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#30DayMapChallenge #2 lines
I revisit one of the coolest small improvements one can do to their maps – create borders explicitly as lines and not as polygon outlines
#rstats #rspatial #dataviz
Blog post: https://kashnitsky.phd/2023/map-borders

#30DayMapChallenge #3 polygons
I love bivariate maps
This map shows which countries have more researchers registered in Publons and where did they contribute more into fuelling the peer-review engine of academia
#dataviz #rspatial #AcademicSky
#rstats code: https://gist.github.com/ikashnitsky/90483ee3c3c230aa874dffb856d6074b

#30DayMapChallenge #4 A Bad Map
I discuss an often neglected element of everyday maps – map projections. It's amazing how much of a difference this small trick makes when you use a projection that fits the data and/or the story
#dataviz #rspatial #rstats
Blog post: ikashnitsky.phd/2023/map-proj

#30DayMapChallenge | 5: Analog map
This was one of my few full-scale printed posters. There are numerous ways to make #dataviz attractive, but they all can never compete with a cat
#rstats code to replicate the poster: https://github.com/ikashnitsky/compare-pop-eu-us

#30DayMapChallenge | 6: Asia
All Danish restaurants from OSM, yellow highlights Asian cuisine restaurants, regions are colored by the percentage of Asian places #dataviz #rspatial
#rstats code: https://github.com/ikashnitsky/30DayMapChallenge/blob/main/src/06-asian-restaurants.R

#30DayMapChallenge | 7: Navigation
When you think of a complicated building, what comes first to your mind? Hogwarts? "Hold my beer" says SDU campus that hides immense (and often unnecessary) complexity behind the outlooks of a huge warehouse in the fields

#30DayMapChallenge | 9: hexagons
I asked bing (who forwards image generation requests to dall•e3) to generate a map of Europe, which all countries represented with hexagons scaled to population size. The outputs look cool but I failed to get roughly what I wanted
#dataviz #generativeAI #demography

#30DayMapChallenge 10: North America
We often forget that Mexico is also North America
w/ José Manuel Aburto we explored early adult mortality in Mexican states using geofaceting and ternary colorcoding #dataviz
#rstats code: https://github.com/ikashnitsky/demres-geofacet
#demography paper: https://doi.org/10.4054/demres.2019.41.17

#30DayMapChallenge | 11: retro
This is more nostalgia than retro — the city I miss most, specifically the part of Moscow that I started exploring on my own at age ~14. I remember a map that looked very much like this toner that I tore out of a phonebook, laminated it and used daily to navigate

#30DayMapChallenge | 12: South America
In my head South America === football
Thus for this day of the challenge I explore the historical outcomes of Copa Libertadores, the main club competition of the continent –– but also
#dataviz #rspatial
#rstats code: https://github.com/ikashnitsky/30DayMapChallenge/blob/main/src/12-south-america-copa.R

#30DayMapChallenge | 13: Choropleth
Women live longer than men — this is a well-known regularity of mortality. Yet, we are too focused on the averages — 25-50% of individual men outsurvive a randomly paired woman
#demography paper: https://doi.org/10.1136/bmjopen-2021-059964
#rstats code: https://github.com/CPop-SDU/outsurvival-in-perspective

#30DayMapChallenge | 14: Europe

This was one of my last fun #dataviz projects at twitter (Apr'22) when it was still vibrant and full of life – I asked my followers to name 5 non-capital cities in Europe, the first that came to their mind. 101 people participated! Here are the named cities:

#30DayMapChallenge | Day 15: OpenStreetMap
Just following the idea of Kyle Walker (shared via LinkedIN [1] I use Mapbox's Cartogram tool [2] that quickly styles the map based on the colors found in an uploaded image
[1]: https://www.linkedin.com/posts/walkerke_30daymapchallenge-activity-7130589379164573696-vAYC/
[2]: https://apps.mapbox.com/cartogram/#8.3/55.14/10.647

#30DayMapChallenge | Day 16: Oceania
A very quick one today – Pacific Ocean view via Google Earth. I tried to get a viewpoint with minimal land. Would be a cool creative #rspatial challenge to calculate the coordinates of the central point for such a view

#30DayMapChallenge | Day 17: Flow
We are an extremely diverse group of researchers at CPop SDU #demography
I attempted to map our migration histories (idea contributed by Julia Callaway). Although it's a tough task due to the hugely varying scales
#rstats code: https://github.com/ikashnitsky/30DayMapChallenge/blob/main/src/17-flow-places-lived.R

#30DayMapChallenge | Day 20: Outdoors
I map all my Strava runs in Odense over the last 6 years, 342 in total.
#rstats code: https://github.com/ikashnitsky/30DayMapChallenge/blob/main/src/20-outdoors-strava.R

Once I accidentally mirrored a map of Europe, this one. And suddenly I was shocked: Wait! Is Italy that wide? Southern England more to the south than most of Poland? Cyprus, where are you?
Appreciate just how much we really on familiar shapes in maps!
#30DayMapChallenge | 22: North is always up

I am a demographer. Most of the times, in maps I care only about humans. Thus, the best thing I can do about Antarctica is to remove it and save the plot space. With #rstats and {sf} it's done in one line.
#30DayMapChallenge | Day 25: Antarctica

I love inset maps as an additional element in #dataviz to facilitate reading the plot. Here is an example from my recent paper on regional differences in older adult mortality in DK
Preprint: https://osf.io/y9ke4
#rstats code: https://github.com/ikashnitsky/mun-non-surv
#30DayMapChallenge | Day 28: Is this a chart or a map?

In the early days of the c19 pandemic, using this map we illustrated w/ José Manuel Aburto how differences in population age structures may matter in the face of the new cause of death
Paper: https://doi.org/10.1016/j.worlddev.2020.105170
#rstats code: https://github.com/ikashnitsky/covid19-nuts3
#30DayMapChallenge | Day 29: Population

#30DayMapChallenge | 30: my favorite
This map represents population age structures as ternary compositions, data produces the colors in this #dataviz — away from grey means more people of specific age groups
w/ Jonas Schöley
Paper: https://doi.org/10.1016/s0140-6736(18)31194-2
#rstats code: https://github.com/ikashnitsky/the-lancet-2018

@ikashnitsky nice. I love that the 2d colormap is stretched to fit the actual age distribution.

@ikashnitsky Poland and Slovakia seem to be a bit particular, neither old nor young. Any explanation?