In the last few decades, sampling theory has been given a substantial boost by the growing availability of geo-referenced finite populations. Unfortunately, geo-referentiation is often incomplete or affected by locational errors for a portion of the units. Spatial sampling methods produce efficient estimates but suffer from consequences of flaws in geo-referentiation. This paper proposes a mixed sampling strategy for finite populations where a portion of the units is not correctly geo-referenced. The strategy exploits the available spatial information in the sampling design and adopts traditional sampling techniques for the remaining part of the population. Statistical properties of the strategy are explained and studied through Monte Carlo experiments on simulated and real data. An analysis of results in terms of efficiency and optimal sample composition is performed. The design-based nature of the proposed approach and its adaptability to several practical situations make it a general and easy-to-implement tool, which can outperform pure spatial sampling designs in terms of efficiency in estimation.

在过去的几十年中，随着地理参考有限人口的日益增加，抽样理论得到了极大的推动。不幸的是，地理参考通常是不完整的，或者受到一部分单元位置误差的影响。空间采样方法可产生有效的估计值，但会遭受地理参考缺陷的后果。本文提出了一种有限人口的混合采样策略，其中部分单元未正确进行地理参考。该策略在抽样设计中利用了可用的空间信息，并对其余人口采用了传统的抽样技术。通过模拟和真实数据的蒙特卡洛实验来解释和研究该策略的统计属性。对效率和最佳样品组成进行结果分析。该方法基于设计的性质及其对几种实际情况的适应性使其成为一种通用且易于实现的工具，在评估效率方面，它可以胜过纯空间采样设计。