A dataset listing Colorado cities by population for 2024.

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.

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).

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

This feature class contains county boundaries for all 64 Colorado counties and 2010 US Census attributes data describing the population within each county.

Feature class representing retail alcohol outlet density at the Health Statistics Region level developed directly from address information from liquor licensee lists that were obtained from the Colorado Department of Revenue-Liquor Enforcement Division (DOR-LED). This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment (CDPHE), including the Alcohol Outlet Density StoryMap. CDPHE nor DOR-LED are responsible for data products made using this publicly available data. It should be stated that neither agency is acting as an active data steward of this map service/geospatial data layer at this point in time. This dataset is representative of Colorado licensing data gathered in January 2024. This data file contains the following attributes:Health Statistics RegionCOUNTIES (Counties included in Health Statistics Region)RegionNewYear Total Population (Total population of the HSR)Average County Population (Average county population among counties in the HSRAverage Outlet Count (Average number of retail alcohol outlets among the counties in the HSR)Total Outlet Count (Total number of retail alcohol outlets in the HSR)Average Rate of Outlets per 10,000 Residents (Average rate of alcohol outlets per 10,000 residents among the counties in the HSR)

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 Dove Creek satellite population, includes a set of 8 total raster files. These include: 4 uncategorized heatmaps illustrating predicted change in Gunnison Sage-grouse habitat suitability across space following habitat restoration actions (either single or combined), and 4 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. 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). Note that Saher and others (2022) indicated that there was insufficient Gunnison sage-grouse use data to develop a summer model for that population. Consequently, we do not present the effect of management actions on sage-grouse habitat for that population during the summer season. Raster file names are coded as follows: DCb = Dove Creek Breeding ahrb_rm = annual herbaceous removal (decrease in cover) combo = combined actions mes_impr = mesic improvements (increase in area) pos_incr = increase percent other sagebrush (non-big sagebrush) C = CATEGORIZED Maps V = UNCATEGORIZED Maps

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 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 map of human habitation was developed, following a modification of Schumacher et al. (2000), by incorporating 2000 U.S Census Data and land ownership. The 2000 U.S. Census Block data and ownership map of the western United States were used to correct the population density for uninhabited public lands. All census blocks in the western United States were merged into one shapefile which was then clipped to contain only those areas found on private or indian reservation lands because human habitation on federal land is negligible. The area (ha) for each corrected polygon was calculated and the 2000 census block data table was joined to the shapefile. In a new field, population density (individuals/ha) corrected for public land in census blocks was calculated . SHAPEGRID in ARC/INFO was used to convert population density values to grid with 90m resolution.

These data represent the predicted (modeled) prevalence of Frequent Mental Distress among adults (Age 18+) for each census tract in Colorado. Frequent Mental Distress is defined as experiencing more than 14 mentally unhealthy days within the past 30 days in which mental health was "not good." Health conditions for measuring mental health include stress, depression, and problems with emotions.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

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 Poncha Pass satellite population, includes a set of 13 total raster files. These include: 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 1 heatmap illustrating predicted changes in suitability following new or worsening plant invasions (cheatgrass, represented by annual herbaceous). 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). Raster file names are coded as follows: PPb = Poncha Pass Breeding PPs = Poncha Pass 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) pns_decr = decrease non-sagebrush shrub cover pns_incr = increase non-sagebrush shrub cover C = CATEGORIZED Maps V = UNCATEGORIZED Maps X = INVASION (categorized) Maps

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 includes a set of 76 total raster files. This includes: 38 heatmaps illustrating predicted change in Gunnison Sage-grouse habitat suitability across space following habitat restoration actions (either single or combined), 32 categorized heatmaps 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 6 heatmaps illustrating predicted changes in suitability following new or worsening plant invasions (for example, cheatgrass, represented by annual herbaceous, or pinyon-juniper). Raster file names are coded by the name of the satellite population, the season the model is representing, the restoration action applied, and whether the maps were categorized. The codes are listed below: CSb = Cerro Summit-Cimarron-Sims Mesa Breeding CSs = Cerro Summit-Cimarron-Sims Mesa Summer CRb = Crawford Breeding CRs = Crawford Summer DCb = Dove Creek Breeding PMb = Pinon Mesa Breeding PMs = Pinon Mesa Summer PPb = Poncha Pass Breeding PPs = Poncha Pass Summer SMb = San Miguel Breeding SMs = San Miguel Summer ahrb_inv = annual herbaceous invasion (increase in cover) ahrb_rm = annual herbaceous removal (decrease in cover) combo = combined actions hrb_incr = increase herbaceous cover lit_incr = increase litter cover mes_impr = mesic improvements (increase in area) pj_inv = pinyon-juniper invasion (increase in area) pj_rm = pinyon-juniper removal (decrease in area) pns_decr = decrease non-sagebrush shrub cover pns_incr = increase non-sagebrush shrub cover pos_incr = increase percent other sagebrush cover (non-big sagebrush cover) sgc_incr = increase sagebrush cover sgh_incr = increase sagebrush height shh_decr = decrease shrub height C = CATEGORIZED Maps V = UNCATEGORIZED Maps X = INVASION (categorized) Maps

These data represent the predicted (modeled) prevalence of Asthma among adults (Age 18+) for each census tract in Colorado. Asthma is defined as ever being diagnosed with Asthma by a doctor, nurse, or other health professional, and still having the condition.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

These data represent the predicted (modeled) prevalence of Fair or Poor Health Status among adults (Age 18+) for each census tract in Colorado. General health status was self-reported as 'fair' or 'poor' as opposed to 'good', 'very good', or 'excellent'.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

This is a swipe map that was created as part of the Domestic Wells in the Great Lakes Basin storymap. The map contains two layers that depict both the population and percentage of the total population of each contiguous U.S. state that use domestic wells for drinking water. Data source: Johnson, T. D., Belitz, K., & Lombard, M. A. (2019). Estimating domestic well locations and populations served in the contiguous US for years 2000 and 2010. Science of the Total Environment 687, 1261-1273. The original data can be found and downloaded here.

U.S. Census Bureau QuickFacts statistics for Sheridan city, 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 Avon 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.

A dataset showing the population of 80013 between 2010 and 2020.