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Map of the Caprivi Link Interconnector between Zambia and Namibia. Copyright ABB.
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This map displays bloom data collected during harmful algal bloom sampling in the Indian River Lagoon in June 2014.
Map of Central Switzerland using QGIS 3.6 and Blender. That's Interlaken between the two lakes.. Just realised how it got its name... :-)
SRTM raster. Using adaptive subdivision surface allowed me to upscale rasters to 14k x 12k. Used QGIS/gdalwarp to prepare heightmap and map tiles. Approx 2x vertical exaggeration. Using top-down orthographic camera.
Map tiles from OpenStreetMap (wmflabs OSM no labels)
Map layer shader uses a 95/5% mixture of Diffuse/Ambient Occlusion. It turns out ambient occlusion works a bit like 'sky view factor' in GIS, and is a LOT faster to compute in Blender, but is less pronounced. Used two soft blackbody sun lamps, NW 4500K, SE 9000K (shadowless fill light)
This map shows ca. 2020 data of where there are 6 or more through lanes on the U.S. Interstate system. Map data from FHWA's National Network dataset.
Taken and originally posted in 2015.
A girl consults a Metro (subway) route map outside the Cité station on the Ile de la Cité, near Sainte-Chapelle and Notre Dame.
I'm closest to the Cajon Fire (13), which is near Lytle Creek, 15-20 minutes away. I have friends who live there and the 10 might be burning the cabin as we speak.
I have so many connections on LinkedIn it became almost unusable and started becoming a repository of business contacts. This visualization, though mesmerizing to me at first and to others as well, is interesting though the groups are spread around time, location, profession and education.
The top and left is more related to my design expertise, the lower right is more my personal life.
Also the edges are filled with people I barely know. Then again in the center it is often the same.
Collection of old maps scanned from books and other print sources Download them all at Photoshop Roadmap.
This visualizations shows the geographic location of 1,139,441,531 ip adresses. The locations are grouped by city, divided into four categories according to the total number of ip adresses belonging to a city. The categories are net-metropoles (more than 10,000,000 IPs), net-capitals (1,000,000 up tp 10,000,000 IPs), net-cities (100,000 up 1,000,000 IPs) and net-villages (fewer than 100,000 IPs). IP adresses that cannot be allocated to a city were left out.
I created this visualization using ActionScript, based on my classes for map projections and polygon maths, which you can download for own usage. The data was extracted from the free GeoLite-City database by MaxMind.