GeoJupyter virtual hackathon

GeoJupyter virtual hackathon (2025-10-16)¶

Please add new agenda items under the New agenda items heading!

• Join us on Zoom

  • What time is the meeting in my time zone?

• Notes from previous hackathons

• GeoJupyter handy links:

  • GitHub org

  • Community calendar

  • Zulip chat

Sign in!¶

Your name / GitHub ID / affiliation / icebreaker

• Matt / @mfisher87 / DSE

• Stefan / @stefanv / BIDS

• Maryam / @Mary-h86 / BIDS

Agenda & notes¶

⚡ (5 minutes) Lightning intros¶

Tell us about you in 30 seconds or less!

🌐 (5 minutes) Lightning demo¶

What’s new? Show & tell. Post on Zulip to request a show & tell slot; by default, QuantStack will demo awesome JupyterGIS progress each meeting!

💡 (5 minutes) What will we hack on?¶

• What do you want to work on today?

  • For ideas, check out the hackathon and good first issue labels on the JupyterGIS project!

• Add your ideas to the “ideas” list below.

• Add your favorite emoji or a + next to ideas you’re excited about.

  • Press the colon (:) key on your keyboard or navigate to “Insert > Emoji” in the menu bar to open the emoji browser.

Ideas¶

• Brainstorm new, well-scoped use cases for Jupyter users analyzing geo data

🪄 (all the minutes) Hack together!¶

Form teams from the ideas generated in the step above!

💬 (10 minutes) Share out¶

Think about: What exciting things did you accomplish? What loose ends remain? Big questions? Big ideas?

Please write for people who don’t have full context; link to related issues and documentation!

• A Jupyter Widget which enables viewing many Python data objects together on a map as layers with a simple, well-typed, well-documented API

  • some_package.explore(da1, da2, gdf1, {data: gdf2, symbology: {"choropleth": {steps: 11, classification: "natural"}})

  • Support rioxarray DataArrays

  • Support geopandas GeoDataFrames

  • Maybe: Support WMTS?

  • Support some curated default symbology options

    • Choropleth: # steps, classification mode, ?

    • Symbol map: shape, min/max size, size variable, color variable

    • Dot density: …

    • Cartogram: Maybe?

  • Support some symbology customization

    • Each symbology option provides

  • Use a Jupyter Server extension to tile raster data under the covers, e.g. rioxarray DataArray -> TiTiler

  • Send vector data to the renderer as binary (geoarrow)

  • Future integration (developed as an independent component): Support a data discovery interface which can help the user find other data they want to integrate with their Notebook analysis

    • Plain language search

    • Produce Python one-liners to bring that dataset into their notebook, e.g. geopandas.read_file(...) and xarray.open_mfdataset(...)

  • Display the data on slippy map widget (e.g. DeckGL)