Exploiting Data and Human Knowledge for Predicting Wildlife Poaching

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.Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

“…Data experts design algorithms purely as technical problems resulting in unusable and unexplainable recommendations in forest decision-making Wagstaff, 2012;Padarian et al, 2020 Inclusion of social and technical context while designing algorithms Predictive algorithms fail to capture social and technical contexts and make simplistic assumptions about social actors, institutions, and their interactions Wagstaff, 2012;Dutta et al, 2016;Mueller et al, 2019;Selbst et al, 2019 Interpretation of ML results in specific contexts to support decision-making Little scholarly tradition within ML community to interpret results in their specific socio-economic and political contexts narrows model interpretability Aertsen et al, 2010;Wagstaff, 2012;Mueller et al, 2019 Uniform model-based predictions to support a given decision Predictive models lack uniformity in their predictions. For the same set of input features and prediction tasks, complex models can generate multiple accurate models with varying details of explanations Adadi and Berrada, 2018;Hall and Gill, 2018 Robust and verified unique causal solutions to a given problem Predictive algorithms are only evaluated by their predictive success and are not optimized to answer causal questions Drake et al, 2006;Aertsen et al, 2010;Nunes and Görgens, 2016;Pearl and Mackenzie, 2018 Full understanding of how predictive algorithm is making decisions Black-box nature of many ML algorithms make it difficult for humans to understand their decisions Naidoo et al, 2012;Mascaro et al, 2014;Kar et al, 2017;Mueller et al, 2019 Big, accurate and appropriate data to support interpretable decisions Lack of data, class imbalance, data sparsity, noise in data quality and presence of spatial and temporal correlation further limits the development of interpretable ML models in forest management Lippitt et al, 2008;Ali et al, 2015;Curtis et al, 2018;Franklin and Ahmed, 2018;Gurumurthy et al, 2018;Gholami et al, 2019;Hethcoat et al, 2019;…”

“…The lack of accurate and adequate data in forestry further limits developing interpretable models (Lippitt et al, 2008;Kar et al, 2017;O'Connor et al, 2017;Curtis et al, 2018;Franklin and Ahmed, 2018;Gurumurthy et al, 2018;Gholami et al, 2019;Hethcoat et al, 2019). Scholars have noticed significant class imbalance, sparsity, and noise in the patrolling datasets they use in predicting wildlife poaching (Bland et al, 2015;Kar et al, 2017;Gurumurthy et al, 2018;Gholami et al, 2019). They also identified geographic and language barriers in collecting and synthesizing data for forest conservation decisions (Gurumurthy et al, 2018).…”

“…Scholars have noticed significant class imbalance, sparsity, and noise in the patrolling datasets they use in predicting wildlife poaching (Bland et al, 2015;Kar et al, 2017;Gurumurthy et al, 2018;Gholami et al, 2019). They also identified geographic and language barriers in collecting and synthesizing data for forest conservation decisions (Gurumurthy et al, 2018). The absence of high-quality data (and lack of computing power and black-box nature of deep learning) is problematic in modeling the physical properties of forest ecosystems, as noticed during forest damage assessment in Bavaria, Germany (Hamdi et al, 2019).…”

“…Forest decision-making can specifically be enhanced if algorithm developers follow specific standards, rules, and best practices to ensure fairness and nondiscrimination (Kroll et al, 2016;Corbett-Davies et al, 2017;Kehl and Kessler, 2017). While crafting any fair ML algorithm to support decision-making, it is important to include social scientists, indigenous peoples, forest management committees and other institutions, and their interactions to get a holistic idea of local decision-making cultures, regulatory norms, and incentive structures in a particular forest decision-making context (Gurumurthy et al, 2018;Selbst et al, 2019). Bringing different stakeholders on one platform may, however, be tedious.…”

“…Inspired by success in other sectors, scholars have started exploring ML for forest conservation and management. Recent examples include predicting wildlife poaching (Gurumurthy et al, 2018), classifying drivers of global forest loss (Curtis et al, 2018), and predicting deciduous tree species composition using unmanned aerial vehicle multispectral data (Franklin and Ahmed, 2018). Other applications include identifying fire risk zones (Sakr et al, 2010;Rodrigues and de la Riva, 2014;Dutta et al, 2016), producing spatially explicit carbon stock maps to monitor forest-based climate change mitigation mechanisms such as reducing emissions from deforestation and forest degradation (REDD+) (Baccini et al, 2012;Mascaro et al, 2014), and detecting subtle changes in forests, forest types, and land use from hyperspectral imagery (Li et al, 2013;Curtis et al, 2018;Holloway and Mengersen, 2018).…”