Chart titles reflect Eurostat topics, but may sometimes be misleading as data sets allow various filters (e.g. age groups, enterprise sizes, regional/national level). All data charts in the regional profiles refer to NUTS 2: recognise this by the NUTS 2 code below  or in brackets beside the title.

Click on the chart title and use the option there to download the charts based on Eurostat data sets as png files. Not all charts include data for all regions or time periods, as they are not always fully available. Furthermore some categories may be missing. Perform further analyses directly with the corresponding Eurostat data set, linked below the chart, where available. Set filters according to your personal interests with the ‘Customise your data set’ option there.

Please note: the values on the scales of the diagrams (percentages, absolute figures) are laid out differently due to large differences in the values for the individual regions. A visual comparison can therefore be misleading. When making a comparison, please check the specific values.

The charts based on Eurostat CSV tables and AI integration are currently in the beta phase. Figures were spot-checked several times per topic. We appreciate information about bugs. Please carefully check the selected filters displayed in the graph before reporting an error. Thank you!

Orient yourself on topics at NUTS 2 level for which no Eurostat data sets are available. The charts marked with the orange ‘AI estimation’ label serve as a guide to a topic. The numbers and rates presented were derived by AI from data and information available on the internet, such as from national statistical offices, labour market service institutions, business support organisations, but also national Eurostat data sets, etc. In this case, the AI correlates data, draws conclusions from national data for NUTS 2 level and incorporates other relevant and available data at NUTS 2 level to increase the accuracy of the estimate. Nevertheless, the figures and rates are only more or less accurate.

Use ‘AI estimation’ data to reflect on your region. Let it inspire you to ask questions that will help your region develop positively in the future. For this process, it can be more helpful to be able to compare somewhat vague data from many different areas than completely accurate data from only a few areas.