What Is Raster Data Structure?


Larry Thompson

What Is Raster Data Structure?

Raster data structure is a way to represent and store spatial data in a grid-like format. It is commonly used to represent continuous phenomena, such as elevation, temperature, and satellite imagery. In this article, we will explore the concept of raster data structure, its characteristics, and its applications.

Characteristics of Raster Data Structure

Raster data structure has several key characteristics that distinguish it from other data structures:

  • Grid-based: Raster data is organized in a grid-like format where each cell, also known as a pixel, represents a discrete unit of information. The size of the cells can vary depending on the resolution of the raster dataset.
  • Regularly spaced: The cells in a raster dataset are regularly spaced and aligned with each other.

    This regularity allows for efficient storage and retrieval of the data.

  • Cell values: Each cell in a raster dataset stores a single value that represents a specific attribute or measurement. These values can be integers or floating-point numbers depending on the nature of the data.

Applications of Raster Data Structure

Raster data structure finds wide applications across various industries and domains:

1. Remote Sensing and Satellite Imagery

Raster datasets are extensively used to store satellite imagery captured by remote sensing devices. These images provide valuable information about land cover, vegetation health, urban development, and more. Raster data structure allows for efficient storage and analysis of these large datasets.

2. Digital Elevation Models (DEMs)

Rasters are commonly used to represent elevation models, also known as DEMs. DEMs provide information about the height or elevation of the Earth’s surface, and they are crucial for various applications such as hydrology, terrain analysis, and 3D visualization.

3. Environmental Modeling

Environmental scientists and researchers use raster data structure to model and analyze various environmental parameters such as temperature, precipitation, soil types, and land cover. Rasters allow for spatially explicit representation of these parameters, enabling better understanding and prediction of environmental processes.

4. Geographic Information Systems (GIS)

Raster data is an integral part of geographic information systems (GIS). GIS software allows users to perform spatial analysis and overlay multiple layers of raster data to gain insights into various phenomena. Rasters can be combined with vector data to create comprehensive GIS datasets.


Raster data structure provides a powerful way to store and analyze spatial data in a grid-like format. Its characteristics such as grid-based organization, regular spacing, and cell values make it ideal for representing continuous phenomena.

The applications of raster data structure are diverse and span across industries like remote sensing, environmental modeling, and GIS. Understanding the fundamentals of raster data structure is essential for anyone working with spatial data.

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