Serial Killers Should Fear This Algorithm: How Data is Transforming Crime Solving
Data has become a powerful tool in many fields, and now it is revolutionizing the way law enforcement tackles unsolved murders. Thomas Hargrove, a retired news reporter, is pioneering this change with his innovative use of data analysis to identify potential serial killers.
The Email That Could Have Changed Everything
On August 18, 2010, a police lieutenant in Gary, Indiana, received an email from Hargrove, suggesting a possible serial killer in the area. The email included detailed spreadsheets created from FBI files showing 14 unsolved murders of women, all strangled, between the ages of 20 and 50. Despite the compelling data, the email was ignored. Follow-up emails and letters to the police chief also went unanswered. This lack of response was a frustrating setback for Hargrove, who believed his analysis could prevent further deaths.
A Data-Driven Approach to Crime Solving
Hargrove’s journey began in 2004 when he stumbled upon the FBI’s Supplementary Homicide Report while researching prostitution statistics. This comprehensive dataset detailed every murder reported to the FBI, sparking Hargrove’s idea to teach a computer to identify serial killers by spotting patterns in the data.
Developing the Algorithm
Hargrove developed an algorithm that uses cluster analysis to identify unsolved murders with common characteristics such as geography, sex, age group, and method of killing. His initial test against the known case of the Green River Killer validated his approach, as the algorithm successfully identified the killer’s victims. This success led Hargrove to uncover clusters of unsolved murders in various cities, indicating the presence of potential serial killers overlooked by local law enforcement.
The Murder Accountability Project
In 2015, after losing his job at Scripps, Hargrove founded the Murder Accountability Project (MAP). This nonprofit organization aims to make FBI murder data more accessible and to identify trends in unsolved murders. MAP’s database includes details on over 638,000 homicides from 1980 to 2014, making it the most comprehensive list of U.S. murders available. The data is open-sourced, allowing anyone with statistical analysis skills to search for serial killers.
Challenges in Law Enforcement
Despite the potential of Hargrove’s data-driven approach, police departments often resist integrating new technologies and data analysis methods. Many departments fail to contribute their data to the FBI, leading to gaps in the national homicide database. Furthermore, issues like staffing shortages and prioritization of resources hinder effective crime solving. Hargrove’s efforts to train law enforcement agencies and raise awareness about the power of data are crucial steps toward overcoming these challenges.
The Impact of Low Clearance Rates
Hargrove’s analysis revealed troubling trends in homicide clearance rates. Despite advances in forensic science, the rate of cleared cases has declined from nearly 90% in the 1960s to around 60% in recent years. In cities with poor clearance rates, the murder rate is nearly double that of cities with higher clearance rates. This correlation underscores the importance of solving murders not only for justice but also for preventing further crimes.
Success Stories and Future Potential
There are examples of cities improving their clearance rates through dedicated efforts. For instance, Philadelphia significantly increased its clearance rate under Mayor Michael Nutter’s administration by prioritizing major crime investigations. Similarly, Santa Ana and Oakland saw improvements by creating specialized units and collaborating with federal agencies.
Summary
Thomas Hargrove’s work with the Murder Accountability Project highlights the transformative potential of data in solving unsolved murders. By leveraging comprehensive data analysis, law enforcement agencies can identify patterns, uncover serial killers, and ultimately save lives. As data-driven methods continue to evolve, they promise to become essential tools in the fight against crime.
How do you think data analysis and new technologies will shape the future of crime solving and law enforcement? Share your thoughts and join the discussion!
