The canonical U.S. political map

The previous posts feature the canonical political map of U.S. presidential elections, the vote margin shift map. The following realization of it, made by NBC News (link), drills down to the counties with the largest Asian-American populations:

How does this map form encode the data?

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The key visual element is the arrow. The arrow has a color, a length and also an angle.

The color scheme is fixed to the canonical red-blue palette attached to America's two major political parties.

The angle of the arrow, as seen in the legend, carries no data at all. All arrows are slanted at the same angles. Not quite; the political party is partially encoded into the angle, as the red arrows slant one way while the blue arrows always slant the other way. The degree of slant is constant everywhere, though.

So only the lengths of the arrows contain the vote margin gain/loss data. The legend shows arrows of two different lengths but vote margins have not been reduced to two values. As evident on the map, the arrow lengths are continuous.

The designer has a choice when it comes to assigning colors to these arrows. The colors found on the map above depicts the direction of the vote margin shift so red arrows indicate counties in which the Republicans gained share. (The same color encoding is used by the New York Times.)

Note that a blue county could have shifted to the right, and therefore appear as a red arrow even though the county voted for Kamala Harris in 2024. Alternatively, the designer could have encoded the 2024 vote margin in the arrow color. While this adds more data to the map, it could wreak havoc with our perception as now all four combinations are possible: red, pointing left; red, pointing right; blue, pointing left; and blue, pointing right.

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To sum this all up, the whole map is built from a single data series, the vote margin shift expressed as a positive or negative percentage, in which a positive number indicates Republicans increased the margin. The magnitude of this data is encoded in the arrow length, ignoring the sign. The sign (direction) of the data, a binary value, is encoded into the arrow color as well as the direction of the arrow.

In other words, it's a proportional symbol map in which each geographical region is represented by a symbol (typically a bubble), and a single numeric measure is encoded in the size of the symbol. In many situations, the symbol's color is used to display a classification of the geographical regions.

The symbol used for the "wind map" are these slanted arrows. The following map, pulled from CNN (link), makes it clear that the arrows play only the role of a metaphor, the left-right axis of political attitude.

This map is essentially the same as the "wind map" used by the New York Times and NBC News, the key difference being that instead of arrows, the symbol is a triangle. On proportional triangle maps, the data is usually encoded in the height of the triangles, so that the triangles can be interpreted as "hills". Thus, the arrow length in the wind map is the hill height in the triangle map. The only thing left behind is the left-right metaphor.

The CNN map added a detail. Some of the counties have a dark gray color. These are "flipped". A flip is defined as a change in "sign" of the vote margin from 2020 to 2024. A flipped county can exhibit either a blue or a red hill. The direction of the flip is actually constrained by the hill color. If it's a red hill, we know there is a shift towards Republicans, and in addition, the county flipped, it must be that Democrats won that county in 2020, and it flipped to Republicans. Similiar, if a blue hill sits on a dark gray county, then the county must have gone for Republicans in 2020 and flipped to Democrats in 2024.

 

Gaining precision by deleting data

The title is a bit of a paradox, isn't it? When we want more precise knowledge about something, we want to gather more data, at greater granularity. But it's not that simple.

Here is the famous "wind map" by the New York Times (link) showing vote margin shifts in the U.S. Presidential elections from 2020 to 2024, at the county level. A red arrow pointing rightward indicates a county in which the voters shifted toward the Republican candidate (Trump). It paints the red wave story loud and clear.

Even though every county is on the map, this map alone doesn't answer all possible questions about vote margin shift. For example, someone might be interested in the vote margin shift in counties with high Hispanic populations. It's impossible to learn this from the above map, even if one has a list of the names of these counties.

The answer is found in the following map, published by NBC News here:

The story is also very clear. This map can be thought of as the NYT map minus the counties that have negligible Hispanic populations. By deleting all unrelated data, the designer highlights the story about Hispanic voters.

The reader can use the tab up top to see partial shift maps that emphasize different demographic groups. Well done!

 

 

 

 

Election coverage prompts good graphics

The election broadcasts in the U.S. are full-day affairs, and they make a great showcase for interactive graphics.

The election setting is optimal as it demands clear graphics that are instantly digestible. Anything else would have left viewers confused or frustrated.

The analytical concepts conveyed by the talking heads during these broadcasts are quite sophisticated, and they did a wonderful job at it.

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One such concept is the value of comparing statistics against a benchmark (or, even multiple benchmarks). This analytics tactic comes in handy in the 2024 election especially, because both leading candidates are in some sense incumbents. Kamala was part of the Biden ticket in 2020, while Trump competed in both 2016 and 2020 elections.

In the above screenshot, taken around 11 pm on election night, the MSNBC host (that looks like Steve K.) was searching for Kamala votes because it appeared that she was losing the state of Georgia. The question of the moment: were there enough votes left for her to close the gap?

In the graphic (first numeric column), we were seeing Kamala winning 65% of the votes, against Trump's 34%, in Douglas county in Georgia. At first sight, one would conclude that Kamala did spectacularly well here.

But, is 65% good enough? One can't answer this question without knowing past results. How did Biden-Harris do in the 2020 election when they won the presidency?

The host touched the interactive screen to reveal the second column of numbers, which allows viewers to directly compare the results. At the time of the screenshot, with 94% of the votes counted, Kamala was performing better in this county than they did in 2020 (65% vs 62%). This should help her narrow the gap.

If in 2020, they had also won 65% of the Douglas county votes, then, we should not expect the vote margin to shrink after counting the remaining 6% of votes. This is why the benchmark from 2020 is crucial. (Of course, there is still the possibility that the remaining votes were severely biased in Kamala's favor but that would not be enough, as I'll explain further below.)

All stations used this benchmark; some did not show the two columns side by side, making it harder to do the comparison.

Interesting side note: Douglas county has been rapidly shifting blue in the last two decades. The proportion of whites in the county dropped from 76% to 35% since 2000 (link).

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Though Douglas county was encouraging for Kamala supporters, the vote gap in the state of Georgia at the time was over 130,000 in favor of Trump. The 6% in Douglas represented only about 4,500 votes (= 70,000*0.06/0.94). Even if she won all of them (extremely unlikely), it would be far from enough.

So, the host flipped to Fulton county, the most populous county in Georgia, and also a Democratic stronghold. This is where the battle should be decided.

Using the same format - an interactive version of a small-multiples arrangement, the host looked at the situation in Fulton. The encouraging sign was that 22% of the votes here had not yet been counted. Moreover, she captured 73% of those votes that had been tallied. This was 10 percentage points better than her performance in Douglas, Ga. So, we know that many more votes were coming in from Fulton, with the vast majority being Democratic.

But that wasn't the full story. We have to compare these statistics to our 2020 benchmark. This comparison revealed that she faced a tough road ahead. That's because Biden-Harris also won 73% of the Fulton votes in 2020. She might not earn additional votes here that could be used to close the state-wide gap.

If the 73% margin held to the end of the count, she would win 90,000 additional votes in Fulton but Trump would win 33,000, so that the state-wide gap should narrow by 57,000 votes. Let's round that up, and say Fulton halved Trump's lead in Georgia. But where else could she claw back the other half?

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From this point, the analytics can follow one of two paths, which should lead to the same conclusion. The first path runs down the list of Georgia counties. The second path goes up a level to a state-wide analysis, similar to what was done in my post on the book blog (link).

Around this time, Georgia had counted 4.8 million votes, with another 12% outstanding. So, about 650,000 votes had not been assigned to any candidate. The margin was about 135,000 in Trump's favor, which amounted to 20% of the outstanding votes. But that was 20% on top of her base value of 48% share, meaning she had to claim 68% of all remaining votes. (If in the outstanding votes, she got the same share of 48% as in the already-counted, then she would lose the state with the same vote margin as currently seen, and would lose by even more absolute votes.)

The reason why the situation was more hopeless than it even sounded here is that the 48% base value came from the 2024 votes that had been counted; thus, for example, it included her better-than-benchmark performance in Douglas county. She would have to do even better to close the gap! In Fulton, which has the biggest potential, she was unable to push the vote share above the 2020 level.

That's why in my book blog (link), I suggested that the networks could have called Georgia (and several other swing states) earlier, if they used "numbersense" rather than mathematical impossibility as the criterion.

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Before ending, let's praise the unsung heroes - the data analysts who worked behind the scenes to make these interactive graphics possible.

The graphics require data feeds, which cover a broad scope, from real-time vote tallies to total votes casted, both at the county level and the state level. While the focus is on the two leading candidates, any votes going to other candidates have to be tabulated, even if not displayed. The talking heads don't just want raw vote counts; in order to tell the story of the election, they need some understanding of how many votes are still to be counted, where they are coming from, what's the partisan lean on those votes, how likely is the result going to deviate from past elections, and so on.

All those computations must be automated, but manually checked. The graphics software has to be reliable; the hosts can touch any part of the map to reveal details, and it's not possible to predict all of the user interactions in advance.

Most importantly, things will go wrong unexpectedly during election night so many data analysts were on standby, scrambling to fix issues like breakage of some data feed from some county in some state.

Adjust, and adjust some more

This Financial Times report illustrates the reason why we should adjust data.

The story explores the trend in economic statistics during 14 years of governing by conservatives. One of those metrics is so-called council funding (local governments). The graphic is interactive: as the reader scrolls the page, the chart transforms.

The first chart shows the "raw" data.

The vertical axis shows year-on-year change in funding. It is an index relative to the level in 2010. From this line chart, one concludes that council funding decreased from 2010 to around 2016, then grew; by 2020, funding has recovered to the level of 2010 and then funding expanded rapidly in recent years.

When the reader scrolls down, this chart is replaced by another one:

This chart contains a completely different picture. The line dropped from 2010 to 2016 as before. Then, it went flat, and after 2021, it started raising, even though by 2024, the value is still 10 percent below the level in 2010.

What happened? The data journalist has taken the data from the first chart, and adjusted the values for inflation. Inflation was rampant in recent years, thus, some of the raw growth have been dampened. In economics, adjusting for inflation is also called expressing in "real terms". The adjustment is necessary because the same dollar (hmm, pound) is worth less when there is inflation. Therefore, even though on paper, council funding in 2024 is more than 25 percent higher than in 2010, inflation has gobbled up all of that and more, to the point in which, in real terms, council funding has fallen by 20 percent.

This is one material adjustment!

Wait, they have a third chart:

It's unfortunate they didn't stabilize the vertical scale. Relative to the middle chart, the lowest point in this third chart is about 5 percent lower, while the value in 2024 is about 10 percent lower.

This means, they performed a second adjustment - for population change. It is a simple adjustment of dividing by the population. The numbers look worse probably because population has grown during these years. Thus, even if the amount of funding stayed the same, the money would have to be split amongst more people. The per-capita adjustment makes this point clear.

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The final story is much different from the initial one. Not only was the magnitude of change different but the direction of change reversed.

Whenever it comes to adjustments, remember that all adjustments are subjective. In fact, choosing not to adjust is also subjective. Not adjusting is usually much worse.

 

 

 

 

The art of making simple things harder

It's no longer a shock when a TV network such as MSNBC plays loose with the scaling of the column heights, as in this recent example:

Hat tip to Mark P. for forwarding the image, and Rachel for the original tweet.

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What's shocking is that the designer appears to believe that the column heights of a column chart can be determined without reference to the data.

There is not a single relationship that has been retained on this chart. The designer just picks whatever size column is desired.

One obvious distortion is between the Biden and Trump columns. Trump's number is about 1/3 of Biden's (120 vs 40), and yet the red column's height is 70% of the blue's.

Furthermore, amongst the red columns, the heights are also haphazard. Trump's number is almost 3 times larger than Haley's; the ratio of column heights is almost 4 times. Haley's number is just a tad higher than DeSantis and yet Haley's column is twice the height of DeSantis.

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There is a further, subtle distortion of the column's widths. By curving the chart canvas, certain columns are widened more than others. The diagram above retains the distorted widths and you can see that the Desantis column is wider than that of Haley's.

Here is what the undistorted column chart looks like:

It's easy to make such a chart in Excel or any charting software, so it's mystery why this type of distortion happens. Did the designer open up an empty canvas and start putting up columns of any size?

An elaborate data vessel

I recently came across the following dataviz showing global oil production (link).

This is an ambitious graphic that addresses several questions of composition.

The raw data show the amount of production by country adding up to the global total. The countries are then grouped by region. Further, the graph presents an oil-and-gas specific grouping, as indicated by the legend shown just below the chart title. This grouping is indicated by the color of the circumference of the circle containing the flag of the country.

This chart form is popular in modern online graphics programs. It is like an elaborate data vessel. Because the countries are lined up around the barrel, a space has been created on three sides to admit labels and text annotations. This is a strength of this chart form.

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The chart conveys little information about the underlying data. Each country is given a unique odd shaped polygon, making it impossible to compare sizes. It’s definitely possible to pick out U.S., Russia, Saudi Arabia as the top producers. But in presenting the ranks of the data, this chart form pales in comparison to a straightforward data table, or a bar chart. The less said about presenting values, the better.

Indeed, our self-sufficiency test exposes the inability of these polygons to convey the data. This is precisely why almost all values of the dataset are present on the chart.

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The dataviz subtly presumes some knowledge on the part of the readers.

The regions are not directly labeled. The readers must know that Saudi Arabia is in the Middle East, U.S. is part of North America, etc. Admittedly this is not a big ask, but it is an ask.

It is also assumed that readers know their flags, especially those of smaller countries. Some of the small polygons have no space left for country names and they are labeled with just flags.

In addition, knowing country acronyms is required for smaller countries as well. For example, in Africa, we find AGO, COG and GAB.

For this chart form the designer treats each country according to the space it has on the chart (except those countries that found themselves on the edges of the barrel). Font sizes, icons, labels, acronyms, data labels, etc. vary.

The readers are assumed to know the significance of OPEC and OPEC+. This grouping is given second fiddle, and can be found via the color of the circumference of the flag icons.

I’d have not assigned a color to the non-OPEC countries, and just use the yellow and blue for OPEC and OPEC+. This is a little edit but makes the search for the edges more efficient.

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Let’s now return to the perception of composition.

In exactly the same manner as individual countries, the larger regions are represented by polygons that have arbitrary shapes. One can strain to compile the rank order of regions but it’s impossible to compare the relative values of production across regions. Perhaps this explains the presence of another chart at the bottom that addresses this regional comparison.

The situation is worse for the OPEC/OPEC+ grouping. Now, the readers must find all flag icons with edges of a specific color, then mentally piece together these arbitrarily shaped polygons, then realizing that they won’t fit together nicely, and so must now mentally morph the shapes in an area-preserving manner, in order to complete this puzzle.

This is why I said earlier this is an elaborate data vessel. It’s nice to look at but it doesn’t convey information about composition as readers might expect it to.

Partition of Europe

A long-time reader sent me the following map via twitter:

This map tells how the major political groups divide up the European Parliament. I’ll spare you the counting. There are 27 countries, and nine political groups (including the "unaffiliated").

The key chart type is a box of dots. Each country gets its own box. Each box has its own width. What determines the width? If you ask me, it’s the relative span of the countries on the map. For example, the narrow countries like Ireland and Portugal have three dots across while the wider countries like Spain, Germany and Italy have 7, 10 and 8 dots across respectively.

Each dot represents one seat in the Parliament. Each dot has one of 9 possible colors. Each color shows a political lean e.g. the green dots represent Green parties while the maroon dots display “Left” parties.

The end result is a counting game. If we are interested in counts of seats, we have to literally count each dot. If we are interested in proportion of seats, take your poison: either eyeball it or count each color and count the total.

Who does the underlying map serve? Only readers who know the map of Europe. If you don’t know where Hungary or Latvia is, good luck. The physical constraints of the map work against the small-multiples set up of the data. In a small multiples, you want each chart to be identical, except for the country-specific data. The small-multiples structure requires a panel of equal-sized cells. The map does not offer this feature, as many small countries are cramped into Eastern Europe. Also, Europe has a few tiny states e.g. Luxembourg (population 660K)  and Malta (population 520K). To overcome the map, the designer produces boxes of different sizes, substantially loading up the cognitive burden on readers.

The map also dictates where the boxes are situated. The centroids of each country form the scaffolding, with adjustments required when the charts overlap. This restriction ensures a disorderly appearance. By contrast, the regular panel layout of a small multiples facilitates comparisons.

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Here is something I sketched using a tile map.

First, I have to create a tile map of European countries. Some parts, e.g. western part, are straightforward. The eastern side becomes very congested.

The tile map encodes location in an imprecise sense. Think about the scaffolding of centroids of countries referred to prior. The tile map imposes an order to the madness - we're shifting these centroids so that they line up in a tidier pattern. What we gain in comparability we concede in location precision.

For the EU tile map, I decided to show the Baltic countries in a row rather than a column; the latter would have been more faithful to the true geography. Malta is shown next to Italy even though it could have been placed below. Similarly, Cyprus in relation to Greece. I also included several key countries that are not part of the EU for context.

Instead of raw seat counts, I'm showing the proportion of seats within each country claimed by each political group. I think this metric is more useful to readers.

The legend is itself a chart that shows the aggregate statistics for all 27 countries.

One bubble is a tragedy, and a bag of bubbles is...

From Kathleen Tyson's twitter account, I came across a graphic showing the destinations of Ukraine's grain exports since 2022 under the auspices of a UN deal. This graphic, made by AFP, uses one of the chart forms that baffle me - the bag of bubbles.

The first trouble with a bag of bubbles is the single bubble. The human brain is just not fit for comparing bubble sizes. The self-sufficiency test is my favorite device for demonstrating this weakness. The following is the European section of the above chart, with the data labels removed.

How much bigger is Spain than the Netherlands? What's the difference between Italy and the Netherlands? The answers don't come easily to mind. (The Netherlands is about 40% the size of Spain, and Italy is about 20% larger than the Netherlands.)

While comparing relative circular areas is a struggle, figuring out the relative ranks is not. Sure, it gets tougher with small differences (Germany vs S. Korea, Belgium vs Portugal) but saying those pairs are tied isn't a tragedy.

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Another issue with bubble charts is how difficult it is to assess absolute values. A circle on its own has no reference point. The designer needs to add data labels or a legend. Adding data labels is an act of giving up. The data labels become the primary instrument for communicating the data, not the visual construct. Adding one data label is not enough, as the following shows:

Being told that Spain's value is 4.1 does little to help estimate the values for the non-labelled bubbles.

The chart does come with the following legend:

For this legend to work, the sample bubble sizes should span the range of the data. Notice that it's difficult to extrapolate from the size of the 1-million-ton bubble to 2-million, 4-million, etc. The analogy is a column chart in which the vertical axis does not extend through the full range of the dataset.

The designer totally gets this. The chart therefore contains both selected data labels and the partial legend. Every bubble larger than 1 million tons has an explicit data label. That's one solution for the above problem.

Nevertheless, why not use another chart form that avoids these problems altogether?

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In Tyson's tweet, she showed another chart that pretty much contains the same information, this one from TASS.

This chart uses the flow diagram concept - in an abstract way, as I explained in previous post.

This chart form imposes structure on the data. The relative ranks of the countries within each region are listed from top to bottom. The relative amounts of grains are shown in black columns (and also in the thickness of the flows).

The aggregate value of movements within each region is called out in that middle section. It is impossible to learn this from the bag of bubbles version.

The designer did print the entire dataset onto this chart (except for the smallest countries grouped together as "other"). This decision takes away from the power of the underlying flow chart. Instead of thinking about the proportional representation of each country within its respective region, or the distribution of grains among regions, our eyes hone in on the data labels.

This brings me back to the principle of self-sufficiency: if we expect readers to consume the data labels - which comprise the entire dataset, why not just print a data table? If we decide to visualize, make the visual elements count!

Visual story-telling: do you know or do you think?

One of the most important data questions of all time is: do you know? or do you think?

And one of the easiest traps to fall into is: I think, therefore I know.

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Visual story-telling can be great but it can also mislead. Deception sometimes happens when readers are nudged to "fill in the blanks" with stuff they think they know, but they don't.

A Twitter reader asked me to look at the map in this Los Angeles Times (paywall) opinion column.

The column promptly announces its premise:

Years of widening economic inequality, compounded by the pandemic and political storm and stress, have given Americans the impression that the country is on the wrong track. Now there’s empirical data to show just how far the country has run off the rails: Life expectancies have been falling.

The writer creates the expectation that he will reveal evidence in the form of data to show that life expectancies have been driven down by economic inequality, pandemic, and politics. Does he succeed?

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The map portrays average life expectancy (at birth) for some mysterious, presumably very recent, year for every county in the United States. From the color legend, we learn that the bottom-to-top range is about 20 years. There is a clear spatial pattern, with the worst results in the south (excepting south Florida).

The choice of colors is telling. Red and blue on a U.S. map has heavy baggage, as they signify the two main political parties in the country. Given that the author believes politics to be a key driver of health outcomes, the usage of red and blue here is deliberate. Throughout the article, the columnist connects the lower life expectancies in southern states to its politics.

For example, he said "these geographical disparities aren't artifacts of pure geography or demographics; they're the consequences of policy decisions at the state level... Of the 20 states with the worst life expectancies, eight are among the 12 that have not implemented Medicaid expansion under the Affordable Care Act..."

Casual readers may fall into a trap here. There is nothing on the map itself that draws the connection between politics and life expectancies; the idea is evoked purely through the red-blue color scheme. So, as readers, we are filling in the blanks with our own politics.

What could have been done instead? Let's look at the life expectancy map side by side with the map of the U.S. 2020 Presidential election.

Because of how close recent elections have been, we may think the political map has a nice balance of red and blue but it isn't. The Democrats' votes are heavily concentrated in densely-populated cities so most of the Presidential election map is red. When placed next to each other, it's obvious that politics don't explain the variance in life expectancy well. The Midwest is deep red and yet they have above average life expectancies. I have circled out various regions that contradict the claim that Republican politics drove life expectancies down.

It's not sufficient to point to the South, in which Republican votes and life expectancy are indeed inversely correlated. A good theory has to explain most of the country.

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The columnist also suggests that poverty is the cause of low life expectancy. That too cannot be gleaned from the published map. Again, readers are nudged to use their wild imagination to fill in the blank.

Data come to the rescue. Here is a side-by-side comparison of the map of life expectancies and the map of median incomes.

A similar conundrum. While the story feels right in the South, it fails to explain the northwest, Florida, and various other parts of the country. Take a look again at the circled areas. Lower income brackets are also sometimes associated with high life expectancies.

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The author supplies a third cause of lower life expectancies: Covid-19 response. Because Covid-19 was the "most obvious and convenient" explanation for the loss of life expectancy during the pandemic, this theory suggests that the red areas on the life expectancy map should correspond to the regions most ravaged by Covid-19.

Let's see the data.

The map on the right shows the number of confirmed cases until June 2021. As before, the correlation holds somewhat in the South but there are notable exceptions, e.g. the Midwest. We also have states with low Covid-19 cases but below-average life expectancy.

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What caused the decline of life expectancy in the U.S. - which began before the pandemic, and has continued beyond - is highly complex, beyond what a single map or a pair of maps or a few pairs of maps could convey. Showing a red-blue map presents a trap for readers to fall into, in which they start thinking, without knowing.

 

Area chart is not the solution

A reader left a link to a Wiki chart, which is ghastly:

This chart concerns the trend of relative proportions of House representatives in the U.S. Congress by state, and can be found at this Wikipedia entry. The U.S. House is composed of Representatives, and the number of representatives is roughly proportional to each state's population. This scheme actually gives small states disporportional representation, since the lowest number of representatives is 1 while the total number of representatives is fixed at 435.

We can do a quick calculation: 1/435 = 0.23% so any state that has less than 0.23% of the population is over-represented in the House. Alaska, Vermont and Wyoming are all close to that level. The primary way in which small states get larger representation is via the Senate, which sits two senators per state no matter the size. (If you've wondered about Nate Silver's website: 435 Representatives + 100 Senators + 3 for DC = 538 electoral votes for U.S. Presidental elections.)

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So many things have gone wrong with this chart. There are 50 colors for 50 states. The legend arranges the states by the appropriate metric (good) but in ascending order (bad). This is a stacked area chart, which makes it very hard to figure out the values other than the few at the bottom of the chart.

A nice way to plot this data is a tile map with line charts. I found a nice example that my friend Xan put together in 2018:

A tile map is a conceptual representation of the U.S. map in which each state is represented by equal-sized squares. The coordinates of the states are distorted in order to line up the tiles. A tile map is a small-multiples setup in which each square contains a chart of the same design to faciliate inter-state comparisons.

In the above map, Xan also takes advantage of the foregrounding concept. Each chart actually contains all 50 lines for every state, all shown in gray while the line for the specific state is bolded and shown in red.

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A chart with 50 lines looks very different from one with 50 areas stacked on each other. California, the most populous state, has 12% of the total population so the line chart has 50 lines that will look like spaghetti. Thus, the fore/backgrounding is important to make sure it's readable.

I suspect that the designer chose a stacked area chart because the line chart looked like spaghetti. But that's the wrong solution. While the lines no longer overlap each other, it is a real challenge to figure out the state-level trends - one has to focus on the heights of the areas, rather than the boundary lines.

[P.S. 2/27/2023] As we like to say, a picture is worth a thousand words. Twitter reader with the handle LHZGJG made the tile map I described above. It looks like this:

You can pick out the states with the key changes really fast. California, Texas, Florida on the upswing, and New York, Pennsylvania going down. I like the fact that the state names are spelled out. Little tweaks are possible but this is a great starting point. Thanks LHZGJG! ]