Light pollution refers to the excessive or misdirected artificial illumination that degrades natural darkness, disrupts ecosystems, and interferes with astronomical observation. As a GIS use case, it involves the collection, integration, and spatial analysis of geospatial data to quantify, monitor, and mitigate the effects of artificial light at night (ALAN) across geographic areas.

GIS technology enables analysts to process satellite-derived imagery — particularly from NOAA's VIIRS and NASA's Black Marble datasets — alongside ancillary layers such as land use classifications, population density, road networks, and protected area boundaries. Through spatial analysis techniques including raster modeling, interpolation, and overlay analysis, GIS professionals can map sky brightness levels, identify high-emission zones, and evaluate light trespass patterns at local, regional, and global scales.

The practical benefits are significant. Municipalities leverage light pollution mapping to optimize street lighting infrastructure, reduce energy consumption, and enforce dark sky ordinances. Conservation agencies use spatial modeling to assess ecological impact corridors, while urban planners integrate these insights into sustainable development frameworks. GIS-driven light pollution analysis ultimately supports evidence-based decision-making for environmental compliance and community well-being.