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Artificial neural networks have been widely applied to spatial modeling and knowledge discovery because of their high-level intelligence and flexibility. Their highly parallel and distributed structure makes them inherently suitable for parallel computing. As the technology of parallel and high-performance computing evolves and computing resources become(More)
Spatially explicit agent-based models have a great potential to mitigate their computational costs by taking advantage of parallel and high-performance computing. However, the spatial dependency and heterogeneity of interactions pose challenges for parallel SE-ABMs to achieve good scalability. This paper applies the principle of data locality to tackle(More)
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