Cellular automata (CA) models are used to analyze and simulate the global phenomenon of urban growth. However, these models are characterized by ignoring spatially heterogeneous transition rules and asynchronous evolving rates, which make it difficult to improve urban growth simulations. In this paper, a partitioned and asynchronous cellular automata (PACA) model was developed by implementing the spatial heterogeneity of both transition rules and evolving rates in urban growth simulations. After dividing the study area into several subregions by k-means and knn-cluster algorithms, a C5.0 decision tree algorithm was employed to identify the transition rules in each subregion. The evolving rates for cells in each regularly divided grid were calculated by the rate of changed cells. The proposed PACA model was implemented to simulate urban growth in Wuhan, a large city in central China. The results showed that PACA performed better than traditional CA models in both a cell-to-cell accuracy assessment and a shape dimension accuracy assessment. Figure of merit of PACA is 0.368 in this research, which is significantly higher than that of partitioned CA (0.327) and traditional CA (0.247). As for the shape dimension accuracy, PACA has a fractal dimension of 1.542, which is the closest to that of the actual land use (1.535). However, fractal dimension of traditional CA (1.548) is closer to that of the actual land use than that of partitioned CA (1.285). It indicates that partitioned transition rules play an important role in the cell-to-cell accuracy of CA models, whereas the combination of partitioned transition rules and asynchronous evolving rates results in improved cell-to-cell accuracy and shape dimension accuracy. Thus, implementing partitioned transition rules and asynchronous evolving rates yields better CA model performance in urban growth simulations due to its accordance with actual urban growth processes. ARTICLE HISTORY

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Today, in the multimedia encoding technology, fractal image coding is an effective coding method without resolution. The effectiveness is because of the high compressing ratio of fractal image coding. But the computational complexity of this coding method is so high that it needs long encoding time. In this paper, a novel fast fractal coding method is constructed to decrease the coding time by the capture of primary additional error values. This method is a universal algorithm, which is independent of image types. First, we abstract the additional error values from classic image coding. Then, we present a method to abstract the primary error values with a given rule of weight. Moreover, the encoding and decoding processes are reformed to store the primary additional error values. Finally, experimental results shows the improved fractal image coding method has higher compressing ratio and better effectiveness (signal to noise ratio) than the classic algorithm.