Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

About this dataset

Context

The dataset tabulates the Speed 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 Speed 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 Speed was 35, a 0% decrease year-by-year from 2022. Previously, in 2022, Speed population was 35, a decline of 0% compared to a population of 35 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Speed decreased by 9. In this period, the peak population was 44 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 Speed is shown in this column.
• Year on Year Change: This column displays the change in Speed 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 Speed Population by Year. You can refer the same here

About this dataset

Context

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

Content

The dataset constitues the following datasets

• Speed, NC 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/.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

Historical chart and dataset showing South Africa population growth rate by year from 1961 to 2023.

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

Plant populations are limited by resource availability and exhibit physiological trade-offs in resource acquisition strategies. These trade-offs may constrain the ability of populations to exhibit fast growth rates under water limitation and high cover of neighbors. However, traits that confer drought tolerance may also confer resistance to competition. It remains unclear how fitness responses to these abiotic conditions and biotic interactions combine to structure grassland communities and how this relationship may change along a gradient of water availability. To address these knowledge gaps, we estimated the low-density growth rates of populations in drought conditions with low neighbor cover and in ambient conditions with average neighbor cover for 82 species in six grassland communities across the Central Plains and Southwestern United States. We assessed the relationship between population tolerance to drought and resistance to competition and determined if this relationship was ..., Cover data These data include a subset of 82 species (113 species-site combinations) that were monitored annually as part of the Extreme Drought in Grasslands Experiment (EDGE). Topographically unform and hydrologically isolated plots were set up across six grassland types (tallgrass prairie, southern mixed-grass prairie, northern mixed-grass prairie, northern shortgrass prairie, southern shortgrass prairie, and desert grassland) and absolute cover of all species in four 1 x 1 m quadrats was estimated yearly from 2012–2017. At each site, ten control plots at each site received ambient rainfall over the experimental period, and ten treatment plots experienced a 66% reduction in growing season precipitation (equivalent to roughly 40–50% over the whole year) using greenhouse rainout shelters equipped with strips of clear corrugated polycarbonate. Additional site and experimental design details are available in Griffin†Nolan et al., (2019). Population growth rates Percent cover was used as..., , # Drought tolerant grassland species are generally more resistant to competition

These data were analyzed and presented in the accompanying paper where we observed a positive correlation between low-density population growth rates in drought and low-density population growth rates in the presence of interspecific neighbors. We also found that high leaf dry matter content and low (more negative) leaf turgor loss point were associated with higher population fitness in drought and with higher neighbor competition.

The EDGE_covers.csv dataset contains aggregated absolute cover estimates and annual population growth summarized from the Extreme Drought in Grassland Experiment (EDGE). The all_pop_data.csv contains the calculated population growth rates for each population and the trait data paired with each population. Finally, the Suppinfo_tableS1_traits.csv dataset was included as supplemental information for the publication. It also has the population-level traits data but i...

Human-induced environmental changes have a direct impact on species populations, with some species experiencing declines while others display population growth. Understanding why and how species populations respond differently to environmental changes is fundamental to mitigate and predict future biodiversity changes. Theoretically, species life-history strategies are key determinants shaping the response of populations to environmental impacts. Despite this, the association between species' life-histories and the response of populations to environmental changes has not been tested. In this study, we analysed the effects of recent land-cover and temperature changes on rates of population change of 1,072 populations recorded in the Living Planet Database. We selected populations with at least 5 yearly consecutive records (after imputation of missing population estimates) between 1992 and 2016, and for which we achieved high population imputation accuracy (in the cases where missing values had to be imputed). These populations were distributed across 553 different locations and included 461 terrestrial amniote vertebrate species (273 birds, 137 mammals, and 51 reptiles) with different life-history strategies. We showed that populations of fast-lived species inhabiting areas that have experienced recent expansion of cropland or bare soil present positive population trends on average, whereas slow-lived species display negative population trends. Although these findings support previous hypotheses that fast-lived species are better adapted to recover their populations after an environmental perturbation, the sensitivity analysis revealed that model outcomes are strongly influenced by the addition or exclusion of populations with extreme rates of change. Therefore, the results should be interpreted with caution. With climate and land-use changes likely to increase in the future, establishing clear links between species characteristics and responses to these threats is fundamental for designing and conducting conservation actions. The results of this study can aid in evaluating population sensitivity, assessing the likely conservation status of species with poor data coverage, and predicting future scenarios of biodiversity change.

Smallegange IM, Lucas S. 2023. DEBBIES to compare life history strategies across ectotherms. Preprint. https://doi.org/10.1101/2023.08.22.554265Demographic models are used to explore how life history traits structure life history strategies across species. This study presents the DEBBIES dataset that contains estimates of eight life history traits (length at birth, puberty and maximum length, maximum reproduction rate, fraction energy allocated to respiration versus reproduction, von Bertalanffy growth rate, mortality rates) for 185 ectotherm species. The dataset can be used to parameterise dynamic energy budget integral projection models (DEB-IPMs) to calculate key demographic quantities like population growth rate and demographic resilience, but also link to conservation status or biogeographical characteristics. Our technical validation shows a satisfactory agreement between observed and predicted longevity, generation time, age at maturity across all species. Compared to existing datasets, DEBBIES accommodates (i) easy cross-taxonomical comparisons, (ii) many data-deficient species, and (iii) population forecasts to novel conditions because DEB-IPMs include a mechanistic description of the trade-off between growth and reproduction. This dataset has the potential for biologists to unlock general predictions on ectotherm population responses from only a few key life history traits.

This dataset contains data from an analysis of differences in growth rate among three different barnacle populations breeding at different latitudes, described in the paper: Boom, Michiel P., van der Jeugd, H.P., Steffani, B., Nolet, B.A., Larsson, K., & Eichhorn, G. (2021), Postnatal growth rate varies with latitude in range-expanding geese – the role of plasticity and day length. Journal of Animal Ecology.

The postnatal growth period is a crucial life stage, with potential lifelong effects on an animal’s fitness. How fast animals grow depends on their life history strategy and rearing environment, and interspecific comparisons generally show higher growth rates at higher latitudes. However, to elucidate the mechanisms behind this gradient in growth rate, intraspecific comparisons are needed.

Recently, barnacle geese expanded their Arctic breeding range from the Russian Barents Sea coast southwards, and now also breed along the Baltic and North Sea coasts. Baltic breeders shortened their migration, while barnacle geese breeding along the North Sea stopped migrating entirely.

We collected cross-sectional data on gosling tarsus length, head length and body mass, and constructed population-specific growth curves to compare growth rates among three populations (Barents Sea, Baltic Sea and North Sea) spanning 17° in latitude.

Growth rate was faster at higher latitudes, and the gradient resembled the latitudinal gradient previously observed in an interspecific comparison of precocial species. Differences in day length among the three breeding regions could largely explain the observed differences in growth rate. In the Baltic, and especially in the Arctic population, growth rate was slower later in the season, most likely because of the stronger seasonal decline in food quality.

Our results suggest that differences in postnatal growth rate between the Arctic and temperate populations are mainly a plastic response to local environmental conditions. This plasticity can increase the individuals’ ability to cope with annual variation in local conditions, but can also increase the potential to re-distribute and adapt to new breeding environments.

Methods We collected biometric data on growing goslings during long-term studies in colonies from three study-populations: 1) A long-distance migratory population breeding in the Arctic in Kolokolkova Bay along the Barents Sea coast (68°35’N, 52°20’E), data collected in 6 years between 2003 and 2015; 2) A short-distance migratory population breeding on Gotland in the Baltic Sea (57°25’N, 18°53’E) data collected in 15 years between 1986 and 2000; 3) A sedentary population breeding in the Netherlands along the North Sea (51°40’N, 4°14’E) data collected in 5 years between 2004 and 2018 (Larsson et al., 1988; Van der Jeugd et al., 2003, 2009; Eichhorn et al., 2010).

Our analysis is based on all measured goslings with known age (Sample sizes: Barents Sea = 392; Baltic Sea = 933; North Sea = 116). Sex was determined based on cloacal inspection. Goslings were weighed in a bag using a Pesola spring scale with an accuracy of ± 5 g (if <600 g) or a digital hand scale or Pesola spring scale with an accuracy of ± 10 g (if >600 g). A calliper (± 0.1 mm) was used to measure the outer length of the bent tarsus. Head length was measured using a ruler (± 1 mm).

The number of daylight hours that had accumulated between hatching and capture was calculated for each gosling. Daylight was determined as the period between dawn and dusk, and was calculated based on the coordinates of the three breeding colonies using the R package “Suncalc” (see associated manuscript referenced above).

We calculated relative hatch dates by centralizing hatch dates within each cohort, because years can differ in onset of spring and consequently in timing of breeding and hatching. For the calculation of the relative hatch date for each gosling, we used the mean hatch date of the colonies (not only of the recaptured goslings), as established from nest monitoring (see associated manuscript referenced above for details).

The datasets associated can be used for the long-term dynamics of the models presented in Ackleh et al., 2019. The study here deals with a more in-depth analysis of these models, including global stability of equilibria, the existence of cycles, and chaos. The main focus of this work is to investigate how the speed of evolution may impact the population dynamics. For the non-evolutionary model developed in Ackleh et al., 2019, we first show that the system has an unstable interior equilibrium and the solution converges to a 6-cycle. This situation is described in figure-1 in the associated manuscript Ackleh et al., 2020. Figure-2 and figure-3 in the text illustrate a unique globally asymptotically stable interior equilibrium for a specific set of parameter values for the non-evolutionary model. For the pure trait model (with no predator-population) of the evolutionary case, it is shown in figure-4 and figure-5 that by increasing the value of the speed of the evolution, it is possible for this model to exhibit a classical period-doubling route to chaos. In figure-5(b), these scenarios are supported by the Lyapunov exponents for the pure-trait model. Figure-7 in the example-3 provided in the text gives the numerical illustration of a specific parameter value that may be finite or infinite for which the dynamics may alter, resulting in the destabilization of the interior equilibrium. Finally, figure-7 shows that the increasing the size of the speed of the evolution, it is possible to have stable cycles and chaos. This example also demonstrates that when the speed of evolution is fast, evolution may have a destabilizing effect on the population dynamics. This dataset supports the publication: Ackleh, A. S., Hossain, M. I., Veprauskas, A., & Zhang, A. (2020). Long-term dynamics of discrete-time predator-prey models: stability of equilibria, cycles and chaos. Journal of Difference Equations and Applications, 26(5), 693–726. doi:10.1080/10236198.2020.1786818.

FishDataSee ReadMe fileDryad.zip

From the AfriPop website..."High resolution, contemporary data on human population distributions are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. The AfriPop project was initiated in July 2009 with an aim of producing detailed and freely-available population distribution maps for the whole of Africa. Based on the approaches outlined in detail here and here, and summarized on the methods page, fine resolution satellite imagery-derived settlement maps are combined with land cover maps to reallocate contemporary census-based spatial population count data. Assessments have shown that the resultant maps are more accurate than existing population map products, as well as the simple gridding of census data. Moreover, the 100m spatial resolution represents a finer mapping detail than has ever before been produced at national extents. The approaches used in AfriPop dataset production are designed with operational application in mind, using simple and semi-automated methods to produce easily updatable maps. Given the speed with which population growth and urbanisation are occurring across much of Africa, and the impacts these are having on the economies, environments and health of nations, such features are a necessity for both research and operational applications."Data Source: AfriPop.org

Demographic parameters are key to understanding population dynamics. Here, we analyse the survival and reproduction of the German wolf population in the 20 years following recolonization. Specifically, we analysed the effects of environmental, ecological, and individual characteristics on i) the survival probability of the population; ii) annual survival rates of age classes; iii) reproduction probability; and iv) reproductive output, measured as the number of detected pups/juveniles. Using the Cox proportional hazards model, we estimated a median survival time of circa 3 years for wolves. Annual survival probabilities were found to be 0.75 for juveniles, 0.75 for subadults, and 0.88 for adults. Survival was lower for juveniles in winter and for subadult males in summer, probably associated with dispersal events. Low habitat suitability was clearly associated with lower survival in juveniles and subadults, but not in adults. Local territory density was related to increased survival. Reproduction probability within a territory was 0.89, but explanatory variables had no effect. Reproductive output was four pups/juveniles on average, positively related to habitat suitability and female experience, but negatively related to territory density. Survival values were very high for the species when compared to other regions. We hypothesize that carrying capacity has not been reached in the study area, thus the survival may decrease in the future if the landscape becomes saturated. Furthermore, our results highlight a spatial pattern in survival and reproduction, with areas of better habitat suitability favouring faster population growth. Thus, targeting conservation measures to low habitat suitability areas will have a strong population effect in the short term by boosting the survival and reproduction of the individuals, while long-term viability should be carefully planned with high suitability areas in mind, as those contain the territories with higher survival and reproduction potential. Methods Wolf individual and territory data for survival and reproduction analyses were provided by the Federal Documentation and Consultation Centre on Wolves (DBBW, www.dbb-wolf.de) and by the Senckenberg Centre for Wildlife Genetics. Information about individuals and territories was grouped into monitoring years (from the 1st of May to the 30th of April next year), starting in 2000 until 2020 (April 2021). Individuals were identified genetically and for the survival analysis, the original dataset was filtered to retain only reliable information on the lifespan of the animals. Thus, individuals with NA ("not available") in the variables 'sex' or 'date of birth' as well as individuals born or died outside the German border were removed, as the environmental data included in the demographic analyses were only available for Germany. Consequently, the status of the individuals (dead, alive) was assessed until April 2021. The age classes were defined as juveniles including pups (0-12 months), subadults (13-24 months), and adults (> 24 months) (Mech and Boitani, 2003). The final dataset contained a total of 1054 individuals. Reproduction data was analysed at the territory level. The number of juvenile counts might be less than the actual number of pups born, thus we defined this variable as ‘minimum reproductive output’. Territories with more than 10 observed pups/ juveniles were removed from analyses to account for the fact that such a high number of pups might stem from a double reproduction and thus belong to one or more females (n = 4). In addition, territories from the first year of pair formation were removed (n = 227), because pairs typically form shortly before or during the breeding season (in autumn or winter) and therefore, there is no opportunity for reproduction in the months prior to the pair formation, which would correspond to the reproduction in the first year in the dataset. The final dataset consisted of 723 entries comprising 205 different territories with data from 1-16 years per territory. Explanatory variables We analysed the survival and reproduction of wolves in relation to environmental and ecological conditions and individual characteristics. For the survival analysis, we used as environmental variables the wolf habitat suitability (Planillo et al., 2024) in an 8 km radius of the territory centroid, wolf local territory density for each year in a 50 km radius, season defined as summer (May-Oct) or winter (Nov-Apr), individual sex and age, with the latter being classified as age classes: juveniles < 12 months, subadults 12 to 24 months, and adults > 24 months old. For the reproduction analysis, environmental and ecological conditions were described by habitat suitability values and local territory density around each breeding territory. As individual characteristics, we included the experience of the reproductive female in the models, measured sequentially as the number of years the same breeding female had reproduced, i.e., the first year that the female reproduced was considered year 1, the second year 2, and so forth. Data Analysis

Survival analysis: Survival analysis was calculated for the whole population and for each of the age classes using Cox Proportional Hazards Regression (Therneau and Grambsch 2000).

Reproduction analysis: We analyzed reproduction patterns for i) the probability of reproduction in a territory and ii) the total number of juveniles per reproductive event. In both cases, data were analysed using generalised linear mixed-effects models (GLMMs) with binomial error distribution and logit link for reproduction probability and Poisson error distribution and log link for the reproductive output. Territory identity was included as a random effect. The total number of years that a territory was monitored in the dataset was included as a weighting variable to avoid an inflated effect of the territories observed only for one year. As explanatory variables, we included the mean habitat suitability of the territory, local territory density in a buffer of 50 km, and the quadratic effect of the experience of the female as fixed effects.

Population growth: We used the values of survival and reproduction to estimate population growth (λ) and contrast it with the observed data. We developed a population matrix model using three age classes, based on obtained values of reproduction and annual survival for the age classes, and used the eigenvalue of the matrix as our λ. We explored the observed population growth values with respect to the effects of the minimum and maximum values of habitat suitability. To compute the lambda for the latter cases, we predicted the survival of juveniles and subadults and the number of pups per reproduction in areas with the lowest and highest observed values of habitat suitability.

Crude rate per 100,000 population: the number of fast food outlets is divided by the population of the area and multiplied by 100,000.

Rationale

The environment in which we live and work has positive and negative effects on our health and wellbeing. One component of the built-up environment is food outlets and the choices they provide. Meals eaten outside of the home tend to be associated with higher calories, and portion sizes tend to be bigger, which can make it more challenging to eat healthily [1,2]. The neighbourhood food environment is one important modifiable determinant of dietary behaviour and obesity [3].

The availability of fast food in our environment is one issue, within a complex system [4], which is associated with a range of negative health outcomes and contributes to the obesogenic nature of some of our neighbourhoods. Fast food is more abundantly available in the most deprived areas of England where obesity in children and adults and the associated health conditions, such as type 2 diabetes, hypertension, and heart disease are most prevalent [5,6].

This indicator is designed to help users understand the number of fast food outlets in an area taking the size of the population into account. It is intended to support national policy making and influence planning activities in local authorities [7] with the aim of reducing the availability of fast food, where this is deemed desirable, in order to improve health outcomes.

References

Sugar reduction programme: industry progress 2015 to 2020 - GOV.UK
Calorie reduction programme: industry progress 2017 to 2021 - GOV.UK
Dietary inequalities: What is the evidence for the effect of the neighbourhood food environment?
A foresight whole systems obesity classification for the English UK biobank cohort
The Association between Fast Food Outlets and Overweight in Adolescents Is Confounded by Neighbourhood Deprivation
The association between the presence of fast-food outlets and BMI
No new fast-food outlets allowed! Evaluating the effect of planning policy on the local food environment in the North East of England

Definition of numerator The numerator is a count, at a specific point in time, of fast food outlets in each geographic area. The inclusion criteria for counting fast food outlets is described in the methodology section below.

Definition of denominator Count of the population in each geographic area from Office for National Statistics (ONS) mid-year population estimates 2023.

Caveats

The Impact of Food Delivery Services In recent years there has been a large growth of food delivery services and meal delivery apps (MDAs). These companies allow customers to order food via mobile apps or websites for delivery to a chosen address. The availability of fast food through MDAs expands the geographic coverage of fast food outlets, increasing the likelihood that customers will order from outlets in neighbouring local authority areas, especially in urban settings. These apps extend the reach of fast food outlets beyond the immediate resident or visiting population.

Cross Local Authority Movements Some individuals may travel to neighbouring local authorities to access fast food outlets. Therefore, data showing fast food outlets within a specific area may underestimate actual exposure for the resident population. Users should consider data from neighbouring areas to gain a more comprehensive understanding of fast food exposure.

The Impact of Non-Resident Populations Movements for work, shopping, entertainment, or tourism also affect exposure. Local authorities with high numbers of fast food outlets per 100,000 residents may have large non-resident populations who are not included in the population denominator but are still exposed to these outlets.

Data Source and Methods The data may not fully capture all fast food outlets. Many businesses are multi-functional—offering dine-in, takeaway, and delivery—and may be categorized as restaurants or cafés, thus excluded from fast food counts. Inclusion based on business names helps mitigate this, but some outlets may still be missed. Conversely, some outlets categorized as ‘Takeaway/sandwich shop’ may not be considered fast food.

Data from the FSA FHRS is presumed accurate, but errors in collection, collation, and entry are possible. Categorization may vary between local authorities. For example, an outlet selling sandwiches and tea might be recorded as a Restaurant/Café/Canteen in one area and as a Takeaway/sandwich shop in another.

Different data sources use varying definitions and categorizations, so numbers may differ across datasets. The data here counts businesses identified as fast food outlets, not direct access for individuals, which is influenced by factors like opening hours, pricing, parking, and delivery options. It reflects premises use rather than individual access.

Life history traits are used to predict asymptotic odds of extinction from dynamic conditions. Less is known about how life history traits interact with stochasticity and population structure of finite populations to predict near-term odds of extinction. Through empirically parameterized matrix population models, we study the impact of life history (reproduction, pace), stochasticity (environmental, demographic), and population history (existing, novel) on the transient population dynamics of finite populations of plant species. Among fast and slow pace and either uniform or increasing reproductive intensity or short or long reproductive lifespan, slow, semelparous species are at the greatest risk of extinction. Long reproductive lifespans buffer existing populations from extinction while the odds of extinction of novel populations decreases when reproductive effort is uniformly spread across the reproductive lifespan. Our study highlights the importance of population structure, pace, a..., We gathered empirically derived stage-based population models from the COMPADRE Plant Matrix Database v6.22.5.0 (created 2022-05-11; Salguero-Gomez et al. 2015) that (1) were ergodic and irreducible, (2) were modelled on an annual time step (Iles et al. 2016), and (3) did not explicitly parse clonal growth into a separate matrix. This subset resulted in 1,606 matrices representing multiple years and/or populations of 317 plant species., , # Data from: Pace and parity predict short-term persistence of small plant populations

Access these datasets on Dryad https://doi.org/10.5061/dryad.2547d7wzv

Empirically derived stage-based population models were collected from the COMPADRE Plant Matrix Database v6.22.5.0 (created 2022-05-11; Salguero-Gomez et al. 2015) that (1) were ergodic and irreducible, (2) were modelled on an annual time step, and (3) did not explicitly parse clonal growth into a separate matrix. This subset resulted in 1,606 matrices representing multiple years and/or populations of 317 plant species.

Life history traits were estimated from the matrix population models using the R package Rage (Jones et al. 2022).

Plant matrix population models were used to simulate asymptotic growth, demographic and environmental stochasticity and test the impact of initial population size, population structure, stochasticity, and life history on the odds of extinction. The impa...

Corridors with good-quality habitats maintain the spatial dynamics of metapopulations by promoting dispersal between habitat patches, potentially buffering populations and communities against continued global change. However, this function is threatened by habitats becoming increasingly fragmented, and habitat matrices becoming increasingly inhospitable, potentially reducing the resilience and persistence of populations. Yet, we lack a clear understanding of how reduced corridor quality interacts with rates of environmental change to destabilise populations. Using laboratory microcosms containing metapopulations of the Collembola Folsomia candida, we investigate the impact of corridor quality on metapopulation persistence under a range of simulated droughts, a key stressor for this species. We manipulated both drought severity and the number of patches affected by drought across landscapes connected by either good or poor-quality corridors. We measured the time of metapopulation extinction, the maximum rate of metapopulation decline, and the variability of abundance among patches as criteria to evaluate the persistence ability of metapopulations. We show that whilst drought severity negatively influenced the time of metapopulation extinction and the increase in drought patches caused metapopulation decline, these results were mitigated by good quality corridors, which increased metapopulation persistence time and decreased both how fast metapopulations declined and the inter-patch variability in abundances. Our results suggest that enhancing corridor quality can increase the persistence of metapopulations, increasing the time available for conservation actions to take effect, and/or for species to adapt or move in the face of continued stress. Given that fragmentation increases the isolation of habitats, improving the quality of habitat corridors may provide a useful strategy to enhance the resistance of spatially structured populations.

Competing theoretical models make different predictions on which life history strategies facilitate growth of small populations. While ‘fast’ strategies allow for rapid increase in population size and limit vulnerability to stochastic events, ‘slow’ strategies and bet-hedging may reduce variance in vital rates in response to stochasticity. We test these predictions using biological invasions since founder alien populations start small, compiling the largest dataset yet of global herpetological introductions and life history traits. Using state-of-the-art phylogenetic comparative methods, we show that successful invaders have fast traits, such as large and frequent clutches, at both establishment and spread stages. These results, together with recent findings in mammals and plants, support ‘fast advantage’ models and the importance of high potential population growth rate. Conversely, successful alien birds are bet-hedgers. We propose that transient population dynamics and differences in...

About this dataset

Context

The dataset tabulates the data for the Speed, KS population pyramid, which represents the Speed population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Speed, KS, is 0.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Speed, KS, is 15.0.
• Total dependency ratio for Speed, KS is 15.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Speed, KS is 6.7.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group for the Speed population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Speed for the selected age group is shown in the following column.
• Population (Female): The female population in the Speed for the selected age group is shown in the following column.
• Total Population: The total population of the Speed for the selected age group is shown in the following column.

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 Speed Population by Age. You can refer the same here