Nathan Yau

If you liked this, you might also enjoy: http://flowingdata.com/ (Great. It seems I've turned into a recommendation bot :)

Haha I wrote about this back in April! http://mapblog.wellingtonstravel.com/post/49107340201/this-is-an-amazi… If you guys like maps, I run an awesome map blog (I'd like to think). My latest is about 8-bit maps: http://mapblog.wellingtonstravel.com Also I really like this, which has tons of maps. The latest is Coffee Place Geography: http://flowingdata.com

Unfortunately, InformationIsBeautiful.net basically exists to be an advertisement for the author's book, The Visual Miscellaneum. Which is a very pretty book--I own it--but not really full of informative infographics. There's some original content in the blog, but far too much of it is reposts of graphics from the book. For a better (or at least more varied) selection of infographics I like: * Chart Porn (http://chartporn.org/) * Flowing Data (http://flowingdata.com/) which also covers datavis software * Information Aesthetics (http://infosthetics.com/) * We Love DataVis (http://datavis.tumblr.com/) (edit: formatting)

I'd recommend this Edward Tufte book for visual data design, it's very good: http://www.amazon.com/Visual-Display-Quantitative-Information-2nd/dp/0… Also, FlowingData is a great site for data visualization: http://flowingdata.com/

"Years You Have Left to Live, Probably"[1], on Nathan Yau's FlowingData[2], reminded me of Lanes. I stumbled on it, selected my gender and age, and the animated distribution sampling began. And the first-ish sample was a 'dead this year', immediate ball plummet. That... left an impression. [1] https://flowingdata.com/2015/09/23/years-you-have-left-to-live-probabl… [2] https://flowingdata.com/

My favorite pie chart: https://flowingdata.com/2008/09/19/pie-i-have-eaten-and-pie-i-have-not… My second favorite: https://xkcd.com/688/

Someone please use this data to make a clone of Weather Spark. Weather Spark came way before Dark Sky. As an ameature professional weather nerd I feel it was the best weather site ever! You could do so much with the data in one unified dashboard style screen! Especially with the integrated historical datasets. Examples: http://3.bp.blogspot.com/-B1LupTaXvSo/TcBNECP0A0I/AAAAAAAAEik/OqXYwhgh… https://flowingdata.com/wp-content/uploads/2011/03/Weatherspark-everyt… https://media.licdn.com/dms/image/C5112AQEuHVOGOd-5fQ/article-cover_im…

One of my favorite graphics is this one detailing the evolution of the Pepsi logo vs. the Coca-Cola logo: https://flowingdata.com/2009/08/13/pepsi-and-coca-cola-logo-design-ove… Of course it was over-dramatized for effect, and the truth emerges further down the page. But it's close enough to true that it stops and makes you think.

Here's a cite: https://www.cambridge.org/core/journals/psychological-medicine/article… Of course it's not the only thing, and can be greatly mediated by other factors. From the book Age of Em, citing that and other sources, "people today tend to be both happier and more productive when they have jobs, autonomy at work, health, beauty, money, marriage, religion, intelligence, extroversion, conscientiousness, agreeableness, and non-neuroticism." I brought up Lisa Simpson because there's this meme: https://flowingdata.com/wp-content/uploads/2008/06/lisa-simpson-graph1… It's a popular take, but it's wrong.

The chances of one getting divorced vary greatly depending on one’s income and ethnicity: https://journeys.dartmouth.edu/censushistory/2016/03/04/divorce-rates-… https://flowingdata.com/2021/05/04/divorce-rates-and-income/

> This study captures P(running into a profession) x P(preference for that profession). I would love to them model P(running into a profession), so that we could get the much juicer preferences of each group for various professions. The page links to a different visualization of the same data, [0] which attempts to control for the prevalence of a job within the population. Maybe it doesn't go far enough for you but that's a pretty big first step in that model. [0] https://flowingdata.com/2017/08/28/occupation-matchmaker/

Not apparent in the original chart, but in the follow-on bubble chart there seems to be a weirdly strong connection between fire inspectors and pumping station operators. https://flowingdata.com/2017/08/28/occupation-matchmaker/ They both seem like very small groups, though, so probably just an amusing bit of statistical noise.

Probably not worth bothering your elderly parents over. After 60 your risk of death from cancer starts declining, because other causes dominates your risk of death from cancer. By 90 the cancer risk is less than 10%. https://flowingdata.com/2016/01/05/causes-of-death/ I would like to see the paper - there are so so many obvious confounding factors (age, tastes, diet, wealth, social group etcetera). Past experience about “miracle” antioxidants (ergothioneine in this case) has shown me how much bollocks there is in popularised science. I would also be interested how robust the results were to removing each dataset. One fraudulent paper could ruin their conclusions?

The clothing industry could really use some a shared, standard set of measurements. Even though bodies are all different, most of them have a lot in common. When three pairs of pants with the same waist "size" can vary in circumference by three or more inches[0], it's all but worthless. [0] http://flowingdata.com/2010/09/30/advertised-vs-actual-waistline/

For those that are surprised that it's "so expensive", LEGO is usually very expensive (and totally worth it.) This is only 119 euros/dollars (same for both) and I know I'll be buying it both for myself and as a gift for my best friend (unless she gets it for herself first.) LEGO sets have an average price per piece of around 10 cents, which has been pretty stable since the late 70s [0]. With 1969 pieces (which is a fantastic number) you'd expect the set to cost $216.59 at the current average of 11 cents per piece, so this is a fantastic deal. It's also a meter high (about 3 foot 4 for the Americans) which is really fantastic. [0] https://flowingdata.com/2013/02/07/analysis-of-lego-brick-prices-over-…

Flowing Data http://flowingdata.com/ FlowingData explores how statisticians, designers, data scientists, and others use analysis, visualization, and exploration to understand data and ourselves.

These are the top blogs I follow in the data visualization space: http://www.edwardtufte.com/tufte/ http://visual.ly/ http://www.thefunctionalart.com/ http://hbr.org/special-collections/insight/visualizing-data http://chartporn.org/ http://www.excelcharts.com/blog/posts/ http://flowingdata.com/ http://datavisualization.ch/ http://visualization.geblogs.com/ No matter how the tools change with time, I am able to keep up to date with the field.

Subscribe to Data Science Weekly: http://www.datascienceweekly.org/ http://blog.zipfianacademy.com/ http://blog.cloudera.com/blog/category/data-science/ http://www.hilarymason.com/ http://mathbabe.org/ http://fivethirtyeight.blogs.nytimes.com/ http://blog.kaggle.com/ http://grepalex.com/ http://pulseblog.emc.com/category/big-data/ http://radar.oreilly.com/ http://flowingdata.com/ http://oreilly.com/data/newsletter.html http://www.gapminder.org/ http://mlcomp.org/

http://flowingdata.com/category/tutorials/ not web-centric but the best source I know of offhand (edit to add ..) http://chimera.labs.oreilly.com/books/1230000000345/index.html is a viz book that uses d3. I haven't read it but the guy who wrote it does good work

I wasn't able to release the original code as it belonged to Facebook, but Nathan Yao wrote a great blog post covering how to do make a similar map (including code): http://flowingdata.com/2011/05/11/how-to-map-connections-with-great-ci… To be honest, 80% of the code was getting the data into the right shape and the other 20% was a slow implementation of the great circle calculations (I didn't know about the geosphere package so I rolled my own). Nathan's code is better.

Do you mean visualising the tree by citations? All of these help, but papercube has a good following. http://flowingdata.com/2012/01/01/visualizing-citations-in-research-li… http://webapps.stackexchange.com/questions/10058/visualization-of-cita… http://papercube.peterbergstrom.com/ http://www.cs.umd.edu/hcil/ase/

Try flowing data's forums (http://flowingdata.com/). It's a very good site.

I would check out Visualizing Data - http://oreilly.com/catalog/9780596514556 Also, I enjoy this site http://flowingdata.com.

http://flowingdata.com/ is a pretty nice blog about data visualization. Both for reposts and commentary of other sources, and some original content. Some of http://flowingdata.com/category/data-underload/ posts are pretty amusing.

http://flowingdata.com/ has a few resources and sources.. I think the answer is - for the really high quality stuff - the end result is normally always Photoshop/Illustrator.

For better examples, check out Information Aesthetics: http://infosthetics.com/ Flowing data: http://flowingdata.com

http://flowingdata.com/ http://junkcharts.typepad.com/ for examples.

My hope is that the new generation of designers/UX people question and reject many of the UI/UX patterns made popular in the past 15 years and go back to the 90s for inspiration. Resources like the Apple Design Guidelines from 1992 linked in the OP is excellent! Perhaps my biggest gripe is that many of these terrible UI/UX patterns are built in at such a low level, it is near impossible for developers to override them in the software they build. For example, I really dislike flat UI and particularly flat scrollbars. But it is near impossible to add scrollbars that look like these in any Windows or Mac app I build: https://flowingdata.com/wp-content/uploads/2024/02/evolution-of-the-sc… Usability has become theatre across Apple products. The sad part is that since Microsoft just seems to copy Apple, over time Windows usability has also degraded severely. I am so frustrated by what Apple, Microsoft and Google have done.

This is a spitball take, but I'm trying it out because I'm more interested in learning how it plays out than actually making a point. Socialization is moving at a slower pace than the shift in economics. The trade deficit starting in the 1970s has overwhelmingly affected goods [1], resulting in the exporting of goods production overseas and leaving the US with a service-based economy[2]. The US at the time had a greater proportion of gender-dependent occupations[3]. The export of goods-producing jobs tended to disproportionately affect men as a result of this gender-occupation dissymmetry[4]. At the same time, people have been socialized with system of gender values. In the past these gender values were congruent with both gendered occupations[5] and gendered occupational values[5]. However, as the proportional of service occupations grew, a portion of men found themselves socialized with values[4] incongruent with the values associated with occupations now available to them. The lag between value socialization and economic realities represent a point of friction and frustration that is expressed as a feeling of being undervalued economically[7]. 1. https://www.stlouisfed.org/-/media/project/frbstl/stlouisfed/Publicati… 2. https://ustr.gov/issue-areas/services-investment/services 3. https://flowingdata.com/2017/09/11/most-female-and-male-occupations-si… 4.…

As a millennial, GenX seems like the most reasonable generation to me. They got the best of a lot of things. (Very anecdotal) * Excellent Music - Top tier Rock and Rap * Moderate on Politics - Saw the evils of Communism, but don't have the rosy eyed image of Capitalism as 2008/2000 hit them at critical ages (20-30) in their lives. * Less involved in identity politics - Went to college before campus activism was based around identity. * Understanding of the Millenial's struggles, cautiously liberal, and stuck in a terrible housing market because Boomers refuse to die. Sadly, The Boomer's stranglehold over politics has affected them the most. This is most evident in this [1] graph, where the GenX wave clearly never came and Millenials are already nipping at their heels with Jon Ossof in the senate. This is visible in in presidential runs too. Obama, McCain, Trump, Bernie, Warren, Kamala and Biden are all Boomers or older. Yang and Pete came closest, which is still embarrassingly far for a generation at the top of the Presidential age bell curve [2]. [1] https://flowingdata.com/2021/02/01/age-generations-in-the-u-s-senate-o… [2] https://upload.wikimedia.org/wikipedia/commons/b/bf/Age_distribution_U…