Ah, uncertainty. We don’t often talk about it, but no data is just absolutely rock-solid truth. That is not the nature of data. Data, and the maps we assemble from them, are models of a complex and multifaceted universe. Even the best most curated content has some amount of uncertainty due to collection unknowns, processing\/editing\/aggregation necessities, the problematic Boolean geometric nature of vector data representing soft or fuzzy phenomena, and just good old universal statistical honesty.<\/p>\n
In this example, I’m using tornado paths as a catch-all phenomenon for illustrating uncertainty. I got this notion from Dr. Joseph Kerski<\/a>, who uses tornadoes as an excellent example of a phenomenon who’s historic data has varying levels of…<\/p>\n There are all sorts of reasons to consider uncertainty when communicating a spatial phenomenon. And there are lots of ways to visually represent that uncertainty in your map, if you choose. To dig deeply into this topic and learn about ways of modeling uncertainty, check out the related work of Ashton Shortridge<\/a>.<\/p>\n In the meantime, let’s dig into just a handful of ways to brew some visual uncertainty into overconfident vector line features.<\/p>\n"},{"acf_fc_layout":"youtube","start_time":"0","end_time":"","youtube_video_url":"\n