Unemployment is one of the few economical phenomena which can be observed at the municipality level. This very detailed local spatial level is not common to analyze on a large scale in international research because of data availability, comparability, and necessary data processing requirements. However, the results may uncover many interesting findings and enables us to reveal multi-level interdependencies. The arrangement of spatial patterns can reveal important regularities hidden at the macro-regional level. These patterns, which result from the trends and challenges (economic, social, pandemic etc.) that society at the economy are facing, could be stable or unstable through the time. A long-term stability of these spatial patterns could be examined by economic geography approaches to socio-spatial resilience. The aim of the research is to localize, analyze and explain these long-term spatial patterns in Europe by using sophisticated statistical methods (multi-level regression analyses, etc.) and spatial methods (hot spot analysis, etc.). These types of methods demand deep knowledge of working with geographical information systems (such as ArcGIS) and statistical system (such as SPSS). The area of interest is Europe, where these spatial patterns can cross national borders. Therefore, this quantitative research requires to be able to use and manage a large database of unemployment data and to be able to interpret this data in a broad spatial context of Europe and its geographical and historical context.
Expected/required skills of the applicant: advanced statistical and GIS skills, SPSS, ArcGIS, GeoDaDeadline is closed
Geography: Spatial patterns of unemployment in Europe: socio-spatial resilience in a multi-level context