New tool - Toolbox to Create Squish Maps

 I've got a new tool to create "squish maps" which are maps in which the location of polygons have been moved around in order to improve the map scale.  You can download this tool by clicking HERE.

Creates squish maps to better show detail in rasters that are seperated by geographic space (see the example in which I "squished" the Hawaiian Islands together in the documentation). Two polygon shapefiles are needed. One has the original true location and the second the edited cartographic locations. The tool then takes a raster and shifts the cells to match the new polygon's location.

Blended image to classification in ArcMap

A while back I did a vegetation classification in ArcMap using data collected from a drone. The method was fairly simple and I was pretty pleased with the result. I wanted to simultaneously display the image and the classified map. I had seen some pretty nifty blended images on the web that were created in Photoshop, but since I don't have Photoshop on my computer I opted to try to figure out how to do this in ArcMap. In general, I followed the steps to this tutorial - https://blogs.esri.com/esri/arcgis/2008/10/14/fade-to-white-background-effect/

However, I took some liberties and deviated from it a bit. My classification was a raster so in order to accommodate that I sliced the raster up into discrete slices going from north to south. For each raster I set the transparency to increase by 7%. Likewise I did the same with the segment outlines (the black lines).

Below is the resulting image. In case you are interested in the actual vegetation here is what each color represents: blue = sagebrush, green = other shrub, pink = cheatgrass+forbs, tan = bare soil, and gray = dead shrub (rare in this image). The UAV image was take by AboveGeo near Doyle, California. The upper portion of the image is intact sagebrush desert while the lower part was previously burned.

Creating non-spatial and "not quite true" spatial figures in GIS

I recently was tasked with creating figures to show some variables collected along a trapping grid. The problem with these data were that the trapping locations were very close to one another yet the distance among the trapping grids was not.  As a result, it was very nearly impossible to show meaningful differences at the scale of the entire study. In the figure on the right you can see the location of the trapping grids.  Each grid had 64 stations arrayed in 16 columns and 4 rows. I've removed any geographic information that may suggest the location of this study for the sake of privacy.

The solution?  What I call a "not quite true" spatial map.  In the figure below we see that each bold box shows a trapping grid  and each smaller box is a trapping station. The colors represent the intensity of some value.  It may be the number of animals caught in the traps at the locations or some habitat variable having to do with plant cover or soil type. The reason why it is "not quite true" spatial is that distances among trapping grids is much larger than they are in real life and distances between individual traps isn't always exactly even. Nonetheless it shows spatial patterns in a succinct and compact form.

To make this map all I had to do was create relative row and column X and Y coordinates. That table was then imported into ArcMap as X and Y data. In this example there were a total of 36 rows and 48 columns. Although this workflow could have taken place using R or python I found that it was quite easy to accomplish in ArcMap.

This is a nice reminder of how GIS can be a powerful tool for all sorts of visualizations, not just for maps. Using GIS we can very easily change color schemes using different kinds of classification s (i.e. natural breaks, quantiles, equal intervals, etc.), edit individual lines and polygons, convert from raster to vector formats.  Some of this stuff is tedious to do in other types of software.

We're probably all familiar with some examples of "not quite true" spatial maps.  Subway maps are a  prime example.  They are designed to show relative space, but distance isn't always accurate in the true geographic sense.  However, we can also take raster GIS outside of the realm of normal geographic space and actually use GIS for displaying things like time series or even data space.  More on that later.