Residential segregation patterns in Istanbul: internal and international migrant profiles

Cities that demand workers for jobs in various segments of employment receive migrants of a wide range of profiles. The locational settlements of the newly arrived migrants are shaped by multiple factors, and their settlement patterns also differ by their profiles. Several different mechanisms operate during the processes of migrants’ integration into the segmented job market and residential settlement in the city which in turn transform residential segregation patterns. It is crucial first of all to explore which job segments are filled by which profile of migrants, where they come from, what their distinctive characteristics are, and where they settle in the city, to start analysing these mechanisms. Data reduction methods help demonstrate the locational segmentation patterns that emerge. However, most of these methods make area-based classifications, with a few exceptions, and the ability of these classifications to capture accurate representations is debated. We claim that this issue can be addressed by making individual-level classifications. This is especially needed when the geographical resolution of the data is low. Classification methods address the problem of nonlinear properties of data which restrict the effectiveness of multivariate models. These extensive and exploratory analyses help to create understandable patterns that are hidden in complex datasets and enable a joint and simultaneous evaluation of social and spatial formations. This study is an attempt to present such an extensive and exploratory analysis. In order to stratify the profiles of migrants who came to Istanbul in the period of 1995–2000, the study implements the combined use of multiple correspondence analysis and cluster analysis by using national population census data.