Albanese points out the New York City Police Department's CompStat, now used by a number of other agencies, is essentially an exercise in data mining. "It's looking more carefully, more systematically at the information police are already collecting," he says. "It's looking at reported crimes and different areas of the city, plotting them on maps, looking at trends, looking at the allocation of police around the city, looking for hotspots."
Law enforcement also can use data mining to marshal support of the community to assist in crime prevention. Armed with data about trends and patterns, police can turn to businesses, school groups and others, and show where help is needed.
"If a lot of theft activity is taking place near a mall, it only makes sense the shopping mall share responsibility for the efforts to prevent crime there," he says.
Long-term, he says, "We want to prevent crime, and crime prevention is really everybody's responsibility."
Police managers who understand data mining can in turn educate the public about data mining and its benefits, as well as address critics.
"Police managers, command staff and public officials always need to be sensitive to public perceptions about how they do business," McCue says. "There's a move toward transparent government. People want to know how we do things, how we analyze data, what data we're looking at."
Working with the city council, legislators or an agency's oversight group is important when technology is upgraded, McCraw says, because it helps alleviate presumptions and misinformation.
Data mining is not an abusive technique to spy on citizens, says McCraw, who testified before Congress on the subject during his tenure with the FBI.
"It's using information technology to locate the information that you need among data you already have," he emphasizes.
Data mining is an analytical process. "The same rules that have always applied to legally permissible means of accessing data are always going to apply," McCue says.
Other criticisms of data mining are that it doesn't work and wastes resources.
"I think they are absolutely wrong," she adds. "We found it does work. When data mining is done by someone who knows data mining, and understands the limitations of law enforcement data and the analytical outcomes sought — or works with someone who does — data mining reduces errors."
While with the Richmond PD, McCue used data mining to reduce gunfire complaints by almost 50 percent on New Year's Eve 2003 and increase the number of illegal weapons seized by 246 percent from the previous year, while using fewer officers.
Some data mining is more difficult than others. Very infrequent events are difficult to model.
"That is where I think it becomes really important law enforcement personnel do the analysis themselves or participate very actively in the analysis," she says.
Despite the fact that measures are taken to reduce errors, errors happen, as they do with anything.
McCue uses a medical analogy to remind that not all errors in law enforcement are equal.
As long as a disease is identified effectively, screening tools are allowed a certain number of errors, or false positives. Yet, there are other situations in which there is no room for error. If someone who is ill is given a wrong antibiotic, an illness might not only not be cured, it could worsen.
Again, people doing data mining must work closely with people who understand law enforcement and criminal behavior so they can make informed decisions about the nature of the errors, which errors are acceptable and which are not, she says.
"Maybe if you put officers in the wrong location, they spend a night in the cold," she says. "That's not necessarily a big deal."
But, she says if you're using data mining to determine motive and you make an error, the danger associated with misdirecting resources can cause a crime to remain unsolved.
In her book, McCue gives the example of creating a model that's 97-percent accurate by always predicting crime will not take place in a certain low crime area. That is unacceptable, she says.
"Getting inside the nature of the errors and making informed decisions is key," she says.
Predicting the need for predictive analysis
Once law enforcement starts looking at data mining, they realize in many ways, they're already doing it, she says.