The Spatial Analyst toolbox provides a set of spatial analysis and modeling tools for raster (cell-based) and feature (vector) data.
The capabilities of Spatial Analyst are broken down into categories or groups of related functionality. Knowing the categories will help you identify which particular tool to use. The table at the end of this section lists all the available toolsets with a description of the capabilities offered by the tools in each.
There are several ways to access Spatial Analyst functionality. With geoprocessing, operations in the Spatial Analyst toolbox can be performed through a Tool dialog box, Python (either at an interactive command line interface or with a script), or a Model. Traditional operations and workflows using map algebra can also be performed in the Python environment. There is also a Raster Calculator available for entering simple map algebra expressions that generate an output raster.
For most tools, when the output is a raster, the location and name you specify for the output raster determines the format in which it is created. When not saving to a geodatabase, specify .tif for a TIFF file format, .crf for a CRF file format, .img for an ERDAS IMAGINE file format, or no extension for an Esri Grid raster format.
Spatial Analyst toolsets
The functional categories of Spatial Analyst are identified below.
The functional categories of raster analysis available with the Spatial Analyst are identified below.
| Toolset | Description |
|---|---|
The Conditional tools allow you to control the output values based on the conditions placed on the input values. The conditions that can be applied are of two types: queries on the attributes or a condition based on the position of the conditional statement in a list. | |
The Density toolset contains tools that calculate the density of input features within a neighborhood around each output raster cell. | |
The Distance tools allow you to perform analysis that accounts for either straight-line (Euclidean) distance or weighted distance. Distance can be weighted by a simple cost (friction) surface or in ways that account for vertical and horizontal restrictions to movement. | |
The Extraction tools allow you to extract a subset of cells from a raster by either the cells' attributes or their spatial location. You can also obtain the cell values for specific locations as an attribute in a point feature class or as a table. | |
The generalization analysis tools are used to either clean up small erroneous data in the raster or generalize the data to get rid of unnecessary detail for a more general analysis. | |
The Groundwater tools can be used to perform rudimentary advection-dispersion modeling of constituents in groundwater flow. The following topics provide background information on the theoretical aspects of the tools as well as some examples of their implementation. The Groundwater tools can be applied individually or used in sequence to model and analyze groundwater flow. | |
The Hydrology tools are used to model the flow of water across a surface. The Hydrology tools can be applied individually or used in sequence to create a stream network or delineate watersheds. | |
The Interpolation tools create a continuous (or prediction) surface from sampled point values. The continuous surface representation of a raster dataset represents some measure, such as the height, concentration, or magnitude (for example, elevation, acidity, or noise level). Surface interpolation tools make predictions from sample measurements for all locations in an output raster dataset, whether or not a measurement has been taken at the location. | |
The local tools are those where the value at each cell location on the output raster is a function of the values from all the inputs at that location. | |
The Math toolset contains tools that perform mathematical operations on rasters. | |
The tools in the Multidimensional Analysis toolset allow you to perform analysis on scientific raster data across multiple variables and dimensions. | |
Multivariate statistical analysis allows the exploration of relationships among many different types of attributes. There are two types of multivariate analysis available: Classification (both Supervised and Unsupervised) and Principal Component Analysis (PCA). | |
Neighborhood tools create output values for each cell location based on the location value and the values identified in a specified neighborhood. The neighborhood type can be either moving or search radius. | |
Overlay analysis tools allow you to apply weights to several input layers, combine them into a single output, and subject to specifications of distribution and shape, identify preferred locations within that result. These tools are commonly used for suitability modeling. | |
The Raster Creation tools generate new rasters in which the output values are based on a constant or a statistical distribution. | |
The Reclass tools provide a variety of methods that allow you to reclassify or change input cell values to alternative values. | |
With the segmentation and classification tools, you can prepare segmented rasters to use in creating classified raster datasets. | |
The solar radiation analysis tools allow you to map and analyze the effects of the sun over a geographic area for specific time periods. | |
With the Surface tools, you can quantify and visualize a terrain landform represented by a digital elevation model. | |
The Zonal tools allow you to perform analysis when the output is a result of computations performed on all cells that belong to each input zone. A zone can be defined as a single area of a particular value, but it can also be composed of multiple disconnected elements, or regions, all having the same value. Zones can be defined by raster or feature datasets. Rasters must be of integer type, and features must have an integer or string attribute field. |