The Usefulness Of Data Science In Law Enforcement

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. 

Data Science For Law Enforcement

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.

Crime Detection With Data Science

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. 

NLP In Law Enforcement

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.

Body Cams

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.

Crime Detection With Data Science

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.

• Logistic regression: This is a well-liked machine learning method (and data science). It is a straightforward binary classification technique that can get trickier. The likelihood of repeat offenders could be estimated using a logistic regression model.

• Clustering algorithms: Additionally, cluster analysis can rate the likelihood of crime and identify whether neighborhoods will experience more excellent or lower crime rates. Several clustering techniques are available, including hierarchical, centroid-based, distribution-based, and density-based. One or more clustering tools can be employed on the basis of the type of data and the purpose of the analysis.

• Convolutional Neural Networks: One of several machine learning and AI models that uses biologically inspired frameworks is CNN. Essentially, the structure of this model mimics the layers between input and output values found in the visual brain using neuronal-type nodes. CNN is a well-liked method for video and image analysis.

• Convolutional Deep Belief Networks: Despite being frequently utilized for video analysis, CDBNs can also be used for audio analysis. This is particularly valid when the study's input values (or values) are high-dimensional data. Several layers make up CBNs, only one of which is visible. In data science for law enforcement, CDBNs can help categorize the likelihood of who is speaking and what is being said in audio recordings.

• Recurrent Neural Networks: RNNs have been employed in the past to recognize voice and text (or handwriting). Within the RNN classification, LTSM networks have also been applied to voice recognition and speech-to-text tasks. RNNs could be used by law enforcement to recreate different sorts of audio transcripts, like witness and victim interviews, and analyze emergency calls.

Conclusion

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.