Most people pick one data type and stick with it. That’s how you end up with broken analysis.
Vector data represents discrete features: buildings, roads, parcels, property boundaries. Each feature is a point, line, or polygon with attributes. Vector answers questions like: Which buildings are in the floodplain? How many schools are within 2km? What’s the perimeter of this zone?
Raster data represents continuous surfaces: elevation, temperature, precipitation, land use classification. A raster is a grid where each cell has a value. Raster answers questions like: What’s the average elevation across this watershed? How much area has temperature above 30°C? Where is vegetation density highest?
The mistake: trying to do raster analysis with vector, or vector analysis with raster. Buffer operations make sense with vector. Zonal statistics make sense with raster. If you’re working in pure vector and need to aggregate continuous data, you’ll get frustrated fast. If you’re in pure raster and need to identify individual features, you’re inefficient.
Modern GIS workflows intentionally convert between both. Start with raster satellite imagery, vectorize meaningful features. Clip raster data using vector boundaries. Rasterize vector layers to run neighborhood analysis. The right tool depends on the question, not the format you started with.
The takeaway: Vector for discrete features and boundaries. Raster for continuous fields and area-based analysis. Master both. Stop choosing one and living with its limits. Real workflows convert between them deliberately. That flexibility is what separates competent analysis from stuck workflows.
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