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).

The 2015 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. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

This is an ArcGIS Online web service updated by the Colorado Parks and Wildlife GIS Unit for distributing Colorado state parks and wildlife species GIS data for public distribution.

This file was updated on December 9, 2024.

Map containing historical census data from 1900 - 2000 throughout the western United States at the county level. Data includes total population, population density, and percent population change by decade for each county. Population data was obtained from the US Census Bureau and joined to 1:2,000,000 scale National Atlas counties shapefile.

Feature class representing retail alcohol outlet density at the census tract 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. The data file contains the following attributes:FIPSTract Name Tract FIPS StateCountyLand Area Square Miles (Area of Land in Square Miles)Water Area SquareMiles (Area of Water in Square Miles)Population Total (Total Population as estimated in ACS 2018-2022)Percent Race White (Percent of population identifying as White as estimated in ACS 2018-2022) Percent Race African American Percent (Percent of population identifying as African American as estimated in ACS 2018-2022)Race American Indian Alaskan Native (Percent of population identifying as American Indian or Alaskan Native as estimated in ACS 2018-2022)Percent Race Asian (Percent of population identifying as Asian as estimated in ACS 2018-2022)Percent Race NHawaiian OPI (Percent of population identifying as Native Hawaiian or Pacific Islander as estimated in 2018-2022)Percent Race Other (Percent of population identifying as another race as estimated in 2018-2022)Percent Ethnicity Hispanic Latino (Percent of population identifying as Hispanic or Latino as estimated in 2018-2022)Percent Ethnicity Not Hispanic or Latino (Percent of population identifying as not Hispanic or Latino as estimated in 2018-2022)Percent Race Minority Race or Hispanic Latino (Percent of population made up of a Race and/or Ethnicity other than White, Non-Hispanic)Percent Population over 24 Years No HS Diploma (Percent of population over 24 years old without a High School Diploma as estimated in 2018-2022)Frequency All Retail Outlets 2024 (All retail alcohol outlets from January 2024)Average Distance Between Outlets in Meters (Average distance in Meters between an alcohol outlet and its nearest neighboring outlet)Frequency Off Premises Outlets 2024 (All Off-premises retail alcohol outlets from January 2024)Frequency On Premises Outlets 2024 (All On-premises retail alcohol outlets from January 2024)Rate Total Outlets per Square Mile (Rate of all retail alcohol outlets per square mile)Rate Total Outlets per 1,000 Residents (Rate of all retail alcohol outlets per 1,000 residents)Rate On Premises Outlets per Square Mile (Rate of On-premises retail alcohol outlets per square mile)Rate Off Premises Outlets per Square Mile (Rate of On-premises retail alcohol outlets per square mile)Rate On Premises Outlets per 1,000 Residents (Rate of on-premises retail alcohol outlets per 1,000 residents)Rate Off Premises Outlets per 1,000 Residents (Rate of off-premises retail alcohol outlets per 1,000 residents)Average Distance Between Outlets in Miles (Average distance in Miles between an alcohol outlet and its nearest neighboring outlet)

The 2019 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. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

The data set was created by preparing fine-scale population-specific Species Distribution Models (SDMs) to map revised PHMA and GHMA areas for each of the six greater sage-grouse populations within the current occupied range of Colorado. First, known presence locations of marked greater sage-grouse were used to train Random Forest and Resource Selection Function (RSF) models to estimate seasonal (e.g., breeding, summer-fall and winter) habitat suitability. Secondly, the seasonal model results were classified into high or low habitat suitability categories and subsequently compiled to produce a year-round habitat suitability map. Third, the resulting year-round habitat suitability maps were used to develop revised PHMA and GHMA areas for each population. Finally, the current occupied range for each population were modified to 1) exclude areas identified as unsuitable habitats and 2) include areas outside of current occupied range where evidence of sage-grouse occupancy exists.Data inputs into the RSF and Random Forest Models included presence data from GPS and VHF collar data provided to Olsson from CPW biologists, which was used to refine the models. A combination of vegetative and topographic predictors were employed at multiple scales in assessing the probability of habitat selection for the populations analyzed in this study. The predictors were analyzed at multiple spatial scales, as the literature demonstrates that habitat selection by a species occurs at some scales and not others (Mayor et al. 2009, Acker et al. 2017). The predictors were measured at five scales: 100 meters (m), 400 m, 1000 m, 1600 m, and 3200 m. These were selected to assess a range of local- to landscape-level scales that may influence habitat selection. Furthermore, these scales are comparable to scales assessed in other contemporary studies concerning habitat selection of greater sage-grouse (Doherty et al. 2010; Rice et al. 2016; Walker et al. 2016).Populations were also analyzed to assess utilization of smaller mapped aspen stands as compared to larger continuous forested stands of aspen and/or mixed-conifer. While greater-sage grouse tend to avoid larger forested areas, they will utilize smaller aspen stands (T. Apa pers. comm. 2016-2018). All presence locations for each population were sampled against mapped aspen stands to calculate 1) the rate of selection for aspen stands by the population, and 2) the acreage of each aspen stand utilized. The sampled stand acreages were subsequently graphed and examined to identify natural breaks in the data. Stands with acreages less than the natural break value and not directly adjacent to other forested stands were classified and analyzed separately as isolated aspen polygons which were included as potentially suitable habitat; the remaining aspen stands were classified as forested and integrated with mixed-conifer forests, which were assumed to be non-suitable habitat.Finally, the distance to forested areas was measured as a vegetative predictor using the Euclidean Distance tool in ArcGIS 10.4, excluding all isolated aspen patches and mixed-conifer patches less than 0.5 acres (and see previous paragraph).Vegetation types were derived from the Colorado Vegetation Classification Project (CVCP), a 25 m resolution raster dataset developed by CPW, which mapped landcover conditions through the periods from 1993to 1997. In addition, vegetation types were also derived from the 2001 LANDFIRE Existing Vegetation Type (EVT) layer for areas adjacent to the study area in Utah and Wyoming to provide complete and continuous vegetation cover for populations abutting the state boundary. The LANDFIRE EVT is a 30 m resolution raster dataset developed by the United States Geological Survey (USGS) mapping landcover conditions from 2001 (LANDFIRE 2001). Vegetative types were classified into biologically relevant classes and subsequently measured as percent-proportion by dividing the number of cells for the particular class by the total number of cells within the radii of the five defined scales using ArcGIS 10.4. The assigned classes of vegetative types varied by population and are detailed in the population-specific reports provided to BLM.Topographic predictors were derived from the 10 m resolution National Elevation Dataset (NED) Digital Elevation Model (DEM) developed and maintained by the USGS. Key topographic predictors include aspect, Compound Topographic Index (CTI), elevation, percent slope, slope position and surface roughness. Aspect and percent slope were calculated in ArcGIS 10.4. CTI, slope position and surface roughness were calculated using the Geomorphology and Gradient Metrics toolbox (Evans et al. 2014). In addition, aspect was subsequently transformed using the TRASP method in the Geomorphology and Gradient Metrics toolbox. To develop the multi-scale predictors, CTI and percent slope were measured as the mean of all values within the radii of the five defined scales; slope position and surface roughness were calculated using the radii of the five defined scales.The following summary of the step-wise procedure was developed to convert the Random Forest and RSF continuous surface model results into revised Habitat Management Area Prescriptions. Details of these methods follow this list:1. Classify all seasonal Random Forest and RSF model results into high and low habitat suitability layers.2. Ensemble all Random Forest and RSF classified seasonal layers to form a single year-round annual habitat layer designating locations as either high or low habitat suitability.3. Convert all highly suitable locations to Priority Habitat Management Areas (PHMA) and all locations designated as low habitat suitability to General Habitat Management Areas (GHMA).4. Classify all areas within a 0.6-mile radius from lek locations having an active or unknown status designation as PHMA, regardless of habitat suitability classification.5. Identify all irrigated agricultural lands and designate interiors as Undesignated Habitat (UDH).6. Review and apply site-specific manual conversions of initial management prescription designations based on CPW biologist and stakeholder input.7. Remove identified non-habitat areas from Current Occupied Range (COR). Expand COR in areas beyond the current population boundary where evidence exists to demonstrate occupation by greater sage-grouse.The previous habitat layer generated by CPW, only two habitat designations prescribed by the BLM ARMPA exist for assigning management approaches for conservation of the Colorado greater sage-grouse populations; PHMA and GHMA. PHMA have the highest conservation value based on a combination of habitat and sage-grouse population characteristics and are managed to minimize disturbance activities through No Surface Occupancy (NSO) stipulations and implementing capped disturbance allowances. GHMA represent areas with lower greater sage-grouse occupancy and generally have marginal habitat conditions with fewer management restrictions that provide greater flexibility in land use activities.The initial step to applying PHMA and GHMA habitat management prescriptions involves converting all areas classified as highly suitable habitat in the population’s year-round classified habitat layer to PHMA, while the remaining low habitat suitability areas are converted to GHMA. Secondly, all lek locations with a CPW-prescribed active or unknown status designation are buffered with a 0.6-mile radius and the entirety of the interior of the buffer area is converted to PHMA. Third, the most recent mapped irrigated agricultural lands data was acquired from the Colorado Division of Water Resources for all applicable populations, then the following procedure described below were implemented to apply the Undesignated Habitat prescription to the interior of all irrigated agricultural lands.Undesignated HabitatThrough the course of this study, an additional management prescription was established by AGNC to address concerns regarding habitat management on privately held irrigated agricultural lands.An Undesignated Habitat(UDH) management prescription was developed to address concerns surrounding the management of privately held irrigated agricultural lands. The UDH prescription is applicable to all populations, excluding the Parachute-Piceance-Roan population (due to a lack of irrigated agricultural lands). UDH are areas of seasonally irrigated and harvested hay fields. These areas are utilized seasonally by sage-grouse, primarily in the late summer and fall, near edges where irrigated fields are adjacent and abutting sagebrush habitats. UDH is considered effective habitat, but it is the long-term irrigation and haying practices which have created and maintain this habitat type, and thus the unimpeded irrigation, haying operations and maintenance are not considered to be a negative impact to sage-grouse. While utilization of the edges of irrigated agricultural lands by sage-grouse is known to vary from population to population, studying grouse utilization on a population-specific basis proved problematic as most populations lacked adequate telemetry locations within irrigated agricultural lands to yield results with any level of confidence. For this reason, the North Park population was selected to analyze in detail due to the high number of telemetry points located within irrigated agricultural lands. Approximately 20 percent of all summer-fall telemetry locations for the North Park population occur within irrigated agricultural lands, compared to less than 1 percent to 3 percent utilization demonstrated in the remaining populations.All summer-fall telemetry locations occurring within irrigated agricultural lands were sampled to calculate the distance each point occurred from the edges of irrigated fields. The distances for each location were plotted in a histogram and subsequently reviewed by CPW and AGNC team consultants, revealing a natural break occurring in the

The smoke sensitive map includes areas in Colorado that meet at least one of the following two criteria:1. Within 5 miles of a Census 2010 tract with population density ≥ 500 people per km².2. Within 3 miles of an overnight residential health care facility.A prescribed fire permit issued the Air Pollution Control Division becomes more restrictive if the project is proposed in a location defined as a smoke sensitive area. The most significant restriction in the permit is the amount of acres that are allowed to be burned. For example, a permit for a prescribed fire in a non-smoke sensitive area may be allowed to burn up to 500 acres under excellent or very good ventilation conditions. The same permit in a smoke sensitive area would only allow 125 acres of burning under the same ventilation conditions.