Georeferenced data often contain missing values, and such missing values can considerably affect spatial modeling. A spatial location model can also suffer from this issue when there are missing values in its geographic distribution of weights. Although general imputation approaches have been developed, one distinguishing fact here is that spatial imputation generally performs better for georeferenced data because it can reflect a fundamental property of those data, that is, spatial autocorrelation or spatial dependency. This paper explores how spatial imputation exploiting spatial autocorrelation can contribute to estimating missing values in a weights surface for location modeling and subsequently improve solutions for spatial optimization, specifically p-median problems using a spatially imputed weights surface. This paper examines two spatial imputation methods, ordinary co-kriging and Moran eigenvector spatial filtering. Their results are compared with conventional linear regression, essentially Expectation-Maximization algorithm results for independent observations of Gaussian random variable cases. Simulation experiments show that spatial imputation produces better results for georeferenced data than simply ignoring any missing values and non-spatial imputation, and appropriately imputed values can enhance spatial optimization solutions, regardless of the number of medians, p.

地理配准数据通常包含缺失值，并且此类缺失值会极大地影响空间建模。当空间位置模型的权重地理分布中存在缺失值时，也可能会遇到此问题。尽管已经开发了通用插补方法，但这里的一个显着事实是，空间插补通常对于地理参考数据表现更好，因为它可以反映这些数据的基本属性，即空间自相关或空间依赖性。本文探讨了利用空间自相关的空间插补如何有助于估计位置建模权重曲面中的缺失值，并随后改进空间优化的解决方案，特别是使用空间插补权重曲面的p中值问题。本文研究了两种空间插补方法：普通协同克里金法和莫兰特征向量空间滤波。他们的结果与传统的线性回归进行了比较，本质上是对高斯随机变量情况的独立观察的期望最大化算法结果。模拟实验表明，对于地理参考数据，空间插补比简单地忽略任何缺失值和非空间插补产生更好的结果，并且无论中位数 p 的数量如何，适当的插补值都可以增强空间优化解决方案。