Finding missing children and unraveling the complex web of human trafficking is no easy task. The relevant datasets are massive and often unstandardized. It can be difficult to find the right data at all, as it often disappears from websites and pages on a regular basis. When data is hard enough for scientists to capture and evaluate, how can law enforcement agencies even begin to get a handle on it? These agencies, with little funding or know-how, need real help if they want to leverage big data and get a grip on human trafficking.

Many efforts to solve crimes with data is actually coming from outside the law department. From community efforts to non-profits and even full business solutions, it seems the world of data science is actively using their skills for good. More importantly, these data solutions are in stark contrast to the more general and vague job of crime prediction, which is becoming more and more common. Many departments already use data to target trouble areas, but for those crimes that involve huge rings and layers of corruption, there’s a lot more work to be done.

The companies using data science to stop human trafficking often use several methods and mimic what regular law enforcement agencies might do on their own. The “Science Against Slavery” Hackathon, was an all-day Hackathon aimed sharing ideas and creating science-based solutions to the problem of human trafficking. Data scientists, students and hackers honed in on data that district attorneys would otherwise never find. Many focused on automating processes so agencies could use the technology with little guidance. Some focused primarily on generating data that could lead to a conviction—which is much easier said than done. One effort from EPIK Project founder Tom Perez included creating fake listings. They could then gather information on respondents, including real world coordinates. Other plans compared photos mined from escort ads and sites to those from missing person reports. Web crawling could eventually lead to geocoding phone numbers or understanding the distribution of buyers and sellers, as well as social network analysis.

Turning Big Data Into Real World Information

Perhaps one of the more famous initiatives comes from the Polaris Project, a project that was started in 2002 and revitalized in 2012 through the use of data science. When the company heard a talk from the CEO of Palantir, a software and data analysis company, it was clear that the fight against human trafficking needed an upgrade—a big one. With some help from Palantir, Polaris was soon armed with new technology and engineers. They began leveraging data from phone calls, company contacts, legal service providers, and every other part of their organization in one simple platform.

Palantir actually helped other companies, like the National Center for Missing and Exploited Children, or NCMEC, in a similar fashion. By combining data from public and private sources, the organization pinpointed 170 different quantitative and qualitative variables per case record. Advanced analytics were required to evaluate tips, of which 31,945 came by phone, 1,669 through online submission, and 787 from SMS. The project also aimed to digitize old records that spanned several decades and import them into a single searchable analyzable structure. All of this data is powerful, but the final step was making it easily accessible. By importing the numerous formats and levels of information into one database, what once took several weeks—or was impossible entirely—could be done in an instant.

The story of one missing 17-year old girl in California has since become the shining example of data triumphing in the world of human trafficking. Using data science, analysts were able to find multiple online posts advertising the missing girl in question for sex. By analyzing over 50 ads, and nine different women spanning five states, analysts didn’t just find the girl—they saw the larger ring and were able to link the pimp to other crimes and victims.

Visualizations and Easy Solutions for Law Enforcement

The BBC has reported on the amount of data available, and how those terabytes aren’t as immediately helpful as the public would like to think. Child sex abuse raids tend to lead to unbelievable amounts of data. Image forensic specialist Johann Hoffman laments, “the problem is, how as a police officer do you go through that huge amount of data? When you are dealing with terabytes there’s no way a human could ever go through it all.” Using analytics, however, has given them an entirely new approach to data. Friendly data platforms and visualizations help generate a larger story that doesn’t require a master’s degree to understand.

There are several more examples, but one particularly interesting area are those data solutions marketed toward law enforcement. One Y combinator startup wants to act as a paid service for law enforcement. It may feel a tad weird to read a tagline like “the right data at the right time can make or break your prosecution,” but these external companies offer the expertise law enforcement employees likely won’t otherwise have access to. Plus, to make the entire concept a bit more palatable, this particular startup, Rescue Forensics, only registers with official law enforcement agencies, as opposed to just anyone who wants to pay up. Most escort advertisements disappear after a few days, making it incredibly difficult to track. Companies like these who focus entirely on data tracking, analysis and storage can keep otherwise lost information alive for those who need it.

The splintered nature of the entire field might also be one of its biggest assets, for the time being. While splintering in some sectors causes huge problems, and ultimately holds users back from progress, the array of approaches in this area is due to just how many people are interested in creating solutions. These different companies come with different backgrounds and goals and will ultimately lead to new and exciting possibilities. Many operate on open-source platforms, meaning we can expect the number of solutions to continue to skyrocket.

Like this article? Subscribe to our weekly newsletter to never miss out!

Previous post

Why IoT developers need open source framework

Next post

Big Data isn’t the problem - data copies are