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.

The North Carolina State Demographer data platform houses the latest data produced by the Office of the State Demographer. The platform allows users to create visualizations, download full (or partial) datasets, and create maps. Registered users can save their visualizations and be notified of dataset updates. This new platform is a subdomain of OSBM’s Log In to North Carolina (LINC) – a service containing over 900 data items including items pertaining to population, labor force, education, transportation, etc. LINC includes topline statistics from the State Demographer’s population estimates and projections while the North Carolina State Demographer data platform includes more detailed datasets for users requiring more detailed demographic information.

The TIGER/Line shapefiles and related database files (.dbf) 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 shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Comprehensive demographic dataset for Eure, NC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) 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) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard Census Bureau geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

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.

The NCREDC in conjunction with Hobbs, Upchurch & Associates developed the digital `P' Sewer System as mapped by individual system owners as required by contract. The data collected will facilitate planning, siting and impact analysis in the 70 individual counties of North Carolina. This file enables the user to make various county-level determinations when used in conjunction with other data layers. This data contains information onP' Sewer systems which are planned or proposed public community system service areas are outside of current service area boundaries where public systems do not currently exist. Type P systems are areas which have been identified as having sufficient need and population density to support viable public systems and which have an existing minimum potential user density of 40 existing potential connections per mile of wastewater collection line. Information includes: system ID, parent system ID, cost estimate to implement. Other coverages exist with sewer lines and other appurtenances. This data was created to assist governmental agencies and others in making resource management decisions through use of a Geographic Information System (GIS).

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.

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.

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.

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.

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.

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.

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.

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

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the “true” explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.

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.

The 2015 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

Urban growth and climate change together complicate planning efforts meant to adapt to increasingly scarce water supplies. Several studies have shown the impacts of urban planning and climate change separately, but little attention has been given to their combined impact on long-term urban water demand forecasting. Here we coupled land and climate change projections with empirically-derived coefficient estimates of urban water use (sum of public supply, industrial, and domestic use) to forecast water demand under scenarios of future population densities and climate warming. We simulated two scenarios of urban growth from 2012 to 2065 using the FUTure Urban-Regional Environment Simulation (FUTURES) framework. FUTURES is an open-source probabilistic land change model designed to address the regional-scale environmental and ecological impacts of urbanization. We simulated an urbanization scenario that continues the historic trend of growth referred to as “Status Quo” and a scenario that simulates patterns of clustered higher density development, referred to as “Urban Infill". We initialized land change projections in 2011 and run forward on an annual time step to 2065. We captured the uncertainty associated with future climate conditions by integrating three Global Climate Models (GCMs), representative of dry, wet, and median future conditions. GCMs follow a continuously increasing greenhouse gas emissions scenario (Representative Concentration Pathway; RCP 8.5). This data release includes: a) land change projections for both urbanization scenarios in a spatial resolution consistent with the National Land Cover Database; b) development-related water demand projections for scenarios of environmental change at the census tract spatial unit summarized by 2030 and 2065; and c) the spatial boundaries of census tracts presented as a shapefile.