A Dynamic Analysis of Interaction Data: U.S. Rail Freight Flows, 1972-81

The purpose of this paper is to extend Poisson regression to the analysis of spatial interactions over time. The methodology involves derivation of models using information methods and calibration using Poisson regression. Poisson regression is then used to analyze interannual variation in U.S. rail freight flows, 1972-81. Findings indicate that import and export variability is less important than is flow variability. Import and export variability is highest for the northeastern United States, but flow variability is highest for the southern and western United States. This has implications for the specification of dynamic models of commodity flow.