Abstract
Spatial scientists wish to understand when space matters in studies dealing with phenomena evolving through time. Predictive models often exploit temporal and spatial autocorrelation latent in space–time datasets to make forecasts for these landscapes. In many cases, temporal autocorrelation offers a superior description of dynamics in a geographic landscape. However, especially after some perturbation event, the memory captured by temporal autocorrelation may become seriously corrupted, with one consequence being that spatial autocorrelation furnishes a superior description of, and forecasts for, a given phenomenon. This chapter discusses the relative importance of these two types of autocorrelation, providing examples of when each type dominates in a geographic landscape.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
