The slope is commonly derived from a digital elevation model (DEM), but other elevation source data may be used. Cell values must be in percent slope, not degree, which is a common default in some GIS software.

%slope equation:

Pixel value range: 0 - 100

Each pixel value contained in the grayscale land use images should represent a unique land class. For example, if the Anderson Level I scheme was used to classify the land cover data:

where (R,G,B) represents the red, green and blue color bands in the image, and class is the land cover type associated with the (R,G,B) value.

This information is entered in the land cover colorable section of the scenario file where pix is the (R,G,B) value and name is the class land cover type.

Pixel value range: 0 - 255

The excluded image defines all locations that are resistant to urbanization. Areas where urban development is considered impossible, open water bodies or national parks for example, are given a value of 100 or greater. Locations that are available for urban development have a value of zero (0).

Pixels may contain any value between (0-100) if the representation of partial exclusion of an area is desired - unprotected wetlands could be an example: Development is not likely, but there is no zoning to prevent it.

Pixel value range: 0 - 255 (values > 100, are read as 100)

The urban extent for the start year, or seed, is used to initialize the model and is the basis for the CA driven urban growth. For calibration, the earliest urban year is used as the seed, and subsequent urban layers, or control years, are used to measure several statistical best fit values. For this reason, at least four urban layers are needed for calibration: one for initialization and three additional for a least-squares calculation.

The definition of "urban extent" is up to the creators of the data set. The model simply requires a binary classification of urban/nonurban. Methods used in the past include digitizing city maps and aerial photographs, thresholding remotely sensed images or block densities from census data.

Pixel value range: 0 = nonurban
0 < n < 256 = urban

The road influenced growth dynamic included in SLEUTH simulates the tendency of urban development to be attracted to locations of increased accessibility. A transportation network can have major influence upon how a region develops. To include this effect in calibration several road layers, that change with the city's growth over time, are desirable. SLEUTH will be initialized with the earliest road layer. As growth cycles, or "time", pass and the date for a more recent road layer is reached, the new layer will be read in and development will proceed from there.

Road network images may be binary (road/non-road) or have relative values:

note that the relative weighting of the two schemes above are equivalent and would have an identical effect if applied to the same data. For more information see road weighting.

Pixel value range:
binary: 0 = non-road, 0 < n < 256 = road
relative: (see above)

In order to give spatial context to the urban extent data, a background image is incorporated into image output. This must be a grayscale image, and a hillshaded DEM (pictured here) is commonly used.

To give further definition to a region, bodies of water may also be represented. This occurs by any pixels in the background image whose values are zero (0) being filled with the WATER color defined in the scenario file. *Note: this will also mean that any heavily shaded locations that have a zero value will also be filled with the WATER color. This can be avoided by remapping any zero values in the hillshade image to one (1) before adding the water mask.

If WATER is defined as black (R,G,B = 0,0,0) zero value pixels will remain black in the output images.