Load-balanced spatial partitioning is crucial for achieving high-efficiency distributed spatial join processing. However, existing spatial partitioning methods focus more on balancing data quantity, and there is much less emphasis on accurately quantifying computational loads and generating partitioning layouts according to the derived loads. To bridge these gaps, we propose a novel partitioning method, i.e. a computational intensity-balanced partitioner (termed CIBPartitioner for short), to enhance the efficiency of distributed spatial join processing by ensuring computational load balance. First, a computational intensity (CI) indicator is defined through theoretical analysis of the time complexity of spatial join processing to quantify the computational loads. Second, a distributed estimation method using grid histograms is introduced to efficiently calculate the distribution of CI. Finally, inspired by the KDBTree, a CI-balanced partitioning scheme is designed to partition the grid cells in the grid histogram according to the CI distribution, which minimizes the CI differences across partitions to achieve a balanced CI layout. Extensive experiments on real-world datasets demonstrate that CIBPartitioner significantly improves computational load balancing and enhances the end-to-end efficiency of distributed spatial join processing compared with popular spatial partitioners, including KDBTree. The source code of CIBPartitioner has been released.

Urban growth has accelerated significantly in recent decades, categorized into two spatial patterns: adjacent and outlying growth. Traditional Cellular Automata (CA)-based models excel at simulating adjacent growth but exhibit limitations in modeling outlying growth, which can be summarized into two issues: 1) the overestimation of neighborhood effects caused by models’ excessive dependence on neighboring cell states; and 2) the ignorance of spatial heterogeneity in the relative importance of land suitability and neighborhood effects on urban growth. To address these problems, a novel CA model with Separate Extraction and Adaptive Fusion of land suitability and neighborhood effects (SEAF-CA) is proposed. In this model, a dual-path convolution structure is employed to extract spatial features from driving factors and cell states; geographical coordinates of each cell then input into a multilayer perceptron to derive spatially varying weights for feature fusion. Finally, the derived conversion probability is integrated with CA to simulate urban growth. Land use data collected from 2000 to 2020 in Wuhan are selected to evaluate the proposed model. Experimental results illustrate that SEAF-CA outperforms three typical CA models, achieving the closest outlying growth proportion to reality and the highest simulation accuracy. In addition, the source code of SEAF-CA is now available at GitHub (https://github.com/ohXu/SEAF-CA).