The future of crime fighting is moving away from reacting to incidents as they occur and towards ‘predicting’ crime in order to prevent it. The LAPD have already spoken about using earthquake models to predict crime ‘aftershocks‘; now, The Australian Crime Comission are scanning massive sets of data to examine criminal threats across the country. They have spent $14.5 million over the last four years developing big data systems to identify these trends, meaning they can take a more proactive approach to identifying and tackling crime.

However, speaking at the CeBIT tech conference in Sydney, ACC chief information officer Maria Milosavljevic was keen to emphasise that their work was less about the idea of predicting specific crimes, and more about examining ‘a threat that is increasing, and predicting that it is going to continue to increase based on what we’ve seen in the past’. Discussing the importance and possibilities of Big Data, she stated ‘We live in an algorithmic age, we live in an age where we have access to a lot of information and we’ve moved to a world where strategy and vision setting can be adjusted on the basis of what we can see in information’.

One advantage the ACC have found in analysing huge amounts of data is that it broadens crime fighting beyond one particular jurisdiction. By having a much wider, national view of crime patterns, they are able to identify which areas are tackling the same problems and pool their resources. Milosavljevic also stated that being able to identify threats faster and with greater accuracy means response time is shorter, and that information can be shared between partners with greater speed and efficiency.

Moving forward, the ACC are looking at how to incorporate more unstructured audio and visual data into their analysis. Milosavljevic highlighted the variety of data beyond text and spreadsheets as one of the main challenges facing the system- ‘There are some tools that allow you to do some things but it’s limited’.


Read more here.

(Image credit: Simon Yeo)

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