A Data-Driven Approach to Spatial Interaction Models of Migration: Integrating and Refining the Theories of Competing Destinations and Intervening Opportunities

Abstract

Traditional spatial interaction (SI) models of migration are susceptible to misspecification when the spatial structure of locations is not properly incorporated. To address this, a novel SI model for migration is introduced that integrates the theories of competing destinations (CD) and intervening opportunities (IO) to capture multiscale spatial structure using the recent generalized additive spatial smoothing (GASS) framework. This GASS CDIO model can identify the appropriate spatial scales to represent the spatial structure of origins and destinations in a data-driven manner. Validation of the model was conducted through two simulation experiments. The first demonstrates that employing the incorrect scale to capture spatial structure in SI models biases the parameter estimates and increases uncertainty. The second demonstrates that the GASS approach reliably recovers accurate parameters by identifying optimal hyperparameters associated with multiple spatial scales. The GASS CDIO model was then applied to U.S. inter-county migration data and compared to several other model specifications. The results reveal the unique spatial structure from the perspective of origins and destinations and illustrate the improved recoverability of anticipated migration relationships. This work suggests that the GASS CDIO model better integrates spatial theories of migration and accounts for the multiscale nature of SI processes.