  Spatial data about individuals or businesses is often aggregated over
polygonal regions to preserve privacy, provide useful insight and support
decision making. Given a particular aggregation of data (say into local
government areas), the re-aggregation problem is to estimate how that same data
would aggregate over a different set of polygonal regions (say electorates)
without having access to the original unit records. Data61 is developing new
re-aggregation algorithms that both estimate confidence intervals of their
predictions and utilize additional related datasets when available to improve
accuracy. The algorithms are an improvement over the current re-aggregation
procedure in use by the ABS, which is manually applied by the data user, less
accurate in validation experiments and provides a single best guess answer. The
algorithms are deployed in an accessible web service that automatically learns
a model and applies it to user-data. This report formulates the re-aggregation
problem, describes Data61's new algorithms, and presents preliminary validation
experiments.
