Add vector data analysis tutorial

[Valyrian-Code]

Adds a new beginner/intermediate tutorial on vector data analysis — the tutorial
set currently has no dedicated vector-focused tutorial, so this fills that gap and
covers the operations users reach for most often.

What it covers

Using the North Carolina schools_wake (points), roadsmajor (lines), and
zipcodes_wake (areas), it works through a single "school accessibility" example:

1. Exploring features and attributes — v.info, v.db.select
2. Selecting by attribute — v.extract (the 95 elementary schools)
3. Buffering — v.buffer (a 500 m road corridor)
4. Selecting by location — v.select (the 15 elementary schools within the corridor)
5. Overlay — v.overlay (road corridor intersected with ZIP areas)
6. Counting per area — v.vect.stats + a d.vect.thematic choropleth of schools per ZIP

Details

• Uses the standard North Carolina sample dataset and the grass.tools API (GRASS 8.5), consistent with the recent NumPy/Landlab tutorial.
• All code was run against GRASS 8.5; the figures are real outputs of the workflow, and the tutorial renders cleanly with Quarto (eval: false).
• Categories: vector, beginner, intermediate, Python.
• Cross-links to the Thematic Maps and Terrain/DEMs tutorials.

Feedback on scope, the choice of example, or wording is very welcome.

[Copilot]

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[veroandreo]

The content is ok for an intro tutorial on basic vector operations. All images need to be fixed, though. Black background does not look nice and they are too big. See other suggestions below.

[Valyrian-Code]

Thanks for the thorough review, @veroandreo! I've pushed an update addressing all of it:

• Images: the transparent areas were rendering as a black background — regenerated all figures composited onto white, at a smaller size (width=500), with the scale bar on a white background box so it stays legible and clear of the features.
• Attribute table first: added v.db.select ... format=json read into a pandas table, so the GLEVEL selection is motivated by what's actually in the data (and it shows the handy JSON-to-table trick).
• Overlay: added an area-within-reach computation (v.to.db + grouped db.select) reporting how much of each ZIP code lies within 500 m of a major road.
• Retitled to "Basic Vector Data Analysis with GRASS", reworded the v.overlay bullet, moved the GRASS-version callout below the setup code, fixed the get-started link, removed the duplicate intro image and the terrain cross-link, and applied the wording suggestions.

Let me know if you'd like any further changes!

[veroandreo]

Thanks @Valyrian-Code! Looks much better now, I left some other minor suggestions here and there.

[Valyrian-Code]

Thanks @veroandreo! Pushed the follow-ups:

• Both tables now show an actual extract: the schools attribute table, and the reach-per-ZIP result. Good call on db.select also having JSON output, so I reused the same pandas pattern there.
• Applied all the wording suggestions.

Ready for another look when you have a moment.

[Valyrian-Code]

Thanks @petrasovaa! Applied the same cleanups here:

• single-string path to gj.init
• .text instead of .stdout for printed output
• JSON read by subscripting the ToolResult directly (tools.v_db_select(...)["records"], same for db.select) instead of json.loads(...)
• scale bar moved to the bottom-right in every figure so it no longer overlays the map

I also mirrored the Tools-API changes in the viewshed PR (#135).

[veroandreo]

All scale bars are cropped in the lower border now. Fix all of them.

[Valyrian-Code]

Fixed, thanks for catching it. Raised the scale bar so the bar, label, and north arrow now sit fully inside the frame in all figures.