Dig Into Data Mining

Enhanced analysis can help law enforcement be more proactive

What's your favorite brand of toilet paper? How about deodorant?

Some stores, especially online retailers, don't have to ask. They already have the answers they're looking for, helping them sell more products. They know what you buy, in what amount, at what price, how you pay, and when you are most likely to come back to restock your supplies.

Moreover, they know who you are, the best ways to deliver advertising to you, and they know what else you are likely to buy at the same time you buy toilet paper or deodorant.

While the specific examples above may not cause envy among law enforcement, the fact that retailers have better analytical capacities than most law enforcement agencies should.

This fact frustrates Colleen "Kelly" McCue, a senior research scientist at RTI International, a non-profit research institute.

"In law enforcement, if you do your analysis wrong, you can compromise public safety," she says.

Before joining RTI International, McCue was program manager for the Richmond (Virginia) Police Department Crime Analysis Unit, where she pioneered the use of data mining and predictive analysis.

Data mining, also referred to as predictive analytics (or analysis), sense making or knowledge discovery, involves the systematic analysis of large data sets using automated methods, she explains. Wanting to help the enforcement community learn more about data mining, she wrote "Data Mining and Predictive Analysis."

McCue is hopeful data mining will become more widespread in law enforcement, because she says it is within the grasp of agencies of all sizes and at all levels. In fact, she says agencies are already data mining to some extent in investigations (determining motive is one example), but they also can use data mining to predict and prevent criminal acts.

A big emphasis today is being placed on counting crime, counting what happened, she says.

"One of the things data mining and predictive analytics allows us to do is move from counting crime to anticipating, preventing and perhaps responding more effectively to it," she says. "We can focus on what we consider to be an effective use of our information and how we want to manage our resources and fight crime. If it is counting crime, that's great. But we know criminal behavior tends to be relatively predictable. By exploiting the data, we can be much more proactive in anticipating and preventing crime than we are now."

The importance of analysis
Data which means nothing to one case could solve another.

"All law enforcement data is very important," says Steve McCraw, director of homeland security in Texas. "A parking ticket, for example, could be a valuable lead in a conspiracy investigation being worked on a series of robberies."

Overall, law enforcement has become very good at collecting and compiling data, especially since the advent of computerized records management systems. Regional sharing initiatives and state-level fusion centers add to the data that individual agencies can tap into. And, national law enforcement data sharing standards help make this possible.

While information sharing initiatives certainly are beneficial, McCue says "don't stop there." Once data is collected in a meaningful fashion, the next step is analysis, she notes.

Unfortunately, McCue adds, the importance of analysis is not a universal understanding today.

Yet, she says the process of analyzing the data is important to:

  1. confirm what you already know and,
  2. discover new information or relationships in data (knowledge discovery).

Jay Albanese, graduate director of criminal justice at Virginia Commonwealth University, says police need information more than ever before and it is increasingly difficult to obtain.

The point at which police solve major crimes has been dropping nationwide over the past 10 to 15 years, he says. One reason is there are more complicated crimes, affiliated with terrorists, organized crime or ethnic minorities, where language can be a barrier.

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