eli knaap

definitely! :)

xref, I wanted to add an example using https://easystac.readthedocs.io/en/latest/ which has super high resolution imagery and uses xarray as their data structure

>nbsphinx appears to already build extract_urban_areas & binary_dasymetric even though they aren't in docs/notebooks? If the same would work for the others all this other nblink could be avoided. 🤣...

>The main problem comes next when we want to use that table to allocate source values into the target geography. Given we have the cross-over table (AoI in Eli's term)...

i think what you're proposing is target density weighting a la https://pubmed.ncbi.nlm.nih.gov/28260826/ ?

it took me awhile, but i realized a similar example that makes obvious sense to me is volume. If you were trying to estimate population at the building-level from like...

hi, thanks for this. We do have [an example](https://github.com/pysal/tobler/blob/master/notebooks/extract_urban_areas.ipynb) that uses copernicus which provides international raster data, but would certainly welcome examples from other sources! Any chance you'd like to...

personally, i think of geoplot more along the lines of [proplot](https://github.com/lukelbd/proplot), in that it provides a nicer-than-default API, sensible aesthetics out of the box, tooling for better layouts, etc. whereas...

I didn't mean to be reductive about geoplot, so apologies if it came off that way! To put it a bit differently, the distinction i see is about the substantive...

while we're at it, one thing it would be nice to add is auto ability to scale linewidths by weight value for nonbinary weights