My Contributions to the First 30DayMapChallenge

Posted by Cédric on Sunday, December 15, 2019

What’s the #30DayMapChallenge?

The #30DayMapChallenge which was initiated by Topi Tjukanov in 2019. The goal is to create a map each day of November with a broad topic provided by Topi, no matter which tool, and to share it with the hashtag “#30DayMapChallenge” on Twitter.

The idea is to create (and publish) maps based on different themes on each day of the month using the hashtag #30DayMapChallenge, You can prepare the maps beforehand, but the main idea is to publish maps from specific topics on specific days listed below. Just include a picture of the map when you post to Twitter with the hashtag. You don’t have to sign up anywhere to participate. There are no restrictions on the tools, technologies and the data you use in your maps. Doing less than 30 is also fine (and actually doing all 30 is really hard!). Happy mapping!

In the end, I made 28 out of 30 maps. Which I believe is quite a success given the challenges on my way (yes, I learned how to plot maps in R but this was maybe only 15% needed to succeed) and that I spontaneously decided to join after sharing a point map on the first of November which coincidentally was the prompt of the first day of the #30DayMapChallenge.

Me somewhen in October: “Uh, there is this #30DayMapChallenge. Let’s keep an eye on it.”
Me on first of November: “Haha, as I would have time to participate.”
Me on second of November: “Now that I have already done one by accident, I also can do all of them.”

Me on first of December: “Uff, what a month!”


My Contributions to the #30DayMapChallenge

Day 1: Points

./Day01_Points/Points_Squirrels.png

Day 2: Lines

./Day02_Lines/Lines_OSMnx_Kiez.png

Day 3: Polygons

./Day03_Polygons/Polygons_GlobalMortality.png Alternatvie version of alcohol and drug disorder

Day 4: Hexagons

./Day04_Hexagons/Hexagons_SchoolDiversity_hex.png Version showing diversity indices for the school year 199495

Day 5: Raster

./Day05_Raster/Raster_GermanyDGM.png

Days 6 to 9: Blue, Green, Red & Yellow

./Day09_Yellow/BlueRedGreenYellow_BerlinPublicTransport.png Day 6: Blue | Day 7: Red | Day 8: Green | Day 9: Yellow

Day 10: Black & White

./Day10_BlackWhite/BlackWhite_CapitalPop_bw.png Alternatvie version with some grey

Day 11: Elevation

./Day11_Elevation/Elevation_Rayshader_custom.png High resolution version | Alternatvie version with annother custom texture

Day 12: Movement

./Day12_Movement/Movement_BerlinByBike.png

Day 13: Tracks

./Day13_Tracks/Tracks_StorksGili.png Read the paper!

Day 14: Boundaries

./Day14_Boundaries/Boundaries_GlobalNeighbors.png

Day 15: Names

./Day15_Names/Names_BerlinRoads.png High resolution version

Day 16: Places

./Day16_Places/Places_ExtremesEarth.png

Day 17: Zones

./Day17_Zones/Zones_TimezonesEarth.png Alternative version with filled countries only

Day 18: Globe

./Day18_Globe/Globe_Projections.png

Day 19: Urban

./Day19_Urban/Urban_GlobalUrbanAreas_bg.png ./Day19_Urban/Urban_GlobalUrbanLands.png Alternative version without land masses

Day 20: Rural

./Day20_Rural/Rural_BerlinRuralAreas.png Alternative green version without roads


Day 21: Environment

./Day21_Environment/Environment_NationalParksUS.png

Day 22: Built Environment

./Day22_BuiltEnvironment/BuiltEnv_BerlinBuildings.png ./Day22_BuiltEnvironment/BuiltEnv_BuildingsMoabit.png Blank version of Moabit

Day 23: Population

./Day23_Population/Population_ChangeGlobal_diff.png Version showing the projections for the coming 5 years and for 20-year periods from 1950 to 2050

Day 24: Statistics

./Day24_Statistics/Statistics_MalariaDeaths.png

Day 25: Climate

./Day25_Climate/Climate_KoppenGeiger.png

Day 26: Hydrology

./Day26_Hydrology/Hydrology_WorldRivers.png

Day 27: Resources

./Day27_Resources/Resources_eMobilityBerlin.png

Day 29: Experimental

./Day29_Experimental/Experimental_PopIntervals.png


Final Thoughts

The challenge was exciting! I learned so many new things, especially about map projections, new standards of geodata processing in R, and data sources. I extensively used the {sf} package and rarely something else. I found the Natural Earth page which provides public domain map data sets. And there is an R package called {rnaturalearth} as well that will from now on my first choice of getting geodata into R—its usage is so simple! I also worked a lot with OpenStreetMap (OSM) data.

Overall, I feel way more confident to work with spatial data in R now thanks to the challenge. And this extends to all things related to #rspatial:

  • searching, loading, and processing geodata
  • using different projections
  • combining several spatial data sets, even from different sources
  • applying uncommon map styles such as creating cartograms or hex tile maps

Lastly, it was great to see all the other contributions and to be part of a community! I learned about other tools out there, was impressed and inspired by the designs of others, have bookmarked several data sets—and most importantly, I was able to build a network of map creators all over the world! Thank you Topi and everyone for contributing, sharing, and caring 💙💚🧡💜