Small area estimation, and spatial statistics

小面积估计模型和空间统计模型分别在抽样调查和公共卫生领域具有重要的现实意义和理论意义。近年来，由于对可靠的小区域统计数据的需求日益增长，小区域估计受到了相当大的关注，这些统计数据在制定政策和计划、分配政府资金、区域规划、地方一级的营销决策和其他用途时都需要使用。小区域估计的应用领域包括区域一级学龄儿童的贫困计数、小地方的收入、人口普查大都市区的月失业率以及当地与健康有关的估计。空间统计指的是以统计学理论为基础的各种技术，这些技术可以确定集群、比较地理区域之间的比率、将系统模式与随机噪声分开、量化测量的不确定性(例如估计)以及在空间范围内评估潜在的预测因素。虽然空间统计对人口和公共健康特别有用，但这些方法也可以应用于包括天文学、农业和经济学在内的不同领域。

Small area estimation and spatial statistical models have practical importance and theoretical interest in survey sampling and public health, respectively. Small area estimation has received considerable attention in recent years due to growing demand for reliable small area statistics that are needed in formulating policies and programs, allocation of government funds, regional planning, marketing decisions at local level, and other uses. Areas of application of small area estimation include poverty counts of school-age children at the area level, income for small places, monthly unemployment rates for Census Metropolitan Areas, and health-related estimates for local areas. Spatial statistics refer to a broad variety of techniques grounded in statistical theory that can identify clusters, compare rates among geographic area, separate systematic patterns from random noise, quantify uncertainty of measures (e.g., estimates), and assess potential predictors in a spatial context. While spatial statistics are particularly useful for population and public health, these methods can also be applied to diverse fields including astronomy, agriculture, and economics.