Analysis of disease data that has an implicit spatio-temporal component (such as disease outbreaks, data generated by surveillance systems and specific hypothesis-based veterinary field research) is a foundation of veterinary epidemiology and preventive medicine. Components of this process include exploratory spatial data analysis (finding interesting patterns), visualisation (showing interesting patterns) and spatial modelling (explaining interesting patterns). Spatio-temporal statistics and tests are valuable when adding precision to qualitative verbal descriptions, facilitating the comparison of distributions and drawing attention to characteristics unlikely to be noticed by visual inspection. Quantifying spatio-temporal patterns is important for understanding how disease phenomena behave. The application of a range of spatio-temporal statistics is illustrated by exploratory spatial data analysis and visualisation of the 2002 outbreak of West Nile virus encephalomyelitis in Texas equines. This large outbreak (1 698 reported cases) consisted of both point (latitude, longitude) and polygon (Texas counties) spatial data with a time component (reported date of onset of clinical disease) and case series and attack rate data. This example highlights the need to use a range of techniques to fully understand the spatio-temporal nature of disease occurrence. With knowledge of how disease occurs in time and space, appropriate and effective disease control, prevention and surveillance programmes can be implemented.