This dataset includes data on 25 transitions of a matrix demographic model of the invasive species Vincetoxicum nigrum (L.) Moench (black swallow-wort or black dog-strangling vine) and Vincetoxicum rossicum (Kleopow) Barb. (pale swallow-wort or dog-strangling vine) (Apocynaceae, subfamily Asclepiadoideae), two invasive perennial vines in the northeastern U.S.A. and southeastern Canada. The matrix model was developed for projecting population growth rates as a result of changes to lower-level vital rates from biological control although the model is generalizable to any control tactic. Transitions occurred among the five life stages of seeds, seedlings, vegetative juveniles (defined as being in at least their second season of growth), small flowering plants (having 1–2 stems), and large flowering plants (having 3 or more stems). Transition values were calculated using deterministic equations and data from 20 lower-level vital rates collected from 2009-2012 from two open field and two forest understory populations of V. rossicum (43°51’N, 76°17’W; 42°48'N, 76°40'W) and two open field populations of V. nigrum (41°46’N, 73°44’W; 41°18’N, 73°58’W) in New York State. Sites varied in plant densities, soil depth, and light levels (forest populations). Detailed descriptions of vital rate data collection may be found in: Milbrath et al. 2017. Northeastern Naturalist 24(1):37-53. Five replicate sets of transition data obtained from five separate spatial regions of a particular infestation were produced for each of the six populations. Note: Added new excel file of vital rate data on 12/7/2018. Resources in this dataset:Resource Title: Matrix model transition data for Vincetoxicum species. File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

Abstract The demographic dynamics of the city of Porto Alegre (Southern Brazil) was characterized, in the last decade, by a reduction in fertility rates, low population growth and an increasing number of elderly people, according to data from the IBGE Census (2010). This indicates, therefore, a demographic transition phase. This study aims to relate the demographic transition to the production of the urban space of Porto Alegre in the intercensal period from 2000 to 2010. Urban space production, specifically property development, is analyzed here through the identification of the relationship between urban growth/population growth and the city’s spatial configuration trends.

The total fertility rate of the world has dropped from around 5 children per woman in 1950, to 2.2 children per woman in 2025, which means that women today are having fewer than half the number of children that women did 75 years ago. Replacement level fertility This change has come as a result of the global demographic transition, and is influenced by factors such as the significant reduction in infant and child mortality, reduced number of child marriages, increased educational and vocational opportunities for women, and the increased efficacy and availability of contraception. While this change has become synonymous with societal progress, it does have wide-reaching demographic impact - if the global average falls below replacement level (roughly 2.1 children per woman), as is expected to happen in the 2050s, then this will lead to long-term population decline on a global scale. Regional variations When broken down by continent, Africa is the only region with a fertility rate above the global average, and, alongside Oceania, it is the only region with a fertility rate above replacement level. Until the 1980s, the average woman in Africa could expect to have 6-7 children over the course of their lifetime, and there are still several countries in Africa where women can still expect to have 5 or more children in 2025. Historically, Europe has had the lowest fertility rates in the world over the past century, falling below replacement level in 1975. Europe's population has grown through a combination of migration and increasing life expectancy, however even high immigration rates could not prevent its population from going into decline in 2021.

About this dataset

Context

The dataset tabulates the Index 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 Index 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 Index was 157, a 1.29% increase year-by-year from 2022. Previously, in 2022, Index population was 155, an increase of 1.31% compared to a population of 153 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Index decreased by 3. In this period, the peak population was 211 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 Index is shown in this column.
• Year on Year Change: This column displays the change in Index 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 Index Population by Year. You can refer the same here

In 2023, it is estimated that the BRICS countries have a combined population of 3.25 billion people, which is over 40 percent of the world population. The majority of these people live in either China or India, which have a population of more than 1.4 billion people each, while the other three countries have a combined population of just under 420 million. Comparisons Although the BRICS countries are considered the five foremost emerging economies, they are all at various stages of the demographic transition and have different levels of population development. For all of modern history, China has had the world's largest population, but rapidly dropping fertility and birth rates in recent decades mean that its population growth has slowed. In contrast, India's population growth remains much higher, and it is expected to overtake China in the next few years to become the world's most populous country. The fastest growing population in the BRICS bloc, however, is that of South Africa, which is at the earliest stage of demographic development. Russia, is the only BRICS country whose population is currently in decline, and it has been experiencing a consistent natural decline for most of the past three decades. Growing populations = growing opportunities Between 2000 and 2026, the populations of the BRICS countries is expected to grow by 625 million people, and the majority of this will be in India and China. As the economies of these two countries grow, so too do living standards and disposable income; this has resulted in the world's two most populous countries emerging as two of the most profitable markets in the world. China, sometimes called the "world's factory" has seen a rapid growth in its middle class, increased potential of its low-tier market, and its manufacturing sector is now transitioning to the production of more technologically advanced and high-end goods to meet its domestic demand.

Chile is in an advanced demographic transition stage with the population over 60 years of age representing 15% of the total population and whose number of elderly has more than doubled between 1990 and 2014. Rapid economic advancement has promoted significant changes in social organization to which the country is not accustomed. The mental health problems of the elderly are particularly challenging to the country's present social and health structures. The prevalence of dementia in people over 60 years exceeds 8% and is even higher in the rural population. There is more training on dementia in the local medical and scientific community, increased awareness within the civilian community but insufficient responsiveness from the state to the broad diagnostic and therapeutic requirements of patients and caregivers. The objective of the present study was to provide an update of the information on dementia in the context of the ageing process in Chile.

Economic growth and modernization of society are generally associated with fertility rate decreases but which forces trigger this is unclear. In this paper we assess how fertility changes with increased labor market participation of women in rural Senegal. Evidence from high-income countries suggests that higher female employment rates lead to reduced fertility rates but evidence from developing countries at an early stage of demographic transition is largely absent. We concentrate on a rural area in northern Senegal where a recent boom in horticultural exports has been associated with a sudden increase in female off-farm employment. Using survey data we show that employed women have a significantly higher age at marriage and at first childbirth, and significantly fewer children. As causal identification strategy we use instrumental variable and difference-in-differences estimations, combined with propensity score matching. We find that female employment reduces the number of children per woman by 25%, and that this fertility-reducing effect is as large for poor as for non-poor women and larger for illiterate than for literate women. Results imply that female employment is a strong instrument for empowering rural women, reducing fertility rates and accelerating the demographic transition in poor countries. The effectiveness of family planning programs can increase if targeted to areas where female employment is increasing or to female employees directly because of a higher likelihood to reach women with low-fertility preferences. Our results show that changes in fertility preferences not necessarily result from a cultural evolution but can also be driven by sudden and individual changes in economic opportunities.

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.

This study explores the relationship between grandparental co-residence and net fertility – measured as the number of children under five – in Mexico across three key phases of its demographic transition: 1930 (pre-transitional), 1970 (population growth), and 2015 (fertility decline). Using census microdata and Poisson and multinomial regression models, we assess how intergenerational household structures interact with family socioeconomic status and cultural context to influence fertility outcomes. A central innovation is the use of a reconstructed 10% sample of the 1930 census, complemented by an imputation strategy to infer kinship ties not recorded in the original data. This enabled one of the earliest large-scale analyses of family co-residence and reproduction in historical Mexico. Findings reveal that the effects of grandparental co-residence vary by context. In 1930, cohabitation with grandmothers – especially in rural indigenous households – was associated with lower fertility, while cohabitation with grandfathers in non-indigenous rural areas corresponded to higher fertility. In 1970, amid pronatalist policies and economic growth, these effects weakened overall but persisted modestly in rural contexts. By 2015, co-residence – particularly with both grandparents – was associated with higher fertility and lower variability in fertility (CV), suggesting a stabilizing role in reproductive behavior. In contrast, households without grandparents exhibited lower fertility and greater heterogeneity, appearing to lead the shift toward reduced fertility. These findings illustrate how extended family structures both reflect and shape reproductive adaptation across shifting demographic contexts. By integrating evolutionary concepts such as cooperative breeding and social learning biases, the study offers insight into how kin networks can either support or constrain fertility depending on historical, socioeconomic, and cultural conditions. In doing so, it also contributes methodologically by addressing the complexity of nested and interactive effects – an essential step for understanding fertility dynamics in culturally diverse populations undergoing demographic transformation.

Tuberculosis (TB) incidence has been in steady decline in China over the last few decades. However, ongoing demographic transition, fueled by aging, and massive internal migration could have important implications for TB control in the future. We collated data on TB notification, demography, and drug resistance between 2004 and 2017 across seven cities in Shandong, the second most populous province in China. Using these data, and age-period-cohort models, we (i) quantified heterogeneities in TB incidence across cities, by age, sex, resident status, and occupation and (ii) projected future trends in TB incidence, including drug-resistant TB (DR-TB). Between 2006 and 2017, we observed (i) substantial variability in the rates of annual change in TB incidence across cities, from -4.84 to 1.52%; (ii) heterogeneities in the increments in the proportion of patients over 60 among reported TB cases differs from 2 to 13%, and from 0 to 17% for women; (iii) huge differences across cities in the annual growths in TB notification rates among migrant population between 2007 and 2017, from 2.81 cases per 100K migrants per year in Jinan to 22.11 cases per 100K migrants per year in Liaocheng, with drastically increasing burden of TB cases from farmers; and (iv) moderate and stable increase in the notification rates of DR-TB in the province. All of these trends were projected to continue over the next decade, increasing heterogeneities in TB incidence across cities and between populations. To sustain declines in TB incidence and to prevent an increase in Multiple DR-TB (MDR-TB) in the future in China, future TB control strategies may (i) need to be tailored to local demography, (ii) prioritize key populations, such as elderly and internal migrants, and (iii) enhance DR-TB surveillance.

In 2025, there are six countries, all in Sub-Saharan Africa, where the average woman of childbearing age can expect to have between 5-6 children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of almost six children per woman, Chad is the country with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

In outbred sexually reproducing populations, age-specific mortality rates reach a plateau in late life following the exponential increase in mortality rates that marks aging. Little is known about what happens to physiology when cohorts transition from aging to late life. We measured age-specific values for starvation resistance, desiccation resistance, time-in-motion and geotaxis in ten Drosophila melanogaster populations: five populations selected for rapid development and five control populations. Adulthood was divided into two stages, the aging phase and the late-life phase according to demographic data. Consistent with previous studies, we found that populations selected for rapid development entered the late-life phase at an earlier age than the controls. Age-specific rates of change for all physiological phenotypes showed differences between the aging phase and the late-life phase. This result suggests that late life is physiologically distinct from aging. The ages of transitions in physiological characteristics from aging to late life statistically match the age at which the demographic transition from aging to late life occurs, in all cases but one. These experimental results support evolutionary theories of late life that depend on patterns of decline and stabilization in the forces of natural selection.

About this dataset

Context

The dataset tabulates the Red Level 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 Red Level 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 Red Level was 434, a 0.70% increase year-by-year from 2022. Previously, in 2022, Red Level population was 431, an increase of 0.47% compared to a population of 429 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Red Level decreased by 111. In this period, the peak population was 545 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 Red Level is shown in this column.
• Year on Year Change: This column displays the change in Red Level 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 Red Level Population by Year. You can refer the same here

In outbred sexually reproducing populations, age-specific mortality rates reach a plateau in late life following the exponential increase in mortality rates that marks aging. Little is known about what happens to physiology when cohorts transition from aging to late life. We measured age-specific values for starvation resistance, desiccation resistance, time-in-motion and geotaxis in ten Drosophila melanogaster populations: five populations selected for rapid development and five control populations. Adulthood was divided into two stages, the aging phase and the late-life phase according to demographic data. Consistent with previous studies, we found that populations selected for rapid development entered the late-life phase at an earlier age than the controls. Age-specific rates of change for all physiological phenotypes showed differences between the aging phase and the late-life phase. This result suggests that late life is physiologically distinct from aging. The ages of transitions in physiological characteristics from aging to late life statistically match the age at which the demographic transition from aging to late life occurs, in all cases but one. These experimental results support evolutionary theories of late life that depend on patterns of decline and stabilization in the forces of natural selection.

This dataset contains a variety of demographic measures (related to fertility, marriage, mortality and migration), plus a range of socio-economic indicators (related to households, age structure, and social class) for the 2000+ Registration Sub Districts (RSDs) in England and Wales for each census year between 1851 and 1911, and for the 600+ Registration Districts of Scotland 1851-1901. The measures have mainly been derived from the computerised individual level census enumerators' books (and household schedules for 1911) enhanced under the I-CeM project. I-CeM does not currently include data for England and Wales 1871, although the project has been able to access a version of the data for that year it does not contain information necessary to calculate many of the variables presented here. Scotland 1911 is also not available. Users should therefore beware that 1871 does not contain data for many of the variables. Additional data has been derived from the tables summarising numbers of births and deaths by year and areas, which were published by the Registrar General of England and Wales in his quarterly, annual and decennial reports of births, deaths and marriages. Data from the decennial reports was obtained from Woods (SN 3552) and we transcribed data from the quarterly and annual reports ourselves. Counts of births and deaths for Scottish Registration Districts were obtained from the Digitising Scotland project at the University of Edinburgh. The dataset builds on SN 8613 and SN 853547 which provide data for a more limited set of variables and for England and Wales only (the same dataset also has two UKDS SN numbers as it was re-routed by UKDS during the deposit process).

This project will present the first historic population geography of Great Britain during the late nineteenth century. This was a period of unprecedented demographic change, when both mortality and fertility started the dramatic secular declines of the first demographic transition. National trends are well established: mortality decline started in childhood and early adulthood, with infant mortality lagging behind, particularly in urban-industrial areas. The fall in fertility was led by the middle classes but quickly spread throughout society. Urban growth was fuelled by movement from the countryside to the city, but there was also considerable migration overseas, particularly from Scotland, although to some extent outmigration was offset by immigration. There was local and regional variation in these patterns, and a contrast between the demographic experiences of Scotland and of England and Wales. Marriage was later in Scotland but fertility within marriage higher, and the improvement in Scottish mortality was slower than that south of the border. However, while there has been research on local and regional patterns within each country, these have mainly been pursued separately, and it is therefore unclear whether there were real national differences or whether there were local demographic continuities across borders, and if so whether they followed economic, occupational, cultural or even linguistic lines. Understanding population processes involves a holistic appreciation of the interaction between the basic demographic components of fertility, mortality, nuptiality and migration, and how they come together, interacting with economic and cultural processes, to create a specific demographic system via the spread of people and ideas. This project is the first to consider a historical population geography of the whole of Great Britain across the first demographic transition, drawing together measures of nuptiality, fertility, mortality and migration for small geographic areas and unpacking how they interacted to produce the more readily available broad-brush national patterns for Scotland and for England and Wales.

We will build on our immensely successful project on the fertility of Victorian England and Wales, which used complete count census data for England and Wales to calculate more detailed fertility measures than ever previously possible for some 2000 small geographic areas and 8 social groups, allowing the investigation of intra-urban as well as urban-rural differences in fertility. The new measures allowed us to examine age patterns of fertility across the two countries for the first time. We were also able to calculate contextual variables from the census data which allowed us to undertake spatial analysis of the influences on fertility over time. As well as academic papers, our previous project presented summary data at a fine spatial resolution in an interactive online atlas, populationspast.org, a major new resource which is already being widely used as a teaching tool in both schools and universities.

In this new project we will calculate comparable measures of fertility and contextual variables using the full count census data for Scotland, 1851 to 1901 inclusive, to complement those for England and Wales....

Abstract Argentina’s fertility transition has exceptional characteristics. Compared to most Latin American countries, Argentina’s fertility declined relatively early and, unlike fertility transitions in Western Europe, this decline did not lead to a high period of natural population growth. By the beginning of the twenty-first century, Argentina seemed to experience fertility stagnation despite women’s increased formal education and labor force participation, and increased availability of contraceptives. Using the 1980, 1991, 2001, and 2010 Population Censuses, I demonstrate that fertility has continued its downward trend from 1980 to 2010. Changes in fertility behaviors are given by a decrease in the mean number of children per woman, but not by an increase in childlessness. However, there is evidence of postponement of childbearing. Results show that although Argentina is completing its first demographic transition, as it has not reached below-replacement fertility yet, this country could show signs of an emerging second demographic transition.

Resumo The demographic dividend has aroused interest among demographers and economists because it is seen as a window of oportunity for the economic development of countries that have experienced a demographic transition. There are reasons to question the sole virtuosity of the pure demographic dividend in economic growth. Crespo-Cuaresma et al. (2014) found that educational expansion has an important role in economic gains during the demographic dividend. To verify these results for the Brazilian case, we performed a decomposition exercise of economic support ratio (ESR), an alternative to demographic dependency ratio, to analyze the first demographic dividend. A simulation, applied for the period from 1970 to 2100 considering three scenarios of educational expansion, shows that educational expansion was and will be responsible for a big share of the economic gains of the Brazilian demographic dividend period, outperforming the change in age structure effect. In addition, an increase in a work-age population with post-secondary education appears to potentialize these results.

About this dataset

Context

The dataset tabulates the Round Rock 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 Round Rock 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 Round Rock was 130,406, a 2.74% increase year-by-year from 2022. Previously, in 2022, Round Rock population was 126,927, an increase of 2.27% compared to a population of 124,106 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Round Rock increased by 67,715. In this period, the peak population was 133,579 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 Round Rock is shown in this column.
• Year on Year Change: This column displays the change in Round Rock 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 Round Rock Population by Year. You can refer the same here

The molecular clock hypothesis is fundamental in evolutionary biology as by assuming constancy of the molecular rate it provides a time frame for evolution. However, increasing evidence shows time dependence of inferred molecular rates with inflated values obtained using recent calibrations. As recent demographic calibrations are virtually non-existent in most species, older phylogenetic calibration points (>1 Ma) are commonly used, which overestimate demographic parameters. To obtain more reliable rates of molecular evolution for population studies, I propose the Calibration of Demographic Transition (CDT) method, which uses the timing of climatic changes over the late glacial warming period to calibrate expansions in various species. Simulation approaches and empirical datasets from a diversity of species (from mollusk to humans) confirm that, when compared to other genealogy-based calibration methods, the CDT provides a robust and broadly applicable clock for population genetics. The resulting CDT rates of molecular evolution also confirm rate heterogeneity over time and among taxa. Comparisons of expansion dates with ecological evidence confirm the inaccuracy of phylogenetically derived divergence rates when dating population-level events. The CDT method opens opportunities for addressing issues such as demographic responses to past climate change and the origin of rate heterogeneity related to taxa, genes, time and genetic information content.