Big Data Analytics and Crime Mapping in African Cities: Implications for Security Governance and Evidence-Based Policing

Abstract

With an emphasis on the consequences for security governance and evidence-based policing, this study attempts
to investigate the use of Big Data analytics and crime mapping in African cities. Although data-driven policing is
becoming more and more popular worldwide, little context-specific study has been done on the particular socioinstitutional
factors influencing its application in African metropolitan settings. Using comparative case studies
of Johannesburg, Nairobi, Accra, and Lagos, the study employs a qualitative, doctrinal, and comparative analytical
method, drawing from recent literature, policy papers, and institutional reports (2024–2026). Routine Activity
Theory, Crime Pattern Theory, and Socio-Technical Systems Theory serve as its theoretical foundations. The
results show that crime hotspot identification, resource allocation, and proactive decision-making are much
improved by Big Data tools, such as Geographic Information Systems (GIS) and predictive police models. The
study contributes to knowledge by integrating criminological theory with digital policing practices in African
contexts. It concludes that sustainable adoption requires not only technological investment but also robust
governance structures to ensure accountability, legitimacy, and public trust in urban security systems.