Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (Short Version)

@article{Gholami2020StayAO,
  title={Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (Short Version)},
  author={Shahrzad Gholami and Lily Xu and Sara Mc Carthy and Bistra N. Dilkina and Andrew J. Plumptre and Milind Tambe and Rohit Singh and Mustapha Nsubaga and Joshua Mabonga and Margaret Driciru and Fredrick O. Wanyama and Aggrey Rwetsiba and Tom Okello and Eric Enyel},
  journal={2020 IEEE 36th International Conference on Data Engineering (ICDE)},
  year={2020},
  pages={1898-1901}
}
Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an… 
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