Fighting crime with big data analytics

With today’s advanced technology, police and law enforcement agencies can fight crime far more proactively than ever before – and can solve the crimes that do occur with greater success.

Dec 9, 2015
By Paul Jacques

With today’s advanced technology, police and law enforcement agencies can fight crime far more proactively than ever before – and can solve the crimes that do occur with greater success.

“One of the best ways to keep communities safe is to prevent crimes from happening in the first place,” explained Sergio Ortega Cruz, worldwide industry solution manager, Public Safety and National Security, at Microsoft, “Yet, until recently, this proved a difficult challenge for law enforcement agencies. The good news is that leading-edge technology now exists to help agencies identify crime patterns on a very detailed level – and it can do so in a highly secure and scalable way.

“For example, Microsoft Azure Data Lake enables law enforcement agencies to store any type of data, including text, images and video of different formats, in a single repository – no matter how much they have.”

He said with all of their data stored in a single place, police officers can then use analytics software to analyse this information using state-of-the-art machine learning algorithms.

They can then easily tap into this information using personal digital assistant and speech recognition software, asking questions of their computers and mobile devices to extract the exact information they need.

“Law enforcement agencies are starting to use tools such as these to predict, anticipate and prevent crime,” said Mr Cruz in a recent public safety and defence blog for Microsoft.

“For example, one US police department we’re working with is using Microsoft technology to aggregate and analyse public safety data streams in real-time. The police department is analysing data from live camera feeds, 911 calls, police reports and other sources to help it proactively fight terrorism and reduce other types of crime.”

Advanced analytics is becoming increasingly used by police to identify criminal patterns ranging from burglaries to murders to domestic violence. It can help officers sift through vast amounts of information, automatically identifying the habits of a repeat burglar, including means of entry, the time of the break-ins, the type of properties targeted and the geographical distance from other burglaries.

“It can also be used to assess burglaries on a larger scale, helping police officers to identify patterns as to when and where break-ins are likely to occur, so they can prevent future burglaries from happening,” added Mr Cruz.

“With detailed information about crime patterns, law enforcement agencies can put more police officers on the street during high-risk situations.”

If police know, for example, where burglaries are likely to take place in specific neighbourhoods during summer months between 10am and 3pm, additional resources can be placed in those areas at those times.

“They can also work with other government agencies to reduce the risk of break-ins, for instance, by offering summer youth programmes in specific neighbourhoods during the hours when burglaries are most likely to happen,” said Mr Cruz.

He said advanced analytics also offers the opportunity to respond more effectively to crimes once they occur.

“Think about the 2013 Boston Marathon terrorist attack. After the bombings, police used social media outlets to collect a vast amount of citizen data. And although sifting through this data required a huge effort, it eventually helped them to track down the suspects. In the future, police departments will have the opportunity to use advanced analytical tools to pull out the most important leads, helping them to solve crimes very quickly.

“With everything from store security cameras to past police reports to evidence from bystanders’ mobile phones, today’s law enforcement officials have an overwhelming amount of information that can help them solve crimes. The trick is to quickly make sense of this data – which is the magic that advanced data analytics provides.

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