respectively.

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Smaller cities have large impacts on West Africa's expanding urban system

Korah,

Wimberly

2024

Sustainable Cities and Society

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Understanding urban growth modeling in Africa: Dynamics, drivers, and challenges

Cited by 4 publications

References 118 publications

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

Smaller cities have large impacts on West Africa's expanding urban system

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