Visualising high-dimensional data in time and space: ideas from the Orca project

Environmental data are frequently high-dimensional with measurements of multiple chemical constituents, plant or animal species, or meteorological variables. Environmental data are also frequently structured with interest in the patterns of variation over time and space. We describe some new data visualization methods from the Orca project that allow the analyst to reduce the dimension of the data without obscuring its basic structure and illustrate these on air pollution data.