@martinfleis for what it's worth I have updated all of the modules according to your recipe and have a backwardly incompatible #pysal #momepy but Shapely 2.0 "compliant" version on GitHub.

This is based on my now-closed pull-request. It passes pytest validation and I'll use this in the meantime.

(With this and the #pyogrio issue at Christmas, which is fixed, I seemed to be inadvertently surfing the bleeding edge somewhat).

Anyway thanks again for all these toolsets and your help today.

Today's update is #Momepy and #Pysal may give unexpected results due to #PyGeos and #Shapely 2.0 compatibility.

The problem is here https://github.com/pysal/momepy/issues/472.

Based on the PR comments #473 https://github.com/pysal/momepy/pull/473, it is a Shapely 2.0 compatibility issue.

Based on guidance here https://github.com/pysal/momepy/pull/473#issuecomment-1465730704 and noting a GeoPandas discussion here https://github.com/geopandas/geopandas/issues/2691#issuecomment-1375890344 I have a workaround and the issue will be fixed.

Thanks to @martinfleis for his work on this and his timely help and advice.

For those interested in such things, I'm going to look at a #pysal #Momepy patch for #shapely2.0 compatibility

For those who are interested in such things, based on the ONS 2021 Census and the National Records Scotland mid-year 2021 population estimates, the population centre of Great Britain is here.

This was based on calculating a mid-line based on cumulative population totals using Topler projection of the census data onto100m width slices north-south and east-west. Using @geopandas, #PySal and #python.

The more I read, study, and do research, the more I feel like spatial statistics should become a major focus for me.

Luckily there are plenty of awesome online resources available for it completely free. #PySAL docs, Geographic Data Science book, #GeoDA docs.

But there's this one book coming up. Spatial Analysis in the Social Sciences. It's apparently a 560-page chonker but hoooooooo boy, gotta have that!