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How the Evolution of Big Data is Influencing Law Enforcement

Image credit: DARPA

The advent of big data and its influence in various industries has been felt and appreciated across the board. The law enforcement sector is one of the fields where big data has been utilized to combat crime by employing advanced technological methods that were not available in the past decades. Although the fact that a computer-based analysis can help to curb a crime before it actually happens is a connotation that seems to be pegged on fiction, law enforcement policymakers and police officers have widely concurred that it is possible to prevent crime by analyzing available data.

Understanding big data in law enforcement

Big data basically refers to the analysis of the massive volumes of available digitized data by using artificial intelligence analytic tools in order to gain vital insights that can help in making informed decisions promptly.

Big data analytics have been utilized in preventing criminals from perpetrating a crime by improving security and predicting a crime beforehand. One of the notable examples is the usage of OLAP by the National Security Agency (NSA) to foil terrorist plans. Police officers also use the big data analysis insights to predict and prevent cyber crime. The Chicago Police Department used a computer-generated list to deploy police officers to the field in 2014 to make ‘custom notification’ visits to persons they had suspected of having the potential to commit crimes. Even though the community may have been outraged and termed it a police profiling feet, it may have helped in curbing crime in that neighborhood. The Los Angeles Police Department also uses artificial intelligence in analyzing huge data sets.

However, there have been counteractive reactions from various communities who keep complaining that the data mining done on the data they leave behind willingly or unknowingly is an infringement on their privacy. Computer algorithms have the power to collect data from identification technologies and tracking devices. Furthermore, in light of the Fourth Amendment and the reasonable suspicion doctrine, it’s challenging to justify initiating investigations and surveillance to arrest a person based on the insights collected from big data analysis.

Policing and predictive technology

Predicting crimes using criminology theories have been around for decades. But employing information techy methods and tools is an innovation that is still evolving, and yet to be fully embraced by most governments and law enforcement agencies. By using the data generated and numbers produced from the computer algorithms, law enforcement agencies can identify potential criminals and crime hot-spots. For example, in 2010, The Los Angeles Police Department used the historical crime permits and a 500-by-500 foot areas strategy to analyze crime prone areas where burglary and automobile theft was on the rise. Through this algorithm-driven approach, it deployed police officers to the volatile spots and managed to reduce crime by 12 percent compared to the previous year.

Analysis of data from social media platforms

Law enforcement agencies can predict crime by recognizing suspicious behavioral patterns of individuals with criminal minds by monitoring them on social media networks. The predictive policing applied on social media networks is based on identifying malicious affiliations, communication threads, family ties, and other forms of exchanges that amount to the potential of committing a crime by using a social network software.

The algorithms from the software will help the police understand the structure of a criminal outfit and its leaders before making a move. For instance, a drug dealing cartel will assume the form of a legit business with financiers or investors, suppliers, distributors, and consumers. By analyzing the data generated from various malicious social media networks or groups using big data analytics, the law enforcement agencies can easily catch the victims before they carry out the crimes.

Summary

In the future, law enforcement agencies are likely to hire a mix of employees to catch up with the evolution of big data analytics. We are likely to see agencies employing more civilian specialists, increased engagement of part-time workers and volunteers, and more contracted big data specialists to analyze the massive data and make it useful for decision support. With the limited resources witnessed in the law enforcement agencies, shared resources, outsourcing, and contracting services will be a viable option.

Written by Lindsey Patterson

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