Big Data is having a significant and wide-reaching impact on our lives. And, it can be found in quite unexpected places. Nate Silver’s prediction of the 2012 US Election has been making headlines on the web for quite some time. The accuracy is surprising, using Big Data to achieve it, probably not so much. However, following are two scenarios where Big Data’s application is less obvious. The first one shows how Big Data analytics has been used and the second one demonstrates the potential of Big Data for dealing with minor urban crime and benefitting communities.

Pirates (on the high seas)

Although we’re now in the 21st century with advanced technology and military hardware at our disposal, piracy still remains a significant problem. Oceans Beyond Piracy, a US-based consultancy, estimated that losses from piracy reached $7Bn¹ in 2011.

However, in 2012 the International Maritime Bureau reported a 54% drop in piracy incidents in the first half of 2012 compared to the same period the year before. The reason for this drop was the fact that law enforcement agencies turned to Big Data to try and prevent piracy. The amount of data available to Government agencies is analysed in order to predict where the incidents may occur in the future. The information is then plotted on a map using advanced mapping software by Esri and used by the relevant maritime and Government authorities².

The results, as we can see, are simply staggering.

… And, graffiti (in the city)

The above example brings us to another potential use of Big Data — reducing graffiti appearances in our cities. According to a report from the Australian Institute of Criminology, graffiti costs around $1.5 billion³ a year in Australia, based on conservative estimates. Graffiti can be classified into several groups with the offenders in each group possessing a number of distinct characteristics. The most prevalent type of graffiti writer, accounting for over 50% of all people engaged with graffiti, is the tagger. The report also lists some specific traits of graffiti writers:

Thus, if we look at how to deal most effectively with graffiti, it is clear that focusing on taggers will provide the “best bang for the buck”. They also are the easiest group to deal with in terms of predicting the location of their future actions since their mobility is limited, because of age. Thus, using data from schools, reports of graffiti incidents to police and local authorities and data from cleaning operations it is possible to build a predictive model in relation to graffiti.

A similar approach has already been applied by the Memphis Police Department in relation to various crimes with great success. The Department is using analytics to predict incidents. The report from their analytical solution may say “You’ve had three robberies that occurred between 10 p.m. and 1 a.m. on Monday”. This allows patrols to be deployed in areas where they will have the biggest impact.

In essence, the approach is similar to the one, mentioned above, used to fight pirates on the high seas. And, in relation to graffiti, it can be equally effective because of the similarities in available data and the characteristics of the crime.





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