Spatial Analyst Tools

The ArcGIS Spatial Analyst extension provides a rich set of spatial analysis and modeling tools for both raster (cell-based) and feature (vector) data.

Spatial Analyst Tools consist of a set of tools, and it will be explained by explaining all the tools as follows:

Math Toolset:

The Math Toolkit contains tools that perform calculations on raster data. The tools are grouped into four main categories:

Math:

Tools at the top level of the Math Toolkit perform basic arithmetic operations on raster data in the following categories: arithmetic, power, exponential, and logarithmic. Tools that change the sign of point values are also included, as well as tools involved in converting values between integers and floating point:

Abs Calculates the absolute value of cells in a raster.
Divide divides two point values on a cell by cell basis.
Exp Calculates the exponential base of cells in a raster.
Exp10 Calculates the base-10 exponential of cells in the raster.
Exp2 Calculates the base 2 exponential of cells in the raster.
Float Converts the value of each cell of the raster to a floating point representation.
Int converts the value of each cell of the raster to an integer by truncation.
Ln Calculates the natural logarithm (base e) of cells in the raster.
Log10 Calculates the base 10 logarithm of cells in a raster.
Minus subtracts the value of the second bullet entry from the value of the first bullet entry on a cell-by-cell basis.
Mod detects the remainder (modulo) of the first raster when dividing the second raster on a cell-by-cell basis.
Negate changes the sign (multiply by -1) of the values of the input raster cell on a cell-by-cell basis.
Plus Adds (sums) two point values on a cell by cell basis.
Power Raises the cell values in the raster to the power of the values in the other raster.
Round Down Returns the next lowest integer value, represented only as a floating point, for each cell in the raster.
Round Up Returns the next higher integer value, represented only as a floating point, for each cell in the raster.
Square Calculates the square of cell values in a raster.
Square Root Calculates the square root of the cell values in the raster.
Times Multiplies two point values on a cell-by-cell basis.

Bitwise Toolset:

Bitwise math toolkit contains tools that perform bit operations on input raster data:

Bitwise And Performs a Bitwise And operation on the binary values of two input points.
Bitwise Left Shift Performs a Bitwise Left Shift operation on the binary values of two input points.
Bitwise Not Performs a Bitwise Not (complement) operation on the binary value of the input raster.
Bitwise Or performs a Bitwise Or operation on the binary values of two input rasters.
Bitwise Right Shift It performs a bit-level right-shift operation on the binary values of two input points.
Bitwise XOr Executing an operation or an exclusive operation on Bitwise eXclusive on the binary values of two input rasters.

Logical Toolset

The Boolean Math Toolkit contains tools for performing Boolean evaluations on raster data in the following categories: Boolean, Combinational, Relational, and Conditional:

Boolean And Performs a Boolean and an operation on the cell values of two of the two entry points. If the two input values are true (non-zero), the output value is 1. If one or both of the inputs are false (zero), the output is 0
Boolean Not Performs a Boolean Not operation on the values of an input point cell. If the input values are true (non-zero), the output value is 0. If the input values are false (zero), the output is 1.
Boolean Or Performs a Boolean Or operation on the cell values of two input point values. If one or both input values are true (non-zero), the output value is 1. If both input values are false (zero), the output is 0.
Boolean XOr Performs a Boolean eXclusive Or operation on the cell values of two input points. If one of the input values is true (non-zero) and the other is false (zero), the output is 1. If both input values are true or both are false, the output is 0.
Combinatorial And Performs a combinatorial operation and an operation on the cell values of two input point values. If the two input values are true (non-zero), the output is a different value for each unique set of input values. If one or both of the inputs are false (zero), the output value is 0.
Combinatorial Or Performs a combinatorial operation or operation on the cell values of two input point values. If the input value is true (non-zero), the output is a different value for each unique set of input values. If both inputs are false (zero), the output value is 0.
Combinatorial XOr Performs a combinatorial operation or operation on the cell values of two input points. If one of the input values is true (non-zero) and the other is false (zero), the output is a different value for each unique set of input values. If both inputs are true or both are false, the output value is 0.
Diff sets the values from the first input that are logically different from the values of the second input on a cell-by-cell basis, so if the values in the input are different, the value in the first input is the output. If the values on the two inputs are identical, the output is 0.
Equal To Performs a relational operation on two inputs on a cell-by-cell basis Returns 1 for cells where the first bitmap equals the second bullet point and 0 for cells not equal to it
Greater Than performs a relational operation greater than two inputs on a cell by cell basis. Returns 1 for cells where the first bullet is greater than the second bullet point and 0 for cells if it is not.
Greater Than Equal Perform a relational operation greater than or equal to two inputs on a cell by cell basis. Returns 1 for cells where the first raster is greater than or equal to the second bullet point and 0 if it is not.
InList Define values from the first entry included in a set of other inputs, on a cell by cell basis. For each cell, if the value of the first raster entry is found in any of the other input lists, then that value will be assigned to the output raster. If not found, the output cell will be NoData.
Is Null Determines the values from the entered raster is NoData on a cell by cell basis. Returns the value 1 if the input value is NoData and 0 for cells that do not exist.
Less Than performs a relational process less than on two inputs on a cell by cell basis. Returns 1 for cells where the first raster is less than the second raster and 0 if it is not.
Less Than Equal Performs a less than or equal relational operation on two inputs on a cell by cell basis. Returns 1 for cells where the first raster is less than or equal to the second raster and 0 where it is not.
Not Equal Performs a relational operation on two inputs on a cell by cell basis. Returns 1 for cells where the value of the first raster is not equal to the second raster and 0 for cells where they are equal.
Over For cell values in the first input that are not 0, the output value will be the value of the first input. When the cell values are 0, the output will be the raster output of the second input.
Test Performs a Boolean evaluation of an input raster using a Boolean expression. When the expression evaluates to true, the value of the output cell is 1. If the expression is false, the value of the output cell is 0.

Trigonometric Toolset:

Trigonometric tools perform many trigonometric calculations on the values in the input raster, and there are several general classes of trigonometric calculations:

ACos to calculate the inverse cosine of cells in raster.
ACosH to calculate the inverse hyperbola of cells in a raster.
Asin to calculate the inverse sine of cells in raster.
ASinH to calculate the inverse hyperbolic sine of cells in a dotted line.
ATan Calculates the inverted shade of cells in the raster.
ATan2 Calculates the inverse tangent of a hyperbola for cells in a raster.
ATanH Calculates the inverse tangent of a hyperbola for cells in a raster line.
Cos to calculate the cosine of cells in raster.
CosH to calculate the hyperbolic cosine of cells in a raster line.
Sin calculates the sine of cells in the raster.
SinH to calculate the hyperbolic sine of cells in raster.
Tan Calculates the shadow of cells in a raster.
TanH Calculates the hyperbolic tangent of cells in a point line.

Multivariate Toolset:

Multivariate statistical analysis allows to explore the relationships between many types of traits

different. Two types of multivariate analysis are available: classification (supervised and unsupervised) and principal components analysis (PCA), the object of classification is to assign each cell in the study area to a class or class. With supervised classification, you have specific knowledge about the study area and can identify representative areas or samples for each semester. Unsupervised classification uses statistical groups that occur naturally in the data to determine which groups the data will be classified into:

Band Collection Statistics

Calculates the statistics for a set of raster bands.

Class Probability

Creates a multiband raster of probability bands, with one band being created for each class represented in the input signature file.

Create Signatures

Creates an ASCII signature file of classes defined by input sample data and a set of raster bands.

Dendrogram

Constructs a tree diagram (dendrogram) showing attribute distances between sequentially merged classes in a signature file.

Edit Signatures

Edits and updates a signature file by merging, renumbering, and deleting class signatures.

Iso Cluster

Uses an isodata clustering algorithm to determine the characteristics of the natural groupings of cells in multidimensional attribute space and stores the results in an output ASCII signature file.

Iso Cluster Unsupervised Classification

Performs unsupervised classification on a series of input raster bands using the Iso Cluster and Maximum Likelihood Classification tools.

Learn more about how the Interactive Supervised Classification tool works

Maximum Likelihood Classification

Performs a maximum likelihood classification on a set of raster bands and creates a classified raster as output.

Principal Components

Performs Principal Component Analysis (PCA) on a set of raster bands and generates a single multiband raster as output.

Neighborhood Toolset:

Neighborhood tools generate output values for each cell location based on the location value and the values specified in a specific neighborhood. Neighborhood type can be either mobile or search radius, and neighborhood relocation can be either nested or non-nested. The focus stats tool uses nested neighborhoods to calculate a specific statistic of cells within a specific neighborhood around each input cell. For example, you might want to find the average or maximum value in a 3 x 3 neighborhood around each cell in the input raster.

A filter is a specific type of focal process that uses a high or low pass filter to highlight or smooth out data. The non-overlapping neighborhood tool, Block Statistics, allows statistics to be calculated in a specific, non-overlapping neighborhood. This tool is particularly useful for changing the resolution of a raster to a coarse cell size. The values assigned to coarse cells can be based on another arithmetic operation, such as the maximum value in the coarse cell instead of using the default nearest nearest neighbor:

Block Statistics

Partitions the input into non-overlapping blocks and calculates the statistic of the values within each block. The value is assigned to all of the cells in each block in the output.

Filter

Performs either a smoothing (Low pass) or edge-enhancing (High pass) filter on a raster.

Focal Flow

Determines the flow of the values in the input raster within each cell's immediate neighborhood.

Focal Statistics

Calculates for each input cell location a statistic of the values within a specified neighborhood around it.

Line Statistics

Calculates a statistic on the attributes of lines in a circular neighborhood around each output cell.

Point Statistics

Calculates a statistic on the points in a neighborhood around each output cell.

Overlay Toolset:

Overlay analysis tools allow you to apply weights to multiple input layers, combine them into a single output, subject to distribution and shape specifications, and specify preferred positions within that result. These tools are commonly used for trapping modeling, and there are several ways to perform superposition analysis. Although the methods differ, they all follow the same general steps for solving a multi-criteria problem. There is a general sequence of steps to follow when conducting this type of analysis,

Using tools from this toolkit as well as other tools available in the spatial analysis toolbox. Since each approach is based on different assumptions, the meaning of the numbers and analysis techniques are approach specific. Which option you choose depends on the problem you're addressing. Use the weighted overlay and weighted addition tools to implement the more general approach of reclassifying and weighting input raster data in an overlay analysis. Fuzzy Overlay and Fuzzy Membership tools use fuzzy logic as a mechanism to address inaccuracies inherent in features and in the geometry of spatial data sets. The zoning tool allows you to locate the best spots or areas within the built-in deck that meet your specific needs. You can control the total area desired, the number of areas that should be distributed between them, the shape of the areas, and how close or far apart the areas are:

Fuzzy Membership

Transforms the input raster into a 0 to 1 scale, indicating the strength of a membership in a set, based on a specified fuzzification algorithm.

A value of 1 indicates full membership in the fuzzy set, with membership decreasing to 0, indicating it is not a member of the fuzzy set.

Fuzzy Overlay

Combine fuzzy membership rasters data together, based on selected overlay type.

Locate Regions

Identifies the best regions, or groups of contiguous cells, from an input utility (suitability) raster that satisfy a specified evaluation criterion and that meet identified shape, size, number, and interregion distance constraints.
This tool uses a parameterized region-growing (PRG) algorithm to grow candidate regions from seed cells by adding neighboring cells to the region that best preserves the specified shape but also maximizes utility for the region. Using a selection algorithm and an evaluation criterion—such as the highest average value—the best region or regions are selected from the candidate regions that meet identified size and spatial constraints. An example of a spatial constraint would be maintaining a certain minimum distance between regions.

Weighted Overlay

Overlays several rasters using a common measurement scale and weights each according to its importance.

Weighted Sum

Overlays several rasters, multiplying each by their given weight and summing them together.

Raster Creation Toolset:

Raster Creation tools create new raster data in which the output values are based on a fixed or statistical distribution, these tools are useful in determining the distribution of a phenomenon. One example of such an analysis is the analysis of whether known bear sightings are randomly distributed within a study site:

Create Constant Raster

Creates a raster of a constant value within the extent and cell size of the analysis window.

Create Normal Raster

Creates a raster of random values with a normal (Gaussian) distribution within the extent and cell size of the analysis window.

Create Random Raster

Creates a raster of random floating-point values between 0.0 and 1.0 within the extent and cell size of the analysis window.

Reclass Toolset:

Reclassification tools provide a variety of methods that allow you to reclassify or change input cell values into alternate values.

The most common reasons for reclassifying data are to achieve the following:

Replace the values based on the new information.
Group certain values together.
Reclassify the values into a common measure (for example, for use in a fit analysis or to create a cost raster for use in the Cost Distance tool).

Assign specific values to NoData or set NoData cells to a value:

Lookup

Creates a new raster by looking up values found in another field in the table of the input raster.

Reclass by ASCII File

Reclassifies (or changes) the values of the input cells of a raster using an ASCII remap file.

Reclass by Table

Reclassifies (or changes) the values of the input cells of a raster using a remap table.

Reclassify

Reclassifies (or changes) the values in a raster.

Rescale by Function

Rescales the input raster values by applying a selected transformation function and then transforming the resulting values onto a specified continuous evaluation scale.

Slice

Slices or reclassifies the range of values of the input cells into zones of equal interval, equal area, or by natural breaks.

Segmentation and Classification Toolset:

With the classification and segmentation tools, you can set up segmented raster data for use in creating labeled raster datasets:

Classify Raster

Classifies a raster dataset based on an Esri classifier definition (.ecd) file and raster dataset inputs.

The .ecd file contains all the information needed to perform a specific type of Esri-supported classification. The inputs to this tool must match the inputs used to generate the required .ecd file.

The .ecd file can be generated from any of the classifier training tools, such as Train Random Trees Classifier or Train Support Vector Machine Classifier.

Compute Confusion Matrix

Computes a confusion matrix with errors of omission and commission and derives a kappa index of agreement and an overall accuracy between the classified map and the reference data.

This tool uses the outputs from the Create Accuracy Assessment Points tool or the Update Accuracy Assessment Points tool.

Compute Segment Attributes

Computes a set of attributes associated with the segmented image. The input raster can be a single-band or 3-band, 8-bit segmented image.

Create Accuracy Assessment Points

Creates randomly sampled points for post-classification accuracy assessment.

A common practice is to randomly select hundreds of points and label their classification types by referencing reliable sources, such as field work or human interpretation of high-resolution imagery. The reference points are then compared with the classification results at the same locations.

Export Training Data For Deep Learning

Uses a remote sensing image to convert labeled vector or raster data into deep learning training datasets. The output is a folder of image chips and a folder of metadata files in the specified format.

Generate Training Samples From Seed Points

Generates training samples from seed points, such as accuracy assessment points or training sample points. A typical use case is generating training samples from an existing source, such as a thematic raster or a feature class.

Inspect Training Samples

Estimates the accuracy of individual training samples. The cross validation accuracy is computed using the previously generated classification training result in an .ecd file and the training samples. Outputs include a raster dataset containing the misclassified class values and a training sample dataset with the accuracy score for each training sample.

Remove Raster Segment Tiling Artifacts

Some regional processes, such as image segmentation, will have inconsistencies near image tile boundaries. This tool corrects segments or objects cut by tile boundaries during the segmentation process performed as a raster function.

This processing step is included in the Segment Mean Shift tool; therefore, it should only be used on a segmented image that was not created from that tool.

Segment Mean Shift

Groups into segments adjacent pixels that have similar spectral characteristics.

Train ISO Cluster Classifier

Generates an Esri classifier definition (.ecd) file using the Iso Cluster classification definition.

This tool performs an unsupervised classification.

Train Maximum Likelihood Classifier

Generates an Esri classifier definition (.ecd) file using the Maximum Likelihood Classifier (MLC) classification definition.

Train Random Trees Classifier

Generates an Esri classifier definition (.ecd) file using the Random Trees classification method.

The random trees classifier is a powerful technique for image classification that is resistant to overfitting and can work with segmented images and other ancillary raster datasets. For standard image inputs, the tool accepts multiple-band imagery with any bit depth, and it will perform the Random Trees classification on a pixel basis or segment, based on the input training feature file.

Train Support Vector Machine Classifier

Generates an Esri classifier definition (.ecd) file using the Support Vector Machine (SVM) classification definition.

Update Accuracy Assessment Points

Updates the Target field in the attribute table to compare reference points to the classified image.

Accuracy assessment takes known points and uses them to assess the validity of the classification model.

Solar Radiation Toolset:

Solar radiation analysis tools enable you to map and analyze the effects of the sun on a geographic area for specific periods of time, and you can perform solar radiation analysis for a specific landscape or location using two methods: The calculations are repeated for each location in the input topographic surface,

To produce insolation maps for an entire geographic area. The Solar Radiation Points Tool is used to calculate the amount of radiated energy for a specific location. Locations can be stored as point features or as x,y coordinates in the location table. Solar radiation calculations can only be done for specific locations, and for diagnostic purposes you can use the Solar Radiation Graphics tool to create graphical representations of the visible sky (field of view map), the position of the Sun in the sky over a period of time (sun map),

and sectors of the sky that affect the amount of incoming solar radiation (sky map). Conceptually, these "maps" are used internally during analysis to calculate the total amount of solar radiation for a given location or area:

Area Solar Radiation

Derives incoming solar radiation from a raster surface.

Points Solar Radiation

Derives incoming solar radiation for specific locations in a point feature class or location table.

Solar Radiation Graphics

Derives raster representations of a hemispherical viewshed, sun map, and sky map, which are used in the calculation of direct, diffuse, and global solar radiation.

Surface Toolset:

With Surface Tools, you can define and visualize the terrain represented by the digital elevation model, starting with a point elevation surface as an input. You can elicit patterns that were not easily apparent in the original surface,

Such as curves, slope angle, steeper slope direction (Aspect), terrain shaded (hillshade) and visibility. Each surface tool provides insight into the surface that can be used as an end in itself or as input to additional analysis:

Aspect

Derives the aspect from each cell of a raster surface.

The aspect identifies the compass direction that the downhill slope faces for each location.

Contour

Creates a feature class of contours from a raster surface.

Contour List

Creates a feature class of selected contour values from a raster surface.

Contour with Barriers

Creates contours from a raster surface. The inclusion of barrier features allows you to independently generate contours on either side of a barrier.

Curvature

Calculates the curvature of a raster surface, optionally including profile and plan curvature.

Cut Fill

Calculates the volume change between two surfaces. This is typically used for cut and fill operations.

Hillshade

Creates a shaded relief from a surface raster by considering the illumination source angle and shadows.

Observer Points

Identifies which observer points are visible from each raster surface location.

Slope

Identifies the slope (gradient or steepness) from each cell of a raster.

Viewshed

Determines the raster surface locations visible to a set of observer features.

Viewshed 2

Determines the raster surface locations visible to a set of observer features using geodesic methods

Visibility

Determines the raster surface locations visible to a set of observer features, or identifies which observer points are visible from each raster surface location.

Zonal Toolset:

Zonal tools allow you to perform analysis where the result is the result of calculations performed on all cells belonging to each input region. A region can be defined as a single region with a certain value, but it can also be made up of several unconnected elements or regions, all of which have the same value. Regions can be specified by raster or feature sets. The raster must be of an integer type, and the features must have an integer or string attribute field. Some area tools define certain properties of the geometry, or shape, for the area input and do not require any other input. Other area tools use the area input to specify locations for which other parameters will be calculated, such as statistics, areas, or value frequencies. There is also a zoning tool that is used to populate specific regions with the lowest value found along the boundaries of the region:

Tabulate Area

Calculates cross-tabulated areas between two datasets and outputs a table.

Zonal Fill

Fills zones using the minimum cell value from a weight raster along the zone boundary.

Zonal Geometry

Calculates the specified geometry measure (area, perimeter, thickness, or the characteristics of ellipse) for each zone in a dataset.

Zonal Geometry as Table

Calculates the geometry measures (area, perimeter, thickness, and the characteristics of ellipse) for each zone in a dataset and reports the results as a table.

Zonal Histogram

Creates a table and a histogram graph that show the frequency distribution of cell values on the Value input for each unique Zone.

Zonal Statistics

Calculates statistics on values of a raster within the zones of another dataset.

Zonal Statistics as Table

Summarizes the values of a raster within the zones of another dataset and reports the results to a table.

Go to Spatial Analyst Tools Part 1

The same topic is available in Arabic from here:

Watch this video from the YouTube channel.

In the same way, as described through this site. Watch the video first, then you can search for any tool by writing its name in the search, the language of the video is Arabic, but English subtitles and any language in the world are available. Good luck and God bless you.

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