Small area estimation, and spatial statistics

小面积估计和空间统计模型分别在调查抽样和公共卫生领域具有实际意义和理论意义。近年来，由于在制定政策和计划、政府资金分配、区域规划、地方一级的营销决策和其他用途中对可靠的小区域统计数据的需求不断增长，小区域估计受到了相当大的关注。小区域估计的应用领域包括地区一级学龄儿童的贫困人数、小地方的收入、人口普查大都市地区的每月失业率以及地方地区的健康估计。空间统计是指以统计理论为基础的各种各样的技术，可以识别集群，比较地理区域之间的比率，从随机噪声中分离系统模式，量化测量的不确定性（例如，估计），并评估空间背景中的潜在预测因子。虽然空间统计对人口和公共卫生特别有用，但这些方法也可应用于天文学、农业和经济学等不同领域。

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.