The police department detects and prevents the time and punishes the felons. Most of the time, police officers face unknown threats looming around to disrupt the peace of society. Whenever we hear data science, we think of all the industries except law enforcement. Regardless of what we perceive, using data science in law enforcement is usual. Data Science is a field in which the data is studied to focus on improving the outcomes, whether it is a production company or society. With the proper study of data, law enforcement can easily prevent crime and take the accused into custody.
Although data science seems a new field of study, Law enforcement has unknowingly utilized it for a long time. There are many cases where city cops have analyzed the places where the majority of the crime occurs and the time when it happens. With such analysis, the patrolling had been increased in those areas at a specific hour of the day, and the crime rate got reduced easily. Let's look at some specific applications of data science in Law Enforcement.
Historically, law enforcement agencies were a disjointed data-sharing matrix. The Installation of the National Crime Information Center, Software for crime mapping, and other assisting management tools like CompStat and OneDOJ have improved the condition. Gathering information from social media has given an upper hand to the data scientist at law enforcement agencies. Such information provides law enforcement agencies an idea of a felony tracking someone's search on Google for learning something unusual that may cause harm to society or to the person itself. Such information can also help gather evidence post the crime is conducted. Moreover, text analytics during the interrogation may also help to understand the nature of the accused, which helps to predict which accused should be further interrogated.
Law enforcement does not just rely upon static data; accurate analysis of the crime requires understanding the answers of the witness, audio recording, and camera footage. That's where NLP plays an important role. AI can analyze emotional distress, but the human element is reasonably necessary. The report on Natural language processing contains computational linguistics and computational psychology.
Everyone owns smartphones nowadays, but body cam is mandatory for police officers in the United States as they can't perform their duties carrying a mobile phone. This video can be too lengthy, or the quality can be inappropriate because of constant movement. In such cases, data scientists can assist in shortening the length of the videos with the help of AI to detect a specific zone in the video which requires alertness. After establishing the alert, the video can be used for further analysis. Moreover, facial recognition can also help with AI tools to notify the cop on duty to remain alert in suspicious situations.
By now, it should be clear that algorithmic accuracy is primarily determined by the skill of the statistician or data scientist and the quality of the data. There are numerous statistical models available for use in law enforcement. The list below is not exhaustive; instead, it is a starting point rather than a detailed description of all possible crime detection models. Furthermore, only broad descriptions are provided because a comprehensive examination of when, how, and why each statistical tool is used is outside the scope of this article.
Data is certainly not new for justice and authority, but technological advancement has made it more accurate and enhanced. Leveraging data science consulting services, Law Enforcement agencies can build software and algorithms to keep their data safe. In the hour of need, those data can be retrieved to solve cases and maintain social harmony.