The Effect of Data Aggregation on a Poisson Regression Model of Canadian Migration

Abstract

A Statistics Canada data set for Canadian migration data at the census division level incorporating information on income tax for 1986 has already been presented. This matrix of 260 × 260 flows was used to calibrate a set of Poisson regression models by utilizing flows for the aggregate population. In this paper, the relatively high spatial resolution is used to test for aggregation effects as the original 260 units are combined to form fewer, synthetic regions with larger areas. A series of simulation experiments are performed with three different aggregation algorithms to create 130, 65, and ultimately 10 (corresponding to the provinces) synthetic regions. Average results from the experiments are compared with the original model. Results are obtained that suggest that, in this case, obvious aggregation effects similar to those observed elsewhere (by Openshaw) are not observed.