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Title: Home
Welcome to the EU Regional Demographic Spatial Analysis Wiki.
This repository houses a comprehensive data analysis of demographic trends across the European Union at the NUTS-3 regional level. We utilize spatial analysis techniques to uncover geographical patterns that non-spatial data analysis might miss.
Title: Data-Preparation
The datasets used in this project contain demographic indicators sourced for the EU NUTS-3 regions. Key variables include:
- Crude Rate of Total Population Change: Highlighting regions with growing or shrinking populations.
- Median Age of Population: Indicating demographic aging across different geographies.
- Net Migration (Plus Statistical Adjustment): Showing population movement into and out of specific regions.
- Population Density: Contextualizing urbanization and rural spread.
The raw data was filtered and cleaned to remove missing values and harmonize the regional codes with the NUTS-3 shapefiles (EU_NUTS3 directory). Cleaned output files are stored as .csv files in the Data - Cleaned directory to easily interface with GeoDa.
Title: Spatial-Autocorrelation
To analyze how demographic variables cluster geographically, we use Spatial Autocorrelation. This methodology tells us whether regions with similar demographic features are geographically close to each other.
We calculated Global Moran's I to test for overall spatial clustering in variables like Population Density and Median Age. A positive Moran's I indicates that regions with similar values tend to cluster together (e.g., older regions bordering other older regions).
We utilized Local Moran's I to identify specific local clusters and spatial outliers:
- High-High Clusters: Regions with high values surrounded by high values.
- Low-Low Clusters: Regions with low values surrounded by low values.
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Spatial Outliers (High-Low / Low-High): Regions that differ significantly from their neighbors.
Results from these models can be found in the
Spatial Autocorrelationdirectory.
Title: Map-Interpretations
The Variable Maps directory contains visual representations of the demographic distributions.
By mapping the data through GeoDa, we are able to easily visualize the divide across the European Union. For example, quantile and standard deviation maps were generated to observe structural divides (e.g., East-West or North-South demographic shifts).
- Aging Populations: Visual clusters show which macro-regions are facing the most significant demographic aging.
- Migration Corridors: Net migration maps illustrate the pull factors of major economic hubs compared to rural outflows.
Title: Conclusions
The spatial analysis demonstrates that demographic trends in the EU are highly regionalized. Population aging and migration do not happen evenly; they are heavily influenced by geographic proximity to economic centers, borders, and urbanized zones.
Understanding these spatial dynamics is crucial for regional planning and targeted policy-making. Spatial autocorrelation proves that treating regions as independent data points ignores the spillover effects of neighboring regions.
For a synthesized overview, please refer to the Presentation.pptx available in the root directory.