The vector and raster storage models are two of the most commonly used storage models in Geographic Information Systems (GIS). Both models have different characteristics and uses, making them suitable for different types of geographic data.
The raster storage model is used to store geographic data that can be represented as a matrix of cells, such as satellite images or thematic maps. Each cell in the matrix represents a value of a specific attribute, such as elevation, temperature, precipitation, or the presence of a particular type of vegetation in that area. The cells are arranged in rows and columns, forming a grid. The spatial resolution of raster data is defined by the size of the cells, meaning that raster data becomes less accurate as the cell size increases.
Raster data is useful for performing surface analysis and representing continuous variables in space. Common operations performed with raster data include interpolation, multispectral analysis, generation of terrain elevation models, and generation of distribution maps of continuous variables. However, raster data is not suitable for representing discrete geographic objects, such as roads or rivers.
On the other hand, the vector storage model is used to store geographic data that can be represented as discrete geographic objects, such as roads, rivers, buildings, or administrative boundaries. These objects are defined by their geometry, such as points, lines, or polygons, and are stored together with the attributes that describe them, such as their name or length.
Vector data is useful for performing object analysis, such as analyzing the connectivity of road networks or analyzing the relationship between geographic objects. They are also commonly used for representing categorical and discrete data, such as political boundaries or the location of cities.
In summary, the raster storage model is used for continuous and surface data, while the vector storage model is used for discrete and geographic objects. Both models are essential in Geographic Information Systems, and the choice of the appropriate model will depend on the nature of the geographic data and the analyses to be performed.
In conclusion, understanding these models is fundamental to the effective management and analysis of geographic data, and their correct selection is crucial for the success of any GIS project.
Main sources: Ayala, V. (2014). Sistemas de Información Geográfica. Editorial de la Universidad Nacional de La Plata.
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