Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface

Area of Science:

• Geospatial analysis
• Environmental science
• Demography

Background:

• Assessing socio-environmental issues requires integrating environmental data with human population density.
• US Census data is a primary population source but often lacks sufficient spatial resolution, particularly across urban-rural gradients.
• Existing population datasets can be problematic for fine-scale analyses.

Purpose of the Study:

• To provide a high-resolution (30-m) population estimate dataset for the contiguous United States.
• To develop an updatable methodology for population distribution based on impervious surfaces.
• To create a valuable resource for socio-environmental problem assessment.

Main Methods:

• Dasymetrically distributing Census block population estimates onto non-transportation impervious surfaces.
• Utilizing remote sensing data for impervious surface area observations.
• Developing an updatable workflow using recent Census data.

Main Results:

• A 30-m resolution population dataset (U.G.L.I.) for the contiguous US was generated.
• The dataset effectively distributes population estimates across impervious surfaces.
• The U.G.L.I. dataset shows favorable comparisons with other population data sources.

Conclusions:

• The U.G.L.I. population dataset offers a significant improvement in spatial resolution for US population data.
• The methodology provides a flexible and updatable approach for population mapping.
• This dataset is a valuable tool for detailed socio-environmental research and planning.