Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.

人口估计对于政府服务，发展项目和公共卫生运动至关重要。这些数据通常是通过全国人口和住房普查获得的。但是，在局部地区，尤其是在发生移民或流离失所的地区，人口估计会很快变得不准确。一些受冲突影响和资源匮乏的国家在10年内没有进行过人口普查。我们开发了一个分层的贝叶斯模型，根据样本区域中的枚举数据以及有关行政边界，建筑物位置，安置类型以及其他与人口密度有关的其他因素的全国性信息，估算小区域的人口数量。我们通过估计尼日利亚全国每10米网格单元中的人口规模证明了该模型。这些网格化的人口估计数和由此得出的区域人口总数都伴随着基于贝叶斯后验概率的不确定性估计。该模型的总体错误率是每公顷67人（绝对残差的平均值）或43％（使用缩放的残差），用于在样本外调查区域（每个大约3公顷）中进行预测，预计总人口的准确性会提高总计面积较大。这种统计方法代表了朝着在没有完整的和最近的人口普查的情况下以高分辨率覆盖全国人口的方向迈出的重要一步，同时还提供了不确定性的可靠估计以支持明智的决策。该模型的总体错误率是每公顷67人（绝对残差的平均值）或43％（使用缩放的残差），用于在样本外调查区域（每个大约3公顷）中进行预测，预计总人口的准确性会提高总计面积较大。这种统计方法代表了朝着在没有完整和近期的人口普查的情况下以全国覆盖率高分辨率估计人口的重要步骤，同时还提供了不确定性的可靠估计以支持明智的决策。该模型的总体错误率是每公顷67人（绝对残差的平均值）或43％（使用缩放的残差），用于在样本外调查区域（每个大约3公顷）中进行预测，预计总人口的准确性会提高总计面积较大。这种统计方法代表了朝着在没有完整的和最近的人口普查的情况下以高分辨率覆盖全国人口的方向迈出的重要一步，同时还提供了不确定性的可靠估计以支持明智的决策。