Seth Goodman

@jgerardsimcock processing time depends largely on the size of the polygons and the resolution of the raster data

In case it is useful for future users looking to address memory issues, I submitted a PR a while back that takes this concept one step further and can split...

Approach from #136 could be leveraged for threshold selection (with 100% being the same as using only_within)

would be interested in seeing a performance comparison of your proof-of-concept with a similar implementation in R's [raster](https://cran.r-project.org/web/packages/raster/index.html) package (see extract function with weights option. note: R implementation uses the...

i went ahead and used your proof-of-concept to replicate the functionality of the R package. initial testing produced identical results and was about 7.5x faster than R. https://github.com/sgoodm/python-rasterstats/tree/cell-weights

Improved original method for calculating percent coverage of cells. Instead of using cell bounding box intersections, it now rasterizes features at a finer resolution than raster source (this scale can...

PR #136 for latest on this

Will need to consider how using percent cover as a selection method impacts nodata stat. May need to be a separate field?

@jhamman I have a fork that includes updated code for this PR and the other PRs (#135, #154) I have submitted, but I have not been maintaining the individual branches...

Hoping to have a chance to get back to this in the next month or two