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 July of 2025.

About this dataset

Context

The dataset tabulates the Midwest City 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 City 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 City was 58,086, a 0.15% increase year-by-year from 2022. Previously, in 2022, Midwest City population was 57,997, a decline of 0.29% compared to a population of 58,164 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Midwest City increased by 4,319. In this period, the peak population was 58,464 in the year 2020. 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 City is shown in this column.
• Year on Year Change: This column displays the change in Midwest City 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 City Population by Year. You can refer the same here

Graph and download economic data for Unemployed Persons in Midwest Census Region (LAURD920000000000004) from Jan 1976 to May 2025 about Midwest Census Region, household survey, unemployment, persons, and USA.

Graph and download economic data for Employed Persons in Midwest Census Region (LASRD920000000000005) from Jan 1976 to May 2025 about Midwest Census Region, household survey, employment, persons, and USA.

This data collection contains current population estimates and per capita money income for counties and minor civil divisions in each state. These estimates were developed to provide updates of the data elements in Federal Revenue Sharing allocations under the state and local Fiscal Assistance Act of 1972. Population estimates recorded in the dataset are for July 1, 1976, while per capita income estimates are for 1975. The units recorded in the data collection include counties, incorporated places, certain towns in New England, New York, and Wisconsin, and townships in other states. Certain Midwestern states may have active minor civil divisions in some counties, but not in others. In additional to these estimates, April 1, 1970, population and 1969 and 1975 per capita money income are included for each area. POPULATION AND INCOME ESTIMATES FOR THE UNITED STATES, 1969-1973 (ICPSR 0078) and POPULATION AND PER CAPITA INCOME ESTIMATES, 1969-1975 (ICPSR 7577) contain similar data for earlier years.

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.

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.

This statistic shows the change in the regional distribution of the U.S. population each decade from 1790 to 2021. In 2021, 17.2 percent of the population in the United States lived in the Northeast.

Milwaukee is the largest city in the state of Wisconsin and the fifth-largest city in the Midwestern United States. The county seat of Milwaukee County, it is on Lake Michigan's western shore. Ranked by estimated 2014 population, Milwaukee was the 31st largest city in the United States.[7] The city's estimated population in 2015 was 600,155.[8] Milwaukee is the main cultural and economic center of the Milwaukee metropolitan area. It is also part of the larger Milwaukee-Racine-Waukesha combined statistical area, which had an estimated population of 2,026,243 in the 2010 census. Milwaukee is also the second most densely populated metropolitan area in the Midwest, surpassed only by Chicago. View our Open Data Policy by selecting the link below, https://city.milwaukee.gov/ImageLibrary/Groups/cityOpenData/MilwaukeeOpenDataPolicy.pdf

One of the pervasive challenges in landscape genetics is detecting gene flow patterns within continuous populations of highly mobile wildlife. Understanding population genetic structure within a continuous population can give insights into social structure, movement across the landscape and contact between populations, which influence ecological interactions, reproductive dynamics, or pathogen transmission. We investigated the genetic structure of a large population of deer spanning the area of Wisconsin and Illinois, USA, affected by chronic wasting disease. We combined multi-scale investigation, landscape genetic techniques and spatial statistical modeling to address the complex questions of landscape factors influencing population structure. We sampled over 2,000 deer and used spatial autocorrelation and a spatial principal components analysis to describe the population genetic structure. We evaluated landscape effects on this pattern using a spatial auto-regressive model within a model selection framework to test alternative hypotheses about gene flow. We found high levels of genetic connectivity, with gradients of variation across the large continuous population of white-tailed deer. At the fine scale, spatial clustering of related animals was correlated with the amount and arrangement of forested habitat. At the broader scale, impediments to dispersal were important to shaping genetic connectivity within the population. We found significant barrier effects of individual state and interstate highways and rivers. Our results offer an important understanding of deer biology and movement that will help inform the management of this species in an area where over-abundance and disease spread are primary concerns.

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.

A dataset listing Wisconsin cities by population for 2024.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Female data was reported at 37.500 % in 2024. This records an increase from the previous number of 26.700 % for 2023. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Female data is updated yearly, averaging 19.700 % from Jun 2018 (Median) to 2024, with 7 observations. The data reached an all-time high of 37.500 % in 2024 and a record low of 13.100 % in 2018. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Female data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Labour Market – Table IN.GBA029: Periodic Labour Force Survey: Annual: Worker Population Ratio: Usual Status: by State: Education Level: Middle.

Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban data was reported at 54.200 % in 2024. This records an increase from the previous number of 51.700 % for 2023. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban data is updated yearly, averaging 48.400 % from Jun 2018 (Median) to 2024, with 7 observations. The data reached an all-time high of 54.200 % in 2024 and a record low of 44.100 % in 2020. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Labour Market – Table IN.GBA029: Periodic Labour Force Survey: Annual: Worker Population Ratio: Usual Status: by State: Education Level: Middle.

Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban: Male data was reported at 79.500 % in 2024. This records an increase from the previous number of 78.400 % for 2023. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban: Male data is updated yearly, averaging 75.900 % from Jun 2018 (Median) to 2024, with 7 observations. The data reached an all-time high of 79.500 % in 2024 and a record low of 71.100 % in 2020. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban: Male data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Labour Market – Table IN.GBA029: Periodic Labour Force Survey: Annual: Worker Population Ratio: Usual Status: by State: Education Level: Middle.

Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban: Female data was reported at 28.900 % in 2024. This records an increase from the previous number of 21.200 % for 2023. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban: Female data is updated yearly, averaging 19.200 % from Jun 2018 (Median) to 2024, with 7 observations. The data reached an all-time high of 28.900 % in 2024 and a record low of 14.900 % in 2018. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Urban: Female data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Labour Market – Table IN.GBA029: Periodic Labour Force Survey: Annual: Worker Population Ratio: Usual Status: by State: Education Level: Middle.

Worker Population Ratio: Usual Status: Education: Middle: West Bengal data was reported at 58.200 % in 2024. This records an increase from the previous number of 53.900 % for 2023. Worker Population Ratio: Usual Status: Education: Middle: West Bengal data is updated yearly, averaging 49.400 % from Jun 2018 (Median) to 2024, with 7 observations. The data reached an all-time high of 58.200 % in 2024 and a record low of 45.300 % in 2018. Worker Population Ratio: Usual Status: Education: Middle: West Bengal data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Labour Market – Table IN.GBA029: Periodic Labour Force Survey: Annual: Worker Population Ratio: Usual Status: by State: Education Level: Middle.

Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Rural data was reported at 59.700 % in 2024. This records an increase from the previous number of 54.800 % for 2023. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Rural data is updated yearly, averaging 49.700 % from Jun 2018 (Median) to 2024, with 7 observations. The data reached an all-time high of 59.700 % in 2024 and a record low of 45.200 % in 2018. Worker Population Ratio: Usual Status: Education: Middle: West Bengal: Rural data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Labour Market – Table IN.GBA029: Periodic Labour Force Survey: Annual: Worker Population Ratio: Usual Status: by State: Education Level: Middle.

The population name, site, year, densities, macroclimates, and microclimates are included. (XLSX)