As of February 2025, there were 5.56 billion internet users worldwide, which amounted to 67.9 percent of the global population. Of this total, 5.24 billion, or 63.9 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 2024. In The Netherlands, Norway and Saudi Arabia, 99 percent of the population used the internet as of April 2024. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Asia was home to the largest number of online users worldwide – over 2.93 billion at the latest count. Europe ranked second, with around 750 million internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2023, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in the Arab States and Africa, with around a ten percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller gender gap. As of 2023, global internet usage was higher among individuals between 15 and 24 years across all regions, with young people in Europe representing the most significant usage penetration, 98 percent. In comparison, the worldwide average for the age group 15–24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

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.

Approximately 43 million people (about 14 percent of the U.S. population) rely on domestic wells as their source of drinking water. Unlike community water systems, which are regulated by the Safe Drinking Water Act, there is no comprehensive national program to ensure that the water is tested to ensure that is it safe to drink. A study published in 2009 from the National Water-Quality Assessment Program of the U.S. Geological Survey that assessed water-quality conditions from 2,100 domestic wells within 48 states reported that more than one in five (23 percent) of the sampled wells contained one or more contaminants at a concentration greater than a human-health benchmark. In addition, there are many activities, e.g., resource extraction, climate change-induced drought, and changes in land use patterns that could potentially affect the quality of the ground water source for domestic wells. The Health Studies Branch (HSB) of the National Center for Environmental Health, Centers for Disease Control and Prevention, created a Clean Water for Health Program to help address domestic well concerns. The goals of this program are to identify emerging public health issues associated with using domestic wells for drinking water and begin to develop a plan to address these issues. As part of this effort, HSB in cooperation with the U.S. Geological Survey has created models to estimate the probability of arsenic occurring at various concentrations in domestic wells in the U.S. Similar work has been done by public health professionals on a state and regional basis. We will present preliminary results of the project, including estimates of the domestic well population that is likely to have arsenic greater than 10 micrograms per liter. Nationwide, we estimate this to be just over 2 million people. Logistic regression model results showing probabilities of arsenic greater than 10 micrograms per liter in domestic wells in the U.S., based on data for arsenic concentrations in domestic wells across the U.S. will be described, as well as the use of data on domestic well use by county in the U.S., to estimate the affected population.

About this dataset

Context

The dataset tabulates the data for the South Carolina population pyramid, which represents the South Carolina 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 South Carolina, is 27.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for South Carolina, is 29.0.
• Total dependency ratio for South Carolina is 56.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for South Carolina is 3.4.

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 South Carolina population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the South Carolina for the selected age group is shown in the following column.
• Population (Female): The female population in the South Carolina for the selected age group is shown in the following column.
• Total Population: The total population of the South Carolina 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 South Carolina Population by Age. You can refer the same here

Chart and table of population level and growth rate for the Los Angeles metro area from 1950 to 2025. United Nations population projections are also included through the year 2035.

The Community Resilience Estimates (CRE) program provides an easily understood metric for how socially vulnerable every neighborhood in the United States is to the impacts of disasters.This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census, CRE, and ACS when using this data.Overview:Community resilience is the capacity of individuals and households within a community to prepare, absorb, respond, and recover from a disaster. Local planners, policy makers, public health officials, emergency managers, and community stakeholders need a variety of estimates to help assess the potential resiliency and vulnerabilities of communities and their constituent populations to help prepare and plan mitigation, recovery, and response strategies. Community Resilience Estimates (CRE) focuses on developing a tool to identify socio-economic vulnerabilities within populations. The 2022 Community Resilience Estimates (CRE) are produced using information on individuals and households from the 2022 American Community Survey (ACS) and the Census Bureau’s Population Estimates Program (PEP). The CRE uses small area modeling techniques that can be used for a broad range of disaster related events (hurricanes, tornadoes, floods, economic shocks, etc.) to identify population concentrations likely to be relatively more impacted by and have greater difficulties overcoming disasters.The end result is a data product which measures social vulnerability more accurately, timely, and address equity concerns differently than other measures.The CRE for Equity dataset provides information about the nation, states, counties, and census tracts from four different data sources. These sources include the Community Resilience Estimates, the American Community Survey, the 2020 Census, and the Census Bureau’s Planning Database. Providing all this information in one dataset allows users quick access to the data on a variety of topics concerning social vulnerability and equity.Data:The ACS is a nationally representative survey with data on the characteristics of the U.S. population. The sample is selected from all counties and county-equivalents and has a sample size of about 3.5 million housing units each year. It is the premier source for timely and detailed population and housing information about our nation and its communities. We also use auxiliary data from the PEP, the Census Bureau’s program that produces and publishes estimates of the population living at a given time within a geographic entity in the U.S. and Puerto Rico. We use population data from the PEP by age group, race and ethnicity, and sex. Since the PEP does not go down to the census tract level, the CRE uses the Public Law 94-171 summary files (PL94) and Demographic Housing Characteristics File (DHC) tables from the 2020 Decennial Census to help produce the population base estimates. Once the weighted estimates are tabulated, small area modeling techniques are used to create the estimates for the CRE. Components of Social Vulnerability (SV): Resilience to a disaster is partly determined by the components of social vulnerability exhibited within a community’s population. To measure these components and construct the community resilience estimates, we designed population estimates based on individual- and household-level components of social vulnerability. These components are binary indicators or variables that add up to a maximum of 10 possible components using data from the ACS. The specific ACS-defined measures we use are as follows: Components of Social Vulnerability (SV) for Households (HH) and Individuals (I):SV 1: Income-to-Poverty Ratio (IPR) < 130 percent (HH). SV 2: Single or zero caregiver household - only one or no individuals living in the household who are 18-64 (HH). SV 3: Unit-level crowding with >= 0.75 persons per room (HH). SV 4: Communication barrier defined as either: Limited English-speaking households1 (HH) orNo one in the household over the age of 16 with a high school diploma (HH). SV 5: No one in the household is employed full-time, year-round. The flag is not applied if all residents of the household are aged 65 years or older (HH). SV 6: Disability posing constraint to significant life activity. Persons who report having any one of the six disability types (I): hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. SV 7: No health insurance coverage (I). SV 8: Being aged 65 years or older (I). SV 9: No vehicle access (HH). SV 10: Households without broadband internet access (HH). Each individual is assigned a 0 or 1 for each of the components based upon their individual or household attributes listed above. It is important to note that SV 4 is not double flagged. An individual will be assigned a 1, if either of the characteristics is true for their household. For example, if a household is linguistically isolated and no one over the age of 16 has attained a high school diploma or more education, the household members are only flagged once. The result is an index that produces aggregate-level (tract, county, and state) small area estimates: the CRE. The CRE provide an estimate for the number of people with a specific number of social vulnerabilities. In its current data file layout form, the estimates are categorized into three groups: zero , one-two, or three plus social vulnerability components. Differences with CRE 2021:The number of census tracts have increased from 84,414 in CRE 2021 to 84,415 in CRE 2022. This is due to the boundary changes in Connecticut implemented in 2022 census data products. To accommodate the boundary change, Connecticut also now has nine planning regions instead of eight counties in CRE 2022.To avoid confusion, the modeled rates are now set to equal zero in CRE 2022 for geographic areas with zero population in universe. To improve the population base estimates, CRE 2022 uses more detailed decennial estimates from the 2020 DHC in addition to PL94, whereas CRE 2021 just used PL94 due to availability at the time. See “2022 Community Resilience Estimates: Detailed Technical Documentation” for more information. Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). This dataset does not contain values for Puerto Rico or Island Areas at any level of geography.Further Information:Community Resilience Estimates Program Website https://www.census.gov/programs-surveys/community-resilience-estimates.htmlCommunity Resilience Estimates Technical Documentation https://census.gov/programs-surveys/community-resilience-estimates/technical-documentation.htmlFor Data Questionssehsd.cre@census.gov

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.

1. Natural populations deliver a wide range of products that provide income for millions of people and need to be exploited sustainably. Large heterogeneity in individual performance within these exploited populations has the potential to improve population recovery after exploitation and thus help sustaining yields over time. 2. We explored the potential of using individual heterogeneity to design smarter harvest schemes, by sparing individuals that contribute most to future productivity and population growth, using the understorey palm Chamaedorea elegans as a model system. Leaves of this palm are an important non-timber forest product and long-term inter-individual growth variability can be evaluated from internode lengths. 3. We studied a population of 830 individuals, half of which was subjected to a 67 % defoliation treatment for three years. We measured effects of defoliation on vital rates and leaf size – a trait that determines marketability. We constructed integral projection models in which vital rates depended on stem length, past growth rate, and defoliation, and evaluated transient population dynamics to quantify population development and leaf yield. We then simulated scenarios in which we spared individuals that were either most important for population growth or had leaves smaller than marketable size. 4. Individuals varying in size or past growth rate responded similarly to leaf harvesting in terms of growth and reproduction. By contrast, defoliation-induced reduction in survival chance was smaller in large individuals than in small ones. Simulations showed that harvest-induced population decline was much reduced when individuals from size and past growth classes that contributed most to population growth were spared. Under this scenario cumulative leaf harvest over 20 years was somewhat reduced, but long-term leaf production was sustained. A three-fold increase in leaf yield was generated when individuals with small leaves are spared. 5. Synthesis and applications This study demonstrates the potential to create smarter systems of palm leaf harvest by accounting for individual heterogeneity within exploited populations. Sparing individuals that contribute most to population growth ensured sustained leaf production over time. The concepts and methods presented here are generally applicable to exploited plant and animal species which exhibit considerable individual heterogeneity.

AbstractOver 18 million ha of forests have been destroyed in the past decade in Canada by the mountain pine beetle (MPB) and its fungal symbionts. Understanding their population dynamics is critical to improving modeling of beetle epidemics and providing potential clues to predict population expansion. Leptographium longiclavatum and Grosmannia clavigera are fungal symbionts of MPB that aid the beetle to colonize and kill their pine hosts. We investigated the genetic structure and demographic expansion of L. longiclavatum in populations established within the historic distribution range and in the newly colonized regions. We identified three genetic clusters/populations that coincide with independent geographic locations. The genetic profiles of the recently established populations in northern British Columbia (BC) and Alberta suggest that they originated from central and southern BC. Approximate Bayesian Computation supports the scenario that this recent expansion represents an admixture of individuals originating from BC and the Rocky Mountains. Highly significant correlations were found among genetic distance matrices of L. longiclavatum, G. clavigera, and MPB. This highlights the concordance of demographic processes in these interacting organisms sharing a highly specialized niche and supports the hypothesis of long-term multipartite beetle-fungus co-evolutionary history and mutualistic relationships. Usage notesLL_suppTable1Microsatellite profiles of 10 loci for 241 Leptographium longiclavatum isolates (isolates in grey are clones). First column is the name of the isolates, and the second represents the location.

TikTok has 102.3 million monthly active users in the US alone. This is forecasted to reach 121.1 million by 2027.

Temporal or spatial variation in selection has the potential to explain long standing evolutionary problems such as evolutionary stasis and the maintenance of genetic variation. Long-term field studies of plants and wild vertebrates have provided some insights, but multigenerational measures of selection in wild invertebrates remain scarce. Short-lived ectothermic animals are likely to experience more pronounced environmental variation across generations than longer-lived and endothermic species. As a result, variation in selection may be particularly significant in these groups. Over ten years, we have monitored an individually tagged population of wild crickets (Gryllus campestris) using a network of up to 133 day-night video cameras. The over a million hours of video that we watched allowed us to capture detailed information about naturally and sexually selected traits and life-history parameters. Over ten discrete generations, population size ranged from 51 to 546 adults. There were..., Study system Our data are the product of WildCrickets, a long-term project monitoring of a wild population of field crickets G. campestris in a meadow in northern Spain (RodrÃguez-Muñoz et al. 2019d). This species has a single generation each year, with the first adults emerging in mid to late April and the last adults dying in mid-July. Individuals of both sexes build burrows as a refuge from predation and bad weather. Most interesting events occur at burrow mouths (Rost and Honegger 1987) with individuals spending only short periods moving between them. This lifestyle allows us to record the adult lives of the entire population in great detail, by attaching unique tags to individuals as they become adult and monitoring the population through daily surveys and a network of up to 133 day/night video cameras. During the adult season, males call from their burrows to attract females and both sexes move around the meadow, displacing members of the same sex from burrows and sharing burrows ..., , # Fluctuating selection among years in a wild insect

https://doi.org/10.5061/dryad.rxwdbrvm8

Description of the data and file structure

This README explains the meaning of each of the variables included in the "VariationSelection" data file. Each row represents an individual. The study is based on the monitoring of a wild population of the field cricket (Gryllus campestris), a univoltine species, over eight consecutive generations.Â

Files and variables

File: VariationSelection.txt

Description:Â Each row represents an individual. This species is univoltine. Missing data are shown as n/a.

Variables

• Fitness:Â Number of adult offspring left for the next generation
• Year:Â Year of the breeding season for that adult
• Sex:Â Female (F) or male (M)
• TWc: Thorax width (mm) centered within year
• TWaY: Thorax width (mm) centered within year and standardised across years
• EDc: Julian emergence date (days from 1st January) ...,

Understanding the causes of population decline is crucial for conservation management. We therefore used genetic analysis both to provide baseline data on population structure and to evaluate hypotheses for the catastrophic decline of the South American sea lion (Otaria flavescens) at the Falkland Islands (Malvinas) in the South Atlantic. We genotyped 259 animals from 23 colonies across the Falklands at 281 bp of the mitochondrial hypervariable region and 22 microsatellites. A weak signature of population structure was detected, genetic diversity was moderately high in comparison with other pinniped species, and no evidence was found for the decline being associated with a strong demographic bottleneck. By combining our mitochondrial data with published sequences from Argentina, Brazil, Chile and Peru, we also uncovered strong maternally directed population structure across the geographical range of the species. In particular, very few shared haplotypes were found between the Falklands and South America, and this was reflected in correspondingly low migration rate estimates. These findings do not support the prominent hypothesis that the decline was caused by migration to Argentina, where large-scale commercial harvesting operations claimed over half a million animals. Thus, our study not only provides baseline data for conservation management but also reveals the potential for genetic studies to shed light upon long-standing questions pertaining to the history and fate of natural populations.

Projections published in 2022 estimated that the population in Italy will decrease in the following years. In January 2024, the Italian population added up to 59 million people, but in 2030 Italians will be 57.5 million individuals. Twenty years later, the population will be around 52.3 million people. Low birth rate and old population The birth rate in Italy has constantly dropped in the last years. In 2023, 6.4 children were born per 1,000 inhabitants, three babies less than in 2002. Nationwide, the highest number of births was registered in the southern regions, whereas central Italy had the lowest number of children born every 1,000 people. More specifically, the birth rate in the south stood at 7 infants, while in the center it was equal to 5.9 births. Consequently, the population in Italy has aged over the last decade. Between 2002 and 2024, the age distribution of the Italian population showed a growing share of people aged 65 years and older. As a result, the share of young people decreased. The European exception Similarly, the population in Europe is estimated to decrease in the coming years. In 2024, there were 740 million people living in Europe. In 2100, the figure is expected to drop to 586 million inhabitants. However, projections of the world population suggest that Europe might be the only continent experiencing a population decrease. For instance, the population in Africa could grow from 1.41 billion people in 2022 to 3.92 billion individuals in 2100, the fastest population growth worldwide.

Methods

Sampling and RAD sequencing

A total of 108 Sceloporus occidentalis were sampled from 87 sites throughout their range in western North America (Fig. 1; Table S1). Double digest RAD sequencing data (ddRADseq) were collected using standard protocols (Peterson et al. 2012). Genomic DNA (500 ng per sample) was double-digested with 20 units each of a rare cutter SbfI (restriction site 5'-CCTGCAGG-3') and a common cutter MspI (restriction site 5'-CCGG-3') in a single reaction with the manufacturer recommended buffer (New England Biolabs) for 8 hours at 37°C. Fragments were purified with SeraPure SpeedBeads before ligation of barcoded Illumina adaptors onto the fragments. The libraries were size-selected (between 415 and 515 bp after accounting for adapter length) on a Blue Pippin Prep size fractionator (Sage Science). The final library amplification used proofreading Taq and Illumina's indexed primers. The fragment size distribution and concentration of each pool was determined on an Agilent 2200 TapeStation, and qPCR was performed to determine library concentrations before multiplexing equimolar amounts of each pool for sequencing on a single Illumina HiSeq 2500 lane (50-bp, single-end reads) at the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley.

Bioinformatics

The raw Illumina reads were filtered and demultiplexed using PyRAD v.3.0.3 (Eaton 2014) and assembled using Stacks v.2.54 (Catchen et al. 2013; Rochette et al. 2019) and STACKS_PIPELINE v.2.4 (Portik et al. 2017). Reads potentially arising from PCR duplicates during sequencing were not explicitly accounted for because they occur at low frequency and presumably do not significantly impact most population genetic parameter estimates (Schweyen et al. 2014). During filtering, sites with < 99% base call accuracy (Phred score = 20) were converted to missing data and reads with ≥ 10% missing sites were discarded. No barcode mismatches were allowed during demultiplexing. Reads were aligned into stacks with a minimum depth of coverage of 5 and a maximum of 2 nucleotide differences between stacks. The minor allele count was set to 2 to eliminate singletons, which reduces errors in model-based clustering methods (Linck & Battey 2019). Loci that were invariant, non-biallelic, or absent from > 20% of samples were removed. Samples with > 70% missing data were also removed. One random variable site per locus was sampled to minimize the chance of retaining physically linked SNPs. The resulting unlinked SNP dataset was used to generate input files for downstream analyses.

Population structure

Two methods were used to estimate population structure. The maximum likelihood method ADMIXTURE v1.3.0 (Alexander et al. 2009) was used to estimate (1) the number of populations (K) and (2) admixture proportions of samples to identify putative "hybrids" of mixed population ancestry. Samples with admixture proportions < 0.95 were considered admixed. The cross-validation errors for analyses from K = 1 to K = 10 were compared to determine which K minimized group assignment error; e.g., the K with the lowest cross-validation (CV) error is the model best supported by the data. However, parametric methods for estimating population structure are sensitive to violations of model assumptions prevalent in natural populations (Lawson et al. 2018). Therefore, Discriminant Analysis of Principle Components (DAPC), a non-parametric method, was also used to estimate population structure as implemented in the R package adegenet (Jombart et al. 2010; Jombart & Ahmed 2011). First, principal components of genetic variation were estimated without any assumptions about grouping information or the underlying population genetic model. Second, the find.clusters function was used to evaluate successive values of K from 1 to 10 with the Bayesian information criterion (BIC) to determine the most likely number of populations. The K value with the lowest BIC score was considered optimal, although it is important to consider that other K values can also provide biologically realistic models.

When multiple SNPs are present across loci, the random selection of one SNP per locus represents one of many possible dataset combinations. To assess the stability and sensitivity of K to this SNP selection procedure, we performed random selection of one SNP per locus 100 times to create 100 replicates of unlinked SNP datasets. These dataset permutations were created using STACKS_PIPELINE. The optimal K values for all 100 replicates were then evaluated with ADMIXTURE and DAPC, and summarized to determine variation in support for K values.

Network and population tree analysis

Three methods were used to visualize intraspecific genetic relationships and estimate relationships among populations. Network methods can depict relationships that are not necessarily bifurcating and can identify admixed samples (Posada and Crandall 2001). A genetic network was constructed from the concatenated SNP data (uncorrected “p” distances) using the NeighborNet algorithm (Bryant and Moulton 2004) in SplitsTree v4.6 (Huson and Bryant 2006). Phylogenetic analyses were conducted using SNAPP v1.5.0 (Bryant et al. 2012) and TreeMix v1.13 (Pickrell & Pritchard 2012). One key difference between these methods is that SNAPP is migration-free and produces strictly bifurcating trees, while TreeMix models migration events as non-bifurcating, internal branches among populations. The population assignments required for the phylogenetic methods were obtained from the ADMIXTURE model for K=5 (see Results). Admixed samples (admixture proportion < 0.95) were excluded prior to using SNAPP to avoid biasing topologies and parameter estimates (Leaché et al. 2014).

SNAPP was implemented in BEAST v2.6.3 via CIPRES (Bouckaert et al., 2019; Miller et al. 2010). The mutation rate priors (u and v) were set to 1, and the Yule birth rate prior (λ) was set to 10. A diffuse prior on the expected divergence between populations (θ) was assigned with a gamma distribution (1, 250) corresponding to a mean of 0.004. Three independent analyses were run for 1 million generations each, sampling every 1000 generations. The posterior distribution of trees was summarized using DensiTree (implemented in BEAST) to visualize uncertainty in the topology and branch lengths. A maximum clade credibility (MCC) tree was summarized using TreeAnnotator (BEAST) after discarding the first 20% of samples as burn-in. Branch lengths were converted to time using a human mutation rate of 1.0 × 10-8 substitutions per site per generation (Lynch, 2010; Scally and Durbin, 2012) and a generation time 2 years for S. occidentalis (Jameson & Allison 1976).

TreeMix was used to estimate a maximum likelihood population tree and infer gene flow between diverged populations. The analysis included all samples that passed missing data thresholds (see Results), including admixed samples. SNPs with missing allele counts in ≥1 population were removed from the input dataset prior to analysis. Block resampling across groups of 100 unlinked SNPs was implemented to model uncertainty in the sample covariance matrix and to effectively explore parameter space. A root position was selected for the population tree according to the topology of the SNAPP tree (see Results). The analysis added 1 – 6 migration events (m) to the tree, and each analysis included 500 bootstrap replicates. Replicates were summarized using the R package OptM (https://CRAN.R-project.org/package=OptM). The value of m that explained most of the variation in the data was determined by comparing the average increase in explained variance with each added migration event and the rate of change in the likelihood with the Evanno method (Evanno et al. 2005).

We further tested for admixed ancestry with the threepop and fourpop tests in TreeMix (Keinan et al., 2007; Reich et al., 2009). The threepop analysis tests for admixture within all possible triplets by evaluating the null hypothesis that the relationship within the triplet can be explained by a simple bifurcating tree with a common ancestor. A significantly negative value of the f3 statistic suggests the presence of admixture within the triplet, and that relationship cannot be captured by a simple bifurcating tree. The fourpop analysis tests for treeness across all possible quartets by evaluating the null hypothesis that two pairs of populations can only be connected by one internal branch. An f4 statistic that significantly deviates from zero suggests the presence of admixture within the quartet, meaning more than one internal branch can connect two pairs of populations.

Demographic models

Demographic models were compared to examine potential gene flow and secondary contact between populations. Divergence scenarios were modeled using joint site frequency spectra (JSFS) in dadi v2.1.0 (Gutenkunst et al. 2009), and model optimization routines were performed using DADI_PIPELINE V3.1.5 (Portik et al. 2017). Parameters of interest included divergence times (T), population sizes (nu), and the amounts and directions of gene flow (m). Primary analyses were conducted on geographically contiguous populations that reflect phylogenetic relationships among populations (see Results). Population abbreviations used were: Southern California (SCA), West Sierra Nevada (WSN), East Sierra Nevada (ESN), Pacific Northwest (PNW), Great Basin (GB). Population sets included WSN/PNW, ESN/GB; and ESN/GB/SCA. Secondary analyses were conducted on geographically contiguous populations that do not reflect direct phylogenetic relationships – these were used to validate whether the presence of admixed individuals at population boundaries can be explained by contemporary gene

The Voter Participation indicator presents voter turnout in Champaign County as a percentage, calculated using two different methods.

In the first method, the voter turnout percentage is calculated using the number of ballots cast compared to the total population in the county that is eligible to vote. In the second method, the voter turnout percentage is calculated using the number of ballots cast compared to the number of registered voters in the county.

Since both methods are in use by other agencies, and since there are real differences in the figures that both methods return, we have provided the voter participation rate for Champaign County using each method.

Voter participation is a solid illustration of a community’s engagement in the political process at the federal and state levels. One can infer a high level of political engagement from high voter participation rates.

The voter participation rate calculated using the total eligible population is consistently lower than the voter participation rate calculated using the number of registered voters, since the number of registered voters is smaller than the total eligible population.

There are consistent trends in both sets of data: the voter participation rate, no matter how it is calculated, shows large spikes in presidential election years (e.g., 2008, 2012, 2016, 2020) and smaller spikes in intermediary even years (e.g., 2010, 2014, 2018, 2022). The lowest levels of voter participation can be seen in odd years (e.g., 2015, 2017, 2019, 2021, 2023).

This data primarily comes from the election results resources on the Champaign County Clerk website. Election results resources from Champaign County include the number of ballots cast and the number of registered voters. The results are published frequently, following each election.

Data on the total eligible population for Champaign County was sourced from the U.S. Census Bureau, using American Community Survey (ACS) 1-Year Estimates for each year starting in 2005, when the American Community Survey was created. The estimates are released annually by the Census Bureau.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because this data is not available for Champaign County, the eligible voting population for 2020 is not included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Population by Sex and Population Under 18 Years by Age.

Sources: Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (10 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (5 October 2023).; Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (7 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; Champaign County Clerk Election History; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (6 March 2017).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).

Access to electricity in sub-Saharan Africa was set to decrease in 2021. Some 597 million people did not have electricity connections in the region that year, while in 2020 electrical energy was inaccessible to 581 million Africans. This means that around five out of every 10 individuals below the Sahara lived in the dark. In rural areas, the situation was even worse: over 70 percent of the population lacked access to electricity. Among Africa’s regions, Central and West Africa registered the most dramatic scenario, with electrification covering less than half of the population.

A new challenge for electrification progress

From 2000 to 2013, the number of people without electricity in sub-Saharan Africa increased annually, peaking at some 612 million individuals. This trend changed, however, between 2014 and 2019. During this period, few countries increased the accessibility to electrical energy, improving the overall conditions in the region. For instance, the access rate in Kenya reached nearly 70 percent – against 36 percent in 2014. Nevertheless, the electrification progress in sub-Saharan Africa has been afterward jeopardized by the coronavirus (COVID-19) pandemic. The economic crisis triggered by the disease worsened the poverty level in Africa, leaving households in vulnerability and unable to afford electrical energy.

Renewables as a path to fight energy poverty

Investments in renewable technologies may play a key role in improving access to electricity in Africa. The continent has abundant hydro, solar, wind, and bioenergy resources. In fact, renewable energy capacity on the continent almost doubled in the last ten years. Similarly, the number of Africans connected to solar mini grids strongly increased, although it still covers a small share of the entire population – revealing a potential for growth in the coming years.

Among member countries of the Organization of Economic Co-operation and Development (OECD), Japan has the highest density of magnetic resonance imaging (MRI) units. Over 57 such units are available per every million of its population. The United States and Greece both follow with rates of around 38 per million inhabitants. Compared to these countries, Mexico had around three MRI units per every million, The density of diagnostic imaging units can be one measurement to define the quality of a country’s health care infrastructure. Why and when MRI is usedThe invention of MRI scanners revolutionized diagnostic imaging as it doesn’t use radiation, but a magnetic field and radio waves. Since ionized radiation as used in CT-scans and X-rays is potentially harmful for the patient, this includes a significant advantage for MRIs. MRI scans are principally used for imaging organs, soft tissues, ligaments, and other parts of the body which are difficult to see. While on the other hand, computer tomography (CT) scanners are more frequently used to show bony structures. Among the global top manufacturers of MRI scanners are General Electric, Siemens, Hitachi, and Philips. The costs of MRIA single scan per MRI could cost up to 4,000 U.S. dollars, and thus double the cost of a scan with CT. The purchase of an MRI scanner could be a major investment for a practice or a hospital, with prices ranging from 150 thousand dollars up to several million dollars. Of course, there are installation and maintenance costs to be taken into account as well. With nearly 40 million MRI scans performed annually in the United States, it’s clear that diagnostic imaging costs are substantial.

TikTok has a significantly larger female user base globally.

How many people use social media? Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

Who uses social media? Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

How much time do people spend on social media? Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

What are the most popular social media platforms? Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

These TikTok user statistics tell the whole story of the new social media giant and give you some insights into the app's future.