Mungiole M, Pickle L W, Simonson K H
Centers for Disease Control and Prevention, National Center for Health Statistics, 6525 Belcrest Rd., Rm. 915, Hyattsville, MD 20782-2003, USA.
Stat Med. 1999 Dec 15;18(23):3201-9. doi: 10.1002/(sici)1097-0258(19991215)18:23<3201::aid-sim310>3.0.co;2-u.
Smoothed data maps permit the reader to identify general spatial trends by removing the background noise of random variability often present in raw data. To smooth mortality data from 798 small areas comprising the contiguous United States, we extended the head-banging algorithm to allow for differential weighting of the values to be smoothed. Actual and simulated data sets were used to determine how head-banging smoothed spike and edge features in the data, and to observe the degree to which weighting affected the results. As expected, spikes were generally removed while edges and clusters of high rates near the U.S. borders were maintained by the unweighted head-banging algorithm. Incorporating weights inversely proportional to standard errors had a substantial effect on smoothed data, for example determining whether observed spikes were retained or removed. The process used to obtain the smoothed data, including the choice of head-banging parameters, is discussed. Results are considered in the context of general spatial trends. Published in 1999 by John Wiley & Sons, Ltd. This article is a U.S. Government work and is in the public domain in the United States.
平滑数据图通过去除原始数据中经常存在的随机变异性背景噪声，使读者能够识别总体空间趋势。为了平滑来自美国本土798个小区域的死亡率数据，我们扩展了“猛撞”算法，以便对要平滑的值进行差异加权。使用实际数据集和模拟数据集来确定“猛撞”算法如何平滑数据中的峰值和边缘特征，并观察加权对结果的影响程度。正如预期的那样，未加权的“猛撞”算法通常会去除峰值，同时保留美国边境附近的高死亡率边缘和聚集区域。纳入与标准误差成反比的权重对平滑数据有重大影响，例如决定观察到的峰值是保留还是去除。本文讨论了用于获取平滑数据的过程，包括“猛撞”参数的选择。结果在总体空间趋势的背景下进行了考量。本文于1999年由约翰·威利父子有限公司出版。本文是美国政府的作品，在美国属于公共领域。
