LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty
Daniel S Adams, Andrew Zimmer, Joseph Tuccillo, Jessica Moehl, Robert Stewart, Sarah Walters, Budhendra Bhaduri, Peter Li, Marie Urban · 2025
Read LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty by Daniel S Adams, Andrew Zimmer, Joseph Tuccillo, Jessica Moehl, Robert Stewart, Sarah Walters, Budhendra Bhaduri, Peter Li, Marie Urban (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12738844.txt.
Description
Daniel Adams and co-authors present LandScan Mosaic in a 2025 paper on mapping where people live. Gridded population data sets place people on a fine map grid and guide disaster planning, city growth, and resource decisions. Most such products give one fixed number per cell and hide the uncertainty behind it, which can breed false confidence.
Their framework uses probabilistic machine learning and Monte Carlo simulation to carry uncertainty through three inputs: building use, floor counts, and occupancy rates. The output is a distribution of counts per cell instead of a single guess. A case study in Iloilo City in the Philippines shows how officials can rank flood-hit areas for help using those ranges.
Catalog record
| CREATOR | Daniel S Adams, Andrew Zimmer, Joseph Tuccillo, Jessica Moehl, Robert Stewart, Sarah Walters, Budhendra Bhaduri, Peter Li, Marie Urban |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 84 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |