Rbanism

@cforgaci.bsky.social @ineszaid.bsky.social @clementinecttn.bsky.social @javiersanmillan.bsky.social @dni-ka.bsky.social

We have just run the 2026 edition of our Data @carpentries.carpentries.org for Geospatial Data with R. Thank you to this special bunch of enthusiastic participants, dedicated helpers and generous instructors. Till next year!

www.tudelft.nl/en/library/d...

Explore Explain S6E6 - Nicola Rennie & Ansgar Wolsing - Data Viz Excellence, Everywhere Welcome to episode 6 of season 6 of Explore Explain, a long-form video and podcast series all about data visualisation design. I am delighted to welcome Nicola Rennie, Data Visualisation Specialist ba...

NEW EPISODE! I chatted with @nrennie.bsky.social & @ansgarw.bsky.social about the #30DayMapChallenge and took a close look at 5 of their respective works.

visualisingdata.com/2026/02/expl...

@clementinecttn.bsky.social
@dni-ka.bsky.social
@javiersanmillan.bsky.social
@ineszaid.bsky.social
@bhausleitner.bsky.social

Geospatial Data Carpentry for Urbanism

🗺️ We are running our new edition of Geospatial Data Carpentry for Urbanism next month (23 & 26 Feb) at TUD!

🙋 Join us to learn practical ways to handle Geospatial Data with R (import, analyse, plot, map & more!).

💻 @carpentries.carpentries.org @cforgaci.bsky.social @paulamartinezl.bsky.social

30DayMapChallenge2025/26Nov-transport at main · Rbanism/30DayMapChallenge2025 Collective #30DayMapChallenge with R. Contribute to Rbanism/30DayMapChallenge2025 development by creating an account on GitHub.

Code

github.com/Rbanism/30Da...

GitHub - sdgis-edu-tud/report-asa2025-groupf: applied-spatial-analytics-2025-create-your-report-asa2025-report-1 created by GitHub Classroom applied-spatial-analytics-2025-create-your-report-asa2025-report-1 created by GitHub Classroom - sdgis-edu-tud/report-asa2025-groupf

References

Spatial Units with MCDA from Unpacking Dresden Project: github.com/sdgis-edu-tu...
Dresden's Administrative Boundary from OpenStreetMap plug-in in QGIS, June, 2025.

Proces (4/4)

Moreover, missing attributes, such as information on the accessibility or classification of waterways, pose a fundamental limitation for creating accurate and meaningful maps.

Process (3/4)

... it is often necessary to visually examine the data to determine the appropriate operations. For example, national borders are defined differently across data sources, and such semantic inconsistencies can strongly affect geometric operations.

Process (2/4)

In addition, since ggplot2 allows flexible control over visualization, the overall workflow in R is also more efficient than that in FME.
One major challenge I encountered during the coding process was related to data quality. Although object attributes can be inspected directly in R,

Process (1/4)

This map primarily uses vector data, sf and ggplot2 . Before implementing the workflow in code, I first experimented with similar processes in QGIS and FME. I found that sf provides all the necessary geometric operations while requiring significantly less processing time than QGIS.

Content (4/4)

However, due to data limitations, it may be somewhat misleading—since not all inland waterways are suitable for freight transport. For instance, the canals in central Amsterdam are clearly not designed for this purpose.

Content (3/4)

Inland, the waterway network is noticeably denser in the western part of the country, with several main waterways extending into neighboring nations. The map primarily focuses on depicting the waterway network, omitting most general topographic details.

Content (2/4)

This map illustrates the country’s water transportation routes, with dashed lines representing sea routes and solid lines indicating inland waterways. It clearly shows that the sea routes converge at two major ports: Rotterdam and Amsterdam.

Content (1/4)

Located at the edge of the European continent, facing the Atlantic Ocean and home to the largest international port in Europe, the Netherlands serves as a gateway for European imports in the era of globalization.

#30DayMapChallenge #30DayMapChallenge2025 #30DayMapChallengeRbanism #30DayMapChallengeR

Day 26: Transport

Netherlands: Gateway of European Import

by Yaying Hao

#GeoData #rstats #DataViz #Maps #SpatialViz

30DayMapChallenge2025/22Nov_naturalEarth at main · Rbanism/30DayMapChallenge2025 Collective #30DayMapChallenge with R. Contribute to Rbanism/30DayMapChallenge2025 development by creating an account on GitHub.

Code

github.com/Rbanism/30Da...

Process

To create this map, I have downloaded from the Natural Earth data center the bathymetry, cities, ports, marine names, as well as coastline of the world, and plotted them in one same map focusing on the area of the Mediterranean Sea.

Content (2/2)

On the same map, you can also see the geographic names of the different parts of the sea, as well as the bigger cities around the Mediterranean as square shapes changing in size depending on the amount of people that live in the area.

Content (1/2)

This maps shows the bathymetry of the Mediterranean Sea, with the ports and their names located around all the costs of this body of water.

#30DayMapChallenge #30DayMapChallenge2025 #30DayMapChallengeRbanism #30DayMapChallengeR

Day 22: Data challenge: natural earth

Ports in the Mediterranean Sea

By Roger Marín de Yzaguirre

#GeoData #rstats #DataViz #Maps #SpatialViz

30DayMapChallenge2025/25Nov-Hexagons at main · Rbanism/30DayMapChallenge2025 Collective #30DayMapChallenge with R. Contribute to Rbanism/30DayMapChallenge2025 development by creating an account on GitHub.

Code

github.com/Rbanism/30Da...

GitHub - sdgis-edu-tud/report-asa2025-groupf: applied-spatial-analytics-2025-create-your-report-asa2025-report-1 created by GitHub Classroom applied-spatial-analytics-2025-create-your-report-asa2025-report-1 created by GitHub Classroom - sdgis-edu-tud/report-asa2025-groupf

References

Spatial Units with MCDA from Unpacking Dresden Project: github.com/sdgis-edu-tu...
Dresden's Administrative Boundary from OpenStreetMap plug-in in QGIS, June, 2025.

Process (3/3)

At the plotting step, the hexagons attribute containing the MCDA score was chosen and the values were visualized in a continuous way using the `scale_fill_viridis_c` by defining the visualization option of 'magma'. At last, the map with description is plotted.

Process (2/3)

After that, the attributes containing the name of the boundary, in our case Dresden, was looked up using `head()` and `unique()` functions to locate the attribute containing the value of "Dresden" before filtering.

Process (1/3)

The the spatial units and Dresden's municipality boundary were assigned as specific values. Then, their CRS was transformed to EPSG: 25832, suitable for Germany projections.

Content (3/3)

The Spatial units consisting of water body were extracted and filled with given score of each criteria. The final scores were collected and calculated in MCDA matrix and entered into the hexagons as synthsized values that is visualized in the final map.

Content (2/3)

The water network was divided into spatial units with 250m diameter shaped in Hexagons for a better partitioning of overlapping zones of the study area. The indicators were Quality_of_Life, Climate_Adaptation and Biodiversity.

Content (1/3)

The map is the visualization of Multi-Criteria Decision Analysis (MCDA) scores of Dresden's streams and Elbe river based on specific criteria defined by the project team working on stream restorations.

#30DayMapChallenge #30DayMapChallenge2025 #30DayMapChallengeRbanism #30DayMapChallengeR

Day 25: Hexagons

MCDA Analysis of Water Network in Dresden

by Soroush Saffarzadeh

#GeoData #rstats #DataViz #Maps #SpatialViz