We often think of predictive analytics in terms of business applications, but this advanced technology can be a game-changer far beyond the corporate world. Today, we're going to dive into an unexpected use case - the realm of wildlife conservation.
Poaching and Illegal Wildlife Trade
Wildlife poaching and illegal trade are grave problems threatening the existence of numerous species across the globe. From rhinos and elephants in Africa to tigers and pangolins in Asia, numerous species are caught in the crosshairs of illegal activities. The illicit wildlife trade stands as one of the most profitable criminal enterprises globally, with an estimated annual worth exceeding $23 billion. Furthermore, poaching poses significant risks to wildlife conservation personnel and park rangers who bravely protect wildlife day in and day out. Tragically, since 2003, more than 1,000 wildlife rangers have lost their lives in the line of duty, battling against poachers to safeguard vulnerable species.
Conventional methods of addressing these issues, such as deploying rangers and monitoring teams, often fall short due to a lack of resources and the sheer vastness of the areas that need to be protected. This is where predictive analytics steps in, offering a much-needed lifeline.
Predictive Analytics to the Rescue
Scientists, in collaboration with data analysts, have begun to employ predictive analytics in their fight against wildlife crimes. They gather data from various sources, including past poaching activities, animal migration patterns, and geographic information.
This data is then analysed to identify patterns and predict potential hotspots for illegal activities. The predictive model, often referred to as the "Poaching Predictive Model," helps in efficient resource allocation by enabling authorities to anticipate poaching activities and strategically position their teams for preventative measures.
Case Study: PAWS (Protection Assistant for Wildlife Security)
An example of this approach in action is the PAWS (Protection Assistant for Wildlife Security) system, a joint project between the University of Southern California and the Uganda Wildlife Authority. PAWS uses machine learning algorithms to predict poaching threats and help rangers identify the most effective patrol routes.
The integration of AI and machine learning in such applications showcases the immense potential these technologies hold in safeguarding vulnerable species and combatting illegal wildlife trade. With the help of predictive analytics, PAWS has been successful in reducing poaching activities, marking a significant victory for wildlife conservation in the region. This strategic application of AI has proven effective, with parks in Cambodia using PAWS to discover and dismantle over 1,000 snares. In Indonesia, a study conducted in 2019 revealed that numerous rangers expressed a strong willingness to utilize PAWS following an informative workshop.
Beyond Poaching: Broader Applications in Conservation
Predictive analytics also offers broader applications in wildlife conservation. It can be used to predict the impacts of climate change on specific species, helping conservationists develop targeted protection strategies. It can also be used to monitor animal populations and migration patterns, providing crucial insights for habitat preservation efforts.
Embracing Predictive Analytics for a Better Tomorrow
This surprising application of predictive analytics underscores the vast potential of data science. It's not only about improving businesses but also about creating a positive impact on our world. As a company at the forefront of business intelligence and data science, we are excited about the transformative potential of these technologies and look forward to exploring further innovative applications.
If you're curious about the potential of predictive analytics and other machine learning algorithms, or how these advanced technologies can provide solutions tailored to your unique business needs, visit our Predictive Analytics page by clicking the button below.