Priviet Social Sciences Journal

Harnessing artificial intelligence for census in Nigeria: Advancing accuracy, efficiency, and governance outcomes

by Inuwa Sani Sani ORCID , Muhammad Dimyati ORCID , Aliyu Aminu Umar
Vol. 5 No. 11 (2025): November 2025

Abstract

Successful administration of national censuses in Nigeria has been a protracted agony plagued by inherent problems, including logistic, political, and methodological issues, which cumulatively have caused delays in enumeration, undercounting, and inconsistency of data. These defects diminish the credibility of demographic data needed for evidence-based governance, economic planning, and equitable resource allocation._. In this study, we explored opportunities for harnessing Artificial Intelligence (AI) to transform census activities in Nigeria through the injection of state-of-the-art computational approaches into the national enumeration exercise. We showcased a multimodal AI pipeline comprising Convolutional Neural Networks (CNNs) for population density estimation from satellite images, Natural Language Processing (NLP) pipelines for address standardization and matching in various languages, and unsupervised anomaly detection algorithms for real-time data quality verification. AI-based enumeration methods were simulated at both national and sub-national levels. CNN-generated heatmaps revealed population concentration trends in Lagos and other states and enabled the precise delineation of high-density urban agglomerations and underserved rural enclaves. The NLP tool generalized well to the linguistically diverse environments in Nigeria, with F1-scores greater than 0.90 for all but a few states for broken address reconciliation. Anomaly detection models built using Isolation Forest algorithms detected anomalous enumeration patterns as flags for potential undercounts or data manipulation. Population pyramid analysis for Lagos revealed an extremely young population structure, consistent with country-wide age trends. These findings provide empirical evidence that AI integration can promote census accuracy, operational efficiency and government effectiveness in Nigeria.

References

  1. Adebayo, A., Eze, U., & Johnson, T. (2023). Assessing the viability of administrative records for census validation in low-income contexts. Population and Development Review, 49(2), 301–323. https://doi.org/10.1111/padr.12543
  2. Akinyemi, A. I., & Isiugo-Abanihe, U. C. (2013). Demographic dynamics and development in Nigeria: Issues and perspectives. Etude de La Population Africaine, 27(2 SUPPL.), 239–248. https://doi.org/10.11564/27-2-471
  3. Bennett, J., Gunter, B., & Wood, C. (2022). Innovation systems and technology adoption in African public service: A review. Public Administration and Development, 42(4), 211–225. https://doi.org/10.1002/pad.1982
  4. Coelho, F. C., Armstrong, M., Saraceni, V., & Lemos, C. (2017). Can Zika Account for the Missing Babies? Frontiers in Public Health, 5. https://doi.org/10.3389/fpubh.2017.00317
  5. Darwish, W., Bolsee, Q., & Munteanu, A. (2019). Plenoptic camera calibration based on microlens distortion modelling. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 146–154. https://doi.org/10.1016/j.isprsjprs.2019.10.008
  6. Everaers, P. (2021). Editorial. In Statistical Journal of the IAOS (Vol. 37, Issue 1, pp. 1–9). IOS Press BV. https://doi.org/10.3233/SJI-210801
  7. Gawusu, S., & Ahmed, A. (2024). Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach. Energy, 304. https://doi.org/10.1016/j.energy.2024.132194
  8. Hafner, S., Georganos, S., Mugiraneza, T., & Ban, Y. (2023). Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda. http://arxiv.org/abs/2303.08511
  9. Kellogg, K. C., Orlikowski, W. J., & Yates, J. (2020). Distributed work relationships: Electronically mediated work interdependence and the impact of emotional expression. Organization Science, 31(6), 1451–1474. https://doi.org/10.1287/orsc.2020.1422
  10. Lee, C.-C., Li, J., Xia, Z., & Xie, L. (2025). Elderly population migration and urban energy consumption: Voluntary relocation or forced displacement? Energy Economics, 149. https://doi.org/10.1016/j.eneco.2025.108725
  11. Michael, T. O., & Odeyemi, M. A. (2017). Nigeria’s Population Policies: Issues, Challenges and Prospects. IBADAN JOURNAL OF THE SOCIAL SCIENCES, 15(1). https://doi.org/10.36108/ijss/7102.51.0101
  12. Müller, I., Taubenböck, H., Kuffer, M., & Wurm, M. (2020). Misperceptions of predominant slum locations? Spatial analysis of slum locations in terms of topography based on earth observation data. Remote Sensing, 12(15). https://doi.org/10.3390/RS12152474
  13. Okon, P., & Ibrahim, S. (2022). Building AI and geospatial capability in Nigerian public institutions: A training collaboration model. Public Administration Review Africa, 15(2), 88–105. https://doi.org/10.48804/paruafrica.2022.88-105
  14. Omoju, O. E., & Abraham, T. W. (2014). Youth bulge and demographic dividend in Nigeria. In African Population Studies (Vol. 27). http://aps.journals.ac.za352
  15. Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302
  16. Reed, H. E., & Mberu, B. U. (2014). Capitalizing on Nigeria’s demographic dividend: reaping the benefits and diminishing the burdens. In African Population Studies (Vol. 27). http://aps.journals.ac.za
  17. Shu, C., & Zhang, H. (n.d.). Neural Programming by Example. www.aaai.org
  18. Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015a). Disaggregating census data for population mapping using Random forests with remotely-sensed and ancillary data. PLoS ONE, 10(2). https://doi.org/10.1371/journal.pone.0107042
  19. Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015b). Disaggregating census data for population mapping using Random forests with remotely-sensed and ancillary data. PLoS ONE, 10(2). https://doi.org/10.1371/journal.pone.0107042
  20. United Nations. (2024). Recommendations for AI Governance in Public Administration. UN Digital Governance Series. https://doi.org/10.18356/UNAI.2024.007
  21. Xu, L., Zhang, Z., & Liu, R. (2021). Technology diffusion in public sector: An empirical study of innovation spread in government institutions. Government Information Quarterly, 38(3), 101596. https://doi.org/10.1016/j.giq.2021.101596
  22. Zeng, Y., Fu, J., Cao, W., Chen, Y., & Yang, Z. (2024). Research Hotspots and Knowledge Framework of Digital Healthcare Service Management from the Perspective of the Silver Economy. Sustainability (Switzerland), 16(22). https://doi.org/10.3390/su16229735
  23. Zhang, K., Pang, Y., Zhang, Y., & Sekimoto, Y. (2024). MobGLM: A Large Language Model for Synthetic Human Mobility Generation. 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024, 629 – 632. https://doi.org/10.1145/3678717.3691311
  24. Zhang, Y., Chen, Y., & Xu, R. (2025). The Health Cost of Rural Banquet Culture: The Mediating Role of Labor Time and Health Decision-Making Evidence from Jiangsu, China. Sustainability (Switzerland), 17(12). https://doi.org/10.3390/su17125318