Public safety is becoming one of the most pressing issues in urban centres across Africa, and Nigeria stands at the forefront of this crisis. With urban populations expanding rapidly due to rural-urban migration, challenges such as crime, traffic congestion, and general safety concerns have surged. The Nigerian Bureau of Statistics reports a 12 percent increase in crime rates for 2022, with the majority of incidents occurring in the country’s largest urban areas, including Lagos, Abuja, Port Harcourt, and Kano. With cities like Lagos holding over 14 million inhabitants, the challenge of maintaining security is daunting. In response to these challenges, artificial intelligence (AI) and machine learning (ML) are becoming indispensable tools for transforming public safety.
AI and ML technologies are being increasingly deployed to enhance public safety by improving crime detection, predictive policing, automated surveillance, and emergency response. These technologies present an opportunity to transform how public safety is managed, allowing for timely, effective, and proactive solutions that have the potential to save both lives and resources. By leveraging data-driven insights, law enforcement agencies and public institutions can make informed decisions and respond swiftly to emerging security threats.
In Nigeria, maintaining public safety is further complicated by the persistent threats of terrorism, kidnapping, cybercrime, and traffic-related fatalities. According to the Federal Road Safety Corps (FRSC), more than 5,000 people died in road accidents in Nigeria in 2021, emphasising the need for intelligent traffic management systems. Kidnapping for ransom is another concern, especially in the northern and central regions of the country, making the use of technology critical for enhancing surveillance and response mechanisms. AI and ML applications in public safety have the potential to significantly alleviate these issues, providing a safer and more secure environment for Nigerians.
One of the most promising uses of AI in Nigerian cities is in the field of predictive policing. By analysing historical crime data, AI models can help identify crime hotspots and forecast where crimes are likely to occur. This approach allows authorities to allocate police resources more efficiently, increasing their ability to deter crime before it happens. In Lagos, predictive policing models are being tested to better allocate law enforcement officers to high-risk areas. By focusing efforts in areas with the highest predicted risk, police forces can preemptively deter criminal activity and enhance public safety outcomes. This approach has been successful in other parts of Africa, such as Cape Town, where the “ShotSpotter” system uses acoustic sensors to detect gunfire, thereby allowing for faster response times by law enforcement. Deploying similar technologies in Nigerian cities would be a step forward in modernising the security apparatus, reducing crime rates, and enhancing the general sense of safety among citizens.
Video surveillance has always been a crucial part of urban safety infrastructure, but its efficiency has been limited by the need for human intervention in monitoring camera feeds. AI and ML, however, can augment traditional surveillance systems by automating the detection of suspicious activities, facial recognition, vehicle tracking, and even behaviour analysis. With Lagos State already installing CCTV cameras across the city, there is great potential for AI technologies to improve these systems by providing real-time analysis and generating alerts for suspicious behaviour. AI-driven video analytics can automatically detect abnormal movement, loitering, or other suspicious activities, allowing authorities to respond in real time. The cost of deploying these systems remains high, with estimates suggesting a total of over $20 million in investment for citywide coverage in major Nigerian cities. However, the potential benefits in terms of crime reduction and enhanced security far outweigh the costs.
Traffic management and accident prevention are other areas where AI and ML can have a substantial impact. Traffic congestion in Nigerian cities is a well-known issue, causing economic losses estimated at around $1 billion annually, according to a report by the Lagos Chamber of Commerce and Industry. AI-based traffic management systems can analyse data from cameras, sensors, and other sources to adjust traffic signals dynamically and minimise congestion. In Nairobi, Kenya, a similar system has been implemented with great success. The intelligent AI-driven traffic system optimises traffic light timings in real time, leading to a significant reduction in congestion. By adopting a similar approach in Lagos and Abuja, Nigeria could see a reduction in both traffic-related economic losses and the number of traffic accidents. AI systems piloted in Abuja are being used to identify traffic violations such as speeding and illegal parking, leveraging machine learning models to analyse live feeds from surveillance cameras to detect infractions in real time.
AI is also being used to combat kidnapping and terrorism, major issues in parts of Nigeria. Predictive analytics have proven useful in identifying patterns related to abductions and even in detecting suspicious social media activity that may signal impending kidnappings. In addition to predictive models, AI-enhanced drones have been deployed in the northeastern region of Nigeria, where they monitor and gather intelligence on terrorist groups. These drones are equipped with advanced computer vision capabilities that can detect unusual gatherings and provide real-time feedback to security agencies. The cost of maintaining drone surveillance is significantly estimated at $500,000 annually per region—yet the value they bring in providing safety and security in terror-prone areas is undeniable.
AI is also transforming emergency response and disaster management in Nigeria. During events like flooding, building collapses, or accidents, AI systems can facilitate more coordinated and faster responses by analysing large volumes of real-time data. AI-based flood prediction models have been developed for some flood-prone areas of Nigeria, using data from satellites, meteorological centres, and local sensors to predict floods and help authorities prepare for emergency response. As the frequency and impact of extreme weather events increase due to climate change, such predictive models will be vital in saving lives and minimising damage.
A core advantage of AI and ML in public safety is their ability to utilise machine learning models for predictive analysis. Regression models are frequently used to forecast crime trends and predict where and when crimes are likely to happen. Deep learning models, such as convolutional neural networks (CNNs), are employed for facial recognition, object detection, and analysing complex images from surveillance cameras. By processing large datasets, these models become increasingly accurate over time, making them invaluable for public safety initiatives.
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Another important tool in AI-driven public safety efforts is computer vision technology. Real-time object detection algorithms like YOLO (You Only Look Once) are used to identify weapons, suspicious activities, and even abandoned objects in crowded spaces. These algorithms can enhance real-time monitoring capabilities, which is crucial for cities with a limited workforce to continuously monitor security cameras. Additionally, natural language processing (NLP) techniques are used to analyse emergency call transcripts, crime reports, and social media posts to identify emerging threats. The real-time analysis of these data points allows for more effective responses to security threats.
Despite the numerous benefits, there are also several challenges to the widespread deployment of AI and ML for public safety in Nigeria. One of the most significant challenges is data quality and availability. AI systems depend heavily on the quality and quantity of data they receive for training and accuracy. In Nigeria, crime data collection and reporting are often inconsistent, fragmented, and plagued by inaccuracies, which makes it difficult to train reliable AI models. Ensuring access to accurate and comprehensive datasets is, therefore, a prerequisite for deploying AI effectively.
Another challenge is privacy concerns. The deployment of AI-powered surveillance systems brings up critical issues of citizens’ privacy. The ethical implications of such technology must be thoroughly addressed, and robust legal frameworks must be put in place to ensure that AI systems are used responsibly. While the Nigerian Data Protection Regulation (NDPR) provides some degree of protection, more comprehensive and specific legislation regarding AI use in surveillance is needed.
The infrastructure limitations in many parts of Nigeria also pose a major barrier to the implementation of AI technologies. AI systems require reliable internet connectivity, power supply, and access to high-performance computing resources, which have yet to be widely available across the country. Public-private partnerships can play an essential role in building the necessary infrastructure, with tech companies partnering with the government to bridge this gap.
Another significant concern is the cost of deploying and maintaining AI-driven public safety systems. From the hardware required for video surveillance to software development and cloud infrastructure for processing large datasets, the costs are high. The budget constraints faced by many Nigerian cities make it challenging to implement these solutions without external funding or partnerships. The economic value, however, lies in potential savings resulting from reduced crime, reduced traffic congestion, and enhanced overall productivity. Studies suggest that enhanced public safety could save Nigeria up to $2 billion annually in reduced crime-related costs, emergency response efficiencies, and improved economic activities.
Job creation is an often-overlooked benefit of AI deployment in public safety. The introduction of AI and ML technologies into Nigerian cities could create thousands of jobs, from technicians and data analysts to drone operators and software engineers. For example, the establishment of a surveillance command centre in Lagos could employ between 500 and 1,000 people, including analysts, engineers, and support staff. The tech industry would also benefit from increased demand for AI specialists and data scientists, leading to job creation in sectors that support public safety infrastructure.
Several case studies from across Africa demonstrate the potential of AI in transforming public safety. In Rwanda, the Kigali City Surveillance Project integrates CCTV cameras with machine learning models to monitor traffic violations and crime hotspots. This has resulted in a significant reduction in crime rates in Kigali, highlighting the potential for Nigerian cities to emulate similar successes. Kenya’s use of AI-driven tools for wildlife poaching prevention also highlights the versatility of these technologies. By analysing data from drones and sensors, Kenyan authorities have managed to curb poaching activities significantly. In Nigeria, a similar approach could be used to monitor critical infrastructure or to counter rural banditry.
To harness the full potential of AI and ML in Nigerian public safety, a multi-faceted strategy is required. Public-private partnerships can help reduce costs, share expertise, and accelerate technology adoption. Collaborations between tech companies, government institutions, and academia are essential for capacity building and infrastructure development. Policy development is also critical—the Nigerian government must formulate policies to ensure ethical AI use, protect citizens’ privacy, and establish clear guidelines for AI in public safety.
Moreover, investment in training and capacity building is key to bridging the skills gap. AI and ML education should be integrated into university curricula, and specialised AI research centres should be established to encourage innovation and train future experts. This is crucial to building a sustainable foundation for AI adoption in Nigeria.
Finally, pilot projects are essential for understanding the practical challenges of deploying AI and ML in public safety. Implementing pilot programs in specific neighbourhoods of Lagos or Abuja can provide valuable insights that can be used to refine the systems before larger-scale deployments. Pilot initiatives can also help gauge public reaction, allowing for more tailored approaches that balance safety needs with privacy considerations.
In conclusion, AI and ML technologies present enormous opportunities to improve public safety in Nigerian cities. From predictive policing to intelligent traffic management and emergency response, AI and ML can address many of the security challenges faced by urban centres in Nigeria. However, their successful deployment will require a concerted effort to overcome challenges related to data quality, privacy, cost, infrastructure, and skills shortages. The economic value these technologies bring, including crime reduction, enhanced safety, and job creation, is immense. With strategic investments and collaboration, Nigeria has the opportunity to build safer, smarter, and more resilient cities that foster economic growth and improve the quality of life for its citizens.
Engr Friday Ogochukwu Ikwuogu M-NSE., is an expert in networks engineering, and infrastructure deployment in Nigeria. He has over a decade of experience in the network’s analytics and AI/ML in finance.
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