By: Nikola Rujkov, Mythili Manirajah, Finn Klebe, and Min Wu
Why did we choose “PredPol” and what does it have to do with Big Data?
In general, working with big data means to analyze th huge amount of data that is very hard to process when using traditional data mining systems and algorithms. “PredPol”, the company we chose for our project, works in the same way. Often the vast amount of data is collected in order to maximize profits, for example, by tailoring advertising to each and every customer individually. Moreover, the collected data is, in the most cases, highly personal and confidential, and possibly even collected without the customers knowing it. We, as a group, had the aim to find other branches in which neither profit maximization, nor the collection of highly confidential data is on top of the agenda. In the case of “PredPol”(Predictive Policing), big data is used to assist the human beings. However, instead of maximizing profits, meeting the noble goal of maximizing citizens’ security is intended to be facilitated.
When we, as a group, came across this company, we found it intriguing to see how big data broadens the opportunities of human beings. Without forestalling the summary of their service, the tool “PredPol” is used in the police branch, to be more precise in criminology. However, it is not used to replace police officers – usually being one of the main issues of advanced technology, but rather to assist and encourage them to use their workforce as effective as possible. We find the overall aim of the tool to make all of us feel safer in our own environment remarkable and likely to be able to constitute a role model of big data usage in a moral way. As a result, we consider the tool worthy enough, to dig deeper in order to find out how it works and if it is able to confirm our first thoughts and aspirations.
“I’m not going to get more money. I’m not going to get more cops. I have to be better at using what I have, and that’s what predictive policing is about… If this old street cop can change the way that he thinks about this stuff, then I know that my [officers] can do the same.”
-Los Angeles Police Chief Charlie Beck
Short summary of the service provided by Predpol
“PredPol” was founded in 2012, based in Santa Cruz, California. “PredPol” is a secure, cloud-based software-as-a-service, developed by a team of PhD mathematicians, criminologists, and social scientists. It is an invaluable added tool that allows our police to use their patrol time more efficiently and helps stop crime before it happens – this technology offers an excellent crime-fighting solution that will ultimately make our lives safer. The core technology includes prediction of drug crime, gang crime, anti-social behavior, and the recently released gun violence prediction tool. In other words, “PredPol” tracks various types of crime: burglary, robbery, vehicle theft, theft from vehicles and gun crime as well as traffic accidents.
Only three criteria of data are used to make predictions – type of crime, place of crime, and time of crime. No personal data is utilized in making these predictions. This predictive policing gives officers a significantly better idea of when and where to be so that they can deter crime. There is a proven track record of crime reduction in communities that have deployed “PredPol”.
First obvious opportunities and threats
PredPol plays a vital role in ensuring public safety because it enables law enforcement agencies to take preemptive steps ahead of crimes and reduce the crime rate in regions that are prone to repeated attacks. “PredPol” saves time, money and energy (logistics) because law enforcement agencies are more focused and only have to deploy their resources when it is necessary. In other words, it ensures maximum efficiency and effective implementation of invaluable resources to prevent crime. In addition, “PredPol” is cost effective because it is based on pre-existing crime records that are safely transferred and analyzed to produce accurate crime predictions. It takes advantage of the available data and reproduces it for a different purpose at a minimal cost as the company only expends money to maintain the software. However, the company could be forced to work with old data if by chance the law enforcement crime database is not updated on a regular basis as the database is the foundation of “PredPol’s” predictions. This, in turn, could affect the company’s relationship with its customers as they could potentially switch to “PredPol’s” competitors whose services might not depend on the law enforcement crime database. In addition, “PredPol’s” predictions are somewhat unreliable in that they only pinpoint the time, day, place and very limited information on the criminal. Law enforcement officials could misjudge innocent individuals and arrest them solely based on the prediction. There are also concerns about the use of predictions to convict a criminal as such predictions are only mere speculations and don’t necessarily reflect the true intentions of the individual on trial. It is unclear whether the predictions are reliable and hence one may wonder whether it is ethically acceptable to carry out convictions based on technological predictions. Moreover, “PredPol” is less effective when considering crimes that less repetitive in nature or the new one for that matter because there is very minimal data on such crimes; hence, PredPol is required to wait until there’s enough data to produce reliable and accurate predictions on such crimes. This, in turn, could be too late in some cases and often times law enforcement officials act based on their intuitions. It is also why law enforcement agencies report a decrease in crimes such as theft and burglary (crimes that are repetitive in nature) whilst reviewing the effectiveness of “PredPol”.
|Name||Distributed Sessions||Meeting Time|
|All members||Summary on Big Data and Social Media||Oct 14th|
|Nikola Rujkov||Key Partners, Key Activities & Key Resources||Oct 14th|
|Min Wu||Value Propositions & Channels||Oct 28th|
|Mythili Manirajah||Customer Relationships & Customer Segments||Nov 11th|
|Finn Klebe||Cost Structure & Revenue Streams||Nov 25th|
Further videos for more information 🙂
“Don’t Even Think about It.” The Economist. The Economist Newspaper, 20 July 2013. Web. 05 Oct. 2016.
Garriss, Kirstin. “Intelligence Based Software Helps Police Predict Crimes.” YOUR4STATE.
Nexstar Broadcasting, Inc, 04 July 2014. Web. 05 Oct. 2016.
“Predictive Policing Pros and Cons.” PredPol RSS. Web. 05 Oct. 2016.
“Proven Results of Our Predictive Policing Software | PredPol.” PredPol RSS. Web. 05 Oct. 2016.