remotePARTS

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: 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.

Last 30 days

This package has been downloaded 226 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,638 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Apr 25, 2024 with 19 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.