![]() ![]() However, interpolation can introduce errors and should be used with caution. Interpolation is commonly used in raster data to fill in gaps or create a smoother surface. Interpolation is the process of estimating values for locations where data is not available. This allows the data to be correctly located in space and used in conjunction with other spatial data. ![]() This means that each cell in the grid must be associated with a specific geographic location. Raster data must be spatially referenced in order to be used in GIS. Spatial referencing is another important characteristic of raster data. Binary data is used to represent presence or absence, such as in land cover classifications. Integer data represents whole numbers, while float data represents decimal numbers. Raster data can come in a variety of data types, including integer, float, and binary. This can make working with raster data computationally intensive, as it requires a large amount of processing power and memory. Raster data can contain a large amount of data, especially if the spatial resolution is high. Data VolumeĪnother characteristic of raster data is its data volume. High spatial resolution data is useful for analyzing small areas or features, while low spatial resolution data is better for analyzing larger areas. The smaller the cell size, the higher the spatial resolution. Spatial resolution refers to the size of the cells in the grid. One of the most important characteristics of raster data is spatial resolution. Now that we know what raster data is, let’s explore the characteristics of raster data that make it unique and important in GIS.
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