The increasing availability of complex survey data, and the continued need for estimates of demographic and health indicators at a fine spatial and temporal scale, which leads to issues of data sparsity, has led to the need for spatio-temporal smoothing methods that acknowledge the manner in which the data were collected. The open source R package SUMMER implements a variety of methods for spatial or spatio-temporal smoothing of survey data. The emphasis is on small-area estimation. We focus primarily on indicators in a low and middle-income countries context. Our methods are particularly useful for data from Demographic Health Surveys and Multiple Indicator Cluster Surveys. We build upon functions within the survey package, and use INLA for fast Bayesian computation. This paper includes a brief overview of these methods and illustrates the workflow of accessing and processing surveys, estimating subnational child mortality rates, and visualizing results with both simulated data and DHS surveys.

翻译：随着复杂抽样调查数据的日益普及，以及对精细时空尺度上人口与健康指标估计的持续需求——这往往导致数据稀疏性问题——亟需发展能够反映数据采集方式的时空平滑方法。开源R包SUMMER实现了多种针对调查数据的空间及时空平滑方法，其重点在于小区域估计。我们主要关注中低收入国家背景下的指标估计，所提方法特别适用于人口健康调查和多指标类集调查数据。本方法基于survey包中的函数构建，并采用INLA进行快速贝叶斯计算。本文简要概述了这些方法，并通过模拟数据和人口健康调查案例，演示了从访问处理调查数据、估计地方级儿童死亡率到结果可视化的完整工作流程。