Jonas Schöley

@jschoeley

Jonas Schöley | Demography and Data Viz and Music | formerly

Vrijeme pridruživanja: lipanj 2017.

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  1. Prikvačeni tweet
    28. lip 2017.

    For a quick look at human mortality over space & time try this:

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  2. 22. sij

    Interesting technique. There's intuitive appeal to thinning lines as cohorts die out. Ties into the larger problem of visualizing scalar fields weighted by a third characteristic. We should compare different techniques in the context of the Lexis surface.

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  3. 7. sij

    "Age patterns of morality" is such a common typo of mine that I maybe should just start to take it seriously.

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  4. proslijedio/la je Tweet
    30. pro 2019.

    Here's my conversation with Donald Knuth, the legendary computer scientist & mathematician. I can imagine no better guest to end the year with than Don, one of the kindest, funniest, and most brilliant people I have ever met. It was an honor beyond words:

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  5. 30. pro 2019.

    Nice application of tricolore () to regional statistics on educational attainment.

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  6. 17. pro 2019.

    Haha. Overlapping small multiples with axis only partially drawn to reduce clutter. Found in the original publication on Taylors-Law ().

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  7. proslijedio/la je Tweet
    23. stu 2019.

    nr 23 (population): Composition of the Population of Brussels, between Belgian, EU and outside of EU nationalities. Definitely needs more work and explanations, but, hey, we're doing one map a day (and I think this is already a nice result!) cc

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  8. 15. stu 2019.
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  9. proslijedio/la je Tweet
    7. stu 2019.

    If we put this all together then the global picture seems to be one where most countries are moving away from drinking cultures which are based around a single type of alcohol. Just look at how the colours jump when this animation loops back round.

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  10. proslijedio/la je Tweet
    30. lis 2019.

    Mapping changes in the relative prevalence of so-called 'deaths of despair' (drug poisoning, suicide and alcohol-specific causes) over age in England. Plotted with the {tricolore} package in

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  11. 28. lis 2019.

    I wonder how many R packages are basically a subset of what library(brms) can deliver (with a wonderful formula interface). Thank you for making Bayesian regression modeling so much easier to get started with.

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  12. 24. lis 2019.

    Why bother with standard errors if our uncertainty around the correct model specification is magnitudes higher than the sampling error from any given model? This feels especially true for demographic forecasts. Can we incorporate modeling uncertainty into the errors?

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  13. 18. lis 2019.

    This may be one more case where it would be desirable to have a strict 1:1 mapping of data dimension to color dimension (i.e. population-density:lightness, direction-of-change:hue, rate-of-change:chroma) but the resulting loss of visual contrast would do more harm than good.

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  14. 18. lis 2019.

    Multidimensional color scales hit again!

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  15. 18. lis 2019.

    Best summary of survival identities I've seen so far. Found in Rinne: The Weibull Distribution.

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  16. 24. ruj 2019.

    DataViz as a medium stratified into genres. This way of thinking naturally leads one to consider fashions in viz. Are there cyclic trends? Also my favorite genre are multidimensional color scales which I believe to be equivalent of being into Scandinavian body horror.

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  17. 23. ruj 2019.

    Next I participate in I'll bring a poster.

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  18. 20. ruj 2019.

    The youth truly owns their movement. Speakers, organizers, stewards, all below drinking age.

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  19. 20. ruj 2019.
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  20. 20. ruj 2019.
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  21. 20. ruj 2019.
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