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Every now and then a dataset comes along that just has to be mapped. This is one of those times.
Bigfoot. Sasquatch. Skookum. Yahoo. Whatever you call it, the towering man-like ape is a folklore staple. From stories of Yeti in the Himalayas to Wildmen in the Pacific Northwest, people have been talking about and trying to find the creature for ages. Occasionally, some form of evidence – like Patterson’s famous 1967 film – emerges and either feeds our fascination or gets dismissed as a hoax. In either case, it’s easy to see why believers search for proof and skeptics remain doubtful.
Through archival work and reports submitted directly to their website, the Bigfoot Field Researchers Organization has amassed a database of thousands of sasquatch sightings. Each report is geocoded and timestamped. Occasionally, even photos and videos of the alleged evidence are included. I’m not quite sure how I stumbled across this, but I’m glad I did.
After crawling the data and converting it to a more convenient format, I mapped and graphed all 3,313 sightings that were reported from 1921 to 2013:
Traffic maps are among the most widely used maps available today. Whether they are accessed through a web browser, an app on your smartphone, or an in-vehicle navigation system, many popular maps include a traffic layer – and for good reason. The proliferation of live traffic information is extremely beneficial. With real-time congestion data we can avoid delays, plan better routes, and get to the places we need to be when we need to be there. Traffic maps simplify this process and clarify the costs and benefits associated with various routes.
This year I was selected to represent the 2013 Big Data Social Science IGERT in the annual poster and video competition hosted by the National Science Foundation. The news came as both an honor and a challenge.
Spatial is special. Accordingly, there are many considerations that need to be made prior to and during the task of visualizing spatial data. Chief among these considerations are your goals and what it is you’re actually trying to accomplish with a map. Are you communicating results you’ve already found? Enabling users to explore the data to discover information on their own, possibly generating new hypotheses?
Last week I had a blast visiting Santa Barbara and Los Angeles. While I enjoyed the wineries and sunshine, the annual meeting of the Association of American Geographers was the highlight of the trip. With so many talks on cartography, GIS, and cyber science, it was my favorite AAG conference yet.
What made AAG even more enjoyable was the acknowledgement of big data within GIS (though I’d argue we’ve all been doing “big data” for decades now).
As part of the NSF IGERT in Big Data Social Science, Pond Lab hosted a public poster session this afternoon.
A total of eleven different posters were presented, covering topics in big data that ranged from text processing and visualizing uncertainty to computing voter sentiment and analyzing data of patients’ heart rates.
I presented work on a GeoVISTA project that leverages geovisual analytics and Twitter to help users make sense of the spatiotemporal variation of place and topic mentions within hundreds of millions of tweets.