Agent-based model validation using Bayesian networks and vector spatial data

Abstract

Validation of agent-based models (ABMs) of land-use change is a significant challenge in current spatial-modelling research and application. During the validation process, model performance and accuracy assessment depend mostly on pixel-by-pixel comparisons. However, in urban land-use planning problems the use of vector spatial data to develop ABMs is becoming more necessary. Hence, improved and robust validation approaches are required for vector-based ABMs. This study presents a novel validation approach for an ABM using vector-based geographic information system and Bayesian networks. The approach creates a unique-polygons map and an object-oriented database. Three indicator variables are calculated to assess the probability of agreement. The indicator variables are nodes in a Bayesian network that is used to evaluate the final agreement of each unique polygon. Further, an index of overall agreement is calculated. The approach was applied to a simulation outcome map generated by an existing Bayesian ne...