ABSTRACT
Four applications of spatial-temporal data modelling are presented. The first three applications are all based on the underlying space-time inseparable modelling structure discussed in Chapter 15 and share a common inferential goal of detecting “unusual” areas with local time trends that differ markedly from the “common” time trend. However, whilst Application 1 assesses whether a geographically targeted crime prevention policy has had a measurable impact on the crime rates in the targeted areas, Applications 2 and 3 are topics within space-time surveillance. Distinguishing the two applications is whether we are dealing with shorter (Application 2) or longer (Application 3) time series. Different time series models are then specified accordingly. Underlying all three applications is a modelling structure in which the common/general time trend is obtained through the space-time separable structure and unusual local behaviours are captured through the space-time interaction component. A common problem in surveillance is the issue of multiple testing (or multiple comparisons), and this is discussed. Application 4 describes a spatial-temporal model to investigate the presence (or absence) of spatial-temporal spillover effects on village-level malaria risk within a district in India, extending the (static) spatial modelling described in Chapter 9.
