The police have long hoped that technology would ease their most vexing problems. Law enforcement is about keeping society safe. So it is no surprise that as society has changed, so too has law enforcement. New technologies, new methods, and new ideas have brought significant change to the profession. The most important recent innovations in technology involve computers and related software that deals with crime detection efficiently. The police are information-dependent and rely on the public as a primary source of information; how the police obtain, process, encode, decode, and use information is critical to understanding their functions. There are at least three types of police information (primary, secondary, and tertiary), intelligence (prospective, retrospective, and applied), and operational strategies (preventive, prospective, and reactive), each of which interacts in a complex fashion with technology. These processes are importantly patterned by police work, especially the role of the patrol officer, and the occupational cultures of policing. Technology is embedded in the social organization; it shapes organizations and is shaped by them. 

Every investigative journey begins with collecting facts about the world. Traditionally, this work has been tough. It could mean relying on an officer’s memory of a license plate to look out for, or long hours searching for the right pieces of information. It often means being there, to see, to hear, and to deter—and no department can be everywhere. However, new technologies, like the Internet of Things (IoT) and smart sensors, can be there when needed. What if this preliminary need for public investigation and involvement is eradicated in order to speed up the process of criminal identification and punishment. Today, the pace of technology is accelerating faster than ever. Technology is changing core aspects of how we interact as a society, and as society changes, so too will the tools, techniques, and concepts the men and women of law enforcement use to keep us safe. 

Police and law enforcement agencies across the country are driving the change, pioneering creative ideas, adapting to changing contexts, and incorporating insights from officers and community partners. To understand how these innovative practices may transform the future, we must begin by understanding the journey an officer takes from evidence to insight. Officers need to be able to assess their environment rapidly, leverage technology as they pursue public safety, mine data for insights on what to do next, scale up their successes, and get deeply involved in their communities.

Smart sensors can be used to compile many different types of information to help officers do their jobs faster and more effectively. New capabilities can log locations, listen for gunshots, stream video, flag license plates, scan databases, and go on virtual patrol, allowing officers unprecedented awareness in their environments.

These capabilities can provide the raw data which more detailed analytics can use to likely enhance efficiencies and expedite investigations. Most importantly, these technologies can help officers be in the right place, at the right time

An increasing number of cities worldwide are relying on surveillance cameras as a tool for preventing crimes and supporting investigations and prosecutions. These smart surveillance cameras detect any unusual activity which gets recorded and alerts the nearby police station about it. 

Police-monitored areas and previous crime

Automated firearm detection is one solution to a growing problem that has no clear policy cure: curbing mass shootings and gun violence. Gun-detection algorithms that alert security personnel when an armed person arrives might reduce the number of victims — though likely not as much as if there were no armed shooters in the first place.

A company called ZeroEyes out of Philadelphia markets a system to police departments that can detect when a person is entering a given facility carrying a gun. It integrates with any number of closed-circuit surveillance systems. The ML algorithm is trained by a team from the company that shows up on location and proceeds to stage mock attacks. Slowly, the algorithm begins to learn what a gun looks like in that specific setting, depending on light, angles, and other conditions.

Thermal imaging to detect concealed guns is an emerging technology. And it’s claiming a success rate of 99 percent. The only constraint is the camera; higher-resolution cameras allow them to get farther away. But the prediction activity is the same.

 Cyberspace constantly remains the greatest source of different illegal activities that include not only new types of crime, such as hacking or malicious codes and programs, such as ‘spam’, but also the migration of traditional crime, such as child pornography, fraud, and copyright infringements.  Machine learning preemptively stamps out cyber threats and bolsters security infrastructure through pattern detection, real-time cybercrime mapping, and thorough penetration testing. These alert the cybercrime officials to take some actions against the offender in the shortest span of time.  

Facial recognition and crime indication-Abnormal activity and behavior tracking. Imagine it were possible to recognize not the faces of people who had already committed crimes, but the behaviors indicating a crime that was about to occur.

A predictive indicator of potential violence, rather than a lagging indicator, such as facial recognition, and carries less political baggage.  More and more, cities and police departments are experimenting with facial recognition to detect the presence of suspects in real-time. 

The two indicators—the identity of someone in a criminal database, the presence of a weapon in the hand of that person—together would provide a far more accurate probability and cut down on false positives. 

The presence of a specific object, like a gun, with a specific person, like someone in a criminal database, serves as an imperfect indicator of violence. The future of predicting violence may rest in the collection of other data related to behavior. 

 In lieu of facial recognition, some police departments may rely on automated license plate readers as an indicator of identity. A company called Reconyx sells a system that can automatically read license plates and has multiple law enforcement clients. This can be used to identify any hit and run accidents, suspicious car tracking, and easily identify law-breaking automobiles.policy efforts to rein in the use of artificial intelligence in surveillance move much slower than the technology itself.

Machine recognition of faces or license plates is a subject of privacy concern because these speak to identity. But any number of data streams may speak to the same thing They include biometric data such as gait or speech, which may be accessible via video streams, or even location patterns, which are detectable via cell phone.

IoT and distributed sensing are about leveraging new technology to gather information about the world and point in the right direction. But what should officers do on the scene? New technologies and practices are developing that can help guide action in the world. Advances in areas such as 5G communication, electronics miniaturization, and augmented reality allow people to see, hear, and act in ways that were previously impossible.

For example, an officer arriving at an unfamiliar situation can now use augmented reality glasses to see pertinent information about prior calls for service from this address, find exits from a building, or see the recent criminal history on the block.4 With this information, an officer could take precautions to protect themselves and even better serve the public.

 Small autonomous drones, for example, can be programmed to follow officers, scout locations, and provide video streams so that no officer ever has to go into any situation truly alone.

We live in a world awash in data. And as many departments deploy technology solutions like augmented reality, body cameras, license plate readers, and smart sensors, they will likely generate more data each day than in their entire analog histories.5 The success of future law enforcement strategies rests on being able to quickly and efficiently harness these immense volumes of data to support investigations and enforcement actions. But these mountains of data are too vast for any human to comb through, even if they dedicated an entire lifetime to searching.

To gain real insight, artificial intelligence (AI) and machine learning will be key to future investigations. For example, each year more than eight million tips on the location of missing children must be analyzed by a team of just 25 investigators.AI is being deployed to help those investigators sift through the data and find the most likely leads to identify exploited children and reunite them with loved ones. AI is already demonstrating its value around the world by helping police in England analyze CCTV video, officers in India find 3,000 missing persons in just four days, and the Dutch to find promising leads in cold cases. 

By analyzing patterns, sensor feeds, and databases of records, AI could help law enforcement identify critical places to be, find key linkages between suspects, and explore other insights hidden in a sea of data. 

I and machine learning can help find the clues to target a patrol or forward an investigation, using simple rule sets to test new hypotheses, identify what works, and scale successes. A similar approach—evidence-based policing—can also be applied to the methods of police work itself. Evidence-based policing includes using advanced forensic techniques to extract more information from the same amount of evidence, but it also goes beyond this to examine the heart of police activities.

Evidence-based policing can analyze data about the outcomes of police interactions to help find the most effective methods and tools while minimizing the use of tactics that tend to make situations worse. In an era when many police officers are being asked to do more and more with fewer and fewer resources, evidence-based policing seeks to pair them with outside assistance, such as academic researchers or computer programmers, to help focus their efforts on the most effective police work. That is exactly what the national police of New Zealand found when they established their Evidence-Based Policing Centre.

Researchers are now able to comb through data and identify where enforcement actions could be most effective (for example, targeting drug distribution at the island nation’s airports) and when officers’ time could be better spent elsewhere. Using these methods, police were able to design more effective strategies to combat the most complex safety issues facing society, from domestic violence to cybercrime, while redirecting other issues to community support structures.Hence, the future of security lies fundamentally in the hands of the people. The mission of law enforcement is the safety of the community, and a strong relationship with the community is critical to the success of every law enforcement organization. Building close ties within the community are about traditional police outreach and local involvement, but it’s also about leveraging tools to help people communicate their needs and their risks directly. New technologies can help police develop their ties to the community, by enabling officers to be more aware of and responsive to the needs of their constituents.

Article dedicated to the restless efforts of police.