Spatiotemporal data often comes in the form of dense arrays, with space and time being array dimensions. Examples include

• raster data
• socio-economic or demographic data,
• environmental variables monitored at fixed stations,
• time series of satellite images with multiple spectral bands,
• spatial simulations, and
• climate model results.

Currently, R does not have infrastructure to handle and analyse such arrays easily. Package raster is probably still the most powerful package for handling this kind of data in memory and on disk, but does not address non-raster time series, rasters time series with multiple attributes, rasters with mixed type attributes, or spatially distributed sets of satellite images.

This project will not only deal with these cases, but also extend the “in memory or on disk” model to that where the data are held remotely in cloud storage, which is a more feasible option e.g. for satellite data collected Today. We will implement pipe-based workflows that are developed and tested on samples before they are evaluated for complete datasets, and discuss the challenges of visualisation and storage in such workflows.