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Agent-Based Models and Complexity Science in the Age of Geospatial Big Data pp 31–44Cite as

Deconstructing Geospatial Agent-Based Model: Sensitivity Analysis of Forest Insect Infestation Model

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Part of the book series: Advances in Geographic Information Science ((AGIS))

Abstract

Agent-based models (ABM) can be used to represent the spatio-temporal dynamics of real world geospatial phenomena, however because of their complexity, they can be difficult to implement and validate. This study uses the invariant-variant validation approach to further model testing of a developed ABM of forest insect infestation representing spatio-temporal dynamics of the emerald ash borer (EAB). The invariant-variant method deconstructs model results to facilitate an improved understanding of the model’s sensitivity to changes in input parameters and focuses on EAB agents’ access to information. Obtained results indicate that the developed EAB agent-based model represents and maintains both process accuracy and spatial similarity.

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Acknowledgments

This study was fully funded by a Natural Sciences and Engineering Research Council (NSERC) Canadian Graduate Scholarship-Doctoral (CGS D) awarded to the first author and Discovery Grant awarded to the second author. The datasets were provided by the Town of Oakville, Canada. The authors are thankful to Compute Canada WestGrid high-performance computing facility for enabling model simulations.

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Authors and Affiliations

  1. Spatial Analysis and Modeling Research Laboratory, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada, V5A 1S6

    Taylor Anderson & Suzana Dragićević

Authors
  1. Taylor Anderson
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  2. Suzana Dragićević
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Corresponding author

Correspondence to Taylor Anderson .

Editor information

Editors and Affiliations

  1. Laboratoire de Géosimulation Environnementale, Department of Geography, Université de Montréal, Montreal, Québec, Canada

    Liliana Perez

  2. Department of Geography, Pennsylvania State University, University Park, Pennsylvania, USA

    Eun-Kyeong Kim

  3. Department of Geography, School of Environment, McGill University, Montreal, Québec, Canada

    Raja Sengupta

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Anderson, T., Dragićević, S. (2018). Deconstructing Geospatial Agent-Based Model: Sensitivity Analysis of Forest Insect Infestation Model. In: Perez, L., Kim, EK., Sengupta, R. (eds) Agent-Based Models and Complexity Science in the Age of Geospatial Big Data. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-65993-0_3

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