Bridging the AI Deployment Gap in Predictive Policing: An Analytical Review of Hybrid Multimodal Methods in Developing-Country Contexts
DOI:
https://doi.org/10.62411/jcta.15962Keywords:
Artificial Intelligence, Crime Prediction, Explainable Artificial Intelligence, Machine Learning, Natural Language Processing, Predictive Policing, Spatial-Temporal Modeling, Smart PolicingAbstract
The rapid growth of crime in Nigeria and other developing countries has intensified the need for proactive, data-driven policing strategies. Predictive policing leverages historical crime data and artificial intelligence (AI) techniques to support crime forecasting, patrol planning, and resource allocation. However, most state-of-the-art predictive policing frameworks have been developed and evaluated in data-rich environments, whereas Nigeria and many developing countries continue to face fragmented crime records, partial digitization, narrative-heavy police data, limited digital infrastructure, and evolving AI governance. This paper presents an analytical review of AI-based predictive policing, focusing on Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), spatial-temporal modeling, graph-based prediction, Explainable Artificial Intelligence (XAI), and ethical AI deployment. The review introduces a taxonomy that organizes the literature according to problem class, model architecture, data modality, interpretability, and deployment requirements. The findings indicate that conventional ML remains effective for structured crime prediction, whereas DL and graph-based approaches provide superior capabilities for temporal forecasting and spatial crime-diffusion analysis. Despite its potential to exploit intelligence embedded in police narratives, witness statements, case files, social media, and other unstructured sources, NLP remains underutilized in operational predictive policing systems. Similarly, XAI is insufficiently integrated into current frameworks, limiting transparency, auditability, bias detection, and public trust. The review further identifies an AI Deployment Gap between developed and developing-country contexts, demonstrating that successful deployment depends not only on predictive accuracy but also on data readiness, local validation, explainability, governance, and institutional capacity. Overall, the review highlights the need for context-aware hybrid ML–NLP–spatial-temporal–XAI frameworks that are computationally efficient, ethically governed, and better suited to the operational realities of predictive policing in Nigeria and other developing countries.References
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