About this dataset

Context

The dataset tabulates the population of Appalachia by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Appalachia. The dataset can be utilized to understand the population distribution of Appalachia by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Appalachia. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Appalachia.

Key observations

Largest age group (population): Male # 45-49 years (99) | Female # 10-14 years (128). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Appalachia population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Appalachia is shown in the following column.
• Population (Female): The female population in the Appalachia is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Appalachia for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Appalachia Population by Gender. You can refer the same here

This research project was designed to demonstrate the contributions that Geographic Information Systems (GIS) and spatial analysis procedures can make to the study of crime patterns in a largely nonmetropolitan region of the United States. The project examined the extent to which the relationship between various structural factors and crime varied across metropolitan and nonmetropolitan locations in Appalachia over time. To investigate the spatial patterns of crime, a georeferenced dataset was compiled at the county level for each of the 399 counties comprising the Appalachian region. The data came from numerous secondary data sources, including the Federal Bureau of Investigation's Uniform Crime Reports, the Decennial Census of the United States, the Department of Agriculture, and the Appalachian Regional Commission. Data were gathered on the demographic distribution, change, and composition of each county, as well as other socioeconomic indicators. The dependent variables were index crime rates derived from the Uniform Crime Reports, with separate variables for violent and property crimes. These data were integrated into a GIS database in order to enhance the research with respect to: (1) data integration and visualization, (2) exploratory spatial analysis, and (3) confirmatory spatial analysis and statistical modeling. Part 1 contains variables for Appalachian subregions, Beale county codes, distress codes, number of families and households, population size, racial and age composition of population, dependency ratio, population growth, number of births and deaths, net migration, education, household composition, median family income, male and female employment status, and mobility. Part 2 variables include county identifiers plus numbers of total index crimes, violent index crimes, property index crimes, homicides, rapes, robberies, assaults, burglaries, larcenies, and motor vehicle thefts annually from 1977 to 1996.

About this dataset

Context

The dataset tabulates the population of Appalachia by race. It includes the population of Appalachia across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Appalachia across relevant racial categories.

Key observations

The percent distribution of Appalachia population by race (across all racial categories recognized by the U.S. Census Bureau): 82.69% are white, 6.89% are Black or African American and 10.42% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Appalachia
• Population: The population of the racial category (excluding ethnicity) in the Appalachia is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Appalachia total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Appalachia Population by Race & Ethnicity. You can refer the same here

The coal mining industry employed 27,632 people in the Appalachian region in 2023, of which around 73 percent worked in underground mines. Since 2010, the minority of the coal mine employees in the region worked in surface mines. During the period in consideration, coal-mining employment in the Appalachian region presented a trend of decline, with figures peaking in 2011, at some 60.2 thousand employees.

Identical observations, conducted 1-4 times per year for 15-20 years at two locations in the southern Appalachians, have yielded quantitative data on populations of six species of salamanders. Although the numbers have fluctuated for various reasons, there has been no trend in the numbers of any of the species. The "world-wide decline of amphibian populations" has not occurred in the two localities studied. Please refer to the methodological summary near each graph on the following web page, http://www.unc.edu/~rhwiley/salamandertrends/ The number of salamanders observed in a 1.5 hour search from the creek southward up the slopes 150 m (average of two trips in September each year).

About this dataset

Context

The dataset tabulates the Appalachia population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Appalachia. The dataset can be utilized to understand the population distribution of Appalachia by age. For example, using this dataset, we can identify the largest age group in Appalachia.

Key observations

The largest age group in Appalachia, VA was for the group of age 10 to 14 years years with a population of 197 (12.22%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Appalachia, VA was the 80 to 84 years years with a population of 12 (0.74%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Appalachia is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Appalachia total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Appalachia Population by Age. You can refer the same here

Appalachian Coal Data were prepared in late 2019 and early 2020 to support technical writing about Appalachian coalfield history, environment, and communities. The data originate from US federal government publications. All data sources are non-copyrighted. The data concern Appalachian and US coal production quantities, pricing, and revenue generation; Appalachian coal’s role in the USA’s energy economy; land areas disturbed by Appalachian coal mining; and selected coal-production, population, economic, and human-health metrics for counties and independent cities in seven states encompassing the Appalachian coalfield classified by coal-production status. The posted data are time series covering differing periods, some extending as far back as the late 1700s and all terminating in recent years, 2011 through 2019. The database was constructed while considering the Appalachian coalfield to include coal-mining areas of Tennessee, eastern Kentucky, West Virginia, Maryland, Pennsylvania, and the southwestern coalfield area of Virginia.

Human populations are rapidly expanding and encroaching on previously undisturbed habitats. Stream salamanders in the southern Appalachian Mountains are a diverse and abundant group threatened by rapid exurban development in high-elevation watersheds. Previous research has demonstrated the sensitivity of salamanders to urbanization, but little research exists describing the mechanisms behind population declines and extirpations. Appalachian stream salamanders are adapted to forested streams with dense overstory and little light, yet following urbanization, light gaps associated with land clearing emerge. Light avoidance behaviors may alter movement behaviors of salamanders, fragmenting populations on opposite sides of light gaps. To study the effects on riparian disturbance on salamanders we established 6 experimental sites with canopy gaps ranging from 13m to 85m in stream length and 2 control sites lacking canopy gaps in May of 2010. Animals were collected within these plots, marked, and translocated to the plot on the opposite side of the gap. To establish detection probabilities in the absence of translocation, we established an additional 10m plot in the forest at each site where individuals were captured, marked, and re-released within this area. Recaptured individuals were measured and in some cases re-marked if those individuals had returned to their capture location.

Four neochoristoderan vertebral centra are described from the latest Cretaceous of New Jersey. One specimen was recovered from the basal transgressive lag of the Navesink Formation in the area of Holmdel Park, New Jersey, and two others were recovered nearby and likely were derived from the same horizon. The fourth was recovered from the Marshalltown sequence in the vicinity of the Ellisdale Dinosaur Site. These vertebrae expand the geographic range of Late Cretaceous neochoristoderes in North America by over 2000 km further east, and represent the first neochoristoderan remains from the Atlantic coastal plain. To discern whether neochoristodere remains are to be expected in New Jersey, and elucidate why neochoristoderes are apparently so rare in Appalachia, we implemented ecological niche modeling to predict the range of suitable habitat for Champsosaurus, the only known genus of Late Cretaceous neochoristoderes. We found that in Appalachia, the ideal habitat of Champsosaurus likely existed slightly further north and west than the Atlantic coastal plain, and New Jersey is likely on or near the margin of this suitable habitat space. These results suggest that the occurrence of neochoristoderes in New Jersey is consistent with the habitat requirements of known Late Cretaceous neochoristoderes. These vertebrae therefore may represent the southern margin of a population of neochoristoderes that lived further inland, where latest Cretaceous sediments are not preserved. The continued recovery of material from Late Cretaceous deposits along the Atlantic coast, and review of existing collections, is encouraged to clarify the true distribution of neochoristoderes in Appalachia.

18 to 64 years Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Appalachia, Virginia by age, education, race, gender, work experience and more.

ARC funds projects that address the four goals identified in the Commission's strategic plan:Increase job opportunities and per capita income in Appalachia to reach parity with the nation.Strengthen the capacity of the people of Appalachia to compete in the global economy.Develop and improve Appalachia's infrastructure to make the Region economically competitive.Build the Appalachian Development Highway System to reduce Appalachia's isolation.Each year ARC provides funding for several hundred projects in the Appalachian Region, in areas such as business development, education and job training, telecommunications, infrastructure, community development, housing, and transportation. These projects create thousands of new jobs; improve local water and sewer systems; increase school readiness; expand access to health care; assist local communities with strategic planning; and provide technical and managerial assistance to emerging businessesARC Website: https://www.arc.gov/Data Download: https://ky.box.com/v/kymartian-KyBnds-ARC-counties

Combined data file for analysis in nexus format. (NEX 421 kb)

Previously, American black bears (Ursus americanus) were thought to follow the pattern of female philopatry and male-biased dispersal. However, recent studies have identified deviations from this pattern. Such flexibility in dispersal patterns can allow individuals greater ability to acclimate to changing environments. We explored dispersal and spatial genetic relatedness patterns across ten black bear populations—including long established (historic), with known reproduction >50 years ago, and newly established (recent) populations, with reproduction recorded <50 years ago—in the Interior Highlands and Southern Appalachian Mountains, United States. We used spatially-explicit, individual-based genetic simulations to model gene flow under scenarios with varying levels of population density, genetic diversity, and female philopatry. Using measures of genetic distance and spatial autocorrelation, we compared metrics between sexes, between population types (historic and recent), and among simulated scenarios which varied in density, genetic diversity, and sex-biased philopatry. In empirical populations, females in recent populations exhibited stronger patterns of isolation-by-distance (IBD) than females and males in historic populations. In simulated populations, low density populations had a stronger indication of IBD than medium to high density populations; however, this effect varied in empirical populations. Condition dependent dispersal strategies may permit species to cope with novel conditions and rapidly expand populations. Pattern-process modelling can provide qualitative and quantitative means to explore variable dispersal patterns, and could be employed in other species, particularly to anticipate range shifts in response to changing climate and habitat conditions.

Lung cancer is the leading cause of cancer-related death for women in the US, including the Appalachian region (North et al. 2013). Although lung cancer rates have declined steadily among men since the 1990s, such decreases have not been seen among women, including alarming mortality rates in Central Appalachia (Paskett et al. 2011; Appalachian Regional Commission 2019). Despite high incidence and mortality rates, few studies focus on lung cancer risk reduction or prevention among this population (Thompson et al. 2021). Interventions to reduce lung cancer risk among Appalachian women need to consider social and environmental contexts, including tobacco policies, insurance access, environmental exposures (e.g., second-hand smoke, radon), historical livelihoods in the region (e.g., farming, mining), and chronic stress (Paoletti et al. 2012; Hahn et al. 2018; Slatore et al. 2010; Stanifer et al. 2022; Alberg et al. 2013). In this scoping review, we seek to identify and summarize existing evidence-based interventions (EBIs) capable of addressing lung cancer risk reduction (e.g., tobacco cessation, smoke-free policies, environmental exposures) among Appalachian women. Studies will be identified by searching PubMed, CINAHL Complete (EBSCOhost), PsycInfo (EBSCOhost), Web of Science Core Collection (Clarivate) and Cochrane Database for Systematic Reviews and Library CENTRAL. The researchers will follow a systematic approach to track data on a given topic and establish central ideas, theories, sources and information gaps across the peer-reviewed literature. We will follow a methodological framework developed by Arksey and O’Malley (2005) and expanded upon by Levac et al. (2010). Two independent reviewers will screen the search results at the abstract level for inclusion. For potentially relevant articles, information to be extracted include: article authors, year published, intervention title, intervention content area of focus, type of assessment/analysis completed, population of focus, Appalachian state(s) of focus, primary mode of delivery, major findings/developed evidence base, and important barriers/facilitators or other social, behavior, or environmental factors to consider. We will utilize Covidence to track, de-duplicate, and finalize included search articles. We will use EndNote for data management to store selected full text articles. Both reviewers will be involved in the screening processes, and conflicts will be handled through the addition of a third reviewer to resolve discrepancies. Through this scoping review, we will describe existing evidence-based interventions, identify important gaps, and highlight next steps for intervention development related to lung cancer risk in this population.

All unique haplotypes/alleles (COI, CAD, KKV, and ITS2) and GenBank #s. (XLSX 53 kb)

Informed conservation of stream fishes requires detailed understanding of the effects of both natural processes and anthropogenic activities on genetic diversity. Brook Trout Salvelinus fontinalis, a salmonid native to eastern North America, typically resides in cold, high-quality stream ecosystems. The species has not only faced historical anthropogenic pressures, but also confronts current and future pressures. In a genetic analysis we used a reduced representation sequencing method (ddRADseq) to characterize 63 individuals from 23 streams where Brook Trout are native in the Appalachian region of Pennsylvania. A total of 2,590 loci passed filtering criteria, and 53% displayed significant association with a major stream drainage basin (Susquehanna or Allegheny; mean FST = 0.085). Mapping of the sequencing reads to the Atlantic Salmon Salmo salar genome revealed no clustering of high interdrainage FST values to specific genome regions. Evidence for genetic heterogeneity within each drainage basin was also detected. Stepwise regression of observed heterozygosity against geographic and environmental features revealed that drainage basin and effective area of watersheds were significant predictors of observed heterozygosity of Brook Trout within streams. Natural features such as waterfalls and major drainage basin, as well as the effects of dams and acid-mine drainage have fragmented habitat and shaped genetic diversity within Brook Trout populations in the Appalachian region of Pennsylvania, overall indicating the vulnerability of this species to increased industrialization.

Like the Breeding Bird Census, the Winter Bird Population Study (WBPS) is a monitoring program that estimates winter bird densities in specific habitat types throughout North America. In addition, the vegetation of the plot is described. Relatively large areas of a single habitat are preferred for WBPS plots. Censusing methodology follows the Cornell Laboratory of Ornithology guidelines. Two permanent plots (the same two used in our BBCs), a ridge-top south-southwest facing 19.3 ha site in 120-130 year old oak-maple forest (elevation 408-448m), and a low elevation east-facing 16.9 ha site in 120-200 year old, oak-maple forest (elevation 265-347m), have been gridded at 30.5 m intervals in the Sanctuary's forest. The two sites are 750m apart. The habitat in each plot is characterized on a Habitat Classification Form supplied by the Cornell Laboratory of Ornithology. A detailed vegetation survey and vegetation mapping of each plot was conducted in 1989 (F. Watson, unpubl. data).

Elevation gradients provide a wealth of habitats for a wide variety of organisms. The southern Appalachian Mountains in eastern United States are known for their high biodiversity and rates of endemism in arthropods, including in high-elevation leaf-litter taxa that are often found nowhere else on earth. Trechus Clairville (Coleoptera: Carabidae) is a genus of litter inhabitants with a near-global distribution and over 50 Appalachian species. These span two subgenera, Trechus s. str. and Microtrechus Jeannel, largely restricted to north and south of the Asheville basin, respectively. Understanding the diversification of these 3–5 mm flightless beetles through geological time can provide insights into how the litter-arthropod community has responded to historical environments, and how they may react to current and future climate change. We identified beetles morphologically and sequenced six genes to reconstruct a phylogeny of the Appalachian Trechus. We confirmed the Asheville Basin as a biogeographical barrier with a split between the north and south occurring towards the end of the Pliocene. Finer scale biogeography, including mountain-range occupancy, was not a reliable indication of relatedness, with group ranges overlapping and many instances of species-, species group-, and subgeneric sympatry. This may be because of the recent divergence between modern species and species groups. Extensive taxonomic revision of the group is required for Trechus to be useful as a bioindicator, but their high population density and speciose nature make them worth additional time and resources.

Some college, associate's degree Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Appalachia, Virginia by age, education, race, gender, work experience and more.

The eastern subterranean termite, Reticulitermes flavipes, currently inhabits previously glaciated regions of the northeastern U.S., as well as the unglaciated southern Appalachian Mountains and surrounding areas. We hypothesized that Pleistocene climatic fluctuations have influenced the distribution of R. flavipes, and thus the evolutionary history of the species. We estimated contemporary and historical geographic distributions of R. flavipes by constructing Species Distribution Models (SDM). We also inferred the evolutionary and demographic history of the species using mitochondrial (cytochrome oxidase I and II) and nuclear (endo‐beta‐1,4‐glucanase) DNA sequence data. To do this, genetic populations were delineated using Bayesian spatial‐genetic clustering, competing hypotheses about population divergence were assessed using approximate Bayesian computation (ABC), and changes in population size were estimated using Bayesian skyline plots. SDMs identified areas in the north with suitable habitat during the transition from the Last Interglacial to the Last Glacial Maximum, as well as an expanding distribution from the mid‐Holocene to the present. Genetic analyses identified three geographically cohesive populations, corresponding with northern, central, and southern portions of the study region. Based on ABC analyses, divergence between the Northern and Southern populations was the oldest, estimated to have occurred 64.80 thousand years ago (kya), which corresponds with the timing of available habitat in the north. The Central and Northern populations diverged in the mid‐Holocene, 8.63 kya, after which the Central population continued to expand. Accordingly, phylogeographic patterns of R. flavipes in the southern Appalachians appear to have been strongly influenced by glacial‐interglacial climate change.