Archive: “Category: data”

2020 Census Apportionment Fun

Posted in data by Adam

1 May 202112 October 2021

In celebration of the release of the 2020 Census data, here are some calculations that show different apportionment scenarios.

Some years back I wrote a Python script to calculate apportionment, and with the Census Bureau releasing the 2020 data, I decided to do some calculations of various apportionments. Others have noted that New York lost a seat by only 89 people. For starters, which states would have gained (or held) a seat if their populations were 100 000 larger?

New York (h)
Ohio (h)
Idaho
West Virginia (h)
Delaware
Arizona

California will have 52 House members for the next decade. How many seats would there need to be so that all states had that many (this means to get Wyoming to 52)? About 29500. Each one would represent around 11 000 people. (This is barred by the Constitution, which sets the minimum at 30 000…. Though, if a state ever has fewer than 30K persons, it will still get one seat.) If this were possible, California alone would have over 3500 seats.

If each seat represented the constitutional minimum of 30 000, how many would that be? (Equivalent to asking how many to get WY to 20 seats, as they have 577K persons.) About 11 000 representatives. According to the Bureau of Labor Statistics, that’s about as many as are currently employed as adhesive bonding machine operators. (There are currently around 25 000 legislators in the country at various levels, according to the same.)

A middle-case: how many seats to get California to 435 representatives—the current total for the whole House? At 3640 seats, California gets 435 of them. Each seat represents about 96 000 people.

Let’s try something less extreme. How many seats to get Wyoming to two seats? A mere 811, less than double the House’s current size. Each member would have about 300K constituents, still more than in 1911 when the current size of the House was set (about 210K in 1913, per Wikipedia, and it was still under 300K per seat in 1929 when the current law was passed). At 811 seats, 24 states would have up to ten seats, 14 up to 20, and 12 with more than 20.

There’s been talk about Puerto Rico and Washington, DC, becoming states. If they were, then under the current census, DC would get one seat and Puerto Rico four. Those five seats would come from California, Colorado, Minnesota, Montana, and Oregon. Assuming all but Montana’s were and stayed Democratic in both cases, it would mostly be a wash in the House. (The census figures do include overseas population for DC, but not for Puerto Rico; it’s unclear if it would have made a difference and I didn’t try to check.)

And if both DC and PR were states, and you wanted WY (and every other state) to have at least two seats, it would take 821 seats. In that case, Puerto Rico would have eight seats.

We can also go back to the first apportionment act, the Apportionment Act of 1792, which set the House at 105 members. You know what’s coming: what if there were only 105 members? California would get 11, and would be the only state with ten or more. Texas would get eight, with both Florida and New York claiming six. A full 27 states would have only one representative.

The United States should clearly expand the House. I favor setting the number such that the smallest state has at least two representatives, so long as every state has a reasonable population to support it. There would be changes to the House in order to handle that many legislators. Some of those changes would be to take advantage of increased human bandwidth, including more total staff, but others which would require limits to how speechmaking could be handled.

Still, the overall effect would be to push more business into committees, where it belongs. The body of the House seldom sees real debate or business, and having too many people to pretend otherwise would be a positive change. Indeed, they would probably assign each member a class and restrict floor activity to a particular class for each day of the week for any kind of open debate (with customs akin to pairing developing to allow for trading by issue). But even then, it would be a limited activity.

The committee-run body makes more sense anyway, as too often legislators outside their expertise already derail important progress of our nation. More members means more committees with more granularity of the scope of each. It would mean more dysfunctional members, but they would also be a smaller percentage due to the size of the body (and they would be heard from less often in the chamber, due to the changes in speaking rules required).

See below for the output for each scenario, with the disclaimer that I did not verify my results. No state redistricting commission (from an alternate universe) should use these results without double-checking!

(2020 apportionment) Seats = 435
'AK:  1', 'AL:  7', 'AR:  4', 'AZ:  9',
'CA: 52', 'CO:  8', 'CT:  5', 'DE:  1',
'FL: 28', 'GA: 14', 'HI:  2', 'IA:  4',
'ID:  2', 'IL: 17', 'IN:  9', 'KS:  4',
'KY:  6', 'LA:  6', 'MA:  9', 'MD:  8',
'ME:  2', 'MI: 13', 'MN:  8', 'MO:  8',
'MS:  4', 'MT:  2', 'NC: 14', 'ND:  1',
'NE:  3', 'NH:  2', 'NJ: 12', 'NM:  3',
'NV:  4', 'NY: 26', 'OH: 15', 'OK:  5',
'OR:  6', 'PA: 17', 'RI:  2', 'SC:  7',
'SD:  1', 'TN:  9', 'TX: 38', 'UT:  4',
'VA: 11', 'VT:  1', 'WA: 10', 'WI:  8',
'WV:  2', 'WY:  1',

(WY parity with 2020 CA) Seats = 29517
'AK:   66', 'AL:  448', 'AR:  269', 'AZ: 638',
'CA: 3528', 'CO:  515', 'CT:  322', 'DE:  88',
'FL: 1923', 'GA:  956', 'HI:  130', 'IA: 285',
'ID:  164', 'IL: 1143', 'IN:  605', 'KS: 262',
'KY:  402', 'LA:  416', 'MA:  627', 'MD: 551',
'ME:  122', 'MI:  899', 'MN:  509', 'MO: 549',
'MS:  264', 'MT:   97', 'NC:  932', 'ND:  70',
'NE:  175', 'NH:  123', 'NJ:  829', 'NM: 189',
'NV:  277', 'NY: 1802', 'OH: 1053', 'OK: 353',
'OR:  378', 'PA: 1160', 'RI:   98', 'SC: 457',
'SD:   79', 'TN:  617', 'TX: 2601', 'UT: 292',
'VA:  771', 'VT:   57', 'WA:  688', 'WI: 526',
'WV:  160', 'WY:   52',

(30K per seat) Seats = 11175
'AK:   25', 'AL: 170', 'AR: 102', 'AZ: 242',
'CA: 1335', 'CO: 195', 'CT: 122', 'DE:  33',
'FL:  728', 'GA: 362', 'HI:  49', 'IA: 108',
'ID:   62', 'IL: 433', 'IN: 229', 'KS:  99',
'KY:  152', 'LA: 157', 'MA: 237', 'MD: 209',
'ME:   46', 'MI: 340', 'MN: 193', 'MO: 208',
'MS:  100', 'MT:  37', 'NC: 353', 'ND:  26',
'NE:   66', 'NH:  47', 'NJ: 314', 'NM:  72',
'NV:  105', 'NY: 682', 'OH: 398', 'OK: 134',
'OR:  143', 'PA: 439', 'RI:  37', 'SC: 173',
'SD:   30', 'TN: 233', 'TX: 985', 'UT: 111',
'VA:  292', 'VT:  22', 'WA: 260', 'WI: 199',
'WV:   61', 'WY:  20',

(CA to 435) Seats = 3640
'AK:   8', 'AL:  55', 'AR:  33', 'AZ: 79',
'CA: 435', 'CO:  63', 'CT:  40', 'DE: 11',
'FL: 237', 'GA: 118', 'HI:  16', 'IA: 35',
'ID:  20', 'IL: 141', 'IN:  75', 'KS: 32',
'KY:  50', 'LA:  51', 'MA:  77', 'MD: 68',
'ME:  15', 'MI: 111', 'MN:  63', 'MO: 68',
'MS:  33', 'MT:  12', 'NC: 115', 'ND:  9',
'NE:  22', 'NH:  15', 'NJ: 102', 'NM: 23',
'NV:  34', 'NY: 222', 'OH: 130', 'OK: 44',
'OR:  47', 'PA: 143', 'RI:  12', 'SC: 56',
'SD:  10', 'TN:  76', 'TX: 320', 'UT: 36',
'VA:  95', 'VT:   7', 'WA:  85', 'WI: 65',
'WV:  20', 'WY:   6',


(Two seats for WY) Seats = 811
'AK:  2', 'AL: 12', 'AR:  7', 'AZ: 18',
'CA: 97', 'CO: 14', 'CT:  9', 'DE:  2',
'FL: 53', 'GA: 26', 'HI:  4', 'IA:  8',
'ID:  5', 'IL: 31', 'IN: 17', 'KS:  7',
'KY: 11', 'LA: 11', 'MA: 17', 'MD: 15',
'ME:  3', 'MI: 25', 'MN: 14', 'MO: 15',
'MS:  7', 'MT:  3', 'NC: 26', 'ND:  2',
'NE:  5', 'NH:  3', 'NJ: 23', 'NM:  5',
'NV:  8', 'NY: 49', 'OH: 29', 'OK: 10',
'OR: 10', 'PA: 32', 'RI:  3', 'SC: 13',
'SD:  2', 'TN: 17', 'TX: 71', 'UT:  8',
'VA: 21', 'VT:  2', 'WA: 19', 'WI: 14',
'WV:  4', 'WY:  2',

(PR/DC as states) Seats = 435
'AK:  1', 'AL:  7', 'AR:  4', 'AZ:  9',
'CA: 51', 'CO:  7', 'CT:  5', 'DC:  1',
'DE:  1', 'FL: 28', 'GA: 14', 'HI:  2',
'IA:  4', 'ID:  2', 'IL: 17', 'IN:  9',
'KS:  4', 'KY:  6', 'LA:  6', 'MA:  9',
'MD:  8', 'ME:  2', 'MI: 13', 'MN:  7',
'MO:  8', 'MS:  4', 'MT:  1', 'NC: 14',
'ND:  1', 'NE:  3', 'NH:  2', 'NJ: 12',
'NM:  3', 'NV:  4', 'NY: 26', 'OH: 15',
'OK:  5', 'OR:  5', 'PA: 17', 'PR:  4',
'RI:  2', 'SC:  7', 'SD:  1', 'TN:  9',
'TX: 38', 'UT:  4', 'VA: 11', 'VT:  1',
'WA: 10', 'WI:  8', 'WV:  2', 'WY:  1',

(Two for WY and has DC/PR as states) Seats = 821
'AK:  2', 'AL: 12', 'AR:  7', 'AZ: 18',
'CA: 97', 'CO: 14', 'CT:  9', 'DC:  2',
'DE:  2', 'FL: 53', 'GA: 26', 'HI:  4',
'IA:  8', 'ID:  5', 'IL: 31', 'IN: 17',
'KS:  7', 'KY: 11', 'LA: 11', 'MA: 17',
'MD: 15', 'ME:  3', 'MI: 25', 'MN: 14',
'MO: 15', 'MS:  7', 'MT:  3', 'NC: 26',
'ND:  2', 'NE:  5', 'NH:  3', 'NJ: 23',
'NM:  5', 'NV:  8', 'NY: 49', 'OH: 29',
'OK: 10', 'OR: 10', 'PA: 32', 'PR:  8',
'RI:  3', 'SC: 13', 'SD:  2', 'TN: 17',
'TX: 71', 'UT:  8', 'VA: 21', 'VT:  2',
'WA: 19', 'WI: 14', 'WV:  4', 'WY:  2',

(Apportionment Act of 1792) Seats = 105
'AK:  1', 'AL:  1', 'AR:  1', 'AZ:  2',
'CA: 11', 'CO:  2', 'CT:  1', 'DE:  1',
'FL:  6', 'GA:  3', 'HI:  1', 'IA:  1',
'ID:  1', 'IL:  4', 'IN:  2', 'KS:  1',
'KY:  1', 'LA:  1', 'MA:  2', 'MD:  2',
'ME:  1', 'MI:  3', 'MN:  2', 'MO:  2',
'MS:  1', 'MT:  1', 'NC:  3', 'ND:  1',
'NE:  1', 'NH:  1', 'NJ:  3', 'NM:  1',
'NV:  1', 'NY:  6', 'OH:  3', 'OK:  1',
'OR:  1', 'PA:  4', 'RI:  1', 'SC:  2',
'SD:  1', 'TN:  2', 'TX:  8', 'UT:  1',
'VA:  2', 'VT:  1', 'WA:  2', 'WI:  2',
'WV:  1', 'WY:  1',

(Humans and states are quantum fluctuations) Seats = ????
'??: ???', '??  ???', '??: ???', '??: ???',
' ?: ???', '??: ???', '  : ???', '??: ???' 
'??: ?  ',      ???', '??: ??     ??: ???',
'??: ???', '??: ???', '??: ???', '??: ???',
'??    ?',                                 
'??  ???', '??:  ??', '??:  ? ', '??: ???',
'??: ? ?', '??: ???', '??: ???', '??: ?    
'  : ???', '??: ?   , '       ', '??: ???',
'??: ???', '??: ???', '??: ???', '??: ? ?' 
'??:             ??', '?   ???', '??: ???',
'??: ???', '??: ???', '??: ???', '??: ?  ',
'? : ???', '??: ?        : ???', '?     ?',
 ??: ???'  '??: ?

Big Data on Small Computers

Posted in data by Adam

15 November 2014

To have the benefits of big data without giving up privacy will undoubtedly require distributed systems.

US motto, e pluribus unum, on the back of a dime — Shows US motto (*e pluribus unum*) on the reverse of a US dime.

One of the great emerging fields of computing is the use of big data and machine learning. This is a process whereby large datasets actually teach computers to do things like translate text, interpret human speech, categorize images, and so on. The problem with this is, so far, it requires large amounts of data and a lot of computing power.

The paradigm is largely opposed to the types of computing people would prefer to do and use. We would rather not send our voice data out to the Internet or have the Internet always listening or watching us to get these benefits of machine learning. But while the advances in technology will allow for us to crunch the data on smaller devices, it will be difficult to have the corpus of data needed for training and use.

It remains to be seen whether smaller datasets or synthesized datasets (where a large dataset is somehow compressed or distilled into the important parts) will emerge. So how do we get big data in our relatively small computers?

It is likely that the problem will provoke the emergence of more distributed systems, something many have wanted and waited for. Distributed systems or collaborative computing allows your computer(s) to participate in computing larger datasets. Projects like the Search for Extra-Terrestrial Intelligence (SETI) have used such distributed computing for over a decade.

The main challenge will be finding ways to break up data to send to the distributed system that protect privacy. That is, if you send the whole voice capture to the distributed system (as you do, AFAIK, with cloud services like Apple’s Siri), you risk the same privacy issues as with the cloud model.

Instead, it should be possible to break up inputs (video or audio) and send portions (possibly with some redundancy, depending on e.g., if word breaks can be determined locally) to several systems and let them each return only a partial recognition of the whole.

It also remains to be seen whether this piecemeal approach will be as functional as the whole-system approach in all cases. While this splitting undoubtedly takes place in whole-systems like Siri, the reassembly and final processing surely takes place over the whole input. That final step may not be easily managed over a distributed system, at least not while protecting privacy.

Consider asking, “what is the time in Rome?” which might be processed as slightly off, due to pronunciation, “what is the dime in Rome?” In a whole-system approach it’s likely easier to infer dime → time at some late step, rather than if each hands back a partial result and the final recipient has less knowledge of how it was made. In a question case like that, the final text is likely targeted to a search engine, which will correct (though it could take the question literally and say, “It is the €0.10 coin.”).

For situations where the voice command lends insufficient context for local correction, it could be a greater challenge.

The good news is that it does look like it’s possible for us to have these distributed systems replace proprietary cloud solutions. The questions are when and how they will emerge, and where they might be weaker.

About the Privacy Argument Against Autocars

Posted in data by Adam

1 November 2014

The privacy argument against self-driving vehicles is broader than its subject, and it’s one we have to solve even if there weren’t to be autocars.

Image of an overgrown field with the remnants of a car visible (back wheels, steering column). — By Ben Salter (Flickr: ben_salter)

One of the arguments against self-driving vehicles is the privacy argument. Won’t you be tracked? Won’t police be able to stop the car? What if the navigation is hacked? And so on.

The problem with this argument is that it avoids the fact that we have the same problem already in many other facets of our lives. The issues are only more obvious and accute when you’re talking about putting your life into the cyberhands of an algorithm.

Society has a real need to confront the security and privacy issues much more directly than it has done. Autocars may raise the issue to higher prominence, which may help us strike a new balance sooner. In that, it could be a feature. But how we ultimately deal with the erosion of barriers to privacy and security is still unsolved.

It will need to be solved even if we stuck to manual cars, of course. But it also needs to be solved with televisions that watch you, phones that listen to you (for voice control), and similar services. It needs to be solved when the day comes that your phone tells a restaurant you’re allergic to something. And so on.

There is a balance to be struck between providing information and retaining privacy. And we have yet to strike it in most cases. Our political world is full of dark money, where donors choose not to reveal themselves while attacking others. Our tax code is full of subtle blind alleys where large companies and the very rich hide their money.

What you buy is tracked, which is one of the reasons that some companies are shunning NFC-based payments like ApplePay. ApplePay would reduce the information they receive when you buy something.

And, of course, online you leave your digital footprints as you jump from reading Eight Exercises that Your Ancestors would Laugh Their Asses Off at You for Doing to ordering food online to reading this blog.

Point is, we’re already being tracked through all manner of invasive tools both in meatspace and in cyberspace. One more meatspace tracking measure does not seem to raise itself in priority above balancing them all correctly and comprehensively.

Even your goods are tracked as they are shipped to you. And you like that. It lets you know when your stuff will get home.

Done right, instead of waiting on someone running late for a meeting, you could see that they’re stuck waiting for an autocar. Done wrong, you might have a surprise party ruined because the birthday human sees that everyone’s at their house. Or couples might catch each other cheating. Or stalkers and criminals will hack the system and use it for evil means.

But the good news is that there are real enough non-totalitarian harms to giving up privacy to make strong arguments for laws and technical designs that let us retain privacy, even in autocars. The balance is yet to be struck, but the reasons are there for it. It may not even be a world we find comfortable, it may be less private than we would like. But there’s no indication it will be as bad as the tracking that’s already going on today.