Vector vs Raster Data

Vector data represents geographic features as points, lines and polygons defined by coordinates, whereas raster data represents geographic space as a regular grid of cells, with each cell storing one or more values.

The difference is not simply file format. Vector and raster are two different ways of modelling geography, and that choice affects what is easy to measure, how boundaries are represented, how scale works and which analytical operations make sense.

A road network is naturally expressed as connected lines. Elevation is naturally expressed as a continuous surface. Land parcels usually need explicit polygon boundaries. Satellite imagery arrives as a grid of measurements. Good GIS work starts by matching the data model to the phenomenon rather than treating vector and raster as interchangeable containers.

Vector represents identifiable features

Vector data works well when the geography can be described as distinct objects with explicit geometry.

A school can be stored as a point, a road as a line and a protected area as a polygon, with each feature carrying attributes such as name, class, ownership or population.

Because the model preserves boundaries explicitly, two adjacent parcel polygons can share a border and one road line can connect to another at a junction. A polygon can also be selected, labelled or related to a record in a table.

This makes vector particularly strong for:

administrative and cadastral boundaries;
transport and utility networks;
facilities and addresses;
parcels and ownership units;
discrete ecological or planning features;
workflows where individual features need identities and attributes.

The trade-off is that continuous phenomena must be turned into discrete features or represented another way. A set of rainfall stations is vector data, but the rainfall field between the stations is not automatically represented by those points.

Raster divides space into cells

Raster data represents a study area as rows and columns of cells. Each cell has a geographic footprint and a value for each band.

A cell might store elevation, temperature, land-cover class, spectral reflectance, population estimate or model probability. For example, a three-band image can store red, green and blue intensity for each pixel, while a classified land-cover raster can store one category code per cell.

Raster is particularly strong for:

satellite and aerial imagery;
elevation and terrain surfaces;
continuous environmental variables;
land-cover classification;
cost and suitability surfaces;
gridded population and climate products;
cell-by-cell map algebra.

The grid makes neighbourhood operations straightforward because every cell has a predictable position relative to surrounding cells. But boundaries are represented through the grid rather than as exact coordinate sequences.

The same phenomenon can exist in either model

Vector and raster are not rigid categories of phenomena.

A forest can be stored as polygons representing mapped forest boundaries or as a raster classification in which each cell is labelled forest or non-forest. Similarly, a river can be a vector centreline or a raster flow network, while population can be attached to administrative polygons or allocated to grid cells.

The two versions answer slightly different questions.

A forest polygon supports explicit boundary measurements and feature-level management, whereas a forest raster supports cell-based overlay with elevation, climate or imagery. Likewise, a gridded population surface can be combined with a flood raster cell by cell, while an official administrative population table preserves the authoritative reporting unit.

The right model depends on what the analysis must preserve.

Resolution affects raster boundaries

Raster boundaries are constrained by cell size and alignment. A coastline represented on a 1-kilometre grid is much more generalised than the same coastline on a 10-metre grid. Increasing the number of cells can represent smaller spatial variation, but it also increases storage and computation.

A finer raster does not automatically make the underlying observation more accurate. It may simply divide the same uncertain or modelled information into smaller cells. Raster Resolution vs Raster Accuracy explains that distinction in detail.

Vector data has its own scale limitations. A polygon boundary may contain thousands of precise-looking vertices even when it was digitised from a coarse source. Coordinate density is not a guarantee of positional accuracy either.

Analysis changes with the data model

Some GIS operations are naturally vector-oriented: network routing relies on connected lines and junctions, point-in-polygon analysis asks which discrete polygon contains a feature, and topology checks relationships between explicit geometries.

Other operations are naturally raster-oriented. Terrain derivatives examine neighbouring elevation cells, suitability models combine several gridded factors, image classification operates on pixel values and neighbourhoods, and cost-distance modelling accumulates movement cost through a surface.

Many operations can be implemented in either model, but the assumptions differ. A vector buffer creates an explicit geometric boundary around a feature. A raster proximity surface records distance in cells across space. Both can answer distance-related questions, but their outputs and downstream uses are not identical.

Conversion is not neutral

Rasterising vector data assigns feature values to cells. The result depends on cell size, grid alignment and the rule used when a feature only partly covers a cell.

Vectorising raster data turns cell regions or edges into geometry. The resulting polygons inherit the stair-step structure and classification of the source grid unless smoothing or generalisation is introduced.

This means conversion can change area, boundary position, topology and the smallest feature that remains visible.

A common mistake is to convert repeatedly between models during a workflow without tracking those decisions. Each conversion can introduce representation choices that become difficult to recover later.

Attributes and bands are organised differently

Vector features can carry many named attributes in a table: a district polygon might have population, code, area and administrative level.

Raster datasets, by contrast, usually organise values by band, with each cell in a band representing the same type of variable across the grid. A multispectral image may contain several wavelength bands; a multiband analytical raster might contain several model outputs.

Rasters can store categorical codes, continuous measurements or probabilities, but the meaning belongs to the band and its metadata rather than to an individual row representing a feature.

Which should you use?

Choose vector when the analysis depends on discrete features, explicit boundaries, networks or rich feature attributes.

Choose raster when the phenomenon is continuous, measured as imagery, naturally gridded, or best analysed through local cell relationships and map algebra.

Use both when the workflow requires it. It is normal to intersect vector administrative areas with raster population or land-cover data, or to derive vector contours from a raster elevation model.

The important question is not which model is more advanced. It is which representation preserves the geographic distinction the task depends on.

References

USGS Lidar Base Specification Glossary. Defines raster cells and describes raster as an array of cells containing values representative of their geographic area.
GDAL Raster documentation. Documents common raster operations and illustrates the grid-oriented processing model used by modern GIS tooling.