In earthquake-stricken area, with the occurrence of aftershocks, heavy rainfall and human activity, the earthquake-induced secondary landslide disaster will threaten people's life and property in a very long period. So, it makes secondary landslide became a research hotspots that draw much attention. The forecasting of natural disaster is considered as a most effective way to prevention or mitigation disaster and the spatial prediction is the base work of landslide disaster research. The aim of this study is to analyze the landslide prediction, taking the case of Beichuan County. Six factors affecting landslide occurrence have been taken into account, including elevation, slope, lithology, seismic intensity, distance to roads and rivers. The correlations of landslide distribution with these factors is calculated, the multiple regression and neural network model are applied to landslide spatial prediction and mapping. The model calculates result is ultimately categorized into four classes. It shows that the high and very high susceptibility areas most distribute in Qushan, Chenjiaba towns, etc., along the rivers and the roads around the area of Longmenshan fault. The precision accuracy using multiple regression models is about 73.7% and the neural network model can be up to 81.28%. It can be concluded that in this study area, the neural network model appears to be more accurate in landslide spatial prediction.

In this paper we empirically evaluate interregional capital mobility in China from 1978 to 2012. We measure the degree of capital mobility as the ability of regions, each as a representative consumer, to engage in intertemporal consumption smoothing through running external imbalances. We estimate a correlated random coefficient model which takes into account the potential correlation of capital mobility with output. Our results show that barriers to capital mobility across regions are still high in China. However, there is a slight increase in capital mobility over time. After 2000, the improvement of the degree of capital mobility becomes stagnant. This may reflect the prevalence of capital market distortion in China.