remotePARTS-package: remotePARTS: Spatiotemporal Autoregression Analyses for Large...

In morrowcj/remotePARTS: Spatiotemporal Autoregression Analyses for Large Data Sets

remotePARTS: Spatiotemporal Autoregression Analyses for Large Data Sets

Description

These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.rse.2021.112678")}). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses.

Author(s)

Maintainer: Clay Morrow [e-mail] (ORCID)

Authors:

• Anthony Ives [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/morrowcj/remotePARTS

• Report bugs at https://github.com/morrowcj/remotePARTS/issues

morrowcj/remotePARTS documentation built on Sept. 17, 2023, 5:42 p.m.