Modelling and Forecasting Urban Population Growth in Nigeria Using Autoregressive Integrated Moving Average (ARIMA) Models
Keywords:
ARIMA model, Box-Jenkins method, differencing, Nigeria, unit root test, urban population growth
Abstract
We used the univariate autoregressive integrated moving average (ARIMA) model with the Box and Jenkins method to model and predict the yearly urban population growth rate in Nigeria from 2023 to 2030, using data spanning from 1961 to 2022. We checked the stationarity of the data obtained from the World Development Indicators using time plots, ACF, PACF, and unit root tests. Initially, the plot and test results suggest non-stationarity in the data; however, after first differencing, it becomes stationary, indicating integration of order one. The combination of the ACF and PACF plots, as well as our judgment and expertise, informed our decision about the AR and MA components used in our model choice. After applying selection criteria like loglikelihood, AIC, and BIC to compare all the results of the fitted ARIMA models, we identified the ARIMA (1, 1, 10) model as the most suitable model. The diagnostic tests carried out also confirm that the residuals of the model are normally distributed and uncorrelated. The model was confirmed to be stable and highly accurate based on the result of the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), and a forecast of the annual urban population growth in Nigeria for a period of 8 years from 2023 to 2030 was made. We discovered a consistent rise in urban population growth in Nigeria throughout the forecast period, which presents both challenges and opportunities for Nigerian cities. Therefore, strategic efforts to accommodate the anticipated population surge and foster sustainable urban development in Nigeria should be enacted.
References
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Kumar, M., & Anand, M. (2014). An application of time series ARIMA forecasting model for predicting sugarcane production in India. Studies in Business and Economics, 9(1), 81-94.
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Agbola, T. (Ed.). (2004). Readings in urban and regional planning. Macmillan Nigeria Publishers Limited.
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Anam, B. E., Ironbar, V. E., Ijim, U. A., Abang, P. O., Duke, O. O., Ojong, F. E., Eba, M.-B. A., Uzoh, E. E.-C., Okorn, F. T., & Bisong, B. D. (2024). Assessing corporate social responsibility and poverty reduction programmes in the emerging markets. Corporate & Business Strategy Review, 5(1), 127–136. https://doi.org/10.22495/cbsrv5i1art13
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Box, G. P. E., & Jenkins, M. (1970). Time series analysis: Forecasting and Control (Revised Edition). Holden Day, San Francisco.
Clement, E. P. (2014). Using normalized bayesian information criterion (BIC) to improve box-jenkins model building. American Journal of Mathematics and Statistics, 4(5), 214-221. https://doi.org/10.5923/j.ajms.20140405.02
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Esowe, I. (2021, January 22). Trend in Nigerian population growth. BusinessDay. Retrieved August 9, 2024, from https://businessday.ng/research-post/article/trend-in-nigerian-population-growth/
Estoque, L. N. C., Fuente, L. M. D. D., Mabborang, R. C., & Molina, M. G. (2022). Forecasting Urban Population Growth in the Philippines Using Autoregressive Integrated Moving Average (ARIMA) Model. EPRA International Journal of Multidisciplinary Research (IJMR), 8(7), 132-153. https://doi.org/10.36713/epra2013
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Fox, S., Bloch, R., & Monroy, J. (2018). Understanding the dynamics of Nigeria’s urban transition: A refutation of the ‘stalled urbanisation’ hypothesis. Urban Studies, 55(5), 947-964. https://doi.org/10.1177/0042098017712688
Gbadamosi, K., & Akanmu, A. (2023). Food security and population growth in Nigeria: The urban agriculture perspective. In Nigeria perspective to population dynamics and sustainable development (Chapter 4). Department of Geography, University of Ilorin.
Gizelis, T. I., Pickering, S., & Urdal, H. (2021). Conflict on the urban fringe: Urbanization, environmental stress, and urban unrest in Africa. Political Geography, 86, 102357. https://doi.org/10.1016/j.polgeo.2021.102357
Gunawan, D., & Febrianti, I. (2023). Ethereum value forecasting model using autoregressive integrated moving average (ARIMA). International Journal of Advances in Social Sciences and Humanities, 2(1), 29–35. https://doi.org/10.56225/ijassh.v2i1.151
Hassan, S. M. (2023). Modelling and Forecasting Somalia GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. East African Journal of Business and Economics, 6(1), 255-264. https://doi.org/10.37284/eajbe.6.1.1356
Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022
Ibrahim, A., Sani, U. M., & Olokojo, V. O. (2023). Forecasting consumer price index and exchange rate using ARIMA models: Empirical evidence from Nigeria. FUDMA Journal of Sciences, 6(6), 114–124. https://doi.org/10.33003/fjs-2022-0606-1136
Jiang, S., Zhang, Z., Ren, H., Wei, G., Xu, M., & Liu, B. (2021). Spatiotemporal characteristics of urban land expansion and population growth in Africa from 2001 to 2019: Evidence from population density data. ISPRS International Journal of Geo-Information, 10(9), 584. https://doi.org/10.3390/ijgi10090584
Kafle, S. C., & Hooda, E. (2023). ARIMA and Exponential Smoothing Model to Forecast Average Annual Precipitation in Bharatpur, Nepal. BMC Journal of Scientific Research, 6(1), 113–125.https://doi.org/10.3126/bmcjsr.v6i1.60959
Koko, A.F., Yue, W., Abubakar, G.A., Hamed, R., & Alabsi, A. A. (2021). Analyzing urban growth and land cover change scenario in Lagos, Nigeria using multi-temporal remote sensing data and GIS to mitigate flooding. Geomatics, Natural Hazards and Risk, 12(1), 631-652. https://doi.org/10.1080/19475705.2021.1887940
Kuddus, M. A., Tynan, E., & McBryde, E. (2020). Urbanization: A problem for the rich and the poor? Public Health Reviews, 41, 1. https://doi.org/10.1186/s40985-019-0116-0
Kuhe, D. A., & Egemba, R. C. (2016). Modelling and forecasting CPI inflation in Nigeria: Application of autoregressive integrated moving average homoskedastic model. Journal of Scientific and engineering Research, 3(2), 57-66.
Kumar, M., & Anand, M. (2014). An application of time series ARIMA forecasting model for predicting sugarcane production in India. Studies in Business and Economics, 9(1), 81-94.
Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butter-worth-Heinemann: London, UK.
Lu, Y., & AbouRizk, S. M. (2009). Automated Box–Jenkins forecasting modelling. Automation in Construction, 18(5), 547–558. https://doi.org/10.1016/j.autcon.2008.11.007
Mahtta, R., Fragkias, M., Güneralp, B., Mahendra, A., Reba, M., Wentz, E. A., & Seto, K. C. (2022). Urban land expansion: the role of population and economic growth for 300+ cities. Npj Urban Sustainability, 2(1), 5. https://doi.org/10.1038/s42949-022-00048-y
Mabogunje, et al. (1978). Shelter Provision in Developing Countries. Surrey: The Gresham Press
Mashi, S., Chup, D., & Agoha, S. (2021). Influence of urban growth on land use/cover in Umuahia, Abia State, Nigeria. Journal of Geography, Environment and Earth Science, 12, 1–14.
National Bureau of Statistics. (2021). Demographic statistical bulletin. A publication of Demographic Statistics Division.
Noor, T. H., Almars, A. M., Alwateer, M., Almaliki, M., Gad, I., & Atlam, E.-S. (2022). SARIMA: A seasonal autoregressive integrated moving average model for crime analysis in Saudi Arabia. Electronics, 11(23), 3986. https://doi.org/10.3390/electronics11233986
Nyoni, T. (2018). Box – Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe. Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87737.
Poudel, O. (2023). Relationship between Defense Expenditure and Economic Growth in Nepal. Unity Journal, 4(1), 208–226. https://doi.org/10.3126/unityj.v4i01.52242
Ruggles, S., & Magnuson, D. L. (2020). Census technology, politics, and institutional change, 1790–2020. Journal of American History, 107(1), 19-51.
Susanti, R., & Adji, A. R. (2020). Analisis Peramalan Ihsg Dengan Time Series Modeling Arima. Jurnal Manajemen Kewirausahaan, 17(1), 97-106.
Ukwayi, J. K., & Okpa, J. T. (2018). Socio-demographic factors affecting the safety of lives and properties in Calabar, Cross River State, Nigeria. Multi-Disciplinary Journal of Research and Development Perspectives, 7(1), 23-25.
UNHCR. (2022, October 28). Millions face harm from flooding across West and Central Africa, UNHCR warns [Press briefing]. Retrieved June 25, 2024, from https://www.unhcr.org/news/briefing-notes/millions-face-harm-flooding-across-west-and-central-africa-unhcr-warns
UN-HABITAT. (2006). National trends in housing – Production practices Volume 4: Nigeria. United Nations Centre for Human Settlements. ISBN 9211312345, 9789211312348.
United Nations Department of Economic and Social Affairs, Population Division. (2014). World urbanization prospects. In Statistical Papers - United Nations (Ser. A), Population and Vital Statistics Report. United Nations. https://doi.org/10.18356/527e5125-en
United Nations Department of Economic and Social Affairs, Population Division. (2019). World urbanization prospects: The 2018 revision. https://doi.org/10.18356/b9e995fe-en
United Nations Department of Economic and Social Affairs, Population Division. (2022). World population prospects: The 2022 revision. Elaboration of data by Worldometer. Retrieved from https://www.worldometers.info/world-population/population-by-country/
United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition. https://doi.org/10.18356/9789213586440
URBANET. (2018, October 23). Infographics: Urbanisation and urban development in Nigeria. Housing and Construction, Integrated Planning. Retrieved June 19, 2024, from https://www.urbanet.info/urbanization-in-nigeria-infographics/
Usman, A., Sulaiman, M. A., & Abubakar, I. (2019). Trend of neonatal mortality in Nigeria from 1990 to 2017 using time series analysis. Journal of Applied Sciences and Environmental Management, 23(5), 865-869. http://dx.doi.org/10.4314/jasem.v23i5.15
Uzoh, E. E.-C., Anam, B. E., & Ironbar, V. E. (2023). Community participation in the provision of educational facilities in public schools and community development in Cross River State. International Journal of Innovation Research in Education, Technology & Social, 10(2), 99-118. https://doi.org/10.48028/iiprds/ijiretss.v10.i2.09
Wan, A. (2018). Chapter 5: Box-Jenkins (ARIMA) Forecasting. Retrieved from http://personal.cb.cityu.edu.hk/msawan/teaching/ms6215/MS6215Ch5.pdf
Williams, T., Green, B., & Johnson, R. (2022). Climate change and resilient housing in Nigeria: Pathways to sustainable urban development. Journal of Climate and Urban Studies, 45(4), 302-318.
World Health Organization. (2010). Hidden cities: Unmasking and overcoming health inequities in urban settings (pp. 3–10). ISBN 978-9241548038.
World Bank Group. (2020, April 20). Urban Development Overview: Development news, research, data | World Bank. Retrieved June 6, 2022, from https://www.worldbank.org/en/topic/urbandevelopment/overview#1
World Bank. (2023, April 03). Urban Development. Retrieved from https://www.worldbank.org/en/topic/urbandevelopment/overview
World Health Organization. (2021, October 29). Urban health. Retrieved from https://www.who.int/news-room/fact-sheets/detail/urban-health
Published
2024-10-14
How to Cite
Olalude, G. A., Esiegbe, E. O., & Alabi, P. A. (2024). Modelling and Forecasting Urban Population Growth in Nigeria Using Autoregressive Integrated Moving Average (ARIMA) Models. European Journal of Science, Innovation and Technology, 4(4), 311-327. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/521
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Copyright (c) 2024 Gbenga Adelekan Olalude, Emmanuel Oluwasegun Esiegbe, Peter Ayo Alabi
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