Have you ever wondered why cities rise and fall, as do empires?
“Charting Culture” is an animation that examines when and how “notable” people are born, stay or migrate, and where they die. Over 12,000 notable historical figures were tracked and the migrations display how people moved from city to city, empire to empire, between 600 B.C. to 2012 A.D.
This animation distils hundreds of years of culture into just five minutes. A team of historians and scientists wanted to map cultural mobility, so they tracked the births and deaths of notable individuals like David, King of Israel, and Leonardo da Vinci, from 600 BC to the present day. Using them as a proxy for skills and ideas, their map reveals intellectual hotspots and tracks how empires rise and crumble
The information comes from Freebase, a Google-owned database of well-known people and places, and other catalogues of notable individuals. The visualization was created by Maximilian Schich (University of Texas at Dallas) and Mauro Martino (IBM).
Did you find anything unusual or surprising about this video?
I was fascinated by the data and by the visualization of it. However, I found it very Euro-centric. The authors did show Japan, briefly, but ignore the US South, the Middle East (the birth place of numbers 0-9), China, and the entire African continent.
Have you ever wondered why data visualization matters? Do you prefer to look simply at numbers in a spreadsheet, or would you rather seen an image of that data?
Many people learn better visually. We all have to crawl through a great deal of data each and every day as well as process the meaning of all of this information. So, why bother with data visualization at all?
To understand that, it helps to understand the principles we strive for in data journalism. At The New York Times, we strongly believe that visualization is reporting, with many of the same elements that would make a traditional story effective: a narrative that pares away extraneous information to find a story in the data; context to help the reader understand the basics of the subject; interviewing the data to find its flaws and be sure of our conclusions. Prettiness is a bonus; if it obliterates the ability to read the story of the visualization, it’s not worth adding some wild new visualization style or strange interface. (From: http://www.niemanlab.org/2011/10/word-clouds-considered-harmful/.)
What do you think are the key concepts for a clear visualization of data? What do you consider a bad info graphic? Do you have a favorite infographic? (This could be a favorite because it is excellent, or a favorite because it is so awesomely bad.)
How many times have you moved from one house or apartment to another in your lifetime? Do you think you know what it takes to move objects, pets, and people? I’ve moved plenty of times in my lifetime, but I am eager to hear of any information that can help me tame the moving beast.
I was thrilled to find an infographic on The Mechanics of Moving. Patrick Garvin created the infographic after helping a colleague move to a new house. He based it on his own notes, plus the comments and feedback of moving company employees. He writes:
In the summer of 2007, I helped one of our editors move his family to a new house near the beach. I noticed that when helping carry large items such as tables and couches that I was designated as the person who had to walk backwards. I mused aloud that there surely was an ettiquette that dictated moving protocol. What started as a joke ended up as a scribble in my graphics notebook and in turn became a full-page graphic a month later. I contacted a few moving companies to get their takes on the mechanics and manners necessary for a sucessful move, then wrote all the text myself. It came out a few weeks before school started for the fall, timed perfectly with the droves of college students who would be moving into the dorms.
The infographic below provides information on how to pack, how to stack furniture in a van or pod, which person of which height should be on which side of a piece of furniture when carrying it, the physical qualities of a good mover, and so forth and so on.
[If you would like to view a more readable version of the infographic, please click on the image below, and then click on it again to reveal the larger image.]
Did you learn anything new from this visualization of the mechanics of moving? Do you have any moving tips or tricks you’d like to share? Is there anything on the infographic that you would delete? Is there any missing information you would add to it?
Have you ever wondered in what locations people swear more or less versus other geographic locations? I can’t say I have, either. Having said that, sometimes too much data can be a wonderful thing — if one has a sense of humor, that is.
Clicking on the above map will take you to the full PDF of the map, which is located on the web site of the North American Cartographic Information Society.
Daniel Huffman is responding to sincere comments about the map on his blog, Cartastrophe.
Are you surprised by any of the information presented in the map? Do you think that Twitter users would swear more or less than non-Twitter users, or do you believe the sample population are representative of the habits of the larger population (both Twitter users and non-Twitter users)?
How can you determine if two fingerprints are merely similar or are an exact match? Is forensics as practiced currently, skill and art — or science?
I was surprised to learn from Sargur Srihari that forensics is not as scientific in its methods as one might think from watching the various TV shows. Neither are the practitioners as unbiased as one might prefer when examining the data. Srihari is working to improve forensic methodology. He writes that one way to improve the methodology is to use “pattern recognition and other computational methods [that] can reduce the bias inherent in traditional criminal forensics” [Srihari, 2010].
He wrote an article in the December 2010 IEEE Spectrum called Beyond C.S.I.: The Rise of Computational Forensics in which he gives an overview of how computational forensics can improve the methods and results of forensic investigation. For example, in the image below, he describes how computers are used to compare a shoe print from a crime scene against a database of known shoe prints.
To view a larger image, please click on it and click on it again on the new page.
Do you believe that computers can make forensic investigations more science than art and skill? Why or why not?
How do you manage all of the distractions from the data and information thrown at you from social media, email, the Web, chat and [insert name of app here]? Well? Not-so-well? Do you focus on one task at a time, or do you multi-task?