Occasionally, I like to go through the QGIS plugins and see what’s new. There are all sorts of handy tools in there, but one in particular caught my eye this time. The Historical Map plugin, by Nicolas Karasiak & Antoine Lomellini, takes on a specific challenge: extracting data from old maps. Of course, you can always digitize this data, but it can be quite time consuming. The plugin leverages image classification techniques to speed up the process. The nice thing about historical maps (such as USGS topos) is that there are usually fairly discrete color codings. Discrete colors takes a lot of the guesswork out of the image classification process, but the techniques can be pretty intimidating if you haven’t done them before.
A popular cartographic element is the venerable hillshade. With a good hillshade and a transparent overlay, you can add a lot of topographic context to your map without being overly distracting. Most of us have probably used a bare earth hillshade before. These are great if you just want to look at the underlying terrain, or if you are covering a wide area.
For close-in work, creating a hillshade from DEM that includes the vegetation canopy can make things much more interesting. However, if you look closely at the canopy hillshade, you’ll notice that many of the terrain features no longer stand out, even where there is little to no vegetation. This is because individual trees and shrubs create more steep areas, which proceed to hog the dark end of the stretched histogram. So what’s a cartographer to do? Continue reading “Taking the Best of Both Worlds for a Better Hillshade”
As some of you know, most of my life for the last two years has revolved around predicting the locations of small headwater streams that are not captured by the venerable National Hydrography Dataset. The NHD is a fantastic resource overall, but it does have its shortcomings, particularly in small, ephemeral channels. Usually, the limitations are fairly predictable, as they result from the intersection of limits of scale, subjective cartographic choices, and deliberate design rules that make a map good for one purpose but not necessarily others. Sometimes, those shortomings manifest themselves in unexpected ways.
So, ArcGIS 10.2 was released last week, with a bunch of exciting new features. Among other things, they turned on multi-core support in a few spatial analyst tools, made it easy to create Python add-ins for the GUI, and have posted code templates on GitHub. All of these features are very exciting, but what I’m most excited about is support for Spatialite databases.
In light of the recent noises coming out of North Korea, I wasn’t too surprised to see an article about their missile ranges. However, I was dismayed by the following infographic:
Ever been stuck at the airport and wondered just where your flight was anyway? Or maybe you wanted to know just how many planes were overhead? A friend shared this awesome site with me that displays live radar info on airline flights, showing you where they are in the world. It’s incredible to see just how many flights are in the air!
In addition to being an informative visualization, if you click on a flight, a sidebar appears with details on the airline, the origin and destination, and position in three dimensions. Clicking on a flight also displays the flight track it has followed to this point. All this is great, but the real icing on the cake is the cockpit view. The website leverages Google Earth to simulate what the view from the cockpit might look like. It’s a bit silly, but how cool is that! Click and enjoy!!
In flow accumulation modeling, a common bit of data needed is the average upslope value of a parameter of interest, such as slope or curvature. In most cases, this is simply a matter of calculating a flow accumulation weighted by the parameter in question, then dividing this by the unweighted flow accumulation. But what if you want average upslope aspect? Since aspect is measured in degrees, and a value of 0° and 360° are the same, a simple arithmetic mean will be useless. So, what to do? In this post, I will walk you through the process of calculating the mean upslope aspect using ArcGIS, and leave you with a working python script that will automate the process.