About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Minneapolis. The dataset can be utilized to gain insights into gender-based income distribution within the Minneapolis population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Minneapolis, among individuals aged 15 years and older with income, there were 172,460 men and 159,598 women in the workforce. Among them, 89,553 men were engaged in full-time, year-round employment, while 72,335 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 4.99% fell within the income range of under $24,999, while 5.46% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 33.80% of men in full-time roles earned incomes exceeding $100,000, while 25.30% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Minneapolis median household income by race. You can refer the same here

This dataset tracks annual diversity score from 2011 to 2012 for Academy Of North Minneapolis vs. Minnesota and Academy Of North Minneapolis School District

This dataset tracks annual diversity score from 1991 to 2023 for Minneapolis Public School District vs. Minnesota

This dataset tracks annual diversity score from 2008 to 2023 for Minneapolis Community and Technical College vs. Minnesota

About this dataset

Context

The dataset presents the median household income across different racial categories in Minneapolis. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Minneapolis population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 61.59% of the total residents in Minneapolis. Notably, the median household income for White households is $93,863. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $93,863.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Minneapolis.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Minneapolis median household income by race. You can refer the same here

This dataset tracks annual diversity score from 2006 to 2016 for Minneapolis Academy Charter School vs. Minnesota and Minneapolis Academy Charter School District

This dataset tracks annual diversity score from 1995 to 2023 for Minneapolis Jr-sr High School vs. Kansas and North Ottawa County School District

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Minneapolis. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Minneapolis, the median household income for the households where the householder is White increased by $15,393(19.62%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $78,470 in 2013 and $93,863 in 2023.
• Black or African American: In Minneapolis, the median household income for the households where the householder is Black or African American increased by $12,778(47.18%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $27,085 in 2013 and $39,863 in 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Minneapolis.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Minneapolis median household income by race. You can refer the same here

This dataset tracks annual diversity score from 1991 to 2023 for Andersen Community vs. Minnesota and Minneapolis Public School District

This dataset tracks annual diversity score from 2013 to 2016 for Minneapolis College Preparatory vs. Minnesota and Minneapolis College Preparatory School District

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Northfield. The dataset can be utilized to gain insights into gender-based income distribution within the Northfield population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Northfield, among individuals aged 15 years and older with income, there were 7,604 men and 8,505 women in the workforce. Among them, 3,019 men were engaged in full-time, year-round employment, while 2,223 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 10.24% fell within the income range of under $24,999, while 10.08% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 21.73% of men in full-time roles earned incomes exceeding $100,000, while 12.28% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/northfield-mn-income-distribution-by-gender-and-employment-type.jpeg" alt="Northfield, MN gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Northfield median household income by gender. You can refer the same here

This dataset tracks annual diversity score from 1999 to 2023 for Takoda Prep vs. Minnesota and Minneapolis Public School District

This is a collection of 5 raster datasets and an ecoregions boundary file that were used to compile the report available here: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/centralUS/GreatLakes/Pages/Reports-and-Data.aspx

The Minnesota Department of Natural Resources provides these datasets clipped to the Minnesota boundary and re-projected to UTM Zone 15N. Detailed metadata for each layer can be found here:
Settings
Landscape Diversity
Local Connectedness
Resilience Score
Above Average Resilience

Brief Summary of Methods

Detailed methods are addressed in the report. Here we provide a short summary of the key resilience inputs and analysis steps.

• Settings: We developed geophysical setting classifications that best correspond to important drivers of species diversity and vegetation types in the project area. This analysis resulted in a set of empirically derived and ecologically relevant geophysical settings on which we can base conservation priorities. The settings fall broadly into bedrock-influenced and surficial soil texture categories.


• Landscape Diversity: To create a standardized metric of Landscape Diversity we transformed two indices (Landform Variety and Wetland Scoreto standardized normal distributions (“Z-scores”with a mean of 0 and standard deviation of 1) and then combined them into a single index. Landforms are the base score of the Landscape Diversity metric—because not all landscapes have wetlands. Where wetlands were present, if the Wetland Score was greater than the Landform Diversity Score, the Landform Diversity Score and the Wetland Score were combined. In these cases, the Landform Variety Score received twice the weight of the Wetland Score. The final map of Landscape Diversity shows the areas estimated to have the most microclimates based on Landform Variety and the Wetland score (when wetlands are present, they increase the Landscape Diversity score of flat landforms).


• Local Connectedness: Local connectedness measures how impaired the structural connections are between natural ecosystems within a local landscape. Connectedness answers the question: “To what extent are ecological flows outward from that cell impeded or facilitated by the surrounding local landscape?” To measure this, each cell is coded with a resistance weight based on land cover, and the theoretical spread of a species or process outward from a focal cell is a function of the resistance values of the neighboring cells and their distance from a focal cell out to a maximum distance (3 km). Based on the possible spread, each cell is given a resultant local connectedness value from 0 (least connected) to 100 (most connected). This score is then converted to a Z-score: The cell score“x” minus the mean scoreof all cells “µ” divided by the standard deviation of all cells.


• Resilience Estimates: We combined the Landscape Diversity and the local connectedness scores into an integrated resilience score. To ensure that the two factors had equal weight in the integrated score we transformed each metric to standardized normalized scores (z-scores) so that each had a mean of zero and a standard deviation of one (this prevents the factor with a larger mean or variance from having more influence). The formula for calculating the z-scores was: the cell score“x”minus the mean scoreof all cells “µ” divided by the standard deviation of all cells “σ”.


• Stratification of Resilience Estimates: To arrive at a final resilience score, we stratified our evaluation of estimated resilience at three geographic stratification levels: ecoregions, settings with ecoregions, and for the entire region. The base result is resilience stratified by ecoregion. We then added in overrides to capture the most resilient areas in each setting and the most resilient areas in the region. The Ecoregional Stratification highlights the most resilient places in each ecoregion and ensures a fair geographic distribution. For the Setting Stratification, we boosted the areas with the highest scores of each geophysical setting within ecoregion. If the Setting Stratification was above average (>.5 SD above the mean) and exceeded the Ecoregion Stratification score, the grid cell received the Setting Stratification score. Finally, we wanted to ensure that the highest scoring places for resilience in the region appeared in the final map. If the Regional results were above average and were higher than the combined ecoregional and setting results, the grid cell received the value of the Regional score. The threshold for “above average” scores for Regional resilience included all scores meeting a “above average” (>1 SD) or “slightly above average (between 0.5 and 1 SD) if the landscape diversity score was also above average. This specification ensured that areas with only slightly above average scores for the region were only included if they also have high landscape diversity. We also corrected the final scores to ensure lands with high intensity corn landuse or surface mining could not have a maximum score above .5 SD, and we also ran a spatial smoothing algorithm to improve transitional mapping within 20km of ecoregional lines.


• Final Resilience Score Legend: Our method identified the sites that scored above or below average in estimated resilience using the -0.5 standard deviations (SD) to 0.5 SD of the range of sites as the definition of average. Although the result is a continuous numeric number for each 30m pixel, our standard legend was as follows:
  
  Far below average (<-2 SD) Most Vulnerable
  Below average (-1 to -2 SD) More Vulnerable
  Slightly below average (-0.5 to -1 SD) Somewhat Vulnerable
  Average (-0.5 to 0.5 SD) Average
  Slightly above average (0.5 to 1 SD) Somewhat Resilient
  Above average (1- 2 SD) More Resilient
  Far above average (>2 SD) Most Resilient

This layer shows lakes meeting criteria for Lakes of Biological Significance (LBS) in Minnesota. Lakes were identified and classified by DNR subject matter experts on objective criteria for four community types (aquatic plants, fish, herptiles, birds).

Unique plant or animal presence was the primary measure of a lake's biological significance. Lakes were rated and grouped for each of the following communities: aquatic plants, fish, birds, and herptiles. Lakes were assigned one of three biological significance classes (outstanding, high, or moderate), which are defined in Section 5: Attributes (below). Many Minnesota lakes have not been sampled for plants and/or animals, so this list of lakes will be periodically revised as additional biological data become available.

The goal of this list was to identify lakes that exhibit the highest quality features within any of the four assessed biological communities (as opposed to identification of lakes that exhibit diversity across communities). Therefore, a lake needed to meet criteria for only one of the community types (aquatic plants, fish, birds, herptiles) to be identified as a Lake of Biological Significance. Occurrences of high-quality features within the community types determined the biological significance rank.

For a detailed description of criteria and analysis used, see: Lakes of Biological Significance 2025 (pdf, 169k)

This dataset tracks annual diversity score from 2013 to 2023 for North High School vs. Minnesota and Minneapolis Public School District

This dataset tracks annual diversity score from 2016 to 2023 for Fair High School vs. Minnesota and Minneapolis Public School District

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within St. Paul Park. The dataset can be utilized to gain insights into gender-based income distribution within the St. Paul Park population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within St. Paul Park, among individuals aged 15 years and older with income, there were 2,439 men and 1,742 women in the workforce. Among them, 1,352 men were engaged in full-time, year-round employment, while 749 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, none fell within the income range of under $24,999, while 5.47% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 21.08% of men in full-time roles earned incomes exceeding $100,000, while 11.48% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for St. Paul Park median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Parkers Prairie. The dataset can be utilized to gain insights into gender-based income distribution within the Parkers Prairie population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Parkers Prairie, among individuals aged 15 years and older with income, there were 390 men and 457 women in the workforce. Among them, 161 men were engaged in full-time, year-round employment, while 145 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 4.97% fell within the income range of under $24,999, while 20% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 9.32% of men in full-time roles earned incomes exceeding $100,000, while 5.52% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Parkers Prairie median household income by race. You can refer the same here

This dataset tracks annual diversity score from 1996 to 2006 for P.m. High School vs. Minnesota and Minneapolis Public School District

This dataset tracks annual diversity score from 1995 to 2011 for City Inc. North High School vs. Minnesota and Minneapolis Public School District