Applying multiple and logistic regression models to investigate periurban processes in Thessaloniki, Greece

Abstract

The paper examines periurbanization processes in the region of Thessaloniki through the application of multiple regression and logistic regression models. A set demographic and socio-economic data are analysed through a principal component analysis, and results are mapped so as to highlight the distributional characteristics. In a next section, the multiple regression model is applied to the study area. The four variables entering the multiple regression model (population size and age characteristics, employment in the tertiary sector, new buildings), which reflect the indications stemming out from the PCA analysis, provide a relatively high multiple correlation coefficient (R = 0.852) accounting for about 72.5% of the population changes. Then, a logistic regression model is used to identify periurban communities presenting urban characteristics and to distinguish them from the rural ones. Results indicate that decentralization towards existing periurban settlements is mostly driven by the construction of new housing and employment in the tertiary sector.