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ISPRS Int. J. Geo-Inf. 2017, 6(4), 93; doi:10.3390/ijgi6040093

A Novel k-Means Clustering Based Task Decomposition Method for Distributed Vector-Based CA Models

The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing,Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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Received: 5 February 2017 / Revised: 20 March 2017 / Accepted: 22 March 2017 / Published: 23 March 2017
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Abstract

More and more vector-based cellular automata (VCA) models have been built to leverage parallel computing to model rapidly changing cities and urban regions. During parallel simulation, common task decomposition methods based on space partitioning, e.g. grid partitioning (GRID) and recursive binary space partitioning (BSP), do not work well given the heterogeneity of VCA parcel tasks. In this paper, to solve this problem, we propose a novel task decomposition method for distributed VCA models based on k-means clustering, named KCP. Firstly, the polygon dataset is converted into points based on centroids, which combines the size of two parcels and the outer distance. A low-cost recursive quad-partition is then applied to decide the initial cluster centers based on parcel density. Finally, neighbor parcels can be allocated into the same subdivision through k-means clustering. As a result, the proposed KCP method takes both the number of tasks and computing complexity into consideration to achieve a well-balanced local workload. A typical urban VCA growth model was designed to evaluate the proposed KCP method with traditional spatial partitioning methods, i.e. GRID and BSP. KCP had the shortest total simulation time when compared with GRID and BSP. During experimental urban growth simulations, the time spent on a single iteration was reduced by 15% with the BSP and by 25% with the GRID method. The total simulation time with a 120 m neighborhood buffer size was reduced by more than one hour to around three minutes with 32 cores. View Full-Text
Keywords: vector-based CA; distributed computing; k-means clustering; task decomposition vector-based CA; distributed computing; k-means clustering; task decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, Z.; Guan, X.; Wu, H.; Gong, J. A Novel k-Means Clustering Based Task Decomposition Method for Distributed Vector-Based CA Models. ISPRS Int. J. Geo-Inf. 2017, 6, 93.

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