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 !
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:
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.
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?
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.
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!
Or we could focus attention on Texas, the state with the greatest market share decline.
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!
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.
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:
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 firstname.lastname@example.org 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!
Adolfo visualized cumulative hurricanes in an annotated line graph:
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)."
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
Andrew chose a view that focused on major hurricanes with a line graph:
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.
Andy (Twitter | LinkedIn) likened hurricane predictions to flipping coins in his colorful view of the data:
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!"
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."
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).
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.
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.
Cindy C. & Amanda D.
Cindy and Amanda worked together on a bar/line combo:
Dangfun plots total and percent Category 3 and above with lines and bars and the headline, "Fewer but Stronger."
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.
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."
Eduardo highlights a declining trend via 30-year buckets:
Gavin also highlights a decreasing overall trend, but also comments on a slight increase in the most damaging storms:
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)."
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."
Heather (Twitter) focuses attention on major hurricanes:
Kat (Twitter) pulled together a couple graphs and annotations into a single view:
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."
Kevin R. created a line graph focusing on the decrease in total hurricanes over time.
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."
Man listed what was less than ideal in the original...
Color bars of the hurricane categories look cluttered. At the first glance, it’s hard to tell what do these different colors tell.
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.
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:
Drop the color bars for categories. Instead, I collapsed the categories into “strong winds” and “major hurricanes.”
Keep only one trend line for the Total Hurricane. Make the headline right inside the chart to make it more prominent.
Drop the major hurricane trend line since I don’t think such trend is significant based on the data.
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…
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.
Matthew chose a horizontal bar chart, emphasizing the most recent decade:
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."
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.
Miguel from Portugal created two views of the data in Excel:
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.
Min (Twitter) chose a side-by-side layout and highlights the proportion of major hurricanes increasing:
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.' "
Olivier (LinkedIn) from Switzerland shared the following comments on the original graph:
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).
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.
Bad dataviz: cumulated bars + cumulated periods. Absolutely meaningless!
Bad dataviz: the data was not including years with no hit. That shall be corrected before plotting a time-based axis.
Raf (Facebook) from Belgium shared the following view:
Rahul (Twitter) said his story would have revolved around the following four views:
Rebeca (LinkedIn) "basically merged the hurricanes into two categories (minor and major) and looked at both frequency and intensity by decade."
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.
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:
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.
Number 5 category should be up high the graph, number 1 should be down
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).
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:
Ryan left the historical data there for context, but focused attention on the more recent decades:
Sam writes, "I have tried to simplify it, while keeping the major features."
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:
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.
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.
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."
Srikanth shares the following views:
Teresa (LinkedIn) says her headline would be “Hurricanes this half century on track with previous.”
Thomas (Profile | Twitter) from Austria used R (especially dplyr and ggplot2) to build the following hurricane timeline:
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."
Last, but not least, Ziwei shares the following stacked area graph, concluding "no strong trends"—
Huge thanks again to everyone who participated for taking the time and sharing your work!
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 havebeencritical 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?
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?
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.
micah: But let me introduce another element here …
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?
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
Shelley Moore Capito
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.
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.
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?
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.
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.
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.”
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.
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.
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.
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.
Unusual personal names collected by onomasticist Elsdon C. Smith for his Treasury of Name Lore, 1967:
Dr. Pacifico D. Quitiquit
Lala Legattee Wiggins
Marietta Avenue Jeeter
St. Elmo Bug
Trammer W. Splown
Ephraim Very Ott
Park A. Carr
May June July
Napoleon N. Waterloo
According to the Veterans Administration, Love’n Kisses Love is a deceased sailor formerly of Bremerton, Washington. Walt Disney employed an animator named T. Hee. Outerbridge Horsey VI was named ambassador to Czechoslovakia in 1963. (“I am the sixth Outerbridge Horsey and my unhappy son is the seventh. In fact, the only trouble with any new post is explaining the name to people.”) Gisella Werberzerck Piffl was a character actress in Australia in 1948. Two police officers who worked together in Long Beach, California, in 1953 were named Goforth and Ketchum. Jack Benny’s wife said that the firm Batten, Barton, Durstine & Osborn (now BBDO) “sounds like a trunk falling down stairs.”
And “When Mrs. Rum of Chicago divorced her husband she was allowed to resume her maiden name of Cork.”
An excellent reflection on the "cone of uncertainty".
(Updated on September 8 and 9. Go to the bottom of the post)
Data visualization isn't just about visualizing data, but also about writing headlines, intros, captions, explainers, and footnotes. I'm right now closely following the news about hurricane Irma —I live in Miami!— and feeling both amazed and terrified by the many great graphics news organizations and independent designers are publishing. As I've just tweeted, beauty is sometimes correlated with terror.
I'm no expert in weather forecasting, but I believe that this is inaccurate. To learn why, go to minute 14:30 in my keynote at Microsoft's Data Insights summit. Here's some of what I said there:
Maps based on cones of uncertainty are quite problematic, as this article by Jen Christiansen, and this other by Robert Kosara explain. Among other reasons, some people don't see in that cone the possible range of paths the center of the hurricane can take, but the size of the hurricane itself.
This happens event to those who, like me, do know how to read this kind of map. I need to consciously struggle with my brain's inclination to see a physical object, and not a probability range. Why? I don't know for sure, but I'll make a conjecture: it's because the representation looks pictorial. The rounded shape of the tip of the cone roughly resembles the shape of a hurricane.
This map is made even more confusing if a black line is placed in the middle of the cone. Just read tweets like this. People may see that line not as a visual aid to emphasize the center of the cone (right), but as the most probable path (wrong).
Going back to the caption, the reason why it sounds wrong to me is related to something most of you probably aren't aware of: the cone of uncertainty doesn't represent the range of all possible paths the hurricane could follow, based on simulations. This excellent paperexplains that the most common cone, the one by NHC, “accurately predicts the ultimate path of the tropical cyclone’s center about 2/3 of the time (J. Franklin 2005, personal communication). In other words, one out of three storm centers directly impact areas outside of the cone.” That's a 66%-33% chance.
Therefore, the caption could say something like this: “Based on predictive simulations of past hurricanes, there are 2 out of 3 chances that the path of the center of the hurricane could be anywhere within this cone, and a 1 out of 3 chance it will be outside of it.” This is longer and clunkier —I'm sure that any copy editor in the audience can improve it!— but truer to reality.
This other map shows the actual uncertainty of predictive simulations quite well; notice the faded lines, corresponding to less probable (but still possible) paths:
UPDATE: It seems that NOAA is listening. See the explanation that they have been tweeting. It ought to be published next to every single cone of uncertainty map out there:
UPDATE 2: The map below, by meteorologist Ryan Maue, is far better than any cone of uncertainty map if your goal is to inform the general public about the risks posed by wind. See it animated. The scale is predicted maximum wind speed in mph.