When it comes to wildlife protection, reporting historic events is utterly inadequate. To protect wildlife, rangers need actionable intelligence.
Creating actionable intelligence, however, is no sinecure. It requires deep understanding of the nature of the threats to wildlife, insight into the behaviour of the threatened animals, and hands-on knowledge of data science and computer programming.
Not many nature conservation organisations have the resources to assemble such intelligence team. To help them, we have formed the Sensing Clues Wildlife Intelligence team. The team is dedicated to developing and leveraging the tools and methods needed to create actionable intelligence.
The Wildlife Intelligence Team works closely together with our partners in the field, who have deep local knowledge and who can interpret the intelligence reports that we co-create. And we work closely together with our technical solution partners, who know the capabilities of their various technologies better than anyone.
The result is actionable information that is trustworthy, specific and timely.
Heat Map. Among the first operational applications is the observation heat map. The map can be created for any topic, always shows the latest information and is (re)created in a few clicks. It is used to inform law enforcement, to monitor biodiversity, and to mitigate human-wildlife conflicts.
WildCAT Heat Map: Presence of deer (prey for the tiger, Living with Tiger project)
The map shown illustrates the presence of deer based on (indirect) animal sightings, including footprints, droppings, and scratch marks. This information is relatively easy to collect and important for biodiversity monitoring. But also for the protection of tigers, because deer is one of its favourite animals of prey. The heat map is actionable as the timeline brings to light time-spatial variations, which may be followed by the tiger and his human competitors, bushmeat poachers.
Likelihood Map. While the observation heat map is valuable for identifying (temporal) hotspots and cold spots, the data becomes more predictive when it is combined with patrol tracking data. It corrects the inevitable bias of observer presence. More importantly, however, colours of the map point in the direction of not-yet-patrolled areas with higher or lower chances to find (signs of presence) of animals, or illegal activities such as poaching (e.g. snares), charcoaling (e.g. kilns), illegal grazing, logging, mining, or any other subject of interest.
WildCAT Likelihood Map: In areas adjacent to yellowish areas the likelihood to find deer is higher then in areas adjacent darker areas.
Learn more
Interested to learn more about our work, about the Wildlife Conservation Analytical Toolbox (WildCAT), or are you interested to work with our Wildlife Intelligence team? Contact us!
Support this important work!
The algorithms to generate the maps shown in this blog are created by the Sensing Clues Wildlife Intelligence team for the PhD project of D.P. Srivastava, who is assessing potential of tiger survival in the urban landscape of Bhopal, India. Through WildCAT, other field partners can use these algorithms, too, or adjust them to their needs.
The above work of the Wildlife Intelligence team is powered by Functional Analytics and DIKW Intelligence, and financially sponsored by Gary Bloom and the MarkLogic Charity Bike Riders.
Do you too want to develop tools for the WildCAT or support our work? Don’t hesitate to contact us or hit the donate button!