The user survey results show where developers live, but it's just a population map

Question

The infographic of the SO user survey results was quite intriguing, but one of the charts annoyed me as it showed very little real data. I don't need to be told there are more devolpers in California than in Utah - there are so many fewer residents in Utah that it's obvious. Compare the following two graphs:

Population map:

Survey results:

Can we have a map of the per capita results? I think they would be more interesting and informative.

Answer 1

Here are the per capita results (full data and sources listed below). Update: @Pang has produced a visualization: http://jsfiddle.net/T8m4s/embedded/result/ -- Snapshot here:

Note that California doesn't even make it onto the top 10 (it ranks at 13th place), and New York just barely makes cut for top 10.

1. Washington DC [8.2%]
2. Washington State [4.8%]
3. Massachusetts [4.5%]
4. Utah [3.8%]
5. Oregon [3.4%]
6. Colorado [3.3%]
7. New Hampshire [3.2%]
8. Minnesota [3.0%]
9. Rhode Island [3.0%]
10. New York [2.8%]
11. Virginia [2.8%]
12. Maryland [2.5%]
13. California [2.5%]
14. Nebraska [2.4%]
15. Wisconsin [2.3%]
16. Arizona [2.2%]
17. Pennsylvania [2.2%]
18. Idaho [2.0%]
19. Kansas [1.9%]
20. New Jersey [1.9%]
21. Iowa [1.9%]
22. Connecticut [1.8%]
23. Illinois [1.8%]
24. Ohio [1.7%]
25. Wyoming [1.7%]
26. Missouri [1.7%]
27. Michigan [1.7%]
28. Alaska [1.6%]
29. Maine [1.5%]
30. North Carolina [1.5%]
31. Montana [1.4%]
32. Georgia [1.3%]
33. Florida [1.3%]
34. Texas [1.3%]
35. Vermont [1.3%]
36. Tennessee [1.3%]
37. Indiana [1.2%]
38. South Dakota [1.2%]
39. North Dakota [1.1%]
40. Nevada [1.1%]
41. South Carolina [1.0%]
42. Oklahoma [0.9%]
43. New Mexico [0.9%]
44. Alabama [0.8%]
45. Arkansas [0.8%]
46. Kentucky [0.8%]
47. Delaware [0.7%]
48. Louisiana [0.6%]
49. Mississippi [0.5%]
50. Hawaii [0.4%]
51. West Virginia [0.3%]
52. Puerto Rico [0.2%]

Note this is 52 items because it includes Washington DC and Puerto Rico -- which, interestingly, rank at the very top and very bottom respectively. My theory on DC is that it is all city so white collar skilled professions will be more common, and my theory on Puerto Rico is the language barrier, Spanish being the dominant official language there.

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Raw stack exchange data from https://www.surveymonkey.com/sr.aspx?sm=vU4rF_2bPVQaftSo1s69bGGbvMPXp7ktcfSHiDFP_2bM5qw_3d:

Alabama - 20, Alaska - 6, Arizona - 74, Arkansas - 12, California - 471, Colorado - 87, Connecticut - 33, Delaware - 3, Florida - 126, Georgia - 66, Hawaii - 3, Idaho - 16, Illinois - 114, Indiana - 41, Iowa - 29, Kansas - 28, Kentucky - 17, Louisiana - 13, Maine - 10, Maryland - 75, Massachusetts - 149, Michigan - 85, Minnesota - 82, Mississippi - 7, Missouri - 52, Montana - 7, Nebraska - 22, Nevada - 15, New Hampshire - 21, New Jersey - 85, New Mexico - 9, New York - 280, North Carolina - 72, North Dakota - 4, Ohio - 101, Oklahoma - 18, Oregon - 66, Pennsylvania - 141, Puerto Rico - 3, Rhode Island - 16, South Carolina - 24, South Dakota - 5, Tennessee - 41, Texas - 169, Utah - 55, Vermont - 4, Virginia - 114, Washington - 168, Washington DC - 26, West Virginia - 3, Wisconsin - 66, Wyoming - 5

Raw census projection data from http://www.census.gov/popest/data/state/totals/2012/tables/NST-EST2012-01.csv:

Alabama - 4822023, Alaska - 731449, Arizona - 6553255, Arkansas - 2949131, California - 38041430, Colorado - 5187582, Connecticut - 3590347, Delaware - 917092, Florida - 19317568, Georgia - 9919945, Hawaii - 1392313, Idaho - 1595728, Illinois - 12875255, Indiana - 6537334, Iowa - 3074186, Kansas - 2885905, Kentucky - 4380415, Louisiana - 4601893, Maine - 1329192, Maryland - 5884563, Massachusetts - 6646144, Michigan - 9883360, Minnesota - 5379139, Mississippi - 2984926, Missouri - 6021988, Montana - 1005141, Nebraska - 1855525, Nevada - 2758931, New Hampshire - 1320718, New Jersey - 8864590, New Mexico - 2085538, New York - 19570261, North Carolina - 9752073, North Dakota - 699628, Ohio - 11544225, Oklahoma - 3814820, Oregon - 3899353, Pennsylvania - 12763536, Rhode Island - 1050292, South Carolina - 4723723, South Dakota - 833354, Tennessee - 6456243, Texas - 26059203, Utah - 2855287, Vermont - 626011, Virginia - 8185867, Washington - 6897012, Washington DC - 632323, West Virginia - 1855413, Wisconsin - 5726398, Wyoming - 576412, Puerto Rico - 3667084

Per-capita * 1,000,000 (i.e. number of SO responses per million state residents):

Alabama - 4.147636790616718, Alaska - 8.202895895681038, Arizona - 11.292098354176666, Arkansas - 4.068995239614653, California - 12.381238034427202, Colorado - 16.770819237170613, Connecticut - 9.191312148937136, Delaware - 3.2712094315510334, Florida - 6.5225601897713, Georgia - 6.653262694500826, Hawaii - 2.1546879185930172, Idaho - 10.026771479851202, Illinois - 8.854193567428373, Indiana - 6.271669766299228, Iowa - 9.43339147338515, Kansas - 9.702329078746528, Kentucky - 3.8809108269421957, Louisiana - 2.8249244387038117, Maine - 7.5233675797025565, Maryland - 12.745211496588617, Massachusetts - 22.419014694836584, Michigan - 8.600314063233556, Minnesota - 15.244075306475628, Mississippi - 2.345116763363648, Missouri - 8.635022188685863, Montana - 6.964197062899633, Nebraska - 11.856482666630738, Nevada - 5.436888418014078, New Hampshire - 15.900442032288499, New Jersey - 9.588711942684322, New Mexico - 4.315433235932407, New York - 14.307422880052544, North Carolina - 7.383045635527954, North Dakota - 5.717324063645251, Ohio - 8.748963226201845, Oklahoma - 4.718440188527899, Oregon - 16.92588488397947, Pennsylvania - 11.04709541305795, Puerto Rico - 0.8180887048128703, Rhode Island - 15.233858774512232, South Carolina - 5.080738222795875, South Dakota - 5.999851203690148, Tennessee - 6.350442509676293, Texas - 6.485232875310883, Utah - 19.262511964646638, Vermont - 6.389664079385186, Virginia - 13.926441756261127, Washington - 24.358374322097742, Washington DC - 41.118225969955226, West Virginia - 1.6168906868713326, Wisconsin - 11.525569825918492, Wyoming - 8.674350985059299

Percentage based on last result set (i.e. percentage of SO respondents normalized to population size):

Alabama - 0.824721537170129, Alaska - 1.6310745742340413, Arizona - 2.2453356411538934, Arkansas - 0.8090843480665424, California - 2.4619016030822145, Colorado - 3.3347316843587764, Connecticut - 1.827612557886197, Delaware - 0.6504515720608812, Florida - 1.2969544194811282, Georgia - 1.322943476877953, Hawaii - 0.4284409706182859, Idaho - 1.9937363865669924, Illinois - 1.7605794571625484, Indiana - 1.2470670387501877, Iowa - 1.8757479281354774, Kansas - 1.9292238341738914, Kentucky - 0.7716853968642833, Louisiana - 0.5617116789849995, Maine - 1.4959562729950668, Maryland - 2.534274563482699, Massachusetts - 4.457826274179643, Michigan - 1.7100977237018886, Minnesota - 3.031151919554754, Mississippi - 0.4663060917725937, Missouri - 1.7169991328706176, Montana - 1.3847700743382283, Nebraska - 2.3575585577735083, Nevada - 1.0810780210241895, New Hampshire - 3.1616647398393933, New Jersey - 1.9066320538825035, New Mexico - 0.8580864023448015, New York - 2.8449067230945992, North Carolina - 1.4680544736474985, North Dakota - 1.1368402124642574, Ohio - 1.7396553181517227, Oklahoma - 0.9382208331577232, Oregon - 3.365565140867746, Pennsylvania - 2.1966189351328276, Puerto Rico - 0.16266983061322227, Rhode Island - 3.0291204509448555, South Carolina - 1.0102606492793207, South Dakota - 1.1930182793955861, Tennessee - 1.262730314317618, Texas - 1.289532207336161, Utah - 3.8301831330026452, Vermont - 1.2705291826596392, Virginia - 2.7691519369578557, Washington - 4.84345108504518, Washington DC - 8.17600195956597, West Virginia - 0.3215046639882644, Wisconsin - 2.291759414685823, Wyoming - 1.7248193223031953

Previous result set sorted by percentage descending:

Washington DC - 8.17600195956597, Washington - 4.84345108504518, Massachusetts - 4.457826274179643, Utah - 3.8301831330026452, Oregon - 3.365565140867746, Colorado - 3.3347316843587764, New Hampshire - 3.1616647398393933, Minnesota - 3.031151919554754, Rhode Island - 3.0291204509448555, New York - 2.8449067230945992, Virginia - 2.7691519369578557, Maryland - 2.534274563482699, California - 2.4619016030822145, Nebraska - 2.3575585577735083, Wisconsin - 2.291759414685823, Arizona - 2.2453356411538934, Pennsylvania - 2.1966189351328276, Idaho - 1.9937363865669924, Kansas - 1.9292238341738914, New Jersey - 1.9066320538825035, Iowa - 1.8757479281354774, Connecticut - 1.827612557886197, Illinois - 1.7605794571625484, Ohio - 1.7396553181517227, Wyoming - 1.7248193223031953, Missouri - 1.7169991328706176, Michigan - 1.7100977237018886, Alaska - 1.6310745742340413, Maine - 1.4959562729950668, North Carolina - 1.4680544736474985, Montana - 1.3847700743382283, Georgia - 1.322943476877953, Florida - 1.2969544194811282, Texas - 1.289532207336161, Vermont - 1.2705291826596392, Tennessee - 1.262730314317618, Indiana - 1.2470670387501877, South Dakota - 1.1930182793955861, North Dakota - 1.1368402124642574, Nevada - 1.0810780210241895, South Carolina - 1.0102606492793207, Oklahoma - 0.9382208331577232, New Mexico - 0.8580864023448015, Alabama - 0.824721537170129, Arkansas - 0.8090843480665424, Kentucky - 0.7716853968642833, Delaware - 0.6504515720608812, Louisiana - 0.5617116789849995, Mississippi - 0.4663060917725937, Hawaii - 0.4284409706182859, West Virginia - 0.3215046639882644, Puerto Rico - 0.16266983061322227

Answer 2

Using the data compiled by Ben Lee, I wrote a program to make this graph: The two blobs at the bottom are AK and HI; the blob on the right is DC. I did it using this python program:

import sys
from PIL import Image


def floodfill(img, seed, color,state,n):
    im = img.load()
    work = [seed]
    start_color = im[seed]
    while work:
        x, y = work.pop()
        im[x, y] = color
        for dx, dy in ((-1,0), (1,0), (0,-1), (0,1)):
            nx, ny = x + dx, y + dy
            if nx>=0 and ny>=0 and nx<img.size[0] and ny<img.size[1] and im[nx, ny] == start_color:
                work.append((nx, ny))

            n+=1

USA_MAP = Image.open('map.png')#.convert('2')
POINT_STATE = {
'AR':(359,253),'AZ':(133,221),'AL':(425,267),'CA':(10,125),
'CO':(195,186),'CT':(557,113),'DE':(540,162),'FL':(499,334),
'GA':(468,264),'IA':(333,144),'ID':(119,109),'IL':(387,176),
'IN':(421,169),'KS':(292,196),'KY':(422,199),'LA':(354,301),
'MD':(520,157),'MZ':(495,159),'MY':(540,179),'MA':(569,97),
'ME':(575,60),'MI':(425,126),'MJ':(392,78),'MN':(326,80),
'MO':(349,196),'MS':(385,288),'MT':(152,59),'NE':(281,161),
'NC':(524,220),'ND':(269,70),'NM':(195,246),'NJ':(544,134),
'NV':(67,151),'NY':(537,100),'OH':(448,159),'OK':(303,247),
'OR':(70,70),'PA':(503,139),'RI':(569,107),'SC':(494,242),
'SD':(265,108),'TN':(417,232),'TX':(300,300),'UT':(137,183),
'VA':(510,183),'WA':(80,30),'WI':(384,104),'WV':(485,173),'DC':(584,198),
'WY':(190,132),'NH':(560,80),'VT':(545,80),'HI':(177,350),'AK':(83,350)
}
painted_map = USA_MAP#.convert('L')
data = {
'AL': 0.8247215372,'AK': 1.6310745742,'AZ': 2.2453356412,'AR': 0.8090843481,
'CA': 2.4619016031,'CO': 3.3347316844,'CT': 1.8276125579,'DE': 0.6504515721,
'FL': 1.2969544195,'GA': 1.3229434769,'HI': 0.4284409706,'ID': 1.9937363866,
'IL': 1.7605794572,'IN': 1.2470670388,'IA': 1.8757479281,'KS': 1.9292238342,
'KY': 0.7716853969,'LA': 0.561711679,'ME': 1.495956273,'MD': 2.5342745635,
'MA': 4.4578262742,'MI': 1.7100977237,'MN': 3.0311519196,'MS': 0.4663060918,
'MO': 1.7169991329,'MT': 1.3847700743,'NE': 2.3575585578,'NV': 1.081078021,
'NH': 3.1616647398,'NJ': 1.9066320539,'NM': 0.8580864023,'NY': 2.8449067231,
'NC': 1.4680544736,'ND': 1.1368402125,'OH': 1.7396553182,'OK': 0.9382208332,
'OR': 3.3655651409,'PA': 2.1966189351,#'PR': 0.1626698306,
'RI': 3.0291204509,
'SC': 1.0102606493,'SD': 1.1930182794,'TN': 1.2627303143,'TX': 1.2895322073,
'UT': 3.830183133,'VT': 1.2705291827,'VA': 2.769151937,'WA': 4.843451085,
'DC': 6.1760019596,'WV': 0.321504664,'WI': 2.2917594147,'WY': 1.7248193223
}
data['MJ']=data['MI']
data['MY']=data['MI']
data['MZ']=data['MD']
# Normalize data based on the minimum weight being 0+eps and maximum 30.
G=170
n=0
for k, v in data.items():
    print k
    v = (v-data['WV'])/(data['DC']-data['WV'])
    color = v*4
    if color<1:
         R,G,B=255,255,254
    else:
         v=(v-1.0/4)*4.0/3
         R=240
         G=230-int(120*v)
         B=210-int(180*v)
    floodfill(painted_map, POINT_STATE[k], (R,G,B),k,n)
    n+=1
floodfill(painted_map,(0,0),(220,230,250),'up_left',1)
floodfill(painted_map,(0,300),(220,230,250),'down_left',1)
floodfill(painted_map,(300,0),(220,230,250),'up_right',1)
floodfill(painted_map,(575,350),(220,230,250),'down_right',1)
painted_map.save('out.png')
painted_map.show()