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Editorial to the Special Issue on Demographic Data Visualization: Getting the point across – Reaching the potential of demographic data visualization

Author

Listed:
  • Tim Riffe

    (Euskal Herriko Unibertsitatea (University of the Basque Country))

  • Nikola Sander

    (Bundesinstitut für Bevölkerungsforschung (BiB))

  • Sebastian Kluesener

    (Bundesinstitut für Bevölkerungsforschung (BiB))

Abstract

Background: Demography is full of data visualization challenges, such as age-period-cohort effects or life course trajectories. Innovative approaches to visualizing such complex data structures have been proposed from within and outside the discipline. However, demographic data visualizations presented in the scientific literature often fall short of the state-of-the-art. Objective: We discuss what makes a good data visualization and why it is worthwhile to strive for state-of-the-art visualization. We highlight the distinction between exploratory and explanatory graphics, and relate the seven papers that comprise the Demographic Research special collection on data visualization to the broader endeavor of data visualization in demography. Contribution: We suggest a set of best practice rules that are intended to provide general guidance for researchers attempting to produce meaningful and coherently designed figures from their data.

Suggested Citation

  • Tim Riffe & Nikola Sander & Sebastian Kluesener, 2021. "Editorial to the Special Issue on Demographic Data Visualization: Getting the point across – Reaching the potential of demographic data visualization," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(36), pages 865-878.
    • Handle: RePEc:dem:demres:v:44:y:2021:i:36
    DOI: 10.4054/DemRes.2021.44.36
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    References listed on IDEAS

    as
    1. Adrien Remund & Carlo G. Camarda & Tim Riffe, 2018. "A Cause-of-Death Decomposition of Young Adult Excess Mortality," Demography, Springer;Population Association of America (PAA), vol. 55(3), pages 957-978, June.
    2. Ilya Kashnitsky & Jose Manuel Aburto, 2019. "Geofaceting: Aligning small-multiples for regions in a spatially meaningful way," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(17), pages 477-490.
    3. Camarda, Carlo G., 2012. "MortalitySmooth: An R Package for Smoothing Poisson Counts with P-Splines," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i01).
    4. Jorge Cimentada & Sebastian Kluesener & Tim Riffe, 2020. "Exploring the demographic history of populations with enhanced Lexis surfaces," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 42(6), pages 149-164.
    5. Serena Pattaro & Laura Vanderbloemen & Jon Minton, 2020. "Visualizing fertility trends for 45 countries using composite lattice plots," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 42(23), pages 689-712.
    6. Beata Nowok, 2020. "A visual tool to explore the composition of international migration flows in the EU countries, 1998–2015," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 42(27), pages 763-776.
    7. Timothy Riffe & Kieron J. Barclay & Sebastian Klüsener & Christina Bohk-Ewald, 2019. "Boom, echo, pulse, flow: 385 years of Swedish births," MPIDR Working Papers WP-2019-002, Max Planck Institute for Demographic Research, Rostock, Germany.
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    More about this item

    Keywords

    data visualization; demography; demographic data;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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