About this dataset

Context

The dataset tabulates the Madison 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 Madison 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 Madison was 280,305, a 1.04% increase year-by-year from 2022. Previously, in 2022, Madison population was 277,414, an increase of 1.80% compared to a population of 272,520 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Madison increased by 70,692. In this period, the peak population was 280,305 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Madison is shown in this column.
• Year on Year Change: This column displays the change in Madison 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 Madison Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Madison, WI population pyramid, which represents the Madison 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 Madison, WI, is 18.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Madison, WI, is 17.3.
• Total dependency ratio for Madison, WI is 36.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Madison, WI is 5.8.

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 Madison population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Madison for the selected age group is shown in the following column.
• Population (Female): The female population in the Madison for the selected age group is shown in the following column.
• Total Population: The total population of the Madison 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 Madison Population by Age. You can refer the same here

Chart and table of population level and growth rate for the Madison metro area from 1950 to 2025.

About this dataset

Context

The dataset tabulates the population of Madison by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Madison. The dataset can be utilized to understand the population distribution of Madison by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Madison. 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 Madison.

Key observations

Largest age group (population): Male # 20-24 years (20,793) | Female # 20-24 years (20,520). 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 Madison population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Madison is shown in the following column.
• Population (Female): The female population in the Madison 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 Madison 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 Madison Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Madison 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 Madison. The dataset can be utilized to understand the population distribution of Madison by age. For example, using this dataset, we can identify the largest age group in Madison.

Key observations

The largest age group in Madison, WI was for the group of age 20 to 24 years years with a population of 41,313 (14.99%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Madison, WI was the 85 years and over years with a population of 3,815 (1.38%). 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 Madison is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Madison 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 Madison Population by Age. You can refer the same here

A dataset listing Wisconsin cities by population for 2024.

About this dataset

Context

The dataset tabulates the data for the Madison, WI population pyramid, which represents the Madison population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 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 Madison, WI, is 19.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Madison, WI, is 16.4.
• Total dependency ratio for Madison, WI is 35.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Madison, WI is 6.1.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Madison population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Madison for the selected age group is shown in the following column.
• Population (Female): The female population in the Madison for the selected age group is shown in the following column.
• Total Population: The total population of the Madison 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 Madison Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Madison, WI population pyramid, which represents the Madison population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Madison, WI, is 18.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Madison, WI, is 17.4.
• Total dependency ratio for Madison, WI is 36.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Madison, WI is 5.8.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Madison population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Madison for the selected age group is shown in the following column.
• Population (Female): The female population in the Madison for the selected age group is shown in the following column.
• Total Population: The total population of the Madison 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 Madison Population by Age. You can refer the same here

NOTE FOR USERS: For local-level projections, such as at a township and municipal-level, please use the original “2018 Series”. This is the data CMAP recommends be used for planning, grant applications, and other official purposes. CMAP is confident in the updated regional-level population projections; however, the projections for township and municipal level populations appear less reflective of current trends in nearterm population growth. Further refinements of the local forecasts are likely needed.CONTENTS:Filename: ONTO2050OriginalForecastData2018.zipTitle: Socioeconomic Forecast Data, 2018 SeriesThis .zip file contains data associated with the original ON TO 2050 forecast, adopted in October 2018. Includes:Excel file of regional projections of population and employment to the year 2050:CMAP_RegionalReferenceForecast_2015adj.xlsx (94kb)Excel file of local (county, municipality, Chicago community area) projections of household population and employment to the year 2050: ONTO2050LAAresults20181010.xlsx (291kb)GIS shapefile of projected local area allocations to the year 2050 by Local Allocation Zone (LAZ): CMAP_ONTO2050_ForecastByLAZ_20181010.shp (19.7mb)Filename: ONTO2050OriginalForecastDocumentation2018.zipTitle: Socioeconomic Forecast Documentation, 2018 SeriesThis .zip file contains PDF documentation of the original ON TO 2050 forecast, adopted in October 2018. Includes:Louis Berger forecast technical report (2016): CMAPSocioeconomicForecastFinal-Report04Nov2016.pdf (2.3mb)Louis Berger addendum (2017): CMAPSocioeconomicForecastRevisionAddendum20Jun2017.pdf (0.6mb)ON TO 2050 Forecast appendix (2018): ONTO2050appendixSocioeconomicForecast10Oct2018.pdf (2.6mb)Filename: Socioeconomic-Forecast-Appendix-Final-October-2022.pdfTitle: Socioeconomic Forecast Appendix, 2022 SeriesDocumentation & results for the updated socioeconomic forecast accompanying the ON TO 2050 plan update, adopted October 2022. PDF, 2.7mbFilename: RegionalDemographicForecast_TechnicalReport_202210.pdfTitle: 2050 Regional Demographic Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional demographic forecast, developed in coordination with the Applied Population Lab at the University of Wisconsin, Madison. PDF, 1.7mbFilename: RegionalEmpForecast_TechnicalReport_202112.pdfTitle: 2050 Regional Employment Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional employment forecast, developed by EBP and Moody's Analytics. PDF, 0.8mbFilename: CMAPRegionalForecastONTO2050update202209.xlsxTitle: Regional Projections, 2022 SeriesProjections of population and employment to the year 2050, produced for the ON TO 2050 plan update adopted October 2022. 60kbFilename: CMAPLocalForecastONTO2050update202210.xlsxTitle: County and Municipal Projections, October 2022 (2022 Series)Projections of population and employment to the year 2050 at the county and municipal level, produced for the ON TO 2050 plan update adopted October 2022.

Human-nature connection (HNC) is a concept derived from investigating the formulation and extent of an individual’s identification with the natural world. This relationship is often characterized as an emotional bond to nature that develops from the contextualized, physical interactions of an individual, beginning in childhood. This outcome presents complexity in evaluating the development of HNC but suggests optimism in the pathways for enhancing lifelong HNC. As urban populations increase, there is a growing recognition worldwide of the potential for urban green space to cultivate HNC and thus shape the environmental identity of urban residents. The results of an online survey of 560 visitors to three community parks (managed primarily to provide a variety of physical, social and cultural opportunities) and three conservation parks (managed primarily to protect native plants and wildlife) in Madison, Wisconsin, USA, were used to investigate HNC. Linear mixed effects models evaluated visitors’ HNC as a function of their (1) literacy and sentiment about wildlife species, (2) park experience, (3) number and frequency of nine childhood and adult recreation experiences, and (4) demographics. Across the park response groups, the number and frequency of childhood and adult recreation experiences was significantly associated with HNC, and this positive association persisted in multiple recreation activities. Furthermore, species literacy and sentiment, visiting a park for 'Nature', and frequent and extended visitation also was significantly associated with HNC by park type. Our research demonstrates the importance of lifelong recreation experiences in the development and enhancement of HNC and provides evidence for differences in the expression of HNC associated with particular attributes of urban park visitors and their views of wildlife. Methods Methodology Study Area Madison has a population of approximately 270,000 residents, covers approximately 260 km2, and is located in south central Wisconsin, USA (US Census Bureau, 2022). Madison is currently the fastest growing city in Wisconsin and is home to the state capital and the University of Wisconsin-Madison (US Census Bureau, 2022). The study area is within the Yahara Watershed, now largely dominated by agricultural and urban land cover, and experiences four distinct seasons (Carpenter et al., 2007, Wisconsin State Climatology Office, 2010).
The six selected parks were based on their classification as a community or conservation park; an estimated visitation rate; a central, western, or eastern location in Madison; and approval from the Madison Parks Division of the City of Madison (Figure 1). The size of the community parks ranged from 19.07 ha to 101.50 ha, and the size of the conservation parks ranged from 24.39 ha to 39.17 ha. The parks can be broadly described as mixed forest ecosystems with open grass areas and low levels of pavement and structural development. Conservation parks contain native grasslands whereas community parks may contain native grasslands and/or mowed turf. By definition, conservation parks are managed to protect native plant and wildlife species, resulting in the inclusion of vegetation and management practices supporting that objective (City of Madison Parks Division, 2022). As a result of their conservation status, recreation therein is limited to physical activities such as hiking and snowshoeing and nature-based activities such as watching birds / wildlife and photography. Dogs are not allowed in conservation parks. Community parks are designed to provide a variety of physical, social, and cultural opportunities, including athletic fields and courts, playgrounds, and picnic shelters. Community parks allow dogs that are leashed and licensed (City of Madison Parks Division, 2022).
Study Population and Survey We conducted an online survey to park visitors in three conservation parks and three community parks in Madison. Our research design was approved by the University of Wisconsin Education and Social/Behavioral Science Institutional Review Board as exempted research. We developed the survey in Google Forms and administered it in the parks using a park-specific quick response (QR) code printed either (1) on posters that were statically accessible to park visitors throughout the study period or (2) on postcards dynamically handed to park visitors at selected times during the study period. The posters were visible outdoors in all six parks from 2021-09-04 through 2021-10-24 (high-use fall period) and from 2022-06-09 through 2022-08-24 (high-use summer period). Postcards were distributed in the six parks on four Saturdays in both September and July from 10.00 to 12.00. These dates and times were selected to coincide with the days and times with the highest number of park visitors, the availability of surveyors, and the approval of the Madison City Parks Division. Each postcard had a unique three-digit number required to access the online survey. Adults (18 years or older) were approached by the surveyor (lead author and/or student assistants trained in research ethics and project specifics) and invited to participate. After verbally agreeing to participate (standard approach for exempted research), each potential respondent was asked three questions to check for nonresponse bias: (1) zip code, (2) year of birth, and (3) main reason for visitation. For poster and postcard respondents who continued on to take the online survey, the first question was a screening for informed consent, with only those who actively acknowledged consent continuing into the study’s content questions.
The online survey consisted of 30 questions, grouped into four categories: (1) literacy and sentiment about wildlife species, (2) recreation and park experience, (3) HNC, and (4) demographics. For species literacy and sentiment, respondents were asked questions evaluating (1) the correct photographic identification of six mammal species, each considered a generalist and likely present in the study parks, and (2) visitor sentiment about each species (Figure 2). For recreation activity, respondents were asked questions about (1) the number and frequency of childhood and adult experiences with bird / wildlife watching, camping, canoeing / kayaking, fishing, gardening, hiking, hunting, nature photography, and picnicking; (2) the main reason for visitation; (3) prior visitation; (4) length of visit; and (5) distance of residence to the park. For HNC, the abbreviated six-item short form of the Nature Relatedness Scale (NR-6) was used, with four statements from NR-Self (1-4) and two statements from NR-Experience (5 and 6):

My connection to nature and the environment is a part of my spirituality. My relationship to nature is an important part of who I am. I feel very connected to all living things and the earth. I always think about how my actions affect the environment. My ideal vacation spot would be a remote, wilderness area. I take notice of wildlife wherever I am.

Demographic questions included age group, educational level, and gender. The survey responses were in the form of a short answer (only identification of species), exclusionary checkboxes, or a 5-point Likert scale response (“Never” to “Very Often” or “Disagree Strongly” to “Agree Strongly”). Wildlife literacy and sentiment questions were accompanied by a corresponding species-specific color photo (Figure 2). Species sentiment was measured by species-specific exclusionary responses: 'I am happy they live at the park’, ‘I think they are important for the park ecosystem', 'I am concerned about their impact on human safety', 'I am concerned that they bring disease', 'I think they are a nuisance', or 'I am unsure how I feel or do not care’. We piloted the survey with a focus group before administering it in the six parks to identify possible issues such as unclear language or challenges in viewing on mobile devices and adjusted our final survey accordingly. All survey responses were anonymous.
Analysis Initial exploratory analysis included a random effect for park type (community and conservation) and a random effect and interaction term for survey type (postcard and poster). The type of park was a significant factor, and the models afterwards were separated into two model sets, one for community park visitors and one for conservation park visitors. A random effect was included for the parks sampled (3 community parks or 3 conservation parks) within the corresponding model set. The type of survey was not a significant random effect, and the data of each type of survey were combined based on the type of park. No differences were found between the potential and actual respondents by postcard with respect to zip code, year of birth, and main reason for visitation. This suggests that nonresponse bias was unlikely.
Mixed-effects linear models were applied using the ‘lme’ function in the 'nlme' package (v3. 1-152; Pinheiro et al., 2021) of the R software, version 4.2.1 (R Core Team, 2019). As our work forwards investigation on the specific factors associated with HNC (using the mean NR-6 score of a respondent) rather than the conventional application of NR-6 as a predictor of pro-environmental behavior or self-assessed well-being, we evaluated factors independently rather than collectively. Separate models were developed for community and conservation park survey data to evaluate HNC as a function of factors within four categories: (1) species literacy and positive species sentiment; (2) number, frequency, and type of outdoor recreation activities of childhood and adulthood; (3) main reason for visitation, prior visitation, length of visit, and distance of residence to the park; and (4) demographic factors (age category, educational level, and gender). Species literacy was calculated as the average of responses recorded in six

This README file was generated on 2023-08-30 by Jennifer Merems. GENERAL INFORMATION

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1. Title of Dataset: Fear before food: Scale-dependence in elk habitat selection
2. Author Information: A. Principal Investigator Contact Information Name: Timothy Van Deelen Institution: University of Wisconsin-Madison: Madison, WI USA Email: trvandeelen@wisc.edu

B. Corresponding Author Contact Information Name: Jennifer Merems Institution: University of Wisconsin-Madison: Madison, WI USA Email: merems@wisc.edu

1. Date of data collection (single date, range, approximate date): 2022-2023
2. Geographic location of data collection: Wisconsin, USA
3. Information about funding source that supported the collection of the data: Wisconsin Department of Natural Resources

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SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: Coordinates could not be given because location information for elk in Wisconsin is protected information. Coordinates are not needed to run the resource s...

Premise of the study: Shifts in abiotic factors can affect many plant traits, including floral volatiles. This study examines the response of floral volatiles to water availability, and whether phenotypic plasticity to water differs among populations. Furthermore, it investigates genetic differentiation in floral volatiles, determines the effect of temperature on phenotypic plasticity to water, and assesses temporal variation in floral scent emission between day and evening, since pollinator visitation differs at those times.

Methods: Rocky Mountain columbine plants (Aquilegia coerulea), started from seeds collected in three wild populations in Colorado, Utah, and Arizona, were grown under two water treatments in a greenhouse in Madison, Wisconsin, USA. One population was also grown under the two water treatments, at two temperatures. Air samples were collected from enclosed flowers using dynamic headspace methods and floral volatiles were identified and quantified by gas chromatography (GC) with mass spectrometry (MS) detection.

Key Results: Emission of three floral volatiles increased in the wetter environment, indicating phenotypic plasticity. The response of six floral volatiles to water differed among populations, suggesting genetic differentiation in phenotypic plasticity. Five floral volatiles varied among populations, and emission of most floral volatiles was greater during the day.

Conclusions: Phenotypic plasticity to water permits a quick response of floral volatiles in changing environments. The genetic differentiation in phenotypic plasticity suggests that phenotypic plasticity can evolve but complicates predictions of the effects of environmental changes on a plant and its pollinators.

About this dataset

Context

The dataset tabulates the Madison town 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 Madison town. The dataset can be utilized to understand the population distribution of Madison town by age. For example, using this dataset, we can identify the largest age group in Madison town.

Key observations

The largest age group in Madison Town, Wisconsin was for the group of age 20 to 24 years years with a population of 881 (14.19%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Madison Town, Wisconsin was the 80 to 84 years years with a population of 25 (0.40%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Madison town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Madison town 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 Madison town Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Madison town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Madison town. The dataset can be utilized to understand the population distribution of Madison town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Madison town. 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 Madison town.

Key observations

Largest age group (population): Male # 20-24 years (512) | Female # 25-29 years (414). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Madison town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Madison town is shown in the following column.
• Population (Female): The female population in the Madison town 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 Madison town 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 Madison town Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Madison Town, Wisconsin population pyramid, which represents the Madison town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Madison Town, Wisconsin, is 16.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Madison Town, Wisconsin, is 14.2.
• Total dependency ratio for Madison Town, Wisconsin is 30.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Madison Town, Wisconsin is 7.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Madison town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Madison town for the selected age group is shown in the following column.
• Population (Female): The female population in the Madison town for the selected age group is shown in the following column.
• Total Population: The total population of the Madison town 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 Madison town Population by Age. You can refer the same here

Biologists aim to explain patterns of growth, reproduction, and ageing that characterize life histories, yet we are just beginning to understand the proximate mechanisms that generate this diversity. Existing research in this area has focused on telomeres but has generally overlooked the telomere’s most direct mediator, the shelterin protein complex. Shelterin proteins physically interact with the telomere to shape its shortening and repair. They also regulate metabolism and immune function, suggesting a potential role in life history variation in the wild. However, research on shelterin proteins is uncommon outside of biomolecular work. Intraspecific analyses can play an important role in resolving these unknowns because they reveal subtle variation in life history within and among populations. Here, we assessed ecogeographic variation in shelterin protein abundance across eight populations of tree swallow (Tachycineta bicolor) with previously documented variation in environmental and life history traits. Using blood gene expression of four shelterin proteins in 12-day old nestlings, we tested the hypothesis that shelterin protein gene expression varies latitudinally and in relation to both telomere length and life history. Shelterin protein gene expression differed among populations and tracked non-linear variation in latitude: nestlings from mid-latitudes expressed nearly double the shelterin mRNA on average than those at more northern and southern sites. However, telomere length was not significantly related to latitude. We next assessed whether telomere length and shelterin protein gene expression correlate with 12-day old body mass and wing length, two proxies of nestling growth linked to future fecundity and survival. We found that body mass and wing length correlated more strongly (and significantly) with shelterin protein gene expression than with telomere length. These results highlight telomere regulatory shelterin proteins as potential mediators of life history variation among populations. Together with existing research linking shelterin proteins and life history variation within populations, these ecogeographic patterns underscore the need for continued integration of ecology, evolution, and telomere biology, which together will advance understanding of the drivers of life history variation in nature. Methods Study populations: Data were collected from 8 populations in the eastern United States, spanning nearly 10 degrees of latitude (Table 1, Fig 2A): Ithaca, New York (42.28°N, 76.29°W); Amherst, Massachusetts (42.22°N, 72.31°W); Linesville, Pennsylvania (41.65°N, 80.43°W); Bloomington, Indiana (39.17°N, 86.53°W); Lexington, Kentucky (38.11°N, 84.49°W); Knoxville, Tennessee (35.90°N, 83.96°W); Davidson, North Carolina (35.53°N, 80.88°W); and Santee, South Carolina (33.49°N, 80.36°W). These populations do not represent the entire breeding range of this species and in particular, do not extend to the northern edge in Canada and Alaska. All methods were approved by institutional IACUCs and conducted with appropriate state and federal permits. Sampling of nestlings: Nest boxes were monitored for hatch dates, but in cases where hatch dates were missed (e.g., due to weather or COVID-related staffing shortages), hatch dates were estimated using existing growth curves (McCarty, 2001; Wolf et al., 2021) and accounted for in all statistical analyses. Data from multiple populations shows that the average peak of postnatal growth occurs around 6-days old (McCarty, 2001; Wolf et al., 2021). Growth then slows and plateaus near adult size by 12-days old, just as feather development accelerates. We targeted 12-day old nestlings because they have just completed the rapid period of postnatal growth. Many studies therefore use morphological data at this critical time period as a proxy of nestling growth (Gebhardt-Henrich & Richner, 1998; Haywood & Perrins, 1992; Magrath, 1991; Martin et al., 2018; McCarty, 2001). Population variation in growth rates occurs primarily after peak growth but does not map neatly onto latitude, at least not in the northern (historical) range where previous research has been focused (Ardia, 2006; McCarty, 2001). We sampled nestlings at 12.03 ± 0.01-days old (hatch day = day 1, range = 10 – 14 days). We sampled ~30 nests per population (Table 1), though logistical constraints prevented collection of RNA in Kentucky. Upon arrival at each nest, we immediately collected whole blood from the brachial vein of 2-3 nestlings per nest (≤ 200 µl, below the maximum suggested volume based on body mass; Gaunt et al., 1997), and we avoided obvious runts with atypical growth. We collected blood in separate tubes for DNA and RNA analyses. We banded nestlings with a USGS band and weighed them to the nearest 0.1g. We also measured flattened wing length using a wing ruler. We stored blood on ice or dry ice in the field, and later stored it at -80°C. Due to limited budgets, we made the decision a priori to conduct laboratory analyses for a single nestling per nest. When possible, we selected the nestling with the median mass. If the median-massed nestling was not bled or failed to produce a sufficient blood sample, we selected the nestling with the closest mass to the median. In nests with even brood sizes, we randomly selected one of the two nestlings with median mass for telomere and gene expression analyses. In all states except Indiana, telomere length and gene expression data come from the same individual. qPCR for Telomere length: We extracted DNA from whole blood (following Wolf et al., 2022) and used primers telc and telg (adapted from Cawthon, 2009) to quantify telomere length relative to the single copy gene GAPDH. Samples were run in triplicate, and mean values were used to calculate the T/S ratio of telomere repeat copy number (T) to our single gene copy number (S) using the formula: 2-∆∆Ct, where ∆∆Ct = (Ct telomere – Ct GAPDH) reference – (Ct telomere – Ct GAPDH) sample. The intraclass correlation coefficient (ICC) for intraplate repeatability was 0.951 ± 0.03 (95% CI = 0.944, 0.957) for GAPDH Ct values and 0.926 ± 0.09 (95% CI = 0.916, 0.935) for telomere Ct values. The ICCs for interplate repeatability were 0.96 ± 0.03 (95% CI = 0.87, 0.98) for GAPDH Ct values, 0.89 ± 0.06 (95% CI = 0.73, 0.95) for telomere Ct values, and 0.79 ± 0.10 (95% CI: 0.54 - 0.90) for the T/S ratio (based on 2-∆Ct values). Plates (n = 13) were balanced by population, sex, relative date of sampling within each population, and brood size. Nestling Sexing Protocol: Nestlings were molecularly sexed using DNA following established methods (Griffiths et al., 1998; Wolf et al., 2022). Shelterin Protein Primer Design: Shelterin proteins are relatively conserved across taxa (de Lange, 2018; Myler et al., 2021) and earlier work has identified at least four shelterin proteins in the chicken (De Rycker et al., 2003; Konrad et al., 1999; Tan et al., 2003; Wei & Price, 2004). Our shelterin protein primer sets were developed using the tree swallow transcriptome (accession #GSE126210; Bentz et al., 2019). TRF2 exhibits multiple variants in passerines, and a BLAST search confirmed that our primer set targets TRF2 in closely related barn swallows (Hirundo rustico). TPP1 and POT1 each have a single transcript in adult tree swallows that is highly expressed across tissues, and BLAST searches confirmed that our primer sets targeted TPP1 and POT1 genes, respectively, in multiple bird species. We also designed primers for RAP1 based on tree swallow transcripts of TRF2IP (TRF2-interacting protein), a common alias for RAP1. However, this study omits TRF1 due to negligible expression in nestling blood, and TIN2 because we could not confidently identify the passerine sequence for TIN2. Thus, altogether we quantified gene expression for four key components of the shelterin complex: TRF2, RAP1, TPP1, and POT1 (primer sequences in Table S1). qPCR for Shelterin Protein Gene Expression: We extracted RNA using a phenol-chloroform-based Trizol method (Invitrogen, Carlsbad, CA) with PhaseLock tubes (5PRIME, #2302830). We synthesized cDNA using 1µg RNA and Superscript III reverse transcriptase (Invitrogen), treated with DNAase (Promega, Madison, WI) and RNase inhibitor (RNAsin N2111, Promega). For each gene of interest, we used the 2-∆∆Ct method of quantification (Livak & Schmittgen, 2001), in which expression is normalized to the geometric mean Ct of two reference genes for each sample (Vandesompele et al., 2002), and relative to a calibrator sample on each plate. Reference genes correct for technical variation in cDNA quantity across samples, and as such, must (i) be highly expressed, (ii) exhibit low variability among samples, and (iii) show no significant variation among biological categories of interest. Our reference genes were PPIA (peptidylprolyl isomerase A; Virgin & Rosvall, 2018) and MRPS25 (Mitochondrial Ribosomal Protein S25; Woodruff et al., 2022). Preliminary work showed that New York samples exhibited markedly higher gene expression of these and a third reference gene (GAPDH). This violates assumption (iii) of the 2-∆∆CT method, and we therefore had to omit New York gene expression data. The remaining six populations exhibited limited among-population variation in reference gene expression (non-significant state differences or ≤ 0.5 Ct of the study-wide average). Samples were run in triplicate alongside No Template Controls (NTCs), using PerfeCta SYBR Green FastMix with low ROX (Quanta Biosciences, Gaithersburg MD) on 384-well plates using an ABI Quantstudio 5 machine with Quantstudio Design & Analysis software (v1.4.3, Thermo Fisher Scientific, Foster City, CA). Each well included 3µL of cDNA diluted 1:50 (or 3µL water, for NTCs) and primers diluted to 0.3µM in a total volume of 10µL. All reactions use the following thermal profile: 10 min at 95°, followed by 40 cycles

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.

42 populations totalling 517 individuals of Ixodes scapularis from different spatial locations were sampled and sequenced to study the neutral variation, population structure, ancestral admixture, genetic connectivity, and landscape influences on gene flow. We began with genomic data preprocessing, variant calling, variant filtering and concordance check. Then we used the finalized dataset in variant call format (VCF) and spatial locations to conduct genetic distance statistics, isolation by distance modeling and calculate summary statstics. Further we used VCF and sample metadata to conduct Pincipal Component analysis and clustering analysis for understanding population structure and ancestral admixture. To understand region-wide gene flow connectivity, we conducted effective migration surface analysis and graph network analyses to visualize dispersal route and extent. Lastly, we processed landscape and ecological data to conduct landscape genomic analyses to understand the impact of l..., Table of ContentsPart 1. Genomic Data Preprocessing, Variant Calling, Variant filtering and concordance1A. Demultiplex and assign ID - PROCESS_RADTAGS in STACKS 2.64Â 1B. Adapter removal using CUTADAPT 3.51C. Read trimming with Trimmomatic 0.391D. Read mapping with reference genome using bwa-mem 0.7.17-r11881E. Alignment file conversion, and then resequencing bam merge - SAMTOOLS 1.16.1Â 1F. Variant calling - GATK 4.4.0.01G. Sample missingness filtering - PLINK2 v2.00a3 SSE4.2Â 1H. Variant filtering - GATK 4.4.0.01I. Variant missingness filtering - VCFTOOLS 0.1.17Â 1J. Genotype imputation in BEAGLE v5.41K. Genotype concordance between non-amplified and amplified ticks - GATKÂ Part 2. Genetic distance statistics, Isolation by distance and Summary stats2A. Genetic distance via ADAGENET 2.1.10, GRAPH4LG 1.8.0, and MMOD 1.3.32B. Isolation by distance and mantel correlogram2C. Expected heterozygosity and nucleotide diversity via POPULATIONS in STACKS 2.64Â 2D. Tajima's D via DADI2E. Rarefied priv..., , # Population structure, ancestral admixture, gene flow, and landscape association of Blacklegged ticks during range expansion in the Midwestern U.S.

Main Author Information Name: Dahn-young Dong ORCID:0000-0001-6284-2738 Institution: University of Wisconsin - Madison Email: ddong22@wisc.edu

Co-Author Information Name: Sean Schoville ORCID:0000-0001-7364-434X Institution: University of Wisconsin - Madison Email: sean.schoville@wisc.edu

Date of data collection: from 2021 to 2023

Geographic location of data collection: See Metadata.xlsx

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: NA

Links to publications that cite or use the data: Genetic and landscape connectivity of Blacklegged ticks during range expansion in the Midwestern U.S. In Review at Molecular Ecology

Links to other publicly accessible locations of the data: NA

Links/relationships to ancillary data sets: NA

Was data...

Characterizing how frequently, and at what life stages and spatial scales, dispersal occurs can be difficult, especially for species with cryptic juvenile periods and long reproductive life spans. Using a combination of mark–recapture information, microsatellite genetic data, and demographic simulations, we characterize natal and breeding dispersal patterns in the long-lived, slow-maturing, and endangered Blanding’s turtle (Emydoidea blandingii), focusing on nesting females. We captured and genotyped 310 individual Blanding’s turtles (including 220 nesting females) in a central Wisconsin population from 2010 to 2013, with additional information on movements among 3 focal nesting areas within this population available from carapace-marking conducted from 2001 to 2009. Mark–recapture analyses indicated that dispersal among the 3 focal nesting areas was infrequent (<0.03 annual probability). Dyads of females with inferred first-order relationships were more likely to be found within the same nesting area than split between areas, and the proportion of related dyads declined with increasing distance among nesting areas. The observed distribution of related dyads for nesting females was consistent with a probability of natal dispersal at first breeding between nearby nesting areas of approximately 0.1 based on demographic simulations. Our simulation-based estimates of infrequent female dispersal were corroborated by significant spatial genetic autocorrelation among nesting females at scales of <500 m. Nevertheless, a lack of spatial genetic autocorrelation among non-nesting turtles (males and females) suggested extensive local connectivity, possibly mediated by male movements or long-distance movements made by females between terrestrial nesting areas and aquatic habitats. We show here that coupling genetic and demographic information with simulations of individual-based population models can be an effective approach for untangling the contributions of natal and breeding dispersal to spatial ecology.

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 Madison. 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 2012 and 2022, 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 Madison, the median household income for the households where the householder is White increased by $6,072(8.48%), between 2012 and 2022. The median household income, in 2022 inflation-adjusted dollars, was $71,577 in 2012 and $77,649 in 2022.
• Black or African American: In Madison, the median household income for the households where the householder is Black or African American increased by $10,710(32.04%), between 2012 and 2022. The median household income, in 2022 inflation-adjusted dollars, was $33,431 in 2012 and $44,141 in 2022.
• 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

https://i.neilsberg.com/ch/madison-wi-median-household-income-by-race-trends.jpeg" alt="Madison, WI median household income trends across races (2012-2022, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-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 Madison.
• 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
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-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 Madison median household income by race. You can refer the same here