Distance & Context Operators

• Calculate the euclidean distance/proximity of each pixel to the nearest of a set of target pixels.
• Calculate spherical distances on the surface of the earth from designated features using spherical trigonometry.
• Generate a distance/proximity surface where distance is measured as the least-cost distance (in terms of time, money, etc.) in moving over a friction surface.
• Generate an image of buffers of a given width around features.
• Compute an anisotropic cost surface that incorporates frictions with different strengths in different directions. Also allows the specification of an isotropic friction image for omni-directional elements. 
• Calculate the dispersion of materials under the influence of anisotropic forces and frictions.
• Compute the resultant force vector (as a magnitude and direction image pair) from two input force vector image pairs. Decompose a force vector (as a magnitude and direction image pair) into X and Y component images. Also compose X and Y component images into a force vector image pair.
• Find the shortest path between one or more specified targets and the source features of a cost or distance surface.
• Assign every pixel to its nearest source feature using a distance or cost surface.
• Relocate features to their nearest linear feature for purposes of network analysis.
• Produce Thiessen polygons (a Voronoi Tessellation) about a set of irregularly distributed points.
• Calculate slope gradient, aspect and analytical hillshading images from a surface model.
• Convolve (strictly correlate) an image with a variable-sized digital filter. Mean, gaussian, median, adaptive box, mode, Laplacian edge-enhancement, high-pass, Sobel edge detector and user-defined filters are accommodated.
• Evaluate relative richness, diversity (entropy), dominance index, fragmentation index, number of different classes, center versus neighbors and binary comparison matrix pattern measures.
• Perform texture analysis of an image, including variability, fractal dimension, frequency and edge analysis using convolution filters.
• Assign unique identifiers to each contiguous grouping of like-value pixels in an image.
• Create an image of areas visible from one or more viewpoints, given an elevation model and viewer height.
• Determine the boundaries of watersheds and subwatersheds given a minimum subwatershed size or a seed image.
• Determine the surplus/deficit balance between supply areas and point demand centers.
• Create new images representing the X and/or Y coordinate of each cell center.