This graph shows the population density in the federal state of North Carolina from 1960 to 2018. In 2018, the population density of North Carolina stood at 213.6 residents per square mile of land area.

Historical population counts from the US Census Bureau census counts of 1970, 1980, 1990, 2000, 2010, and 2020. Certified population estimates prepared by the State Demographer beginning 1981. Population density for selected years. The certified population estimates are as estimated for the given vintage year and may be different from the revised estimates and the intercensal (smoothed) estimates also produced by the State Demographer. Census counts for April 1 of given year, population estimates for July 1 of given year.

In 2023, about 13.3 percent of the population in North Carolina was between the ages of 25 and 34 years old. A further 13 percent of the population of North Carolina was between the ages of 35 and 44 years old in that year.

84,80 (persons per sq. km) in 2022.

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

Total population, population density, migration, and voting age population by year. Includes population estimates and projections prepared by the NC State Demographer.

Household data are collected as of March.

As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf):

Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.

We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.

Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. 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 2016 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.

1 computer laser optical disc ; 4 3/4 in. Selected block-level data from Summary tape file 1B, including total population, age, race, and Hispanic origin, number of housing units, tenure, room density, mean contract rent, mean value, and mean number of rooms in housing units. ISO 9660 format.

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of North Carolina. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of North Carolina. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for North Carolina. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F72805M4; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of North Carolina. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of North Carolina. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for North Carolina. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F72805M4

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.

Baseflow grab samples and flow measurements were collected bi-weekly from 27 urbanized catchments in the NC piedmont over periods of one to fours years between Fall 2013 and Fall 2019. A subset of 13 catchments were sampled for isotopic nitrate in 2018. Sampled catchments were selected to approximate the median and span most of the range in metrics of landcover, infrastructure, and population density for developed NHD+ catchments in the Haw and Upper Neuse River basins. Land cover, infrastructure, topography, geology, and hydrogeomorphic position of development features were characterized for the study area. This data supports the findings of the manuscript "The sources and transport of baseflow N loading across a developed rural-urban gradient" submitted to WRR for review in Nov. 2021.

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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 United States senior living market, valued at $112.93 billion in 2025, is projected to experience robust growth, driven by several key factors. The aging population, coupled with increasing life expectancy and a rising prevalence of chronic health conditions requiring assisted care, are significant contributors to market expansion. Technological advancements in senior care, such as telehealth and remote monitoring, are also fueling demand for innovative and efficient senior living solutions. Furthermore, a shift in preferences towards independent living options that provide a sense of community and support, as opposed to solely relying on family caregivers, is boosting market growth. The segment breakdown reveals a diversified market with Assisted Living, Independent Living, and Memory Care facilities leading the way. Key states like New York, Illinois, California, North Carolina, and Washington represent significant regional concentrations, reflecting population density and economic factors. The competitive landscape includes both large national players like Brookdale Senior Living and Sunrise Senior Living, as well as smaller regional providers, indicating a dynamic and evolving market structure. The projected Compound Annual Growth Rate (CAGR) of 5.86% from 2025 to 2033 indicates a significant expansion of the market over the forecast period. However, several factors could influence this trajectory. Rising healthcare costs and potential regulatory changes related to senior care could pose challenges. Additionally, maintaining staffing levels within the industry, addressing workforce shortages, and ensuring quality care will be crucial for sustained growth. Despite these challenges, the fundamental demographic trends point toward a consistently growing market. Strategic investments in infrastructure, technology, and workforce development will be critical for operators to capitalize on opportunities within the expanding senior living sector. Recent developments include: July 2023: Spring Cypress senior living site expansion is set to open at the end of 2024 and will consist of three phases. The first phase of the expansion will include 19 independent-living, two-bedroom cottages. The second phase will include 24 townhomes. The third phase will feature 95 apartments. The final phase will feature a resort with several luxury amenities., Apr 2023: For seniors looking for innovative, high-quality care, Avista Senior Living is transitioning away from its SafelyYou partnership to empower safer, more personalized dementia care with real-time, AI video and remote clinical experts 24/7.. Key drivers for this market are: 4., Increase in Aging Population Driving the Market4.; Healthcare and Long-term Care Needs Driving the Market. Potential restraints include: 4., Increase in Aging Population Driving the Market4.; Healthcare and Long-term Care Needs Driving the Market. Notable trends are: Senior Housing Witnessing Increased Demand.

This study compared marginal and conditional modeling approaches for identifying individual, park and neighborhood park use predictors. Data were derived from the ParkIndex study, which occurred in 128 block groups in Brooklyn (New York), Seattle (Washington), Raleigh (North Carolina), and Greenville (South Carolina). Survey respondents (n = 320) indicated parks within one half-mile of their block group used within the past month. Parks (n = 263) were audited using the Community Park Audit Tool. Measures were collected at the individual (park visitation, physical activity, sociodemographic characteristics), park (distance, quality, size), and block group (park count, population density, age structure, racial composition, walkability) levels. Generalized linear mixed models and generalized estimating equations were used. Ten-fold cross validation compared predictive performance of models. Conditional and marginal models identified common park use predictors: participant race, participant education, distance to parks, park quality, and population >65yrs. Additionally, the conditional mode identified park size as a park use predictor. The conditional model exhibited superior predictive value compared to the marginal model, and they exhibited similar generalizability. Future research should consider conditional and marginal approaches for analyzing health behavior data and employ cross-validation techniques to identify instances where marginal models display superior or comparable performance.

Predator-prey interactions are important to regulating populations and structuring communities but are affected by many dynamic, complex factors, across larges-scales, making them difficult to study. Integrated population models (IPMs) offer a potential solution to understanding predator-prey relationships by providing a framework for leveraging many different datasets and testing hypotheses about interactive factors. Here, we evaluate the coyote-deer (Canis latrans – Odocoileus virginianus) predator-prey relationship across the state of North Carolina (NC). Because both species have similar habitat requirements and may respond to human disturbance, we considered net primary productivity (NPP) and urbanization as key mediating factors. We estimated deer survival and fecundity by integrating camera trap, harvest, biological and hunter observation datasets into a two-stage, two-sex Lefkovich population projection matrix. We allowed survival and fecundity to vary as functions of urbanization, NPP and coyote density and projected abundance forward to test eight hypothetical scenarios. We estimated initial average deer and coyote densities to be 11.83 (95% CI: 5.64, 20.80) and 0.46 (95% CI: 0.02, 1.45) individuals/km2, respectively. We found a negative relationship between current levels of coyote density and deer fecundity in most areas which became more negative under hypothetical conditions of lower NPP or higher urbanization, leading to lower projected deer abundances. These results suggest that coyotes could have stronger effects on deer populations in NC if their densities rise, but primarily in less productive and/or more suburban habitats. Our case study provides an example of how IPMs can be used to better understand the complex relationships between predator and prey under changing environmental conditions. Methods Survival and harvest rates: We used the dynamic N-mixture model of Zipkin et al. (2014) to estimate stage and sex-based survival and harvest rates from stage-at-harvest data collected statewide from 2012-2017 over all 100 counties of North Carolina. The stage-at-harvest data were collected by county each year for two stages for male deer (adults and fawns about to transition to adulthood (i.e., button bucks)) and does. We assumed that all button bucks were fawns and all females were adults. The census took place right before fawns transitioned to adulthood and we considered all fawns to reach adulthood at one year of age. Fawn:doe ratio: To represent hunted populations, we used 2017 hunter observation data from each county of NC. Hunters documented what species they observed on their hunts, given the number of hours they spent hunting, to get an index of abundance. The location of these observations was known only to the county level. Hunters were instructed to report their hunting activity even if no wildlife was observed (Fuller et al. 2018). For use in our model, we removed all observations made over bait and averaged observations of hunters that remained in the same hunting stand for multiple days instead of treating those days as independent samples. Litter size: To provide explicit information about litter size we used fertility data collected by the NCWRC. Fertility data (number of fetuses/doe) are recorded by a subset of hunters each year as part of biological data collection.

Stream fish abundance data was collected seasonally in three 30m sections of stream from 1984 to 1995. Population estimates were obtained using electrofishing (3 pass removal). Habitat availability measurements were recorded biannually along with the electrofishing data starting in fall 1988. Data was collected each fall and spring from 1988-1995, in order to examine changes in fish assemblage structure along the habitat gradient. We used strong inference with Akaike’s Information Criterion (AIC) to assess the processes capable of explaining long-term (1984–1995) variation in the per capita rate of change of mottled sculpin (Cottus bairdi) populations in the Coweeta Creek drainage (USA). We sampled two fourth- and one fifth-order sites (BCA [uppermost], BCB, and CC [lowermost]) along a downstream gradient, and the study encompassed extensive flow variation.

We examined the reach-scale distributions of three fish species to determine which biotic and abiotic factors are influential in the fishes distributions.