The 1998 Dress Rehearsal was conducted as a prelude to the United States Census of Population and Housing, 2000, in the following locations: (1) Columbia, South Carolina, and surrounding areas, including the town of Irmo and the counties of Chester, Chesterfield, Darlington, Fairfield, Kershaw, Lancaster, Lee, Marlboro, Newberry, Richland, and Union, (2) Sacramento, California, and (3) Menominee County, Wisconsin, including the Menominee American Indian Reservation. This collection contains map files showing various levels of geography (in the form of Census Tract Outline Maps, Voting District/State Legislative District Outline Maps, and County Block Maps), TIGER/Line digital files, and Corner Point files for the Census 2000 Dress Rehearsal sites. The Corner Point data files contain the bounding latitude and longitude coordinates for each individual map sheet of the 1998 Dress Rehearsal Public Law (P.L.) 94-171 map products. These files include a sheet identifier, minimum and maximum longitude, minimum and maximum latitude, and the map scale (integer value) for each map sheet. The latitude and longitude coordinates are in decimal degrees and expressed as integer values with six implied decimal places. There is a separate Corner Point File for each of the three map types: County Block Map, Census Tract Outline Map, and Voting District/State Legislative District Outline Map. Each of the three map file types is provided in two formats: Portable Document Format (PDF), for viewing, and Hewlett-Packard Graphics Language (HP-GL) format, for plotting. The County Block Maps show the greatest detail and the most complete set of geographic information of all the maps. These large-scale maps depict the smallest geographic entities for which the Census Bureau presents data -- the census blocks -- by displaying the features that delineate them and the numbers that identify them. These maps show the boundaries, names, and codes for American Indian/Alaska Native areas, county subdivisions, places, census tracts, and, for this series, the geographic entities that the states delineated in Phase 2, Voting District Project, of the Redistricting Data Program. The HP-GL version of the County Block Maps is broken down into index maps and map sheets. The map sheets cover a small area, and the index maps are composed of multiple map sheets, showing the entire area. The intent of the County Block Map series is to provide a map for each county on the smallest possible number of map sheets at the maximum practical scale, dependent on the area size of the county and the density of the block pattern. The latter affects the display of block numbers and feature identifiers. The Census Tract Outline Maps show the boundaries and numbers of census tracts, and name the features underlying the boundaries. These maps also show the boundaries and names of counties, county subdivisions, and places. They identify census tracts in relation to governmental unit boundaries. The mapping unit is the county. These large-format maps are produced to support the P.L. 94-171 program and all other 1998 Dress Rehearsal data tabulations. The Voting District/State Legislative District Outline Maps show the boundaries and codes for voting districts as delineated by the states in Phase 2, Voting District Project, of the Redistricting Data Program. The features underlying the voting district boundaries are shown, as well as the names of these features. Additionally, for states that submit the information, these maps show the boundaries and codes for state legislative districts and their underlying features. These maps also show the boundaries of and names of American Indian/Alaska Native areas, counties, county subdivisions, and places. The scale of the district maps is optimized to keep the number of map sheets for each area to a minimum, but the scale and number of map sheets will vary by the area size of the county and the voting districts and state legislative districts delineated by the states. The Census 2000 Dress Rehearsal TIGER/Line Files consist of line segments representing physical features and governmental and statistical boundaries. The files contain information distributed over a series of record types for the spatial objects of a county. These TIGER/Line Files are an extract of selected geographic and cartographic information from the Census TIGER (Topological

This collection provides 100-percent data from the Census 2000 Dress Rehearsal conducted in 1998 in the following locations: (1) Columbia, South Carolina, and surrounding areas, including the town of Irmo and the counties of Chester, Chesterfield, Darlington, Fairfield, Kershaw, Lancaster, Lee, Marlboro, Newberry, Richland, and Union, (2) Sacramento, California, and (3) Menominee County, Wisconsin, including the Menominee American Indian Reservation. The collection includes data on population, race, Hispanic/Latino origin, age, sex, marital status, family type and presence of own children, household relationship, household type and size, and group quarters. There are 104 population (P) and 42 housing (H) tables that provide data down to the block level. There are 29 additional population tables that provide data down to the census tract level. Also provided are accompanying map files, including Census Block and Census Tract Maps, in two formats, Portable Document Format (PDF) for viewing and Hewlett-Packard Graphics Language (HP-GL) for plotting large-scale maps. The Corner Point files contain the bounding latitude and longitude coordinates for each individual map sheet of the 1998 Dress Rehearsal 100-Percent Summary Files map products.

U.S. Census Bureau QuickFacts statistics for Fountain Inn city, South Carolina. 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.

Provides regional identifiers for county based regions of various types. These can be combined with other datasets for visualization, mapping, analyses, and aggregation. These regions include:Metropolitan Statistical Areas (Current): MSAs as defined by US OMB in 2023Metropolitan Statistical Areas (2010s): MSAs as defined by US OMB in 2013Metropolitan Statistical Areas (2000s): MSAs as defined by US OMB in 2003Region: Three broad regions in North Carolina (Eastern, Western, Central)Council of GovernmentsProsperity Zones: NC Department of Commerce Prosperity ZonesNCDOT Divisions: NC Dept. of Transportation DivisionsNCDOT Districts (within Divisions)Metro Regions: Identifies Triangle, Triad, Charlotte, All Other Metros, & Non-MetropolitanUrban/Rural defined by:NC Rural Center (Urban, Regional/Suburban, Rural) - 2020 Census designations2010 Census (Urban = Counties with 50% or more population living in urban areas in 2010)2010 Census Urbanized (Urban = Counties with 50% or more of the population living in urbanized areas in 2010 (50,000+ sized urban area))Municipal Population - State Demographer (Urban = counties with 50% or more of the population living in a municipality as of July 1, 2019)Isserman Urban-Rural Density Typology

Vintage 2024 population projections of North Carolina counties produced by the State Demographer of the North Carolina Office of State Budget & Management. Population by sex (male/female) and single years of age for the total population. Includes the total population and median age for July 1, 2020 through July 1, 2060. Includes revised population estimates for 2020-2022, 2023 certified population estimate, and July 1, 2024 through July 1, 2060 population projections.

This layer summarizes the social vulnerability index for populations within each county in the United States at scales 1:3m and below. It answers the question “Where are the areas of relatively higher risk within this county?” from the perspective of social vulnerability. For emergency response planning and hazard mitigation, populations can be assessed from a perspective of their vulnerability to various hazards (fire, flood, etc). Physical vulnerability refers to a population’s exposure to specific potential hazards, such as living in a designated flood plain. Social vulnerability refers to potential exposure due to population and housing characteristics: age, low income, disability, home value or other factors. For example, low-income seniors may not have access to a car to simply drive away from an ongoing hazard such as a flood. ESRI applied a model from Susan Cutter, University of South Carolina, Hazards Research Lab, Department of Geography (http://webra.cas.sc.edu/hvri/), to generate this data using current year demographics at the block group level. The map is designed to be displayed with semi-transparency of about 50% for overlay on other base-maps, which is reflected in the legend for the map.

Map shows the population of Native Americans in the state of South Carolina by Census Block.Its shown in comparison with a map showing percentage of Native American children aged 5-19 by census block. These locations indicate more children than adults and therefore possibly a greater need for afterschool education programs and community outreach to encourage continued education.The coutnies of South Carolina are overlain to provide a more digestable reference region.

Vintage 2024 Population projections by race, sex and age group for North Carolina counties. Includes population by race (American Indian/Alaska Native), Asian & Pacific Islander (Asian), Black, White, Other (includes persons identified as two or more races). In some counties not all race groups will be reported separately. For population of less than 250 for any race group, the population by age will be reported within the other category and the "group n" for the other category show a number larger than 1 indicating that the other category includes population from other race groups that are separately reported for other counties. For this reason, users should take care in aggregating race group population across counties.

Historical population as enumerated and corrected from 1790 through 2020. North Carolina was one of the 13 original States and by the time of the 1790 census had essentially its current boundaries. The Census is mandated by the United States Constitution and was first completed for 1790. The population has been counted every ten years hence, with some limitations. In 1790 census coverage included most of the State, except for areas in the west, parts of which were not enumerated until 1840. The population for 1810 includes Walton County, enumerated as part of Georgia although actually within North Carolina. Historical populations shown here reflect the population of the respective named county and not necessarily the population of the area of the county as it was defined for a particular census. County boundaries shown in maps reflect boundaries as defined in 2020. Historic boundaries for some counties may include additional geographic areas or may be smaller than the current geographic boundaries. Notes below list the county or counties with which the population of a currently defined county were enumerated historically (Current County: Population counted in). The current 100 counties have been in place since the 1920 Census, although some modifications to the county boundaries have occurred since that time. For historical county boundaries see: Atlas of Historical County Boundaries Project (newberry.org)County Notes: Note 1: Total for 1810 includes population (1,026) of Walton County, reported as a Georgia county but later determined to be situated in western North Carolina. Total for 1890 includes 2 Indians in prison, not reported by county. Note 2: Alexander: *Iredell, Burke, Wilkes. Note 3: Avery: *Caldwell, Mitchell, Watauga. Note 4: Buncombe: *Burke, Rutherford; see also note 22. Note 5: Caldwell: *Burke, Wilkes, Yancey. Note 6: Cleveland: *Rutherford, Lincoln. Note 7: Columbus: *Bladen, Brunswick. Note 8: Dare: *Tyrrell, Currituck, Hyde. Note 9: Hoke: *Cumberland, Robeson. Note 10: Jackson: *Macon, Haywood. Note 11: Lee: *Moore, Chatham. Note 12: Lenoir: *Dobbs (Greene); Craven. Note 13: McDowell: *Burke, Rutherford. Note 14: Madison: *Buncombe, Yancey. Note 15: Mitchell: *Yancey, Watauga. Note 16: Pamlico: *Craven, Beaufort. Note 17: Polk: *Rutherford, Henderson. Note 18: Swain: *Jackson, Macon. Note 19: Transylvania: *Henderson, Jackson. Note 20: Union: *Mecklenburg, Anson. Note 21: Vance: *Granville, Warren, Franklin. Note 22: Walton: Created in 1803 as a Georgia county and reported in 1810 as part of Georgia; abolished after a review of the State boundary determined that its area was located in North Carolina. By 1820 it was part of Buncombe County. Note 23: Watauga: *Ashe, Yancey, Wilkes; Burke. Note 24: Wilson: *Edgecombe, Nash, Wayne, Johnston. Note 25: Yancey: *Burke, Buncombe. Note 26: Alleghany: *Ashe. Note 27: Haywood: *Buncombe. Note 28: Henderson: *Buncombe. Note 29: Person: Caswell. Note 30: Clay: Cherokee. Note 31: Graham: Cherokee. Note 32: Harnett: Cumberland. Note 33: Macon: Haywood.

Note 34: Catawba: Lincoln. Note 35: Gaston: Lincoln. Note 36: Cabarrus: Mecklenburg.
Note 37: Stanly: Montgomery. Note 38: Pender: New Hanover. Note 39: Alamance: Orange.
Note 40: Durham: Orange, Wake. Note 41: Scotland: Richmond. Note 42: Davidson: Rowan. Note 43: Davie: Rowan.Note 44: Forsyth: Stokes. Note 45: Yadkin: Surry.
Note 46: Washington: Tyrrell.Note 47: Ashe: Wilkes. Part III. Population of Counties, Earliest Census to 1990The 1840 population of Person County, NC should be 9,790. The 1840 population of Perquimans County, NC should be 7,346.

The latest 8 and 10 digit HUC boundaries, along with the calculated US Census population within each subbasin and watershed for 2020, 2010, and 2000.

HUC boundaries are from the USGS National Hydrography Watershed Boundary Dataset. US Census 2020, 2010, and 2000 Block Data was acquired through NC OneMap.

Subbasin and watershed population estimates were derived from the 2020, 2010, and 2000 Block population data from the US Census. The ArcGIS Tool "Summarize Within" was used to calculate the total population within each subbasin and watershed for each census period. As census blocks and HUC boundaries do not always coincide, the calculated population is only an estimate and is not to be used as an exact figure.

County Boundary for Pitt County North Carolina - This dataset only contains one polygon representing the Pitt County boundary. This dataset is maintained in collaboration between Pitt County Tax Administration and Pitt County Management Information Systems. For specific questions regarding the data you may contact the Pitt County MIS department at 252-902-3800 OR contact Pitt County Tax Administration at 252-902-3400.Pitt County is a county located in the U.S. state of North Carolina. As of the 2010 census, the population was 168,148, making it the seventeenth-most populous county in North Carolina. The county seat is Greenville. Pitt County comprises the Greenville, NC Metropolitan Statistical Area. As one of the fastest growing centers in the state, the county has seen a population boom since 1990.

The latest 8 and 10 digit HUC boundaries, along with the calculated US Census population within each subbasin and watershed for 2020, 2010, and 2000.

HUC boundaries are from the USGS National Hydrography Watershed Boundary Dataset. US Census 2020, 2010, and 2000 Block Data was acquired through NC OneMap.

Subbasin and watershed population estimates were derived from the 2020, 2010, and 2000 Block population data from the US Census. The ArcGIS Tool "Summarize Within" was used to calculate the total population within each subbasin and watershed for each census period. As census blocks and HUC boundaries do not always coincide, the calculated population is only an estimate and is not to be used as an exact figure.

In the face of sea level rise and as climate change conditions increase the frequency and intensity of tropical storms along the north-Atlantic Coast, coastal areas will become increasingly vulnerable to storm damage, and the decline of already-threatened species could be exacerbated. Predictions about response of coastal birds to effects of hurricanes will be essential for anticipating and countering environmental impacts. This project will assess coastal bird populations, behavior, and nesting in Hurricane Sandy-impacted North Carolina barrier islands. The project comprises three components: 1) ground-based and airborne lidar analyses to examine site specific selection criteria of coastal birds; 2) NWI classification habitat mapping of DOI lands to examine habitat change associated with Hurricane Sandy, particularly in relation to coastal bird habitat; and 3) a GIS-based synthesis of how patterns of coastal bird distribution and abundance and their habitats have been shaped by storms such as Hurricane Sandy, coastal development, population density, and shoreline management over the past century. We will trace historic changes to shorebird populations and habitats in coastal North Carolina over the past century. Using historic maps and contemporary imagery, the study will quantify changes in shorebird populations and their habitats resulting from periodic storms such as Hurricane Sandy in 2012, to development projects such as the Intracoastal Waterway early in the last century, as well as more recent urban development. We will synthesize existing data on the distribution and abundance of shorebirds in North Carolina and changes in habitats related to storms, coastal development, inlet modifications, and shoreline erosion to give us a better understanding of historic trends for shorebirds and their coastal habitats. Historic data on the distribution and abundance of shorebirds are available from a variety of sources and include bird species identification, _location, activity, habitat, and band data. Habitat maps of federal lands in the study area will be created using National Wetlands Inventory mapping standards to assess storm impacts on available nesting habitat. Ground-based LIDAR and high-accuracy GPS data will be collected to develop methods to estimate shorebird nest elevation and microtopography to make predictions about nest site selection and success. Microtopography information collected from lidar data in the area immediately surrounding nest site locations will be used to analyze site specific nesting habitat selection criteria related to topography, substrate (coarseness of sand or cobble), and vegetation cover. The data will be used in future models to assess storm impacts on nest locations, predict long-term population impacts, and influence landscape-scale habitat management strategies that might lessen future impacts of hurricanes on coastal birds and lead to better restoration alternatives.

The following webmap contains individual web layers showing critical habitat for 5 DPSs of Atlantic Sturgeon. Information on each layer is detailed below:SturgeonAtlantic_AtlanticSubspecies_SouthAtlanticDPS_20170817: This dataset depicts the river lengths along which Critical Habitat has been designated (82 FR 39160, August 17, 2017) for the South Atlantic DPS of Atlantic Sturgeon. Critical habitat includes all of the river along the specified segment, from the ordinary high water mark of one riverbank to the ordinary high water mark of the opposing riverbank of the mainstem of the river, to the downstream limit at the bank-to-bank transect of the specified segment. For clarification of the critical habitat definition, please refer to the maps and narrative descriptions in the CFR. It is a product of the NOAA Fisheries Service’s Southeast Regional Office (SERO). This dataset includes boundaries for the following Regulated Areas: Critical Habitat South Atlantic Distinct Population Segment of Atlantic Sturgeon: Edisto River, Combahee River, Salkehatchie River, Savannah River, Ogeechee River, Altamaha River, Satilla River and St. Marys River. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are considered to be approximate representations and are NOT an OFFICIAL record for the exact Area boundaries. For information on the official legal definition refer to the Use Constraints metadata section.SturgeonAtlantic_AtlanticSubspecies_NewYorkBightDPS_20170817: This dataset depicts the river lengths along which Critical Habitat has been designated (82 FR 39160, August 17, 2017) for the New York Bight DPS of Atlantic Sturgeon. Critical habitat includes all of the river along the specified segment, from the ordinary high water mark of one riverbank to the ordinary high water mark of the opposing riverbank of the mainstem of the river, to the downstream limit at the bank-to-bank transect of the specified segment. For clarification of the critical habitat definition, please refer to the maps and narrative descriptions in the CFR. It is a product of the NOAA Fisheries Service’s Greater Atlantic Regional Fisheries Office (GARFO). This dataset includes boundaries for the following Regulated Areas: Critical Habitat for New York Bight Distinct Population Segment of Atlantic Sturgeon: Connecticut River, Housatonic River, Hudson River, and Delaware River. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are considered to be approximate representations and are NOT an OFFICIAL record for the exact Area boundaries. For information on the official legal definition refer to the Use Constraints metadata section.SturgeonAtlantic_AtlanticSubspecies_GulfofMaineDPS_20170817: This dataset depicts the river lengths along which Critical Habitat has been designated (82 FR 39160, August 17, 2017) for the Gulf of Maine distinct population segment (DPS) of Atlantic Sturgeon. Critical habitat includes all of the river along the specified segment, from the ordinary high water mark of one riverbank to the ordinary high water mark of the opposing riverbank of the mainstem of the river, to the downstream limit at the bank-to-bank transect of the specified segment. For clarification of the critical habitat definition, please refer to the maps and narrative descriptions in the CFR. It is a product of the NOAA Fisheries Service’s Greater Atlantic Regional Fisheries Office (GARFO). This dataset includes boundaries for the following Regulated Areas: - Critical Habitat for Gulf of Maine Distinct Population Segment of Atlantic Sturgeon: Penobscot River, Kennebec River, Androscoggin River, Piscataqua River, and Merrimack River. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are considered to be approximate representations and are NOT an OFFICIAL record for the exact Area boundaries. For information on the official legal definition refer to the Use Constraints metadata section.SturgeonAtlantic_AtlanticSubspecies_ChesapeakeBayDPS_20170817:This dataset depicts the river lengths along which Critical Habitat has been designated (82 FR 39160, August 17, 2017) for the Chesapeake Bay distinct population segment (DPS) of Atlantic Sturgeon. Critical habitat includes all of the river along the specified segment, from the ordinary high water mark of one riverbank to the ordinary high water mark of the opposing riverbank of the mainstem of the river, to the downstream limit at the bank-to-bank transect of the specified segment. For clarification of the critical habitat definition, please refer to the maps and narrative descriptions in the CFR. It is a product of the NOAA Fisheries Service’s Greater Atlantic Regional Fisheries Office (GARFO). This dataset includes boundaries for the following Regulated Areas: - Critical Habitat for Chesapeake Bay Distinct Population Segment of Atlantic Sturgeon: Nanticoke River, Potomac River, Rappahannock River, York River, and James River. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are considered to be approximate representations and are NOT an OFFICIAL record for the exact Area boundaries. For information on the official legal definition refer to the Use Constraints metadata section.SturgeonAtlantic_AtlanticSubspecies_CarolinaDPS20170817: This dataset depicts the river lengths along which Critical Habitat has been designated (82 FR 39160, August 17, 2017) for the Carolina DPS of Atlantic Sturgeon. Critical habitat includes all of the river along the specified segment, from the ordinary high water mark of one riverbank to the ordinary high water mark of the opposing riverbank of the mainstem of the river, to the downstream limit at the bank-to-bank transect of the specified segment. For clarification of the critical habitat definition, please refer to the maps and narrative descriptions in the CFR. It is a product of the NOAA Fisheries Service’s Greater Atlantic Regional Fisheries Office (GARFO). Dataset includes boundaries for the following Regulated Areas: Critical Habitat Carolina Distinct Population Segment of Atlantic Sturgeon: Roanoke River, Tar-Pamlico River, Neuse River, Cape Fear River, Pee Dee River, Black River, Santee River and Cooper River. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are considered to be approximate representations and are NOT an OFFICIAL record for the exact Area boundaries. For information on the official legal definition refer to the Use Constraints metadata section.Link to NOAA Fisheries final rule pageLink to eCFRLink to InPortLink to NOAA Fisheries Critical Habitat Webpage

The 2020 TIGER/Line Shapefiles contain current geographic extent and boundaries of both legal and statistical entities (which have no governmental standing) for the United States, the District of Columbia, Puerto Rico, and the Island areas. This vintage includes boundaries of governmental units that match the data from the surveys that use 2020 geography (e.g., 2020 Population Estimates and the 2020 American Community Survey). In addition to geographic boundaries, the 2020 TIGER/Line Shapefiles also include geographic feature shapefiles and relationship files. Feature shapefiles represent the point, line and polygon features in the MTDB (e.g., roads and rivers). Relationship files contain additional attribute information users can join to the shapefiles. Both the feature shapefiles and relationship files reflect updates made in the database through September 2020. To see how the geographic entities, relate to one another, please see our geographic hierarchy diagrams here.Census Urbanized Areashttps://www2.census.gov/geo/tiger/TIGER2020/UACCensus Urban/Rural Census Block Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php2020 TIGER/Line and Redistricting shapefiles:https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2020.htmlTechnical documentation:https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdfTIGERweb REST Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_restmapservice.htmlTIGERweb WMS Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.htmlThe legal entities included in these shapefiles are:American Indian Off-Reservation Trust LandsAmerican Indian Reservations – FederalAmerican Indian Reservations – StateAmerican Indian Tribal Subdivisions (within legal American Indian areas)Alaska Native Regional CorporationsCongressional Districts – 116th CongressConsolidated CitiesCounties and Equivalent Entities (except census areas in Alaska)Estates (US Virgin Islands only)Hawaiian Home LandsIncorporated PlacesMinor Civil DivisionsSchool Districts – ElementarySchool Districts – SecondarySchool Districts – UnifiedStates and Equivalent EntitiesState Legislative Districts – UpperState Legislative Districts – LowerSubminor Civil Divisions (Subbarrios in Puerto Rico)The statistical entities included in these shapefiles are:Alaska Native Village Statistical AreasAmerican Indian/Alaska Native Statistical AreasAmerican Indian Tribal Subdivisions (within Oklahoma Tribal Statistical Areas)Block Groups3-5Census AreasCensus BlocksCensus County Divisions (Census Subareas in Alaska)Unorganized Territories (statistical county subdivisions)Census Designated Places (CDPs)Census TractsCombined New England City and Town AreasCombined Statistical AreasMetropolitan and Micropolitan Statistical Areas and related statistical areasMetropolitan DivisionsNew England City and Town AreasNew England City and Town Area DivisionsOklahoma Tribal Statistical AreasPublic Use Microdata Areas (PUMAs)State Designated Tribal Statistical AreasTribal Designated Statistical AreasUrban AreasZIP Code Tabulation Areas (ZCTAs)Shapefiles - Features:Address Range-FeatureAll Lines (called Edges)All RoadsArea HydrographyArea LandmarkCoastlineLinear HydrographyMilitary InstallationPoint LandmarkPrimary RoadsPrimary and Secondary RoadsTopological Faces (polygons with all geocodes)Relationship Files:Address Range-Feature NameAddress RangesFeature NamesTopological Faces – Area LandmarkTopological Faces – Area HydrographyTopological Faces – Military Installations

In the face of sea level rise and as climate change conditions increase the frequency and intensity of tropical storms along the north-Atlantic Coast, coastal areas will become increasingly vulnerable to storm damage, and the decline of already-threatened species could be exacerbated. Predictions about response of coastal birds to effects of hurricanes will be essential for anticipating and countering environmental impacts. This project will assess coastal bird populations, behavior, and nesting in Hurricane Sandy-impacted North Carolina barrier islands. The project comprises three components: 1) ground-based and airborne lidar analyses to examine site specific selection criteria of coastal birds; 2) NWI classification habitat mapping of DOI lands to examine habitat change associated with Hurricane Sandy, particularly in relation to coastal bird habitat; and 3) a GIS-based synthesis of how patterns of coastal bird distribution and abundance and their habitats have been shaped by storms such as Hurricane Sandy, coastal development, population density, and shoreline management over the past century. We will trace historic changes to shorebird populations and habitats in coastal North Carolina over the past century. Using historic maps and contemporary imagery, the study will quantify changes in shorebird populations and their habitats resulting from periodic storms such as Hurricane Sandy in 2012, to development projects such as the Intracoastal Waterway early in the last century, as well as more recent urban development. We will synthesize existing data on the distribution and abundance of shorebirds in North Carolina and changes in habitats related to storms, coastal development, inlet modifications, and shoreline erosion to give us a better understanding of historic trends for shorebirds and their coastal habitats. Historic data on the distribution and abundance of shorebirds are available from a variety of sources and include bird species identification, location, activity, habitat, and band data. Habitat maps of federal lands in the study area will be created using National Wetlands Inventory mapping standards to assess storm impacts on available nesting habitat. Ground-based LIDAR and high-accuracy GPS data will be collected to develop methods to estimate shorebird nest elevation and microtopography to make predictions about nest site selection and success. Microtopography information collected from lidar data in the area immediately surrounding nest site locations will be used to analyze site specific nesting habitat selection criteria related to topography, substrate (coarseness of sand or cobble), and vegetation cover. The data will be used in future models to assess storm impacts on nest locations, predict long-term population impacts, and influence landscape-scale habitat management strategies that might lessen future impacts of hurricanes on coastal birds and lead to better restoration alternatives.

This data represents five-digit ZIP Code areas used by the U.S. Postal Service. This is an ArcGIS Online item directly from Esri. For more information see https://www.arcgis.com/home/item.html?id=8d2012a2016e484dafaac0451f9aea24.

The latest 12-Digit HUC boundaries, along with the calculated US Census population within each subwatershed area. HUC boundaries are from the USGS National Hydrography Watershed Boundary Dataset. US Census 2020, 2010, and 2000 Block Data was acquired through NC OneMap.Subwatershed population estimates were derived from the 2020, 2010, and 2000 Block population data from the US Census. The ArcGIS Tool "Summarize Within" was used to calculate the total population within each subwatershed for each census period. As census blocks and subwatershed boundaries do not always coincide, the calculated population is only an estimate and is not to be used as an exact figure.

Geographic information systems (GIS) can be used to map mosquito larval and adult habitats and human populations at risk for mosquito exposure and possible arbovirus transmission. Along with traditional methods of surveillance-based targeted mosquito control, GIS can help simplify and target efforts during routine surveillance and post-disaster (e.g., hurricane-related flooding) to protect emergency workers and public health. A practical method for prioritizing areas for emergency mosquito control has been developed and is described here. North Carolina (NC) One Map was used to identify state-level data layers of interest based on human population distribution and mosquito habitat in Brunswick, Columbus, Onslow, and Robeson Counties in eastern NC. Relevant data layers were included to create mosquito control treatment areas for targeted control and an 18-step protocol for map development is discussed. This protocol is expected to help state, territorial, tribal, and/or local public health officials and associated mosquito control programs efficiently create treatment area maps to improve strategic planning in advance of a disaster. This protocol may be applied to any NC county and beyond, thereby increasing local disaster preparedness.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,