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China's urbanization has attracted many scholars' attention due to its significant impact on socioeconomic sustainability. Many studies have explored the spatial pattern and effects of the factors influencing urban expansion. However, the spatiotemporal dynamics integrating spatial and temporal dimensions and the spatial scales of the influencing factors are always ignored. This study applied the framework of exploratory space–time data analysis (ESTDA) to investigate the spatiotemporal dynamics of urban expansion across 342 cities in China from 1990 to 2017 and, further, used multiscale geographically weighted regression (MGWR) to estimate the effects of influencing factors on urban expansion. We found that urban expansion had an obvious south–north division, and yet the effects of influencing factors usually showed an east–west division. We also found that the dynamic local spatial dependency of urban expansion was accompanied by a volatile coevolution process and inclined to transfer from heterogeneity to homogeneity, and homogeneity tended to be stable. The coevolution of urban expansion between cities and other neighboring ones became stronger with increases in time and regional integration. These findings support the use of customized urban planning for specific regions in different spatial dependence to improve land-use efficiency and coordinate regional development. [ABSTRACT FROM AUTHOR]

This paper discusses extensions of GAP-trees from three aspects and its implementation based on non-topological structure in order to enhance access to large vector data sets. First of all, we apply cartographic generalization rules to build a generalization procedure of the GAP-tree, which makes coarse representations more consistent with human cognition. Second, we replace the three-dimensional (pseudo-) Reactive-tree index with a 2D R-tree index and a B-tree index to improve the system efficiency. Finally, we compress a binary GAP-tree into multi-way GAP-trees in order to reduce data redundancy. The shallower multi-way GAP-trees not only eliminate redundant data but also accelerate the system's response time. The extensions have been successfully implemented in PostgreSQL. A test of Beijing's land-use data at the 1:10 000 scale demonstrates that the extended GAP-trees are efficient, compact, and easy to implement. [ABSTRACT FROM AUTHOR]