What is Data Science in Law Enforcement

What is Data Science in Law Enforcement

by Jia Wang, Minseok Song

Introduction

Imagine an ideal society that a crime never happens. Law enforcement predicts when, where and how a person commits a crime. Data scientists at the police data center analyze data that have been accumulated over tens of years and strive to find the next crime scene. Police are dispatched to a predicted location before a crime happens and arrests a suspect. This will sound familiar to those who have watched the movie, Minority Report.  The movie is science fiction, but this is something that can actually happen in the future by harnessing the power of data science. In the following sections, we will look at what data science means in law enforcement and why it is important.

About Data Science in Law Enforcement

We can explain data science in law enforcement with two major fronts – predictive policing and departmental efficiency. Predictive policing, as described in the introduction, refers to a technique that data experts analyze big data and predict a possible crime. This area, however, is not new, as explained in an IBM blog (Wills, 2016). Law enforcement has been taking advantage of data analytics since the mid-1990s.  But, full realization of data science in crime prevention started a few years ago.

One good example of predictive policing can be found in an article about data science and law enforcement (Heinze, 2014). Smart Policing is an initiative launched in the U.S. Under the initiative, data experts used GIS techniques and predictive analytics to select “hot spots” that are highly likely crime locales. Thanks to this program, the Philadelphia police could focus on the selected areas and could reduce crimes by 39%.

                        

What would departmental efficiency mean? When police can predict crimes, we can generate fewer wastes. Police will be able to reduce false alarms, cold cases and deployments. These reductions will lead to saving in budget. According to Evaluation of the Shreveport Predictive Policing Experiment, a study by RAND corporation (Priscillia Hunt, 2014), crime predictions by data science will allow officers ‘to develop strategies for highly focused, specific areas, which allowed units to be more effective’.

To sum up, data science in law enforcement means data analytical practices with which police can reduce crimes and run their operation more efficiently. 

    

Smart Policing is training Philadelphia corps to be crime analysts using data science

(Image from: http://technical.ly/philly/2014/02/18/philadelphia-police-smart-policing-crime-scientists/)

Why should we care?

In law enforcement, accurate interpretation of data will play a crucial role. Failure of this task can lead to a loss of someone’s life or create a social unrest. Data science will help prevent crimes and, as a result, allow police officers to have fewer situations that they have to use their force. This will save innocent lives and stop a situation from developing into social turmoil. We all are aware of what excessive force by police can trigger in our society. Furthermore, an article on the importance of data analysis in policing (Blevins, 2013) stated that ‘currently, many law enforcement agencies are trying to do more with fewer resources’.  It has been proven in many industries that data science can be used to cut cost a lot.

Therefore, data science in law enforcement can be a key to a safer society and effective police operation. This is why we should care about it.

Here is a video link about crime reduction in Durham that can give you the idea about the importance of data science in helping the law enforcement: https://www.youtube.com/watch?v=sj_ItgsvEUo

 

References

Blevins, K. R. (2013, 5 26). The Importance of Research and Analysis in Policing. Retrieved 5 21, 2016, from EKU: http://plsonline.eku.edu/insidelook/importance-research-and-analysis-policing

Heinze, J. (2014, 9 5). Fighting Crime with Data: How Law Enforcement is Leveraging Big Data Analytics to Keep Us Safe. Retrieved 5 21, 2016, from BetterBuys: https://www.betterbuys.com/bi/fighting-crime-with-data/

Priscillia Hunt, J. S. (2014). RAND. Retrieved 5 21, 2016, from Evaluation of the Shreveport Predictive Policing Experiment: http://www.rand.org/content/dam/rand/pubs/research_reports/RR500/RR531/RAND_RR531.pdf

Wills, T. (2016, 3 2). Anticipating criminal behavior: Data science and the future of predictive policing. Retrieved 5 21, 2016, from IBM Big Data & Analytics Hub: http://www.ibmbigdatahub.com/blog/anticipating-criminal-behavior-data-science-and-future-predictive-policing

Juliana Reyes(2014, 2 18).‘Smart policing’ movement training Philly cops to be data scientists.Retrieved 5 22,2016, from Technical: http://technical.ly/philly/2014/02/18/philadelphia-police-smart-policing-crime-scientists/