The terms “big data,” “artificial intelligence,” and “algorithms” are commonly found in advertisements for new security tools but often serve as nothing more than buzzwords with no substantive backing. Now there is a new buzzword that delivers what it promises: geospatial intelligence.
What is geospatial intelligence? The traditional approach to geospatial intelligence involved analyzing satellite imagery and gathering data such as the number of tanks on a neighboring country’s border or assessing a country’s nuclear program by measuring the number of cement trucks and type of construction material at a secret location. Now geospatial intelligence compiled from mobile phone location data is becoming an increasingly popular investigative tool due to the vast amounts of information that can be quickly deciphered into actionable intelligence. Geospatial intelligence vendors now allow customers to identify known and unknown shoplifters, boosters, fences, black market distribution, and more with only a few clicks online.
The National Retail Federation estimates organized retail crime (ORC) cause $62 billion in annual losses to retailers across the country. Even though retailers focus on stopping these criminals from taking their products off shelves, shoplifters continue to become increasingly bold including robberies, smash‑and‑grabs, and flash mob‑style thefts. Fundamental security principles have been used to prevent, deter, and investigate these criminal activities for decades, but they continue to fall short of helping shut down ORC. With geospatial intelligence and advanced analytics, it is now possible to identify criminals and their patterns before, during, and after they commit crimes.
Next Level EBR
Advanced geospatial intelligence is similar to current exception based reporting. The exception reporting tool (EBR), primarily when used at the point of sale (POS), has proven valuable in the detection of theft and fraud. Programmers and investigators have worked together to identify transactions or performance metrics that raise red flags, also known as anomalies. A typical example of an exception report is an employee using the void transaction button statistically more than others, indicating that merchandise is possibly being stolen at the POS. Other exception reports can be much more complex and discover many areas of theft and fraud in a company.
As cheaper processing power, lower storage costs, and big data become more mainstream in everyday life, geospatial intelligence has stepped in to provide ways of combating theft and fraud with location-based red flags and suspicious patterns. Like POS exception reporting, companies are producing algorithms that uncover theft based on how people travel in and around store locations. By reviewing billions of signals compiled from data brokers, patterns of how consumers travel from point A to point B can reveal investigative insights, lead generations, and actionable intelligence instantly. Some vendors even offer the ability to review IP address location patterns to quickly investigate cybercrime and other online activity in ways that were impossible for civilians and companies to conduct in the past.
Big Data from Small Devices
In one example using mobile phone locations, multiple consumer patterns can be uncovered quickly when examining the typical retail location to identify possible criminal behavior. Consider how often a consumer might visit more than one big-box retailer on the same day when shopping. It may be expected that they needed help finding an item at store #1 and drove to store #2 in search of a product. Another consumer may be an auditor at a big-box retailer and visit multiple stores daily. And now we have many cases of workers for companies like Instacart and Uber who pick up numerous items each day from multiple retailers. Unlike single shoplifters or even regular customers, boosters travel from retailer to retailer, sometimes hitting multiple stores in multiple cities daily. Some geospatial intelligence vendors have analyzed these patterns millions of times and identified anomalies that have now been converted into algorithms that can uncover these patterns in seconds, highlighting the suspicious travel patterns indicative of boosting. Subsequent geospatial data can analyze multiple booster travel patterns and look for common relationships between devices. Quickly, unknown fences, shipping companies, and other suspicious locations are identified for further investigations. Even basic searches, often called “patterns of life,” can show investigators where a device has traveled dating back years.
Putting Two and Two Together
In addition, geospatial intelligence can be utilized with a spatial‑temporal analysis search to verify when two or more people have been together anywhere in the world. A simple‑to‑understand model would be instantly knowing if employees from company A ever had lunch, private meetings, or inappropriate relationships with any employee from competitor company B in the past few years. These patterns can also apply to boosters meeting up with partners and fences, conflicts of interest, intellectual property theft, and other types of crime involving sex trafficking, cartels, and organized crime activity. The use cases for geospatial intelligence are unlimited.
All new technology, particularly investigative or surveillance services, raises privacy concerns, and geospatial intelligence is no exception. Adding to the fears, there is a fundamental lack of understanding of how geospatial intelligence companies operate and the safeguards that are in place for the use of these tools. While geospatial intelligence is legal because of how data is collected, processed, and used, regulations are beginning to emerge. Vendors who offer geospatial intelligence are aware of the concerns, and many have data controls in place to ensure that even these impressive data sets comply with the General Data Protection Regulations and California Consumer Privacy Act.
A few vendors are even offering filters that limit searches of sensitive locations including hospitals, places of worship, and other sites that may not have investigational value for the average user. While these tools are beneficial to law enforcement and retailers, it is the availability and use of the data themselves, as well as trust in surveillance practices, that the public at large is uncertain about. More transparency about geospatial intelligence can help to alleviate these concerns.
With the growth of geospatial intelligence since the 1950s, new applications have been developed to support investigations and identify criminal activities. What was once the simple reconnaissance of areas using imagery has evolved into an industry ruled by big data and leveraging acquired signals and signatures to define and track predictive behavior. As this data grows and new algorithms are developed, the potential uses for investigations become infinite. Users agree that a single search can instantly transform any investigation into a positive conclusion, making the opportunities provided invaluable in the pursuit of crime and criminals.
Using geospatial data to intersect time and space is simply the next step to safeguarding our communities and businesses with everything from predictive policing to bolstering our understanding of the criminal mind.
Vijay Richard is the founder and chief operating officer of Geogentia, a geospatial intelligence company specializing in using big data to uncover criminal activity. With years of experience in the Department of Defense and the intelligence community, Richard has focused on merging tools and tradecraft into easy-to-use platforms for local, state, federal, and corporate customers.