Visualizing analytical spatial autocorrelation components latent in spatial interaction data: An eigenvector spatial filter approach

Four decades ago, Curry (1972) argued that spatial autocorrelation (i.e., local distance and configuration effects) and distance decay (i.e., global distance effects) intermingle in the estimation of spatial interaction model specifications. Today, massive computer power available with a desktop PC offers the necessary resources to account for these spatial autocorrelation effects within spatial interaction. Simple gravity model respecifications have been analyzed by Griffith (2007) and by Fischer and Griffith (2008). These results have been extended to a doubly-constrained gravity model respecification by Griffith, 2009a and Griffith, 2009b. This paper summarizes results from a further refinement of this doubly-constrained respecification, namely differentiating within and between areal unit flows. In doing so, an eigenfunction-based spatial filter description of spatial autocorrelation is constructed, which lends itself to visualization of analytical spatial autocorrelation components. Results are illustrated with the 2000 journey-to-work dataset for the extended state of Pennsylvania, USA.

Research highlights
► Spatial interaction model specification accounts for spatial autocorrelation in flows.
► Model specification differentiates within from between areal unit distance decay.
► Constructed eigenfunction spatial filters describe spatial autocorrelation in flows.
► Maps of spatial filters visualize analytical spatial autocorrelation components.
► Illustrations employ 2000 Pennsylvania journey-to-work flows.