Population disaggregation is a spatial analysis technique that redistributes aggregated demographic data from coarse administrative boundaries — such as census tracts or municipalities — into finer geographic units, producing high-resolution population distribution surfaces. Using GIS technology, analysts combine source population counts with ancillary geospatial data layers, including land use classifications, building footprints, road networks, nighttime light imagery, and remotely sensed data, to model where people actually reside within a given area.

The process typically employs dasymetric mapping, areal interpolation, or machine learning-assisted approaches within GIS platforms to generate gridded population datasets at resolutions as granular as 100 meters or less. This methodology accounts for uninhabited zones — water bodies, industrial areas, and protected lands — that uniform distribution methods incorrectly assign population values.

Practical applications span disaster risk assessment, public health resource allocation, transportation planning, humanitarian response, and environmental impact analysis. By transforming blunt administrative statistics into spatially accurate population estimates, disaggregation enables geospatial analysts to support more precise, evidence-based decision-making across government, academic, and private sector contexts where understanding human settlement patterns is operationally critical.