融合空间自相关的空间数据预测模型

空间数据具有空间自相关的特性，使多元线性回归模型不适合于空间数据预测；空间自相关模型由于考虑了空间信息，可以用于空间预测，但时间耗费较大。为此，在研究多元线性回归模型的基础上，把空间信息加入到输入变量中，再把新的输入变量输入到多元线性回归模型估计模型参数，最后进行空间数据预测。实验结果表明，该方法能取得与空间自相关模型几乎相同的预测效果，且计算代价更小。

Prediction Models for Spatial Data Based on Spatial Autocorrelation

Because spatial data have the characteristic of spatial autocorrelation,it makes the MLS(Multivariate Linear Regression) model unfit to spatial prediction.Due to account for spatial information,the SAR(Spatial Auto-Regression) model can be used for spatial prediction,but it is computationally very expensive.We add spatial information into input variables by replacing each input variables with the weighted average of its neighbors and feed the new input variables to a MLS model to estimate model parameters,a...