The simulation of rural land use systems in general, and rural settlement dynamics in particular, has developed with synergies of theory and methods for decades. Three current issues are: linking spatial patterns and processes, representing hierarchical relations across scales, and considering nonlinearity to address complex non-stationary settlement dynamics. We present a hierarchical simulation model to investigate complex rural settlement dynamics in Nang Rong, Thailand. This simulation uses sub-models to allocate new villages at three spatial scales. Regional and sub-regional models, which involve a nonlinear space–time autoregressive model implemented in a neural network approach, determine the number of new villages to be established. A dynamic village niche model, establishing a pattern–process link, was designed to enable the allocation of villages into specific locations. Spatiotemporal variability in model performance indicates that the pattern of village location changes as a settlement frontier advances from rice-growing lowlands to higher elevations. Simulation experiments demonstrate that this simulation model can enhance our understanding of settlement development in Nang Rong and thus gain insight into complex land use systems in this area.

中文翻译：

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泰国模拟村庄位置：包含神经网络方法的多尺度模型

几十年来，农村土地利用系统的模拟，特别是农村聚落动态，在理论和方法的协同作用下得到了发展。当前的三个问题是：将空间模式和过程联系起来，表示跨尺度的层次关系，以及考虑非线性以解决复杂的非平稳沉降动态。我们提出了一个分层模拟模型来研究泰国南荣复杂的农村聚落动态。该模拟使用子模型在三个空间尺度上分配新村庄。区域和次区域模型，涉及在神经网络方法中实施的非线性时空自回归模型，决定了要建立的新村庄的数量。一个动态的村庄生态位模型，建立模式-过程链接，旨在将村庄分配到特定位置。模型性能的时空变异性表明，随着聚落边界从种植水稻的低地向更高的海拔发展，村庄位置的模式发生了变化。模拟实验表明，该模拟模型可以增强我们对南荣聚落发展的理解，从而深入了解该地区复杂的土地利用系统。