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ropensci / chopin

Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing
https://niehs.github.io/chopin/
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add climate applications #59

Closed kyle-messier closed 8 months ago

kyle-messier commented 9 months ago

As we move towards writing the manuscript, let's consider our target audience and the interest in climate and health modeling. The README should reflect common applications in climate and health.

sigmafelix commented 9 months ago

@Spatiotemporal-Exposures-and-Toxicology Sure, I am considering ERA5 from ECMWF or PRISM data use cases. I realized that the climate data are suitable for par_multirasters as outputs from climate models or thematic prediction models are moderately resolved (1+ kilometers). A guide for the advantage of choosing par_* functions by potential cases will be helpful.

sigmafelix commented 8 months ago

Since most climate datasets are spatially coarsely resolved (1+ km), parallel processing underperformed compared to single-core processing. exactextractr::exact_extract does pretty good job in sparing memory and attaining performance. I switching the focus of the vignette to demonstrate how to run chopin functions for parallel processing and tips for determining what strategy to choose when users have large amount of data with various spatial resolutions.

sigmafelix commented 8 months ago

Resolved by #61