IDEAS home Printed from https://ideas.repec.org/a/eee/lauspo/v99y2020ics0264837720309583.html
   My bibliography  Save this article

Assessing urban growth in Ghana using machine learning and intensity analysis: A case study of the New Juaben Municipality

Author

Listed:
  • Nyamekye, Clement
  • Kwofie, Samuel
  • Ghansah, Benjamin
  • Agyapong, Emmanuel
  • Boamah, Linda Appiah

Abstract

Population growth coupled with economic, housing and environmental factors have significantly contributed into accelerated land use change in the New Juaben Municipality of Ghana. These factors have caused destruction of natural habitat and increased natural hazards such as flooding in the Municipality. Monitoring land use/land cover change is essential in respect to the dynamics of both human and natural factors that affect the biophysical and biochemical properties of the land surface. This research investigates the transitions among the major land use/land cover categories in the Municipality as a highly populated urban region that is facing some environmental challenges such as deforestation and degradation of the environment. Random Forest was adopted for the classification of 1985, 1991, 2002 and 2015 land cover maps while the analysis of the dynamics was conducted using intensity analysis. The unique contribution of this article is the combine usage of machine learning algorithm and intensity analysis to assess the changes in land use/land cover. The results showed that 1985–1991 and 2002–2015 periods experience fast change and the land use transformation has been accelerating over the whole period. The major changes were caused by the Built-up and Agricultural activities constituting 21.24 % and 13.19 % respectively in the category level. It is recommended that, authorities should consider several structural transformation measures within Ghana, including inter-sectoral land use harmonization policies (e.g. the Land Use and Spatial Planning Act 2016), land use planning and legal reforms to help address the underlying drivers of urban led deforestation.

Suggested Citation

  • Nyamekye, Clement & Kwofie, Samuel & Ghansah, Benjamin & Agyapong, Emmanuel & Boamah, Linda Appiah, 2020. "Assessing urban growth in Ghana using machine learning and intensity analysis: A case study of the New Juaben Municipality," Land Use Policy, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:lauspo:v:99:y:2020:i:c:s0264837720309583
    DOI: 10.1016/j.landusepol.2020.105057
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264837720309583
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.landusepol.2020.105057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ali Kourosh Niya & Jinliang Huang & Hazhir Karimi & Hamidreza Keshtkar & Babak Naimi, 2019. "Use of Intensity Analysis to Characterize Land Use/Cover Change in the Biggest Island of Persian Gulf, Qeshm Island, Iran," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    2. Robert Gilmore Pontius & Yan Gao & Nicholas M. Giner & Takashi Kohyama & Mitsuru Osaki & Kazuyo Hirose, 2013. "Design and Interpretation of Intensity Analysis Illustrated by Land Change in Central Kalimantan, Indonesia," Land, MDPI, vol. 2(3), pages 1-19, July.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Yilmazer, Seckin & Kocaman, Sultan, 2020. "A mass appraisal assessment study using machine learning based on multiple regression and random forest," Land Use Policy, Elsevier, vol. 99(C).
    5. Mads Christensen & Jamal Jokar Arsanjani, 2020. "Stimulating Implementation of Sustainable Development Goals and Conservation Action: Predicting Future Land Use/Cover Change in Virunga National Park, Congo," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    6. Qu, Shijin & Hu, Shougeng & Li, Weidong & Wang, Hui & Zhang, Chuanrong & Li, Quanfeng, 2020. "Interaction between urban land expansion and land use policy: An analysis using the DPSIR framework," Land Use Policy, Elsevier, vol. 99(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jules Degila & Ida Sèmévo Tognisse & Anne-Carole Honfoga & Sèton Calmette Ariane Houetohossou & Fréjus Ariel Kpedetin Sodedji & Hospice Gérard Gracias Avakoudjo & Souand Peace Gloria Tahi & Achille Ep, 2023. "A Survey on Digital Agriculture in Five West African Countries," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    2. Changchun Feng & Hao Zhang & Liang Xiao & Yongpei Guo, 2022. "Land Use Change and Its Driving Factors in the Rural–Urban Fringe of Beijing: A Production–Living–Ecological Perspective," Land, MDPI, vol. 11(2), pages 1-18, February.
    3. Dadirai Matarira & Onisimo Mutanga & Maheshvari Naidu & Terence Darlington Mushore & Marco Vizzari, 2023. "Characterizing Informal Settlement Dynamics Using Google Earth Engine and Intensity Analysis in Durban Metropolitan Area, South Africa: Linking Pattern to Process," Sustainability, MDPI, vol. 15(3), pages 1-20, February.
    4. Iana Rufino & Slobodan Djordjević & Higor Costa de Brito & Priscila Barros Ramalho Alves, 2021. "Multi-Temporal Built-Up Grids of Brazilian Cities: How Trends and Dynamic Modelling Could Help on Resilience Challenges?," Sustainability, MDPI, vol. 13(2), pages 1-21, January.
    5. Ze Zhou & Bin Quan & Zhiwei Deng, 2023. "Effects of Land Use Changes on Ecosystem Service Value in Xiangjiang River Basin, China," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    6. Sun, Jianing & Zhou, Tao & Wang, Di, 2022. "Relationships between urban form and air quality: A reconsideration based on evidence from China’s five urban agglomerations during the COVID-19 pandemic," Land Use Policy, Elsevier, vol. 118(C).
    7. Andrea Urgilez-Clavijo & David Rivas-Tabares & Anne Gobin & Juan de la Riva, 2024. "Comprehensive Framework for Analysing the Intensity of Land Use and Land Cover Change in Continental Ecuadorian Biosphere Reserves," Sustainability, MDPI, vol. 16(4), pages 1-21, February.
    8. Xiaofang Sun & Guicai Li & Junbang Wang & Meng Wang, 2021. "Quantifying the Land Use and Land Cover Changes in the Yellow River Basin while Accounting for Data Errors Based on GlobeLand30 Maps," Land, MDPI, vol. 10(1), pages 1-18, January.
    9. Zhiwei Deng & Bin Quan, 2022. "Intensity Characteristics and Multi-Scenario Projection of Land Use and Land Cover Change in Hengyang, China," IJERPH, MDPI, vol. 19(14), pages 1-18, July.
    10. Adjei-Poku, Bernard & Afrane, Samuel K. & Amoako, Clifford & Inkoom, Daniel K.B., 2023. "Customary land ownership and land use change in Kumasi: An issue of chieftaincy sustenance?," Land Use Policy, Elsevier, vol. 125(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dadirai Matarira & Onisimo Mutanga & Maheshvari Naidu & Terence Darlington Mushore & Marco Vizzari, 2023. "Characterizing Informal Settlement Dynamics Using Google Earth Engine and Intensity Analysis in Durban Metropolitan Area, South Africa: Linking Pattern to Process," Sustainability, MDPI, vol. 15(3), pages 1-20, February.
    2. Valentin Ouedraogo & Kwame Oppong Hackman & Michael Thiel & Jaiye Dukiya, 2023. "Intensity Analysis for Urban Land Use/Land Cover Dynamics Characterization of Ouagadougou and Bobo-Dioulasso in Burkina Faso," Land, MDPI, vol. 12(5), pages 1-20, May.
    3. Xiaofang Sun & Guicai Li & Junbang Wang & Meng Wang, 2021. "Quantifying the Land Use and Land Cover Changes in the Yellow River Basin while Accounting for Data Errors Based on GlobeLand30 Maps," Land, MDPI, vol. 10(1), pages 1-18, January.
    4. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    5. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    6. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    7. Shuangqing Sheng & Wei Song & Hua Lian & Lei Ning, 2022. "Review of Urban Land Management Based on Bibliometrics," Land, MDPI, vol. 11(11), pages 1-25, November.
    8. Lin Meng & Wentao Si, 2022. "The Driving Mechanism of Urban Land Expansion from 2005 to 2018: The Case of Yangzhou, China," IJERPH, MDPI, vol. 19(23), pages 1-14, November.
    9. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    10. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    11. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    12. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    14. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    15. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    16. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    17. Natalia Pardo-Lorente & Anestis Gkanogiannis & Luca Cozzuto & Antoni Gañez Zapater & Lorena Espinar & Ritobrata Ghose & Jacqueline Severino & Laura García-López & Rabia Gül Aydin & Laura Martin & Mari, 2024. "Nuclear localization of MTHFD2 is required for correct mitosis progression," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    18. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    19. Zander S. Venter & Adam Sadilek & Charlotte Stanton & David N. Barton & Kristin Aunan & Sourangsu Chowdhury & Aaron Schneider & Stefano Maria Iacus, 2021. "Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis," IJERPH, MDPI, vol. 18(23), pages 1-12, November.
    20. Zhiwei Deng & Bin Quan, 2022. "Intensity Characteristics and Multi-Scenario Projection of Land Use and Land Cover Change in Hengyang, China," IJERPH, MDPI, vol. 19(14), pages 1-18, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:lauspo:v:99:y:2020:i:c:s0264837720309583. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joice Jiang (email available below). General contact details of provider: https://www.journals.elsevier.com/land-use-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.