As America Turns 250 Years Old, the Census is Not Far Behind at 236

The decennial census, mandated by the framers of the Constitution, has evolved along with our nation over the course of their shared history.

Population tool: How will populations across the world change in the 21st century?

We created an interactive tool that lets you test how changes in fertility rates, life expectancy, and migration rates will change future populations.

Jonas Schöley (@jschoeley.com)

demoscapes.org is a visual atlas of demographic surfaces hosted at @mpidr.bsky.social. It is also my passion project growing out of my 2016 human mortality database explorer. Did you publish something containing Lexis surfaces? Consider reaching out and having your work featured on the site. [contains quote post or other embedded content]

Geospatial foundation-model embeddings improve population estimation unevenly across space and scale

Reliable subnational population estimates are essential for applications, yet remain difficult where censuses are sparse, outdated or spatially coarse. Existing population-mapping workflows rely on hand-built geospatial covariates, such as settlement extent, night-time lights, and environmental conditions, which must be assembled and harmonised across scales and geographies. Geospatial foundation models offer an alternative by learning reusable representations of place from more multifaceted and heterogeneous data sources. Here, we benchmark Population Dynamics Foundation Model (PDFM) embeddings against the harmonised geospatial covariates for subnational population estimation in Brazil, Nigeria and the United States. Under geographically structured validation, PDFM increased predictive fit by a median of 20.1% (IQR: 10.0-33.2%, across country-model comparisons) reduction in unexplained variance, and reduced Kullback-Leibler divergence by 23.2% (9.2-26.2%). However, these gains were uneven. PDFM was most advantageous where the geospatial covariates weakly characterised settlement context, such as larger and less-developed subnational areas. Moreover, PDFM performance was scale-coupled with embeddings providing less flexible transfer across spatial aggregations than geospatial covariates. These findings showed that geospatial foundation-model representations of place can improve population estimation in data poor settings, but their benefits break down predictably under spatial scale mismatch, revealing a fundamental limitation of current geospatial AI.

A Blended Data Approach to Measuring Monthly Housing Starts: Satellite Imagery, Survey Data and More!

Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.

Portraits of Population

An archive of 700 hand-drawn charts and graphics from India's census, and why they were made

A Quiet Driver of Population Change