Infant mortality refers to the death of a live-born child before reaching one year of age, typically expressed as the number of deaths per 1,000 live births within a defined geographic area. As a GIS use case, infant mortality analysis leverages spatial analysis techniques to examine the geographic distribution, clustering, and underlying determinants of early childhood death across regions, communities, and administrative boundaries.

GIS technology enables analysts to integrate diverse geospatial data sources — including census demographics, healthcare facility locations, socioeconomic indicators, environmental exposure data, and vital statistics records — into a unified spatial framework. Through geostatistical methods such as kernel density estimation, spatial autocorrelation, and hotspot analysis, GIS professionals can identify high-risk areas, detect statistically significant mortality clusters, and uncover correlations between infant death rates and contributing factors like access to prenatal care or environmental hazards.

The practical benefits are substantial: public health agencies can prioritize resource allocation, target intervention programs with geographic precision, monitor longitudinal trends through temporal mapping, and communicate findings effectively to policymakers using compelling, evidence-based cartographic visualizations. This spatial intelligence ultimately supports data-driven strategies aimed at reducing preventable infant deaths.