Geospatial analysis of disease investigation data improves data standardisation and validation and enhances pathogen detection. Grid-based surveillance systems for Newcastle disease in southern California and for bovine tuberculosis on Molokai Island, Hawaii, demonstrate the importance of this approach to operational planning. In addition, as shown by a bovine tuberculosis study in wildlife on Molokai Island, a lattice grid can be used to develop zonal strategies for disease regulatory actions. In risk mapping, disease risk distribution is compared with the distribution of risk factors to identify potential determinants of risk. This process is being applied to North American waterfowl migratory routes to identify geographic areas with high concentrations of migratory waterfowl so that a spatially targeted sampling strategy for use in surveillance operations can be designed. Finally, while farm location data are needed to model pathogen spread through susceptible animal populations, this information is generally unavailable to analysts and modellers. Recently, a farm location and animal population simulator application was developed in which agricultural census data is distributed to create a farm location dataset representative of an agricultural commodity within a specific geographic area.