This dataset contains the following ".PDF", ".R", and ".RData" files: (1) A PDF file "Description of the SimuBP function.PDF"; (2) R scripts for Algorithm 1 (SimuBP), Algorithm 2, and Algorithm 3; (3) R scripts for Simulations S1a, S1b, S1c, S2a, S2b, S2c, and S3a; (4) An R script "pLD.R" used in Simulation S1c. (5) Results generated in Simulations S1a, S1b, S1c, S2a, S2b, and S3a.

With plasmid-mediated antibiotic resistance thriving and threatening to become a serious public health problem, it is paramount to increase our understanding of the forces that enable the spread and maintenance of drug resistance genes encoded in mobile genetic elements. The relevance of plasmids as vehicles for the dissemination of antibiotic resistance genes, in addition to the extensive use of plasmid-derived vectors for biotechnological and industrial purposes, has promoted the in-depth study of the molecular mechanisms controlling multiple aspects of a plasmids’ life cycle. This body of experimental work has been paralleled by the development of a wealth of mathematical models aimed at understanding the interplay between transmission, replication, and segregation, as well as their consequences in the ecological and evolutionary dynamics of plasmid-bearing bacterial populations. In this review, we discuss theoretical models of plasmid dynamics that span from the molecular mechanisms of plasmid partition and copy-number control occurring at a cellular level, to their consequences in the population dynamics of complex microbial communities. We conclude by discussing future directions for this exciting research topic.

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Aim: Land use change, climate change, and shifts to disturbance regimes make successful wildlife management challenging, particularly when ongoing urbanization constrains habitat and movement. Preserving and maintaining landscape connectivity is a potential strategy to support wildlife responding to these stressors. Using a novel model framework, we determined the population-level benefit of a set of identified potential corridors for spotted owl population viability.

Location: Southern California, United States.

Methods: Combining habitat suitability and dynamic metapopulation models, we compared the benefit of corridors to the Southern California spotted owl population, measured as the increase in the expected minimum abundance, both now and under a future climate. Our approach considered key corridor characteristics important to conservation decisions, namely, corridor irreplaceability and local population network benefit.

Results: We identified two corridors likely to increase So...

These files contain individual graphs tracking population dynamics in 104 individual Arctic/Subarctic Alaska communities, over the years from 1990 to 2022. The numerical data underlying these graphs have been archived separately with the Arctic Data Center: Hamilton, L.C. 2023. “Annual population, natural increase and net migration for rural Alaska communities 1990–2022.” Dataset archived with the NSF Arctic Data Center. https://arcticdata.io/catalog/view/doi:10.18739/A28K74Z2B The purpose of this "graphical library" is to provide visualizations of 1990-2022 population change for each town or village in a format that is simple to download, share, and apply to other purposes such as planning, proposals or case studies. The files (identical pdf and PowerPoint versions) include a brief rationale, illustration of the numerical database organization, description of sources, citations and links to published articles, and explanation of the graphical style. These notes are followed by 104 individual graphs, one per slide, organized by boroughs or census areas.

From the abstract of the referenced paper:

Randomly amplified polymorphic DNA markers (RAPDs) were applied in a cephalopod population study. Samples of the squid Moroteuthis ingens taken from around the Falkland Islands and Macquarie Island were used to test a null hypothesis that M. ingens forms a single, panmictic population in the Southern Ocean. Six of the 8 arbitrary RAPD primers screened produced a total of 30 reproducible polymorphic bands. Analysis of RAPD allele frequencies demonstrated high levels of variation between individuals but little variation between two sample sites. Although the differentiation between the two sites was low, subtle population structure was detected and the null hypothesis was rejected. The implications of low genetic differentiation between the two sites are briefly discussed in terms of possible egg and para-larval drift facilitated via the circumpolar current.

The PDF document available for download also includes a number of data tables.

See also ASAC project 1242 (ASAC_1242), Biology of Southern Ocean squid, ecological importance and potential commercial implications - a preliminary assessment.

This data package contains model data that were used to support conclusions drawn in “Declines in low-elevation subalpine populations outpace growth in high-elevation populations with warming”, by Conlisk et al. 2017. Experimental data collected at field sites within the Alpine Treeline Warming Experiment (ATWE), and data from long-term observational plots were collected on Niwot Ridge, Colorado, USA and used to formulate models contained within the folder “Model_archive” in the zipped folder “Conlisk_etal_JEcol2017_model_archive12022020.zip”. The contents of this compressed folder are described in the data user's guide attached to this archive. There are two folders within the zipped folder - “EngelmannSpruce” and “LimberPine” - for each of the two species in the paper. Models are stored as text files and .sch files can also be opened as text files. However, please note that all these files are specific to the RAMAS Metapop population modeling software, and you will need the program in order to be able to run these models. There are two separate documents, both named “Conlisk_JofEcology_SI_01262017” within “Model_archive”. One is a Microsoft Word file, and the other is a PDF. The former can be opened with Microsoft Word, and the latter can be opened by Adobe Acrobat Reader, or any software compatible with a PDF. ------------------ 1. Species distribution shifts in response to climate change require that recruitment increase beyond current range boundaries. For trees with long lifespans, the importance of climate-sensitive seedling establishment to the pace of range shifts has not been demonstrated quantitatively. 2. Using spatially explicit, stochastic population models combined with data from long-term forest surveys, we explored whether the climate-sensitivity of recruitment observed in climate manipulation experiments was sufficient to alter populations and elevation ranges of two widely distributed, high-elevation North American conifers. 3. Empirically observed, warming-driven declines in recruitment led to rapid modeled population declines at the low-elevation, “warm edge” of subalpine forest and slow emergence of populations beyond the high-elevation, “cool edge”. Because population declines in the forest occurred much faster than population emergence in the alpine, we observed range contraction for both species. For Engelmann spruce, this contraction was permanent over the modeled time horizon, even in the presence of increased moisture. For limber pine, lower sensitivity to warming may facilitate persistence at low elevations – especially in the presence of increased moisture – and rapid establishment above treeline, and, ultimately, expansion into the alpine. 4. Synthesis. Assuming 21st century warming and no additional moisture, population dynamics in high-elevation forests led to transient range contractions for limber pine and potentially permanent range contractions for Engelmann spruce. Thus, limitations to seedling recruitment with warming can constrain the pace of subalpine tree range shifts.

RMPWO_Population_By_COB: This dataset represents population change by number and percent for those counties in the area of the BLM (Bureau of Land Management) Resource management Plan for Western Oregon (RWO). http://www.blm.gov/or/plans/rmpswesternoregon/files/prmp/RMPWO_V2_Chapter_3_Socioeconomics.pdf p606 (Table 3-143)

AbstractClassical population genetic theory generally assumes either a fully haploid or fully diploid life cycle. However, many organisms exhibit more complex life cycles, with both free-living haploid and diploid stages. Here we ask what the probability of fixation is for selected alleles in organisms with haploid-diploid life cycles. We develop a genetic model that considers the population dynamics using both the Moran model and Wright-Fisher model. Applying a branching process approximation, we obtain an accurate fixation probability assuming that the population is large and the net effect of the mutation is beneficial. We also find the diffusion approximation for the fixation probability, which is accurate even in small populations and for deleterious alleles, as long as selection is weak. These fixation probabilities from branching process and diffusion approximations are similar when selection is weak for beneficial mutations that are not fully recessive. In many cases, particularly when one phase predominates, the fixation probability differs substantially for haploid-diploid organisms compared to either fully haploid or diploid species. Usage notesSupplementary Mathematica FileMathematica file deriving key results in the manuscript, including the simulations reported in the figures.SupplementaryFile.nbSupplementary Mathematica File (PDF)PDF version of the Mathematica file deriving key results in the manuscript, including the simulations reported in the figures.SupplementaryFile.pdf

Projected Population by Single Year of Age, Sex, Race, Hispanic Origin, and Nativity for the United States: 2016-2060 // Source: U.S. Census Bureau, Population Division // There are four projection scenarios: 1. Main series, 2. High Immigration series, 3. Low Immigration series, and 4. Zero Immigration series. // Note: Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. // For detailed information about the methods used to create the population projections, see https://www2.census.gov/programs-surveys/popproj/technical-documentation/methodology/methodstatement17.pdf. // Population projections are estimates of the population for future dates. They are typically based on an estimated population consistent with the most recent decennial census and are produced using the cohort-component method. Projections illustrate possible courses of population change based on assumptions about future births, deaths, net international migration, and domestic migration. The Population Estimates and Projections Program provides additional information on its website: https://www.census.gov/programs-surveys/popproj.html.

Jellyfish (pelagic Cnidarians and Ctenophores) form erratic and seemingly unpredictable blooms with often large, transient effects on ecosystem structure. To rapidly capitalize on favorable conditions, jellyfish can employ different life histories, which are either a life cycle with one annual sexual reproduction event and an overwintering benthic stage (metagenic life cycle), or continuous reproduction and a holoplanktonic life cycle. However, the links between life history, blooms, and environmental variability are unclear. Here, we examine how environmental variability can drive the bloom dynamics of typical jellyfish in coastal enclosed or semi-enclosed temperate ecosystems. With a simple community model, we reproduce typical seasonalities of the two strategies and trophic cascades triggered by abundant jellyfish, demonstrating how erratic blooms can be generated by irregular changes in the environment. Consistent with literature observations, we predict that metagenic jellyfish dominate early in the season, compared to holoplanktonic organisms, and are favored by increased seasonality. Our results reveal possible mechanisms driving coastal patterns of jellyfish blooms, and factors that are important for the outcome of competition between jellyfish with different life cycles. Such knowledge is important for our understanding of jellyfish blooms, which have large consequences for human activities and well-being, and may improve our ability to predict and manage local ecosystems.

About this dataset

Context

The dataset tabulates the Los Angeles 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 Los Angeles 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 Los Angeles was 3.82 million, a 0.05% decrease year-by-year from 2022. Previously, in 2022, Los Angeles population was 3.82 million, a decline of 0.26% compared to a population of 3.83 million in 2021. Over the last 20 plus years, between 2000 and 2023, population of Los Angeles increased by 118,340. In this period, the peak population was 3.98 million in the year 2019. 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 Los Angeles is shown in this column.
• Year on Year Change: This column displays the change in Los Angeles 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 Los Angeles Population by Year. You can refer the same here

These data are part of the Herring Program of the Exxon Valdez Oil Spill Trustee Council, project numbers 13120111-Q, 14120111-Q, and 16120111-Q, which is a multi-faceted study to determine why herring populations in Prince William Sound remain depressed since the early 1990s. One of the main goals of the Herring Program is to improve the ability to predict herring populations through research and monitoring. This dataset involves the development of a modified version of the age-structure-analysis (ASA) model currently used by the Alaska Department of Fish and Game (ADF&G) to predict herring biomass levels in Prince William Sound. This modified model is a Bayesian ASA model that uses Markov chain Monte Carlo (MCMC) to generate posterior distributions for model parameters and output. The Bayesian ASA model is written for the Automatic Differentiation Model Builder (ADMB) programming framework (Fournier et al., 2012). This dataset includes a written description of the Bayesian model, model files for ADMB (.R, .ctl, TPL, .dat, .PIN formats), model output files as code (.R and .RData formats) and tabular data (.csv format), and graphical summaries (in .pdf format), including posterior predictive intervals, parameter distributions, and key model outputs. Anyone who wishes to run the Bayesian ASA must download ADMB (http://www.admb-project.org/downloads) and R (https://www.r-project.org/).

The annual population growth in Ethiopia saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 2.52 percent. Yet 2023 saw the lowest population growth in Ethiopia with 2.52 percent. Annual population growth refers to the change in the population over time, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Uganda and Somalia.

Predators can have strong roles in structuring communities defined by foundation species. Accumulating evidence shows that predation on reef-building oysters can be intense and potentially compromise efforts to restore or conserve these globally decimated foundation species. However, understanding the controls on variation in oyster predation strength is impeded by inconsistencies in experimental methodologies. To address this challenge, we conducted the first meta-analysis to quantify the magnitude, uncertainty, and drivers of predator effects on oysters. We synthesized 384 predator-exclusion experiments from 49 peer-reviewed publications over 45 years of study (1977 to 2021). We characterized geographic and temporal patterns in oyster predation experiments, determined the strength of predator effects on oyster mortality and recruitment, and assessed how predation varies with oyster size, environmental conditions, the predator assemblage, and experimental design. Predators caused an average 4.3× increase in oyster mortality and 46% decrease in recruitment. Predation increased with oyster size and varied with predator identity and richness. Unexpectedly, we found no effects of latitude, tidal zone, or tidal range on predation strength. Predator effects differed with experiment type and tethering method, indicating the importance of experimental design and the caution warranted in extrapolating results. Our results quantify the importance of predation for oyster populations and suggest that consideration of the drivers of oyster predation in restoration and conservation planning may hasten recovery of these lost coastal foundation species.

About this dataset

Context

The dataset tabulates the Cedar Rapids 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 Cedar Rapids 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 Cedar Rapids was 135,958, a 0.32% decrease year-by-year from 2022. Previously, in 2022, Cedar Rapids population was 136,392, a decline of 0.35% compared to a population of 136,874 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Cedar Rapids increased by 14,470. In this period, the peak population was 137,758 in the year 2020. 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 Cedar Rapids is shown in this column.
• Year on Year Change: This column displays the change in Cedar Rapids 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 Cedar Rapids Population by Year. You can refer the same here

This archive reproduces a table titled "Table 3.1 Boone county population size, 1990 and 2000" from Wang and vom Hofe (2007, p.58). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses Census API to retrieve data, reproduce the table, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration and management. The Census API is used to obtain population counts from the 1990 and 2000 Decennial Census (Summary File 1, 100% data). All downloaded data are maintained in the notebook's temporary working directory while in use. The data are also stored separately with this archive.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code to perform the following functions:install/import necessary Python packagesintroduce a Census API Querydownload Census data via CensusAPI manipulate Census tabular data calculate absolute change and percent changeformatting numbersexport the table to csvThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the Census API downloads. The notebook could be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

Black-headed Ibis (Threskiornis melanocephalus Latham, 1790), also known as Oriental White Ibis, Indian White Ibis, and Black-necked Ibis, is a species of wading bird of ibis family Threskiornithidae, breeds in South and Southeast Asia from India to west and as far east as Japan. It is the only native ibis species in its range that has an overall white plumage with a black neck and head. The down-curved beak and legs are also black. Though often referred to as a wetland species, the black-headed ibis also forages in natural and man-made habitats. This species of ibis nests only during the rainy season. To understand population trend, nesting ecology, and potential habitats of this species, nine months (March-September, 2023) field study was undertaken in eight Talukas of Anand District, Central Gujarat, India. Among physical, phytological and ornithological parameters, following findings were observed and recorded [Refer Data File: 2024 Mendeley Data Elsevier Black-headed Ibis Population Dynamics Nesting Ecology Hiren B Soni.pdf (Attached)]. Plant Height (m) (Min. 3.10-March, Max. 6.90-April, Mean: 5.51), Plants (N=129) (Min. 4.00-September, Max. 35.00-August, Mean: 18.43), Plants (N=129) (Min. 4.00-September, Max. 35.00-August, Mean: 18.43), Nests (N=1518): Min. 64.00-September, Max. 404.00-April, Mean: 216.86, Individuals (N=2742): Min. 108.00-September, Max. 780.00-April, Mean: 391.71), Nest Height (m): Min. 2.20-March, Max. 6.20-April, Mean: 4.73), Outer Canopy (N): Min. 57.00-September, Max. 376.00-April, Mean: 202.71), Inner Canopy (N): Min. 7.00-March & September, Max. 42.00-September, Mean: 14.14), Upper Canopy (N): Min. 36.00- September, Max. 248.00-April, Mean: 138.14), Middle Canopy (N): Min. 16.00- August, Max. 154.00-March, Mean: 65.57), Lower Canopy (N): Min. 2.00- May, Max. 56.00-March, Mean: 14.57), Birds (N=1528): Min. 64.00- September, Max. 404.00-April, Mean: 218.29), Nest Position: North (Min. 11.00-June, Max. 122.00-April, Mean: 63.57), East (Min. 22.00-September, Max. 187.00-March, Mean: 90.14), South (Min. 6.00-June, Max. 75.00-April, Mean: 20.00), West (Min. 20.00-September, Max. 100.00-March, Mean: 42.29), Nesting Birds (N=1512): Min. 64.00-September, Max. 404.00-April, Mean: 216.00). This ‘Near Threatened’ species is undergoing a population reduction, which is suspected to be moderately rapid, and faces threats like hunting and disturbance at breeding colonies to drainage and conversion of foraging habitats to agriculture. It is vulnerable to drainage, disturbance, pollution, agricultural conversion, destruction of roosting and nesting sites, hunting, and collection of eggs and nestlings from colonies. Regular monitoring the population across its range, colonies at agricultural land, assessing effects of threats on population levels, and conducting local education programmes to discourage hunting and disturbance to encourage the protection of nesting areas in Anand District, Central Gujarat, India is warranted.

In 2023, the population of Africa was projected to grow by 2.34 percent compared to the previous year. The population growth rate on the continent has been constantly over 2.3 percent from 2000 onwards, and it peaked at 2.59 percent between 2012 and 2013. Despite a slowdown in the growth rate, the continent's population will continue to increase significantly in the coming years. The second-largest population worldwide In 2022, the total population of Africa amounted to around 1.4 billion. The number of inhabitants had grown steadily in the previous decades, rising from approximately 810 million in 2000. Driven by a decreasing mortality rate and a higher life expectancy at birth, the African population was forecast to increase to about 2.5 billion individuals by 2050. Africa is currently the second most populous continent worldwide after Asia. However, forecasts showed that Africa could gradually close the gap and almost reach the size of the Asian population in 2100. By that year, Africa might count 3.9 billion people, compared to 4.7 billion in Asia. The world's youngest continent The median age in Africa corresponded to 18.8 years in 2023. Although the median age has increased in recent years, the continent remains the youngest worldwide. In 2023, roughly 40 percent of the African population was aged 15 years and younger, compared to a global average of 25 percent. Africa recorded not only the highest share of youth but also the smallest elderly population worldwide. As of the same year, only three percent of Africa's population was aged 65 years and older. Africa and Latin America were the only regions below the global average of 10 percent. On the continent, Niger, Uganda, and Angola were the countries with the youngest population in 2023.

About this dataset

Context

The dataset tabulates the Uvalde 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 Uvalde 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 Uvalde was 15,436, a 0.30% increase year-by-year from 2022. Previously, in 2022, Uvalde population was 15,390, an increase of 0.30% compared to a population of 15,344 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Uvalde decreased by 811. In this period, the peak population was 16,681 in the year 2009. 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 Uvalde is shown in this column.
• Year on Year Change: This column displays the change in Uvalde 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 Uvalde Population by Year. You can refer the same here