Women workers taken for a loop or four

I was drawn to the following chart in Business Insider because of the calendar metaphor. (The accompanying article is here.)

Businessinsider_payday

Sometimes, the calendar helps readers grasp concepts faster but I'm afraid the usage here slows us down.

The underlying data consist of just four numbers: the wage gaps between race and gender in the U.S., considered simply from an aggregate median personal income perspective. The analyst adopts the median annual salary of a white male worker as a baseline. Then, s/he imputes the number of extra days that others must work to attain the same level of income. For example, the median Asian female worker must work 64 extra days (at her daily salary level) to match the white guy's annual pay. Meanwhile, Hispanic female workers must work 324 days extra.

There are a host of reasons why the calendar metaphor backfired.

Firstly, it draws attention to an uncomfortable detail of the analysis - which papers over the fact that weekends or public holidays are counted as workdays. The coloring of the boxes compounds this issue. (And the designer also got confused and slipped up when applying the purple color for Hispanic women.)

Secondly, the calendar focuses on Year 2 while Year 1 lurks in the background - white men have to work to get that income (roughly $46,000 in 2017 according to the Census Bureau).

Thirdly, the calendar view exposes another sore point around the underlying analysis. In reality, the white male workers are continuing to earn wages during Year 2.

The realism of the calendar clashes with the hypothetical nature of the analysis.

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One can just use a bar chart, comparing the number of extra days needed. The calendar design can be considered a set of overlapping bars, wrapped around the shape of a calendar.

The staid bars do not bring to life the extra toil - the message is that these women have to work harder to get the same amount of pay. This led me to a different metaphor - the white men got to the destination in a straight line but the women must go around loops (extra days) before reaching the same endpoint.

Redo_businessinsider_racegenderpaygap

While the above is a rough sketch, I made sure that the total length of the lines including the loops roughly matches the total number of days the women needed to work to earn $46,000.

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The above discussion focuses solely on the V(isual) corner of the Trifecta Checkup, but this data visualization is also interesting from the D(ata) perspective. Statisticians won't like such a simple analysis that ignores, among other things, the different mix of jobs and industries underlying these aggregate pay figures.

Now go to my other post on the sister (book) blog for a discussion of the underlying analysis.

 

 


Where are the Democratic donors?

I like Alberto's discussion of the attractive maps about donors to Democratic presidential candidates, produced by the New York Times (direct link).

Here is the headline map:

Nyt_demdonormaps

The message is clear: Bernie Sanders is the only candidate with nation-wide appeal. The breadth of his coverage is breath-taking. (I agree with Alberto's critique about the lack of a color scale. It's impossible to know if the counts are trivial or not.)

Bernie's coverage is so broad that his numbers overwhelm those of all other candidates except in their home bases (e.g. O'Rourke in Texas).

A remedy to this is to look at the data after removing Bernie's numbers.

Nyt_demdonormap_2

 

This pair of maps reminds me of the Sri Lanka religions map that I revisualized in this post.

Redo_srilankareligiondistricts_v2

The first two maps divide the districts into those in which one religion dominates and those in which multiple religions share the limelight. The third map then shows the second-rank religion in the mixed-religions districts.

The second map in the NYT's donor map series plots the second-rank candidate in all the precincts that Bernie Sanders lead. It's like the designer pulled off the top layer (blue: Bernie) to reveal what's underneath.

Because all of Bernie's data are removed, O'Rourke is still dominating Texas, Buttigieg in Indiana, etc. An alternative is to pull off the top layer in those pockets as well. Then, it's likely to see Bernie showing up in those areas.

The other startling observation is how small Joe Biden's presence is on these maps. This is likely because Biden relies primarily on big donors.

See here for the entire series of donor maps. See here for past discussion of New York Times's graphics.


Powerful photos visualizing housing conditions in Hong Kong

I was going to react to Alberto's post about the New York Times's article about economic inequality in Hong Kong, which is proposed as one origin to explain the current protest movement. I agree that the best graphic in this set is the "photoviz" showing the "coffins" or "cages" that many residents live in, because of the population density. 

Nyt_hongkong_apartment_photoviz

Then I searched the archives, and found this old post from 2015 which is the perfect response to it. What's even better, that post was also inspired by Alberto.

The older post featured a wonderful campaign by human rights organization Society for Community Organization that uses photoviz to draw attention to the problem of housing conditions in Hong Kong. They organized a photography exhibit on this theme in 2014. They then updated the exhibit in 2016.

Here is one of the iconic photos by Benny Lam:

Soco_trapped_B1

I found more coverage of Benny's work here. There is also a book that we can flip on Vimeo.

In 2017, the South China Morning Post (SCMP) published drone footage showing the outside view of the apartment buildings.

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What's missing is the visual comparison to the luxury condos where the top 1 percent live. For these, one can  visit the real estate sites, such as Sotheby's. Here is their "12 luxury homes for sales" page.

Another comparison: a 1000 sq feet apartment that sits between those extremes. The photo by John Butlin comes from SCMP's Post Magazine's feature on the apartment:

Butlin_scmp_home

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Also check out my review of Alberto's fantastic, recent book, How Charts Lie.

Cairo_howchartslie_cover

 

 


SCMP's fantastic infographic on Hong Kong protests

In the past month, there have been several large-scale protests in Hong Kong. The largest one featured up to two million residents taking to the streets on June 16 to oppose an extradition act that was working its way through the legislature. If the count was accurate, about 25 percent of the city’s population joined in the protest. Another large demonstration occurred on July 1, the anniversary of Hong Kong’s return to Chinese rule.

South China Morning Post, which can be considered the New York Times of Hong Kong, is well known for its award-winning infographics, and they rose to the occasion with this effort.

This is one of the rare infographics that you’d not regret spending time reading. After reading it, you have learned a few new things about protesting in Hong Kong.

In particular, you’ll learn that the recent demonstrations are part of a larger pattern in which Hong Kong residents express their dissatisfaction with the city’s governing class, frequently accused of acting as puppets of the Chinese state. Under the “one country, two systems” arrangement, the city’s officials occupy an unenviable position of mediating the various contradictions of the two systems.

This bar chart shows the growth in the protest movement. The recent massive protests didn't come out of nowhere. 

Scmp_protestsovertime

This line chart offers a possible explanation for burgeoning protests. Residents’ perceived their freedoms eroding in the last decade.

Scmp_freedomsurvey

If you have seen videos of the protests, you’ll have noticed the peculiar protest costumes. Umbrellas are used to block pepper sprays, for example. The following lovely graphic shows how the costumes have evolved:

Scmp_protestcostume

The scale of these protests captures the imagination. The last part in the infographic places the number of protestors in context, by expressing it in terms of football pitches (as soccer fields are known outside the U.S.) This is a sort of universal measure due to the popularity of football almost everywhere. (Nevertheless, according to Wikipedia, the fields do not have one fixed dimension even though fields used for international matches are standardized to 105 m by 68 m.)

Scmp_protestscale_pitches

This chart could be presented as a bar chart. It’s just that the data have been re-scaled – from counting individuals to counting football pitches-ful of individuals. 

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Here is the entire infographics.


Three estimates, two differences trip up an otherwise good design

Reader Fernando P. was baffled by this chart from the Perception Gap report by More in Common. (link to report)

Moreincommon_perceptiongap_republicans

Overall, this chart is quite good. Its flaws are subtle. There is so much going on, perhaps even the designer found it hard to keep level.

The title is "Democrat's Perception Gap" which actually means the gap between Democrats' perception of Republicans and Republican's self-reported views. We are talking about two estimates of Republican views. Conversely, in Figure 2 (not shown), the "Republican's Perception Gap" describes two estimates of Democrat views.

The gap is visually shown as the gray bar between the red dot and the blue dot. This is labeled perception gap, and its values are printed on the right column, also labeled perception gap.

Perhaps as an after-thought, the designer added the yellow stripes, which is a third estimate of Republican views, this time by Independents. This little addition wreaks havoc. There are now three estimates - and two gaps. There is a new gap, between Independents' perception of Republican views, and Republican's self-reported views. This I-gap is hidden in plain sight. The words "perception gap" obstinately sticks to the D-gap.

***

Here is a slightly modified version of the same chart.

Redo_perceptiongap_republicans

 

The design focuses attention on the two gaps (bars). It also identifies the Republican self-perception as the anchor point from which the gaps are computed.

I have chosen to describe the Republican dot as "self-perception" rather than "actual view," which connotes a form of "truth." Rather than considering the gap as an error of estimation, I like to think of the gap as the difference between two groups of people asked to estimate a common quantity.

Also, one should note that on the last two issues, there is virtual agreement.

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Aside from the visual, I have doubts about the value of such a study. Only the most divisive issues are being addressed here. Adding a few bipartisan issues would provide controls that can be useful to tease out what is the baseline perception gap.

I wonder whether there is a self-selection in survey response, such that people with extreme views (from each party) will be under-represented. Further, do we believe that all survey respondents will provide truthful answers to sensitive questions that deal with racism, sexism, etc.? For example, if I am a moderate holding racist views, would I really admit to racism in a survey?

 

 


A chart makes an appearance in my new video

Been experimenting with short videos recently. My latest is a short explainer on why some parents are willing to spend over a million dollars to open back doors to college admissions. I even inserted a chart showing some statistics. Click here to see the video.

 

Also, subscribe to my channel to see future episodes of Inside the Black Box.

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Here are a couple of recent posts related to college admissions.

  • About those so-called adversity scores (link)
  • A more detailed post on various college admissions statistics (link)

Morphing small multiples to investigate Sri Lanka's religions

Earlier this month, the bombs in Sri Lanka led to some data graphics in the media, educating us on the religious tensions within the island nation. I like this effort by Reuters using small multiples to show which religions are represented in which districts of Sri Lanka (lifted from their twitter feed):

Reuters_srilanka_religiondistricts

The key to reading this map is the top legend. From there, you'll notice that many of the color blocks, especially for Muslims and Catholics are well short of 50 percent. The absence of the darkest tints of green and blue conveys important information. Looking at the blue map by itself misleads - Catholics are in the minority in every district except one. In this setup, readers are expected to compare between maps, and between map and legend.

The overall distribution at the bottom of the chart is a nice piece of context.

***

The above design isolates each religion in its own chart, and displays the spatial spheres of influence. I played around with using different ways of paneling the small multiples.

In the following graphic, the panels represent the level of dominance within each district. The first panel shows the districts in which the top religion is practiced by at least 70 percent of the population (if religions were evenly distributed across all districts, we expect 70 percent of each to be Buddhists.) The second panel shows the religions that account for 40 to 70 percent of the district's residents. By this definition, no district can appear on both the left and middle maps. This division is effective at showing districts with one dominant religion, and those that are "mixed".

In the middle panel, the displayed religion represents the top religion in a mixed district. The last panel shows the second religion in each mixed district, and these religions typically take up between 25 and 40 percent of the residents.

Redo_srilankareligiondistricts_v2

The chart shows that other than Buddhists, Hinduism is the only religion that dominates specific districts, concentrated at the northern end of the island. The districts along the east and west coasts and the "neck" are mixed with the top religion accounting for 40 to 70 percent of the residents. By assimilating the second and the third panels, the reader sees the top and the second religions in each of these mixed districts.

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This example shows why in the Trifecta Checkup, the Visual is a separate corner from the Question and the Data. Both maps utilize the same visual design, in terms of forms and colors and so on, but they deliver different expereinces to readers by answering different questions, and cutting the data differently.

 

P.S. [5/7/2019] Corrected spelling of Hindu.


Say it thrice: a nice example of layering and story-telling

I enjoyed the New York Times's data viz showing how actively the Democratic candidates were criss-crossing the nation in the month of March (link).

It is a great example of layering the presentation, starting with an eye-catching map at the most aggregate level. The designers looped through the same dataset three times.

Nyt_candidatemap_1

This compact display packs quite a lot. We can easily identify which were the most popular states; and which candidate visited which states the most.

I noticed how they handled the legend. There is no explicit legend. The candidate names are spread around the map. The size legend is also missing, replaced by a short sentence explaining that size encodes the number of cities visited within the state. For a chart like this, having a precise size legend isn't that useful.

The next section presents the same data in a small-multiples layout. The heads are replaced by dots.

Nyt_candidatemap_2

This allows more precise comparison of one candidate to another, and one location to another.

This display has one shortcoming. If you compare the left two maps above, those for Amy Klobuchar and Beto O'Rourke, it looks like they have visited roughly similar number of cities when in fact Beto went to 42 compared to 25. Reducing the size of the dots might work.

Then, in the third visualization of the same data, the time dimension is emphasized. Lines are used to animate the daily movements of the candidates, one by one.

Nyt_candidatemap_3

Click here to see the animation.

When repetition is done right, it doesn't feel like repetition.