Available with Spatial Analyst license.
The ArcGIS Spatial Analyst extension provides a rich set of spatial analysis and modeling tools for both 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.
See the Spatial Analyst extension help to learn more about the product, its capabilities, and how to perform analysis with it.
Spatial Analyst toolsets
The functional categories of Spatial Analyst are identified below.
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, those being either queries on the attributes or a condition based on the position of the conditional statement in a list.
With the Density tools, you can calculate the density of input features within a neighborhood around each output raster cell.
The Distance tools allow you to perform distance analysis in the following ways:
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.
With the local tools, you can combine the input rasters, calculate a statistic on them, or evaluate a criterion for each cell on the output raster based on the values of each cell from multiple input rasters.
Map Algebra is a way to perform spatial analysis by creating expressions in an algebraic language. With the Raster Calculator tool, you can easily create and run Map Algebra expressions that output a raster dataset.
The general Math tools apply a mathematical function to the input. These tools fall into several categories. The arithmetic tools perform basic mathematical operations, such as addition and multiplication. There are tools that perform various types of exponentiation operations, which includes exponentials and logarithms in addition to the basic power operations. The remaining tools are used either for sign conversion or for conversion between integer and floating point data types.
The bitwise math tools compute on the binary representation of the input values.
The Logical Math tools evaluate the values of the inputs and determine the output values based on Boolean logic. The tools are grouped into four main categories: Boolean, Combinatorial, Logical, and Relational.
Trigonometric Math tools perform various trigonometric calculations on the values in an input raster.
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 can be of two types: 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.
The solar radiation analysis tools enable you to map and analyze the effects of the sun over a geographic area for specific time periods.
With the Segmentation and Classification tools, you can prepare segmented rasters to use in creating classified raster datasets.
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 where the output is a result of computations performed on all cells that belong to each input zone. A zone can be defined as being one 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.