NEAT approach for testing and validation of geospatial network agent-based model processes: case study of influenza spread

ABSTRACT

Agent-based models (ABM) are used to represent a variety of complex systems by simulating the local interactions between system components from which observable spatial patterns at the system-level emerge. Thus, the degree to which these interactions are represented correctly must be evaluated. Networks can be used to discretely represent and quantify interactions between system components and the emergent system structure. Therefore, the main objective of this study is to develop and implement a novel validation approach called the NEtworks for ABM Testing (NEAT) that integrates geographic information science, ABM approaches, and spatial network representations to simulate complex systems as measurable and dynamic spatial networks. The simulated spatial network structures are measured using graph theory and compared with empirical regularities of observed real networks. The approach is implemented to validate a theoretical ABM representing the spread of influenza in the City of Vancouver, Canada. Results demonstrate that the NEAT approach can validate whether the internal model processes are represented realistically, thus better enabling the use of ABMs in decision-making processes.

KEYWORDS:

• Geographic information systems
• network science
• agent-based modelling
• model testing
• validation

Acknowledgments

This study was fully funded by a Natural Sciences and Engineering Research Council (NSERC) Canadian Graduate Scholarship-Doctoral (CGS D) and the Discovery Grant awarded to the first and second author respectively. We are thankful for valuable comments and feedback provided by the journal Editors and the anonymous reviewers.

Data and codes availability statement

The data and codes that support the findings of this study are available through figshare.com using the public DOI 10.6084/m9.figshare.10010417

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada [Canadian Graduate Scholarship-Doctoral, Discovery Grant].

Notes on contributors

Taylor Anderson

Dr. Taylor Anderson is an Assistant Professor in the Department of Geography and Geoinformation Systems at George Mason University in Fairfax, Virginia, USA. Her research focuses on integrating networks and geographic automata systems for representing and analyzing a variety of complex spatial-temporal phenomena ranging from ecological to urban systems.

Suzana Dragićević

Dr. Suzana Dragićević is a Professor in the Department of Geography and director of the Spatial Analysis and Modeling (SAM) Research Laboratory, Simon Fraser University, Canada. Her research program is within the multidisciplinary domains of geographic information systems and science (GIS), complexity science, artificial intelligence and soft computing for the analysis and modeling of complex dynamic geospatial systems. Application areas include land-use/land-cover change, urban growth, forestry and landscape ecology.