Spatial patterns of mortality in the United States: A spatial filtering approach

Kyran Cupido, Petar Jevtić, Antonio Paez

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Within the field of spatial analysis, filtering has emerged as a powerful modelling technique to retrieve systematic patterns of geographical variation. In this work, spatial filtering is introduced in the context of modelling mortality for the first time in actuarial literature. Specifically, the objective of this research is to identify patterns in the spatial distribution of mortality rates for the sixty-five and older age group at the county level for the contiguous United States. The analysis carried out pertains to the spatial autocorrelation of these rates over time. The spatial filtering methodology is applied to uncover latent spatial patterns. Furthermore, the analysis reveals the extent to which these spatial patterns remained consistent across the years in the study. Results show the existence of spatial dependencies leading to an accompanying spatial filter. Thus, incorporating spatial filters as an exploratory tool in spatial analysis proves of considerable use when it comes to enriching traditional mortality models.

Original languageEnglish (US)
Pages (from-to)28-38
Number of pages11
JournalInsurance: Mathematics and Economics
StatePublished - Nov 2020


  • Eigenvectors
  • Mortality
  • Spatial autocorrelation
  • Spatial filtering
  • Spatial patterns
  • Weights matrix

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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