#30DayMapChallenge 2025

There is a daily challenge to produce a map. Here is my response.

After enjoying the galleries of maps published during the last year’s challenges, I decided to publish my own in 2025. You can find the instructions here.

I decided to focus on mapping with R as much as possible. The results will be uploaded to Mastodon and LinkedIn.

Enjoy them!

Day 1 – Points

Challenge Classic: Map with point data (e.g., individual locations, points of interest, clusters). Focus on effective symbolization and density visualization.

Occurrences of papyrus (Cyperus papyrus L.) worldwide according to GBIF. Orange dots are the occurrences in Africa (its natural range). Yellow dots are the occurrences outside of Africa (introduced range). The map uses the Robinson projection.

Day 2 – Lines

Challenge Classic: Map linear features (e.g., roads, rivers, migration paths, flow lines). Explore line thickness, color, and direction to convey information.

Just one line displaying the longest span for continental Chile.

Day 3 – Polygons

Challenge Classic: Create a map focused on area features (e.g., administrative regions, land use, boundaries). Use fills, patterns, and choropleth techniques.

The islands of the Magellanic fjords are classified into quartiles according to their surface area.

Day 4 – My Data

Map something personal using your own dataset. Visualize GPS traces, your commute, or a unique, small dataset you created. (Need simple data? Try geojson.io).

The background is from OpenStreetMap, masked with a dark layer, and scratched by the locations of track points collected over several years.

Day 5 – Earth

Classical Elements 1/4: Focus on the tangible and grounded. Map landforms, geology, soil, agriculture, elevation, or anything solid beneath your feet.

Northness estimated by the difference in mean elevation towards south minus towards north. The location is the Siebengebirge in Germany. Contour lines show the elevation in m asl.

Day 6 – Dimensions

Map beyond 2D. Visualize data using 3D models, extrusions (building heights), depth, time (as a dimension), or an unconventional multivariate approach.

The population size of the South American countries is represented by the area of the rectangles. The centroid and aspect ratio of each rectangle are preserved from the bounding boxes of the respective polygons in the background.

Day 8 – Urban

(World Urbanism Day) Map the built environment: dense street networks, highrises, urban sprawl, city infrastructure, or population density within a metro area.

The area is divided into blocks using the highway feature from OpenStreetMap. Blocks are classified primarily by size. Small blocks are expected to indicate urban areas.

Day 9 – Analog

Step away from the screen! Create your map using traditional methods (e.g., pen, pencil, paint, collage, physical models). Show the handmade process!

I used to prepare maps by hand, using ink and printed labels, for our scientific publications at the Geobotanical Lab in Valdivia. The map shown here was prepared for my PhD thesis and depicts sample locations (points A to D) south of the city of Cholchol (Araucanía, Chile). The post-scan processing steps are omitted here.

Day 11 – Minimal Map

Challenge yourself to use the fewest possible elements (color, line weight, labels) while keeping the map clear, useful, and informative.

While working as a cartographer, I learned about various techniques for generalizing features on maps. Here, I applied the Douglas–Peucker algorithm (R package rmapshaper) to reduce the number of nodes. Note that this article recommends Visvalingam as an alternative. The choropleth shows the area size of the German states, calculated using the original polygons (I was too lazy to find the population data).

Day 13 – 10 minute map

Start the timer! The maximum allowed time to design and produce this map is 10 minutes. Focus on speed, simplicity, and core communication.

This is a personal map showing important places from my childhood in the countryside. The Llavería got its name from the old farm equipment storage buildings (llaves means “keys” in Spanish). Until the Chilean land reform of the 1960s, the place was a large estate managed according to medieval standards. In 1985, a powerful earthquake destroyed most of the remaining structures from that period, including the Llavería.

Day 14 – Data challenge: OpenStreetMap

Use OpenStreetMap (OSM) data as your primary source. Map your favorite feature, contribute back to the project, or style the map in an interesting way.

I am attempting an artistic display of road networks by using buffers according to road categories and assigning random colors to the polygons between roads. The area includes Bergheim and Müllekoven (Troisdorf), where I currently live.