Modeling spatio-temporal relationships: retrospect and prospect

Abstract

Interest in space–time modeling is experiencing a resurgence, in part because more and more sizeable space–time datasets are becoming readily available. Currently techniques to describe these data, many of which have existed for years, are being utilized and improved. This paper surveys general categories of these techniques (i.e., autoregressive-integrated-moving-average models, space–time autoregressive models, three-dimensional geostatistical models, and panel data models), in retrospect, demonstrates a future prospect (i.e., spatial filtering models), and suggests important topics for incorporation into a research agenda, including ones pertaining to non-normal random variables, panel data models, space–time heterogeneity, missing data, and distributional properties of space–time filters.