Trending Topics

An introduction to how AI is transforming real time crime centers

AI provides the ability to process and analyze vast quantities of data from multiple sources in real-time, aiding threat assessment and strategic planning



One of the primary benefits of integrating AI into Real-Time Crime Centers (RTCCs) is that it provides the ability to process and analyze vast quantities of data from multiple sources in real-time, aiding threat assessment and strategic planning.

Enhanced by AI and machine learning, pattern recognition is crucial for strategic planning and threat assessment in law enforcement. It helps identify crime trends, hotspots and prevalent criminal tactics, facilitating the strategic allocation of resources, patrol scheduling and the implementation of crime prevention strategies.

In terms of threat assessment, pattern recognition can pinpoint emerging types of crime or novel criminal methods, alerting to potential threats and enabling early proactive measures.

Let’s review how some of these AI algorithms work.

Image and video analysis

AI can quickly process and interpret visual data, finding patterns and anomalies that humans may miss. It can use machine learning algorithms to examine large datasets, anticipate future crimes, locate crime hotspots and propose solutions.

AI can also compare data to find connections between incidents that may seem unrelated, helping to solve crimes. Moreover, AI can be designed to avoid biases that can influence human decision-making, ensuring decisions are based on facts and patterns rather than beliefs or stereotypes.

AI uses deep learning models like Convolutional Neural Networks (CNNs) to analyze images and videos. CNNs can learn to recognize patterns in visual data, such as how a car or a person looks like.

Once trained, these models can analyze new images and recognize these patterns in realtime. For example, it can identify a car involved in a hit-and-run incident by recognizing the pattern of pixels that make up the car’s features such as make, model and color.

Predictive analytics and machine learning

Predictive analytics uses past data to make forecasts about the future.

One way that AI is integrated into RTCCs is by using past data to estimate probable criminal activities or locations. AI models can find patterns and trends in crime data and generate predictions based on various factors, such as time, location, weather and events.

This approach can mitigate biases, including profiling, location-based disparities and other factors that might lead to uneven policing practices and biased crime data analysis, by developing algorithms focused on equitable law enforcement. The advantage of incorporating such algorithms lies in their ability to account for broader, pertinent contexts to promote fairness in policing.

This is often done using machine learning models. Machine Learning involves learning patterns from data and making predictions or decisions. A machine learning model could be trained on previous data to predict which areas are more likely to have higher accidents during certain times, allowing for more effective distribution of police resources. A model might be trained on past crime data to forecast future crime locations.

Natural Language Processing (NLP)

Natural Language Processing is about understanding and generating human language in a way that is both meaningful and useful.NLP involves several sub-fields, including sentiment analysis, topic modeling and entity recognition.

Sentiment analysis involves determining the sentiment (positive, negative, neutral) of a piece of text.

Topic modeling involves identifying the main topics that a piece of text is about.

Entity recognition involves identifying named entities (like people, places, and organizations) in a piece of text.

These tasks are often accomplished using machine learning models like Recurrent Neural Networks (RNNs) or Transformers. For example, body cam footage indicating a sudden increase in negative sentiment could indicate the need for additional resources.

Anomaly detection

Anomaly detection involves identifying data points that are significantly different from the norm. This is often done using statistical methods. For example, a simple anomaly detection algorithm might flag any data point that is more than three standard deviations away from the mean. For instance, it can also alert police on a street takeover for issues such as illegal street races or gatherings.

Data fusion

Data fusion involves combining data from multiple sources to get a more complete picture of a situation. This can be a complex task, as it involves dealing with data that might be in different formats, have different levels of granularity, or be about different aspects of the situation. For instance, in the case of a reported burglary, the center could pull up surveillance footage of the area, any social media posts about suspicious activity, records of recent emergency calls, and even the property records of the building.

Real-time processing

Real-time processing involves analyzing data as it comes in, rather than in batches. This is often accomplished using stream processing techniques, which involve processing data on the fly as it arrives. As data comes in, whether it’s surveillance footage, social media posts, or emergency calls, the system processes it immediately.

AI in action

Let’s consider a hypothetical situation where a city’s Real Time Crime Center (RTCC) uses AI to enhance its operations.

Imagine a hit-and-run incident involving pedestrians. A bystander quickly posts about the incident on social media, describing the car involved. The RTCC’s AI system, monitoring social media in real-time, picks up this post. Using Natural Language Processing (NLP), it determines the negative sentiment of the post and identifies key entities - the car’s description.

Simultaneously, the city’s surveillance cameras capture scenes across the city. The AI system, using Image and Video Analysis techniques like Convolutional Neural Networks (CNNs), analyzes the footage in real-time. It identifies the car involved in the incident by recognizing the pattern of pixels that make up the car’s make, model, and color as well as breadcrumbs.

The sudden increase in social media posts about the incident and the detection of the car in the surveillance footage is flagged as an anomaly by the Anomaly Detection system. This alerts the police to a potential hit-and-run suspect vehicle.

Meanwhile, the Predictive Analytics system, trained on historical crime, patterns, traffic data, external databases and video analytics predicts that the car is likely to head toward a particular neighborhood for police interception.

The Data Fusion system combines all this information — the social media post, the surveillance footage, a 911 call, the anomaly alert, and the prediction — to provide a comprehensive picture of the situation. It even pulls up recent emergency calls and property records of the neighborhood predicted by the system.

All this happens in seconds, allowing dispatchers and supervisors to make comprehensive evidence-based decisions immediately to deploy resources in the proper locations maintaining victim treatment, order and subject apprehension in the most efficient manner possible.

No longer do you have supervisors and officers guessing based on their individual experience, but instead a virtual and immediate collaboration putting several responders in the right place at the right time. This is quick and efficient and now officers are directed toward the predicted neighborhood, leading to the successful interception of the car involved in the hit-and-run incident while sufficient personnel tend to the victims.

Future of AI in policing

As AI technologies continue to advance and evolve, they will have a significant impact on the future of policing and RTCCs. Some of the emerging AI integration platforms that I expect to emerge are Responsible AI Dashboards, Multimodal collaboration, real-time officer wellness monitoring, deepfake detection and alerts, sewage analysis contrasted with drug and microbiomes, sound and environmental detection, digital currency correlation, cybercrime correlation contrasted with physical locations and a global network of RTCCs.

Learn how this agency effectively uses advanced technology for both large-scale event management and real-time crime prevention and response
Discover how these high-tech hubs are changing the landscape of law enforcement
“Our mission is to produce video and other intelligence in real time and then disseminate that to our stakeholder”
Andrea Cortez is a 17-year law enforcement veteran and analyst at Elk Grove (Calif.) Police Department’s Real-Time Information Center
Meet Andrea Cortez, a 17-year law enforcement veteran and analyst at Elk Grove (Calif.) Police Department’s Real-Time Information Center
Make sure your RTCC is prepared to manage your growing use of technology-driven policing
Philip Lukens served as the Chief of Police in Alliance, Nebraska from December 2020 until his resignation in September 2023. He began his law enforcement career in Colorado in 1995. He is known for his innovative approach to policing. As a leading expert in AI, he has been instrumental in pioneering the use of artificial intelligence in tandem with community policing, significantly enhancing police operations and optimizing patrol methods.

His focus on data-driven strategies and community safety has led to significant reductions in crime rates and use of force. Under Lukens’ leadership, his agency received the Victims Services Award in 2022 from the International Association of Chiefs of Police. He is a member of the IACP-PPSEAI Committee - Human Trafficking Committee, PERF, NIJ LEADS, Future Policing Institute Fellow and ASEBP Board Member. He holds a Bachelor of Science in Criminology from Colorado Technical University. He has also earned multiple certifications, including Northwestern School of Police Staff and Command, PERF’s Senior Management Institute for Police, Supervisor Institute with FBI LEEDA, and IACP’s Leadership in Police Organizations.

Connect on LinkedIn.