I’ve been working on base cartography for the research area in Rwanda. Unlike here in Cleveland, we have some great topography to work with, so we can leverage that for basemaps. But, it’s such a beautiful landscape, I didn’t want to sell these hillshades short by doing a halfway job, so I’ve been diving deep. Background First, some legacy. I read three great blog posts … Continue reading Gorilla research in Musanze, Rwanda: Hillshades continued
In previous posts here1, here2, and here3 discussed a then and future trip to Rwanda to help with GIS and gorilla research. No more in depth write up yet, but I’ve been working on some of the cartographic products to show in the background of maps. Since Rwanda is so beautifully hilly (read: mountainous) and the research is focused on the Virunga Mountains (volcanoes) themselves, … Continue reading Gorilla research in Musanze, Rwanda: Hillshades!
Last week, post Boston Code Sprint, I spent a couple of hours playing with bee data, specifically bee keeper data for Norfolk County Massachusetts. My friend Eric (a bee keeper of 4 hives in said county) says that worker bees can fly as far as 3 miles for nectar, but after that approximate limit, they hit diminishing returns relative to the calories they burn. Proximity … Continue reading Way beyond red-dot fever– bees hives and overlapping home ranges
In a further exploration of using PovRay to do fast calculation of Voronoi polygons, let’s look to a real stream system as an example. Here’s where the magic comes out, and where medial axes are found. Here’s the povray code: Continue reading Fast Calculation of Voronoi Polygons in PovRay– Applied
Yet further abuse to follow in the application of PovRay for spatial analyses– be forwarned… . Calculating Voronoi polygons is a useful tool for calculating the initial approximation of a medial axis. We densify the vertices on the polygon for which we want a medial axis, and then calculate Voronoi polygons on said vertices. I’ll confess– I’ve been using ArcGIS for this step. There. I … Continue reading Fast Calculation of Voronoi Polygons in PovRay
From earlier post: “I’ve managed to pilot most of a fast high resolution landscape position workflow with PovRay as my magic tool. The final steps I hope to pipe through PostGIS Raster. In the meantime a screenshot and description: blues are riparian, raw ocre, etc upland categories, grey is mostly flat lake plain and mid slopes, all derived from just a high res DEM input … Continue reading Landscape Position: Conclusion? (part 2)
I’ve managed to pilot most of a fast high resolution landscape position workflow with PovRay as my magic tool. The final steps I hope to pipe through PostGIS Raster. In the meantime a screenshot and description: blues are riparian, raw ocre, etc upland categories, grey is mostly flat lake plain and mid slopes, all derived from just a high res DEM input (no hydro lines … Continue reading Landscape Position: Conclusion?
I typed that last one too quickly– too many typos, but my wife says I’m not supposed to revise blogs, but move on… . So, for clarity, let’s talk a little more about McNab indices. Field-derived McNab indices are a measure of average angle from the observer to the horizon (mesoscale landform index), or from the observer to another field person a set distance away, … Continue reading Landscape Position and McNab Indices (cont.)
Ok, your average terrain-only based viewshed (view is from a road to the southeast, viewshed is in cyan): Note that based on these estimates, we should be able to see most of the valley walls from this little slice of road. I don’t buy that. That section of road is pretty closed in with trees. Let’s add them: As you may see, just a little … Continue reading Povray Viewshed with Trees (finally) (cont.)