About this dataset

Context

The dataset tabulates the data for the Midwest City, OK population pyramid, which represents the Midwest City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Midwest City, OK, is 34.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Midwest City, OK, is 27.6.
• Total dependency ratio for Midwest City, OK is 61.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Midwest City, OK is 3.6.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Midwest City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Midwest City for the selected age group is shown in the following column.
• Population (Female): The female population in the Midwest City for the selected age group is shown in the following column.
• Total Population: The total population of the Midwest City for the selected age group is shown in the following column.

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 Midwest City Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Midwest population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Midwest across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Midwest was 286, a 0.70% increase year-by-year from 2022. Previously, in 2022, Midwest population was 284, a decline of 0.35% compared to a population of 285 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Midwest decreased by 99. In this period, the peak population was 420 in the year 2013. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Midwest is shown in this column.
• Year on Year Change: This column displays the change in Midwest population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Midwest Population by Year. You can refer the same here

Resident Population in the Midwest Census Region was 69596.58400 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in the Midwest Census Region reached a record high of 69596.58400 in January of 2024 and a record low of 26359.00000 in January of 1900. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in the Midwest Census Region - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for Unemployed Persons in Midwest Census Region (LASRD920000000000004) from Jan 1976 to Apr 2025 about Midwest Census Region, household survey, unemployment, persons, and USA.

About this dataset

Context

The dataset tabulates the Midwest population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Midwest. The dataset can be utilized to understand the population distribution of Midwest by age. For example, using this dataset, we can identify the largest age group in Midwest.

Key observations

The largest age group in Midwest, WY was for the group of age 50 to 54 years years with a population of 42 (19.27%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Midwest, WY was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Midwest is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Midwest total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Midwest Population by Age. You can refer the same here

Graph and download economic data for Employed Persons in Midwest Census Region (LAURD920000000000005) from Jan 1976 to Apr 2025 about Midwest Census Region, household survey, employment, persons, and USA.

U.S. Census Bureau QuickFacts statistics for Midwest City city, Oklahoma. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Midwest by race. It includes the distribution of the Non-Hispanic population of Midwest across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Midwest across relevant racial categories.

Key observations

Of the Non-Hispanic population in Midwest, the largest racial group is White alone with a population of 205 (96.70% of the total Non-Hispanic population).

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: This column displays the racial categories (for Non-Hispanic) for the Midwest
• Population: The population of the racial category (for Non-Hispanic) in the Midwest is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Midwest total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Midwest Population by Race & Ethnicity. You can refer the same here

Graph and download economic data for Health Insurance Coverage: People Not Covered in the Midwest Census Region (DISCONTINUED) (CMWRHICNOTCOV) from 1999 to 2012 about covered, Midwest Census Region, health, insurance, persons, and USA.

This dataset includes microsatellite genotypes for 8,454 brook trout from 188 wild Midwestern populations and 26 hatchery strains of both Midwest and eastern (Atlantic seaboard) origin. Each individual was genotyped at either 5 or 7 loci.

About this dataset

Context

The dataset tabulates the population of Midwest by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Midwest. The dataset can be utilized to understand the population distribution of Midwest by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Midwest. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Midwest.

Key observations

Largest age group (population): Male # 50-54 years (36) | Female # 60-64 years (16). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Midwest population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Midwest is shown in the following column.
• Population (Female): The female population in the Midwest is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Midwest for each age group.

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 Midwest Population by Gender. You can refer the same here

Graph and download economic data for Consumer Unit Characteristics: Number of People in CU by Region: Residence in the Midwest Census Region (CXU980010LB1103M) from 1984 to 2023 about Midwest Census Region, consumer unit, residents, persons, and USA.

Replication data and code for the manuscript "Modeling Shifts in Community Corrections Populations following COVID-19: Evidence from a Midwest Metropolitan Area."

The phenology of critical biological events in aquatic ecosystems are rapidly shifting due to climate change. Growing variability in phenological cues can increase the likelihood of trophic mismatches, causing recruitment failures in commercially, culturally, and recreationally important fisheries. We tested for changes in spawning phenology of regionally important walleye (Sander vitreus) populations in 194 Midwest US lakes in Minnesota, Michigan, and Wisconsin spanning 1939-2019 to investigate factors influencing walleye phenological responses to climate change and associated climate variability, including ice-off timing, lake physical characteristics, and population stocking history. Data from Wisconsin and Michigan lakes (185 and 5 out of 194 total lakes, respectively) were collected by the Wisconsin Department of Natural Resources (WDNR) and the Great Lakes Indian Fish and Wildlife Commission (GLIFWC) through standardized spring walleye mark-recapture surveys and spring tribal harvest season records. Standardized spring mark-recapture population estimates are performed shortly after ice-off, where following a marking event, a subsequent recapture sampling event is conducted using nighttime electrofishing (typically AC – WDNR, pulsed-DC – GLIFWC) of the entire shoreline including islands for small lakes and index stations for large lakes (Hansen et al. 2015) that is timed to coincide with peak walleye spawning activity (G. Hatzenbeler, WDNR, personal communication; M. Luehring, GLIFWC, personal communication; Beard et al. 1997). Data for four additional Minnesota lakes were collected by the Minnesota Department of Natural Resources (MNDNR) beginning in 1939 during annual collections of walleye eggs and broodstock (Schneider et al. 2010), where date of peak egg take was used to index peak spawning activity. For lakes where spawning location did not match the lake for which the ice-off data was collected, the spawning location either flowed into (Pike River) or was within 50 km of a lake where ice-off data were available (Pine River) and these ice-off data were used. Following the affirmation of off-reservation Ojibwe tribal fishing rights in the Ceded Territories of Wisconsin and the Upper Peninsula of Michigan in 1987, tribal spearfishers have targeted walleye during spring spawning (Mrnak et al. 2018). Nightly harvests are recorded as part of a compulsory creel survey (US Department of the Interior 1991). Using these records, we calculated the date of peak spawning activity in a given lake-year as the day of maximum tribal harvest. Although we were unable to account for varying effort in these data, a preliminary analysis comparing spawning dates estimated using tribal harvest to those determined from standardized agency surveys in the same lake and year showed that they were highly correlated (Pearson’s correlation: r = 0.91, P < 0.001). For lakes that had walleye spawning data from both agency surveys and tribal harvest, we used the data source with the greatest number of observation years. Ice-off phenology data was collected from two sources – either observed from the Global Lake and River Ice Phenology database (Benson et al. 2000)t, or modeled from a USGS region-wide machine-learning model which used North American Land Data Assimilation System (NLDAS) meteorological inputs combined with lake characteristics (lake position, clarity, size, depth, hypsography, etc.) to predict daily water column temperatures from 1979 - 2022, from which ice-off dates could be derived (https://www.sciencebase.gov/catalog/item/6206d3c2d34ec05caca53071; see Corson-Dosch et al. 2023 for details). Modeled data for our study lakes (see (Read et al. 2021) for modeling details), which performed well in reflecting ice phenology when compared to observed data (i.e., highly significant correlation between observed and modeled ice-off dates when both were available; r = 0.71, p < 0.001). Lake surface area (ha), latitude, and maximum depth (m) were acquired from agency databases and lake reports. Lake class was based on a WDNR lakes classification system (Rypel et al. 2019) that categorized lakes based on temperature, water clarity, depth, and fish community. Walleye stocking history was defined using the walleye stocking classification system developed by the Wisconsin Technical Working Group (see also Sass et al. 2021), which categorized lakes based on relative contributions of naturally-produced and stocked fish to adult recruitment by relying heavily on historic records of age-0 and age-1 catch rates and stocking histories. Wisconsin lakes were divided into three groups: natural recruitment (NR), a combination of stocking and natural recruitment (C-ST), and stocked only (ST). Walleye natural recruitment was indexed as age-0 walleye CPE (number of age-0 walleye captured per km of shoreline electrofished) from WDNR and GLIFWC fall electrofishing surveys (see Hansen et al. 2015 for details). We excluded la

ObjectiveThere is currently inconclusive evidence regarding the relationship between recidivism and mental illness. This retrospective study aimed to use rigorous machine learning methods to understand the unique predictive utility of mental illness for recidivism in a general population (i.e.; not only those with mental illness) prison sample in the United States.MethodParticipants were adult men (n = 322) and women (n = 72) who were recruited from three prisons in the Midwest region of the United States. Three model comparisons using Bayesian correlated t-tests were conducted to understand the incremental predictive utility of mental illness, substance use, and crime and demographic variables for recidivism prediction. Three classification statistical algorithms were considered while evaluating model configurations for the t-tests: elastic net logistic regression (GLMnet), k-nearest neighbors (KNN), and random forests (RF).ResultsRates of substance use disorders were particularly high in our sample (86.29%). Mental illness variables and substance use variables did not add predictive utility for recidivism prediction over and above crime and demographic variables. Exploratory analyses comparing the crime and demographic, substance use, and mental illness feature sets to null models found that only the crime and demographics model had an increased likelihood of improving recidivism prediction accuracy.ConclusionsDespite not finding a direct relationship between mental illness and recidivism, treatment of mental illness in incarcerated populations is still essential due to the high rates of mental illnesses, the legal imperative, the possibility of decreasing institutional disciplinary burden, the opportunity to increase the effectiveness of rehabilitation programs in prison, and the potential to improve meaningful outcomes beyond recidivism following release.

In 2020, about 82.66 percent of the total population in the United States lived in cities and urban areas. As the United States was one of the earliest nations to industrialize, it has had a comparatively high rate of urbanization over the past two centuries. The urban population became larger than the rural population during the 1910s, and by the middle of the century it is expected that almost 90 percent of the population will live in an urban setting. Regional development of urbanization in the U.S. The United States began to urbanize on a larger scale in the 1830s, as technological advancements reduced the labor demand in agriculture, and as European migration began to rise. One major difference between early urbanization in the U.S. and other industrializing economies, such as the UK or Germany, was population distribution. Throughout the 1800s, the Northeastern U.S. became the most industrious and urban region of the country, as this was the main point of arrival for migrants. Disparities in industrialization and urbanization was a key contributor to the Union's victory in the Civil War, not only due to population sizes, but also through production capabilities and transport infrastructure. The Northeast's population reached an urban majority in the 1870s, whereas this did not occur in the South until the 1950s. As more people moved westward in the late 1800s, not only did their population growth increase, but the share of the urban population also rose, with an urban majority established in both the West and Midwest regions in the 1910s. The West would eventually become the most urbanized region in the 1960s, and over 90 percent of the West's population is urbanized today. Urbanization today New York City is the most populous city in the United States, with a population of 8.3 million, while California has the largest urban population of any state. California also has the highest urbanization rate, although the District of Columbia is considered 100 percent urban. Only four U.S. states still have a rural majority, these are Maine, Mississippi, Montana, and West Virginia.

About this dataset

Context

The dataset tabulates the population of Midwest City by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Midwest City across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 53.03% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Midwest City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Midwest City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Midwest City Population by Race & Ethnicity. You can refer the same here

A dataset listing Wisconsin cities by population for 2024.

Mean FSTT (millimeters and percentage), SD, and SE divided by sex.

Sample distribution by sex and age group.