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What Is Raster Analysis? Introduction for GIS Professionals

Updated: Aug 14

One of the fundamental methods in Geographic Information Systems (GIS) is raster analysis, which gives experts the ability to precisely model, examine, and comprehend continuous spatial data. Raster analysis provides strong capabilities to handle massive geographic datasets and derive useful insights, regardless of your field of expertise—environmental modelling, hydrology, land use planning, or remote sensing.


What Is Raster Analysis?


The process of applying spatial operations to raster datasets, or grid data, in which each cell (or pixel) has a value that represents information like temperature, elevation, land cover, or spectral reflectance, is known as raster analysis.

Raster data is especially well-suited for expressing continuous events and facilitating cell-by-cell mathematical operations, in contrast to vector data (points, lines, and polygons).


Every cell in raster analysis is geo-referenced, which means that its location on the surface of the Earth matches its position. This enables the use of spatial computations that merge several raster layers or convert unprocessed satellite imagery into insightful analytical results.


Raster Analysis (Elevation Data)
Raster Analysis (Elevation Data)


Raster Data Model in GIS


GIS workers should comprehend the essential elements of the raster data model before delving into raster analytic workflows:


  • Spatial detail is determined by cell size (resolution). A 10x10 meter region on the ground is represented by each pixel in a raster with a 10m resolution.

  • Data Type: Floating-point (continuous data, such as elevation or rainfall) or integer (categorical data, such as land cover classes).

  • Spatial reference is defined by the coordinate system (e.g., WGS84, UTM).

  • Bands: Multi-band rasters, which are frequently used in remote sensing, store multiple values per cell, such as RGB or multispectral imaging, whereas single-band rasters store a single value per cell.


Raster Analysis (Satellite Imagery)
Raster Analysis (Satellite Imagery)


Core Raster Analysis Techniques


Raster analysis includes a number of processes that support various GIS workflows. Important methods consist of:


  1. Local Operations


  • Use one or more rasters to operate on a cell-by-cell basis.

  • For example, NDVI from multispectral imaging is calculated as (NIR - Red) / (NIR + Red).

  • Applications include mapping the ground surface temperature and evaluating the health of the vegetation.


  1. Focal (Neighbourhood) Operations


  • To calculate a new value, take into account the neighbours of a cell.

  • Example: Using a DEM to calculate slope and aspect.

  • Applications include hydrological flow direction analysis and terrain modelling.


  1. Zonal Operations


  • Compile values from a vector or raster dataset into specified zones.

  • Rainfall averages by watershed polygon, for instance.

  • Applications include biological habitat evaluation and watershed management.


  1. Global Operations


  • To generate a single result, use every raster cell.

  • Identifying the highest temperature in a climate model dataset is one example.


  1. Map Algebra


  • A mathematical framework that uses statistical, logical, or arithmetic operations to merge raster layers.

  • Example: Combining proximity, land cover, and slope rasters to map suitability.


Common Raster Analysis Tools and Software


Many raster analysis features are available in contemporary GIS platforms. Among the frequently used tools are:


  • ArcGIS Spatial Analyst: sophisticated tools for hydrology, surface analysis, and raster modelling.

  • GDAL integration and open-source processing are features of QGIS Raster Analysis Plugins.

  • Large datasets benefit greatly from GRASS GIS's high-performance raster computing.

  • Planetary-scale raster analysis via the cloud is provided by Google Earth Engine (GEE).


Advanced Applications of Raster Analysis


In high-level geographic modelling, raster analysis is essential for the following:


  • Hydrological Modelling: Using DEMs to map flood danger, flow direction, and accumulation.

  • Climate Change Studies: Analysis of downscaled climate models for precipitation and temperature.

  • Classifying satellite imagery through both supervised and unsupervised methods is known as remote sensing classification.

  • Land surface temperature and land cover data are used to create urban heat island mapping.

  • Creating representations of hillshades, slopes, and aspects is known as 3D surface modelling.


Any GIS practitioner who wants to accurately model continuous spatial phenomena must be proficient in raster analysis. It makes it possible to turn unprocessed geospatial data into insightful knowledge that informs decisions in a variety of fields, including environmental monitoring, urban planning, and disaster relief.


GIS professionals may fully utilise raster analysis and meet the expectations of contemporary geospatial applications by being proficient in raster data structures, operations, and optimisation approaches.


For more information or any questions regarding the raster analysis, please don't hesitate to contact us at


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