Since it was trivial to calculate the centre of the city, I thought I’d do something a little more complex: calculate the centre of the city, weighted for population. I scraped the 2006 population data by ward from the City of Toronto: Ward Profiles pages; hooray for curl and regexes. Realising that I had no information on population distribution within wards, I made a good old engineering assumption: we could idealize the population as a point mass at the centroid of each ward, and then calculate the centre of mass by balancing moments around the X and Y axis. (I mean, c’mon – it’s the sort of thing we all think about daily … isn’t it? Guys … hello … anyone there?)
I’ll spare you the details until after the jump, but I calculate the human centre of Toronto to be at 43.717794°N, 79.390299°W – that’s in Blythwood Ravine, just south of Blythwood Road. We should have a picnic there …
I’ve transformed all the Open Toronto data into the one CRS, UTM Zone 17 NAD83(CSRS), aka EPSG 2958. Combining the ward centroids with the population data, we get:
ward | xcentre | ycentre | 2006 population |
1 | 613061.3 | 4844146.6 | 59830 |
2 | 614805.5 | 4840302.0 | 53660 |
3 | 615174.7 | 4834294.6 | 50415 |
4 | 617768.5 | 4836958.9 | 53275 |
5 | 618929.9 | 4832315.2 | 57260 |
6 | 619572.4 | 4829261.3 | 56620 |
7 | 617472.3 | 4844566.8 | 49165 |
8 | 620979.9 | 4846977.1 | 47895 |
9 | 622033.3 | 4843760.4 | 44920 |
10 | 624905.2 | 4846553.0 | 61580 |
11 | 620766.3 | 4838224.8 | 59870 |
12 | 622055.6 | 4840047.1 | 53755 |
13 | 622735.3 | 4834354.9 | 50640 |
14 | 625449.4 | 4833661.4 | 50640 |
15 | 624880.8 | 4841050.4 | 60545 |
16 | 627581.4 | 4842532.8 | 51790 |
17 | 624903.7 | 4837407.9 | 50830 |
18 | 625917.3 | 4834970.2 | 45620 |
19 | 627681.1 | 4834083.5 | 49845 |
20 | 629103.3 | 4834749.5 | 59545 |
21 | 626934.8 | 4838564.6 | 47085 |
22 | 629222.9 | 4839354.9 | 59905 |
23 | 627289.9 | 4847806.5 | 79435 |
24 | 630438.7 | 4850084.3 | 58805 |
25 | 630699.0 | 4844136.6 | 55420 |
26 | 632936.3 | 4841208.7 | 60585 |
27 | 630518.7 | 4836915.8 | 67840 |
28 | 631290.6 | 4833067.2 | 58920 |
29 | 633153.2 | 4838703.6 | 44420 |
30 | 633874.3 | 4835110.5 | 51235 |
31 | 636074.0 | 4839975.7 | 52430 |
32 | 636535.9 | 4836957.3 | 55410 |
33 | 632984.6 | 4848926.5 | 57350 |
34 | 634520.4 | 4844926.9 | 56895 |
35 | 639025.5 | 4841743.5 | 56750 |
36 | 641455.1 | 4841624.3 | 51390 |
37 | 638364.8 | 4845440.1 | 62325 |
38 | 641766.3 | 4847162.9 | 63310 |
39 | 635724.0 | 4851688.8 | 54545 |
40 | 636386.0 | 4848580.4 | 61140 |
41 | 639199.5 | 4851985.8 | 67325 |
42 | 644298.3 | 4853536.7 | 74075 |
43 | 644624.2 | 4847389.2 | 53480 |
44 | 648030.9 | 4849745.2 | 58235 |
To get the weighted average we simply divide the sum of (ward centre * population) by the sum of the population. Transforming the numbers back using invproj gets us the geographic coordinate of the centre point. Easy when you’ve no idea what you’re doing …
3 replies on “toronto’s human centre”
This is really cool! What kind of GIS software are you using?
[…] … pretty close to the one I’ve already worked out by ward. […]
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