Census geographic areas are used by the Census Bureau to collect, tabulate, and aggregate decennial census data, and are also used in more frequent demographics reports like the annual American Community Survey (ACS). Three levels of areal geography are available from MassGIS (with layer name in parentheses): Blocks, Block Groups, and TractsSee the datalayer metadata for full details.Map service also available.

This map shows the diversity index of the population in the USA in 2010 by block group. "The diversity index summarizes racial and ethnic diversity. The index shows the likelihood that two people, chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity). For example, a diversity index of 59 means there is a 59 percent probability that two people randomly chosen would belong to different race or ethnic groups." -Esri DemographicsIt calls to the 2010 Census service with attributes related to race and ethnicity. The field PctNonWhite calculates the total percentage of non-white population by subtracting the Total white population from the reported population total. This yields the total non-white population (Field "TotNonWhite"). This number was then divided by the total reported population and multipled by 100 to yield a percetage of the population that is non-white (Field "PctNonWhite"). Original data sourced from: https://tpc.maps.arcgis.com/home/item.html?id=04a8fbbf59aa48ebbc646ba2bc8d9b1c

Census Blocks data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain block boundaries with associated 2020 Census demographic data.

The Census 1960 hosted feature layer displays polygon geometry and attributes of the tracts and population from the 1960 US Census in East Baton Rouge Parish, Louisiana.

The Census Bureau (https://www.census.gov/) maintains geographic boundaries for the analysis and mapping of demographic information across the United States. Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau releases the results of this county as demographic data with geographic identifiers so that maps and analysis can be performed on the US population. There are little more Census Tracts within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared to 2010.Created/Updated: Updated on September 2023, to merged Long Beach Breakwater land-based tracts silver polygons into bigger tract 990300 as per 2022 TIGER Line Shapefiles, and to update Santa Catalina Islands and San Clemente Islands tract boundary based on DPW City boundaries (except 599000 tract in Avalon). Updated on Sep 2022 and Dec 2022, to align tract boundary along city boundaries. Created on March 2021. How was this data created? This geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/on February, 2021 and customized for LA County. Data Fields:1. CT20 (TRACTCE20): 6-digit census tract number, 2. Label (NAME20): Decimal point census tract number.

Historical dataset showing total population for North America by year from 1950 to 2025.

This is a predominance and size map representing the votes for Tyler Kistner, Angie Craig and Paula Overby by using a diverging color and classified scheme. The pros for this map are that you can clearly see who had majority of the votes by adjusting the size of the circles. It's also good because you are able to choose the colors you would like, which makes it easier to understand the map. The cons for this map are that many of the circles overlap, so it's not as clear until you zoom in.

Census Block is the smallest geographic unit used by the United States Census Bureau for tabulation of 100-percent data (data collected from all houses, rather than a sample of houses). The number of blocks in the United States, including Puerto Rico, for the 2010 Census was 11,155,486.Census blocks are grouped into block groups, which are grouped into census tracts. There are on average about 39 blocks per block group. Blocks typically have a four-digit number; the first number indicates which block group the block is in. For example, census block 3019 would be in block group 3.Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The population of a census block varies greatly. As of the 2010 census, there were 4,871,270 blocks with a reported population of zero, while a block that is entirely occupied by an apartment complex might have several hundred inhabitants. Census blocks covering the entire country were introduced with the 1990 census. Before that, back to the 1940 census, only selected areas were divided into blocks. Census blocks are maintained within the Administration Feature and is dissolved out weekly. Administration is a polygon feature consisting of the smallest statistical areas bounded by visible features such as roads, streams, railroad tracks, and mountain ridges, as well as by nonvisible boundaries such as jurisdictional limits, school district, public safety boundaries, voting precincts, and census blocks. This methodology allows for single stream editing to move coincidental boundaries across many aggregate datasets simultaneously. Administration is maintained though an ArcGIS topology class in conjunction with County Parcels and Zoning. The topology prevents self-intersection and gaps, while ensuring complete coverage amongst the participating features.

The 2015 cartographic boundary shapefiles 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.

Data from the Oak Ridge National Laboratory, LandScan Global Population 1998 Database. The LandScan Global Population Project is a worldwide population database at 30 x 30 (arc second) resolution for estimating ambient populations at risk. Best available census counts are distributed to cells based on probability coefficients which, in turn, are based on road proximity, slope, land cover, and nighttime lights. Global coverage has been completed. Verification and validation (V&V) studies have been conducted routinely for all regions and more extensively for portions of the Middle East and the Southwestern United States. Data-set has been exported as Binary format.

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection . Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

As included in this EnviroAtlas dataset, the community level domestic water use is calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking, hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). Water use estimates in this EnviroAtlas-defined study area range from 84 to 110 GPD. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (http://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset portrays the total number of historic places located within each Census Block Group (CBG). The historic places data were compiled from the National Register of Historic Places, which provides official federal lists of districts, sites, buildings, structures and objects significant to American history, architecture, archeology, engineering, and culture. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Valdez-Cordova Census Area, AK was 1284.00000 Persons in January of 2019, according to the United States Federal Reserve. Historically, Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Valdez-Cordova Census Area, AK reached a record high of 1636.00000 in January of 2010 and a record low of 1186.00000 in January of 2018. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Valdez-Cordova Census Area, AK - last updated from the United States Federal Reserve on November of 2025.

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry _location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the winter season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Webster County, GA was 0.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Webster County, GA reached a record high of 9.00000 in January of 2019 and a record low of 0.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Webster County, GA - last updated from the United States Federal Reserve on December of 2025.

The Population Projections for the State of Tennessee, produced for the Tennessee State Data Center, contain projections for each county in Tennessee by race, age, and sex for each year from 2018 to 2070. To drive the map element within the 2018 Boyd Center Population Projections Dashboard, key county-level indicators, were summarized for 2018 and 2040 and presented as percentages of total population. They are joined with county boundary from the 2018 TIGER Data.Variables for the following indicators are included:Percent working age population (age 20 to 64) for 2018 and 2040Percent senior population (age 65+) for 2018 and 2040Percent of minority population for 2018 and 2040Minority population is presented as the sum of three racial and ethnic definitions from the projection. These include:Black Non-HispanicAll HispanicOther non-Hispanic, including two or more racesA series of county-level geography classification fields, to enable map filtering are also provided:Development DistrictsUrban/Rural Classification from the Tennessee Department of Economic and Community DevelopmentMetropolitan/micropolitan statistical areas defined by the U.S. Office of Management and Budget in 2018.The 2018 base year population estimates for Tennessee Counties are from the 2018 Vintage Estimates of Population and Housing Units produced by the US Census Bureau.

Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Clare County, MI was 134.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Clare County, MI reached a record high of 320.00000 in January of 2009 and a record low of 119.00000 in January of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Not Hispanic or Latino, American Indian and Alaska Native Alone (5-year estimate) in Clare County, MI - last updated from the United States Federal Reserve on November of 2025.

PDF Map of FCC Form 477 provider reported maximum download speeds by census block for January - June 2020. This map seeks to highlight areas that are undeserved by terrestrial broadband (fiber/cable/dsl on the ground), with "underserved" defined as down/up speeds less than 25/3 Mbps. These data represent a static snapshot of provider reported coverage between January 2020 and June 2020. Maps also depict the locations of federally recognized tribes, Alaskan communities, ANCSA and borough boundaries. Broadband coverage is represented using provider reported speeds under the FCC Form 477 the amalgamated broadband speed measurement category based on Form 477 "All Terrestrial Broadband" as a proxy for coverage. This field is unique to the NBAM platform. These maps do not include satellite internet coverage (and may not include microwave coverage through the TERRA network for all connected areas). This map was produced by DCRA using data provided by NTIA through the NBAM platform as part of a joint data sharing agreement undertaken in the year 2021. Maps were produced using the feature layer "NBAM Data by Census Geography v4": https://maps.ntia.gov/arcgis/home/item.html?id=8068e420210542ba8d2b02c1c971fb20Coverage is symbolized using the following legend:No data avalible or no terrestrial coverage: Grey or transparent< 10 Mbps Maximum Reported Download: Red10-25 Mbps Maximum Reported Download: Orange25-50 Mbps Maximum Reported Download: Yellow50-100 Mbps Maximum Reported Download: Light Blue100-1000 Mbps Maximum Reported Download: Dark Blue _Description from layer "NBAM Data by Census Geography v4":This layer is a composite of seven sublayers with adjacent scale ranges: States, Counties, Census Tracts, Census Block Groups, Census Blocks, 100m Hexbins and 500m Hexbins. Each type of geometry contains demographic and internet usage data taken from the following sources: US Census Bureau 2010 Census data (2010)USDA Non-Rural Areas (2013)FCC Form 477 Fixed Broadband Deployment Data (Jan - Jun 2020)Ookla Consumer-Initiated Fixed Wi-Fi Speed Test Results (Jan - Jun 2020)FCC Population, Housing Unit, and Household Estimates (2019). Note that these are derived from Census and other data.BroadbandNow Average Minimum Terrestrial Broadband Plan Prices (2020)M-Lab (Jan - Jun 2020) Some data values are unique to the NBAM platform: US Census and USDA Rurality values. For units larger than blocks, block count (urban/rural) was used to determine this. Some tracts and block groups have an equal number of urban and rural blocks—so a new coded value was introduced: S (split). All blocks are either U or R, while tracts and block groups can be U, R, or S. Amalgamated broadband speed measurement categories based on Form 477. These include:99: All Terrestrial Broadband Plus Satellite98: All Terrestrial Broadband97: Cable Modem96: DSL95: All Other (Electric Power Line, Other Copper Wireline, Other)Computed differences between FCC Form 477 and Ookla values for each area. These are reflected by six fields containing the difference of maximum, median, and minimum upload and download speed values. The FCC Speed Values method is applied to all speeds from all data sources within the custom-configured Omnibus service pop-up. This includes:

Geography: State, County, Tract, Block Group, Block, Hex Bins geographiesData source: all data within the Omnibus, i.e. FCC, Ookla, M-LabRepresentation: comparison tables and single speed values