Spatial differentiation in forest transition was measured in terms of space transition and function transi-tion using the exploratory spatial data analysis method (ESDA) and data from 2004—2014 for Zunyi city, Guizhou province, China. The validity of factors affecting forest transition was analyzed by constructing radial basis function neural networks (RBFNN) based on the data processing system (DPS). Our results will provide references for scientific understanding of the potential mechanism underlying forest transition in mountainous areas. We found that Global Moran'sI of space transition and function transition of forest land was 0.0336 and 0.2323, respectively. This suggests a significant positive correlation in spatial distribution of space transition and function transition of forest land, and significant spatial aggregation. The Global Moran'sI of function transition was higher than that of space transition, and the spatial aggregation characteristics of function transition were more significant than for space transition. The Global Moran's I at each time period tended to increase, and the spatial aggregation degree of the function transition and space transition was further enhanced. Hot and cold spots of space transition of forest land stably evolved, suggesting a minor spatial difference in forest land among different administrative units at the county level. The number of hot spots at the county level in function transition increased. Hot spots were intensively distributed at the western edge and continuously distributed in the northeast. The space transition and function transition of forest land were both greatly influenced by urbanization rate and second and third industries. The de-velopment of urbanization and industrialization was the main factor driving forest transition, suggesting a positive role of economic growth on forest transition in mountainous areas. The development of urbanization and industri-alization is an effective approach to forest transition in mountainous areas.%本文利用研究区2004-2014年统计资料,运用ESDA方法从空间形态和功能形态两方面对森林转型空间分异进行测度,并在DPS环境下构建径向基神经网络模型,分析了森林转型的影响因素效用,为科学认识山区森林转型发生机制提供了参考.研究结果如下:(1)研究期间空间形态转型和功能形态转型全局Moran's I指数分别为0.0336、0.2323,表明森林空间形态和功能形态在空间分布上体现出显著的正相关性,空间集聚特征显著.功能形态转型整体全局Moran's I高于空间形态转型,前者空间集聚特征更明显.各时段全局Moran's I指数呈上升态势,两者空间集聚程度进一步增强.(2)森林空间形态转型的冷热点格局演化特征稳定,表明县级行政单元间森林数量增加的空间差异小;功能形态转型的县域热点数目上升,热点以西缘为集中分布区逐渐演变至东北缘为中心的连片发展模式.(3)森林的空间形态转型和功能形态转型均受城镇化率和二、三产业比影响效用最大,城镇化与工业化发展是驱动区域森林转型的主要因素,凸显了经济增长在山区森林转型过程中所发挥的积极作用.未来,推动城镇化、工业化发展是促进山区森林转型的有效路径.
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