Ranking regression analysis of spatiotemporal variables

Socioeconomic systems are often characterized by spatiotemporal structured data sets. If many potentially explanatory variables are available, a ranking of their relevance is desirable. A numerical procedure is presented which allows for a stepwise selective regression analysis of such variables based on least square principles. The optimal set of key socioeconomic factors is obtained from a large number of variables by an orthogonalization procedure. This orthogonalization takes into account the correlation between the variables which have been already been selected and the remaining set. Time delays of variables are also considered. As an example the key factor analysis of regional utilities of the migratory system of the Federal Republic of Germany is treated.