NEighborhood MOdeling (NEMO) provides transparent parallelism to develop parallel spatial data processing. It addresses the following parallel issues: architecture and machine independence; communication bottlenecks; data visualization; casualty errors; load balancing; and data coherence. NEMO is capable of processing three types of time consuming raster neighborhood models: cellular automata; propagation; and neighborhood analysis. NEMO achieves this flexibility by including five components to its design: the three application drivers such as the cellular automata driver, propagation driver, and neighborhood analysis automata driver; and the display manager and raster database manager.

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Conference Proceedings of the 1996 8th Annual ACM Symposium on Parallel Algorithms and Architectures
Citation
Hutchinson, D. (D.), Kuttner, L. (L.), Lanthier, M, Maheshwari, A, Nussbaum, D, Roytenberg, D. (D.), & Sack, J.-R. (1996). Parallel neighbourhood modeling: Research summary. In Annual ACM Symposium on Parallel Algorithms and Architectures (pp. 204–207).