Understanding raster and vector geospatial data

Written by
Ray Gillinder
June 24, 2024

Understanding raster and vector data is fundamental in the world of GIS. These two data formats are the building blocks of spatial analysis, each offering unique advantages for representing and analyzing geographic information. But what exactly is raster and vector data, and what are the differences between the two? This article aims to answer these questions.

In short, you can begin to think about them like this: raster data is like a photograph made up of pixels, with each pixel storing specific information such as color, temperature, or elevation; making it ideal for continuous data. Vector data, on the other hand, is like a drawing where points, lines, and polygons are used to represent features; perfect for mapping discrete elements like roads and property boundaries.

Why it's important to know the difference between raster and vector data

Knowing the difference between raster and vector data is crucial for several reasons:

Having the right output for the job 

Different GIS tasks require different types of data. For instance, terrain analysis and remote sensing often use raster data, while urban planning and transportation modeling typically rely on vector data. Understanding which type of data is appropriate for a given task ensures the best results.

Data visualization

The way data is visualized in GIS depends on whether it is raster or vector. Raster data provides detailed images, while vector data offers clear, precise representations of boundaries and networks. Knowing the difference allows you to create more effective and informative visualizations tailored to your needs.

The type of analysis and insights needed

Different types of spatial analysis require different data models. Raster data is suitable for analyses involving continuous surfaces, such as slope or temperature gradient analysis. Vector data is better for network analysis, like finding the shortest path between points or managing utility networks. Using the appropriate data type ensures accurate and relevant analysis outcomes.

Data storage and management

Raster data, due to its pixel-based structure, can become very large, especially at high resolutions. Vector data, on the other hand, tends to be more storage-efficient for mapping discrete features. Knowing the differences helps in managing data storage efficiently and making informed decisions about data resolution.

Let’s explore what each of these data types are in more detail.

Raster data

An orthophoto processed and visualized in Birdi

Raster data is made up of pixels (tiny squares), each representing a specific area on the Earth's surface. The size of each pixel, known as the Ground Sampling Distance (GSD), determines the resolution of the raster data. A smaller GSD means higher resolution and more detail, while a larger GSD means lower resolution and less detail. Each pixel has a value that represents information, such as color, elevation, or temperature.

Characteristics of raster data:

  • Grid-based: Composed of rows and columns of pixels, similar to a digital photo.
  • Continuous data representation: Suitable for representing continuous phenomena like elevation, temperature, and land cover.
  • Resolution: Defined by the size of the pixels; higher resolution means smaller pixels and more detail.

Types of raster data:

Elevation models (DEM, DTM): Raster data representing elevation values across a terrain surface. Digital Elevation Models (DEM) and Digital Terrain Models (DTM) are examples used for terrain analysis, flood modeling, and viewshed analysis.

Orthophotos (Orthomosaics): High-resolution, georeferenced raster images created by stitching together aerial photographs. Orthomosaics are corrected for distortions caused by terrain relief and camera perspective, used for accurate mapping, land use planning, and environmental monitoring.

Satellite imagery: Raster data captured by satellites, providing visual representations of the Earth's surface. Used for monitoring land cover changes, agricultural assessments, and urban planning.

Land cover and land use maps: Raster datasets categorizing types of land cover (e.g., forest, urban, water) or land use (e.g., residential, commercial, agricultural).

Climate and weather data: Raster datasets capturing meteorological variables such as temperature, precipitation, and humidity, used in climate modeling and environmental studies.

Raster Data Formats:

Raster data formats include:

  • GeoTIFF (.tif) - A widely used format for georeferenced raster images, supporting both spatial data and metadata.
  • JPEG (.jpg) - Commonly used for compressed photographic images, though not typically used for GIS analysis due to lossy compression.
  • PNG (.png) - Another raster format that supports transparency and lossless compression, suitable for web-based mapping and GIS applications.

Vector Data

Contours processed and visualized in Birdi

Vector data represents geographic features using points, lines, and polygons, defined by x, y, and sometimes z coordinates to include elevation or height information. Unlike raster data, which is grid-based, vector data provides precise spatial representations.

Characteristics of vector data:

  • Coordinate-Based: Uses x, y, and optionally z coordinates to define the shape and location of features, allowing for three-dimensional representation.
  • Discrete Data Representation: Ideal for representing discrete features like roads, property boundaries, and landmarks.
  • Scalability: Vector data can be easily scaled without losing detail, making it suitable for maps of various scales.

Types of vector data:

Points: Vector data representing discrete locations defined by x, y coordinates. Used for mapping specific features like landmarks, wells, or monitoring stations.

Lines (Polylines): Vector data representing sequences of points connected by straight or curved segments. Used for mapping features such as roads, rivers, and pipelines.

Polygons: Vector data representing enclosed areas defined by a series of connected points. Used for mapping features like administrative boundaries, land parcels, and lakes.

Networks: Vector data representing interconnected sets of points and lines, used for modeling transportation networks (roads, railways), utility networks (electricity, water), and communication networks.

Contours: Vector data derived from raster DEMs, representing lines connecting points of equal elevation. Used for terrain visualization, engineering design, and landscape analysis.

Vector data formats:

Vector data formats include:

  • Shapefile (.shp) - A popular vector format consisting of multiple files (.shp, .shx, .dbf) that store geometric and attribute data.
  • GeoJSON (.geojson) - A format for encoding a variety of geographic data structures using JavaScript Object Notation (JSON).
  • KML/KMZ (.kml, .kmz) - Keyhole Markup Language and its compressed counterpart, used for displaying geographic data.

Comparing raster and vector data

Raster and vector data formats each have distinct characteristics suited for different types of spatial data and analysis tasks.

Data structure:

  • Raster: Grid-based format with each cell holding a single value, ideal for continuous data like elevation and satellite imagery.
  • Vector: Coordinate-based format using points, lines, and polygons, suitable for discrete data such as roads and administrative boundaries.

Best uses:

  • Raster: Best for continuous spatial data, offering detailed representations but larger file sizes.
  • Vector: Ideal for precise representations of discrete features, scalable without significant file size increase.

Resolution and detail:

  • Raster: Resolution determined by pixel size, higher resolution provides more detail but increases file size.
  • Vector: Resolution is flexible, maintaining detail at various scales without file size increase.

Data analysis:

  • Raster: Suited for complex spatial analyses like surface modeling and habitat suitability assessments.
  • Vector: Effective for network analyses such as shortest path finding and utility network management.

Both raster and vector data are essential components of GIS, each with their unique strengths and applications. Raster data excels in representing continuous phenomena and providing detailed imagery, while vector data is perfect for mapping discrete features and enabling precise analyses. By understanding the differences between these data types and their best uses, you can effectively harness the power of GIS to analyze and visualize spatial data, leading to better decision-making and a deeper understanding of the world around us.

Ray Gillinder
Ray is an Operations Manager at Birdi, and also our Head Drone Pilot and resident expert on all things drone-related. When Ray isn't supporting the runnings of Birdi, he's out flying drones and capturing data at any number of sites.