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Related Experiment Videos

Application of a weighted head-banging algorithm to mortality data maps.

M Mungiole1, L W Pickle, K H Simonson

  • 1Centers for Disease Control and Prevention, National Center for Health Statistics, 6525 Belcrest Rd., Rm. 915, Hyattsville, MD 20782-2003, USA. mmungiole@mail.arl.mil

Statistics in Medicine
|December 22, 1999
PubMed
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This study refined the head-banging algorithm for smoothing mortality data, improving spatial trend identification. Differential weighting significantly impacted results, determining the retention of data spikes and edge features.

Area of Science:

  • Spatial analysis
  • Biostatistics
  • Public health

Background:

  • Raw mortality data often contains noise obscuring spatial trends.
  • Identifying general spatial trends requires data smoothing techniques.

Purpose of the Study:

  • To extend the head-banging algorithm for smoothing mortality data from 798 small areas in the contiguous U.S.
  • To assess the impact of differential weighting on smoothing results, particularly for spike and edge features.

Main Methods:

  • Extended the head-banging algorithm to incorporate differential weighting.
  • Utilized actual and simulated data sets to evaluate smoothing performance.
  • Analyzed the effect of weights inversely proportional to standard errors.

Main Results:

Related Experiment Videos

  • The unweighted head-banging algorithm generally removed spikes while preserving edges and clusters of high rates near borders.
  • Differential weighting substantially influenced smoothed data, affecting the retention or removal of spikes.
  • The choice of head-banging parameters and weighting significantly impacted the final smoothed data.

Conclusions:

  • The modified head-banging algorithm effectively smooths mortality data for identifying spatial trends.
  • Differential weighting is crucial for accurately representing spatial patterns and features in mortality data.
  • The study provides insights into parameter selection for robust spatial smoothing.