Volume 42 - Article 6 | Pages 149–164
Exploring the demographic history of populations with enhanced Lexis surfaces
|Date received:||27 Feb 2019|
|Date published:||22 Jan 2020|
|Keywords:||data visualization, historical demography, Lexis surface, mortality|
|Additional files:||readme.42-6 (text file, 6 kB)|
|demographic-research.42-6 (zip file, 1 MB)|
Background: Lexis surfaces are widely used to analyze demographic trends across periods, ages, and birth cohorts. When used to visualize rates or trends, these plots usually do not convey information about population size. The failure to communicate population size in Lexis surfaces can lead to misinterpretations of mortality or other conditions that populations face. For example, high mortality rates at very high ages have historically been experienced by only a small proportion of a population or cohort.
Objective: We propose enhanced Lexis surfaces that include a visual representation of population size. The examples we present demonstrate how such plots can give readers a more intuitive understanding of the demographic development of a population over time.
Methods: Visualizations are implemented using an R-Shiny application, building upon perception theories.
Results: We present example plots for enhanced Lexis surfaces that show trends in cohort mortality and first-order differences in cohort mortality developments. These plots illustrate how adding the cohort size dimension allows us to extend the analytical potential of standard Lexis surfaces.
Contribution: Our enhanced Lexis surfaces improve conventional depictions of period, age, and cohort trends in demographic developments of populations. An online interactive visualization tool based on Human Mortality Database data allows users to generate and export enhanced Lexis surfaces for their research. The R code to generate the application (and a link to the deployed application) can be accessed at https://github.com/cimentadaj/lexis_plot.
Jorge Cimentada - Max-Planck-Institut für Demografische Forschung, Germany
Sebastian Kluesener - Bundesinstitut für Bevölkerungsforschung, Germany
Tim Riffe - Max-Planck-Institut für Demografische Forschung, Germany
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