Taking It for a Test Drive: A Hybrid Spatio-Temporal Model for Wildlife Poaching Prediction Evaluated Through a Controlled Field Test
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Springer, Cham
Abstract
Worldwide, conservation agencies employ rangers to protect
conservation areas from poachers. However, agencies lack the manpower
to have rangers effectively patrol these vast areas frequently. While past
work has modeled poachers’ behavior so as to aid rangers in planning
future patrols, those models’ predictions were not validated by extensive
field tests. In this paper, we present a hybrid spatio-temporal model that
predicts poaching threat levels and results from a five-month field test of
our model in Uganda’s Queen Elizabeth Protected Area (QEPA). To our
knowledge, this is the first time that a predictive model has been evaluated
through such an extensive field test in this domain. We present two
major contributions. First, our hybrid model consists of two components:
(i) an ensemble model which can work with the limited data common to
this domain and (ii) a spatio-temporal model to boost the ensemble’s predictions
when sufficient data are available. When evaluated on real-world
historical data from QEPA, our hybrid model achieves significantly better
performance than previous approaches with either temporally-aware
dynamic Bayesian networks or an ensemble of spatially-aware models.
Second, in collaboration with the Wildlife Conservation Society and
Uganda Wildlife Authority, we present results from a five-month controlled
experiment where rangers patrolled over 450 sq km across QEPA.
We demonstrate that our model successfully predicted (1) where snaring
activity would occur and (2) where it would not occur; in areas where
we predicted a high rate of snaring activity, rangers found more snares and snared animals than in areas of lower predicted activity. These findings
demonstrate that (1) our model’s predictions are selective, (2) our
model’s superior laboratory performance extends to the real world, and
(3) these predictive models can aid rangers in focusing their efforts to
prevent wildlife poaching and save animals.
Description
Keywords
Predictive models, Ensemble techniques Graphical models, Field test evaluation, Wildlife protection Wildlife poaching
Citation
Gholami, S., Ford, B., Fang, F., Plumptre, A., Tambe, M., Driciru, M., ... & Mabonga, J. (2017, September). Taking it for a test drive: a hybrid spatio-temporal model for wildlife poaching prediction evaluated through a controlled field test. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 292-304). Springer, Cham.