The Effect on Attribute Prediction of Location Uncertainty in Spatial Data
The Effect on Attribute Prediction of Location Uncertainty in Spatial Data
复制标题
空间数据位置不确定性对属性预测的影响
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
N. Cressie
中科院分区：
文献类型：
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作者：
John Gabrosek;N. Cressie
如果基准面包含位置信息，则将其视为空间基准面。通常，还有属性信息，其分布取决于其位置。因此，位置信息中的错误可能导致属性信息中的错误，这最终反映在从数据得出的推断中。我们提出了一个统计模型，将定位误差到空间数据分析。我们研究了位置误差对空间滞后、协方差函数和最优空间线性预测（即克里金）的影响。我们表明，调整后的位置误差的克里金的形式是相同的，没有调整的位置误差的克里金。然而，位置误差改变了解释变量矩阵、样本点之间的协方差矩阵以及样本点与预测位置之间的协方差向量中的条目。我们调查，通过模拟，变化的趋势，测量误差，位置误差，空间依赖范围，样本大小和预测位置的影响，对克里金后，没有调整位置误差。当位置误差较大时，调整位置误差后的克里金法在预测偏差和均方预测误差方面的表现明显优于未调整位置误差的克里金法。
A datum is considered spatial if it contains location information. Typically, there is also attribute information, whose distribution depends on its location. Thus, error in location information can lead to error in attribute information, which is reflected ultimately in the inference drawn from the data. We propose a statistical model for incorporating location error into spatial data analysis. We investigate the effect of location error on the spatial lag, the covariance function, and optimal spatial linear prediction (that is, kriging). We show that the form of kriging after adjusting for location error is the same as that of kriging without adjusting for location error. However, location error changes entries in the matrix of explanatory variables, the matrix of covariances between the sample sites, and the vector of covariances between the sample sites and the prediction location. We investigate, through simulation, the effect that varying trend, measurement error, location error, range of spatial dependence, sample size, and prediction location have on kriging after and without adjusting for location error. When the location error is large, kriging after adjusting for location error performs markedly better than kriging without adjusting for location error, in terms of both the prediction bias and the mean squared prediction error.