Shared posts

10 Jan 15:03

President Roosevelt's Reading Habits While in Office

by /u/hpmetsfan
Jdinoto

Fascinating! A collection of books that Teddy Roosevelt read, along with letters he sent to the authors!

10 Jan 14:32

Trump’s Lawsuits over three decades.

by /u/LazzzyButtons
Jdinoto

USA Today did a surprisingly good job here! Usually their dataviz is among the worst, but this is clear, factual, and well designed. Good job!

15 Dec 14:00

Since Florida won't implement a restaurant grading system, I pulled the public records and made one myself. [OC]

by /u/cpare
Jdinoto

For anyone who eats at restaurants in Florida.

07 Dec 19:53

12 ideas to become a competent data visualization thinker

by Jorge Camoes
Jdinoto

Some very solid advice.

It began with a tweet:

In spite of being a notorious Excel Brute Forcer (thanks, Elijah!), I was invited for a presentation at JMP and was working on it (and answering 5 interesting questions for them). This tweet felt like a great starting point because, as I said to Lindsey, “becoming a data viz thinker” is not a common formulation. I ended up structuring my presentation around 12 ideas that could be relevant for this goal.

The presentation was yesterday, 26 October, and it was recorded, so I’ll add a link here as soon as it becomes available.  Meanwhile, let me summarize those 12 ideas, many of them can be found in my book, but not all. Please note that JMP users are mostly scientists, engineers and similar fauna.

  1. I couldn’t care less about data visualization. Starting with a bang but I really mean it: not everything needs to be visualized. Often there are other methods of data exploration and communication and they complement each other. That’s why in the Anscombe Quartet you need both the charts and the statistical metrics. If you have to make a chart, make it count. Don’t replace information overload with chart overload.
  2. Data matters. The expression “data visualization” was carefully designed to make you think that (counting the letters) you’ll spend more than 70% of your time designing cool “visualizations”, while in reality the opposite is true: you’ll spend most of your time minimizing errors, structuring the data, making sure the concepts are the right ones, and much more. Often, managers or clients fail to understand the resource-intensive nature of the task. They think it magically happens.
  3. Perception and society matter. Being aware of internal mechanisms (the eye-brain system) and external mechanisms (social rules, corporate culture, peer pressure, audience profile) should impact how we communicate visually.
  4. Data mapping and design. Creating new chart types is easy because we basically map data points to a 2D plane and after that everything is design. Thinking at that level of abstraction is interesting not only because your communication can become more flexible but also helps when moving between tools.
  5. Data is interpretation. From the moment you collect the data to the moment you read someone else’s chart interpretation is always present. Torture the data to come up with multiple interpretations and points of view. Even Minard’s Napoleon March, in spite of all variables, is an interpretation (that the Russians will probably disagree with). What makes a good chart is how good it is at saying what what it wants to say. Among other things, this means that it should be a good data pre-processing system that allows the brain to focus on higher level tasks. But data visualization is not enough: you have to have the contextual knowledge to detect and interpret patterns.
  6. Data visualization is a process. Not a linear one. Be aware of the questions you ask. They often reveal not only what you want to know but also what you actually know. Better questions mean better understanding. It’s interesting to have a classification of questions and see how they can be paired to chart types (better: chart designs). A pie chart with 50 slices is not necessarily bad: usually a visualization fails not because there are too many data points but because the author doesn’t understand the data or doesn’t care about the message.
  7. Rules of engagement. Attracting people’s attention with decoration is lazy. There are other effective methods that should be considered first (the data itself, chart titles, avoiding defaults, self-interest…)
  8. Aesthetics and emotions. Stephen Few and David McCandless. Nuff said.
  9. Emotional tone. Define a subdued emotional framework for multiple charts, never The Crying Boy style. Match tone and data (fun with the Titanic data set?). Be aware of the addiction to sugary data visualization.
  10. Complex simplicity. Simplicity is not minimalism or removing junk. Remove the irrelevant, minimize the accessory, adjust the necessary and add the useful.
  11. Using color. Avoid cliches like the plague and color to prettify. Think of it as stimuli that should be managed (intensity, function, symbolic meaning). The aesthetic dimension of color is an afterthought for non-designers. Use a professionally designed color palette and never the default one.
  12. Go beyond the single graph. Structured, matrix style visualizations: small multiples, trellis displays. Animation as stacked small multiples. For free-form visualizations (dashboards, infographics) find a coherent narrative or visual landscape. Use Ben Schnidermans’ Visual Information-Seeking Mantra. For the overview, use gateway charts (simple, perhaps playful charts like pies or gauges that can lead to more addictive and complex charts). Never use gateway charts by themselves. When exploring, often focus + context is often better than filtering.

So, this is a summary of my presentation in 26 October at SAS/JMP in London. I did have a great time there and people were very nice. I had no previous contact with JMP and the people behind it, except Xan Gregg, with whom I talk from time to time on Twitter.

Full disclosure: I was payed for this presentation. At no time I was asked to talk about the product and I have no financial motivation to do so. I will probably write about it in the future, just like I talk about Excel, Tableau or PowerBI. If there is any change I’ll disclose it as well.

The original post is titled 12 ideas to become a competent data visualization thinker , and it came from The Excel Charts Blog .

05 Dec 17:14

AXIOMATIC movie

Jdinoto

Sounds like an interesting short film!

Canadian indie filmmaker K.S. Kuperis has made a short film of my story "Axiomatic" (17 min, free to watch online).
05 Dec 17:12

bigblueboo: fooled ya brain

Jdinoto

Oh this is a good one!



bigblueboo:

fooled ya brain

04 Dec 15:57

Random Access CharacterProject from GLKT generates surreal...

Jdinoto

This is surreal and whimsical.





















Random Access Character

Project from GLKT generates surreal walking characters made with random objects and can save your results in GIF format:

Random Access Character is a procedural character generator.

It lets you generate an infinite number of different characters, made from various objects and textures.  Morphology, colors, movements, patterns are mixed together to create a unique blend and generate a unique character every time.

You can save an animated GIF of your favorite find, that will be upoaded to imgur. You can then share your encounter to the world !

It is available for PC, Mac and Linux here

GLKT have a tumblr blog @glkitty here

30 Nov 14:08

Winners of the 2017 Information is Beautiful Awards

by /u/GreenFrog76
28 Nov 18:53

up / dn

Jdinoto

This is clever.



up / dn

10 Nov 14:02

The Trump Effect - visualizing the impact of Trump's first year in office

by /u/LMGagne
Jdinoto

Really nice policy-based data visualization. Not character - this focuses on policy. High quality material.

06 Nov 14:04

Visual comparison showing how Trump responds to terror attacks.

by /u/2big_2fail
Jdinoto

Not surprising, but this isn't anything new. Before Trump this seems an accurate portrayal of how the network news stations responded.

06 Nov 14:02

[Do not drink and drive. Book a cab or call a friend]. Percentage of US car crash fatalities where driver blood alcohol level was .01 and above, 1999–2012

by /u/daldablade
Jdinoto

Don't drink and drive please.

06 Nov 14:02

Database of every politician and their involvement in the Paradise Papers

by /u/fuckyousir69
Jdinoto

Saving this for later.

06 Nov 13:48

visualizing change via slopegraph

by Cole Nussbaumer Knaflic
Jdinoto

Slopegraphs are great.

Elizabeth Ricks recently joined the storytelling with data team after spending the past decade in various analytical roles in the healthcare, manufacturing, retail and payments processing industries. Most recently, she was Assistant Vice President of Analytics for Bank of America Merchant Services, where she strengthened her data storytelling skills by using the key lessons covered in the storytelling with data workshop. Elizabeth has a passion for helping her audience understand the "so-what?" when communicating with data. Join me in welcoming Elizabeth and her first blog post here! You can connect with her on LinkedIn or Twitter.  

Communicating the “so what?” is fundamental to telling a story with data and I can’t overemphasize the importance of choosing an intuitive visual. Often our story is lost, simply because because we’ve chosen a graph that forces the audience to do more work than necessary. Today’s post illustrates this transformation with a real-world (de-identified!) example.

Imagine you’re a marketing analyst tasked with evaluating your product’s market share and communicating the growth opportunity to your senior marketing leadership team. You’ve gathered the data on the 14 states in which you operate and visualized your market share over the past decade in this bar chart:

blogslopegraph1.png

This graph is functionally adequate. It’s thoughtfully designed using pre-attentive attributes. The color blue cues us where to look first (that’s our market share now!), which allows our second series (our market share then) to fade to the back.  

Additionally, horizontal bar charts have many visual advantages as outlined in storytelling with data:

  • Familiar and easy to read

  • Useful for long category names

  • Align well to how we typically process information: starting from top left and zig-zagging across the page so that we process the category names before interpreting the data

We see this final point demonstrated here, as a quick vertical scan makes it relatively easy to see that our product’s market share is down in every state, except Michigan and Oregon.

That’s fantastic if that’s the end of the story. However our task is twofold: we also need to communicate how our market share has changed over time and our recommendation for the opportunity. With the current design, how easily can you see which state(s) had the greatest decline in market share? Between Michigan and Oregon, which had the greatest improvement?  

blogslopegraph2.png

As the designer of this information, we are asking our time-crunched marketing executives to do a lot of work to scan the graph and make 14 different comparisons. Never make the audience do more work than necessary to understand a graph! Perhaps a different visual would make the task easier.

Enter the slopegraph.  

The slopegraph is a visually intuitive way to see what’s changing in your data. For a deeper analysis of the beauty of slopegraphs, check out this post.

Let’s instead connect the data points with a line. Notice where your eyes go first now.    

blogslopegraph3.png

A few interesting things emerge. We can immediately see that some states have higher rates of change than others, both positive and negative. That’s the "so-what" what we want our audience to understand!

We can further improve by using color to focus our audience’s attention on specific takeaways. For example, we might use blue to highlight the positive story: we’ve improved in 2 states!

blogslopegraph4.png

Or we could focus attention on Texas, the state with the greatest market share decline.  

blogslopegraph5.png

Finally, we’d add a call to action emphasizing how the audience should use this information. Remember, we always want our audience to do something!

blogslopegraph6.png

In conclusion, if your “so-what?” is what’s changed over time, then the slopegraph can be an extremely effective visual. If interested, you can download the Excel file with the above graphs.  

From a formatting standpoint, slopegraphs can take some time to set up. However, that’s time well invested if it means your audience clearly understands the story. Here’s a handy Excel template to get you started.   

06 Nov 13:48

how you would visualize hurricanes

by Cole Nussbaumer Knaflic
Jdinoto

An amazing array of Hurricane data visualization!

Hurricane_ALL.png

A few weeks ago, I posted a visual from The Economist on hurricanes and invited readers to makeover the graph and let me know what headline they would put on it. I was excited by the variety (and number!) of responses from all over the world. Thanks for your patience awaiting this follow-up post: it took a bit of time to pull 60 makeovers together in a sensible way!

First, let me summarize some of what I saw. People used a variety of tools (mainly Excel and Tableau, but also R/ggplot2, D3, Python, STATA, PowerBI, and others). Folks also visualized the data in various ways (lines, bars, stacked bars, area, bubbles, dot plots, maps, and more). Many people used multiple graphs. Some pulled in other data points (e.g. barometric pressure, wind, number of deaths, cost of damage). Many people chose to highlight the lack of pattern/trend in the data or otherwise changed the headline and takeaway(s) called out.

Here is the original graph from The Economist:

Economist_hurricane.png

Common points raised about the above included:

  • Belief that the original headline ("Hurricanes in America have become less frequent") was misleading.
  • Raising doubt as to whether the way hurricanes are measured/categorized has been consistent enough through history to start with such an early point in time.
  • Unease at the inconsistent time intervals on x-axis.
  • Uncertainty regarding years with no hurricanes (whether missing data or really no hurricanes, whether/how this is accounted for in original graph).
  • Questioning of the value/validity of the trend lines, given that the apparent (and calculated) lack of correlation. Uncertainty expressed at why recent data points weren't included.

It's clear you had fun with this. There were a number of comments simply expressing excitement about the challenge and it seemed folks found it to be a stimulating exercise. I'm happy you thought so and would love to do more of these. Stay tuned on that front. I should mention also that I did not personally participate in this challenge—it would have been unfair after seeing all of your amazing remakes and I decided my time would be better spent compiling and sharing back all of the great work you've done.

A couple notes to those who submitted makeovers: first off, THANK YOU for taking the time and sharing your work. In my copying/pasting/condensing, if I've misrepresented anything or failed to include a social media profile you'd like to have linked, please send a note with specifics to makeover@storytellingwithdata.com and I'll take care of it. The makeovers are posted below in alphabetical order by first name + last initial (I omitted full last names in respect of those who would rather remain anonymous). If you thought you submitted a makeover but don't see it here, please send a note with your makeover to the address above and I'll add it (I think I got them all, but you never know). Also, I'll apologize up front for any fuzzy visuals—that's my doing (not yours)—getting everything into a common form for posting here was more challenging than one might imagine! 


READER MAKEOVERS:


Adolfo H.

Adolfo visualized cumulative hurricanes in an annotated line graph:

Hurricane_Adolfo.png

Alex I.

Alex (LinkedIn) from Warsaw, Poland, recapped the following changes in Excel: "aggregated into 2 groups (major, non-major), tried to communicate 2 things with title: 1) about trend in overall number and majors; 2) long time gap since last major, pushed back/out non-data (gridlines, non-major series, subtitles), eye-catchy color for majors, labels for majors—to see that trend is stable for them, extra callout for recent majors back in 2005, downward sloping trendline for total number (although, here it's kinda cheaty, since last decade is only 6 years, but I decided not to reveal this cheat), and made order with Y axis (more clear I guess)."

Hurricane_Aleksy.png

Alessandro N.

Alessandro said he'd group categories together, as illustrated below, and accompany this graph with the following 50-year stats:

1900-1950: -3.1% total number of hurricanes, +60% hurricanes force >=4, -6.5% hurricane force <=3

1950 - 2000: - 24.2% total number of hurricanes, -12.5% hurricanes force >=4, -24.4% hurricanes force <=3

Hurricane_Alessandro.png

Andrew E.

Andrew chose a view that focused on major hurricanes with a line graph:

Hurricane_AndrewE.png

Andrew M.

Andrew illustrated the hurricane data through three views, plotting both actual (bars) and moving average (lines) for number of storms, number of strong storms, and wind speed.

Hurricane_Andrew1.png Hurricane_Andrew2.png Hurricane_Andrew3.png

Andy C.

Andy (Twitter | LinkedIn) likened hurricane predictions to flipping coins in his colorful view of the data:

Hurricane_Andy.png

Ariane M. & Marina C. & Luciana B.

Ariane, Marina, and Luciana decided to keep the headline but group the years in a different way. They said: "We are using 5 because we don't have complete information for our current decade (2011 - 2020). So we're afraid of comparing apples and oranges. Another option of our group was not to mention the growing trend for category 3 hurricanes. We believe it would change the headlines completely!"

Hurricane_Ariane.png

Ben B.

Ben (Twitter) used Tableau to visualize the hurricane data. Here is the Tableau Public version.

Hurricane_Ben.png

Billy W.

Billy (Reddit) was the very first to submit a makeover for this challenge. He writes, "There’s no significant trend on that chart (definitely not a downward one!), and the author does his/her readers a disservice by implying otherwise."

Hurricane_Billy.png

Bridge M.

Bridge (Twitter) created two views of the data, along with the following explanation.

Version A: In sticking with the original headline (and not reading the article),  I came up with this (assuming I'd drop in the Source, and Major* qualifier would be included further below). 

Hurricane_Bridge1.png

Version B: In perhaps sensationalizing the headline, I took a slightly different approach, partially to better handle the white space, but also because it's what I'd actually imagine reading.

Hurricane_Bridge2.png

Budana P.

Budana's suggested headline is: "Major hurricanes in America have become more frequent." With the following graph and comments:

  • This is a time series data. So a line chart is the go to.
  • I did not see value added in the message conveyed by segregation of hurricanes categories into 5, instead I grouped them into 2.
  • I omitted the current 10 years data point since we are currently short by 3 years. Presenting this point (2011-2016) would be prone to bias interpretation of the trends.
  • I added an emphasis of upward trends since 1971 to the most recent data point, highlighting the upward trending of both minor and major hurricanes enclosed in a gray rectangle.
Hurricane_Budana.png

Cindy C. & Amanda D.

Cindy and Amanda worked together on a bar/line combo:

Hurricane_CindyAmanda.png

Colin W.

Colin (Twitter) created an interactive visual on Tableau Public (where you can hover over specific data points and see details). Below is a screenshot. He also posted about his work—and how he applied the storytelling with data process—on his blog.

Hurricane_Colin.png

Craig D.

Craig (Twitter) from Syndey posed the question, "Are severe hurricanes occurring more often?" He wrote about his process on his blog. Also check it out in interactive form on Tableau Public.

Hurricane_Craig.png

Dangfun P.

Dangfun plots total and percent Category 3 and above with lines and bars and the headline, "Fewer but Stronger."

Hurricane_Dangfun.png

Daniel H.

Daniel (blog) from Germany created the following in R, noting, "I decided early on that I wanted to do a visual that includes all data from the source (instead of showing only aggregated versions) to give a visual representation of all the noise and randomness we're seeing there. Also I trashed a few variations with additional data (like death count and damage for the hurricanes) feeling that it made the graph way too complicated to read. It should be possible to get a comparable result in Excel by adding transparency to the plot points (it makes scatterplots sooo much nicer and adds density information)." He also blogged about his approach.

Hurricane_Daniel.png

Divya R.

Divya questions the original headline, writing: "Ignore the horrible overlapping interval labels (every 5 years), but you see how the slope for all categories is only slightly decreasing and in all cases the Standard Error band (colored haze) allows for the possibility of the trend line to go in the exact opposite direction? So one cannot strongly assert that hurricanes are decreasing across categories. I'm using same scale to show that even relative frequency (higher for low category earthquakes) doesn't dampen the possibility but actually has larger SE bands."

"I've seen a number of your posts point out the above effect where artificial precision induces a false accuracy. I'm approaching the same from a core stats perspective. [This] doesn't make for a very good graph, let alone for a headline, but in favor of effective data communication over pure viz, it's a point pertinent to convey."

Hurricane_Divya.png

Eduardo M.

Eduardo highlights a declining trend via 30-year buckets:

Hurricane_Eduardo.jpg

Gavin M.

Gavin also highlights a decreasing overall trend, but also comments on a slight increase in the most damaging storms:

Hurricane_Gavin.png

Glenn K.

Glenn shows two views of the data, along with his comments: "Show the raw data as 10 year rolling averages. While this introduces a lag, it creates a trend that isn't dependent on grouping the data into decades, which is independent of the frequency. Only show total hurricanes and major hurricanes. Show the ratio of major to total, to see if hurricane intensity is increasing (it isn't, but total frequency is increasing)."

Hurricane_Glenn1.png Hurricane_Glenn2.png

Gregg F.

Gregg (Twitter) from the UK shows two views of the data, commenting, "I had a think about this graph and the biggest problem for me was the dataset used. While hurricanes making landfall in the US affect more people, this view misses the bigger trend of the total number of Atlantic hurricanes. If the trend of total Atlantic hurricanes is increasing then the trend of hurricanes hitting the US will increase as well."

"Second, I would include an additional graph to show that the storms making landfall in the US is random and that it follows a typical statistical distribution."

"I think that these charts show the key takeaway from the data: The number of storms and intensity of storms varies each year but is generally on the rise. It naturally follows the more storms instead of less will hit the US in the coming years."

Hurricane_Gregg1.png Hurricane_Gregg2.png

Heather A. 

Heather (Twitter) focuses attention on major hurricanes:

Hurricane_Heather.png

Ivett K.

Ivett (Twitter) from Budapest says, "I enjoyed redesigning that stacked bar chart because there was another reason why I read again Cleveland's Graphical Perception study and your book. By the way I also learned a lot about the hurricanes." She also blogged about her process and visual perception in dataviz

Hurricane_Ivett.png

Jason C.

Jason plotted total and major hurricanes, drawing attention to and projecting a full decade for the final point of data:

Hurricane_Jason.png

Jon L.

Jon built a tool to explore the data in D3 and posted this plus his thoughts and some animated GIFs of the tool in action on GitHub.

Hurricane_Jon.png

Kat G.

Kat (Twitter) pulled together a couple graphs and annotations into a single view:

Hurricane_Kat.png

Kettki D.

Kettki in India writes, "The major concern of mine in ET_NOAA version was the absence of data, 'years when the hurricane did not made it to the landfall' and it’s an important part missing from the data. I thought seeing the pattern here is more important than the numbers, especially when we are analysing centuries of data together. And that the exploratory analysis would be the better approach to this. I agree that it is a challenging to showcase more than 150 years of information on small real-estate, and it made me thinking all over again. To begin with I was not in the favour of stack bar charts (as I did not think, adding # of storms would depict the right information) but now after working on this, it made me wonder."

Hurricane_Kettki.jpg

Kevin K.

Kevin (Twitter | blog) created the following and posted on Tableau Public.

Hurricane_KevinK.png

Kevin R.

Kevin R. created a line graph focusing on the decrease in total hurricanes over time.

Hurricane_KevinR.png

Leonard M.

Leonard shared the following: "Since the goal of the chart was to show that major hurricane landfalls are trending upwards, I got rid of the background column chart showing hurricane counts per year. I found the column chart distracting: the counts fluctuate so wildly from decade to decade that it leads the viewer to question the accuracy of the trend. I also made the line continuous, rather than bucketed by decade."

"In my title, I would have coloured the words "major hurricane" to match the red line, perhaps negating the need for a legend altogether. The tool I used to do this (Power BI) doesn't have that option though."

"I do wonder how meaningful regression analysis is on such a dataset, given that measuring wind-speed in 1901 was surely less accurate than it was in 2001. Clearly, The Economist feels comfortable with it though."

Hurricane_Leonard.jpg

Man H.

Man listed what was less than ideal in the original...

  1. Color bars of the hurricane categories look cluttered. At the first glance, it’s hard to tell what do these different colors tell.
  2. The blue/green bars are for the category 1-2 strong winds. Logically, people would think the green dashed line is for the category 1-2 winds too (just like the red dashed line for red Major Hurricane). However, this green line is for “All Hurricanes”. This is confusing.
  3. Headline says “all hurricanes become less frequent”. This misses the important fact that chart also tells: Major hurricanes increased.

...followed by what was done to simplify, make more readable, and deliver a clearer message:

  1. Drop the color bars for categories. Instead, I collapsed the categories into “strong winds” and “major hurricanes.” 
  2. Keep only one trend line for the Total Hurricane. Make the headline right inside the chart to make it more prominent.
  3. Drop the major hurricane trend line since I don’t think such trend is significant based on the data.
Hurricane_Man.png

Marco H.

Marco did a ton of analysis in STATA, outlining a number of observations and illustrating in tabular and graphical form (I've included just a subset here), which he summarizes in the following: 

"For me the summary is, when we consider yearly data we find no evidence for statistically significant linear trends over time for major or all hurricanes over time. Moreover, different choices of periods of time or different starting points can produce different results: looking at the last ten decades shows us an almost significant downtick in major hurricanes." 

Marco also writes: 

  • The Economist shouldn’t have used that last half decade in the graphic. It’s not representing the same ten year brackets, so just misleads the eye. What does it add? We don’t have data for the rest of the decade yet.
  • They shouldn’t have said NOAA produced this data if The Economist is the analyst and NOAA is just the source.
  • The choice of linear regression line of best fit is pretty hard to justify in data that takes small positive integers as outcomes. Best to look at Poisson, negative binomial or even better, time series regressions like ARIMA that allow you to model subtle lags in the data (e.g. the last three years affect this year).
  • One could go on…
Hurricane_Marco.png

Mark E.

Mark (Twitter) writes, "I’m not sure the outcome is sensational enough to justify a headline, but if I were to offer one it would be something neutral, such as 'Around one in three hurricanes exceed 178mph.' " Mark also blogged about his process.

Hurricane_Mark.png

Matthew P.

Matthew chose a horizontal bar chart, emphasizing the most recent decade:

Hurricane_Matthew.png

Meike G.

Meike points out, "Great example of how data can be used to push an agenda! That's why I chose 2 versions for my makeover—one keeping the original headline (replacing "America" with "US" though), and one to tell a different story."

"Some design decisions I took: changing the x-axis labels to make them easier to understand, removing the category 1-5 distinction and just showing one development per graph (Total vs. % Major Hurricanes/Total), removing y-axis labels and labelling first and last value instead, removing trend lines, removing gridlines. Apart from that, I chose to remove the 2010's in the second graph—because as recent events have sadly shown, the hurricanes have not stopped in 2016. I left it in in the first graph, though, because that's what was done in the original version and it reinforces that message."

Hurricane_Meike1.png Hurricane_Meinke2.png

Michelle M.

Michelle's headline would be, "September: The Most Violent Month For Hurricanes." She says, "I experimented with a few things, and the biggest pattern that I noticed was that most hurricanes occur in September. Not too surprising, but I had fun making the data interesting to play with anyway!" Here is the Tableau Public version.

Hurricane_Michelle.png

Miguel C.

Miguel from Portugal created two views of the data in Excel:

Hurricane_Miguel1.png Hurricane_Miguel2.png

Mike C.

Mike (Twitter) writes, "The top half is the true re-viz of what The Economist was trying to say; the bottom half is a more in-depth interactive for viewers to engage with." Here is the Tableau Public version.

Hurricane_Mike1.png

Min M.

Min (Twitter) chose a side-by-side layout and highlights the proportion of major hurricanes increasing:

Hurricane_Min.png

Neil R.

Neil (Twitter) chose a "dumbbell" view, blogging about how to frame the title in dataviz and his process. You can also view this makeover on Tableau Public.

Hurricane_Neil.png

Nicolas D.

Nicolas chose a line graph to depict the data:

Hurricane_Nicolas.png

Olesia H.

Olesia used Python's matplotlib library, editing with Inkscape afterward. She says, "Unlike journalists from The Economist, I've decided to highlight the lack of pattern in hurricane data. Don't want to sound like climate change denialist but the trends shown in the Economist's graph may very well be just statistical flukes and the NOAA overview cited in the article explicitly says that 'It is premature to conclude that human activities – and particularly greenhouse gas emissions that cause global warming – have already had a detectable impact on Atlantic hurricane or global tropical cyclone activity.' "

Hurricane_Olesia.png

Olivier C.

Olivier (LinkedIn) from Switzerland shared the following comments on the original graph:

  1. Misleading choice of data. While the world is speaking about hurricanes in the context of climate change, the Economist graph refers to hurricanes LANDING on US coast. And actually the online source implies a certain radius from a given point, limited to 200km so probably some counting in missing! On top of that, the data includes 2016 only. Considering that 2017 is already a record year (which would highly impact statistical averages and trends), not featuring 2017 is also misleading. Note also that while Hurricane Sandy (in 2012) was not recorded as major (downgraded to CAT2 just before landing) and did cause enormous damages... So data should have been based on total of hurricanes in the Atlantic rather than only ones hitting US coasts. And based on those data, the conclusion would have been much more relevant. The decrease in frequency since 2005 could actually be just a shift of route (hurricane not hitting US or hitting other lands before and thus declining).
  2. Misleading use of statistical tool which lead to wrong conclusion! The fitting curves are meaningless in this data. One year could very well unbalance the entire trend. As a matter of fact, adding 2017 is changing everything, and fitting curve trend inverse itself in 1950...! This is because the data shows a very consistent frequency rather than changing trend.
  3. Bad dataviz: cumulated bars + cumulated periods. Absolutely meaningless!
  4. Bad dataviz: the data was not including years with no hit. That shall be corrected before plotting a time-based axis.

Here is the online version of the makeover on Plotly.

Hurricane_Olivier.png

Paul W.

Paul (LinkedIn) enjoyed trying to be a little artistic with it while still showcasing the analysis from the original version and adding a non-time based trend to it. You can view his makeover on Tableau Public.

Hurricane_Paul.png

Raf M.

Raf (Facebook) from Belgium shared the following view:

Hurricane_Raf.png

Rahul S.

Rahul (Twitter) said his story would have revolved around the following four views:

Hurricane_Rahul12.png Hurricane_Rahul34.png

Rebeca P.

Rebeca (LinkedIn) "basically merged the hurricanes into two categories (minor and major) and looked at both frequency and intensity by decade."

Hurricane_Rebeca.png

Rob B.

Rob (Twitter) writes, "The Economist writer is clearly trying to articulate that the incidence of severe hurricanes has increased over time. Possibly an agenda linked to climate change, arguing that climate change is making hurricane season worse for America. They've loosely managed to portray this—as the trend lines show a falling absolute number of hurricanes, and a rising absolute number of severe hurricanes. But why not just plot the relative frequency of severe hurricanes during each time period?" Here is his Tableau Public Dashboard.

Hurricane_Rob.png

Robert C.

Robert (Twitter | LinkedIn) wrote a 3-part series on his process (post 1, post 2, post 3), culminating in the following view. His interactive viz can be found here and he'd accompany with a headline like "Should we be concerned?"

Hurricane_RobertC.png

Robert vO.

Robert (LinkedIn) felt the original headline was "plain misleading!" He goes on to say:

"Overall, there is no significant linear correlation between year and number of hurricanes (r=-0.12), although this you wouldn't expect a large correlation, this could mean something. However, the the correlation between year and category 3 plus hurricanes is just plain zero. So bascially, the data is going all over the place without a clear trent for heavy hurricanes. So you shouldn't show a trendline and if you do make sure it is flat."

"Although a bit boring, this is the most relevant conclusion which should be reflected in both the headline and graph. The headline could be something like 'Every era will suffer hurricane hits' or 'Hurricanes are as bad as they were 100 years ago'. Well, I'm obviously not a copywriter but you catch my drift."

"The graph itself is not that bad, I have definitely seen worse. The color coding makes sense, as does the packing of years. The legenda could be a bit more clear and the trend lines just have to go because there aren't any."

"I would do a couple of things differently to support the main idea that there is no trend in total number of hurricanes or 3 plus category hurricanes:

  1. Combining years in packages in 5 instead of 10 as it dilutes the variability in the data; some packaging makes sense to keep a sense of the bigger picture.
  2. Number 5 category should be up high the graph, number 1 should be down
  3. 2016 has to go, as it is just a single year, it would suggest a period of not much going on.

You could also just show the total number of hurricanes and the major one (second graph).

Hurricane_Robert1.png Hurricane_Robert2.png

Rody Z.

Rody wanted to focus on major vs minor hurricanes that make U.S. landfall, while giving scope of the total. Here is the Tableau Public version.

Hurricane_Rody.png

Ron P.

Ron notes, "I could see what they were trying to do. They were hoping to find a trend by filtering 166 years worth of data into decade-wide bins on a stacked bar graph. I liked the attempt, but wondered if there was a better way to filter the data. So, rather than binning it into ten-year buckets, I applied a 20-year moving average filter to all the data and plotted the results on separate line graphs." He shares the following two visuals:

Hurricane_Ron1.png Hurricane_Ron2.png

Ryan T.

Ryan left the historical data there for context, but focused attention on the more recent decades:

Hurricane_Ryan.png

Sam B.

Sam writes, "I have tried to simplify it, while keeping the major features."

Hurricane_Sam.png

Sharon R.

Sharon's proposed title would be: "No significant change observed in hurricane frequency since 1851. Cost and damage of storms has increased markedly and since the early 1990s." She goes on to write the following.

"Here are the main things that concerned me with The Economist’s viz:

  1. It was hard to read and see any (real/significant) trends emerge because there was too much detail that could have been presented better. Instead of showing all 5 hurricane categories in a stack bar chart, the authors could have clustered the storms into “Major” - all hurricanes with a 3 or above measure on the Saffir-Simpson scale - and “Minor” (scoring <3). This makes it easier on the reader’s eyes with minimal loss of data integrity.
  2. The year grouping both looks sloppy (font size, year format) and skews the trend line. Removing any grouping (simply plotting all the years on the x-axis) reveals a very minimal decline in frequency for minor and negligible increase in major hurricanes over the entire period. It is questionable whether these changes are statistically significant based on the R2 values.
  3. There is a significant piece of data missing in the article and in the STORYTELLING: individual hurricane damage and how that trends over time. Specifically, some of the most damaging and costliest hurricanes measured 3 (Katrina, $105B estimated damage) or below (Harvey, category 2, est. damages at $180B). Therefore increases and other trends based on a hurricane's category assignment do not tell the full story, IMO. An article in Slate covers the topic of finding better metrics for measuring hurricanes."

"In my analysis I looked also at the damage data (in terms of cost) for the top 30 costliest tropical cyclones in the US (taken from NOAA, http://www.nhc.noaa.gov/pdf/nws-nhc-6.pdf). While damages are ascribed to only 30 of the 289 hurricanes in the data set (and this is less than ideal I realize), even with the limited data available it is clear the damages have increased dramatically over the last 60 years, across storm categories."

Hurricane_Sharon1.png Hurricane_Sharon2.png Hurricane_Sharon3.png

Srikanth P.

Srikanth shares the following views:

Hurricane_Srikanth.png

Teresa B.

Teresa (LinkedIn) says her headline would be “Hurricanes this half century on track with previous.” 

Hurricane_Teresa.png

Thomas O.

Thomas (Profile | Twitter) from Austria used R (especially dplyr and ggplot2) to build the following hurricane timeline:

Hurricane_Thomas.png

Tim F.

Tim (blog) shared the following:

Hurricane_Tim.png

Todd L.

Todd writes, "So this took me waay longer than it should have. I was trying to create a calculated field in the pivot table to show just Major Hurricanes so I would have two values columns (Total and Major) but I couldn't figure it out. I ended up just hardcoding the data, which is disappointing."

"I may have forgotten some of the ideas in your book/website so this is a good refresher.  [Here] is my best attempt. I wanted to extend the trend line more to the left but I can't seem to do it....but I at least think I'm in the right ballpark! PS. Grouping by 15 year increments seems to work better than 10 year increments b/c using the latter a) creates more bars and more clutter and b) creates a partial category for the last plot (2011-2016.)"

"FYI I realized after I sent it that the blue text could be worded better. Maybe something like 'Trend in total hurricanes doesn't align with climate change trends' is better. I just thought it needed a more impactful takeaway."

Hurricane_Todd.png

Ziwei Z.

Last, but not least, Ziwei shares the following stacked area graph, concluding "no strong trends"—

Hurricane_Ziwei.png

Huge thanks again to everyone who participated for taking the time and sharing your work!

06 Nov 13:46

Are Anti-Trump Republicans Really Anti-Trump?

by A FiveThirtyEight Chat
Jdinoto

Look at the list for Dems... quite interesting.

Welcome to FiveThirtyEight’s weekly politics chat. The transcript below has been lightly edited.


micah (Micah Cohen, politics editor): Today’s topic: If you’re a Republican elected official, what qualifies you as anti-Trump?

Why are we talking about this? Well, there are plenty of GOP senators — Susan Collins, Lisa Murkowski, John McCain, Jeff Flake, Bob Corker, etc. — who have been critical of the president but haven’t necessarily done much about it legislatively. So many people on the left call the whole narrative that they’re standing up to Trump BS.

So, to start us off: How much do you think these elected Republicans are doing to restrain Trump?

natesilver (Nate Silver, editor in chief): Well, they weren’t doing very much at first. Then more signs of resistance emerged over the summer.

In some ways, we’re still waiting for the pivotal tests, though.

What if Trump fires special counsel Bob Mueller, who’s investigating his campaign and potential Russian collusion? What if he pardons Jared Kushner? What if he tries to appoint to his Cabinet someone who’s an obvious hack?

perry (Perry Bacon Jr., senior writer): The Russia sanctions bill was significant in that Congress passed it despite administration objections. The hearing with fired FBI Director James Comey was too. So was prominent GOP senators basically all-but-ordering the president not to fire Attorney General Jeff Sessions. Republicans, particularly in the Senate, are doing more to resist Trump than the liberal conventional wisdom seems to hold.

harry (Harry Enten, senior political writer): Yeah, you are seeing some more outward signs of resistance. You have both Flake and Corker not running for re-election in order, it seems, to be able to critique Trump to their fullest ability. And just this week, McCain put out this tweet:

Now, do those count? Otherwise, I think the Russia bill was the first step, so I concur with Mr. Bacon.

natesilver: Wait — so Flake and Corker not running for re-election is a sign of resistance?

Not sure I buy that, Enten.

harry: I see it that way. Here’s why: Yes, they didn’t run, probably in part because they thought they might lose. That’s especially the case for Flake. But they could have decided to change course. They could have sucked up to Trump. Instead, they chose not to run and to criticize the president.

perry: If you think Trump will go down as the worst president in modern U.S. history and that he breaks lots of important norms along the way, then they are still not doing nearly enough. If you grade them based on their deep desire to 1. get re-elected, 2. please the Fox News base, and 3. get tax cuts and conservative judges, then the level of resistance in the GOP that we’re seeing seems more significant, with Collins/Corker/Flake/McCain at the more resisting end of the spectrum.

harry: I wonder if any of them think Trump is the worst president.

natesilver: Well, Flake was probably going to lose anyway. But Corker is popular enough that he could have stayed in the Senate as a sort of Susan Collins type.

harry: Corker’s numbers slid. I’m not sure he would have won necessarily if he really wanted to critique Trump.

micah: Yeah, isn’t the idea that he would have become far more unpopular by speaking out against Trump?

perry: I think many of these Republican senators believe Trump is uniquely terrible. That is what Flake and Corker are getting at: Let’s use our inside voices outside. If he is terrible, let’s tell people.

natesilver: Corker might have lost. But now you’re almost guaranteed to have someone more Trump-friendly in that Senate seat.

micah: So much of this comes down to how big of a threat you think the president is, right? If you’re on the left and you see Trump as a clear and present danger, then of course you’d be underwhelmed by the anti-Trumpiness of the GOP.

perry: Right.

micah: But let me introduce another element here …

THE FIVETHIRTYEIGHT TRUMP SCORE!!!!

Nate, can you give the people a snappy description of what this is?

natesilver: It’s how often a member of Congress votes the way that Trump wants.

That’s it. It’s pretty simple. It’s a measure of roll-call votes.

micah: So people have been throwing around Flake’s and McCain’s and Corker’s Trump scores — which are all very high — as evidence that their criticism of Trump is hollow.

That seems silly to me, but what do you all think?

natesilver: It’s certainly possible that you could agree with Trump on his legislative priorities but also think he’s a danger to the Republic. In that case, you might have a high Trump score, since most of what’s reflected in it is legislation.

harry: Can I just note that there’s nothing new about measures like the Trump score? People have been tracking stuff like this for years. What’s different here is that we’re doing it in real time. It’s more about the interpretation that some people are taking.

natesilver: Yeah. We’re doing it in real time. And our scores are more transparent — it’s more obvious what they mean.

perry: Micah and I have had this debate a lot internally. So we can have it publicly now.

I appreciate the work of my colleagues in creating this tool. And it explains some things really well. But I see these liberals saying, “Well, Trump is with Flake 90 percent of the time.” Flake wrote a book trashing Trump. Trump wanted Flake out of the Senate. Something is not being captured there.

And the other challenge is that Trump is often very disengaged from the legislative process. So the things that get voted on are really the Paul Ryan-Mitch McConnell priorities, or put differently, the Koch brothers agenda. I know why we are calling it a Trump score, but I at times worry that that communicates to the audience that Trump has defined priorities on a lot of legislation, some of which I doubt he knows exist.

natesilver: I don’t know. It’s a tool. Like any tool, it can be misused.

micah: Perry has outlined the fairest criticism.

But like … don’t the Trump scores simply show that Trump hasn’t pushed an agenda distinct from normal GOP orthodoxy?

perry: Right.

I think people are misusing/misunderstanding the tool.

micah: We could rename it the “GOP Congress-Trump Legislative Agreement Score.”

natesilver: I mean, the scores show that the Republican agenda and the Trump agenda have become pretty well aligned.

The lowest Trump score among Republicans (Collins at 81 percent) is much higher than the highest Trump score among Democrats (Joe Manchin at 54 percent).

harry: Also, the Republican senators with the lowest Trump scores aren’t surprising; they tend to be the senators widely recognized as the most anti-Trump: Collins, Rand Paul, McCain, Corker, etc.

GOP senators by Trump score
SENATOR STATE TRUMP SCORE
1 Roy Blunt Missouri 96.2%
2 John Boozman Arkansas 96.2
3 Bill Cassidy Louisiana 96.2
4 Thad Cochran Mississippi 96.2
5 John Cornyn Texas 96.2
6 Orrin Hatch Utah 96.2
7 John Hoeven North Dakota 96.2
8 Pat Roberts Kansas 96.2
9 Mike Rounds South Dakota 96.2
10 John Thune South Dakota 96.2
11 Thom Tillis North Carolina 96.2
12 Roger Wicker Mississippi 96.2
13 Richard Burr North Carolina 96.1
14 Mitch McConnell Kentucky 96.1
15 Marco Rubio Florida 96.1
16 Dan Sullivan Alaska 95.9
17 Johnny Isakson Georgia 94.4
18 John Barrasso Wyoming 94.2
19 Ted Cruz Texas 94.2
20 Cory Gardner Colorado 94.2
21 James Inhofe Oklahoma 94.2
22 Tim Scott South Carolina 94.2
23 Richard Shelby Alabama 94.2
24 Shelley Moore Capito West Virginia 94.1
25 Jerry Moran Kansas 94.1
26 David Perdue Georgia 94.1
27 Lamar Alexander Tennessee 94.0
28 Tom Cotton Arkansas 92.3
29 Mike Crapo Idaho 92.3
30 Steve Daines Montana 92.3
31 Mike Enzi Wyoming 92.3
32 Joni Ernst Iowa 92.3
33 Deb Fischer Nebraska 92.3
34 Chuck Grassley Iowa 92.3
35 Ron Johnson Wisconsin 92.3
36 John Kennedy Louisiana 92.3
37 James Lankford Oklahoma 92.3
38 Rob Portman Ohio 92.3
39 Todd Young Indiana 92.3
40 Dean Heller Nevada 90.4
41 Mike Lee Utah 90.4
42 James Risch Idaho 90.4
43 Ben Sasse Nebraska 90.2
44 Pat Toomey Pennsylvania 90.2
45 Jeff Flake Arizona 90.0
46 Luther Strange Alabama 90.0
47 Lindsey Graham South Carolina 88.5
48 Bob Corker Tennessee 86.3
49 Lisa Murkowski Alaska 86.3
50 John McCain Arizona 84.0
51 Rand Paul Kentucky 84.0
52 Susan Collins Maine 80.8

natesilver: Yeah, it does a pretty decent job.

perry: Yeah, that actually is perfect in capturing the anti-Trump wing in the Senate. Although, it is strange that Luther Strange is there.
But broader point: I don’t expect someone like Flake, who is quite conservative, to vote against tax cuts because Trump supports them.

natesilver: Right, but it’s reasonable to point out that someone like Collins — despite occasionally disagreeing with Trump, including on important issues — is still quite an asset to him, compared with a Democrat from Maine.

micah: Well, this gets us back to how you judge Republican resistiness — there are people who think Trump is such a threat to the nation that Republicans should be blocking appointments/legislation even if they support them on substance. There are people who think they should switch parties! If you subscribe to that theory, then the Trump score does count as evidence that the McCains and Flakes of the world haven’t done much.

As Nate just said, Collins is still an asset overall.

natesilver: There haven’t been many appointments lately — and Trump has mostly sidestepped making controversial ones.

harry: I mean, these are still Republicans.

perry: This is an interesting question. Tom Price had a bunch of controversial behavior well before he was confirmed as health and human services secretary and well before he resigned after the plane stuff. Should Flake/Corker/McCain have not voted for him? (They did.) Would they do that today?

natesilver: That’s why I’m saying the big tests are still ahead.

harry: What are the big tests? Do we know them yet?

natesilver: Ultimately, some of the resistance will have to come in the form of roll-call votes — like rebuffing his Cabinet nominees or (gulp) even voting to impeach him.

perry: Right, but taxes is the wrong issue on which to judge GOP resistance. Nominations and appointments are right. So are U.S. attorneys, foreign policy appointments, people who could be involved in Russia stuff: Like if Secretary of State Rex Tillerson were to leave and Trump wanted to appoint an even more pro-Russia person. Or if his U.S. attorney appointment in New York seems to be someone with obvious ties to Trump who won’t prosecute crimes by Trump allies.

natesilver: Congress could pass legislation that would make it more difficult to fire Mueller. The fact that they haven’t yet is a good point for the critics.

micah: Or Congress could pass laws aimed at curbing Trump’s potential corruption/conflict of interest stuff.

perry: Yes.

harry: Of course, a number of GOP senators have also said that Trump shouldn’t fire Mueller.

perry: Like this is a serious idea: Republicans should join with Democrats to block any U.S. attorney nominee who Trump has personally met with.

And, yeah, the fact that the pro-Mueller bills have not moved is telling.

natesilver: People are also within their rights to be skeptical of Republicans standing up to Trump based on how the 2016 primaries went down. Trump, famously, received very few endorsements from Republican elected officials. But as we learned, there’s a big difference between failing to endorse and actually resisting someone.

harry: By the way, Flake has not signed onto a bill that would make it harder for Trump to fire Mueller.

micah: I think what we’re seeing is a number of Republican senators who are anti-Trump on non-policy issues (protecting Mueller/rule of law/etc.) and pro-Trump on policy (which is basically just pro-GOP). … BUT they’re active on the policy things and passive on the non-policy things.

That’s the key: active vs. passive.

natesilver: Right. There’s been an impressive amount of passive resistance to Trump and not (yet) very much active resistance.

perry: I guess it’s somewhat hard to be active on non-policy things, since Congress doesn’t really vote on those, right?

micah: Couldn’t they, though?

perry: Is active resistance politically possible in the Republican Party of today?

Politicians perhaps should do things that are political risks. But they almost never do.

micah: That’s a hard question to answer, Perry. My first instinct is “no.”

But maybe that’s simply a case of expectations.

What would happen if every Republican senator up for re-election in 2018 simultaneously came out and broke with Trump in a sustained way?

perry: Well, on Russia sanctions it happened.

He whined, signed the bill and the party people won the fight.

If every senator up for re-election did that, they would all increase their chances of losing to a Steve-Bannon-backed candidate. There is no safety in numbers when the number is fairly small — only six Senate Republicans are up in 2018 (not counting Flake, Corker or the Alabama special election). If every House Republican did that, that would be different. It would be something like 240 people.

harry: I think we’re seeing a major resistance to resisting Trump in that fashion. Look what’s happening in the Alabama Senate race. Roy Moore has said a lot of stuff outside the mainstream, and he was welcomed into Washington with open arms before he’s even won the seat. The fear of losing is really, really powerful. Distancing themselves from that part of the Republican base is not tenable because it would mean, in their mind, losing the election.

perry: A good test will be if Mitt Romney ever says a negative thing about Trump again, since he is rumored to be considering a Senate run in Utah if Orrin Hatch retires.

micah: Clare is on vacation, but in her honor I feel compelled to say …

MITT!!!!!

Anyway, give me a little more detail on what we would see the key tests being for Republicans in Congress.

natesilver: I continue to think the three nuclear-level events are:

  1. Trump firing Mueller.
  2. Trump pardoning people in his inner circle.
  3. Mueller returning with an obstruction of justice finding or something equally severe against Trump.

micah: Let’s talk short of nuclear-level, though.

What would the ramp-up tests be?

natesilver: Part of the problem is that there aren’t that many ramp-up tests.

micah: Interesting!

harry: Again, what are you breaking with Trump on? The GOP Congress and Trump agree on most major policy questions — hence those high Trump scores.

micah: The Federal Reserve chair has to be confirmed by the Senate, right?

Other appointments?

And what about investigations?

Proactive anti-corruption laws?

Seems like there are a lot of options?

harry: I mean, if they started passing those anti-corruption laws, that would be a sign of something.

natesilver: If Trump tried to appoint, say, Rudy Giuliani to something, that would be interesting. But Trump has actually played it pretty carefully on appointments so far.

perry: Giuliani. New Jersey Gov. Chris Christie.

I think it will be fairly hard to have clear votes on things because McConnell will signal to the White House when the votes are there and when they’re not.

micah: You think the Senate would balk at Christie?

perry: Let’s not debate Christie. He will not be nominated.

micah: Lol.

natesilver: Yeah, and that’s another flaw with roll-call measures — they don’t measure things that never get to the floor.

harry: Why the hate toward Mr. 14 Percent Approval? (Also known as Chris Christie.)

natesilver: BTW, Congress could be doing a lot more on its own to investigate Trump, and they could make more of those investigations public.

micah: Yeah, that would qualify as a ramp-up test to me.

natesilver: Mueller — and the media — have devoted a lot of resources to investigating Trump, obviously, but that doesn’t mean that Congress couldn’t ramp up its investigations too.

perry: And that is an important place where Congress is not pushing back on Trump.

In fact, Republicans in Congress have moved in a pro-Trump, anti-Hillary Clinton direction in terms of investigations.

harry: That’s why I think you said to watch Richard Burr, right, Perry? To watch some of those investigations.

perry: GOP super-partisans have basically captured all of the committees but Burr’s. That’s important.

micah: OK, final question: Is it accurate to call Collins, Flake, Corker, McCain or any other Senate Republican “anti-Trump”?

perry: I call them Trump-skeptical. Maybe that’s cautious, but it’s more accurate, I think.

micah: I like that terminology.

harry: My problem with this is it’s difficult to call someone anti-Trump when they are agreeing with him a lot on policy. I would call some of them Trump-headaches. I know, lame phrase.

natesilver: You could call them “anti-Trump-curious.”

micah: lol

perry: Jennifer Rubin and Bill Kristol are anti-Trump. I can’t think of a Republican member of Congress who is really anti-Trump.

They are anti-Trumpism.

If we think of Trumpism as being more about nationalism, white identity politics, norms-bashing, institution-breaking, media-slamming, then Flake, Corker, etc., are against that. But not really Trump policies.

harry: For an anti-Trumper, the disagreements with Trump’s behavior, etc., have to override policy agreement. I don’t think we see that yet in Congress.

micah: Yeah, the passive vs. active seems like the dividing line.

Let’s call them “passively anti-Trump curious.”

01 Nov 12:56

A 'joyplot' of doom: After mass shootings and terrorist attacks, spikes of global attention fade quickly

by /u/laughlander
Jdinoto

Excellent article about attention spans after violent tragedies.

31 Oct 14:05

You need to know something

by Mark Liberman
Jdinoto

This is delightful!

I'm happy to see that Google Translate is still turning (many types of) meaningless character sequences into spoken-word poetry. Repetitions of single hiragana characters are an especially reliable source — here's "You need to know something":


And "I feel a strange feeling":

And "Stay free to leave":

Repeated combinations also often work — here's repetitions of "Ga mama" rendered as "Let it go / As it is":

These works also often reveal some interesting things about the letter-to-sound part of Google's current text-to-speech algorithms, e.g. the performance of that last one:

And as usual, if you ask for it again, you get a performance that sounds like the system has had a drink or five:

Here are the inputs in case you want to try them:


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かかかかかかかかかか
かかかかかかかかかかか
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かかかかかかかかかかかかかかかかかか
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まま
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24 Oct 21:15

Finding yoga-poses constellations in the night with neural network

by /u/sataky
Jdinoto

Amazing!

24 Oct 20:54

Cave Distribution in Kentucky, Tennessee, Alabama, and Georgia (KTAG) [OC]

by /u/chucksutherland
Jdinoto

Very interesting blog, lots of other neat cave maps there too!

24 Oct 20:47

Analyzing the best cities for Amazon's HQ2

by /u/rantdurdenwu
Jdinoto

Will it be one of their top 5?

24 Oct 20:46

Where Your Elements Came From - Great APOD today

by /u/box110a
Jdinoto

Fantastic period table interpretation!

23 Oct 13:53

Steel Mesh Kraken Sunken Off British Virgin Islands to Create an Artificial Reef

by SA Rogers
Jdinoto

This is beautiful!

[ By SA Rogers in Destinations & Sights & Travel. ]

Perched atop the Kodiak Queen, a former WW2-era Navy fuel barge, this 80-foot ‘Kraken’ now serves as the base of an artificial reef and marine research station on the ocean floor near the British Virgin Islands. The project, entitled BVI Art Reef, accomplishes a range of goals all at once: saving a decorated ship from destruction, transplanting coral to a new site in the hopes that it will flourish, creating an epic dive site and underwater art gallery, and providing a new habitat for marine life.

Photographer Owen Buggy documented the process, from the early stages of building the massive sea monster to sinking it in April 2017 to checking out the results a few months later. Sunken off the coast of the island Virgin Gorda with the help of tugboats and helicopters, the installation is already helping to rehabilitate heavily over-fished marine populations. Filmmaker Rob Sorrenti also got some great footage, presented as a documentary entitled ‘The Kodiak Queen,’ which is due for release in early 2018.

“This is the story of learning from past lessons and coming together to create something greater; rooted in joy and fueled by the power of play,” reads the BVI website. “This is the story of a group of friends from around the world who fell in love with the BVIs… and turned a weapon of war into a platform for unity – and a catalyst for new growth. This charitable kick-off in the British Virgin Islands combine art, ocean conservation, world history, marine science and economy… to solve a series of challenges in the BVIs by asking: how can we use play and collaboration to install permanent solutions that boost the local economy, secure the prosperity of these pristine islands for generations to come?”

“Our solution: a fantasy art eco-dive and ocean conservation site that puts the BVIs on the map as having one of the most unique and meaningful dive sites in the world… and one of the most forward-thinking approaches to creative problem solving that secures the education of its youth, and the health and prosperity of this island nation.”

Get updates on the project at the BVI Art Reef Facebook page.

Larger Than Life: 10 Monuments Honoring Nelson Mandela

Though Nelson Mandela's passing will spur the creation of memorials worldwide, a number of larger-than-life tributes are already extant or planned. Nelson Mandela Capture Monument (images ...

Valley of the Dolls: Missing People Replaced with Puppets

It sounds like the premise of a science fiction film, but deep in a rural valley of Japan there is a town where eerily lifelike dolls have been slowly replacing actual residents of Nagoro for ...

House of Eternal Return: Trippy Exhibit Owned by George RR Martin

Game of Thrones author George R.R. Martin purchased a sprawling abandoned bowling alley in Santa Fe, New Mexico so an art collective called Meow Wolf could transform it into a bizarre and ...

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09 Oct 18:46

What men and women think about their partners’ careers and help at home

Jdinoto

I feel like this could be a good bit of data to re-visualize.

ACROSS the Western world, women greatly outnumber men in lower-level jobs, such as clerical and administrative positions, whereas managerial and senior jobs are mostly held by men.
09 Oct 18:46

The “bump stocks” used in the Las Vegas shooting may soon be banned

AMERICA is still in shock after its deadliest mass shooting in modern history. On October 1st Stephen Paddock, a 64-year-old man from Mesquite, Nevada, broke a pair of windows in a 32nd-floor hotel room in Las Vegas and opened fire on the crowd at a nearby country-music concert.
09 Oct 18:45

Landslide for the "Did Not Vote" Candidate in the 2016 Election!

by Randy
Jdinoto

1) Voter Suppression and 2) Candidates who do not represent the interests of the average citizen.

From BrilliantMaps, this is the Did Not Vote Election Map, showing the magnitude if all voting-eligible adults that did not actively vote in the 2016 Presidential election. A Presidential candidate needs 270 Electoral College votes to win. The "Did Not Vote" candidate would have have gathered 41% of the total votes from the voting eligible population, and 471 votes from the Electoral College! A Landslide!

The map above shows what the 2016 US Presidential Election results would have been if votes not cast for Hillary, Trump or one of the third party candidates had gone to fictional candidate “Did Not Vote.”

 As a percentage of eligible voters, Clinton received 28.43% (65,845,063) of all votes compared to Trump’s 27.20% (62,980,160) and Did Not Vote’s 44.37%(102,731,399).

Total voter turnout was estimated to be 55.3% of the voting age population and 59.0% of the voting eligible population.

Map created using 270 To Win, based on reddit user Taillesskangaru’s posts here and updated here.

Disclaimer: The map above was accurate as of January 17th, 2017. Totals below were true at the time of writing but may no longer currently be accurate as additional votes and recounts are conducted.

Thanks to Mike Wirth for sharing on Facebook!

09 Oct 15:29

Too much rain for a rainbow

by Kenneth Field
Jdinoto

Excellent review of cartography in hurricane reporting. Whatever your views of the NYT are, their data visualization team is first rate.

National Weather Service today updated its rainbow colour scheme because of the unprecedented deluge caused by Hurricane Harvey in Texas.


Bravo for NWS in modifying its cartographic approach given a change in the phenomena it's mapping. Except they didn't do a very good job.

Old:


New:



The previous classification had 13 classes. the new one simply adds two more at the top end to deal with larger rain totals. In fact, all they've done is added detail to the 'greater than 15 inches' class and sub-divided it into three classes '15-20', '20-30' and 'greater than 30'. It'd be pedantic of me to note they still have overlapping classes (they do) but the bigger problem is they retained the same rainbow colour scheme and then added two more colours...a brighter indigo and then a pale pink.

Does that light pink area in the new map above look more to you? Or perhaps a haven of relative stillness and tranquility amongst the utter chaos of the disaster?  Yes, the colours are nested and so we can induce increases and decreases simply through the natural pattern - but the light pink could just as easily be seen as a nested low set of values than the more it is supposed to represent.

For a colour scheme that is trying to convey magnitude...more rain...more more more, you need a scheme that people perceive as more, more, more too. Different hues do not, perceptually, do that. Light pink does not suggest hideous amounts of rain compared to the dark purples it is supposed to extend.

We see light as less and dark as more. Going through a rainbow scheme where lightness changes throughout (the mid light yellow at '1.5-2.0' inches is a particular problem) isn't an effective method. Simply adding colours to the end of an already poor colour scheme and then making the class representing the largest magnitude the very lightest colour is weak symbology. But then , they've already used all the colours of the rainbow so they're out of options!

The very least they should have done is re-calibrated the classes to make the largest class encompass the new, out-of-all-known-range range. You can't simply add more classes when you're already maxed-out of options for effective symbolisation.

Better still, look around and learn how it should be done. The Washington Post has made a terrific map using a colour scheme that does have a subtle hue shift but whose main perceptual feature is the shift in lightness values. So we see more, more more as the colour scheme gets darker. It's simple. it really is.


The scientific community continues to use poor colour schemes and poor cartography to communicate to the general public. At least the mainstream media is doing a much better job.

[Update 29.8.2017 to include the New York Times piece]

New York Times today published one of the best maps I have seen in a long while. I mean 'best maps' of anything, not just the continuing deluge in Texas. Its simplicity belies its complexity and that's the trick with good cartography. Here's a pretty lo-res grab but go to the site and take a look.



They've got the colours spot on, A slight hue shift to emphasize light to dark but cleverly hooking into the way in which we 'see' deeper water as darker blue. Of course, it isn't really deeper blue but the way light is reflected, refracted and absorbed by water gives us that illusion. So, it acts as a visual anchor that we can relate to.

There's other symbology too - small gridded proportional circles that show the heaviest rainfall in each hour. The map is an animation so this gives a terrific sense of the pulsing nature of the movement of successive waves of rain (literally, waves!). The colours morph towards the higher end as the animation plays to build a cumulative total. This also has the effect of countering the natural change blindness we see when we're trying to recall the proportional symbols.

The two symbols work in harmony. And then, for those who want detail a hover gets you a graph showing the per hour total over the last few days.



These aren't the only maps in the NYT piece. The article is full of them. Each one carefully designed to explore a specific aspect of the disaster: the history of storms, reports, evacuations etc.

It's maps like those from The Washington Post and New York Times that prove that good cartography does exist and it matters. We really don't deserve the sort of maps that NWS pumps out. They're just really awful to look at, fail on a cognitive level and prove they haven't the first clue about how to effectively communicate their own science and data.

The irony is that the NYT map uses the NWS data of the rainfall data to make their own version and prove that it's perfectly possible to make terrific maps that communicate and which once again give us more reasons to #endtherainbow. Well played.

#endtherainbow





09 Oct 15:26

Share of Politico stories mentioning the word "Poll" over time [OC]

by /u/j_yazman
Jdinoto

The comments section contains a link to the data itself: https://github.com/joshyazman/random-projects/tree/master/politico-scrape

06 Oct 14:04

I do these interactive data vis projects where people use push pins and string to add data to a board, and then I digitize them in Adobe Illustrator afterwards. [OC]

by /u/whiskeyfoxtrot_
Jdinoto

This is great!

06 Oct 13:49

Excel is the graveyard of charts, no!

by junkcharts
Jdinoto

A good note about excel charts, they're not entirely worthless.

It's true that Excel is responsible for large numbers of horrible charts. I just came across a typical example recently:

Ewolff_meanmedianincome

This figure comes from Edward Wolff's 2012 paper, "The Asset Price Meltdown and the Wealth of the Middle Class." It's got all the hallmarks of Excel defaults. It's not a pleasing object to look at.

However, it's also true that Excel can be used to make nice charts. Here is a remake:

Redo_meanmedianincome2

This chart is made almost entirely in Excel - the only edit I made outside Excel is to decompose the legend box.

It takes five minutes to make the first chart; it takes probably 30 minutes to make the second chart. That is the difference between good and bad graphics. Excel users: let that be your inspiration!