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.

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

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 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.

A dataset listing Colorado cities by population for 2024.

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)

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.

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).

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

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.

This is a layer package created by the Colorado Parks and Wildlife GIS Unit in 2017 for distributing Colorado state parks and wildlife GIS data for public distribution.

This file was updated on February 7, 2017.

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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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.

U.S. Census Bureau QuickFacts statistics for Parker town, 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.

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.

Population Estimate, Total, Hispanic or Latino (5-year estimate) in Colorado County, TX was 6218.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Population Estimate, Total, Hispanic or Latino (5-year estimate) in Colorado County, TX reached a record high of 6463.00000 in January of 2022 and a record low of 4852.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Hispanic or Latino (5-year estimate) in Colorado County, TX - last updated from the United States Federal Reserve on July of 2025.

Resident Population in Colorado County, TX was 21.47500 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in Colorado County, TX reached a record high of 21.47500 in January of 2024 and a record low of 17.00000 in January of 1972. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in Colorado County, TX - last updated from the United States Federal Reserve on July of 2025.

U.S. Census Bureau QuickFacts statistics for Rio Grande 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.

Habitat restoration efforts to conserve wildlife species are often conducted along a range of local site conditions, with limited information available to gauge relative outcomes for habitat suitability among sites and identify those that may lead to the greatest returns on restoration investment. We leveraged existing resource selection function models to generate heatmaps of spatially varying habitat suitability improvement potential for the Gunnison Sage-grouse (Centrocercus minimus) based on a suite of habitat restoration actions deployed across crucial habitats within six remaining satellite populations. We first simulated expected change in model covariates (habitat features) from a suite of restoration actions (increasing sagebrush, herbaceous, or litter cover, non-sagebrush shrub management, installation of mesic improvement structures, and removal of invasive plants) to generate modified input layers for each. We then reran the original models using these modified layers and calculated the predicted change in habitat suitability across space. The resulting heatmaps identify areas with the greatest improvement potential for each restoration action to help guide strategic restoration planning for the species. This data release, for the Crawford satellite population, includes a set of 14 total raster files. These include: including 6 uncategorized heatmaps illustrating predicted change in Gunnison Sage-grouse habitat suitability across space following habitat restoration actions (either single or combined), 6 categorized heatmaps additionally showing areas where 1) new habitat was created, 2) non-habitat remained non-habitat despite management interventions, or 3) negative changes in suitability were observed, and 2 heatmaps illustrating predicted changes in suitability following new or worsening plant invasions (cheatgrass, represented by annual herbaceous, or pinyon-juniper). Habitat restorations vary by population depending on the reference model. We only ran management action simulations when the reference model had covariates suitable for the simulation (for example, pinyon juniper removal was only run when pinyon juniper was a covariate; See Saher and others 2022 for model details). CRb = Crawford Breeding CRs = Crawford Summer ahrb_rm = annual herbaceous removal (decrease in cover) ahrb_inv = annual herbaceous invasion (increase in cover) combo = combined actions mes_impr = mesic improvements (increase in area) pj_inv = pinyon-juniper invasion (increase in area) pj_rm = pinyon-juniper removal (decrease in area) sgc_incr = increase sagebrush cover C = CATEGORIZED Maps V = UNCATEGORIZED Maps X = INVASION (categorized) Maps