Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow. STRUCTURE, the most highly cited of several clustering-based methods, was developed to provide robust estimates without the need for populations to be determined a priori. STRUCTURE introduces the problem of selecting the optimal number of clusters and as a result the ΔK method was proposed to assist in the identification of the ‘true’ number of clusters. In our review of 1,264 studies using STRUCTURE to explore population subdivision, studies that used ΔK were more likely to identify K=2 (54%, 443/822) than studies that did not use ΔK (21%, 82/386). A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both ΔK and STRUCTURE to fully explore population subdivision. Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, ΔK frequent...

European countries are experiencing population decline and the tacit assumption in most analyses is that the decline may have detrimental welfare effects. In this paper we use a survey among the population in the Netherlands to discover whether population decline is always met with fear. A number of results stand out: population size preferences differ by geographic proximity: at a global level the majority of respondents favors a (global) population decline, but closer to home one supports a stationary population. Population decline is clearly not always met with fear: 31 percent would like the population to decline at the national level and they generally perceive decline to be accompanied by immaterial welfare gains (improvement environment) as well as material welfare losses (tax increases, economic stagnation). In addition to these driving forces it appears that the attitude towards immigrants is a very strong determinant at all geographical levels: immigrants seem to be a stronger fear factor than population decline.

Philippines Population Projection 2020 to 2025 by Admin3

This data is about the Philippines population projection count 2020 to 2025 by city/municipality (admin3) based on 2015 Census

Location

Philippines

Data Source

Philippines Statistics Authority

Contributor

OCHA Philippines

ResourcesMapTeacher guide Student worksheetGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Science standardsAPES: III. B. – Population biology concepts.APES: II.B.1. – Human population dynamics - historical population sizes; distribution; fertility rates; growth rates and doubling times; demographic transition; age-structure diagrams.Learning outcomesStudents will predict total historical population trends from age-structure information.Students will relate population growth to k (carrying capacity) or r (reproductive factor) selective environmental conditions.

The United Nations Energy Statistics Database (UNSTAT) is a comprehensive collection of international energy and demographic statistics prepared by the United Nations Statistics Division. The 2004 version represents the latest in the series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The database contains comprehensive energy statistics for more than 215 countries or areas for production, trade and intermediate and final consumption (end-use) for primary and secondary conventional, non-conventional and new and renewable sources of energy. Mid-year population estimates are included to enable the computation of per capita data. Annual questionnaires sent to national statistical offices serve as the primary source of information. Supplementary data are also compiled from national, regional and international statistical publications. The Statistics Division prepares estimates where official data are incomplete or inconsistent. The database is updated on a continuous basis as new information and revisions are received. This metadata file represents the population statistics during the expressed time. For more information about the country site codes, click this link to the United Nations "Standard country or area codes for statistical use": https://unstats.un.org/unsd/methodology/m49/overview/

Population exposures to ozone from APEX modeling for combinations of potential future air quality and demographic change scenarios. This dataset is not publicly accessible because: Total file size is too large (73 GB) to be included in ScienceHub. It can be accessed through the following means: data files are stored at L:\PRIV\DIONISIO\ACE118\APEXFiles\RCode\Rfiles. Format: 73 GB, 2,300 files (saved as .rsav R data files). This dataset is associated with the following publication: Dionisio, K., C. Nolte, T. Spero, S. Graham, N. Caraway, K. Foley, and K. Isaacs. Characterizing the impact of projected changes in climate and air quality on human exposures to ozone. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 27(3): 260-270, (2017).

The dataset, provided both in comma-separated values (.csv) and the more informative Stata (.dta) format, contains place/year demographic data on more than 300 rural Alaska communities annually for 1990 to 2022 -- about 10,000 place/years. For each of the available place/years, the data include population estimates from the Alaska Department of Labor and Workforce Development or (in Census years) from the US Census. For a subset consisting of 104 northern or western Alaska (Arctic/subarctic) towns and villages, the dataset also contains yearly estimates of natural increase (births minus deaths) and net migration (population minus last year's population plus natural increase). Natural increase was calculated from birth and death counts provided confidentially to researchers by the Alaska Health Analytics and Vital Records Section (HAVRS). By agreement with HAVRS, the community-level birth and death counts are not available for publication. Population, natural increase, and net migration estimates reflect mid-year values, or change over the past fiscal rather than calendar year. For example, the natural increase value for a community in 2020 is based on births and deaths of residents from July 1, 2019 to June 31, 2020. We emphasize that all values here are best estimates, based on records of the Alaska government organizations. The dataset contains 19 variables: placename Place name (string) placenum Place name (numeric) placefips Place FIPS code year Year borough Borough name boroughfips Borough FIPS code latitude Latitude (decimal, - denotes S) longitude Longitude (decimal, - denotes W) town Village {0:pop2020<2,000} or town {1:pop2020>2,000} village104 104 selected Arctic/rural communities {0,1} arctic43 43 Arctic communities {0,1}, Hamilton et al. 2016 north37 37 Northern Alaska communities {0,1), Hamilton et al. 2016 pop Population (2022 data) cpopP Change in population, percent natinc Natural increase: births-deaths natincP Natural increase, percent netmig Net migration estimate netmigP Net migration, percent nipop Population without migration Three of these variables flag particular subsets of communities. The first two subsets (43 or 37 places) were analyzed in earlier publications, so the flags might be useful for replications or comparisons. The third subset (104 places) is a newer, expanded group of Arctic/subarctic towns and villages for which natural increase and net migration estimates are now available. The flag variables are: If arctic43 = 1 Subset consisting of 43 Arctic towns and villages, previously studied in three published articles: 1. Hamilton, L.C. & A.M. Mitiguy. 2009. “Visualizing population dynamics of Alaska’s Arctic communities.” Arctic 62(4):393–398. https://doi.org/10.14430/arctic170 2. Hamilton, L.C., D.M. White, R.B. Lammers & G. Myerchin. 2012. “Population, climate and electricity use in the Arctic: Integrated analysis of Alaska community data.” Population and Environment 33(4):269–283. https://doi.org/10.1007/s11111-011-0145-1 3. Hamilton, L.C., K. Saito, P.A. Loring, R.B. Lammers & H.P. Huntington. 2016. “Climigration? Population and climate change in Arctic Alaska.” Population and Environment 38(2):115–133. https://doi.org/10.1007/s11111-016-0259-6 If north37 = 1 Subset consisting of 37 northern Alaska towns and villages, previously analyzed for comparison with Nunavut and Greenland in a paper on demographics of the Inuit Arctic: 4. Hamilton, L.C., J. Wirsing & K. Saito. 2018. “Demographic variation and change in the Inuit Arctic.” Environmental Research Letters 13:11507. https://doi.org/10.1088/1748-9326/aae7ef If village104 = 1 Expanded group consisting of 104 communities, including all those in the arctic43 and north37 subsets. This group includes most rural Arctic/subarctic communities that had reasonably complete, continuous data, and 2018 populations of at least 100 people. These data were developed by updating older work and drawing in 61 additional towns or villages, as part of the NSF-supported Arctic Village Dynamics project (OPP-1822424).

The associated files contain the CLUMPAK (Kopelman et al. 2015) outputs generated from STRUCTURE (Falush et al. 2003, Pritchard et al. 2000) analyses of populations simulations generated in EasyPop (Balloux 2001), where K = 1, 2, or 3

Report on Demographic Data in New York City Public Schools In Response to Local Law No. 59 2016 Notes "Enrollment counts are based on the October 31st Audited Register for 2015."

Student demographic and enrollment data by Pre-K for All Site from 2015-16 through 2017-18

Use of genetic methods to estimate effective population size (N^e) is rapidly increasing, but all approaches make simplifying assumptions unlikely to be met in real populations. In particular, all assume a single, unstructured population, and none has been evaluated for use with continuously distributed species. We simulated continuous populations with local mating structure, as envisioned by Wright's concept of neighborhood size (NS), and evaluated performance of a single-sample estimator based on linkage disequilibrium (LD), which provides an estimate of the effective number of parents that produced the sample (N^b). Results illustrate the interacting effects of two phenomena, drift and mixture, that contribute to LD. Samples from areas equal to or smaller than a breeding window produced estimates close to the NS. As the sampling window increased in size to encompass multiple genetic neighborhoods, mixture LD from a two-locus Wahlund effect overwhelmed the reduction in drift LD from i...

In California in 2022, 20.5 percent of students enrolled in K-12 public schools were white, 11.9 percent were Asian, and 56.2 percent were Hispanic. In the United States overall, 44.7 percent of K-12 public school students were white, 5.5 percent were Asian, and 28.7 percent were Hispanic.

The North-Eastern region (NER) of India, comprising of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland and Tripura, is a hot spot for genetic diversity and the most probable origin of rice. North-east rice collections are known to possess various agronomically important traits like biotic and abiotic stress tolerance, unique grain and cooking quality. The genetic diversity and associated population structure of 6,984 rice accessions, originating from NER, were assessed using 36 genome wide unlinked single nucleotide polymorphism (SNP) markers distributed across the 12 rice chromosomes. All of the 36 SNP loci were polymorphic and bi-allelic, contained five types of base substitutions and together produced nine types of alleles. The polymorphic information content (PIC) ranged from 0.004 for Tripura to 0.375 for Manipur and major allele frequency ranged from 0.50 for Assam to 0.99 for Tripura. Heterozygosity ranged from 0.002 in Nagaland to 0.42 in Mizoram and gene diversity ranged from 0.006 in Arunachal Pradesh to 0.50 in Manipur. The genetic relatedness among the rice accessions was evaluated using an unrooted phylogenetic tree analysis, which grouped all accessions into three major clusters. For determining population structure, populations K = 1 to K = 20 were tested and population K = 3 was present in all the states, with the exception of Meghalaya and Manipur where, K = 5 and K = 4 populations were present, respectively. Principal Coordinate Analysis (PCoA) showed that accessions were distributed according to their population structure. AMOVA analysis showed that, maximum diversity was partitioned at the individual accession level (73% for Nagaland, 58% for Arunachal Pradesh and 57% for Tripura). Using POWERCORE software, a core set of 701 accessions was obtained, which accounted for approximately 10% of the total NE India collections, representing 99.9% of the allelic diversity. The rice core set developed will be a valuable resource for future genomic studies and crop improvement strategies.

Population genetic structure can provide valuable insights for conserving genetic resources and understanding population evolution, but it is often underestimated when using the most popular method and software, STRUCTURE and delta K, to assess. Although the hierarchical STRUCTURE analysis has been proposed early to overcome the above potential problems, this method was just utilized in a few studies and its reliability needs to be further tested. In this study, the genetic structure of populations of Wikstroemia monnula was evaluated by sequencing 12 nuclear microsatellite loci of 905 individuals from 38 populations. The STRUCTURE analysis suggested the most likely number of clusters was two, but using multi-hierarchical structure analysis, almost every population was determined with an endemic genetic component. The latter result is consistent with the extremely low gene flow among populations and a large number of unique cpDNA haplotypes in this species, indicating one level of structure analysis would extremely underestimate its genetic component. The simulation analysis shows the number of populations and the genetic dispersion among populations are two key factors to affect the estimation of K value using the above tools. When the number of populations is more than a certain amount, K always is equal to 2, and when a simulation only includes few populations, the underestimation of K value also may occur only if these populations consist of two main types of significantly differentiated genetic components. Our results strongly support that the hierarchical STRUCTURE analysis is necessary and practicable for the species with lots of subdivisions.

Imputed genotype dataset of K-SoyNAM population

In 2023, there were approximately 500 thousand more deaths than births recorded in Russia. That was almost half as low compared to the previous year, when the largest drop in natural population increase was recorded in Russia. A positive natural increase was recorded in 1990 and from 2013 to 2016, with the highest value measured at roughly 333 thousand persons in 1990.

This dataset contains data on population by sex and age on the basis of the results of the Census Data of Estonia, which was carried out on 28 December 1922. Dataset "Estonian Population by Sex and Age in 1922 Census Data" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

Comprehensive population and demographic data for 10 K Village

This dataset contains data on population movement (population, marriages, births, deaths, infant deaths (under 1 year), natural increase of population) in Estonia in Livonia Province (within the current borders of Estonia and Latvia) in 1897-1914. Dataset "Population Movement in Livonia Province (Estonia and Latvia), 1897-1914" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

Complexity of infection and population diversity.