Jakub NowosadโจWhatโs new in geocomputation this year?
The #geocompx 2025 update covers project milestones, current work, and future plans.
Robin LovelaceHappy to see new newest member of the #geocompx family up on the geocompx.org website: Spatial Data Visualization with tmap is an awesome book that is still in progress ๐๏ธ Well worth a read to launch your reproducible map making journey ๐
Chapter 8 of Geocomputation with Python is all about cartography:
๐จ Designing maps
๐ผ๏ธ Static maps with .plot & rasterio.plot.show
๐ Interactive maps with .explore
Chapter 7 of Geocomputation with Python shows how to:
๐น Find spatial datasets (geoportals & download tools)
๐น Work with different file formats (strengths & limits)
๐น Read & write vector + raster files efficiently
Mario Angst#30DayMapChallenge (10 minute map)
This is trees tended by the city of Zรผrich, sized by tree crown diameter.
Done in Python, with loading data into duckdb and graphing on lonboard. The map is actually clickable and gives you meta-info about the trees when clicking on a tree.
I wanted to actually intersect this with street names containing tree names, but 10 minutes were not enough for that :).
Crazy how quick you can get to something with open government data (OGD) and these modern #geocompx tools I am just discovering...
OGD data found via:
https://opendata.swiss/de/dataset/baumkataster/resource/d0ecd9ca-45fb-499b-9171-aac76b00fb42
My contribution to today's #30DayMapChallenge (Minimal Map)
This is a map of two months of cycling to work, two days a week, between Zรผrich and Winterthur (and exploring my options for gravel and beauty).
Done with duckdb + lonboard in Python. Repo here: https://codeberg.org/marioangst/winti-zh-gpx
I am quite new to doing spatial data, coming very much from the non-spatial data science side, but I'm having fun and I've got to say the #geocompx resources out there are really cool nowadays.
Confused about coordinate reference systems (CRSs)? ๐งญ๐๐
Chapter 6 of Geocomputation with Python dives into:
- Geographic vs projected CRSs (lon/lat vs meters)
- Getting, setting & reprojecting CRSs
- Why ignoring CRSs can break your analysis ๐จ
Working with raster + vector together in Python? ๐บ๏ธ๐๐
Chapter 5 of geocompx talks about:
- Cropping & masking rasters with vectors
- Extracting raster values via vector data
- Raster โ๏ธ vector conversion (polygonize & rasterize)
Want to transform the geometry of spatial data in Python? โจ๐๐
Chapter 4 of geocompy covers:
- Vector: simplify, buffer, centroid, clip, unions, affine transforms
- Raster: crop, resample, aggregate, align datasets
Spatial operations in Python? ๐๐๐
Chapter 3 of Geocomputation with Python covers:
- Vector: spatial joins, subsetting, aggregation, etc.
- Raster: map algebra (local, focal, zonal, global), tiling & merging