GIS. Data Generalization

GIS data contains more spatial information than needed for the scale of the map.

Generalization is used in GIS to reduce detail in data.

Generalization can be achieved by removing details such as minor roads, county boundaries, or small features like nooks and crannies of a coastline or meanderings of a stream.

Generalization results in less spatial accuracy as the details are simplified.

Calculations based on generalized data, such as length, perimeter, or area, may have errors.

GIS data is often more detailed than necessary for creating maps at a certain scale. Generalization is the process of simplifying the data to make it more suitable for the purpose. For instance, for a small-scale map of the United States, it's not necessary to show every road or coastline in detail. Removing such minor details can be achieved through generalization.

Generalization can involve removing minor details, showing only major features, or smoothing out lines. However, this process may lead to less spatial accuracy in the data. Therefore, calculations based on the generalized data, such as length, perimeter, or area, may not be entirely accurate.

Aggregating zones of data.

smoothing data edges

reducing the resolution of a raster.

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