GPS sensors have an inherent positional uncertainty that is often neglected in environmental modeling. In this article we study the propagation of positional uncertainty in grid-based geo-information systems. The probability is obtained that a point has an actual position outside the raster cell in which it was observed. For multiple points, these probabilities serve as weights to update a raster value and adjust for positional uncertainty. We show the effect of positional uncertainty propagation by means of simulations using rasters with different levels of spatial autocorrelation, as well as an illustration of a real-world example in which we estimate exposure to air pollution. We found that the propagated uncertainty was highest when the spatial autocorrelation is low, with a root mean squared error of 7% compared to 1% for the high spatial autocorrelation scenario. We conclude that positional uncertainty propagates through environmental models and propose a simple probabilistic method to account for it.

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关于环境模型中位置不确定性传播的注释

GPS 传感器具有固有的位置不确定性，在环境建模中经常被忽略。在本文中，我们研究了基于网格的地理信息系统中位置不确定性的传播。获得一个点在其被观察到的栅格单元之外具有实际位置的概率。对于多个点，这些概率用作更新栅格值并针对位置不确定性进行调整的权重。我们通过使用具有不同空间自相关水平的栅格进行模拟来展示位置不确定性传播的影响，并举例说明我们估计空气污染暴露的真实示例。我们发现，当空间自相关较低时，传播的不确定性最高，均方根误差为 7%，而高空间自相关情景为 1%。我们得出结论，位置不确定性通过环境模型传播，并提出了一种简单的概率方法来解释它。