Latest Contributions by ColeHeap

In ArcMap the functionality of 3D Analyst for making a profile graph was more robust and less limiting. A user could generate a profile graph of just about any raster regardless of the raster data whether the raster depicts elevation or not. For example, one may deal with infrared flyover data that is used to identify pockets of heat that are near-surface. Different snapshots through time can show seasonal swings of ground temperature, as well as the dissipation of a potential geothermal source. In ArcMap one can interpolate a line and then make a profile graph all in the click of several buttons! And the data set being used did not have to have elevation values in feet, meters, kilometers, or kitty cats! It did not matter what the units were as the subsequent profile graph could be customized. The output of the graph units can better be defined and depicted base on what the user is dealing with. This functionality should be brought into ArcGIS Pro so users do not have to default to ArcMap to be able to complete their non-elevation-centric exercises and analyses.  ... View more

Other names this algorithm goes by include liner projection or projected slope. This is a surface estimation method created by Davis (1987). This is a two-part procedure, in which a weighted average of slopes projected from the nearest neighboring data points around each grid node are used to estimate the value at the node. Initially, the slope of the surface at every data point must be estimated. The nearest n neighboring observations around a data point are found and each is weighted inversely to its distance from the data point. A linear trend surface is then fitted to these weighted observations. The constant term of the fitted regression equation is adjusted so the plane passes exactly through the data point. The slope of the trend surface is used as the local dip. If at least five points cannot be found around the data point or if the set of simultaneous equations for the fitted plane cannot be solved, the coefficients of a global linear trend are used to estimate the local slope. The slope coefficients are saved for each data point.   The second part of the algorithm estimates the value of the surface at the grid nodes. A search procedure finds n nearest neighboring data points around the node to be estimated. The X,Y coordinates of the grid node are substituted into each of the local trend surface equations associated with these data points, in effect projecting these local dipping planes to the location of the node. An average of these estimates is then calculated, weighting each slope by the inverse of the distance between the grid node and the data point associated with the slope. If a data point lies at or very near a grid intersection, the value of the data point is used directly as the value of the grid node.   This method is great for maintaining natural geologic features, such as steep slopes (generally with a dip greater than 45 deg). And, as outlined above, is an excellent method for maintaining a trend from an area with great control and projecting that trend into an area of sparse, limited, or no control. ... View more