Uncovering temporal changes in Europe's population density patterns using a data fusion approach

Area of Science:

• Geographic Information Science
• Demography
• Urban Studies

Background:

• Accurate human population distribution data is essential for urban planning, disaster risk management, and infrastructure development.
• Official population statistics often fail to capture dynamic population densities due to human mobility.
• Existing spatio-temporal population assessments lack detail and broad geographical coverage, with mobile-phone data facing availability and consistency challenges.

Purpose of the Study:

• To develop and validate a European Union-wide population grid dataset.
• To incorporate intraday and monthly population variations using a novel approach.
• To provide high-resolution (1 km²) population data accounting for human mobility.

Main Methods:

• A multi-layered dasymetric approach was employed.
• Integration of official statistics with emerging geospatial data sources.
• Validation of the generated population grids across the European Union.

Main Results:

• A comprehensive EU-wide dataset of population grids was produced at 1 km² resolution.
• The dataset successfully accounts for intraday and monthly population variations.
• Daytime population in European city centers is estimated to be 1.9 times higher than nighttime population.

Conclusions:

• The developed method provides a more accurate representation of population distribution by including mobility patterns.
• This high-resolution dataset offers valuable insights for urban studies, disaster management, and policy-making.
• The findings highlight significant spatio-temporal population density variations within European cities.