Researchers and commercial beekeepers increasingly use geographic information systems (GIS) to score candidate apiary sites objectively instead of relying on intuition. Each raster cell of a landscape is scored on multiple layers — forage availability, water access, terrain slope and known risk factors — which are then combined into a single weighted suitability surface. This scene renders that surface as a bar-height landscape: taller, greener columns are more suitable sites for placing hives; short, red columns are poor sites.
Real apiary-siting studies often layer dozens of raster inputs — floral resource maps derived from land-cover classification, digital elevation models, pesticide registries and even road-noise buffers — inside tools like QGIS or ArcGIS before ranking candidate sites with a weighted overlay identical in principle to this simplified four-factor model.
A raster suitability surface for placing beehives: candidate sites are scored on forage access, water proximity, terrain flatness and distance from risk zones, then rendered as a scored 3D landscape of colored columns.
Each grid cell's height and color encode a weighted-overlay suitability score, the same core method GIS practitioners use to rank apiary sites objectively across floral, hydrological, topographic and risk layers.
Adjust the four factor-weight sliders to see the scored landscape reshape in real time, toggle the top-15% filter to isolate the best cluster of sites, and randomize the landscape to test the methodology on a new terrain.
Real GIS apiary-siting studies often stack dozens of raster layers — floral resource indices from land-cover classification, digital elevation models, and pesticide-use registries — before running a weighted overlay like this one.