ArcGIS Spatial Analyst
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ArcGIS Spatial Analyst is an extension to ArcGIS Desktop that provides powerful tools for comprehensive, raster-based spatial modeling and analysis. Using ArcGIS Spatial Analyst, you can derive new information from your existing data, analyze spatial relationships, build spatial models, and perform complex raster operations.
Statistical Cell In a local function, the value at each location on the output raster is a function of the input values at that location. When computing a local function, you can combine input rasters, calculate a statistic, or evaluate a criterion for each cell in an output raster based on the values of each cell from multiple input rasters. For example, by using cell-based statistics, the user can visualize where a desert may spread over a 10-year period. Based on this information, a developer may choose an alternate location for a new golf course because of the decreased water supply that will occur in the near future for this area. Neighborhood Neighborhood functions create output values for each cell location based on the value for that location and the values identified in a neighborhood specified by the user. The statistics calculated for the neighborhood act as a moving window that scans across the data. Neighborhood statistics can be overlapping or nonoverlapping. Overlapping neighborhood functions, or focal functions, generally calculate a specified statistic within the neighborhood. For example, you may want to find the mean or maximum value in a three-by-three-cell neighborhood. Variations of the overlapping neighborhood statistics function are the high- and low-pass filter functions to smooth and accentuate data. The nonoverlapping neighborhood functions, or block functions, allow statistics to be calculated in a specified nonoverlapping neighborhood. The block functions are commonly used for aggregating raster data to a coarser cell size. The values assigned to the coarser cells can be based on some other calculation, such as the maximum value in the coarser cell, as opposed to using the default nearest-neighbor interpolation. In addition to the predefined statistics and filters, you can also create your own custom filters by specifying neighborhoods and weight values such as edge detection filters. Multivariate The Multivariate Statistics tools allow exploration of relationships between many different data layers or types of attributes. This collection of tools supports supervised and unsupervised classification and principal component analysis. These tools can be used not only for traditional image processing applications, such as transforming a multispectral image into a categorized land-cover map, but also for other statistical analyses of multivariate data, such as terrain stratification or habitat analyses. Surface Surface Visiting every location in a study area to measure the height, magnitude, or concentration of a phenomenon is often difficult or expensive. Instead, you can measure the phenomenon at strategically dispersed sample locations and create a continuous surface by predicting values for all other locations. Input points can be either randomly or regularly spaced or based on some sampling scheme. ArcGIS Spatial Analyst provides inverse distance weighted (IDW), kriging, and spline interpolation, as well as polynomial trend and natural neighbor methods, which can be used to estimate elevation, rainfall, temperature, chemical dispersion, or other spatially continuous phenomena. ArcGIS Spatial Analyst can also create nontraditional surfaces using various other functions. These include the ability to derive a density surface showing the density of objects, such as number of people per square kilometer; distance-based surfaces showing distance to various features, such as retail stores; and other surfaces. Using the derived surfaces, users can then directly display this new data, such as elevation from a terrain surface, or color-coded density areas for crime analysis. The inverse distance weighted and spline methods are referred to as deterministic interpolation methods because they assign values to locations based on the surrounding measured values. A second family of interpolation methods consists of geostatistical methods such as kriging, which are based on statistical models that include autocorrelation, the statistical relationship among the measured points. These geostatistical techniques not only have the capability to produce a prediction surface but also provide some measure of the certainty or accuracy of the predictions. Surface These topographic derivatives give you the power to effectively relate your data to real-world terrain and analyze how variations in the topography will affect the problem in question.  Elevation contours, measured spot elevations, streams, and lakes in this map have all been used as input to create a surface.  Slope Analysis Distance Analysis ArcGIS Spatial Analyst provides several distance mapping tools for measuring straight-line (Euclidean) distance and distance measured in terms of other factors such as slope, current road infrastructure, and land use. Calculating the accumulated cost of traveling, or mapping distance, can provide the user with additional data with which to make decisions. For example, the accumulated least cost of travel to a number of processing mills can be calculated while taking into consideration obstacles to travel. Road and waterway costs can then be assigned to restrict travel. Euclidean distance and cost distance are two main ways to perform distance analysis in ArcGIS Spatial Analyst. The Euclidean distance functions measure straight-line distance from each cell to the closest source. Not only can you determine allocation, but you can also calculate the distance and direction to the closest source. The cost-weighted distance function modifies Euclidean distance by equating distance with the cost to travel through any given cell. For example, it may be shorter to climb over the mountain to the destination but faster to walk around it. The cost allocation function identifies the least costly source cell based on accumulated travel cost. The cost direction function provides a road map identifying the route to take from any cell to the nearest source. Using the cost distance functions, you can create distance and direction rasters and compute the least-cost or shortest path from a chosen destination to your source point. The path distance functions add additional factors beyond the cost surface to account for actual travel distance over the terrain.
Suitability Modeling
Modeling The Raster Generalization tools are used to either clean up small erroneous pixels in the raster or generalize the data to remove or smooth out unnecessary detail. The erroneous pixels may be unclassified data originating from a satellite image, unnecessary lines or text originating from a scanned paper map, or imported data from another source.
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