Raster data can undergo similar spatial operations to the geoprocessing tools available for vector datasets. Although the actual computation of these operations is significantly different from their vector counterparts, their conceptual underpinning is similar. The geoprocessing techniques covered here include both single-layer (Section 8.1.1 “Single Layer Analysis”) and multiple-layer (Section 8.1.2 “Multiple Layer Analysis”) operations.

Single Layer Analysis

Reclassifying, or recoding, a dataset is one of the first steps undertaken during raster analysis. Reclassification is the single-layer process of assigning a new class or range value to all pixels in the dataset based on their original values (Figure 8.1 “Raster Reclassification.” For example, an elevation grid commonly contains a different value for nearly every cell within its extent. These values could be simplified by aggregating each pixel value in a few discrete classes (i.e., 0–100 = “1,” 101–200 = “2,” 201– 300 = “3,” etc.). This simplification allows for fewer unique values and cheaper storage requirements. In addition, these reclassified layers are often used as inputs in secondary analyses, such as those discussed later in this section.

As described in Chapter 7: Vector Data Analysis, buffering creates an output dataset containing a zone (or zones) of a specified width around an input feature. In the case of raster datasets, these input features are given as a grid cell or a group of grid cells containing a constant value (e.g., buffer all cells whose value = 1). Buffers are particularly suited for determining the influence area around interest features. Whereas buffering vector data results in a precise area of influence at a specified distance from the target feature, raster buffers tend to be approximations representing those cells that are within the specified distance range of the target (Figure 8.2 “Raster Buffer around a Target Cell(s)”). Most geographic information system (GIS) programs calculate raster buffers by creating a grid of distance values from the center of the target cell(s) to the center of the neighboring cells and then reclassifying those distances such that a “1” represents those cells composing the original target, a “2” represents those cells within the user-defined buffer area, and a “0” represents those cells outside of the target and buffer areas. These cells could also be further classified to represent multiple ring buffers by including values of “3,” “4,” “5,” and so forth to represent concentric distances around the target cell(s).

A raster dataset can also be clipped, similar to a vector dataset (Figure 8.3 “Clipping a Raster to a Vector Polygon Layer”). Here, the input raster is overlain by a vector polygon clip layer. The raster clip process results in a single raster identical to the input raster but shares the extent of the polygon clip layer.

Raster overlays are relatively simple compared to their vector counterparts and require less computational power (Burroughs, 1983). However, despite their simplicity, it is essential to ensure that all overlain rasters are co-registered (i.e., spatially aligned), cover identical areas, and maintain equal resolution (i.e., cell size). If these assumptions are violated, the analysis will fail, or the resulting output layer will be flawed. Therefore, several methodologies are used to perform a raster overlay (Chrisman, 2002).

The mathematical raster overlay is the most common overlay method. The numbers within the aligned cells of the input grids can undergo any user-specified mathematical transformation. Following the calculation, an output raster contains a new value for each cell (Figure 8.4 “Mathematical Raster Overlay”). As you can imagine, there are many uses for such functionality. In particular, a raster overlay is often used in risk assessment studies where various layers are combined to produce an outcome map showing high-risk/reward areas.

The Boolean raster overlay method represents a second powerful technique. As discussed in Chapter 6, “Data Characteristics and Visualization,” the Boolean connectors AND, OR, and XOR can be employed to combine the information of two overlying input raster datasets into a single output raster. Similarly, the relational raster overlay method utilizes relational operators (<, <=, =, <>, >, and =>) to evaluate the conditions of the input raster datasets. In the Boolean and relational overlay methods, cells that meet the evaluation criteria are coded in the output raster layer with a 1. At the same time, those evaluated as false receive a value of 0.

However, the simplicity of this methodology can also lead to easily overlooked errors in interpretation if the overlay needs to be appropriately designed. For example, assume that a natural resource manager has two input raster datasets she plans to overlay; one showing the location of trees (“0” = no tree; “1” = tree) and one showing the location of urban areas (“0” = not ur-ban; “1” = urban). Suppose she hopes to find the location of trees in urban areas. In that case, a simple mathematical sum of these datasets will yield a “2” in all pixels containing a tree in an urban area. Similarly, if she hopes to find the location of all treeless (or “non-tree,” nonurban areas, she can examine the summed output raster for all “0” entries, and finally, suppose she hopes to locate urban, treeless areas. In that case, she will look for all cells containing a “1.” Unfortunately, the cell value “1” is coded into each pixel for nonurban tree cells. Indeed, the input pixel values and overlay equation in this example will yield confounding results due to the poorly devised overlay scheme.