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

Thinking back, the ARC/INFO INTERSECT command is probably where I fell in love with GIS. I think what I loved more than the function itself was the circle and squares they used in the old ARC/INFO user manual. The new user manual sort of replicates that, but not as nicely as the original I'm afraid: Once again, I digitized my own example to look like Esri's: Let's look at the query to perform the topological intersection of the two layers. The key here is using the PostGIS

Having just tackled Symmetrical Difference, we'll move to the murderer's row of topological operations: ERASE, INTERSECT, IDENTITY, and UNION. Esri has a nice description of the Erase function , and I actually created a similar looking layer to the example they use: The code is relatively straightforward: the key to this query is in line 3 where we use ST_Difference to find those areas that don't intersect one another. From there, in lines 6 and 7 we perform a join on the a

Today we'll start exploring some of the topological functions in ARC/INFO such as Symmetrical Difference, Erase, Intersect, Identity, and Union. These are known as the overlay toolset in ArcGIS Pro . But, years and years ago, these tools were the bread and butter of what made ARC/INFO such a powerful tool. Funny side conversation and name drop In 1990 I was in Redlands, CA benchmarking ARC/INFO.  We had a really “difficult” task for them: overlay 25,000 hexagons with 25,000

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

We'll continue our look into ESRI commands in PostGIS by examining another simple task: calculating the Mean Center of a data set. To illustrate these concepts, I will be showing examples from my book Statistical Problem Solving in Geography .  In my textbook, I have an example of 7 data points: So, we can recreate the same results with the following SQL: this query gets the average X and Y coordinate values for a point layer by first converting the geometry into X and Y val

Our previous post showed how to extend the mean center of a geographic dataset to incorporate the weighted mean center in PostGIS.  Today’s post examines the SQL code necessary to generate the central feature for a geographic data set.  Recall from my textbook An Introduction to Statistical Problem Solving in Geography  (third edition), the formula and computation of the Central Point. This is where the power of spatial SQL gets used.  As you can see, finding the central poin

If you've worked with ARC/INFO (or any GIS really), you often have to make a determination about the coordinate system of your data: if you want to reproject the data into another coordinate system, you use PROJECT , but if you want to simply overwrite the coordinate system of a layer, you would use DEFINE PROJECTION . PostGIS allows you to do both commands rather easily. So today, I'll show you both of them. I often find that I need to do these commands a lot, especially

Rather than starting with the easiest or hardest function, I thought I'd start somewhere in the middle - for GIS experts I don't want to bore you, and for novices, I don't want to blow you away with something more complex. But, after this post, we'll work through some easy queries and move on to more advanced queries. For those of you who used ARC/INFO, you probably found that the NEAR function was one of your best friends.  I used it every week to transfer attributes from

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This semester was quite busy, as I oversaw 9 Master's projects in our MS in Geographic Information Science Management program (MSGISM). ...

This semester was quite busy, as I oversaw 9 Master's projects in our MS in Geographic Information Science Management program (MSGISM). ...