🗺️ GIS-Based Apiary Site Selection Methodology (2D Map)

About this simulation

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 2D companion renders that same surface as a top-down heatmap: warmer green cells are more suitable sites for placing hives, red cells are poor sites.

How the score is built

• Forage layer — cells near flowering forage patches (the dotted clusters) score higher; bees rarely forage efficiently far beyond their patch.
• Water layer — cells at a comfortable buffer distance from the lake score highest; cells inside the water are invalid, and sites far from any water score low.
• Terrain flatness — flatter ground (computed from a synthetic elevation field via a finite-difference gradient) scores higher because steep slopes make hive access, drainage and airflow harder to manage.
• Risk avoidance — cells far from the hazard zone (the red hatched rectangle, representing pesticide spray drift) score higher.
• The four layers are combined as a weighted sum, normalized by the total weight, exactly like a weighted overlay analysis in a real GIS.

Controls

• Forage / Water / Terrain flatness / Risk avoidance — relative weight (0–100) each factor contributes to the combined score.
• Show top 15% sites only — dims all but the best-scoring cells so the strongest candidate cluster stands out.
• Show layer contours — outlines the lake, forage patches and hazard zone on the map.
• ↺ Randomize landscape — regenerates the forage patches, lake, hazard zone and elevation field positions.
• Hover any cell to read its exact score and factor breakdown; click to drop a candidate marker and compare it against the computed best site.

Did you know?

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.