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 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.
So, let’s say you need a map. And let’s say you don’t know much about this whole GIS thing. Wouldn’t it be nice if you could just take your spreadsheet of addresses or lat/long points and make a map, just like you would make a chart or graph? Well, the folks at ESRI, MAPCITE, and the LibreOffice Community all thought so, and have developed spreadsheet extensions to help you do just that. This has huge potential for spatially empowering the spreadsheet-enabled public, as well as reserving valuable GIS analyst time for more complex projects.
These extensions have a huge range in terms of number of capabilities and end-user cost (free to as much as you care to pay). Over the next few weeks, my mission is to compare these mapping extensions and see how they stack up. Which one is the “best”? What do you get for your money? Just how good of a map can you make with a spreadsheet, anyway? I’ll be exploring these questions, one extension at a time, and summarizing the results in a final post at the end. Stay tuned for review #1: MAPCITE’s Excel®™ Add-In.