Exploiting Data and Human Knowledge for Predicting Wildlife Poaching

TL;DR

This paper presents a novel approach that combines limited real-world data with expert human knowledge to improve wildlife poaching prediction models, demonstrating enhanced accuracy in conservation efforts.

Contribution

It introduces methods to incorporate expert knowledge into predictive models for poaching, addressing data scarcity and uncertainty in conservation contexts.

Findings

Increased prediction accuracy with human knowledge integration

Effective data augmentation from expert input

Improved resource allocation for anti-poaching efforts

Abstract

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