Taking It for a Test Drive: A Hybrid Spatio-Temporal Model for Wildlife Poaching Prediction Evaluated Through a Controlled Field Test

Gholami, Shahrzad; Ford, Benjamin J.; Fang, Fei; Plumptre, Andrew J.; Tambe, Milind; Driciru, Margaret; Wanyama, Fred; Rwetsiba, Aggrey; Nsubaga, Mustapha; Mabonga, Joshua

doi:10.1007/978-3-319-71273-4_24

Cited by 23 publications

(21 citation statements)

References 5 publications

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“…It is important to note that this does step does not correspond to validation. Validation of the strategy can be done using real-life tests, but is known to be difficult in practice [37,38].…”

Section: Verification Of Optimal Strategiesmentioning

confidence: 99%

An Agent-Based Empirical Game Theory Approach for Airport Security Patrols

2020

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Airports are attractive targets for terrorists, as they are designed to accommodate and process large amounts of people, resulting in a high concentration of potential victims. A popular method to mitigate the risk of terrorist attacks is through security patrols, but resources are often limited. Game theory is commonly used as a methodology to find optimal patrol routes for security agents such that security risks are minimized. However, game-theoretic models suffer from payoff uncertainty and often rely solely on expert assessment to estimate game payoffs. Experts cannot incorporate all aspects of a terrorist attack in their assessment. For instance, attacker behavior, which contributes to the game payoff rewards, is hard to estimate precisely. To address this shortcoming, we proposed a novel empirical game theory approach in which payoffs are estimated using agent-based modeling. Using this approach, we simulated different attacker and defender strategies in an agent-based model to estimate game-theoretic payoffs, while a security game was used to find optimal security patrols. We performed a case study at a regional airport, and show that the optimal security patrol is non-deterministic and gives special emphasis to high-impact areas, such as the security checkpoint. The found security patrol routes are an improvement over previously found security strategies of the same case study.

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Section: Verification Of Optimal Strategiesmentioning

confidence: 99%

An Agent-Based Empirical Game Theory Approach for Airport Security Patrols

2020

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“…In this case, experts play an essential role in the process of validating the model. In some cases, real‐life experiments can be done (Ford, ; Gholami et al., ), potentially improving validity of the model. Furthermore, operational aspects of the models can more readily be validated using data.…”

Section: Absrim: Agent‐based Security Risk Managementmentioning

confidence: 99%

AbSRiM: An Agent‐Based Security Risk Management Approach for Airport Operations

2019

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Security risk management is essential for ensuring effective airport operations. This article introduces AbSRiM, a novel agent‐based modeling and simulation approach to perform security risk management for airport operations that uses formal sociotechnical models that include temporal and spatial aspects. The approach contains four main steps: scope selection, agent‐based model definition, risk assessment, and risk mitigation. The approach is based on traditional security risk management methodologies, but uses agent‐based modeling and Monte Carlo simulation at its core. Agent‐based modeling is used to model threat scenarios, and Monte Carlo simulations are then performed with this model to estimate security risks.The use of the AbSRiM approach is demonstrated with an illustrative case study. This case study includes a threat scenario in which an adversary attacks an airport terminal with an improvised explosive device. The approach provides a promising way to include important elements, such as human aspects and spatiotemporal aspects, in the assessment of risk. More research is still needed to better identify the strengths and weaknesses of the AbSRiM approach in different case studies, but results demonstrate the feasibility of the approach and its potential.

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“…[9] designs an ensemble of decision trees which incorporate spatial correlation of poaching to account for the undetected instances. [6] provides a hybrid model that combines decision trees and Markov Random Fields [6] to exploit the spatio-temporal correlation of poaching activities. [7] proposes to weigh the negative data points in the training set based on patrol effort so as to account for the label uncertainty.…”

Section: Related Workmentioning

confidence: 99%

“…Rangers record their findings, including animal signs and poaching activity signs, e.g., snares placed by poachers during the patrol, and therefore one can analyze these records to get insights of the poaching patterns. There has been several previous work that provided predictive tools through designing machine learning models trained and evaluated using real-world data from two conservation sites in Uganda [6,7,9,14]. In these work, a dataset is created based arXiv:1805.05356v1 [cs.CY] 14 May 2018 on past patrols and geographical information.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Data and Human Knowledge for Predicting Wildlife Poaching

Swaminathan

Zhang

et al. 2018

Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies

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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.

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Taking It for a Test Drive: A Hybrid Spatio-Temporal Model for Wildlife Poaching Prediction Evaluated Through a Controlled Field Test

Cited by 23 publications

References 5 publications

An Agent-Based Empirical Game Theory Approach for Airport Security Patrols

An Agent-Based Empirical Game Theory Approach for Airport Security Patrols

AbSRiM: An Agent‐Based Security Risk Management Approach for Airport Operations

Exploiting Data and Human Knowledge for Predicting Wildlife Poaching

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