Two of my favorite things: my trusty ARC/INFO mug from 1990, and my trusty dog Pepper, painted in the style of Andrew Wyeth's Homer . At the recent FOSS4gNA conference , I had the pleasure of hearing about some of the new features in PostGIS. One such set of features were the Coverage related features ( ST_CoverageInvalidEdges , ST_CoverageUnion , ST_CoverageSimplif y ). These functions made certain adjustments in the data structure to allow for faster spatial operations. L

In previous posts we determined the nearest neighbor and replicated the NEAR command. Sometimes, the nearest neighbor isn't enough. Perhaps we want the three nearest neighbors. We'd call that k nearest neighbor , with k , being the number of nearest neighbors we want. Looking back at the figure from my textbook An Introduction to Statistical Problem Solving in Geography , we have our 7 points: Instead of the nearest neighbor, we might be interested in the 3 nearest neigh

Our previous post showed how to identify the central feature in a geographic layer using PostGIS.  Today’s post will show how to calculate the nearest neighbor index. Recall from An Introduction to Statistical Problem Solving in Geography  (third edition), the formula and computation of the nearest neighbor index. This isn't rocket science, but you'll start to see that things will start to get a little more complex as we build this one out. Again, let's do this in stages.

In my previous post we saw how easy it was to compute the mean center of a geographic dataset in PostGIS.  Today’s post examines the SQL code necessary to generate the weighted mean center for a geographic data set.  Recall from An Introduction to Statistical Problem Solving in Geography  (third edition),  the formula and computation of weighted mean center and the 7 point data set used. We'll solve this one in steps, as well, so you know how the SQL functions can build on on