Weather Channel, You’re Bored, Go Home

https://weather.com/storms/hurricane/news/2018-09-12-hurricane-florence-weird-hurricane-tropical-storm-tracks

Hurricane Florence Joins the Ranks of Weirdest Hurricane and Tropical Storm Tracks

Were you just looking for something to make a headline?

https://en.wikipedia.org/wiki/Hurricane_Ophelia_(2005)
https://en.wikipedia.org/wiki/Hurricane_Hanna
https://en.wikipedia.org/wiki/Hurricane_Jeanne
https://en.wikipedia.org/wiki/Tropical_Storm_Nicholas
https://en.wikipedia.org/wiki/Hurricane_Ophelia_(2017)
https://en.wikipedia.org/wiki/Hurricane_Kyle_(2002)
https://en.wikipedia.org/wiki/Hurricane_Roxanne
https://en.wikipedia.org/wiki/Hurricane_Bonnie_(1992)
https://en.wikipedia.org/wiki/Hurricane_Arlene_(1987)
https://en.wikipedia.org/wiki/Hurricane_Nadine
https://en.wikipedia.org/wiki/Hurricane_Ginger

Advertisements

MIT Buildings, Numbered More Consistently to Their Numbering Patterns

In this map, I renumber some MIT buildings to numbers that more closely follow the helpful geographical patterns in numberings that the rest of the buildings suggest.

mit_building_renumberings

Alternative solution: number 10, 20, 30, and 40 as 9, 19, 39, and 49, de-emphasizing that the buildings are on the central axis, but avoiding slight problems with syllable-parsing ambiguity (thirty one-ten versus thirty-one ten).

(ERRATA: W98 should be grey (but I never specified what red and grey are supposed to mean, so I can say this isn’t an error, right?). The ‘k’ hanging out on Memorial Drive shouldn’t be there.)

Guess the Statistic

Guess what each of these statistics is, given the top 8 countries.

Statistic 1
1. Denmark
2. Canada
3. Russia
4. Norway
5. United States
6. Finland
7. Sweden
8. Iceland

Statistic 2
1. Australia
2. China
3. Thailand
4. India
5. Israel
6. Mexico
7. United States
8. Philippines

Statistic 3
1. United States
2. El Salvador
3. Turkmenistan
4. Maldives
5. Cuba
6. Thailand
7. Bahamas
8. Seychelles

Statistic 4
1. India
2. Palau
3. Côte d’Ivoire
4. Pakistan
5. United States
6. Australia
7. Nigeria
8. Tanzania
(though there exist good arguments some positional switches should happen)

Statistic 5
1. Bolivia*
2. Ecuador*
3. Colombia
4. Ethiopia
5. Bhutan
6. Eritrea
7. Yemen
8. Mexico
*though there exists a good argument that Ecuador and Bolivia switch places

Statistic 6
1. Russia
2. Kyrgyzstan
3. Canada
4. United States
5. Indonesia
6. Norway
7. Tajikistan
8. Argentina

Statistic 7
1. Venezuela
2. San Marino
3. Costa Rica
4. Panama
5. Ecuador
6. Uruguay
7. Colombia
8. Iceland

Statistic 8
1. India
2. Pakistan
3. China
4. United Kingdom
5. Bangladesh
6. Indonesia
7. Brazil
8. United States

Guessing Populations

I decided to sit down and guess the current populations of (a) each US state and (b) each sovereign country. Given how often I work with this sort of data, I’d expect to be pretty good at this by now. I think I definitely did do pretty well, at least for the US. Below’s a map summary of results of where my guesses landed, although, of course, if you wish to try this yourself, you should probably not continue reading yet.

usa_guess

North Dakota turned out to be the state for which I most overestimated the population, at 131% of the actual population. I attribute this to overconsidering the effect of its recent boom. I’m not sure how to explain how I underestimated Connecticut’s population so substantially; it was the extreme in the other direction, at 61% of the actual population.

countries_guess

I was really, really shocked when I consulted the list of actual populations and found out Madagascar was over 6 times as populous as I guessed it would be. Kuwait was a mighty surprise too. The most populous country that I failed to guess the population to within 80% to 125% of the actual was Argentina.

Interestingly, my knowledge that Bahrain was ridiculously densely populated didn’t end up actually helping me, because I had no good mental estimator of how large Bahrain as an island was anyway.

Geographical Leaders in 8 Different Stats in Pokémon Go

This post consists of 16 charts, showing the top 12 countries and the top 12 cities in 8 Pokémon Go statistics: XP, Distance Walked, Pokémon Caught, Trainings, Battles Won, Berries Fed, Hours Defended, and Legendary Raids Won, as measured by the sum of the quantities of the top 25 players in the country or city, according to the last release of TL40 leaderboards. I used a convention that for a player whose statistic is “Not Disclosed”, I used the number for the next highest player that isn’t “Not Disclosed”, and if there isn’t such a next highest player with quantity disclosed, I used “0”.

Country Leaderboards

country_xp

country_distance

country_pokemon

Continue reading “Geographical Leaders in 8 Different Stats in Pokémon Go”

How I Define Regions of the United States

Different people define regions of the United States differently. Here’s where I draw the boundaries for 22 regions. Ample notes follow.

.regions

  • Each state is in exactly one of Northeast, South, Midwest, and West, usually referred to with “The” before (for instance, “The Midwest”). This is the top-level division. Divisions of the United States should occur with this property for the four major regions.
  • This being said, quite a few of the boundaries among the four major regions are rather debatable, particularly between The Midwest and The South. I tend to refer to Oklahoma, Missouri, Kentucky, and West Virginia as The Midwest, but it’s definitely quite valid to consider some subset of them The South as well. Honestly, I believe I am myself sometimes inconsistent about this. Note that categorizing all four states as The Midwest makes The South coterminous with the Former Confederacy, and there’s a fair argument about making The South constitute more than that (and calling this set the “Former Confederacy”). I am less sympathetic to considering Maryland and Delaware as The South. I am not sympathetic at all to considering Illinois, Indiana, Ohio, Pennsylvania, or New Jersey The South. That’s just wrong.
  • The Colonial States include not just the 13 states corresponding to the original 13 colonies, but also states that are not these 13 states but were part of the 13 original colonies. Note that this doesn’t include, for instance, Kentucky for Virginia, because that acquisition really happened way too late.
  • New England plus the Mid-Atlantic is precisely The Northeast.
  • The Great Lakes States are in fact precisely the set of states that border the Great Lakes somewhere. Of course, that causes the awkwardness that a Great Lakes State borders the Atlantic Ocean.
  • The Deep South is precisely the states Goldwater won minus Goldwater’s home state.
  • The Former Free States comprises of three disjoint portions: a large chunk of states in the general Northeast, Kansas, and Oregon and California on the Pacific coast.
  • The Mountain States are precisely the states for which the majority of the state is in Mountain Time.
  • The Pacific States are the states with a coast on the Pacific Ocean. Note that this is not the set of states in Pacific Time. For this reason, I think considering Nevada a Pacific State is actually an okay definition, although not optimal.
  • There’s quite a fair argument to consider Nevada the Southwest as well, really. Half the state’s population is already that Southwest.
  • Viewing the finished map, one (lack of) feature that surprises me is the fact that northing separates Wyoming from Colorado. I think my gut expected that at least something significant distinguishes the two. Maybe some name for “cluster of states in the north with barely any population” could help make that separation happen.

The Harvard MBTA Station: Resolving a Failure of Geographic Intuition

The Harvard station is rather special among MBTA Red Line stations. It’s a double-decker station, and also much, much curvier than other stations. But most notable to me—and, as I’ve found out, not just me—is that the station manages to evade geographical sense. On the inbound platform, my navigational intuition tells me the train should be coming from the right, but instead the train comes from the left. There are also others among the geographically-inclined that agree that it really feels like the train should come from the right. And yet, this is not just a small discrepancy in intuition, where a direction is a mild angle off. This is a navigational intuition failure of 180 degrees.

The entrance I (and most of my friends) usually take to go down to the Harvard station platform starts right in the Harvard Square bend of Massachusetts Avenue (which the Red Line runs under), heading eastward, along the avenue. After going down an escalator or flight of stairs, the path turns sharply left, heading down another escalator or flight of stairs. Here, the path branches into two curved paths heading out: one to the left, heading to the 71 and 73 buses, and one to the right, arcing over to the platforms, along the way splitting into one path going to the outbound platform and one going to the inbound platform, which is under the outbound platform. So, like this:

harvard_apparent

Thus, one would expect, after mentally processing this path, that the train comes from the right. Yet the train actually comes from the left.

There aren’t floor plans of the station available online, at least as far as I can tell, so I returned to the station recently just to figure out what’s going on once and for all.

It turns out this is what actually happens.

harvard_actual

That is, of the 180-degree discrepancy, only about 90 degrees are from miscalibration of angles and curves in the path: the first turn is sharper; the second turn is shallower. The rest of the discrepancy is due to where the platform actually is being only about 90 degrees offset from the mental model of where the platform is, specifically, before the bend in the route. And in fact, looking into the tunnel towards the right at the Harvard station platform, one can confirm that the path taken by the train turns left after entering.

Interestingly, once I entered the platform with my mind set on figuring out what’s really going on, the explanation unraveled itself without need of additional tools or a map. That one time, focused on the problem, was more useful to entangling this mystery than the entire 5 years prior during which the station just caught me off guard.