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Conversion between data models VectorRaster. Conversion between data models.

Date post: 16-Dec-2015
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Conversion between data models Vector Raster
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Page 1: Conversion between data models VectorRaster. Conversion between data models.

Conversion between data models

Vector Raster

Page 2: Conversion between data models VectorRaster. Conversion between data models.
Page 3: Conversion between data models VectorRaster. Conversion between data models.

Conversion between data models

Page 4: Conversion between data models VectorRaster. Conversion between data models.
Page 5: Conversion between data models VectorRaster. Conversion between data models.

Choosing an appropriate cell size is not always simple. You must balance your application's need for spatial resolution with practical requirements for quick display, processing time, and storage.

Page 6: Conversion between data models VectorRaster. Conversion between data models.

Problems associated with conversion:

– Loss of Detail– Loss of Accuracy– Stair Stepping (raster to vector)– Changes to the original data

Page 7: Conversion between data models VectorRaster. Conversion between data models.

Choosing between data models

Often depends on

– Type of entity or phenomena represented. – (discrete or continuous)

– Available storage. – Expected types of analysis. – Expertise of human operators.– Level of accuracy desired.

“Raster is faster but vector is corrector”

Page 8: Conversion between data models VectorRaster. Conversion between data models.

Choosing between data modelsRaster is useful when:

– Working with continuous data types– Good for large area analyses – Good for surface analysis– Mathematical modeling– Spatial detail isn’t important

Vector is useful when:– Working with discrete data types – Good for small study areas – Spatial detail is important (When “close enough” isn’t really

good enough). – When topology is needed for the analysis


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