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Banditry and National Development in Nigeria: A Spatio-Temporal Machine Learning Approach to Conflict Prediction


Authors : Musa Tanimu Karatu; Dauda John Tanimu

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/33pz7f2p

Scribd : https://tinyurl.com/mrvcmvbm

DOI : https://doi.org/10.38124/ijisrt/26mar092

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Abstract : Banditry has emerged as one of the most severe internal security challenges confronting Nigeria, with far-reaching implications for national development, governance, and social stability. This study applies spatio-temporal machine learning techniques to analyse and predict patterns of banditry incidents across Nigerian Local Government Areas (LGAs) between 2015 and 2024. Drawing on Routine Activity Theory, Social Disorganisation Theory, and Government–Development Theory, the research integrates socio-economic, environmental, and security-related variables with historical conflict data comprising over 20,000 recorded incidents. A comparative modelling framework using Random Forest (RF), Long ShortTerm Memory (LSTM), and Convolutional LSTM (ConvLSTM) models is implemented to capture non-linear relationships, temporal dependencies, and spatial diffusion of violence. Results indicate that ConvLSTM outperforms other models in forecasting high-risk locations and attack severity, demonstrating the value of Spatio-temporal deep learning for conflict prediction. The findings reveal persistent hotspots in areas characterised by weak state presence, high poverty levels, arms proliferation, and recurrent attack cycles. The study concludes that machine-learning-driven early-warning systems can enhance proactive security planning, optimised resource allocation, and mitigate the developmental impacts of banditry in Nigeria.

Keywords : Banditry; National Development; Conflict Prediction; Machine Learning; Spatio-Temporal Analysis; Nigeria.

References :

  1. Adegbami, A., & Kugbayi, O. (2024). Armed Banditry and Challenges of National Development: Is Nigeria’s Governance System Failing? Institutiones Administrationis, 4(1), 103-114.
  2. Alkali, A. A. (2025). Armed Banditry, Nigeria, National Security. Journal of CEEAS, 1(1), 45-61.
  3. Alkali, S. (2025). Political manipulation of insecurity in Nigeria: Implications for governance and stability. Abuja: National Security Review.
  4. AP News. (2024, March 8). Why schoolchildren are being abducted in northern Nigeria. https://apnews.com/article/2b5537957ce605ca06576d99a0756901
  5. Financial Times. (2024, March 13). Nigeria hit by wave of food looting as economic crisis deepens. https://www.ft.com/content/8a69bc80-fa5a-4beb-8989-fbd04d2330ac
  6. Odalonu, B. H. (2023). Implications of Escalating Banditry on National Security in Nigeria. Afropolitan Journal of Humanities, Culture and Education Research, 3(2), 15-28.
  7. Ojo, J. S., Aina, F., & Oyewole, S. (2024). Armed Banditry in Nigeria. Palgrave Macmillan.
  8. Ojo, T., Aina, F., & Oyewole, J. (2024). Banditry, governance, and rural insecurity in northern Nigeria. Journal of African Security Studies, 15(2), 45–67. https://doi.org/10.1080/afsec.2024.0015
  9. Osasona, O., Bello, M., & Chukwuma, E. (2023). Cattle rustling, kidnapping, and the economics of insecurity in Nigeria. African Journal of Criminology, 10(1), 21–39. https://doi.org/10.1080/afjcrim.2023.0021
  10. Reuters. (2024). Nigeria lifts five-year mining ban in Zamfara amid improved security. Reuters. Retrieved from https://www.reuters.com
  11. Reuters. (2024, December 23). Nigeria resumes mining in Zamfara state on improved security. https://www.reuters.com/world/africa/nigeria-resumes-mining-zamfara-state-improved-security-2024-12-23/
  12. Thompson, S. T. (2025). Exploring Banditry in Nigeria. Security Journal, 38(1), 77-94.
  13. UNIDIR. (2023). Insecurity and banditry in Nigeria: Humanitarian and developmental impacts. Geneva: United Nations Institute for Disarmament Research.
  14. Wikipedia Contributors. (2025). 2022 Zamfara massacres. Wikipedia. https://en.wikipedia.org/wiki/2022_Zamfara_massacres
  15. Wikipedia Contributors. (2025). Nigerian bandit conflict. Wikipedia. https://en.wikipedia.org/wiki/Nigerian_bandit_conflict

Banditry has emerged as one of the most severe internal security challenges confronting Nigeria, with far-reaching implications for national development, governance, and social stability. This study applies spatio-temporal machine learning techniques to analyse and predict patterns of banditry incidents across Nigerian Local Government Areas (LGAs) between 2015 and 2024. Drawing on Routine Activity Theory, Social Disorganisation Theory, and Government–Development Theory, the research integrates socio-economic, environmental, and security-related variables with historical conflict data comprising over 20,000 recorded incidents. A comparative modelling framework using Random Forest (RF), Long ShortTerm Memory (LSTM), and Convolutional LSTM (ConvLSTM) models is implemented to capture non-linear relationships, temporal dependencies, and spatial diffusion of violence. Results indicate that ConvLSTM outperforms other models in forecasting high-risk locations and attack severity, demonstrating the value of Spatio-temporal deep learning for conflict prediction. The findings reveal persistent hotspots in areas characterised by weak state presence, high poverty levels, arms proliferation, and recurrent attack cycles. The study concludes that machine-learning-driven early-warning systems can enhance proactive security planning, optimised resource allocation, and mitigate the developmental impacts of banditry in Nigeria.

Keywords : Banditry; National Development; Conflict Prediction; Machine Learning; Spatio-Temporal Analysis; Nigeria.

Paper Submission Last Date
31 - March - 2026

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