Smart thermometer company Kinsa allows users of its product to upload their temperature readings along with their location via an app. In normal times, the US Health Weather Map is used to predict influenza trends and compare outbreaks to previous years.
But this year is not normal. Kinsa is suggesting that the Atypical tab on their map might be useful as a predictor for where the next Covid-19 breakout may occur.
As you can see on the map – it looks like the next hotspots after New York and New Jersey are the beaches of Florida (remember the Spring Breakers?) and, for some reason, two counties in Colorado.
Overall trend data for the United States also shows something is amiss.
I’ll be watching this page and update when appropriate.
The Center for Systems Science and Engineering (CSSE) at Johns Hopkins is pulling together data from the WHO and CDC and two Chinese health site, the NHC and Dingxiangyuan into a Google Sheet the drives the visualization above.
You can read more about the map and how they put it together on their blog.
In celebration of the diversity of the 116th congress, I’m sharing this beautiful visualization illustrating the diverse origin of immigrants to the United States over the years.
Data visualization with a poetic take on the data — historical immigration to the U.S. is shown as a set of tree rings (1830-2015). As time advances, the tree grows, forming rings of immigration. Each ring corresponds to a decade. Cells are deposited in layers, and each cell corresponds to 100 immigrants that arrived in that decade from a specific region outside the U.S.
Pedro Cruz is an Assistant Professor in information visualization at Northeastern University and his work above was one of the winners in the Kantar Information is Beautiful awards. If you like this kind of stuff, you really should check out the other winners.
The New York Times data blog, The Upshot has a data-dump piece illustrating the impact of the recession on various sectors of the economy. You can see the full hairball image at the top of the post but what I’ve highlighted is how the digital transformation of publishing has impacted the media business.
Up top you see that jobs associated with the printed word (as in, on paper) have suffered while those jobs involved with digital production have spiked upward. More specific details in the image below.
I had coffee with someone today and we got to talking about the economy in Japan. From what I was reading, the recent consumption tax hikes were hitting the Japanese economy hard but what this person told me was more nuanced. While old manufacturing jobs, the traditional backbone of the economy that Western journalists look to for an indication of health, were suffering (think Sony, Hitachi, Panasonic), newer businesses around design, software development and mobile advertising were booming.
Broad brushstrokes always gloss over the finer details.
I’ve written about Eric Fischer’s work before (Digital Cartography, Digital Contrails). His work takes massive amounts of data and plots them geo-spatially to create beautiful maps. His latest piece shows Twitter and Flickr around the San Francisco Bay.
Red dots are locations of Flickr pictures. Blue dots are locations of Twitter tweets. White dots are locations that have been posted to both.
Interesting to see how much of the activity, especially Twitter, are located along major streets. Looks like a lot of Tweets are being sent from behind the wheel.
Kristian Luoma from Finland pinged me yesterday, curious why I still used Foursquare. When I lived in Finland, we were one of the first people on the ground to use the app and we used to compete on who would retain the mayorship of Helsinki’s Vantaa Airport.
I no longer really care about being the Mayor of someplace but I told Kristian that I still use Foursquare regularly as a personal journal of places I’ve been. I have all my check-ins written to a Google Calendar (I use an IFTTT recipe to do this) so that I can quickly check where I’ve been when needed as a reference.
I also religiously check the recommendations left by others. I find the smaller, explorer-minded crowd on Foursquare more interesting than those on Yelp.
But what I really like about Foursquare is the collective data that you get after logging your location over time. I’ve written about them before (Timemachine, 2010 Infographic, and WeePlaces)
There’s a new one that I missed released for Foursquare day back in April.
What do you do when you have access to the twitter firehose and a top notch geo-visualization artist? Make beautiful maps of course! Gnip and Eric Fischer got together with MapBox and plotted millions of tweets by location, language, and device to come up with some fantastic interactive maps.
The map above is Tokyo and the blue dots represent the location of geo-stamped tweets by people identified by their tweet history as locals while those dots in red are “tourists” who normally tweet from somewhere outside the region. The map tells you a couple of things.
Most tourists are tweeting (photo-sharing?) from the major city centers. I can recognize Shibuya, Shinjuku, Marunouchi, Yokohama, Ueno, Ikebukuro, maybe the Rainbow Bridge?
If you’re familiar with Tokyo, you can see that people tend to tweet while on the train.
This second point reminds me of something I read in Wired a couple of years ago. In an experiment, researches placed oat flakes in a pattern that resembled the major city centers in Tokyo. Then they place a culture of slime mold in the middle and let the culture figure out how best to harvest or “move around” the oat flakes across the pattern. What they found was that the mold grew a series of tunnels that matched the patterns found on the metropolitan rail system.
What works on a large scale also fits a pattern at a much smaller scale.
I’ve written about Location Traces as Art before. Even before the crazy NSA/Snowden tracking scandal broke it was a well-known fact that the phone companies had a wealth of data about us. Aggregated en-mass in platforms such as twitter, this data can paint an pretty amazing picture of the world around us. A couple more maps from the Gnip/Fischer/MapBox collaboration.
It’s a little hard to see but this is a map of the world that shows which type of twitter client is used when a tweet is made. The Red is iPhone, Green is Android, and Purple is Blackberry. Looks like Spain is big on Android (for twitter anyway) while Saudi Arabia, Mexico, and Southeast Asia are Blackberry strongholds (where BBM is huge).
If we look at my neighborhood, you can see that I mostly live in an iPhone town except for a Oakland/San Leandro which is more into Android. I know what you’re thinking, The Atlantic already wrote about it. When you see lots of green, it usually signifies a less affluent area.
Foursquare and Twitter both released experiments that let you look at a visualization of your activity on that service over time. One is art that happens to convey information and tell a story of your travels thru time and space. The other is a functional dashboard that is designed to give you and idea of how effective you are in getting your message out to others.
The Fourquare visualization is sponsored by Samsung and is clearly branded so. It’s a well done, if slightly solipsistic, eye-candy. Foursquare got their money up front on this one and used it so their users (including me) would have something beautiful that they wanted to share along with their sponsor.
The Twitter timeline (read The Next Web for details on how to get to yours) shows mentions and follow/unfollow activity along with details about specific tweets and how they performed. There’s also a screen that shows your follower growth over time along with some basic demographic information. It’s all business though, a reason for the normal folks to login and poke around their ad platform and think about spending money on some of twitter’s ad products. Tucked at the bottom of their graph is a sober message reminding us that we’re browsing through a business tool.
And there, my friend, is the difference between these two efforts:
Foursquare: Zoom through time and space as you visualize all your check-ins
Twitter: The data reported on this page is an estimate, and should not be considered official for billing purposes.
A quick glance at the wordle tag cloud analysis and it looks like wordle favored the lengthy Sarah Lacy piece on Facebook/Netscape. A longer piece means more words which would explain why Facebook, IPO, and Netscape are so big in word cloud.
Is such a view useful? Is there some way to improve this?