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Poaching continues to be a significant threat to the conservation of wildlifeand the associated ecosystem. Estimating and predicting where the poachers havecommitted or would commit crimes is essential to more effective allocation ofpatrolling resources. The real-world data in this domain is often sparse, noisyand incomplete, consisting of a small number of positive data (poaching signs),a large number of negative data with label uncertainty, and an even largernumber of unlabeled data. Fortunately, domain experts such as rangers canprovide complementary information about poaching activity patterns. However,this kind of human knowledge has rarely been used in previous approaches. Inthis paper, we contribute new solutions to the predictive analysis of poachingpatterns by exploiting both very limited data and human knowledge. We proposean approach to elicit quantitative information from domain experts through aquestionnaire built upon a clustering-based division of the conservation area.In addition, we propose algorithms that exploit qualitative and quantitativeinformation provided by the domain experts to augment the dataset and improvelearning. In collaboration with World Wild Fund for Nature, we show thatincorporating human knowledge leads to better predictions in a conservationarea in Northeastern China where the charismatic species is Siberian Tiger. Theresults show the importance of exploiting human knowledge when learning fromlimited data.

Exploiting Data and Human Knowledge for Predicting Wildlife Poaching