Chart and table of population level and growth rate for the state of Colorado from 1900 to 2024.

This graph shows the population density in the federal state of Colorado from 1960 to 2018. In 2018, the population density of Colorado stood at ** residents per square mile of land area.

About this dataset

Context

The dataset tabulates the data for the Colorado population pyramid, which represents the Colorado 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 Colorado, is 26.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Colorado, is 22.6.
• Total dependency ratio for Colorado is 48.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Colorado is 4.4.

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

This layer presents the U.S. Census County (or County Equivalent) boundaries of the United States in the 50 states and the District of Columbia, sourced from 2023 Census TIGER/Line data and includes the estimated annual population total of each County.This layer is updated annually. The geography is sourced from U.S. Census Bureau 2023 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2023 estimated total population from the Esri demographics team.This ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

Resident Population in Colorado was 5957.49300 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in Colorado reached a record high of 5957.49300 in January of 2024 and a record low of 543.00000 in January of 1900. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in Colorado - last updated from the United States Federal Reserve on July of 2025.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Census tracts are small, relatively permanent geographic entities within counties (or the statistical equivalents of counties) delineated by a committee of local data users. Generally, census tracts have between 2,500 and 8,000 residents and boundaries that follow visible features. When first established, census tracts are to be as homogeneous as possible with respect to population characteristics, economic status, and living conditions. (www.census.gov)

These data contain selected census tract level demographic indicators (estimates) from the 2015-2019 American Community Survey representing the population density by square mile (land area).

Actual and predicted population data by gender and age from the Department of Local Affairs (DOLA), from 1990 to 2040.

Abstract

MEJ aims to create easy-to-use, publicly-available maps that paint a holistic picture of intersecting environmental, social, and health impacts experienced by communities across the US.

With guidance from the residents of impacted communities, MEJ combines environmental, public health, and demographic data into an indicator of vulnerability for communities in every state. MEJ’s goal is to fill an existing data gap for individual states without environmental justice mapping tools, and to provide a valuable tool for advocates, scholars, students, lawyers, and policy makers.

Methodology

The negative effects of pollution depend on a combination of vulnerability and exposure. People living in poverty, for example, are more likely to develop asthma or die due to air pollution. The method MEJ uses, following the method developed for CalEnviroScreen, reflects this in the two overall components of a census tract’s final “Cumulative EJ Impact”: population characteristics and pollution burden. The CalEnviroScreen methodology was developed through an intensive, multi-year effort to develop a science-backed, peer-reviewed tool to assess environmental justice in a holistic way, and has since been replicated by several other states.

CalEnviroScreen Methodology:

• Population characteristics are a combination of socioeconomic data (often referred to as the social determinants of health) and health data that together reflect a populations' vulnerability to pollutants. Pollution burden is a combination of direct exposure to a pollutant and environmental effects, which are adverse environmental conditions caused by pollutants, such as toxic waste sites or wastewater releases. Together, population characteristics and pollution burden help describe the disproportionate impact that environmental pollution has on different communities.

• Every indicator is ranked as a percentile from 0 to 100 and averaged with the others of the same component to form an overall score for that component. Each component score is then percentile ranked to create a component percentile. The Sensitive Populations component score, for example, is the average of a census tract’s Asthma, Low Birthweight Infants, and Heart Disease indicator percentiles, and the Sensitive Populations component percentile is the percentile rank of the Sensitive Populations score.

• The Population Characteristics score is the average of the Sensitive Populations component score and the Socioeconomic Factors component score. The Population Characteristics percentile is the percentile rank of the Population Characteristics score.

• The Pollution Burden score is the average of the Pollution Exposure component score and one half of the Environmental Effects component score (Environmental Effects may have a smaller effect on health outcomes than the indicators included the Exposures component so are weighted half as much as Exposures). The Pollution Burden percentile is the percentile rank of the Pollution Burden score.

• The Populaton Characteristics and Pollution Burden scores are then multiplied to find the final Cumulative EJ Impact score for a census tract, and then this final score is percentile-ranked to find a census tract's final Cumulative EJ Impact percentile.

• Census tracts with no population aren't given a Population Characteristics score.

• Census tracts with an indicator score of zero are assigned a percentile rank of zero. Percentile rank is then only calculated for those census tracts with a score above zero.

• Census tracts that are missing data for more than two indicators don't receive a final Cumulative EJ Impact ranking.

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We investigated population dynamics in chorus frogs (Pseudacris maculata) relative to extrinsic (air temperatures and snowpack) and intrinsic (density dependence) characteristics at 2 sites in Colorado, USA. We used capture--mark-recapture (cmr) data (i.e., 1 or 0, provided here) and a Bayesian model framework to assess our a priori hypotheses about interactions among covariates and chorus frog survival and population growth rates. Files include: Cameron_Lily_cmr_NOV2020.csv, Cameron_Matthews_cmr_NOV2020.csv, and Cameron_covariates_NOV2020.csv. Data associated with paper by Kissel et al. 2021.

These data contain selected census tract level demographic indicators (estimates) from the 2015-2019 American Community Survey representing the population density by square mile (land area).

A dataset listing Colorado cities by population for 2024.

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

U.S. Census Bureau QuickFacts statistics for Routt County, Colorado. 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 data for the Colorado Springs, CO population pyramid, which represents the Colorado Springs 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 Colorado Springs, CO, is 27.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Colorado Springs, CO, is 21.9.
• Total dependency ratio for Colorado Springs, CO is 49.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Colorado Springs, CO is 4.6.

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

Resident Population in Colorado Springs, CO (MSA) was 777.63400 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in Colorado Springs, CO (MSA) reached a record high of 777.63400 in January of 2024 and a record low of 540.11100 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in Colorado Springs, CO (MSA) - last updated from the United States Federal Reserve on July of 2025.

U.S. Census Bureau QuickFacts statistics for Boulder County, Colorado. 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 is a comment on the preliminary Congressional Commission redistricting map. Along with providing feedback on that map, it offers a draft alternative that better meets the criteria of the Colorado Constitution. As background, I participated in redistricting initiatives in South Bend, Indiana, in the mid-1980s and for Indiana legislative seats after the 1990 census. I didn’t engage with redistricting during the rest of my 20-year military career. After retiring, and while serving as Public Trustee for El Paso County, I participated in redistricting efforts at the county and city level. I also stood for El Paso County Clerk in 2010. I have lived in Colorado since 2000. The draft alternative map is created using Dave’s Redistricting App (DRA) and can be found at https://davesredistricting.org/join/346f297c-71d1-4443-9110-b92e3362b105. I used DRA because it was more user-friendly in that it allows selection by precinct and by city or town, while the tool provided by the commission seems to allow only selection by census block (or larger clusters). The two tools also use slightly different population estimates, but this will be resolved when the 2020 data are released in August. These comments acknowledge that any map created using estimated populations will need to change to account for the actual census data.

Description of Draft Alternative

    My process started by

identifying large-scale geographic communities of interest within Colorado: the Western Slope/mountain areas, the Eastern Plains, Colorado Springs/El Paso County, the North Front Range, and Denver Metro. Two smaller geographic communities of interest are Pueblo and the San Luis Valley—neither is nearly large enough to sustain a district and both are somewhat distinct from their neighboring communities of interest. A choice thus must be made about which other communities of interest to group them with. El Paso County is within 0.3% of the optimal population, so it is set as District 5. The true Western Slope is not large enough to sustain a district, even with the obvious addition of Jackson County. Rather than including the San Luis Valley with the Western Slope, the preliminary commission map extends the Western Slope district to include all of Fremont County (even Canon City, Florence, and Penrose), Clear Creek County, and some of northern Boulder County. The draft alternative District 3 instead adds the San Luis Valley, the Upper Arkansas Valley (Lake and Chaffee Counties, and the western part of Fremont County), Park and Teller Counties, and Custer County. The draft alternative District 4 is based on the Eastern Plains. In the south, this includes the rest of Fremont County (including Canon City), Pueblo, and the Lower Arkansas Valley. In the north, this includes all of Weld County, retaining it as an intact political subdivision. This is nearly enough population to form a complete district; it is rounded out by including the easternmost portions of Adams and Arapahoe Counties. All of Elbert County is in this district; none of Douglas County is. The draft alternative District 2 is placed in the North Front Range and includes Larimer, Boulder, Gilpin, and Clear Creek Counties. This is nearly enough population to form a complete district, so it is rounded out by adding Evergreen and the rest of Coal Creek in Jefferson County. The City and County of Denver (and the Arapahoe County enclave municipalities of Glendale and Holly Hills) forms the basis of draft alternative District 1. This is a bit too large to form a district, so small areas are shaved off into neighboring districts: DIA (mostly for compactness), Indian Creek, and part of Marston. This leaves three districts to place in suburban Denver. The draft alternative keeps Douglas County intact, as well as the city of Aurora, except for the part that extends into Douglas County. The map prioritizes the county over the city as a political subdivision. Draft alternative District 6, anchored in Douglas County, extends north into Arapahoe County to include suburbs like Centennial, Littleton, Englewood, Greenwood Village, and Cherry Hills Village. This is not enough population, so the district extends west into southern Jefferson County to include Columbine, Ken Caryl, and Dakota Ridge. The northwestern edge of this district would run along Deer Creek Road, Pleasant Park Road, and Kennedy Gulch Road. Draft alternative District 8, anchored in Aurora, includes the rest of western Arapahoe County and extends north into Adams County to include Commerce City, Brighton (except the part in Weld County), Thornton, and North Washington. In the draft alternative, this district includes a sliver of Northglenn east of Stonehocker Park. While this likely would be resolved when final population totals are released, this division of Northglenn is the most notable division of a city within a single county other than the required division of Denver. Draft alternative District 7 encompasses what is left: The City and County of Broomfield; Westminster, in both Jefferson and Adams Counties; Federal Heights, Sherrelwood, Welby, Twin Lakes, Berkley, and almost all of Northglenn in western Adams County; and Lakewood, Arvada, Golden, Wheat Ridge, Morrison, Indian Hills, Aspen Park, Genesee, and Kittredge in northern Jefferson County. The border with District 2 through the communities in the western portion of Jefferson County would likely be adjusted after final population totals are released.

Comparison of Maps

Precise Population Equality
    The preliminary commission

map has exact population equality. The draft alternative map has a variation of 0.6% (4,239 persons). Given that the maps are based on population estimates, and that I left it at the precinct and municipality level, this aspect of the preliminary map is premature to pinpoint. Once final population data are released, either map would need to be adjusted. It would be simple to tweak district boundaries to achieve any desired level of equality. That said, such precision is a bit of a fallacy: errors in the census data likely exceed the 0.6% in the draft map, the census data will be a year out of date when received, and relative district populations will fluctuate over the next 10 years. Both the “good-faith effort†and “as practicable†language leave room for a bit of variance in service of other goals. The need to “justify any variance†does not mean “no variance will be allowed.†For example, it may be better to maintain unity in a community of interest or political subdivision rather than separate part of it for additional precision. The major sticking point here is likely to be El Paso County: given how close it seems to be to the optimal district size, will it be worth it to divide the county or one of its neighbors to achieve precision? The same question would be likely to apply among the municipalities in Metro Denver.

Contiguity
    The draft alternative map

meets this requirement. The preliminary commission map violates the spirit if not the actual language of this requirement. While its districts are connected by land, the only way to travel to all parts of preliminary Districts 3 and 4 without leaving the districts would be on foot. There is no road connection between the parts of Boulder County that are in District 3 and the rest of that district in Grand County without leaving the district and passing through District 2 in either Gilpin or Larimer Counties. There also is no road connection between some of the southwestern portions of Mineral County and the rest of District 4 without passing through Archuleta or Hinsdale Counties in District 3.

Voting Rights Act
    The preliminary staff

analysis assumes it would be possible to create a majority-minority district; they are correct, it can be done via a noncompact district running from the west side of Denver up to Commerce City and Brighton and down to parts of northeastern Denver and northern Aurora. Such a district would go against criteria for compactness, political subdivisions, and even other definitions of communities of interest. Staff asserts that the election of Democratic candidates in this area suffices for VRA. Appendix B is opaque regarding the actual non-White or Hispanic population in each district, but I presume that if they had created a majority-minority district they would have said so. In the draft alternative map, District 8 (Aurora, Commerce City, Brighton, and Thornton) has a 39.6% minority population and District 1 (Denver) has a 34.9% minority population. The proposals are similar in meeting this criterion.

Communities of Interest
    Staff presented a long list

of communities of interest. While keeping all of these intact would be ideal, drawing a map requires compromises based on geography and population. Many communities of interest overlap with each other, especially at their edges. This difficulty points to a reason to focus on existing subdivisions (county, city, and town boundaries): those boundaries are stable and overlap with shared public policy concerns. The preliminary commission map chooses to group the San Luis Valley, as far upstream as Del Norte and Creede, with Pueblo and the Eastern Plains rather than with the Western Slope/Mountains. To balance the population numbers, the preliminary commission map thus had to reach east in northern and central Colorado. The commission includes Canon City and Florence

U.S. Census Bureau QuickFacts statistics for Edwards CDP, Colorado. 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.