Available with Spatial Analyst license.

The functionality of the Spatial Analyst extension in ArcGIS is available through a large number of geoprocessing tools. You can use these tools individually to accomplish specific, detailed tasks. If you need to perform the same operation on multiple inputs or run a sequence of operations to model and analyze complex spatial relationships, you can automate your workflow by running geoprocessing tools inside a model or a scripting environment such as Python.

There is a wide range of analytic capabilities in Spatial Analyst. These capabilities can be categorized into groups of related functionality and are therefore organized into corresponding geoprocessing toolsets. The following table lists these toolsets and provides a brief description of the capabilities provided by each. Click the links to find out more.

Toolset | Description |
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The Conditional tools allow control of the output values based on the conditions placed on the input values. The conditions that can be applied are either attribute queries or are based on the position of the conditional statement in a list. A simple attribute query might be: If a cell value is a negative number, assign it to 0; otherwise, keep the original value for the location. | |

By calculating density, you spread input values over a surface. The magnitude at each sample location (line or point) is distributed throughout a landscape, and a density value is calculated for each cell in the output raster. For example, density analysis can take population counts assigned to town centers and distribute the people throughout the landscape more realistically. | |

The two main ways to perform distance analysis are by Euclidean distance and by cost distance. The Euclidean Distance tools measure straight-line distance from each cell to the closest source, where the source identifies the objects of interest. The Cost Distance (or cost-weighted distance) tools extend the Euclidean distance by adding a cost factor to travel through any particular cell. Further complexity of distance can be modeled with the Path Distance tools, which can incorporate vertical and horizontal restrictions to movement. There are also tools to identify corridors and paths on the landscape. An example application of Distance tools is to determine that while it may be shorter to climb over the mountain in a direct path to the destination, in fact it would be easier and thus faster to walk around it. | |

The Extraction tools allow you to extract (clip out) a subset of cells by either their spatial location or the cells' attributes. The location can be identified by a particular shape, such as a circle or polygon. A logical query on the attribute values can be used to define the cells to be extracted. An example is extracting cells higher than 100 meters in elevation from a surface raster. | |

Sometimes a raster dataset contains data that is erroneous or irrelevant to the analysis at hand or is more detailed than you need. The Generalization tools assist with identifying such areas and automating the assignment of more reliable values to the cells that make up the areas. For example, if a raster dataset was derived from the classification of a satellite image, it may contain many small, isolated areas that are classified incorrectly. By using the Generalization tools, you can clean up the data. | |

The Groundwater tools can be used to perform rudimentary advection-dispersion modeling of constituents in groundwater. A typical application of these tools is determining if a chemical spill might contaminate wells that provide drinking water. | |

Hydrology tools simulate the flow of water over an elevation surface. With them, you can create stream networks, determine drainage basins, and model flood events. | |

Surface Interpolation tools create a continuous (or prediction) surface from sampled point values. While the continuous surface representation of a raster dataset is typically used for elevation (height), it can also represent other phenomena such as soil pH, pollution concentration, or noise. | |

With a Local tool, the value at each location on the output raster is a function of the input values at that cell location from multiple input rasters. The output values can be a statistic calculated from the inputs or identify the unique combinations of input values. For example, with a series of annual precipitation rasters, you could find the mean precipitation for a 10-year period or how many years the precipitation exceeded 650 mm. | |

Map Algebra expressions can be entered into the Raster Calculator tool to perform spatial analysis. | |

A full suite of mathematical operations can be applied to rasters. These allow for the arithmetic manipulation or logical evaluation of values in input rasters. | |

Multivariate statistical analysis allows the exploration of relationships between many different types of attributes. The two main types of multivariate analyses available are: - Classification (both supervised and unsupervised)
- Principal component analysis (PCA)
Classification is typically used for processing multiband imagery data into a single classified raster, such as a land cover layer. PCA can be used, for example, to predict the biomass (the dependent variable) at each location given the quantities of precipitation, soil type, aspect, and temperature (the independent variables). | |

Neighborhood tools create output values for each cell location based on the value for the location and the values identified in the specified neighborhood. The neighborhood moves through the input raster, calculating the output value for each before moving on to the next neighborhood. The neighborhoods can be of two types, overlapping or nonoverlapping. - In the overlapping neighborhood type of operation, also called a focal, convolution, or roving window operation, each cell in the input is iterated through. The particular cell value and the values of the cells contained within the specified neighborhood around it are used to calculate the output value. This is repeated for each of the cells in the input.
- In the nonoverlapping type of operation, the neighborhood constitutes a block of cells for which the calculation is performed. Once the value is calculated, it is written to all the cells in that block in the output raster. This is repeated for the next adjacent and nonoverlapping block until all the input has been processed.
For example, the Focal Statistics tool allows you to find the mean (average) or maximum value in a 3 x 3 neighborhood around each cell in the input raster. | |

The Raster Creation tools create new rasters in which the output values are based either on a constant value or a statistical distribution. There are two types of distribution: random or normal (Gaussian). | |

Reclassifying your data simply means replacing input cell values with new output cell values. You can reclassify your data in several ways: by individual values or by ranges, intervals, or area or through an alternative value. Some common reasons for reclassifying your data include - Replacing individual values based on new information
- Grouping certain values together
- Reclassifying values to a common scale
- Assigning specific cell values to NoData
- Setting NoData cells to a value
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With the Segmentation and Classification tools, you can prepare segmented rasters to use in creating classified raster datasets. | |

Using the Solar Radiation analysis tools, you can calculate incoming solar insolation (global, direct, and diffuse radiation) across a geographic area or for specific point locations. Using an input surface DEM, you can determine the amount of radiant energy that is received from the sun across a landscape for a given period of time. | |

With these tools, you can derive new information about a surface dataset. For each location, you can determine the angle of the surface (slope), the steepest downslope direction (aspect), or the second derivative of the surface (curvature). You can also generate a line dataset that connects locations of equal value (contours), create a shaded relief, calculate the volume changes between two surfaces, and determine the visibility of locations. | |

A zone is defined by all input cells that have the same value. Various statistics can be calculated for the cells in each zone, and a specified geometry measure of the zone can also be determined. Zones can be used to determine the areas or distributions of values in another dataset. For example, with Zonal tools, you can find the perimeter length of each zone in a raster or determine the number of endangered species (the value input) within each land parcel (the zone input). |