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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 | 8: Africa
Population growth is unequal around the globe due to the timing of demographic transition. Lately, the African population has been growing the fastest, and soon it will be even larger on the global population map #dataviz
#rstats code: https://github.com/ikashnitsky/30DayMapChallenge/blob/77d444be79c5170c83b2b6049847df7b5ec04536/src/08-africa-wpp.R

#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 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?