What is Raster Data?

Raster data is a type of digital data that uses a grid of cells or pixels to represent information. It is also known as grid or image data. Each cell in the grid represents a different value or attribute, such as pixel, elevation, temperature, or color.

It is commonly used in Geographic Information Systems (GIS) and remote sensing applications to create maps, evaluate land use patterns, monitor environmental changes, and perform other tasks.

Examples of raster data used under GIS are as follows:

1. Digital Elevation Model (DEM): Represents the elevation and slopes of the earth’s surface and is important for applications such as topographic maps and land use planning.
2. Temperature Data: Important for applications such as climate change, agricultural planning, and natural disasters.
3. Satellite Images: Used in topics such as forests, agricultural lands, water resources and mining sources, vegetation, water quality, forest fires, and environmental monitoring.
4. Land Use Data: Important for applications such as land planning, natural resource management, and environmental impact analysis.
5. Climate Data: Represents data collected from meteorological stations or satellites and is important for applications such as climate change, water resource management, and natural disasters.

Categorical (Discrete) Raster Data: These are spatially discrete and categorized data. They typically include segmented data, such as land cover types or land use categories.

Continuous Raster Data: These are measurement data that are continuous integers and can take any value within a range. They typically include elevation and temperature data.

1. Representation: Raster data is represented by a grid of cells or pixels, while vector data is represented by points, lines, and polygons.
2. Usage: Raster data is generally more practical and straightforward to use compared to vector data. However, in terms of storage space, unlike vector data, it is large for many datasets without compression.
3. Accuracy: Raster data is generally less accurate than vector data because it can only represent values at the resolution of the grid cells. Vector data can represent more accurate spatial features.
4. Editing: Vector data is generally easier to edit and maintain compared to raster data because individual features can be edited without affecting other features.

1. Select the appropriate software: Use a GIS software such as MapInfo Pro to convert raster data to vector data.
2. Prepare the raster data: Prepare the raster data by cleaning and filtering out unwanted data to obtain accurate results.
3. Select the conversion tool: Select the appropriate conversion tool for raster-to-vector conversion from the software. This tool may be found under the vector conversion or image analysis toolset.
4. Adjust the conversion parameters: Adjust the conversion parameters according to your requirements, such as the scale, resolution, and attributes.
5. Run the conversion tool: Run the conversion tool and wait for the output vector data to be generated. The output data can be further edited and improved as needed.
6. Verify the results: Verify the accuracy of the vector data by comparing it with the original raster data and performing necessary quality control processes.

Using the Polygonize option in MapInfo Pro Advanced module, you can convert your raster data into vector data.

1. Continuous representation: Raster data can represent continuous data such as temperature, elevation, or precipitation that cannot be represented with vector data.
2. Spatial resolution: Raster data can represent high spatial resolution, allowing for detailed analysis of spatial data.
3. Easy to Use: Raster data is relatively easy to understand and analyze, as it can be visualized as an image where different colors represent different values. This makes it easier for non-experts to work with spatial data.
4. Compatibility: Raster data can be easily combined with other raster data such as satellite imagery to create composite images.
5. Efficiency: Working with large data sets becomes more efficient with raster data.
6. Flexibility: Raster data can be used in a wide range of applications, such as environmental modeling, urban planning, and natural resource management.

The raster data storage methods commonly used in GIS applications are:

1. GeoTIFF Format: It contains a header that specifies the spatial reference system, coverage, and other metadata about the data. GeoTIFF is widely supported by GIS software and is interactive with other spatial data formats.
2. ERDAS Image Format: ERDAS Image is a specialized raster data format widely used in remote sensing applications. It includes advanced compression algorithms that can reduce the storage space required for large data sets.
3. NetCDF Format (Network Common Data Form): NetCDF is a data storage format commonly used in atmospheric and oceanographic research.
4. HDF Format (Hierarchical Data Format): HDF includes advanced compression techniques that can reduce the storage space required for large data sets.
5. Cloud-Based Storage: With the increasing availability of cloud computing, raster data can also be stored in cloud-based storage systems such as Amazon S3 or Google Cloud Storage.
6. Mutli-Resolution Raster (.mrr): The MRR data format is a format specific to MapInfo Pro software. It provides high performance even for very large raster data sets, and can process terabyte-scale data sets.