• United States



by Dave Gradijan

Study Results to Be Used for Missing Persons Matrix

Aug 02, 20063 mins
CSO and CISOData and Information Security

Results from a PhD study into missing persons around Australia will soon be in use by the New South Wales (NSW) Police Force to develop a standardized procedure for locating missing people.

The five-year study, on which Charles Sturt University (CSU) and the NSW Police Force collaborated, discovered 26 common variables on why people go missing. These variables, or flag points, will be integrated as a search matrix within the missing persons database to provide a percentage risk factor based on missing-person behavior.

Shaunagh Foy, then a student of Forensic Psychology at CSU, worked on the project as part of her PhD and said the researchers initially wanted 60 variables, but were unable to obtain this due to a lack of available missing-persons data.

The project started in 1999 and was completed in 2004. Foy said initially there was only sparse missing-person data to work with.

“Each year 30,000 people are recorded missing; if you break that down it is about 22 people a day in NSW. Cases are often ambiguous and it’s hard to tell what has happened to [people],” Foy said.

“Senior staff at NSW Police wanted to try and understand missing persons further, as there was no research available; only basic studies from the National Center of Missing and Exploited Children in the U.S. We wanted to look at basic demographic data, but in terms of behavior there are no studies on why adults run away.

“My study involved looking at closed cases like where runaways had returned, those who had committed suicide and those who were the victims of foul play. I looked at the background of each person, circumstances, letters and even newspaper articles, detectives’ summary reports and even autopsy reports, microfiche and homicide reports.”

Foy said that once the data was collated, it was analyzed using a basic comparative statistic. Then the entire data was mined using the j48part algorithm on Weka to identify a hierarchy of the most important predictive characteristics of missing people.

The School of Information Technology at CSU is developing a user interface for the software to run on the police mainframe.

Foy said the goal of the project is using the search power of the mainframe to analyze data for officers in the field.

“The idea is to port this information into a mainframe for data mining and eventually look at using fuzzy logic to search the missing-person data. Software is currently being developed so if police were to enter the details of a missing person into a handheld device, it would send the information wirelessly to the mainframe, and the learning system would return a likely reason for a person going missing as well as a percentage risk factor,” Foy said.

“The more information fed in, the more accurate is the prediction of what has happened to that missing person.”

By Michael Crawford, Computerworld Australia

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