Detection of spatiotemporal changepoints: a generalised additive model approach

Music City Center 

In contrast with other fields, it is well accepted in Climatology that climate time series contain changes over time; whether as part of station moves, and/or evolving climate dynamics. There are many different (statistical) ways to model these changes over time. This talk introduces the field of changepoint detection as the simplest departure from the assumption of constant (statistical) properties over time. Following an introduction to the benefits and limitations of changepoint models we will touch on a recent development in detecting changepoints in spatio-temporal data motivated by air quality.

Air quality is an important measure for both ongoing public health and as part of climate modelling. Changes in the spatio-temporal distribution of air quality are important in the short term, e.g. for managing biohazards, and in the longer term for informing climate scenarios or predicting response to climate forcings. We present a spatio-temporal changepoint method that utilises a generalised additive model (GAM) dependent on the 2D spatial location and the observation time to account for the underlying spatio-temporal process.