Smoke signals, in the context of GIS and geospatial analysis, refer to the spatial monitoring, modeling, and visualization of smoke plume behavior resulting from wildfires, prescribed burns, industrial emissions, or other combustion events. This use case leverages satellite imagery, remote sensing data, atmospheric modeling, and real-time sensor networks integrated within GIS platforms to track smoke dispersion patterns across geographic extents. Analysts utilize raster-based spatial analysis, wind vector data, and terrain modeling to predict plume trajectories and assess air quality impacts at the community level. Geospatial data layers — including land cover classification, population density, and meteorological inputs — are combined to generate dynamic smoke forecast maps and risk assessments. Practical benefits include enhanced emergency management decision-making, evacuation route planning, and public health advisories based on particulate concentration mapping. Additionally, GIS-driven smoke signal analysis supports regulatory compliance monitoring, wildfire behavior research, and environmental impact assessments. Tools such as ESRI ArcGIS, QGIS, and NASA's FIRMS platform are commonly employed to deliver accurate, actionable, and operationally scalable smoke dispersion intelligence for land management and public safety agencies.