Educational Attainment in the Goodland Township, Itasca County, Minnesota (Township)

Educational Attainment#1

Highest level of education among people aged 25 years and older.
Scope: population of Itasca County and the Goodland Township
Goodland Township
Itasca County
0%10%20%30%40%50%60%CountHigher Degree1H.S. Diploma2No H.S. Diploma232.048193%34.166974%32.048193%32.0%13364.096386%59.130060%64.096386%64.1%2663.855422%6.702965%3.855422%3.9%16
Count number of people in category
1Post-Secondary Degree 2H.S. = High School

Relative Educational Attainment#2

Highest level of education among people aged 25 years and older, as percentage more or less than Itasca County at large.
Scope: population of Itasca County and the Goodland Township
0%%ref.Higher Degree1H.S. Diploma2No H.S. Diploma2-6.201256%-6.201256%-6.2%32.048%32.0%34.167%34.2%8.398986%8.398986%+8.4%64.096%64.1%59.130%59.1%-42.481850%-42.481850%-42.5%3.855%3.86%6.703%6.70%
% percentage with given level in the Goodland Township
ref. percentage with given level in Itasca County
1Post-Secondary Degree 2H.S. = High School

Detailed Educational Attainment#3

Highest level of education among people aged 25 years and older.
Scope: population of Itasca County and the Goodland Township
Goodland Township
Itasca County
0%5%10%15%20%25%30%CountDoctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None1.445783%0.569091%1.445783%1.4%61.686747%1.248923%1.686747%1.7%79.397590%5.364833%9.397590%9.4%397.710843%15.073213%7.710843%7.7%3211.807229%11.910914%11.807229%11.8%4930.361446%28.149994%30.361446%30.4%12633.734940%30.980066%33.734940%33.7%1403.373494%5.011074%3.373494%3.4%140.481928%1.387351%0.481928%0.5%20.000000%0.000000%0.0%0.304540%0
Count number of people in category
1Degree 2Diploma or equivialent 3H.S. = High School

Detailed Relative Educational Attainment#4

Highest level of education among people aged 25 years and older, as percentage more or less than Itasca County at large.
Scope: population of Itasca County and the Goodland Township
-100%0%+100%%ref.Doctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None154.051449%154.051449%+154.1%1.446%1.45%0.569%0.57%35.056086%35.056086%+35.1%1.687%1.69%1.249%1.25%75.170222%75.170222%+75.2%9.398%9.40%5.365%5.36%-48.844062%-48.844062%-48.8%7.711%7.71%15.073%15.1%-0.870507%-0.870507%-0.9%11.807%11.8%11.911%11.9%7.855959%7.855959%+7.9%30.361%30.4%28.150%28.1%8.892406%8.892406%+8.9%33.735%33.7%30.980%31.0%-32.679225%-32.679225%-32.7%3.373%3.37%5.011%5.01%-65.262736%-65.262736%-65.3%0.482%0.48%1.387%1.39%-100.000000%-100.000000%-100.0%0.000%0%0.305%0.30%
% percentage with given level in the Goodland Township
ref. percentage with given level in Itasca County
1Degree 2Diploma or equivialent 3H.S. = High School

Educational Attainment Sex Ratio#5

Percentage more likely men or women are to have various highest levels of education among people aged 25 years and older.
Scope: population of Itasca County and the Goodland Township
More Females More Males
Itasca County
Goodland Township
50%0%FMHigher Degree1H.S. Diploma2No H.S. Diploma2-82.099018%-9.426830%-82.099018%82%41.346%41.3%22.705%22.7%38.164251%0.768729%38.164251%38%53.846%53.8%74.396%74.4%-65.865385%-65.865385%66%48.588795%4.808%4.81%2.899%2.90%
F females in category as a percentage of all females
M males in category as a percentage of all males
1Post-Secondary Degree 2H.S. = High School

Detailed Educational Attainment Sex Ratio#6

Percentage more likely men or women are to have various highest levels of education among people aged 25 years and older.
Scope: population of Itasca County and the Goodland Township
More Females More Males
Itasca County
Goodland Township
1,000%500%0%FMDoctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3-1,000.000000%-1,000.000000%> 1000%157.950025%2.885%2.88%0.000%0%-1,000.000000%-1,000.000000%> 1000%6.995949%3.365%3.37%0.000%0%-59.230769%-22.829470%-59.230769%59%11.538%11.5%7.246%7.25%-12.788462%-0.458160%-12.788462%13%8.173%8.17%7.246%7.25%-87.330317%-23.411511%-87.330317%87%15.385%15.4%8.213%8.21%-4.472777%21.637427%21.637427%22%27.404%27.4%33.333%33.3%55.292051%5.587796%55.292051%55%26.442%26.4%41.063%41.1%-32.692308%-32.692308%33%54.254428%3.846%3.85%2.899%2.90%-1,000.000000%-1,000.000000%> 1000%21.023170%0.962%0.96%0.000%0%
F females in category as a percentage of all females
M males in category as a percentage of all males
1Degree 2Diploma or equivialent 3H.S. = High School

Bachelor's Degrees By Age#7

Percentage of age cohort whose highest degree is a Bachelor's.
Scope: population of Itasca County and the Goodland Township
Goodland Township
Itasca County
0%5%10%15%B.X.Total65+45-6435-4425-349.230769%11.897005%9.230769%9.2%121306.878307%14.494341%6.878307%6.9%131898.163265%19.514203%8.163265%8.2%4496.382979%18.806306%6.382979%6.4%347
B.X. number of people with a Bachelor's degree
Total number of people in age cohort

Relative Bachelor's Degrees By Age#8

Percentage of population whose highest degree is a Bachelor's, as a percentage more or less than Itasca County at large.
Scope: population of Itasca County and the Goodland Township
60%50%40%30%20%10%0%%ref.65+45-6435-4425-34-22.410981%-22.410981%22.4%9.231%9.23%11.897%11.9%-52.544880%-52.544880%52.5%6.878%6.88%14.494%14.5%-58.167571%-58.167571%58.2%8.163%8.16%19.514%19.5%-66.059371%-66.059371%66.1%6.383%6.38%18.806%18.8%
% percentage in the Goodland Township with a Bachelor's degree
ref percentage in Itasca County with a Bachelor's degree

Bachelor's Degrees Sex Ratio By Age#9

Percentage more likely men or women are to have their highest degree be a Bachelor's.
Scope: population of Itasca County and the Goodland Township
More Females More Males
Itasca County
Goodland Township
1,000%500%0%FM65+45-6435-4425-34-58.360656%-58.360656%58.4%51.113574%11.475%11.5%7.246%7.25%127.393617%9.035452%127.393617%127.4%4.211%4.21%9.574%9.57%-1,000.000000%-46.151128%-1,000.000000%> 1000%14.815%14.8%0.000%0%-76.000000%-52.163932%-76.000000%76.0%8.000%8.00%4.545%4.55%
F female degree holders as a percentage of all females
M male degree holders as a percentage of all males

Median Earnings by Educational Attainment#10

Among population 25 years old and over with earnings.
Scope: population of Itasca County and the Goodland Township
Goodland Township
Itasca County
$0k$20k$40k$60kCount%Graduate Degree1Some CollegeH.S. Diploma2Total$67,734.000000$57,962.000000$67,734.000000$67.7k5212.530%12.5%$44,167.000000$31,080.000000$44,167.000000$44.2k17542.169%42.2%$25,833.000000$25,026.000000$25,833.000000$25.8k14033.735%33.7%$43,182.000000$31,252.000000$43,182.000000$43.2k415100.000%100%
Count number of people with given highest level of educational attainment
% percentage of people with given highest level of educational attainment
1or professional degree 2H.S. = High School

Median Earnings by Educational Attainment#11

By sex among population 25 years old and over with earnings.
Scope: population of Itasca County and the Goodland Township
Female Male
Itasca County
Goodland Township
Shaded bar tips show excess over facing bar.
$0k$50kFMBachelor's DegreeSome CollegeH.S. Diploma2Total$-41,042.000000$-32,984.000000$-41,042.000000$41.0k$41,042.000000$44,896.000000$32,984.000000$13,492.000000$85,938.000000$85.9k1715$-24,583.000000$-22,824.000000$-24,583.000000$24.6k$24,583.000000$46,667.000000$22,824.000000$19,892.000000$71,250.000000$71.3k8986$-22,917.000000$-16,059.000000$-22,917.000000$22.9k$22,917.000000$18,750.000000$16,059.000000$16,244.000000$41,667.000000$41.7k5585$-36,250.000000$-24,273.000000$-36,250.000000$36.3k$36,250.000000$15,469.000000$24,273.000000$16,888.000000$51,719.000000$51.7k208207
F females with given highest level of educational attainment
M males with given highest level of educational attainment
1or professional degree 2H.S. = High School

Employment by Educational Attainment#12

Percentage of population that is employed by highest level of educational attainment among the population aged 25 to 64 years old.
Scope: population of Itasca County and the Goodland Township
Goodland Township
Itasca County
0%20%40%60%80%100%CountBachelor's Degree1Some College2H.S. Diploma3No H.S. Diploma352.631579%79.470323%52.631579%52.6%3073.846154%72.950259%73.846154%73.8%9681.052632%64.545172%81.052632%81.1%77100.000000%52.515091%100.000000%100.0%3
Count employed people with given educational attainment aged 25 to 64 years old
1or higher 2two or four year institution 3H.S. = High School

Lacking High School Diploma By Race#13

Percent of racial or ethnic group lacking a high school diploma (or equivalent).
Scope: population of Itasca County and the Goodland Township
Female Male
Itasca County
Goodland Township
Shaded bar tips show excess over facing bar.
5%0%5%10%MFWhite1Other1-2.941176%-2.267157%-5.298869%-5.208333%5.2%2.941176%5.298869%2.445678%2.941176%2.9%610-0.000000%-8.357349%0.000000%0.0%0.000000%0.000000%8.357349%5.609962%0.000000%0.0%00
M number of males in category
F number of females in category
1non-Hispanic 2excluding black and Asian Hispanics

College Graduates By Race#14

Percent of racial or ethnic group with a bachelor's degree or higher.
Scope: population of Itasca County and the Goodland Township
Female Male
Itasca County
Goodland Township
Shaded bar tips show excess over facing bar.
100%50%0%50%100%MFWhite1Other1-13.235294%-6.556373%-22.397806%-0.406717%-19.791667%19.8%13.235294%22.397806%13.235294%13.2%2738-100.000000%-9.212481%-10.960429%-100.000000%100.0%100.000000%0.000000%9.212481%100.000000%100.0%316
M number of males in category
F number of females in category
1non-Hispanic 2excluding black and Asian Hispanics

Map of Educational Attainment by Tract in the Goodland Township

Coarse: Post-Secondary Degree Educational Attainment by Tract#15

Percentage of the population 25 years and older with given highest level of educational attainment
29.1%29.8%30.4%31.1%31.7%32.4%

Coarse: High School Diploma Educational Attainment by Tract#16

Percentage of the population 25 years and older with given highest level of educational attainment
61.0%62.2%63.4%64.5%65.6%66.8%

Coarse: No High School Diploma Educational Attainment by Tract#17

Percentage of the population 25 years and older with given highest level of educational attainment
4.11%4.60%5.08%5.56%6.04%6.52%

Detailed: Doctorate Degree Educational Attainment by Tract#18

Percentage of the population 25 years and older with given highest level of educational attainment
0.44%0.51%0.57%0.64%0.70%0.77%

Detailed: Professional Degree Educational Attainment by Tract#19

Percentage of the population 25 years and older with given highest level of educational attainment
0.83%0.91%0.97%1.04%1.11%1.18%

Detailed: Master's Degree Educational Attainment by Tract#20

Percentage of the population 25 years and older with given highest level of educational attainment
2.86%3.23%3.60%3.97%4.34%4.71%

Detailed: Bachelor's Degree Educational Attainment by Tract#21

Percentage of the population 25 years and older with given highest level of educational attainment
9.9%11.0%12.0%13.1%14.1%15.2%

Detailed: Associate's Degree Educational Attainment by Tract#22

Percentage of the population 25 years and older with given highest level of educational attainment
12.786%12.799%12.812%12.824%12.837%12.850%

Detailed: Some College Educational Attainment by Tract#23

Percentage of the population 25 years and older with given highest level of educational attainment
29.42%29.88%30.33%30.78%31.23%31.69%

Detailed: High School Diploma or Equivalent Educational Attainment by Tract#24

Percentage of the population 25 years and older with given highest level of educational attainment
31.6%32.4%33.0%33.7%34.4%35.1%

Detailed: Some High School Educational Attainment by Tract#25

Percentage of the population 25 years and older with given highest level of educational attainment
3.23%3.63%4.03%4.42%4.81%5.21%

Detailed: Less than High School Educational Attainment by Tract#26

Percentage of the population 25 years and older with given highest level of educational attainment
0.68%0.80%0.91%1.02%1.12%1.24%

Detailed: None Educational Attainment by Tract#27

Percentage of the population 25 years and older with given highest level of educational attainment
0.074%0.098%0.123%0.147%0.172%0.197%
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Educational Attainment by County Subdivision in the Grand Rapids Area

There are 58 county subdivisions in the Grand Rapids Area. This section compares the Goodland Township to the 50 most populous county subdivisions in the Grand Rapids Area and to those entities that contain or substantially overlap with the Goodland Township. The least populous of the compared county subdivisions has a population of 96.

No H.S. Diploma by County Subdivision#28

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of the Goodland Township, selected other county subdivisions in the Grand Rapids Area, and entities that contain the Goodland Township
0%5%10%15%20%Count#Squaw LakeKinghurst TownshipKinghurstZempleCarpenter TownshipCarpenterBowstring LakeBowstring LkBigforkMax TownshipUnited States of AmericaUnited StatesKeewatinArbo TownshipGood Hope TownshipGood HopeBalsam TownshipBalsamDeer RiverMidwestWest North CentralSpang TownshipSpangStokes TownshipStokesSand Lake TownshipSand LkNashwauk TownshipNashwaukBoveyGrattan TownshipGrattanBigfork TownshipBigforkFeeley TownshipFeeleyWirt TownshipMorse TownshipMorseBowstring TownshipBowstringMinnesotaGreenway TownshipGreenwayColeraineGrand Rapids Metro AreaGrand RapidsItasca CountyItascaOteneagen TownshipOteneagenNashwauk-Keewatin School DistrictNashwauk-KeewatinGrand RapidsSplithand TownshipSplithandDeer River TownshipDeer RivMarcell TownshipMarcellBearville TownshipBearvilleArdenhurst TownshipArdenhurstCohassetLawrence TownshipLawrenceEffieLone Pine TownshipLone PneBlackberry TownshipBlackberrySago TownshipTaconiteLake Jessie TownshipLk JessieDeer LakeLa PrairieNortheast ItascaNE ItascaGoodland TownshipGoodlandHarris TownshipHarrisLittle Sand LakeLittle Sand LkWabana TownshipWabanaTrout Lake TownshipTrout LkWildwood TownshipWildwoodWarba21.126761%21.126761%21.1%15121.052632%21.052632%21.1%20217.741935%17.741935%17.7%11317.708333%17.708333%17.7%34416.738197%16.738197%16.7%117515.223881%15.223881%15.2%51613.131313%13.131313%13.1%13713.020590%13.020590%13.0%27,818,38027.8M12.104539%12.104539%12.1%88811.918605%11.918605%11.9%82911.650485%11.650485%11.7%121010.429448%10.429448%10.4%511110.361068%10.361068%10.4%661210.244520%10.244520%10.2%4,641,8354.64M9.174633%9.174633%9.2%1,278,0511.28M9.036145%9.036145%9.0%15138.994709%8.994709%9.0%17148.633094%8.633094%8.6%12158.573655%8.573655%8.6%110168.560311%8.560311%8.6%44178.510638%8.510638%8.5%4188.292683%8.292683%8.3%17198.260870%8.260870%8.3%19208.235294%8.235294%8.2%7217.852194%7.852194%7.9%34227.526882%7.526882%7.5%14237.376055%7.376055%7.4%270,121270k7.240143%7.240143%7.2%101246.712565%6.712565%6.7%78256.702965%6.702965%6.7%2,1796.702965%6.702965%6.7%2,1796.701031%6.701031%6.7%13266.622517%6.622517%6.6%1806.454131%6.454131%6.5%496276.349206%6.349206%6.3%12286.172840%6.172840%6.2%30295.934718%5.934718%5.9%20305.555556%5.555556%5.6%8315.405405%5.405405%5.4%6325.292062%5.292062%5.3%106335.248619%5.248619%5.2%19345.161290%5.161290%5.2%8355.014749%5.014749%5.0%17364.893964%4.893964%4.9%30374.724409%4.724409%4.7%6384.712042%4.712042%4.7%18394.680851%4.680851%4.7%11404.460094%4.460094%4.5%114414.150198%4.150198%4.2%21423.862213%3.862213%3.9%37433.855422%3.855422%3.9%16443.685703%3.685703%3.7%91453.271028%3.271028%3.3%7462.752294%2.752294%2.8%12472.570093%2.570093%2.6%22481.801802%1.801802%1.8%2490.000000%0.000000%0.0%050
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County Subdivision#29

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of the Goodland Township, selected other county subdivisions in the Grand Rapids Area, and entities that contain the Goodland Township
0%10%20%30%Count#Wabana TownshipWabanaWildwood TownshipWildwoodMinnesotaHarris TownshipHarrisUnited States of AmericaUnited StatesCohassetLone Pine TownshipLone PneWest North CentralStokes TownshipStokesMidwestSand Lake TownshipSand LkMarcell TownshipMarcellTrout Lake TownshipTrout LkGrand RapidsFeeley TownshipFeeleyBearville TownshipBearvilleDeer LakeColeraineNortheast ItascaNE ItascaGrand Rapids Metro AreaGrand RapidsItasca CountyItascaArbo TownshipBalsam TownshipBalsamLa PrairieSplithand TownshipSplithandGoodland TownshipGoodlandDeer River TownshipDeer RivGood Hope TownshipGood HopeZempleLawrence TownshipLawrenceWirt TownshipSpang TownshipSpangSquaw LakeSago TownshipBoveyEffieBowstring TownshipBowstringBowstring LakeBowstring LkNashwauk-Keewatin School DistrictNashwauk-KeewatinGreenway TownshipGreenwayArdenhurst TownshipArdenhurstCarpenter TownshipCarpenterKeewatinKinghurst TownshipKinghurstBigfork TownshipBigforkBlackberry TownshipBlackberryLake Jessie TownshipLk JessieWarbaDeer RiverMax TownshipNashwauk TownshipNashwaukOteneagen TownshipOteneagenLittle Sand LakeLittle Sand LkBigforkMorse TownshipMorseTaconiteGrattan TownshipGrattan38.073394%38.073394%38.1%166136.936937%36.936937%36.9%41234.240609%34.240609%34.2%1,253,9371.25M31.915755%31.915755%31.9%788330.315023%30.315023%30.3%64,767,78764.8M30.304543%30.304543%30.3%607430.088496%30.088496%30.1%102530.056536%30.056536%30.1%4,186,9564.19M29.100529%29.100529%29.1%55628.952816%28.952816%29.0%13,118,64213.1M28.057554%28.057554%28.1%39726.706231%26.706231%26.7%90826.285047%26.285047%26.3%225926.063761%26.063761%26.1%2,0031024.782609%24.782609%24.8%571124.305556%24.305556%24.3%351224.100156%24.100156%24.1%6161322.547332%22.547332%22.5%2621422.546973%22.546973%22.5%2161522.256060%22.256060%22.3%7,23522.256060%22.256060%22.3%7,23521.656977%21.656977%21.7%1491621.267894%21.267894%21.3%1041720.750988%20.750988%20.8%1051820.634921%20.634921%20.6%391920.240964%20.240964%20.2%842020.164609%20.164609%20.2%982119.417476%19.417476%19.4%202219.354839%19.354839%19.4%122319.337017%19.337017%19.3%702418.823529%18.823529%18.8%162518.674699%18.674699%18.7%312616.901408%16.901408%16.9%122716.535433%16.535433%16.5%212816.147860%16.147860%16.1%832916.129032%16.129032%16.1%253016.129032%16.129032%16.1%303115.593705%15.593705%15.6%1093215.526122%15.526122%15.5%42214.623656%14.623656%14.6%2043314.414414%14.414414%14.4%163414.062500%14.062500%14.1%273513.892710%13.892710%13.9%1013613.684211%13.684211%13.7%133713.658537%13.658537%13.7%283812.724307%12.724307%12.7%783911.914894%11.914894%11.9%284011.842105%11.842105%11.8%94111.773940%11.773940%11.8%754211.111111%11.111111%11.1%114310.911925%10.911925%10.9%1404410.309278%10.309278%10.3%20459.345794%9.345794%9.3%20469.253731%9.253731%9.3%31479.006928%9.006928%9.0%39488.900524%8.900524%8.9%34498.510638%8.510638%8.5%450
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County Subdivision#30

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of the Goodland Township, selected other county subdivisions in the Grand Rapids Area, and entities that contain the Goodland Township
0%2%4%6%8%10%Count#Stokes TownshipStokesWabana TownshipWabanaHarris TownshipHarrisSplithand TownshipSplithandWildwood TownshipWildwoodLone Pine TownshipLone PneMinnesotaTrout Lake TownshipTrout LkUnited States of AmericaUnited StatesSago TownshipGoodland TownshipGoodlandWest North CentralLake Jessie TownshipLk JessieMidwestSand Lake TownshipSand LkCohassetDeer River TownshipDeer RivFeeley TownshipFeeleyCarpenter TownshipCarpenterColeraineDeer LakeArbo TownshipGrand Rapids Metro AreaGrand RapidsItasca CountyItascaNortheast ItascaNE ItascaEffieGrand RapidsMarcell TownshipMarcellLa PrairieBowstring LakeBowstring LkBowstring TownshipBowstringNashwauk-Keewatin School DistrictNashwauk-KeewatinArdenhurst TownshipArdenhurstTaconiteBearville TownshipBearvilleBalsam TownshipBalsamSpang TownshipSpangKeewatinOteneagen TownshipOteneagenGreenway TownshipGreenwayBlackberry TownshipBlackberryBoveyDeer RiverNashwauk TownshipNashwaukMorse TownshipMorseLawrence TownshipLawrenceBigforkLittle Sand LakeLittle Sand LkBigfork TownshipBigforkMax TownshipSquaw LakeGood Hope TownshipGood HopeWarbaKinghurst TownshipKinghurstGrattan TownshipGrattanZempleWirt Township10.052910%10.052910%10.1%1918.715596%8.715596%8.7%3825.143783%5.143783%5.1%12734.232804%4.232804%4.2%843.603604%3.603604%3.6%453.539823%3.539823%3.5%1263.458748%3.458748%3.5%126,664127k3.387850%3.387850%3.4%2973.336158%3.336158%3.3%7,127,6737.13M3.149606%3.149606%3.1%483.132530%3.132530%3.1%1392.978981%2.978981%3.0%414,980415k2.978723%2.978723%3.0%7102.893416%2.893416%2.9%1,311,0191.31M2.877698%2.877698%2.9%4112.845731%2.845731%2.8%57122.674897%2.674897%2.7%13132.608696%2.608696%2.6%6142.083333%2.083333%2.1%4152.065404%2.065404%2.1%24161.917058%1.917058%1.9%49171.889535%1.889535%1.9%13181.818014%1.818014%1.8%5911.818014%1.818014%1.8%5911.565762%1.565762%1.6%15191.290323%1.290323%1.3%2201.197137%1.197137%1.2%92211.186944%1.186944%1.2%4221.185771%1.185771%1.2%6231.144492%1.144492%1.1%8241.075269%1.075269%1.1%2251.066961%1.066961%1.1%290.900901%0.900901%0.9%1260.785340%0.785340%0.8%3270.694444%0.694444%0.7%1280.613497%0.613497%0.6%3290.602410%0.602410%0.6%1300.550206%0.550206%0.6%4310.515464%0.515464%0.5%1320.501792%0.501792%0.5%7330.489396%0.489396%0.5%3340.389105%0.389105%0.4%2350.156986%0.156986%0.2%1360.000000%0.000000%0.0%0370.000000%0.000000%0.0%0380.000000%0.000000%0.0%0390.000000%0.000000%0.0%0400.000000%0.000000%0.0%0410.000000%0.000000%0.0%0420.000000%0.000000%0.0%0430.000000%0.000000%0.0%0440.000000%0.000000%0.0%0450.000000%0.000000%0.0%0460.000000%0.000000%0.0%0470.000000%0.000000%0.0%0480.000000%0.000000%0.0%0490.000000%0.000000%0.0%050
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).

Under-Education Sex Ratio by County Subdivision#31

Percentage more likely men are than women to not have a high school diploma (or equivalent) among people aged 25 years and older.
Scope: population of the Goodland Township, selected other county subdivisions in the Grand Rapids Area, and entities that contain the Goodland Township
Female
Male
10x5x0x5x10xFM#Little Sand LkZempleBlackberryHarrisMax TownshipKinghurstBearvilleSand LkSplithandEffieKeewatinOteneagenDeer LakeBalsamCarpenterBowstringFeeleyMarcellSago TownshipLawrenceWirt TownshipSquaw LakeGood HopeStokesBoveyArbo TownshipGrand RapidsItascaNashwaukTrout LkColeraineGrand RapidsBowstring LkTaconiteNE ItascaNashwauk-KeewatinWabanaSpangWest North CentralMinnesotaMidwestUnited StatesDeer RivLa PrairieDeer RiverMorseBigforkArdenhurstGreenwayLk JessieGrattanBigforkCohassetGoodlandLone PneWildwood-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%5.983%5.98%1-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%33.333%33.3%2-1.000000x-1.000000x7.006780x7.006780x7.01x1.258%1.26%8.814%8.81%3-1.000000x-1.000000x6.195165x6.195165x6.20x1.003%1.00%6.211%6.21%4-1.000000x-1.000000x5.390000x5.390000x5.39x4.082%4.08%22.000%22.0%5-1.000000x-1.000000x5.100000x5.100000x5.10x6.667%6.67%34.000%34.0%6-1.000000x-1.000000x3.171429x3.171429x3.17x2.703%2.70%8.571%8.57%7-1.000000x-1.000000x3.132353x3.132353x3.13x4.225%4.23%13.235%13.2%8-1.000000x-1.000000x3.031915x3.031915x3.03x3.158%3.16%9.574%9.57%9-1.000000x-1.000000x2.886076x2.886076x2.89x2.632%2.63%7.595%7.59%10-1.000000x-1.000000x2.818323x2.818323x2.82x6.336%6.34%17.857%17.9%11-1.000000x-1.000000x2.443548x2.443548x2.44x3.960%3.96%9.677%9.68%12-1.000000x-1.000000x2.369541x2.369541x2.37x2.606%2.61%6.175%6.17%13-1.000000x-1.000000x2.351417x2.351417x2.35x6.198%6.20%14.575%14.6%14-1.000000x-1.000000x2.263282x2.263282x2.26x9.859%9.86%22.314%22.3%15-1.000000x-1.000000x2.196970x2.196970x2.20x4.598%4.60%10.101%10.1%16-1.000000x-1.000000x2.056497x2.056497x2.06x5.357%5.36%11.017%11.0%17-1.000000x-1.000000x2.035185x2.035185x2.04x3.822%3.82%7.778%7.78%18-1.000000x-1.000000x1.848485x1.848485x1.85x3.279%3.28%6.061%6.06%19-1.000000x-1.000000x1.814721x1.814721x1.81x3.636%3.64%6.599%6.60%20-1.000000x-1.000000x1.750000x1.750000x1.75x5.714%5.71%10.000%10.0%21-1.000000x-1.000000x1.727273x1.727273x1.73x15.789%15.8%27.273%27.3%22-1.000000x-1.000000x1.678571x1.678571x1.68x8.511%8.51%14.286%14.3%23-1.000000x-1.000000x1.639610x1.639610x1.64x6.931%6.93%11.364%11.4%24-1.000000x-1.000000x1.636238x1.636238x1.64x6.593%6.59%10.788%10.8%25-1.000000x-1.000000x1.502215x1.502215x1.50x9.538%9.54%14.327%14.3%26-1.000000x-1.000000x1.485888x1.485888x1.49x5.397%5.40%8.020%8.02%-1.000000x-1.000000x1.485888x1.485888x1.49x5.397%5.40%8.020%8.02%-1.000000x-1.000000x1.446877x1.446877x1.45x7.057%7.06%10.211%10.2%27-1.000000x-1.000000x1.437710x1.437710x1.44x2.108%2.11%3.030%3.03%28-1.000000x-1.000000x1.420259x1.420259x1.42x5.592%5.59%7.942%7.94%29-1.000000x-1.000000x1.376353x1.376353x1.38x5.493%5.49%7.561%7.56%30-1.000000x-1.000000x1.351552x1.351552x1.35x14.245%14.2%19.253%19.3%31-1.000000x-1.000000x1.314560x1.314560x1.31x4.023%4.02%5.288%5.29%32-1.000000x-1.000000x1.301585x1.301585x1.30x3.354%3.35%4.366%4.37%33-1.000000x-1.000000x1.275522x1.275522x1.28x5.836%5.84%7.444%7.44%-1.000000x-1.000000x1.265502x1.265502x1.27x2.415%2.42%3.057%3.06%34-1.000000x-1.000000x1.170732x1.170732x1.17x8.333%8.33%9.756%9.76%35-1.000000x-1.000000x1.137506x1.137506x1.14x8.598%8.60%9.780%9.78%-1.000000x-1.000000x1.136388x1.136388x1.14x6.913%6.91%7.856%7.86%-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.105842x1.105842x1.11x5.858%5.86%6.478%6.48%36-1.000000x-1.000000x1.032567x1.032567x1.03x4.082%4.08%4.215%4.21%37-1.000000x-1.000000x1.002882x1.002882x1.00x10.345%10.3%10.375%10.4%38-1.002183x-1.002183x1.00x1.000000x1.000000x7.860%7.86%7.843%7.84%39-1.010417x-1.010417x1.01x1.000000x1.000000x8.333%8.33%8.247%8.25%40-1.018182x-1.018182x1.02x1.000000x1.000000x5.455%5.45%5.357%5.36%41-1.110090x-1.110090x1.11x1.000000x1.000000x7.635%7.63%6.878%6.88%42-1.317857x-1.317857x1.32x1.000000x1.000000x5.357%5.36%4.065%4.07%43-1.350000x-1.350000x1.35x1.000000x1.000000x10.000%10.0%7.407%7.41%44-1.541209x-1.541209x1.54x1.000000x1.000000x18.132%18.1%11.765%11.8%45-1.560273x-1.560273x1.56x1.000000x1.000000x6.495%6.49%4.163%4.16%46-1.658654x-1.658654x1.66x1.000000x1.000000x4.808%4.81%2.899%2.90%47-2.815385x-2.815385x2.82x1.000000x1.000000x7.692%7.69%2.732%2.73%48-10.000000x-10.000000x> 10.0x1.000000x1.000000x3.704%3.70%0.000%0%49
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 49 by male-to-female sex ratio

Over-Education Sex Ratio by County Subdivision#32

Percentage more likely men are than women to have a professional or doctorate degree (e.g., MBA, PhD, or MD) among people aged 25 years and older.
Scope: population of the Goodland Township, selected other county subdivisions in the Grand Rapids Area, and entities that contain the Goodland Township
Female
Male
10x5x0x5x10xFM#KeewatinLa PrairieLk JessieEffieSpangCarpenterBowstringBearvilleArdenhurstCohassetSand LkColeraineGreenwayWabanaTrout LkArbo TownshipHarrisMidwestWest North CentralUnited StatesMinnesotaGrand RapidsItascaStokesDeer LakeDeer RivWildwoodSago TownshipMarcellNE ItascaGrand RapidsSplithandBowstring LkFeeleyLone PneNashwauk-KeewatinDeer RiverBlackberryBoveyBalsamTaconiteGoodlandOteneagen-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%1.099%1.10%1-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%2.299%2.30%2-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%5.691%5.69%3-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%2.532%2.53%4-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%1.220%1.22%5-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%3.306%3.31%6-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%2.020%2.02%7-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%1.429%1.43%8-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%1.786%1.79%9-1.000000x-1.000000x7.981607x7.981607x7.98x0.619%0.62%4.937%4.94%10-1.000000x-1.000000x3.132353x3.132353x3.13x1.408%1.41%4.412%4.41%11-1.000000x-1.000000x2.665291x2.665291x2.67x1.151%1.15%3.069%3.07%12-1.000000x-1.000000x2.297111x2.297111x2.30x0.299%0.30%0.688%0.69%13-1.000000x-1.000000x1.958515x1.958515x1.96x5.797%5.80%11.354%11.4%14-1.000000x-1.000000x1.891142x1.891142x1.89x2.342%2.34%4.429%4.43%15-1.000000x-1.000000x1.618713x1.618713x1.62x1.445%1.45%2.339%2.34%16-1.000000x-1.000000x1.601766x1.601766x1.60x3.926%3.93%6.289%6.29%17-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.572017x1.572017x1.57x2.329%2.33%3.662%3.66%-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.481015x1.481015x1.48x2.798%2.80%4.144%4.14%-1.000000x-1.000000x1.385205x1.385205x1.39x1.525%1.53%2.113%2.11%-1.000000x-1.000000x1.385205x1.385205x1.39x1.525%1.53%2.113%2.11%-1.000000x-1.000000x1.275253x1.275253x1.28x8.911%8.91%11.364%11.4%18-1.000000x-1.000000x1.134858x1.134858x1.13x1.792%1.79%2.033%2.03%19-1.000000x-1.000000x1.128880x1.128880x1.13x2.510%2.51%2.834%2.83%20-1.055556x-1.055556x1.06x1.000000x1.000000x3.704%3.70%3.509%3.51%21-1.081967x-1.081967x1.08x1.000000x1.000000x3.279%3.28%3.030%3.03%22-1.146497x-1.146497x1.15x1.000000x1.000000x1.274%1.27%1.111%1.11%23-1.152441x-1.152441x1.15x1.000000x1.000000x1.677%1.68%1.455%1.46%24-1.480726x-1.480726x1.48x1.000000x1.000000x1.410%1.41%0.952%0.95%25-1.649123x-1.649123x1.65x1.000000x1.000000x5.263%5.26%3.191%3.19%26-1.652422x-1.652422x1.65x1.000000x1.000000x1.425%1.42%0.862%0.86%27-2.107143x-2.107143x2.11x1.000000x1.000000x3.571%3.57%1.695%1.69%28-2.346154x-2.346154x2.35x1.000000x1.000000x5.128%5.13%2.186%2.19%29-2.515310x-2.515310x2.52x1.000000x1.000000x1.513%1.51%0.602%0.60%-10.000000x-10.000000x> 10.0x1.000000x1.000000x0.345%0.34%0.000%0%30-10.000000x-10.000000x> 10.0x1.000000x1.000000x0.943%0.94%0.000%0%31-10.000000x-10.000000x> 10.0x1.000000x1.000000x0.733%0.73%0.000%0%32-10.000000x-10.000000x> 10.0x1.000000x1.000000x1.240%1.24%0.000%0%33-10.000000x-10.000000x> 10.0x1.000000x1.000000x1.724%1.72%0.000%0%34-10.000000x-10.000000x> 10.0x1.000000x1.000000x6.250%6.25%0.000%0%35-10.000000x-10.000000x> 10.0x1.000000x1.000000x0.990%0.99%0.000%0%36
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 36 by male-to-female sex ratio

Educational Attainment by County Subdivision in Minnesota

There are 2,761 county subdivisions in Minnesota. This section compares the Goodland Township to the 50 most populous county subdivisions in Minnesota and to those entities that contain or substantially overlap with the Goodland Township. The least populous of the compared county subdivisions has a population of 22,226.

No H.S. Diploma by County Subdivision#33

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of the Goodland Township, selected other county subdivisions in Minnesota, and entities that contain the Goodland Township
0%5%10%15%Count#FaribaultAustinBrooklyn CenterBrooklyn CtrSt. PaulUnited States of AmericaUnited StatesRichfieldMinneapolisMidwestBrooklyn ParkFridleyWest North CentralWinonaMaplewoodInver Grove HeightsInver Grv HtsShakopeeBloomingtonSt. CloudCrystalMinnesotaOwatonnaCoon RapidsRosevilleGrand Rapids Metro AreaGrand RapidsItasca CountyItascaNashwauk-Keewatin School DistrictNashwauk-KeewatinDuluthRochesterBurnsvilleHastingsMankatoNew BrightonOakdaleChaskaMoorheadBlaineApple ValleyCottage GroveElk RiverSavageFarmingtonSt. Louis ParkSt. Louis PkAndoverGoodland TownshipGoodlandRamseyEaganWhite Bear LakeWhite Bear LkPrior LakeRosemountLakevilleEden PrairieShoreviewChamplinWoodburyPlymouthMinnetonkaChanhassenMaple GroveEdina16.891386%16.891386%16.9%2,706116.529027%16.529027%16.5%2,682216.036673%16.036673%16.0%3,131313.278342%13.278342%13.3%24,61324.6k413.020590%13.020590%13.0%27,818,38027.8M11.017383%11.017383%11.0%2,795510.958428%10.958428%11.0%29,34729.3k610.244520%10.244520%10.2%4,641,8354.64M10.167910%10.167910%10.2%5,01479.793424%9.793424%9.8%1,81189.174633%9.174633%9.2%1,278,0511.28M7.914094%7.914094%7.9%1,20597.910263%7.910263%7.9%2,172107.772967%7.772967%7.8%1,831117.542599%7.542599%7.5%1,837127.540581%7.540581%7.5%4,729137.521396%7.521396%7.5%2,285147.446682%7.446682%7.4%1,250157.376055%7.376055%7.4%270,121270k7.196882%7.196882%7.2%1,237167.021578%7.021578%7.0%3,023176.821133%6.821133%6.8%1,681186.702965%6.702965%6.7%2,1796.702965%6.702965%6.7%2,1796.622517%6.622517%6.6%1806.456536%6.456536%6.5%3,427196.300465%6.300465%6.3%4,713206.224589%6.224589%6.2%2,572216.110006%6.110006%6.1%952225.855251%5.855251%5.9%1,220235.686185%5.686185%5.7%873245.387794%5.387794%5.4%1,017255.326215%5.326215%5.3%858265.311348%5.311348%5.3%1,253275.219035%5.219035%5.2%2,154284.998974%4.998974%5.0%1,705294.937132%4.937132%4.9%1,123304.603205%4.603205%4.6%721314.312955%4.312955%4.3%812324.216911%4.216911%4.2%584334.052866%4.052866%4.1%1,429344.031177%4.031177%4.0%812353.855422%3.855422%3.9%163.724508%3.724508%3.7%610363.667395%3.667395%3.7%1,646373.645924%3.645924%3.6%631383.629499%3.629499%3.6%596393.564652%3.564652%3.6%526403.487209%3.487209%3.5%1,325413.205970%3.205970%3.2%1,392423.194655%3.194655%3.2%612433.108874%3.108874%3.1%488443.024874%3.024874%3.0%1,317452.612169%2.612169%2.6%1,406462.317073%2.317073%2.3%893472.309237%2.309237%2.3%368482.095945%2.095945%2.1%966492.079742%2.079742%2.1%74850
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County Subdivision#34

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of the Goodland Township, selected other county subdivisions in Minnesota, and entities that contain the Goodland Township
0%20%40%60%Count#EdinaEden PrairieChanhassenMinnetonkaWoodburyPlymouthSt. Louis ParkSt. Louis PkShoreviewMaple GroveEaganLakevilleMinneapolisNew BrightonRosevillePrior LakeChaskaRosemountRochesterApple ValleySavageBloomingtonSt. PaulBurnsvilleChamplinMoorheadRichfieldShakopeeMankatoFarmingtonDuluthInver Grove HeightsInver Grv HtsWinonaMinnesotaAndoverWhite Bear LakeWhite Bear LkBlaineOakdaleElk RiverCottage GroveCrystalUnited States of AmericaUnited StatesWest North CentralMaplewoodBrooklyn ParkSt. CloudMidwestRamseyHastingsOwatonnaFridleyCoon RapidsGrand Rapids Metro AreaGrand RapidsItasca CountyItascaAustinBrooklyn CenterBrooklyn CtrGoodland TownshipGoodlandFaribaultNashwauk-Keewatin School DistrictNashwauk-Keewatin68.687093%68.687093%68.7%24,70424.7k162.175545%62.175545%62.2%26,99627.0k260.253514%60.253514%60.3%9,602358.886871%58.886871%58.9%22,69522.7k458.692207%58.692207%58.7%25,55425.6k557.488156%57.488156%57.5%30,94330.9k655.889276%55.889276%55.9%19,70619.7k754.601451%54.601451%54.6%10,46010.5k850.665018%50.665018%50.7%23,35123.4k950.398824%50.398824%50.4%22,62022.6k1048.278766%48.278766%48.3%18,34418.3k1147.670489%47.670489%47.7%127,663128k1247.267635%47.267635%47.3%7,2571346.445382%46.445382%46.4%11,44611.4k1445.472261%45.472261%45.5%7,4671544.248557%44.248557%44.2%7,1281644.076986%44.076986%44.1%6,5041743.718250%43.718250%43.7%32,70332.7k1843.384056%43.384056%43.4%14,79714.8k1943.102990%43.102990%43.1%8,1152040.791530%40.791530%40.8%25,58225.6k2139.969357%39.969357%40.0%74,08874.1k2238.511617%38.511617%38.5%15,91315.9k2337.561317%37.561317%37.6%5,8962437.153999%37.153999%37.2%8,7652536.544602%36.544602%36.5%9,2712636.468898%36.468898%36.5%8,8822736.144174%36.144174%36.1%7,5312835.764315%35.764315%35.8%4,9532935.389427%35.389427%35.4%18,78418.8k3034.649346%34.649346%34.6%8,1623134.244056%34.244056%34.2%5,2143234.240609%34.240609%34.2%1,253,9371.25M34.091248%34.091248%34.1%6,8673333.633790%33.633790%33.6%5,8213433.485172%33.485172%33.5%13,82013.8k3531.447341%31.447341%31.4%5,9363630.760391%30.760391%30.8%4,8183730.616372%30.616372%30.6%6,9643830.400334%30.400334%30.4%5,1033930.315023%30.315023%30.3%64,767,78764.8M30.056536%30.056536%30.1%4,186,9564.19M30.016753%30.016753%30.0%8,2424029.516142%29.516142%29.5%14,55514.6k4129.150757%29.150757%29.2%8,8564228.952816%28.952816%29.0%13,118,64213.1M28.410062%28.410062%28.4%4,6534328.059816%28.059816%28.1%4,3724427.682104%27.682104%27.7%4,7584527.190136%27.190136%27.2%5,0284625.993543%25.993543%26.0%11,19111.2k4722.256060%22.256060%22.3%7,23522.256060%22.256060%22.3%7,23521.410083%21.410083%21.4%3,4744820.631018%20.631018%20.6%4,0284920.240964%20.240964%20.2%8418.795256%18.795256%18.8%3,0115015.526122%15.526122%15.5%422
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County Subdivision#35

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of the Goodland Township, selected other county subdivisions in Minnesota, and entities that contain the Goodland Township
0%5%10%Count#EdinaRochesterShoreviewRosevilleMinnetonkaWoodburyEden PrairieMinneapolisSt. PaulPlymouthSt. Louis ParkSt. Louis PkNew BrightonEaganDuluthWinonaMankatoChanhassenMaple GroveMaplewoodLakevilleChamplinMinnesotaSt. CloudUnited States of AmericaUnited StatesBloomingtonGoodland TownshipGoodlandWest North CentralApple ValleyMidwestAustinChaskaWhite Bear LakeWhite Bear LkInver Grove HeightsInver Grv HtsMoorheadBlaineBurnsvillePrior LakeRosemountFridleyShakopeeRichfieldSavageGrand Rapids Metro AreaGrand RapidsItasca CountyItascaOakdaleRamseyBrooklyn ParkCottage GroveCrystalOwatonnaAndoverFaribaultFarmingtonElk RiverCoon RapidsBrooklyn CenterBrooklyn CtrHastingsNashwauk-Keewatin School DistrictNashwauk-Keewatin11.352388%11.352388%11.4%4,08318.768248%8.768248%8.8%6,55927.532495%7.532495%7.5%1,44337.458205%7.458205%7.5%1,83847.368967%7.368967%7.4%2,84057.214222%7.214222%7.2%3,14166.425758%6.425758%6.4%2,79076.241155%6.241155%6.2%16,71416.7k85.920847%5.920847%5.9%10,97511.0k95.718532%5.718532%5.7%3,078105.249723%5.249723%5.2%1,851114.722204%4.722204%4.7%725124.400428%4.400428%4.4%1,975134.278609%4.278609%4.3%2,271144.236175%4.236175%4.2%645154.218660%4.218660%4.2%879163.852912%3.852912%3.9%614173.692855%3.692855%3.7%1,702183.590939%3.590939%3.6%986193.521423%3.521423%3.5%1,338203.510225%3.510225%3.5%551213.458748%3.458748%3.5%126,664127k3.367347%3.367347%3.4%1,023223.336158%3.336158%3.3%7,127,6737.13M3.138055%3.138055%3.1%1,968233.132530%3.132530%3.1%132.978981%2.978981%3.0%414,980415k2.958337%2.958337%3.0%1,009242.893416%2.893416%2.9%1,311,0191.31M2.884260%2.884260%2.9%468252.874170%2.874170%2.9%463262.692552%2.692552%2.7%466272.623535%2.623535%2.6%618282.564537%2.564537%2.6%605292.500485%2.500485%2.5%1,032302.405615%2.405615%2.4%994312.174045%2.174045%2.2%357322.134725%2.134725%2.1%315332.103612%2.103612%2.1%389342.003695%2.003695%2.0%488351.876306%1.876306%1.9%476361.859032%1.859032%1.9%350371.818014%1.818014%1.8%5911.818014%1.818014%1.8%5911.769443%1.769443%1.8%334381.727928%1.727928%1.7%283391.673021%1.673021%1.7%825401.653038%1.653038%1.7%376411.638270%1.638270%1.6%275421.605771%1.605771%1.6%276431.558854%1.558854%1.6%314441.510612%1.510612%1.5%242451.509134%1.509134%1.5%209461.283279%1.283279%1.3%201471.272850%1.272850%1.3%548481.126818%1.126818%1.1%220491.123163%1.123163%1.1%175501.066961%1.066961%1.1%29
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).

Under-Education Sex Ratio by County Subdivision#36

Percentage more likely men are than women to not have a high school diploma (or equivalent) among people aged 25 years and older.
Scope: population of the Goodland Township, selected other county subdivisions in Minnesota, and entities that contain the Goodland Township
Female
Male
1x0x1x2xFM#FarmingtonRamseyEden PrairieMoorheadGrand RapidsItascaOwatonnaMaplewoodElk RiverSt. Louis PkShoreviewShakopeeAustinHastingsNashwauk-KeewatinFridleyCoon RapidsDuluthBrooklyn CtrRichfieldFaribaultWest North CentralMinnesotaMidwestPrior LakeChanhassenUnited StatesChamplinBlaineAndoverCrystalCottage GroveLakevilleRosemountWinonaRochesterBloomingtonSt. PaulBurnsvilleEaganMinneapolisInver Grv HtsMankatoApple ValleyMaple GroveChaskaSt. CloudWoodburyRosevilleOakdaleBrooklyn ParkEdinaPlymouthMinnetonkaWhite Bear LkSavageGoodlandNew Brighton-1.000000x-1.000000x2.003868x2.003868x2.00x2.835%2.84%5.681%5.68%1-1.000000x-1.000000x1.569928x1.569928x1.57x2.887%2.89%4.532%4.53%2-1.000000x-1.000000x1.513482x1.513482x1.51x2.564%2.56%3.881%3.88%3-1.000000x-1.000000x1.505859x1.505859x1.51x4.258%4.26%6.411%6.41%4-1.000000x-1.000000x1.485888x1.485888x1.49x5.397%5.40%8.020%8.02%-1.000000x-1.000000x1.485888x1.485888x1.49x5.397%5.40%8.020%8.02%-1.000000x-1.000000x1.416944x1.416944x1.42x5.995%6.00%8.495%8.49%5-1.000000x-1.000000x1.413357x1.413357x1.41x6.619%6.62%9.355%9.36%6-1.000000x-1.000000x1.411657x1.411657x1.41x3.821%3.82%5.394%5.39%7-1.000000x-1.000000x1.398134x1.398134x1.40x3.411%3.41%4.769%4.77%8-1.000000x-1.000000x1.317049x1.317049x1.32x2.784%2.78%3.666%3.67%9-1.000000x-1.000000x1.299444x1.299444x1.30x6.595%6.59%8.569%8.57%10-1.000000x-1.000000x1.289646x1.289646x1.29x14.496%14.5%18.694%18.7%11-1.000000x-1.000000x1.288751x1.288751x1.29x5.348%5.35%6.892%6.89%12-1.000000x-1.000000x1.275522x1.275522x1.28x5.836%5.84%7.444%7.44%-1.000000x-1.000000x1.269201x1.269201x1.27x8.625%8.62%10.946%10.9%13-1.000000x-1.000000x1.245609x1.245609x1.25x6.293%6.29%7.839%7.84%14-1.000000x-1.000000x1.237646x1.237646x1.24x5.794%5.79%7.171%7.17%15-1.000000x-1.000000x1.171442x1.171442x1.17x14.825%14.8%17.367%17.4%16-1.000000x-1.000000x1.163628x1.163628x1.16x10.209%10.2%11.880%11.9%17-1.000000x-1.000000x1.151611x1.151611x1.15x15.625%15.6%17.994%18.0%18-1.000000x-1.000000x1.137506x1.137506x1.14x8.598%8.60%9.780%9.78%-1.000000x-1.000000x1.136388x1.136388x1.14x6.913%6.91%7.856%7.86%-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.126917x1.126917x1.13x3.416%3.42%3.849%3.85%19-1.000000x-1.000000x1.122496x1.122496x1.12x2.180%2.18%2.448%2.45%20-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.101837x1.101837x1.10x2.962%2.96%3.263%3.26%21-1.000000x-1.000000x1.079970x1.079970x1.08x5.025%5.03%5.427%5.43%22-1.000000x-1.000000x1.079583x1.079583x1.08x3.877%3.88%4.185%4.19%23-1.000000x-1.000000x1.078552x1.078552x1.08x7.176%7.18%7.740%7.74%24-1.000000x-1.000000x1.037512x1.037512x1.04x4.848%4.85%5.030%5.03%25-1.000000x-1.000000x1.032815x1.032815x1.03x3.432%3.43%3.544%3.54%26-1.000159x-1.000159x1.00x1.000000x1.000000x3.565%3.56%3.564%3.56%27-1.021534x-1.021534x1.02x1.000000x1.000000x7.996%8.00%7.828%7.83%28-1.041504x-1.041504x1.04x1.000000x1.000000x6.423%6.42%6.167%6.17%29-1.052680x-1.052680x1.05x1.000000x1.000000x7.724%7.72%7.337%7.34%30-1.060004x-1.060004x1.06x1.000000x1.000000x13.651%13.7%12.879%12.9%31-1.063523x-1.063523x1.06x1.000000x1.000000x6.407%6.41%6.024%6.02%32-1.064491x-1.064491x1.06x1.000000x1.000000x3.778%3.78%3.549%3.55%33-1.092939x-1.092939x1.09x1.000000x1.000000x11.450%11.4%10.476%10.5%34-1.106734x-1.106734x1.11x1.000000x1.000000x8.152%8.15%7.366%7.37%35-1.109043x-1.109043x1.11x1.000000x1.000000x6.154%6.15%5.549%5.55%36-1.118002x-1.118002x1.12x1.000000x1.000000x5.261%5.26%4.705%4.71%37-1.121003x-1.121003x1.12x1.000000x1.000000x2.209%2.21%1.971%1.97%38-1.127409x-1.127409x1.13x1.000000x1.000000x5.637%5.64%5.000%5.00%39-1.137927x-1.137927x1.14x1.000000x1.000000x8.019%8.02%7.047%7.05%40-1.150185x-1.150185x1.15x1.000000x1.000000x3.224%3.22%2.803%2.80%41-1.159893x-1.159893x1.16x1.000000x1.000000x7.291%7.29%6.286%6.29%42-1.170896x-1.170896x1.17x1.000000x1.000000x5.763%5.76%4.922%4.92%43-1.209052x-1.209052x1.21x1.000000x1.000000x11.076%11.1%9.161%9.16%44-1.212019x-1.212019x1.21x1.000000x1.000000x2.263%2.26%1.867%1.87%45-1.236671x-1.236671x1.24x1.000000x1.000000x2.877%2.88%2.327%2.33%46-1.242432x-1.242432x1.24x1.000000x1.000000x2.550%2.55%2.053%2.05%47-1.253553x-1.253553x1.25x1.000000x1.000000x4.017%4.02%3.204%3.20%48-1.284305x-1.284305x1.28x1.000000x1.000000x4.851%4.85%3.777%3.78%49-1.658654x-1.658654x1.66x1.000000x1.000000x4.808%4.81%2.899%2.90%-1.669127x-1.669127x1.67x1.000000x1.000000x7.046%7.05%4.221%4.22%50
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Over-Education Sex Ratio by County Subdivision#37

Percentage more likely men are than women to have a professional or doctorate degree (e.g., MBA, PhD, or MD) among people aged 25 years and older.
Scope: population of the Goodland Township, selected other county subdivisions in Minnesota, and entities that contain the Goodland Township
Female
Male
10x5x0xFM#WinonaWhite Bear LkRosemountAndoverShoreviewMaple GroveOakdaleShakopeeEdinaFridleyEden PrairieOwatonnaMankatoHastingsRamseyMaplewoodMinnetonkaNew BrightonRochesterMoorheadWoodburyMidwestPlymouthWest North CentralRosevilleRichfieldChaskaSt. CloudAustinCrystalUnited StatesCoon RapidsCottage GroveMinnesotaDuluthSt. Louis PkFaribaultInver Grv HtsGrand RapidsItascaEaganBrooklyn CtrSt. PaulPrior LakeFarmingtonSavageBrooklyn ParkLakevilleBlaineMinneapolisChamplinApple ValleyBurnsvilleBloomingtonChanhassenElk RiverNashwauk-KeewatinGoodland-1.000000x-1.000000x3.549966x3.549966x3.55x1.887%1.89%6.698%6.70%1-1.000000x-1.000000x2.892036x2.892036x2.89x1.445%1.45%4.179%4.18%2-1.000000x-1.000000x2.517561x2.517561x2.52x1.236%1.24%3.112%3.11%3-1.000000x-1.000000x2.444960x2.444960x2.44x0.905%0.90%2.212%2.21%4-1.000000x-1.000000x2.158302x2.158302x2.16x4.894%4.89%10.562%10.6%5-1.000000x-1.000000x2.044267x2.044267x2.04x2.469%2.47%5.048%5.05%6-1.000000x-1.000000x2.027520x2.027520x2.03x1.214%1.21%2.461%2.46%7-1.000000x-1.000000x1.989459x1.989459x1.99x1.358%1.36%2.702%2.70%8-1.000000x-1.000000x1.952817x1.952817x1.95x7.877%7.88%15.382%15.4%9-1.000000x-1.000000x1.877228x1.877228x1.88x1.459%1.46%2.739%2.74%10-1.000000x-1.000000x1.843636x1.843636x1.84x4.554%4.55%8.396%8.40%11-1.000000x-1.000000x1.842122x1.842122x1.84x1.143%1.14%2.106%2.11%12-1.000000x-1.000000x1.831940x1.831940x1.83x2.992%2.99%5.480%5.48%13-1.000000x-1.000000x1.824245x1.824245x1.82x0.798%0.80%1.456%1.46%14-1.000000x-1.000000x1.818953x1.818953x1.82x1.219%1.22%2.218%2.22%15-1.000000x-1.000000x1.816195x1.816195x1.82x2.593%2.59%4.709%4.71%16-1.000000x-1.000000x1.778120x1.778120x1.78x5.399%5.40%9.600%9.60%17-1.000000x-1.000000x1.712068x1.712068x1.71x3.517%3.52%6.021%6.02%18-1.000000x-1.000000x1.656189x1.656189x1.66x6.670%6.67%11.046%11.0%19-1.000000x-1.000000x1.655114x1.655114x1.66x1.942%1.94%3.214%3.21%20-1.000000x-1.000000x1.596470x1.596470x1.60x5.629%5.63%8.987%8.99%21-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.583257x1.583257x1.58x4.465%4.46%7.069%7.07%22-1.000000x-1.000000x1.572017x1.572017x1.57x2.329%2.33%3.662%3.66%-1.000000x-1.000000x1.559618x1.559618x1.56x5.912%5.91%9.220%9.22%23-1.000000x-1.000000x1.551084x1.551084x1.55x1.481%1.48%2.298%2.30%24-1.000000x-1.000000x1.548982x1.548982x1.55x2.267%2.27%3.511%3.51%25-1.000000x-1.000000x1.544665x1.544665x1.54x2.633%2.63%4.067%4.07%26-1.000000x-1.000000x1.544387x1.544387x1.54x2.283%2.28%3.525%3.53%27-1.000000x-1.000000x1.532997x1.532997x1.53x1.305%1.30%2.000%2.00%28-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.506415x1.506415x1.51x1.028%1.03%1.548%1.55%29-1.000000x-1.000000x1.492523x1.492523x1.49x1.332%1.33%1.989%1.99%30-1.000000x-1.000000x1.481015x1.481015x1.48x2.798%2.80%4.144%4.14%-1.000000x-1.000000x1.465286x1.465286x1.47x3.496%3.50%5.123%5.12%31-1.000000x-1.000000x1.442441x1.442441x1.44x4.342%4.34%6.262%6.26%32-1.000000x-1.000000x1.419491x1.419491x1.42x1.234%1.23%1.752%1.75%33-1.000000x-1.000000x1.410631x1.410631x1.41x2.190%2.19%3.089%3.09%34-1.000000x-1.000000x1.385205x1.385205x1.39x1.525%1.53%2.113%2.11%-1.000000x-1.000000x1.385205x1.385205x1.39x1.525%1.53%2.113%2.11%-1.000000x-1.000000x1.383309x1.383309x1.38x3.713%3.71%5.137%5.14%35-1.000000x-1.000000x1.367180x1.367180x1.37x0.959%0.96%1.311%1.31%36-1.000000x-1.000000x1.274509x1.274509x1.27x5.228%5.23%6.663%6.66%37-1.000000x-1.000000x1.248780x1.248780x1.25x1.936%1.94%2.418%2.42%38-1.000000x-1.000000x1.247281x1.247281x1.25x1.347%1.35%1.681%1.68%39-1.000000x-1.000000x1.240291x1.240291x1.24x1.659%1.66%2.058%2.06%40-1.000000x-1.000000x1.220015x1.220015x1.22x1.515%1.52%1.848%1.85%41-1.000000x-1.000000x1.179960x1.179960x1.18x3.234%3.23%3.816%3.82%42-1.000000x-1.000000x1.160580x1.160580x1.16x2.321%2.32%2.693%2.69%43-1.000000x-1.000000x1.124528x1.124528x1.12x5.872%5.87%6.603%6.60%44-1.000000x-1.000000x1.068161x1.068161x1.07x3.397%3.40%3.629%3.63%45-1.000000x-1.000000x1.031011x1.031011x1.03x2.916%2.92%3.006%3.01%46-1.000000x-1.000000x1.023622x1.023622x1.02x2.379%2.38%2.435%2.44%47-1.000000x-1.000000x1.018945x1.018945x1.02x3.110%3.11%3.169%3.17%48-1.019385x-1.019385x1.02x1.000000x1.000000x3.889%3.89%3.815%3.81%49-2.497130x-2.497130x2.50x1.000000x1.000000x1.828%1.83%0.732%0.73%50-2.515310x-2.515310x2.52x1.000000x1.000000x1.513%1.51%0.602%0.60%-10.000000x-10.000000x> 10.0x1.000000x1.000000x6.250%6.25%0.000%0%
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Educational Attainment by County Subdivision in the Midwest

There are 19,478 county subdivisions in the Midwest. This section compares the Goodland Township to the 50 most populous county subdivisions in the Midwest and to those entities that contain or substantially overlap with the Goodland Township. The least populous of the compared county subdivisions has a population of 112,821.

No H.S. Diploma by County Subdivision#38

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of the Goodland Township, selected other county subdivisions in the Midwest, and entities that contain the Goodland Township
0%10%20%30%Count#Aurora TownshipAuroraCenter TownshipCtrKansas CityClevelandWayne TownshipWayneDetroitMilwaukeeNorth TownshipNDaytonChicagoProviso TownshipProvisoRockford TownshipRockfordSt. LouisWarrenGrand RapidsToledoBlue TownshipCincinnatiAkronSt. PaulSterling HeightsSterling HtsUnited States of AmericaUnited StatesThornton TownshipThorntonWichitaKaw TownshipOmahaBloomingdale TownshipBloomingdaleColumbusMaine TownshipMaineTopekaMinneapolisSpringfield TownshipSpringfieldWorth TownshipWorthMidwestLawrence TownshipLawrenceSioux FallsWest North CentralCapital TownshipCapitalWheeling TownshipWheelingPalatine TownshipPalatineSchaumburg TownshipSchaumburgMinnesotaLincolnWashington TownshipWashingtonGrand Rapids Metro AreaGrand RapidsItasca CountyItascaNashwauk-Keewatin School DistrictNashwauk-KeewatinOlatheFargoCedar RapidsYork TownshipMilton TownshipMiltonMadisonDowners Grove TownshipDowners GrvGoodland TownshipGoodlandLisle TownshipLisleOverland ParkAnn Arbor33.464284%33.464284%33.5%29,81429.8k123.165532%23.165532%23.2%22,92622.9k222.659362%22.659362%22.7%21,21821.2k321.582420%21.582420%21.6%54,98155.0k421.037720%21.037720%21.0%18,48918.5k521.003600%21.003600%21.0%90,67890.7k617.485105%17.485105%17.5%63,80063.8k717.062713%17.062713%17.1%17,39117.4k816.928359%16.928359%16.9%14,88914.9k916.905373%16.905373%16.9%309,770310k1015.636222%15.636222%15.6%16,11016.1k1115.608074%15.608074%15.6%18,24118.2k1215.466266%15.466266%15.5%33,80433.8k1315.286174%15.286174%15.3%14,05914.1k1414.563179%14.563179%14.6%17,64317.6k1514.332972%14.332972%14.3%26,34326.3k1614.241047%14.241047%14.2%15,25815.3k1713.761583%13.761583%13.8%26,28626.3k1813.591726%13.591726%13.6%17,95118.0k1913.278342%13.278342%13.3%24,61324.6k2013.132171%13.132171%13.1%12,21212.2k2113.020590%13.020590%13.0%27,818,38027.8M12.974232%12.974232%13.0%14,37014.4k2212.568464%12.568464%12.6%31,23131.2k2312.455744%12.455744%12.5%15,16315.2k2412.198899%12.198899%12.2%35,05035.1k2512.171847%12.171847%12.2%9,3752611.376979%11.376979%11.4%60,83160.8k2711.132206%11.132206%11.1%10,96511.0k2811.032803%11.032803%11.0%9,3772910.958428%10.958428%11.0%29,34729.3k3010.767091%10.767091%10.8%11,14911.1k3110.678962%10.678962%10.7%11,17511.2k3210.244520%10.244520%10.2%4,641,8354.64M10.179618%10.179618%10.2%8,161339.702326%9.702326%9.7%9,123349.174633%9.174633%9.2%1,278,0511.28M8.623103%8.623103%8.6%6,837358.424707%8.424707%8.4%9,343368.392669%8.392669%8.4%6,695377.739434%7.739434%7.7%7,226387.376055%7.376055%7.4%270,121270k7.092309%7.092309%7.1%11,88611.9k396.804680%6.804680%6.8%6,403406.702965%6.702965%6.7%2,1796.702965%6.702965%6.7%2,1796.622517%6.622517%6.6%1806.424130%6.424130%6.4%5,331416.055621%6.055621%6.1%4,270425.981653%5.981653%6.0%5,099435.461095%5.461095%5.5%4,715445.266517%5.266517%5.3%4,126454.836248%4.836248%4.8%7,351464.020265%4.020265%4.0%4,166473.855422%3.855422%3.9%163.522812%3.522812%3.5%2,870483.377424%3.377424%3.4%4,282493.157717%3.157717%3.2%2,05750
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County Subdivision#39

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of the Goodland Township, selected other county subdivisions in the Midwest, and entities that contain the Goodland Township
0%20%40%60%Count#Ann ArborOverland ParkMilton TownshipMiltonLisle TownshipLisleMadisonDowners Grove TownshipDowners GrvWashington TownshipWashingtonPalatine TownshipPalatineYork TownshipMinneapolisOlatheWheeling TownshipWheelingSchaumburg TownshipSchaumburgKaw TownshipSt. PaulMaine TownshipMaineFargoLincolnLawrence TownshipLawrenceChicagoCapital TownshipCapitalColumbusOmahaMinnesotaCincinnatiSt. LouisGrand RapidsBloomingdale TownshipBloomingdaleCedar RapidsUnited States of AmericaUnited StatesSioux FallsWest North CentralMidwestWichitaSterling HeightsSterling HtsTopekaSpringfield TownshipSpringfieldProviso TownshipProvisoWorth TownshipWorthMilwaukeeGrand Rapids Metro AreaGrand RapidsItasca CountyItascaRockford TownshipRockfordGoodland TownshipGoodlandAkronCenter TownshipCtrNorth TownshipNThornton TownshipThorntonToledoDaytonWarrenWayne TownshipWayneKansas CityClevelandAurora TownshipAuroraBlue TownshipNashwauk-Keewatin School DistrictNashwauk-KeewatinDetroit72.810169%72.810169%72.8%47,43047.4k158.823344%58.823344%58.8%74,57874.6k257.681507%57.681507%57.7%45,19045.2k356.900171%56.900171%56.9%46,35646.4k456.337583%56.337583%56.3%85,63285.6k553.762123%53.762123%53.8%55,71155.7k650.610540%50.610540%50.6%47,62347.6k749.860853%49.860853%49.9%39,77539.8k849.728972%49.728972%49.7%42,93542.9k947.670489%47.670489%47.7%127,663128k1046.512581%46.512581%46.5%38,59838.6k1146.409378%46.409378%46.4%51,46851.5k1241.792515%41.792515%41.8%39,02039.0k1341.581304%41.581304%41.6%50,61950.6k1439.969357%39.969357%40.0%74,08874.1k1539.511462%39.511462%39.5%38,91838.9k1638.241175%38.241175%38.2%26,96527.0k1737.146608%37.146608%37.1%62,25462.3k1836.577273%36.577273%36.6%29,32429.3k1936.546320%36.546320%36.5%669,666670k2036.298510%36.298510%36.3%28,78028.8k2134.685843%34.685843%34.7%185,460185k2234.651487%34.651487%34.7%99,56199.6k2334.240609%34.240609%34.2%1,253,9371.25M33.783048%33.783048%33.8%64,52964.5k2433.007421%33.007421%33.0%72,14372.1k2532.904381%32.904381%32.9%39,86339.9k2632.623147%32.623147%32.6%25,12725.1k2731.793440%31.793440%31.8%27,10227.1k2830.315023%30.315023%30.3%64,767,78764.8M30.259813%30.259813%30.3%28,45328.5k2930.056536%30.056536%30.1%4,186,9564.19M28.952816%28.952816%29.0%13,118,64213.1M28.877567%28.877567%28.9%71,75771.8k3027.532180%27.532180%27.5%25,60325.6k3127.007248%27.007248%27.0%22,95423.0k3226.964567%26.964567%27.0%27,92127.9k3326.237989%26.237989%26.2%27,03327.0k3425.217641%25.217641%25.2%26,38926.4k3523.517740%23.517740%23.5%85,81285.8k3622.256060%22.256060%22.3%7,23522.256060%22.256060%22.3%7,23521.592552%21.592552%21.6%25,23525.2k3720.240964%20.240964%20.2%8420.213821%20.213821%20.2%26,69726.7k3820.139240%20.139240%20.1%19,93119.9k3919.650916%19.650916%19.7%20,02920.0k4018.971090%18.971090%19.0%21,01221.0k4118.048021%18.048021%18.0%33,17133.2k4217.635555%17.635555%17.6%15,51115.5k4317.280259%17.280259%17.3%15,89315.9k4416.826535%16.826535%16.8%14,78814.8k4516.167409%16.167409%16.2%15,13915.1k4616.073076%16.073076%16.1%40,94640.9k4715.929601%15.929601%15.9%14,19214.2k4815.821208%15.821208%15.8%16,95117.0k4915.526122%15.526122%15.5%42213.833543%13.833543%13.8%59,72359.7k50
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County Subdivision#40

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of the Goodland Township, selected other county subdivisions in the Midwest, and entities that contain the Goodland Township
0%5%10%15%Count#Ann ArborMadisonDowners Grove TownshipDowners GrvWashington TownshipWashingtonMinneapolisYork TownshipOverland ParkLisle TownshipLisleSt. PaulKaw TownshipMilton TownshipMiltonSt. LouisCincinnatiLincolnChicagoFargoCapital TownshipCapitalOmahaMaine TownshipMainePalatine TownshipPalatineMinnesotaWheeling TownshipWheelingColumbusUnited States of AmericaUnited StatesTopekaGrand RapidsGoodland TownshipGoodlandLawrence TownshipLawrenceWest North CentralSpringfield TownshipSpringfieldCedar RapidsMidwestSioux FallsOlatheCenter TownshipCtrSchaumburg TownshipSchaumburgWichitaProviso TownshipProvisoAkronBloomingdale TownshipBloomingdaleRockford TownshipRockfordMilwaukeeWorth TownshipWorthClevelandGrand Rapids Metro AreaGrand RapidsItasca CountyItascaToledoNorth TownshipNSterling HeightsSterling HtsKansas CityDetroitDaytonWayne TownshipWayneNashwauk-Keewatin School DistrictNashwauk-KeewatinWarrenBlue TownshipAurora TownshipAuroraThornton TownshipThornton17.877867%17.877867%17.9%11,64611.6k19.171173%9.171173%9.2%13,94013.9k27.658384%7.658384%7.7%7,93637.306290%7.306290%7.3%6,87546.241155%6.241155%6.2%16,71416.7k56.197734%6.197734%6.2%5,35165.940071%5.940071%5.9%7,53175.933545%5.933545%5.9%4,83485.920847%5.920847%5.9%10,97511.0k95.896414%5.896414%5.9%7,178105.800061%5.800061%5.8%4,544114.880448%4.880448%4.9%10,66710.7k124.729595%4.729595%4.7%9,034134.568292%4.568292%4.6%7,656144.485379%4.485379%4.5%82,18982.2k154.336789%4.336789%4.3%3,058164.233985%4.233985%4.2%3,357174.059919%4.059919%4.1%11,66511.7k184.030539%4.030539%4.0%3,970193.487439%3.487439%3.5%2,782203.458748%3.458748%3.5%126,664127k3.407574%3.407574%3.4%3,779213.357865%3.357865%3.4%17,95418.0k223.336158%3.336158%3.3%7,127,6737.13M3.233245%3.233245%3.2%2,748233.174629%3.174629%3.2%3,846243.132530%3.132530%3.1%133.083448%3.083448%3.1%2,472252.978981%2.978981%3.0%414,980415k2.911721%2.911721%2.9%3,015262.905776%2.905776%2.9%2,477272.893416%2.893416%2.9%1,311,0191.31M2.841677%2.841677%2.8%2,672282.830666%2.830666%2.8%2,349292.602914%2.602914%2.6%2,576302.551250%2.551250%2.6%2,382312.519246%2.519246%2.5%6,260322.213918%2.213918%2.2%2,281332.163955%2.163955%2.2%2,858342.113682%2.113682%2.1%1,628352.067272%2.067272%2.1%2,416362.043126%2.043126%2.0%7,455371.958049%1.958049%2.0%2,049381.927780%1.927780%1.9%4,911391.818014%1.818014%1.8%5911.818014%1.818014%1.8%5911.642065%1.642065%1.6%3,018401.575684%1.575684%1.6%1,606411.439893%1.439893%1.4%1,339421.388310%1.388310%1.4%1,300431.318660%1.318660%1.3%5,693441.257490%1.257490%1.3%1,106451.183365%1.183365%1.2%1,040461.066961%1.066961%1.1%291.047058%1.047058%1.0%963470.997751%0.997751%1.0%1,069480.936111%0.936111%0.9%834490.876686%0.876686%0.9%97150
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).

Under-Education Sex Ratio by County Subdivision#41

Percentage more likely men are than women to not have a high school diploma (or equivalent) among people aged 25 years and older.
Scope: population of the Goodland Township, selected other county subdivisions in the Midwest, and entities that contain the Goodland Township
Female
Male
1x0x1xFM#Grand RapidsItascaNashwauk-KeewatinCedar RapidsLincolnDetroitOmahaToledoThorntonKansas CityMadisonLawrenceFargoProvisoRockfordBlue TownshipWest North CentralMinnesotaWayneMidwestTopekaMilwaukeeKaw TownshipDaytonSioux FallsUnited StatesOlatheWorthNOverland ParkAuroraWheelingGrand RapidsSchaumburgChicagoWichitaColumbusClevelandPalatineCapitalWarrenSpringfieldYork TownshipSt. LouisWashingtonBloomingdaleCtrCincinnatiAkronAnn ArborSt. PaulLisleMinneapolisSterling HtsDowners GrvMiltonMaineGoodland-1.000000x-1.000000x1.485888x1.485888x1.49x5.397%5.40%8.020%8.02%-1.000000x-1.000000x1.485888x1.485888x1.49x5.397%5.40%8.020%8.02%-1.000000x-1.000000x1.275522x1.275522x1.28x5.836%5.84%7.444%7.44%-1.000000x-1.000000x1.243824x1.243824x1.24x5.354%5.35%6.660%6.66%1-1.000000x-1.000000x1.238529x1.238529x1.24x6.342%6.34%7.854%7.85%2-1.000000x-1.000000x1.237056x1.237056x1.24x18.951%19.0%23.443%23.4%3-1.000000x-1.000000x1.230289x1.230289x1.23x10.968%11.0%13.494%13.5%4-1.000000x-1.000000x1.227011x1.227011x1.23x12.945%12.9%15.884%15.9%5-1.000000x-1.000000x1.203250x1.203250x1.20x11.909%11.9%14.330%14.3%6-1.000000x-1.000000x1.189752x1.189752x1.19x20.748%20.7%24.685%24.7%7-1.000000x-1.000000x1.189278x1.189278x1.19x4.425%4.43%5.263%5.26%8-1.000000x-1.000000x1.181964x1.181964x1.18x9.406%9.41%11.117%11.1%9-1.000000x-1.000000x1.176963x1.176963x1.18x5.565%5.56%6.550%6.55%10-1.000000x-1.000000x1.174964x1.174964x1.17x14.460%14.5%16.990%17.0%11-1.000000x-1.000000x1.168755x1.168755x1.17x14.456%14.5%16.896%16.9%12-1.000000x-1.000000x1.161774x1.161774x1.16x13.244%13.2%15.387%15.4%13-1.000000x-1.000000x1.137506x1.137506x1.14x8.598%8.60%9.780%9.78%-1.000000x-1.000000x1.136388x1.136388x1.14x6.913%6.91%7.856%7.86%-1.000000x-1.000000x1.132245x1.132245x1.13x19.779%19.8%22.395%22.4%14-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.125507x1.125507x1.13x10.423%10.4%11.732%11.7%15-1.000000x-1.000000x1.123984x1.123984x1.12x16.526%16.5%18.574%18.6%16-1.000000x-1.000000x1.115695x1.115695x1.12x11.779%11.8%13.142%13.1%17-1.000000x-1.000000x1.114342x1.114342x1.11x16.070%16.1%17.907%17.9%18-1.000000x-1.000000x1.110589x1.110589x1.11x9.201%9.20%10.219%10.2%19-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.107847x1.107847x1.11x6.105%6.11%6.764%6.76%20-1.000000x-1.000000x1.099747x1.099747x1.10x10.199%10.2%11.216%11.2%21-1.000000x-1.000000x1.096598x1.096598x1.10x16.317%16.3%17.894%17.9%22-1.000000x-1.000000x1.095440x1.095440x1.10x3.232%3.23%3.540%3.54%23-1.000000x-1.000000x1.086583x1.086583x1.09x32.102%32.1%34.882%34.9%24-1.000000x-1.000000x1.079846x1.079846x1.08x8.111%8.11%8.758%8.76%25-1.000000x-1.000000x1.078480x1.078480x1.08x14.031%14.0%15.132%15.1%26-1.000000x-1.000000x1.077209x1.077209x1.08x7.467%7.47%8.043%8.04%27-1.000000x-1.000000x1.068021x1.068021x1.07x16.372%16.4%17.486%17.5%28-1.000000x-1.000000x1.059528x1.059528x1.06x12.215%12.2%12.942%12.9%29-1.000000x-1.000000x1.052237x1.052237x1.05x11.098%11.1%11.678%11.7%30-1.000000x-1.000000x1.046686x1.046686x1.05x21.119%21.1%22.105%22.1%31-1.000000x-1.000000x1.039581x1.039581x1.04x8.233%8.23%8.559%8.56%32-1.000000x-1.000000x1.039194x1.039194x1.04x8.470%8.47%8.802%8.80%33-1.000000x-1.000000x1.037373x1.037373x1.04x15.022%15.0%15.584%15.6%34-1.000000x-1.000000x1.028857x1.028857x1.03x10.622%10.6%10.928%10.9%35-1.000000x-1.000000x1.027464x1.027464x1.03x5.391%5.39%5.539%5.54%36-1.000000x-1.000000x1.018183x1.018183x1.02x15.334%15.3%15.613%15.6%37-1.000000x-1.000000x1.013146x1.013146x1.01x6.764%6.76%6.853%6.85%38-1.000000x-1.000000x1.007207x1.007207x1.01x12.130%12.1%12.217%12.2%39-1.000000x-1.000000x1.006204x1.006204x1.01x23.095%23.1%23.239%23.2%40-1.000000x-1.000000x1.001229x1.001229x1.00x13.754%13.8%13.771%13.8%41-1.001506x-1.001506x1.00x1.000000x1.000000x13.601%13.6%13.581%13.6%42-1.031649x-1.031649x1.03x1.000000x1.000000x3.205%3.21%3.107%3.11%43-1.060004x-1.060004x1.06x1.000000x1.000000x13.651%13.7%12.879%12.9%44-1.073696x-1.073696x1.07x1.000000x1.000000x3.642%3.64%3.392%3.39%45-1.092939x-1.092939x1.09x1.000000x1.000000x11.450%11.4%10.476%10.5%46-1.131063x-1.131063x1.13x1.000000x1.000000x13.912%13.9%12.300%12.3%47-1.140051x-1.140051x1.14x1.000000x1.000000x4.269%4.27%3.744%3.74%48-1.194783x-1.194783x1.19x1.000000x1.000000x5.710%5.71%4.779%4.78%49-1.208375x-1.208375x1.21x1.000000x1.000000x12.117%12.1%10.027%10.0%50-1.658654x-1.658654x1.66x1.000000x1.000000x4.808%4.81%2.899%2.90%
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Over-Education Sex Ratio by County Subdivision#42

Percentage more likely men are than women to have a professional or doctorate degree (e.g., MBA, PhD, or MD) among people aged 25 years and older.
Scope: population of the Goodland Township, selected other county subdivisions in the Midwest, and entities that contain the Goodland Township
Female
Male
10x5x0xFM#RockfordNDaytonSioux FallsCapitalLawrenceDowners GrvAkronLisleWichitaYork TownshipMaineWarrenWheelingBlue TownshipSpringfieldOverland ParkMiltonGrand RapidsOlatheOmahaFargoWashingtonLincolnMidwestBloomingdaleWest North CentralKansas CityAuroraUnited StatesAnn ArborTopekaMinnesotaSterling HtsPalatineSchaumburgColumbusMadisonGrand RapidsItascaKaw TownshipMilwaukeeCedar RapidsClevelandCincinnatiSt. PaulSt. LouisChicagoWayneCtrToledoMinneapolisWorthDetroitProvisoThorntonNashwauk-KeewatinGoodland-1.000000x-1.000000x2.117268x2.117268x2.12x1.353%1.35%2.866%2.87%1-1.000000x-1.000000x2.111054x2.111054x2.11x1.033%1.03%2.181%2.18%2-1.000000x-1.000000x1.989047x1.989047x1.99x0.860%0.86%1.711%1.71%3-1.000000x-1.000000x1.962539x1.962539x1.96x1.928%1.93%3.783%3.78%4-1.000000x-1.000000x1.901348x1.901348x1.90x2.990%2.99%5.686%5.69%5-1.000000x-1.000000x1.879271x1.879271x1.88x2.206%2.21%4.146%4.15%6-1.000000x-1.000000x1.852471x1.852471x1.85x5.456%5.46%10.107%10.1%7-1.000000x-1.000000x1.846298x1.846298x1.85x1.550%1.55%2.862%2.86%8-1.000000x-1.000000x1.815266x1.815266x1.82x4.271%4.27%7.754%7.75%9-1.000000x-1.000000x1.792728x1.792728x1.79x1.819%1.82%3.261%3.26%10-1.000000x-1.000000x1.792314x1.792314x1.79x4.498%4.50%8.062%8.06%11-1.000000x-1.000000x1.788417x1.788417x1.79x2.939%2.94%5.256%5.26%12-1.000000x-1.000000x1.768853x1.768853x1.77x0.769%0.77%1.361%1.36%13-1.000000x-1.000000x1.766097x1.766097x1.77x2.485%2.48%4.389%4.39%14-1.000000x-1.000000x1.761699x1.761699x1.76x0.737%0.74%1.298%1.30%15-1.000000x-1.000000x1.751445x1.751445x1.75x2.146%2.15%3.759%3.76%16-1.000000x-1.000000x1.732234x1.732234x1.73x4.415%4.41%7.647%7.65%17-1.000000x-1.000000x1.695067x1.695067x1.70x4.357%4.36%7.385%7.38%18-1.000000x-1.000000x1.689013x1.689013x1.69x2.382%2.38%4.023%4.02%19-1.000000x-1.000000x1.678153x1.678153x1.68x2.131%2.13%3.576%3.58%20-1.000000x-1.000000x1.669388x1.669388x1.67x3.061%3.06%5.110%5.11%21-1.000000x-1.000000x1.627456x1.627456x1.63x3.304%3.30%5.377%5.38%22-1.000000x-1.000000x1.610803x1.610803x1.61x5.701%5.70%9.183%9.18%23-1.000000x-1.000000x1.596978x1.596978x1.60x3.524%3.52%5.628%5.63%24-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.586045x1.586045x1.59x1.649%1.65%2.616%2.62%25-1.000000x-1.000000x1.572017x1.572017x1.57x2.329%2.33%3.662%3.66%-1.000000x-1.000000x1.570028x1.570028x1.57x1.087%1.09%1.707%1.71%26-1.000000x-1.000000x1.550495x1.550495x1.55x0.737%0.74%1.143%1.14%27-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.522839x1.522839x1.52x14.269%14.3%21.730%21.7%28-1.000000x-1.000000x1.481288x1.481288x1.48x2.641%2.64%3.912%3.91%29-1.000000x-1.000000x1.481015x1.481015x1.48x2.798%2.80%4.144%4.14%-1.000000x-1.000000x1.476996x1.476996x1.48x1.170%1.17%1.728%1.73%30-1.000000x-1.000000x1.457167x1.457167x1.46x2.851%2.85%4.154%4.15%31-1.000000x-1.000000x1.436165x1.436165x1.44x2.115%2.11%3.037%3.04%32-1.000000x-1.000000x1.413654x1.413654x1.41x2.801%2.80%3.959%3.96%33-1.000000x-1.000000x1.387878x1.387878x1.39x7.705%7.70%10.693%10.7%34-1.000000x-1.000000x1.385205x1.385205x1.39x1.525%1.53%2.113%2.11%-1.000000x-1.000000x1.385205x1.385205x1.39x1.525%1.53%2.113%2.11%-1.000000x-1.000000x1.373250x1.373250x1.37x4.975%4.97%6.832%6.83%35-1.000000x-1.000000x1.368590x1.368590x1.37x1.742%1.74%2.385%2.38%36-1.000000x-1.000000x1.346658x1.346658x1.35x2.491%2.49%3.354%3.35%37-1.000000x-1.000000x1.286599x1.286599x1.29x1.699%1.70%2.186%2.19%38-1.000000x-1.000000x1.280610x1.280610x1.28x4.179%4.18%5.351%5.35%39-1.000000x-1.000000x1.274509x1.274509x1.27x5.228%5.23%6.663%6.66%40-1.000000x-1.000000x1.273345x1.273345x1.27x4.319%4.32%5.500%5.50%41-1.000000x-1.000000x1.238186x1.238186x1.24x4.026%4.03%4.985%4.99%42-1.000000x-1.000000x1.223931x1.223931x1.22x1.068%1.07%1.307%1.31%43-1.000000x-1.000000x1.213574x1.213574x1.21x2.357%2.36%2.860%2.86%44-1.000000x-1.000000x1.141406x1.141406x1.14x1.539%1.54%1.757%1.76%45-1.000000x-1.000000x1.124528x1.124528x1.12x5.872%5.87%6.603%6.60%46-1.000000x-1.000000x1.084697x1.084697x1.08x1.883%1.88%2.042%2.04%47-1.000000x-1.000000x1.046557x1.046557x1.05x1.291%1.29%1.351%1.35%48-1.000000x-1.000000x1.042857x1.042857x1.04x2.171%2.17%2.264%2.26%49-1.706858x-1.706858x1.71x1.000000x1.000000x1.072%1.07%0.628%0.63%50-2.515310x-2.515310x2.52x1.000000x1.000000x1.513%1.51%0.602%0.60%-10.000000x-10.000000x> 10.0x1.000000x1.000000x6.250%6.25%0.000%0%
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Educational Attainment by County Subdivision in the United States

There are 35,600 county subdivisions in the United States. This section compares the Goodland Township to the 50 most populous county subdivisions in the United States and to those entities that contain or substantially overlap with the Goodland Township. The least populous of the compared county subdivisions has a population of 547,300.

No H.S. Diploma by County Subdivision#43

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of the Goodland Township, selected other county subdivisions in the United States, and entities that contain the Goodland Township
0%10%20%Count#San Antonio CentralSan Antonio CntrlBronxSan BernardinoLos AngelesSouthwest DallasSW DallasEl PasoAnaheim-Santa Ana-Garden GroveAnaheim-Santa An…FresnoHoustonMiamiDetroitNortheast DallasNE DallasEast San Gabriel ValleyE San Gabriel VlyFort WorthBrooklynLong Beach-LakewoodLong Bch-LakewoodQueensSan Fernando ValleySan Fernando VlyMilwaukeePhiladelphiaOntarioChicagoBaltimoreOceanside-EscondidoOceanside-Escond…Las VegasPhoenixBostonSacramentoOrlandoAustinDenverUnited States of AmericaUnited StatesManhattanSan DiegoTampaSan JoseSan FranciscoTucsonAlbuquerqueTownship 1:Charlotte1, CharlotteColumbusTulsaSalt Lake CitySalt Lk CityTown of HempsteadHempsteadMidwestNorthwest HarrisNW HarrisWashingtonAtlantaWest North CentralSeattleNortheast TarrantNE TarrantMinnesotaGrand Rapids Metro AreaGrand RapidsItasca CountyItascaNashwauk-Keewatin School DistrictNashwauk-KeewatinPlanoSeattle EastGoodland TownshipGoodland28.998944%28.998944%29.0%126,922127k128.837398%28.837398%28.8%261,022261k227.582802%27.582802%27.6%135,754136k324.837401%24.837401%24.8%434,160434k423.987048%23.987048%24.0%141,940142k523.756417%23.756417%23.8%81,30881.3k622.645542%22.645542%22.6%248,699249k722.107626%22.107626%22.1%92,47292.5k821.922900%21.922900%21.9%457,347457k921.818150%21.818150%21.8%149,688150k1021.003600%21.003600%21.0%90,67890.7k1120.783651%20.783651%20.8%207,494207k1220.461493%20.461493%20.5%128,038128k1320.041576%20.041576%20.0%106,726107k1420.017506%20.017506%20.0%351,046351k1519.402518%19.402518%19.4%73,10573.1k1619.344706%19.344706%19.3%315,505316k1719.344408%19.344408%19.3%244,983245k1817.485105%17.485105%17.5%63,80063.8k1917.390838%17.390838%17.4%180,039180k2017.129346%17.129346%17.1%71,39771.4k2116.905373%16.905373%16.9%309,770310k2216.548703%16.548703%16.5%69,83969.8k2315.999398%15.999398%16.0%70,21470.2k2415.906523%15.906523%15.9%198,651199k2514.952077%14.952077%15.0%308,569309k2614.325556%14.325556%14.3%63,64263.6k2713.900512%13.900512%13.9%103,047103k2813.679368%13.679368%13.7%53,75853.8k2913.668915%13.668915%13.7%78,23878.2k3013.638703%13.638703%13.6%63,86563.9k3113.020590%13.020590%13.0%27,818,38027.8M12.996284%12.996284%13.0%160,845161k3212.985760%12.985760%13.0%206,792207k3312.979618%12.979618%13.0%57,75857.8k3412.789590%12.789590%12.8%153,604154k3512.559735%12.559735%12.6%84,02484.0k3612.262851%12.262851%12.3%70,40970.4k3712.089143%12.089143%12.1%52,21752.2k3811.553484%11.553484%11.6%61,37861.4k3911.376979%11.376979%11.4%60,83160.8k4011.299086%11.299086%11.3%43,65343.7k4110.435356%10.435356%10.4%64,29464.3k4210.384134%10.384134%10.4%54,23054.2k4310.244520%10.244520%10.2%4,641,8354.64M10.135583%10.135583%10.1%47,67947.7k4410.045421%10.045421%10.0%46,51046.5k459.538552%9.538552%9.5%37,37337.4k469.174633%9.174633%9.2%1,278,0511.28M8.833573%8.833573%8.8%66,22066.2k478.418271%8.418271%8.4%31,44331.4k487.376055%7.376055%7.4%270,121270k6.702965%6.702965%6.7%2,1796.702965%6.702965%6.7%2,1796.622517%6.622517%6.6%1805.714681%5.714681%5.7%20,64420.6k494.913852%4.913852%4.9%20,32620.3k503.855422%3.855422%3.9%16
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County Subdivision#44

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of the Goodland Township, selected other county subdivisions in the United States, and entities that contain the Goodland Township
0%20%40%60%Count#ManhattanSeattle EastWashingtonSan FranciscoPlanoSan JoseAtlantaSeattleBostonDenverAustinTownship 1:Charlotte1, CharlotteTown of HempsteadHempsteadSan DiegoNortheast TarrantNE TarrantNorthwest HarrisNW HarrisChicagoColumbusLos AngelesMinnesotaBrooklynSan Fernando ValleySan Fernando VlyNortheast DallasNE DallasSalt Lake CitySalt Lk CityAlbuquerqueTampaTucsonOceanside-EscondidoOceanside-Escond…TulsaQueensUnited States of AmericaUnited StatesHoustonWest North CentralBaltimoreLong Beach-LakewoodLong Bch-LakewoodMidwestPhoenixOrlandoAnaheim-Santa Ana-Garden GroveAnaheim-Santa An…SacramentoEast San Gabriel ValleyE San Gabriel VlyOntarioPhiladelphiaMiamiFort WorthSouthwest DallasSW DallasMilwaukeeFresnoGrand Rapids Metro AreaGrand RapidsItasca CountyItascaLas VegasEl PasoGoodland TownshipGoodlandBronxSan BernardinoNashwauk-Keewatin School DistrictNashwauk-KeewatinSan Antonio CentralSan Antonio CntrlDetroit60.434640%60.434640%60.4%747,953748k157.691220%57.691220%57.7%238,638239k255.360186%55.360186%55.4%256,316256k354.793235%54.793235%54.8%366,564367k454.142756%54.142756%54.1%195,588196k549.985845%49.985845%50.0%600,334600k649.803731%49.803731%49.8%195,136195k749.626221%49.626221%49.6%372,018372k846.377869%46.377869%46.4%206,036206k945.718966%45.718966%45.7%214,085214k1044.843364%44.843364%44.8%256,674257k1141.989192%41.989192%42.0%223,068223k1238.767308%38.767308%38.8%202,458202k1338.701072%38.701072%38.7%616,296616k1438.547398%38.547398%38.5%143,978144k1536.877886%36.877886%36.9%173,478173k1636.546320%36.546320%36.5%669,666670k1734.685843%34.685843%34.7%185,460185k1834.593243%34.593243%34.6%604,693605k1934.240609%34.240609%34.2%1,253,9371.25M34.081468%34.081468%34.1%597,685598k2033.231104%33.231104%33.2%420,848421k2133.191099%33.191099%33.2%331,364331k2233.118385%33.118385%33.1%204,048204k2332.636312%32.636312%32.6%140,967141k2432.512191%32.512191%32.5%144,676145k2532.087814%32.087814%32.1%184,237184k2631.198303%31.198303%31.2%136,915137k2731.110082%31.110082%31.1%120,191120k2830.595789%30.595789%30.6%499,006499k2930.315023%30.315023%30.3%64,767,78764.8M30.080133%30.080133%30.1%627,520628k3030.056536%30.056536%30.1%4,186,9564.19M29.719611%29.719611%29.7%125,423125k3129.113198%29.113198%29.1%109,693110k3228.952816%28.952816%29.0%13,118,64213.1M28.760152%28.760152%28.8%593,529594k3328.535877%28.535877%28.5%112,142112k3428.412939%28.412939%28.4%312,038312k3528.231204%28.231204%28.2%209,283209k3627.276345%27.276345%27.3%170,682171k3726.758411%26.758411%26.8%111,532112k3826.349720%26.349720%26.3%272,786273k3926.011156%26.011156%26.0%178,455178k4024.665414%24.665414%24.7%131,349131k4123.764821%23.764821%23.8%140,625141k4223.517740%23.517740%23.5%85,81285.8k4322.486319%22.486319%22.5%94,05694.1k4422.256060%22.256060%22.3%7,23522.256060%22.256060%22.3%7,23521.702666%21.702666%21.7%271,037271k4521.071885%21.071885%21.1%72,12072.1k4620.240964%20.240964%20.2%8419.131504%19.131504%19.1%173,169173k4716.375473%16.375473%16.4%80,59580.6k4815.526122%15.526122%15.5%42214.929012%14.929012%14.9%65,34165.3k4913.833543%13.833543%13.8%59,72359.7k50
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County Subdivision#45

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of the Goodland Township, selected other county subdivisions in the United States, and entities that contain the Goodland Township
0%5%10%Count#WashingtonManhattanSan FranciscoBostonAtlantaSeattleSan JoseSeattle EastDenverSan DiegoAustinBaltimoreLos AngelesAlbuquerquePlanoChicagoTucsonTown of HempsteadHempsteadTampaMiamiNortheast DallasNE DallasSalt Lake CitySalt Lk CityHoustonPhiladelphiaSan Fernando ValleySan Fernando VlyTownship 1:Charlotte1, CharlotteOceanside-EscondidoOceanside-Escond…MinnesotaBrooklynSacramentoColumbusUnited States of AmericaUnited StatesTulsaGoodland TownshipGoodlandPhoenixLong Beach-LakewoodLong Bch-LakewoodWest North CentralMidwestOrlandoFresnoAnaheim-Santa Ana-Garden GroveAnaheim-Santa An…QueensNorthwest HarrisNW HarrisNortheast TarrantNE TarrantEast San Gabriel ValleyE San Gabriel VlyOntarioFort WorthLas VegasMilwaukeeGrand Rapids Metro AreaGrand RapidsItasca CountyItascaSan BernardinoSan Antonio CentralSan Antonio CntrlEl PasoSouthwest DallasSW DallasBronxDetroitNashwauk-Keewatin School DistrictNashwauk-Keewatin12.461852%12.461852%12.5%57,69857.7k110.788423%10.788423%10.8%133,520134k27.558053%7.558053%7.6%50,56350.6k37.340604%7.340604%7.3%32,61132.6k46.889819%6.889819%6.9%26,99527.0k56.641855%6.641855%6.6%49,79049.8k66.255912%6.255912%6.3%75,13475.1k76.036065%6.036065%6.0%24,96825.0k85.725842%5.725842%5.7%26,81226.8k95.247128%5.247128%5.2%83,55883.6k105.058711%5.058711%5.1%28,95529.0k114.934352%4.934352%4.9%20,82420.8k124.659072%4.659072%4.7%81,44181.4k134.631274%4.631274%4.6%20,00420.0k144.617088%4.617088%4.6%16,67916.7k154.485379%4.485379%4.5%82,18982.2k164.461087%4.461087%4.5%25,61425.6k174.441262%4.441262%4.4%23,19423.2k184.325266%4.325266%4.3%19,24719.2k194.025531%4.025531%4.0%27,61827.6k203.827408%3.827408%3.8%38,21138.2k213.792624%3.792624%3.8%23,36723.4k223.737871%3.737871%3.7%77,97878.0k233.720350%3.720350%3.7%38,51538.5k243.601468%3.601468%3.6%45,61045.6k253.490252%3.490252%3.5%18,54218.5k263.466073%3.466073%3.5%15,21115.2k273.458748%3.458748%3.5%126,664127k3.384967%3.384967%3.4%59,36259.4k283.380196%3.380196%3.4%25,05825.1k293.357865%3.357865%3.4%17,95418.0k303.336158%3.336158%3.3%7,127,6737.13M3.140490%3.140490%3.1%12,13312.1k313.132530%3.132530%3.1%133.010825%3.010825%3.0%62,13562.1k323.009440%3.009440%3.0%11,33911.3k332.978981%2.978981%3.0%414,980415k2.893416%2.893416%2.9%1,311,0191.31M2.893233%2.893233%2.9%11,37011.4k342.828959%2.828959%2.8%11,83311.8k352.763004%2.763004%2.8%30,34430.3k362.756960%2.756960%2.8%44,96545.0k372.682967%2.682967%2.7%12,62112.6k382.630994%2.630994%2.6%9,827392.484535%2.484535%2.5%15,54715.5k402.324315%2.324315%2.3%9,688412.173239%2.173239%2.2%11,57311.6k422.155077%2.155077%2.2%26,91426.9k432.043126%2.043126%2.0%7,455441.818014%1.818014%1.8%5911.818014%1.818014%1.8%5911.807306%1.807306%1.8%8,895451.792414%1.792414%1.8%7,845461.745764%1.745764%1.7%5,975471.704476%1.704476%1.7%10,08610.1k481.664253%1.664253%1.7%15,06415.1k491.318660%1.318660%1.3%5,693501.066961%1.066961%1.1%29
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).

Under-Education Sex Ratio by County Subdivision#46

Percentage more likely men are than women to not have a high school diploma (or equivalent) among people aged 25 years and older.
Scope: population of the Goodland Township, selected other county subdivisions in the United States, and entities that contain the Goodland Township
Female
Male
1x0x1xFM#Grand RapidsItasca1, CharlotteNashwauk-KeewatinDetroitBaltimoreTulsaSW DallasWest North CentralMinnesotaMidwestFresnoMilwaukeeUnited StatesNE DallasHoustonHempsteadOrlandoPhiladelphiaOntarioSan BernardinoAustinAtlantaFort WorthPhoenixChicagoAlbuquerqueWashingtonSacramentoColumbusSalt Lk CityDenverNE TarrantTampaOceanside-EscondidoOceanside-Escond…NW HarrisBronxPlanoBrooklynSan Antonio CntrlSan Fernando VlyTucsonAnaheim-Santa Ana-Gdn GrvAnaheim-Santa An…Los AngelesLas VegasBostonE San Gabriel VlyQueensMiamiManhattanSeattleLong Bch-LakewoodSeattle EastSan JoseSan DiegoSan FranciscoEl PasoGoodland-1.000000x-1.000000x1.485888x1.485888x1.49x5.397%5.40%8.020%8.02%-1.000000x-1.000000x1.485888x1.485888x1.49x5.397%5.40%8.020%8.02%-1.000000x-1.000000x1.285784x1.285784x1.29x10.185%10.2%13.096%13.1%1-1.000000x-1.000000x1.275522x1.275522x1.28x5.836%5.84%7.444%7.44%-1.000000x-1.000000x1.237056x1.237056x1.24x18.951%19.0%23.443%23.4%2-1.000000x-1.000000x1.179669x1.179669x1.18x15.289%15.3%18.036%18.0%3-1.000000x-1.000000x1.177766x1.177766x1.18x10.417%10.4%12.268%12.3%4-1.000000x-1.000000x1.160109x1.160109x1.16x22.304%22.3%25.875%25.9%5-1.000000x-1.000000x1.137506x1.137506x1.14x8.598%8.60%9.780%9.78%-1.000000x-1.000000x1.136388x1.136388x1.14x6.913%6.91%7.856%7.86%-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.127206x1.127206x1.13x20.830%20.8%23.479%23.5%6-1.000000x-1.000000x1.123984x1.123984x1.12x16.526%16.5%18.574%18.6%7-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.099797x1.099797x1.10x19.814%19.8%21.791%21.8%8-1.000000x-1.000000x1.099188x1.099188x1.10x20.902%20.9%22.975%23.0%9-1.000000x-1.000000x1.092101x1.092101x1.09x9.951%9.95%10.868%10.9%10-1.000000x-1.000000x1.090284x1.090284x1.09x13.111%13.1%14.294%14.3%11-1.000000x-1.000000x1.090163x1.090163x1.09x16.700%16.7%18.206%18.2%12-1.000000x-1.000000x1.088515x1.088515x1.09x16.417%16.4%17.871%17.9%13-1.000000x-1.000000x1.081887x1.081887x1.08x26.536%26.5%28.709%28.7%14-1.000000x-1.000000x1.078150x1.078150x1.08x13.148%13.1%14.176%14.2%15-1.000000x-1.000000x1.073220x1.073220x1.07x9.215%9.21%9.889%9.89%16-1.000000x-1.000000x1.071348x1.071348x1.07x19.379%19.4%20.762%20.8%17-1.000000x-1.000000x1.069008x1.069008x1.07x14.467%14.5%15.465%15.5%18-1.000000x-1.000000x1.068021x1.068021x1.07x16.372%16.4%17.486%17.5%19-1.000000x-1.000000x1.066879x1.066879x1.07x11.714%11.7%12.497%12.5%20-1.000000x-1.000000x1.064309x1.064309x1.06x9.751%9.75%10.378%10.4%21-1.000000x-1.000000x1.053268x1.053268x1.05x13.558%13.6%14.280%14.3%22-1.000000x-1.000000x1.052237x1.052237x1.05x11.098%11.1%11.678%11.7%23-1.000000x-1.000000x1.051272x1.051272x1.05x10.176%10.2%10.697%10.7%24-1.000000x-1.000000x1.049933x1.049933x1.05x13.307%13.3%13.971%14.0%25-1.000000x-1.000000x1.040742x1.040742x1.04x8.257%8.26%8.594%8.59%26-1.000000x-1.000000x1.040003x1.040003x1.04x12.734%12.7%13.244%13.2%27-1.000000x-1.000000x1.039910x1.039910x1.04x15.694%15.7%16.321%16.3%28-1.000000x-1.000000x1.038394x1.038394x1.04x9.953%9.95%10.335%10.3%29-1.000000x-1.000000x1.030539x1.030539x1.03x28.448%28.4%29.316%29.3%30-1.000000x-1.000000x1.023151x1.023151x1.02x5.652%5.65%5.783%5.78%31-1.000000x-1.000000x1.018960x1.018960x1.02x19.845%19.8%20.221%20.2%32-1.000000x-1.000000x1.003929x1.003929x1.00x28.944%28.9%29.058%29.1%33-1.000000x-1.000000x1.003840x1.003840x1.00x19.308%19.3%19.383%19.4%34-1.006471x-1.006471x1.01x1.000000x1.000000x12.301%12.3%12.222%12.2%35-1.008020x-1.008020x1.01x1.000000x1.000000x22.734%22.7%22.553%22.6%36-1.013004x-1.013004x1.01x1.000000x1.000000x24.995%25.0%24.674%24.7%37-1.016645x-1.016645x1.02x1.000000x1.000000x16.036%16.0%15.774%15.8%38-1.022333x-1.022333x1.02x1.000000x1.000000x14.476%14.5%14.159%14.2%39-1.026902x-1.026902x1.03x1.000000x1.000000x20.723%20.7%20.180%20.2%40-1.027762x-1.027762x1.03x1.000000x1.000000x19.596%19.6%19.067%19.1%41-1.030897x-1.030897x1.03x1.000000x1.000000x22.136%22.1%21.473%21.5%42-1.044246x-1.044246x1.04x1.000000x1.000000x13.260%13.3%12.698%12.7%43-1.049689x-1.049689x1.05x1.000000x1.000000x9.047%9.05%8.619%8.62%44-1.056864x-1.056864x1.06x1.000000x1.000000x19.921%19.9%18.849%18.8%45-1.057675x-1.057675x1.06x1.000000x1.000000x5.050%5.05%4.774%4.77%46-1.127548x-1.127548x1.13x1.000000x1.000000x13.555%13.6%12.021%12.0%47-1.168454x-1.168454x1.17x1.000000x1.000000x13.975%14.0%11.960%12.0%48-1.228758x-1.228758x1.23x1.000000x1.000000x13.883%13.9%11.298%11.3%49-1.276282x-1.276282x1.28x1.000000x1.000000x26.394%26.4%20.680%20.7%50-1.658654x-1.658654x1.66x1.000000x1.000000x4.808%4.81%2.899%2.90%
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Over-Education Sex Ratio by County Subdivision#47

Percentage more likely men are than women to have a professional or doctorate degree (e.g., MBA, PhD, or MD) among people aged 25 years and older.
Scope: population of the Goodland Township, selected other county subdivisions in the United States, and entities that contain the Goodland Township
Female
Male
10x5x0xFM#El PasoSalt Lk CityPlanoTulsaAlbuquerqueNE DallasFort WorthOrlandoPhoenixTucson1, CharlotteMidwestNW HarrisLas VegasWest North CentralSeattle EastSan Antonio CntrlSan DiegoUnited StatesSan JoseHempsteadFresnoNE TarrantMinnesotaTampaSan Fernando VlyE San Gabriel VlyHoustonSan BernardinoColumbusMiamiAtlantaGrand RapidsItascaMilwaukeeAnaheim-Santa Ana-Gdn GrvAnaheim-Santa An…SacramentoPhiladelphiaOceanside-EscondidoOceanside-Escond…Los AngelesAustinBaltimoreWashingtonQueensSW DallasBrooklynManhattanChicagoSeattleBostonBronxOntarioLong Bch-LakewoodDenverSan FranciscoDetroitNashwauk-KeewatinGoodland-1.000000x-1.000000x2.132933x2.132933x2.13x1.146%1.15%2.445%2.44%1-1.000000x-1.000000x2.053360x2.053360x2.05x2.488%2.49%5.109%5.11%2-1.000000x-1.000000x1.782054x1.782054x1.78x3.355%3.35%5.978%5.98%3-1.000000x-1.000000x1.720683x1.720683x1.72x2.338%2.34%4.022%4.02%4-1.000000x-1.000000x1.707718x1.707718x1.71x3.458%3.46%5.905%5.91%5-1.000000x-1.000000x1.692229x1.692229x1.69x2.857%2.86%4.836%4.84%6-1.000000x-1.000000x1.684272x1.684272x1.68x1.637%1.64%2.757%2.76%7-1.000000x-1.000000x1.660799x1.660799x1.66x2.196%2.20%3.647%3.65%8-1.000000x-1.000000x1.659024x1.659024x1.66x2.280%2.28%3.783%3.78%9-1.000000x-1.000000x1.621815x1.621815x1.62x3.429%3.43%5.561%5.56%10-1.000000x-1.000000x1.611479x1.611479x1.61x2.711%2.71%4.369%4.37%11-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.579303x1.579303x1.58x2.100%2.10%3.317%3.32%12-1.000000x-1.000000x1.576623x1.576623x1.58x1.677%1.68%2.643%2.64%13-1.000000x-1.000000x1.572017x1.572017x1.57x2.329%2.33%3.662%3.66%-1.000000x-1.000000x1.557664x1.557664x1.56x4.733%4.73%7.372%7.37%14-1.000000x-1.000000x1.540473x1.540473x1.54x1.423%1.42%2.192%2.19%15-1.000000x-1.000000x1.529565x1.529565x1.53x4.164%4.16%6.370%6.37%16-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.514156x1.514156x1.51x4.979%4.98%7.539%7.54%17-1.000000x-1.000000x1.488724x1.488724x1.49x3.608%3.61%5.371%5.37%18-1.000000x-1.000000x1.484269x1.484269x1.48x2.293%2.29%3.404%3.40%19-1.000000x-1.000000x1.481801x1.481801x1.48x2.138%2.14%3.168%3.17%20-1.000000x-1.000000x1.481015x1.481015x1.48x2.798%2.80%4.144%4.14%-1.000000x-1.000000x1.476874x1.476874x1.48x3.518%3.52%5.196%5.20%21-1.000000x-1.000000x1.460302x1.460302x1.46x2.944%2.94%4.299%4.30%22-1.000000x-1.000000x1.451775x1.451775x1.45x2.041%2.04%2.963%2.96%23-1.000000x-1.000000x1.424636x1.424636x1.42x3.092%3.09%4.404%4.40%24-1.000000x-1.000000x1.423621x1.423621x1.42x1.501%1.50%2.137%2.14%25-1.000000x-1.000000x1.413654x1.413654x1.41x2.801%2.80%3.959%3.96%26-1.000000x-1.000000x1.395174x1.395174x1.40x3.384%3.38%4.722%4.72%27-1.000000x-1.000000x1.391590x1.391590x1.39x5.799%5.80%8.070%8.07%28-1.000000x-1.000000x1.385205x1.385205x1.39x1.525%1.53%2.113%2.11%-1.000000x-1.000000x1.385205x1.385205x1.39x1.525%1.53%2.113%2.11%-1.000000x-1.000000x1.368590x1.368590x1.37x1.742%1.74%2.385%2.38%29-1.000000x-1.000000x1.362600x1.362600x1.36x2.347%2.35%3.199%3.20%30-1.000000x-1.000000x1.359928x1.359928x1.36x2.888%2.89%3.927%3.93%31-1.000000x-1.000000x1.341930x1.341930x1.34x3.216%3.22%4.316%4.32%32-1.000000x-1.000000x1.310218x1.310218x1.31x3.011%3.01%3.945%3.95%33-1.000000x-1.000000x1.308108x1.308108x1.31x4.045%4.05%5.292%5.29%34-1.000000x-1.000000x1.303834x1.303834x1.30x4.384%4.38%5.716%5.72%35-1.000000x-1.000000x1.303196x1.303196x1.30x4.332%4.33%5.645%5.65%36-1.000000x-1.000000x1.300117x1.300117x1.30x10.922%10.9%14.199%14.2%37-1.000000x-1.000000x1.285200x1.285200x1.29x2.428%2.43%3.121%3.12%38-1.000000x-1.000000x1.265486x1.265486x1.27x1.515%1.51%1.917%1.92%39-1.000000x-1.000000x1.262281x1.262281x1.26x3.021%3.02%3.814%3.81%40-1.000000x-1.000000x1.245683x1.245683x1.25x9.673%9.67%12.050%12.1%41-1.000000x-1.000000x1.238186x1.238186x1.24x4.026%4.03%4.985%4.99%42-1.000000x-1.000000x1.228890x1.228890x1.23x5.961%5.96%7.326%7.33%43-1.000000x-1.000000x1.221037x1.221037x1.22x6.643%6.64%8.112%8.11%44-1.000000x-1.000000x1.202385x1.202385x1.20x1.526%1.53%1.834%1.83%45-1.000000x-1.000000x1.198236x1.198236x1.20x2.119%2.12%2.539%2.54%46-1.000000x-1.000000x1.181998x1.181998x1.18x2.766%2.77%3.269%3.27%47-1.000000x-1.000000x1.129665x1.129665x1.13x5.378%5.38%6.075%6.08%48-1.000000x-1.000000x1.098720x1.098720x1.10x7.194%7.19%7.905%7.90%49-1.000000x-1.000000x1.046557x1.046557x1.05x1.291%1.29%1.351%1.35%50-2.515310x-2.515310x2.52x1.000000x1.000000x1.513%1.51%0.602%0.60%-10.000000x-10.000000x> 10.0x1.000000x1.000000x6.250%6.25%0.000%0%
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Definitions

Educational attainment is reported in terms of the highest level of education obtained by persons aged 25 years and older. On this page, we report on both detailed and coarse categorizations of the levels of education in the population. The detailed levels of educational attainment are as follows:

  • None: no formal education at all
  • Less than High School: some education, but stopped short of high school
  • Some High School: some high school education, but no high school diploma (or equivalent)
  • Some College: high school diploma (or equivalent), and some college, but no post-secondary degree
  • Associate's Degree: highest post-secondary degree is an associate's degree (including occupational or academic degrees)
  • Bachelor's Degree: highest post-secondary degree is a bachelor's degree (e.g., BA, BS, AB)
  • Master's Degree: highest post-secondary degree is a master's degree (e.g., MA, MS, MENG, MSW)
  • Professional Degree: highest post-secondary degree is a professional degree (e.g., MD, DDC, JD)
  • Doctorate Degree: highest post-secondary degree is a doctorate degree (e.g., PhD, EdD)

The coarse levels of educational attainment are as follows:

  • No High School Diploma: does not have a high school diploma or equivalent
  • High School Diploma: has a high school diploma or equivalent and possibly some college, but no post-secondary degrees
  • Higher Degree: has some post-secondary degree (associate's, bachelor's, master's, professional, doctorate)

For additional information about the data presented on this site, including our sources, please see the About Page.

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Demographics

Income

Education

More Maps to Explore

State:
Minnesota
County:
Itasca
Metro Area:
Grand Rapids Area
ZIP Codes:
55736, 55742, 55746, 55775, 55793
Unified School District:
Nashwauk-Keewatin
Congressional District:
MN-8
State Senate District:
MN-6
State House District:
MN-6A
Neighboring Cities:
Hibbing
Neighboring Townships:
Cedar Valley, Feeley, Greenway, Lone Pine, Sago, Wawina
Neighboring Unorganized Territories:
Janette Lake, Little Sand Lake
Nearby Townships:
Blackberry, Nashwauk, Splithand, Toivola, Trout Lake