The Future of Law Enforcement Intelligence and Technology

Law enforcement intelligence and technology are on the rise, with predictive policing, Internet of Things (IoT) devices, and situational awareness being some hot topics on the radar. What will these trends mean for law enforcement leadership in future years?

Predictive policing is a technique used by police to make predictions about the likelihood of crime in a specific area. This can help officers to better target and respond to crime. For instance, the Los Angeles Strategic Extraction and Restoration (LASER) program helps predict hot spots of gun violence.

Using predictive policing software has allowed law enforcement agencies to deploy more police to high-risk neighborhoods. However, there are several ethical concerns with the practice. In addition to privacy issues, predictive policing can perpetuate biases among officers and residents.

One potential problem with predictive policing is the feedback loop it creates. Police officers may assume benign activities in low-risk neighborhoods are suspicious. For example, an officer might think that a 16-year-old cutting through his backyard is a child committing a crime. But in reality, the child is going to school.

Another possible problem is that data input into predictive algorithms is only sometimes accurate. Vendors often need to include key data sources, leading to inaccurate predictions.

When IoT devices are coupled with artificial intelligence, data can be collected faster and can be used to identify patterns in the criminal justice system. This has the potential to improve the efficiency of law enforcement agencies and help protect public safety. It will also increase the community's trust in the police.

While the benefits of IoT devices for law enforcement are clear, the technology raises ethical and privacy questions. There is a need for police to work with new partners to ensure that all parties are protected.

Currently, police in the United States rely on a manual and isolated approach to data collection. Although IoT offers the opportunity to collect and store data quickly, this will require additional resources. An IoT database can be used to inform stakeholders and train communities.

A database can contain technical documentation, IoT data, and vulnerabilities. These can be used to improve police training, support investigations, and assist in the prosecution of crime.

Situational awareness is a critical skill in law enforcement. It enables police officers to respond quickly and effectively. In today's world, a rapid response can make a big difference to the safety and security of the public. Using this technology, police officers can detect gunshots, track the location of individuals, and monitor suspicious activity.

Situational awareness refers to up-to-date knowledge of the environment, such as a person's location or the elements that make up their surroundings. This information is then used to develop an action plan. These plans can include the use of emergency equipment or surveillance cameras.

Situational awareness was introduced as a foundational theory for sound decision-making in military planning. Law enforcement and aviation also benefit from the capability. A growing number of cities are using this technology to protect civilians and help police officers make better decisions.

There are several different approaches to developing a situation awareness model. Some models are based on learning techniques. Others are based on a computational architecture or a case-based reasoning model.