“Drones Fight Poachers" has an undeniable sexiness to it as a news narrative. Who doesn’t want to read about flying killer robots battling machete-wielding criminals chasing innocent animals on the wild African plains? The instant appeal of a high-tech solution to a pervasive low-tech problem is also why Silicon Valley giant Google has given the World Wildlife Fund (WWF) $5 million for drones to stop poaching. But to actually stop poachers, WWF should focus less on drones and more on math—and some lessons learned in Iraq and Afghanistan.
University of Maryland computer scientist Thomas Snitch is applying a mathematical forecasting model he developed for use by the military in Iraq and Afghanistan to Africa. Snitch is trying to overcome poaching networks’ advantages in money, opportunity, and manpower using his military model to put park rangers in the right places to intercept rhinoceros killers.
Illegal poaching and terrorism don’t seem to have much to do with another. In fact, the relationship is deeply interwoven. First, there’s the money. The State Department recently warned that the proceeds from illegal poaching go to African terrorist organizations like the Lord’s Resistance Army, the Janjaweed and al-Shabaab. But that’s not the only connection. The tools of poaching also look like tools of war. In places like South Africa’s Kruger National Park, here’s how that war appears. On one side are park rangers who make $150 per month protecting endangered animals such as the black rhino. Opposing them are networks of poachers who are more numerous, better armed, and better funded. Today, one rhino horn sells for $50,000 a kilogram on the black markets of Vietnam, according to commonly cited figures from researcher D. Graham-Rowe. The price can reach more than $1 million. It’s one reason why there were more than 1,000 illegal rhino killings in South Africa last year according to South Africa’s Ministry of Environmental Affairs.
The problem with the way groups like the WWF use drones is that they treat unarmed drones as a silver bullet, says Snitch. They succumb to the temptation to spend too much money on systems that are difficult for the rangers to work with, that are costly to repair, and that won’t actually stop poachers. They also don’t know how to use the drones correctly. “I ask these people all the time, ‘You have this device. Where do you plan to fly?’ ‘Well,’ they say ‘we'll fly back and forth.’ OK, Kruger is the size of Israel.” It’s a math problem. One he’s faced before.
In 2010, Snitch’s day job was trying to predict where insurgents in Iraq were going to plant roadside bombs. He approached the problem as one of incremental elimination. If he could figure out those spots where the insurgents were definitely not going to place IEDs then he could make sure that the most vulnerable areas were covered.
“We looked at every IED explosion over the last five years and pinpointed [the explosions] on the maps,” Snitch explained to a group gathered at the University of Maryland in the spring of 2013. “On top of that we overlaid where the U.S. troops were when they were hit and what roads they were on. We then took drone intelligence and human intelligence. We came to the conclusion that when an IED blast goes off there’s a 90-percent chance that the bomb factory is between 685 and 750 meters from the explosion. Does that tell you the exact house? No. But the commander knows the perimeter to start looking. We looked at the patterns of where the insurgents moved, what coverage they had, and where they can hide.”
His biggest asset in anticipating roadside-bomb sites was high-resolution satellite imagery. “We were able to develop a map of Baghdad down to 16 inches … very precise location and terrain.” This data made it easier to understand the factors that made one IED site more attractive than another. Though Sunni-on-Shiite violence was common, the primarily Sunni insurgents weren’t likely to plant bombs in Shiite areas because the risk of being in the wrong place at the wrong time was too high.
Neighborhood dynamics also played a key role in bomb placement. Which families lived on which streets? Which areas got foot traffic, and at which times of day? How much sunlight or shade does a particular street corner receive? How close is the nearest escape route? Where does it go? Understanding how all of these variables relate mathematically to influence the behavior of someone looking to plant a bomb was once unfathomable. It becomes possible with lots of satellite data, which, in 2010, was also opening up for civilian use. Now, a lower price and greater number of publicly available pictures from space is what may save endangered species like the black rhino.