In
spatial analysis with
geographic information systems, both
raster and
vector data are used. Importantly, when working with spatially aggregate data (either in vector or raster) at a coarse resolution, it is impossible to make assumptions about what that data looks like at a finer resolution. Doing so would commit the
ecological fallacy. Aggregating data spatially has a statistical smoothing effect due to the
scale effect.
Raster Arbia's law was first invoked when working with raster datasets.
Spatial resolution in remote sensing is related to the smallest pixel size within an image, and one value is returned for the area within a pixel. The coarser the
image resolution (the larger the pixel) in a remotely sensed image, the larger the area that will be represented with the same value. Thus, a coarse resolution has a soothing effect on the image, making land cover appear more homogenous than an image with a fine spatial resolution.
Vector When working with vector datasets, the same effect is present as in Raster. With Vector datasets in GIS, it is often necessary to aggregate data into discreet spatial enumeration units (often referred to as aerial units), such as county boundaries or national borders. The
Modifiable Areal Unit Problem, or MAUP, arises from the countless possible ways to divide up the same area of land. The same area may not appear very homogenous when the aerial units are smaller. == Controversy==