World Population by Time Zone

Cory Doctorow, scifi author and BoingBoing co-founder, once wrote a scifi novel called Eastern Standard Tribe (available free). It was fun read but what I enjoyed most was his idea that people would belong to a "tribe" based on their time zone. In Doctorow's world, your loyalties lie not with the country of your birth but with the people who are up when you are.

This evening I was thinking about this novel and idly wondered which tribe would be the biggest? In other words, which time zone is the most highly populated?

Looking at a map, the answer was obvious: it had to be UTC+8 which includes not only China but Malaysia, the Philippines, and more.

Still, the fun of such a frivolous question is less in the answer than in the answering, so I fired up Mathematica and a few calculations later generated this graph.

I cheated a little by using a simplifying assumption: if a country has multiple time zones, I divide its population evenly between them. This inaccuracy doesn't change the fact that our top three are... <drumroll>
  1. UTC+8: China and others
  2. UTC+5.5: India and others
  3. UTC+1: Western Europe and a good chunk of Africa

According to Mathematica, there are 39 different time zones ranging from UTC-11.5 to UTC+14. I wonder if anyone has visited them all? Now that would be a glorious adventure! :-)
12 responses
I wrote a couple of quick scripts which used the data from geonames.org

This allowed me to grab the city's population and timezone. This gives me more accurate results per time zone than simply dividing the country's population over the number of timezones.

I only got 38 timezones and 1 of those is null.

array(38) {
[1]=>
int(453405509)
[4]=>
int(16148664)
["4.5"]=>
int(6566168)
[-4]=>
int(20405357)
[13]=>
int(3891624)
[-3]=>
int(76074295)
[-2]=>
int(86563541)
[""]=>
int(1170)
[-11]=>
int(32972)
[8]=>
int(389081095)
["10.5"]=>
int(1711432)
["9.5"]=>
int(189580)
[10]=>
int(8414368)
[11]=>
int(14767834)
[2]=>
int(248920607)
[6]=>
int(43014568)
[0]=>
int(125839835)
[3]=>
int(165858133)
[-5]=>
int(194527172)
[-6]=>
int(149243547)
[-8]=>
int(48973771)
[-7]=>
int(27286339)
["-3.5"]=>
int(244494)
["6.5"]=>
int(12052142)
[-10]=>
int(1570203)
[5]=>
int(80552953)
[-1]=>
int(364193)
[7]=>
int(89389874)
[9]=>
int(155950211)
["5.5"]=>
int(261789654)
["3.5"]=>
int(38740621)
[12]=>
int(118327)
[14]=>
int(3710)
["11.5"]=>
int(880)
["5.75"]=>
int(3636530)
["13.75"]=>
int(6288)
[-9]=>
int(593817)
["-4.5"]=>
int(20511606)
}

Brian,

Thanks a lot for your comment, I appreciate you taking up the challenge of making these results more accurate!

I think there's something to be said for the geonames data though. When I add all your population numbers together they only come to 2,746,443,084, far below the earth's pop.

FWIW my stats, when summed, come to 6,926,242,503.

Hi Paul,

Just wondering where you got your population figures for each time from?
From Mathematica.
I had a dream a few nights ago about the population of time zone and you have given me a great answer. Thank you so much.
Many thanks for your comment Bob. Glad I could answer your dream question :-)
Could you give me the numbers you've used for this graph, please
Could you please share the population per timezone data with us?
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