Future Law Enforcement

Future Law Enforcement

by Jia Wang, Minseok Song

During the last few weeks, we have talked about the application of data science in the law enforcement area and its positive impact. Now, let us move on to talk about the future of law enforcement.

Future of Law Enforcement

In the future, criminal rate would reduce and law enforcement departments would have more efficiency and higher productivity. Let us discuss about how these can be achieved.

Public Safety with Internet of Things/Everything

The world we live in will soon embrace IoT and IoE, according to an IoT report by Cisco (Cisco, 2014). Public safety in the future will be managed by these two strong platforms - this combined platform can be also called as a newly coined term, PSIOT (Tom Wills, 2016). Instead of working within de-centralized systems or devices, law enforcement departments will be able to work on the connected platforms. Everything will be connected from tiny wearable sensors to centralized data centers. The following picture shows the potential changes brought by PSIOT:

Four main changes brought by IoE

(Image from: http://www.nascio.org/events/sponsors/vrc/Public%20Safety%20Justice%20and%20the%20Internet%20of%20Everything.pdf)

PSIOT will enable police officers and agencies to get and share real-time information. Mobile devices such as phones and tablets harnessing telecommunication technology will become more powerful and more responsive to incidents. For example, police officers can send incident data to policing centers and judges by using tablets inside their police car. Data can be accessed at any time and any places. Ken Boal, the vice president of Cisco, stated in the report, Public Safety, Justice, and the Internet of Everything (Cisco, 2014), that “Officers won’t be a situation blind. Internet of Everything brings together people, process, data, and things to make networked connections more relevant and valuable than ever before.”

Law enforcement departments would understand data better with the help of PSIOT. Instead of reading statistic data, rich video data will be available for the officers. Moreover, data would be more secure under the PSIOT umbrella. Only registered objects can access to data, which can enforce the defense against criminal hacking (Cisco, 2014). However, will this technology be free from any risk?

Potential Problems

Network Risk

As we discussed above, law enforcement is becoming more and more dependent on the Internet and networks. This can be a double-edged sword. Networks bring the information tunnels for the police to get a good control on incident situations. However, the police would lose control once the network or platform, which will become their daily working dependency, crashes. Also, criminals’ hacking on the Internet would cause serious confidential crisis.

Social Media

The police use social media to get data for criminal analysis, to get control on officers’ actions and to post issues for public safety. However, people post their daily life and personal information, which can lead to a crime. People can post anything, even the prohibited, with little fear because they are sitting behind the Internet. Police officers may also threaten not only themselves but also their family. The article, Social Media and Law Enforcement Potential Risks, has pointed that the home satellite images of officers’ may be obtained by Internet players when officers issue the posting citations (Gwendolyn Waters, 2012).

Benefits

Criminal & Cost

Thanks to the technologies such as data science, cloud computing and telecommunication, the criminal rate is expected to decline and working cost would go down thanks to the growing productivity.

Reduced Officer Death

The number of death of police officers will reduce. As Harris said in the article, Number of Police Officer Killings Drops, Reversing 2014 Spike, FBI Data Shows, there is more and more training in the law enforcement departments to potentially reduce bad situations and confrontations (Jon Schuppe, 2016). The figure below shows the declining trend of officers’ deaths.

Police officers’ deaths record

(Image from: http://www.nbcnews.com/news/us-news/number-police-officer-killings-drops-reversing-2014-spike-fbi-data-n574746)

Conclusion

The data science technology will provide values to the law enforcement sector. But, external factors might hinder the advancement. For example, a report about the impact of economic downturn on the police explained that an economic downturn can stop adoption of new technologies due to reduced investment (COPS, 2016).

We cannot clearly define the future of law enforcement due to the external factors like economy. But, throughout a series of our blogs, we have learnt that data science will always provide a break-through solution for the police to step forward amid difficulties. We should welcome data science in law enforcement.

References

Gwendolyn Waters.(2012). Social Media and Law Enforcement: Potential Risks. Retrieved 6 9, 2016, from The FBI: https://leb.fbi.gov/2012/november/social-media-and-law-enforcement-potential-risks

Cisco.(2014). Public Safety, Justice, and the Internet of Everything. Retrieved 6 8, 2016, from: http://www.nascio.org/events/sponsors/vrc/Public%20Safety%20Justice%20and%20the%20Internet%20of%20Everything.pdf

Jay Fortenbery, M.J.A..(2016, 2 10). Law Enforcement Organizations: Possibilities and Challenges for the Future. Retrieved 6 8, 2016, from The FBI: https://leb.fbi.gov/2016/february/law-enforcement-organizations-possibilities-and-challenges-for-the-future

Tom Wills.(2016, 3 2). Anticipating criminal behavior: Data science and the future of predictive policing.Retrieved 6 8,2016, from IBM Big Data&Analytics Hub: http://www.ibmbigdatahub.com/blog/anticipating-criminal-behavior-data-science-and-future-predictive-policing

Jon Schuppe.(2016, 5 16). Number of Police Officer Killings Drops, Reversing 2014 Spike, FBI Data Shows. Retrieved 6 9, 2016, from NBC NEWS: http://www.nbcnews.com/news/us-news/number-police-officer-killings-drops-reversing-2014-spike-fbi-data-n574746

COPS. (2016). The Impact of the Economic Downturn on American Police Agencies.Retrieved 6 9,2016, from COPS: http://www.cops.usdoj.gov/Default.asp?Item=2602

Cutting-edge Data Science Techniques

Cutting-edge Data Science Techniques

by Jia Wang, Minseok Song

Cutting-edge technologies are critical in the law enforcement area. Police should have more advanced techniques and tools than criminals have to protect innocent civilians. Since the authorities well understand this fact, they make efforts to use recent technologies. For example, the U.S. law enforcement recently adopted technologies such as GPS tagger, handheld narcotics analyzer, body camera and thermal imaging (Neese, 2015). There are more recent technologies, but we learnt that data science is the centerpiece of them. In this blog, we will first look at cutting-edge data science technologies and their current challenges.

Technology in policing control

(Image from: http://www.bigdatacloud.com/predictive-policing-the-future-of-law-enforcement/)

Crime Predictive Analytical Software

To prevent crimes, police should be able to know criminals’ patterns and predict their next moves. Predictive analytical software can give these powers to the police. An article from Law Enforcement Publications and Conferences reveals that Memphis Police Department (MPD) used IBM SPCC analytics software to reduce violent crime cases and achieved a 29 percent decrease (Phelps, 2010). In the article, John F. Williams, a crime analysis unit manager with MPD, commented that, “We were able to pinpoint when we needed to place our officers in those areas. We saw a tremendous decrease in those areas. From that, we knew we had latched onto something successful.”


Machine Learning for Anti-money Laundering

Machine learning has been being actively used to track anti-money laundering (AML) crimes. A report, Big Shifts What’s Next in AML by Booz Allen Hamilton, highlighted that the next big shift in the fight against financial crime is advanced machine learning (Hamilton, 2014). This report introduced machine learning algorithms including unsupervised similarity clustering, supervised training based on validated true positives and true negatives and binary classification. Data scientists applied these techniques to hundreds of thousands of money transactions to build a predictive model. This model helped them to identify illegitimate traffics and catch criminals.


Image Processing for Identification

Since 2010, the Stockton California Police Department has been using a state-of-the-art device called Mobile Biometric Device (Anderson, 2014). This hand-held device is capable of recognizing suspect’s identification not only by fingerprints but also by facial photos. Once a police takes a photo or scans a fingerprint, this device sends the image to the central database. Then, the database starts analyzing the incoming data with image processing and machine learning techniques. In the article, Erin Mettler, the Stockton Police Department’s fiscal planning and research manager said that, “With the traditional method, you would have to dust the print, lift it with lifting tape, take it back to the office… It could take a day. It could be a week later, depending on the type of crime.” After adopting technology, they could reduce the process to 20 minutes in maximum.


Current Challenges & Limitations

These breakthrough technologies, however, have some limitations. In an article that analyzed problems of data science in law enforcement (Moraff, 2014), the author pointed out that, since predictive analysis is heavily dependent upon data from individual police agencies or software providers, the quality of data can be unreliable. And data quality and content can be inconsistent from city to city, which can decrease analysis accuracy.

No privacy under policing data science

(Image from: https://medium.com/homeland-security/no-privacy-no-security-6fd25bc238c8#.wqf36fu1u/)

In addition, the police databases store not only criminals’ information, but also innocent individuals’ private data. Beth Pearsal at National Institute of Justice in U.S., in his article (Pearsall, 2010) stressed that law enforcement should transparently use these information and form a social consensus in their communities. Communities should have confidence that law enforcement use their data in a right way.


References

Hamilton, B. A. (2014, 1 1). BIG SHIFTS WHAT’S NEXT IN AML. Retrieved 6 3, 2016, from BIG SHIFTS WHAT’S NEXT IN AML: https://www.boozallen.com/content/dam/boozallen/documents/2015/09/machine-learning-and-data-science.pdf

Moraff, C. (2014, 12 15). The Problem With Some of the Most Powerful Numbers in Modern Policing. Retrieved 6 3, 2016, from NEXTCITY: https://nextcity.org/daily/entry/predictive-policing-crime-stats-data-measure

Neese, B. (2015, 3 27). Saving Lives with Technology: Cutting-Edge Law Enforcement Tools. Retrieved 6 3, 2016, from SOUTHERUNIVERSITY: http://online.seu.edu/law-enforcement-tools/

Pearsall, B. (2010, 6 1). Predictive Policing: The Future of Law Enforcement? Retrieved 6 3, 2016, from Office of Justice Program: http://www.nij.gov/journals/266/pages/predictive.aspx

Phelps, C. (2010, 12 1). IBM predictive analytics help slash crime rates in Memphis. Retrieved 6 3, 2016, from Law Enforcement Publications and Conferences : http://www.hendonpub.com/resources/article_archive/results/details?id=1530