Every US County I Remember the Name For

This is a map of US counties and county-equivalents for which I currently remember their names.



Alaska is Smaller than Larger-than-you-think

You may have seen several visualizations or statistics on how massively large the state of Alaska is: a juxtaposition of Alaska onto the contiguous states, or a reading on how many degrees of longitude Alaska spans. These often drive home the point that Alaska is much larger than one might be convinced it actually is, from maps of the United States where Alaska is in a small inset, and the such.

Indeed, Alaska’s contribution to American area is substantial, without which the United States would actually be smaller than Brazil. It is by quite a margin the largest state. It is also easy, however, to arrive, particularly from such visuals, to the conclusion that Alaska is larger than it really is.

There are two factors at play here:

  1. Alaska is very far north, and thus is portrayed as disproportionately large in popular projections like Mercator.
  2. Much of Alaska’s impression of massiveness comes from significantly long “appendages”: the mightily extended Aleutian Islands and the also impressive Alaskan Panhandle. There is a lot of not-Alaska within Alaska’s bounding box.

When looking at the main mass of Alaska, here’s what we find from some comparisons with Texas:

  • The distance from Barrow to Anchorage is 1164 km. The distance from Amarillo to Brownsville is 1117 km.
  • The distance from Nome to Tok is 1099 km. The distance from El Paso to Houston is 1086 km.
  • One can’t actually fit Texas inside Alaska, no matter how one rotates it.

Alaska really does just have seriously extended protrusions: Juneau is 924 km from Anchorage, nearly four-fifths the height of the main part of Alaska. Ketchikan is 1246 km from Anchorage: around this height, and closer to Seattle than to Anchorage.

The Imbalance of the Hemispheres

Earth’s Northern and Southern Hemispheres are drastically unalike. Substantially more of Earth’s land is in the Northern than in the Southern Hemisphere. Whereas the planet overall is substantially more ocean than land, land and ocean are notably closer to balance in the Northern Hemisphere, whereas the Southern Hemisphere is uncontestedly dominated by ocean.

Correlated to this but also to other factors, the Northern Hemisphere hosts an even more lopsided proportion of Earth’s human population: more than 7 of 8 humans on Earth live North of the Equator. In fact, so much of the land of the Southern Hemisphere is so sparsely populated that 1 in 5 humans in the Southern Hemisphere live on the Indonesian island of Jawa (Java).

What I really want to highlight here, though, is a drastic discrepancy in climate. Water has a massively higher specific heat than most of the composition of continents, so much of the Southern Hemisphere is significantly more moderated in seasonal variations than the Northern. Furthermore, the Southern Hemisphere is plain out of land in mid- and mid-high-latitudes, and at the intersection of the population distribution and this land distribution, there are many populated areas of the Northern Hemisphere that climatologically have no Southern Hemisphere equivalent. In the Southern Hemisphere, there is tropical, then there’s a gradual easing that makes it slightly into temperate territory, and then there’s Antarctica.

A particularly stark factor in which to behold discrepancies: snow. The Northern Hemisphere is dotted with large cities that regularly experience snow in the winter: Chicago, Toronto, New York, Berlin, Belgrade, Tehran, Kabul, and Seoul. There’s just no such thing in the Southern Hemisphere.

Even the cities on east coasts of the southern ends of non-Antarctic continents receive barely any snow: Hobart on Tasmania, Dunedin on the South Island of New Zealand (Te Waipounamu), Port Elizabeth in South Africa, and even Maseru, the capital of Lesotho with quite some elevation. One who’s willing to travel to the very southern reaches of South America could finally find some significant snow in Ushuaia, but Ushuaia really doesn’t count as anywhere near a large city. In none of these cities will one find a July where below freezing is the norm like the frozen Januaries of places like Chicago (sidenote: even Boston on average spends more of January below than above freezing historically, but this balance has become questionable with global warming). For lots of snow, one needs to start going up the Andes. Otherwise, the next step is straight to polar Antarctica (where for much of the continent, it still doesn’t snow much; it’s a desert after all).

It is worth mentioning that there are a handful of sub-Antarctic islands that somewhat bridge the gap between climate on Antarctica and climate on other continents, but they only do as much bridging as they can while being small and so moderated by the waters around. This is how amusing Bouvet Island’s climate chart is.

Answers: US Statistics-Identification Puzzles

This filler text is intended to make it harder to accidentally see answers in post preview applications.
This map charts number of syllables in the name.

This map charts number of distinct main interstate freeways (number <100).

3) [note the posted ERRATUM]
This map charts number of counties of at least a million people.

This map charts number of national parks.

This map charts number of years before 19th Amendment ratification when women were granted suffrage.

This map charts length of the road named after the state in Washington, DC (miles).

Failures in Referential Nomenclature

Suppose you heard the term “inaccessible island rail”. What do you think this term refers to? When I heard it, my mind conjured an image of a train line that connected really inaccessible islands.

And that sounds weird. Did someone undertake a project just to create such a rail line? It sounds incredibly costly. And it also sounds like it’d be something cool enough that I would’ve heard about it by now. Nevertheless, what else could this term refer to?

It turns out that a rail is a type of bird. Go figure. So it’s a type of bird that only lives in really remote islands. That makes much more sense than the train situation. Okay.

Except that that isn’t even specifically what this species of bird is. It is a species of rail that only lives on one island, literally named Inaccessible Island. It’s slightly southwest of Tristan da Cunha.

So actually, I slightly lied in the text in the first paragraph, by lowercasing “inaccessible” and “island”. But here’s the thing: you don’t hear capitalization in verbal speech. The uppercase letter hints would not be available to you if someone was orally communicating the term for this bird species to you (and even if you were reading this in text, maybe you would’ve thought the capitalization was probably for other emphasis than to hint that it referred to an island literally named that way). Also note that whereas realizing ‘rail’ probably did not refer to the context of trains could have happened via considering the context of the sentence in which it is used, context would very likely not have helped hint at the ‘Inaccessible Island’ issue.

I claim that ‘Inaccessible Island’ is a poor choice of name for this island. Names should be useful, distinguishing handles, and this name is not that. It was an attempt to reference the island’s inaccessibility, but it decided to do so via a term that would naturally be used anyway to describe islands, thus vastly increasing possibilities of confounding in all terms that refer to it. Calling the sort of bird a ‘rail’ is also unhelpful, but this part is not as problematic, for the reasons stated above.

This sort of naming failure in attempting to make a reference or hint at a metaphor is pervasive in computer science. When looking back at my learning process for many ideas in computer science, I find that this was a massive reason I often got stuck or was confused. People that name tools or ideas relating to computers often try to give them names that refer to parallel entities or processes outside the world of computers, and in doing so make usage of terms often extremely ambiguous.

Continue reading “Failures in Referential Nomenclature”

Dual Frontier Analysis

I. Introduction, with Example in Population and Area of Countries and Country-Like Entities

In this post, I introduce a way of looking at correlated data I will term “dual frontier analysis”.

What motivates this idea? Often, we like to compare entities via a certain “rate”, how much of one quantity there is for a unit amount of another quantity, across a set of entities. One example of this is population density. But if you, like me, have glanced at a population density chart of, say, the countries, you may have had one of the same first reactions as I have had: “the top of the chart is pretty much just a listing of city-states!” You might then proceed with questioning whether it really makes sense to compare this quantity for city-states versus for “more normal” countries. Maybe we want a way of looking at this data that better captures what our prior idea of what an “impressively high” or “impressively low” population density is: Bangladesh’s population density definitely “feels” more impressive, even if it’s not as numerically high as Bahrain’s.

There are probably solutions to this problem involving designing a prior distribution of likeliness of one variable in terms of the other, and then comparing percentiles along respective distributions, but going down this path requires crunching a lot of numbers and, more importantly, extensive knowledge in the ideas being analyzed already.

Here is another solution: output the data on the dual frontiers. If two attributes are somewhat correlated, a scatterplot for entities in these attributes probably looks something like this.


What we’re outputting is this.


That is, we’re outputting entities for which no other entity has both more of one attribute and less of the other attribute than this entity.

In this way, we would capture, for instance, the country with the highest population density among countries of similar size. (We could even extend this to become a quantitative metric for entities not on this frontier: the percentage of the way an entity is from one frontier to the other.)

One could also look at an entity in this data and compare it to neighboring entities and see how much larger in one attribute another entity must be to be larger in the other attribute as well (as otherwise, this entity would also be in the frontier), which shows how prominently impressive a particular entity is in the ratio.

Continue reading “Dual Frontier Analysis”