Many models can be used to fill the gaps caused by incomplete geospatial data. But not all are valid. To study the validity of geospatial information diffusion model, in this article, two judging criteria are suggested to check if a model is valid for filling a gap unit. The root mean squared error of a model with a given sample after removing a test point is called datum error of the model. The error between real value and estimated value of the test point is called forecasting error of the model. The first criterion says that, when the average forecasting error is less than the average datum error, the model is invalid. The second criterion says that, the smaller the errors, the more valid the model. The results of computer simulation show that geospatial information diffusion model is more valid than the geographically weighted regression and the back propagation neural network.

中文翻译：

---

有两个判断标准，可以检查模型的有效性，以填补由不完整的地理空间数据引起的空白。

许多模型可以用来填补由不完整的地理空间数据引起的空白。但并非全部有效。为了研究地理空间信息扩散模型的有效性，本文提出了两种判断标准，以检验该模型是否可以有效地填充间隙单元。移除测试点后具有给定样本的模型的均方根误差称为模型的基准误差。测试点的实际值和估计值之间的误差称为模型的预测误差。第一个标准说，当平均预测误差小于平均基准误差时，该模型无效。第二个标准说，误差越小，模型的有效性越高。