Identification of temporal and spatial relationships in environmental data, and presentation of numerical methods to quantify autocorrelation as a tool for investigating natural phenomena. Practical applications will be examined using public-domain records, such as climate datasets at various spatio-temporal scales. All examples will employ the open-source R software.