Chart and table of population level and growth rate for the state of West Virginia from 1900 to 2024.

About this dataset

Context

The dataset tabulates the West Virginia population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of West Virginia across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of West Virginia was 1.77 million, a 0.03% decrease year-by-year from 2023. Previously, in 2023, West Virginia population was 1.77 million, a decline of 0.20% compared to a population of 1.77 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of West Virginia decreased by 36,715. In this period, the peak population was 1.86 million in the year 2012. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the West Virginia is shown in this column.
• Year on Year Change: This column displays the change in West Virginia population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 West Virginia Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the West Virginia population by year. The dataset can be utilized to understand the population trend of West Virginia.

Content

The dataset constitues the following datasets

• West Virginia Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

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/.

Resident Population in West Virginia was 1769.97900 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in West Virginia reached a record high of 2006.00000 in January of 1950 and a record low of 959.00000 in January of 1900. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in West Virginia - last updated from the United States Federal Reserve on September of 2025.

About this dataset

Context

The dataset tabulates the Huntington population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Huntington across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Huntington was 45,325, a 0.68% decrease year-by-year from 2022. Previously, in 2022, Huntington population was 45,634, a decline of 1.26% compared to a population of 46,215 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Huntington decreased by 6,311. In this period, the peak population was 51,636 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Huntington is shown in this column.
• Year on Year Change: This column displays the change in Huntington population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Huntington Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Parkersburg population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Parkersburg across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Parkersburg was 29,025, a 0.63% decrease year-by-year from 2022. Previously, in 2022, Parkersburg population was 29,208, a decline of 0.85% compared to a population of 29,457 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Parkersburg decreased by 4,646. In this period, the peak population was 33,671 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Parkersburg is shown in this column.
• Year on Year Change: This column displays the change in Parkersburg population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Parkersburg Population by Year. You can refer the same here

Figure S1. Mig-seq primers used in the current study.

Dataset S1. SNP data in .vcf format for Lonicera japonica.

Figure S2: STRUCTURE analyses. Left: Delta K plot showing the optional number of ancestral population clusters (based on Evanno et al. 2015 method). Right: Ancestry plots from analysis with ParallelStructure for k = 3 (above) and k = 5 (below). Colors correspond to each ancestral cluster.

About this dataset

Context

The dataset tabulates the West Point population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of West Point across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of West Point was 3,536, a 2.70% increase year-by-year from 2022. Previously, in 2022, West Point population was 3,443, an increase of 0.06% compared to a population of 3,441 in 2021. Over the last 20 plus years, between 2000 and 2023, population of West Point increased by 612. In this period, the peak population was 3,536 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the West Point is shown in this column.
• Year on Year Change: This column displays the change in West Point population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 West Point Population by Year. You can refer the same here

In 2020, about 82.66 percent of the total population in the United States lived in cities and urban areas. As the United States was one of the earliest nations to industrialize, it has had a comparatively high rate of urbanization over the past two centuries. The urban population became larger than the rural population during the 1910s, and by the middle of the century it is expected that almost 90 percent of the population will live in an urban setting. Regional development of urbanization in the U.S. The United States began to urbanize on a larger scale in the 1830s, as technological advancements reduced the labor demand in agriculture, and as European migration began to rise. One major difference between early urbanization in the U.S. and other industrializing economies, such as the UK or Germany, was population distribution. Throughout the 1800s, the Northeastern U.S. became the most industrious and urban region of the country, as this was the main point of arrival for migrants. Disparities in industrialization and urbanization was a key contributor to the Union's victory in the Civil War, not only due to population sizes, but also through production capabilities and transport infrastructure. The Northeast's population reached an urban majority in the 1870s, whereas this did not occur in the South until the 1950s. As more people moved westward in the late 1800s, not only did their population growth increase, but the share of the urban population also rose, with an urban majority established in both the West and Midwest regions in the 1910s. The West would eventually become the most urbanized region in the 1960s, and over 90 percent of the West's population is urbanized today. Urbanization today New York City is the most populous city in the United States, with a population of 8.3 million, while California has the largest urban population of any state. California also has the highest urbanization rate, although the District of Columbia is considered 100 percent urban. Only four U.S. states still have a rural majority, these are Maine, Mississippi, Montana, and West Virginia.

About this dataset

Context

The dataset tabulates the West Point population by year. The dataset can be utilized to understand the population trend of West Point.

Content

The dataset constitues the following datasets

• West Point, VA Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

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/.

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.

In 1998, formal demographic censusing of wild ginseng (Panax quinquefolius L.) populations was initiated in West Virginia. By 2004, thirty populations had been added to the census effort, spanning seven states (IN-2, KY-6, MD-1, NY-2, PA-2, VA-5, WV-12) and a wide variety of land use histories and eastern deciduous forest communities. The censusing effort continued without interruption at all populations until June, 2016. Annually, each population was visited twice. The first visit generally occurred between late May and the end of June. The second visit generally occurred in the first three weeks of August. The purpose of the spring census was to assess the population status at the time of year when the largest number of individuals were visible aboveground (post-germination, prior to substantial losses due to browsing and other causes). Detailed measures of plant size were made, with an emphasis on total leaf area calculation. In addition, a variety of plant condition notations were made, with the ultimate goal of determining mortality and recruitment in the population, as well as individual size transitions. The primary purpose of the second census each year was to assess seed production on each plant. In addition, further notations of plant condition were made to assess changes over the growing season. To maintain methodological consistency with field personnel turnover, the lead author participated in fieldwork throughout the study, visiting each population at least once every two years. In addition, after being trained themselves, graduate students trained undergraduate conservation interns to assure consistent methods were used each year. The data are suitable for demographic modeling, and the unique spatial and temporal extent allow the exploration of important questions about variability in population growth and viability of ginseng, America’s premiere wild harvested medicinal plant.

1.Natural and anthropogenic forest canopy disturbances significantly alter forest dynamics and lead to multi-dimensional shifts in the forest understorey. An understorey plant's ability to exploit alterations to the light environment caused by canopy disturbance leads to changes in population dynamics. The purpose of this work was to determine if population growth of a species adapted to low light increases in response to additional light inputs caused by canopy disturbance, or alternatively, declines due to long-term selection under low light conditions. 2.To address this question, we quantified the demographic response of an understorey herb to three contrasting forest canopy disturbances (ice storms, tent caterpillar defoliation and lightning strikes) that encompass a broad range of disturbance severity. We used a model shade-adapted understorey species, Panax quinquefolius, to parameterize stage-based matrix models. Asymptotic growth rates, stochastic growth rates and simulations of transient dynamics were used to quantify the population-level response to canopy disturbance. Life table response experiments were used to partition the underlying controls over differences in population growth rates. 3.Population growth rates at all three disturbed sites increased in the transition period immediately after the canopy disturbance relative to the transition period prior to disturbance. Stochastic population models revealed that growth rates increased significantly in simulations that included disturbance matrices relative to those simulations that excluded disturbance. Additionally, transient models indicated that population size (n) was larger for all three populations when the respective disturbance matrix was included in the model. 4.Synthesis Obligate shade species are most likely to be pre-adapted to take advantage of canopy gaps and light influx to a degree, and this pre-adaptation may be due to long-term selection under dynamic old growth forest canopies. We propose a model whereby population performance is represented by a parabolic curve where performance is maximized under intermediate levels of canopy disturbance. This study provides new evidence to aid our understanding of the population-level response of understorey herbs to disturbances whose frequency and intensity are predicted to increase as global climates continue to shift.