This statistic displays the forecast of the Indian population in New Zealand from 2013 to 2038, by age group. The Indian population in New Zealand between 40 and 64 years old is projected to be around *** thousand people by the year 2038.

Demographic projections provide an indication of future trends in the size and composition of the population, labour force, families and households. National Projections are produced at the national level (New Zealand) for the population (total, Māori, Pacific, Asian, and European ethnic groups), families, households and labour force. This dataset contains 2018-base projections of the European or Other (including New Zealander), Maori, Asian, Pacific, Middle Eastern/Latin American/African, Chinese, Indian, and Samoan ethnic populations usually living in New Zealand (released May 2021). These projections have the estimated resident population of each ethnic group at 30 June 2018 as a base.

About this dataset

Context

This list ranks the 5 cities in the Indian River County, FL by New Zealander population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by New Zealander Population: This column displays the rank of city in the Indian River County, FL by their New Zealander population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• New Zealander Population: The New Zealander population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as New Zealander. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Indian River County New Zealander Population: This tells us how much of the entire Indian River County, FL New Zealander population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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 expansion of human settlements over the past few centuries is responsible for an unprecedented number of invasive species introductions globally. An important component of biological invasion management is understanding how introduction history and post-introduction processes have jointly shaped present-day distributions and patterns of population structure, diversity, and adaptation. One example of a successful invader is the European starling (Sturnus vulgaris), which was intentionally introduced to numerous countries in the 19th century, including Aotearoa New Zealand, where it has become firmly established. We used reduced-representation sequencing to characterise the genetic population structure of the European starling in New Zealand, and compared the population structure to that present in sampling locations in the native range and invasive Australian range. We found that population structure and genetic diversity patterns suggested restricted gene flow from the majority of New Zealand to the northmost sampling location (Auckland). We also profiled genetic bottlenecks and shared outlier genomic regions, which supported historical accounts of translocations between both Australian subpopulations and New Zealand, and provided evidence of which documented translocation events were more likely to have been successful. Using these results as well as historic demographic patterns, we demonstrate how genomic analysis complements even well-documented invasion histories to better understand invasion processes, with direct implication for understanding contemporary gene flow and informing invasion management. Methods Sample Collection A total of 106 starling specimen samples were obtained from various contributors within New Zealand from five geographically distinct locations between May 2022 and October 2023. Sampling covered three locations in the North Island, specifically in the Auckland region (AUK: n=18), the Manawatū-Whanganui region (WHA: n=12), the Wellington region (WEL: n=40) and two in the South Island in the Marlborough region (MRL: n=15) and Canterbury region (CAN: n=21). In addition to the newly obtained samples, we also incorporated sequence data from the native European range (Antwerp, Belgium; ANT: n=15, Newcastle, United Kingdom; NWC: n=15, Monks Wood, United Kingdom; MKW: n=15), as well as two locations from within the invasive Australian range (Orange; ORG: n=15, McLaren Vale; MLV: n=15) from a previously published Diversity Arrays Technology Pty Ltd sequencing (DArT-seq) dataset. DNA Extraction and Sequencing Extracted DNA from the newly collected New Zealand samples was sent to Diversity Arrays for sequencing. Sequencing was performed on an Illumina Hiseq2500/Novaseq6000. Raw Sequence Processing The previously published raw DArT-seq data, along with the MRL samples (January 2023 sequencing batch) were demultiplexed using stacks v2.2 process_radtags, while also discarding low quality reads (-q), reads with uncalled bases (-c), and rescuing barcodes and RAD-Tag cut sites (-r). It was not necessary to perform this step on the remainder of the new raw sequence data because DArT performed in-house demultiplexing using a proprietary bioinformatic pipeline. For all the data, we used fastp v0.23.2 to remove adapter sequences and in the same step filtered reads for a minimum Phred quality score of 22 (-q 22) and a minimum length of 40 (-l 40). Both batches of sequence data produced as part of this study were additionally length trimmed to reduce the read length of the newer sequence data to match the base length of the older sequence data (-b 69). Mapping, Variant Calling, and Filtering We used the program bwa v0.7.17 to index the reference genome S. vulgaris vAU1.0 and align the trimmed DArT reads using the bwa aln function (-B 5 to trim the first 5 base pairs of each read), which is optimised for single-end short reads. This was then followed by the bwa samse function for producing the SAM formatted output files containing the alignments and their respective base qualities. Alignments were then sorted and indexed using samtools v1.16.1, and single nucleotide polymorphisms (SNPs) were subsequently called and annotated using bcftools v1.16 with the mpileup (-a "DP,AD,SP", --ignore-RG) and call (-mv, -f GQ) functions. We removed known technical replicates and identified relatives from the data. vcftools v0.1.15 was used to remove indels (--remove-indels), and quality filter for a minimum site quality score of 30 (--minQ30), minimum genotype quality score of 20 (--minGQ 20), and minimum and maximum depth of coverage of 5 (--minDP 5) and 100 (--maxDP 100). Then, to account for batch effects that may impact the sequenced loci, we kept only SNPs present in at least 50% of the individuals in each sampling location. We ran one final filtering step to ensure appropriate levels of missingness and rare alleles using the following parameters: maximum missingness per site of 30% (--max-missing 0.7), minor allele count of 5 (--mac 5), and a minimum and maximum allele per locus of 2 (--min-alleles 2 --max-alleles 2), resulting in a dataset containing 19,174 SNPs and 141 individuals.

A total of 183 myna tissue samples in ethanol from India, New Zealand, Australia, South Africa, Hawaii and Fiji between 1975–1989 were received from the Royal Ontario Museum (ROM). A further 193 euthanized mynas were obtained from myna control programs from contributors in New Zealand between 2017–2020, and muscle tissue was subsampled from each individual. DNA was extracted from the ROM tissue samples using the DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer's protocols. DNA was extracted from the New Zealand tissue samples using the Monarch Genomic DNA Purification Kit (NEB) following the manufacturer's protocols. DNA concentration was measured using a Qubit 2.0 Fluorometer (Thermo Fisher Scientific). DNA was diluted to standardized concentrations of 50–100 ng/μL, and sent to Diversity Arrays Technology Pty Ltd company (DArT P/L) for further processing. Samples from 363 individuals were successfully sequenced, including 13 duplicate samples, using the proprietary Div...

Migrants from the United Kingdom have long been Australia’s primary immigrant group and in 2023 there were roughly 960 thousand English-born people living in Australia. India and China held second and third place respectively with regard to Australia’s foreign-born population. The relative dominance of Asian countries in the list of top ten foreign-born residents of Australia represents a significant shift in Australia’s immigration patterns over the past few decades. Where European-born migrants had previously overshadowed other migrant groups, Australian migration figures are now showing greater migration numbers from neighboring countries in Asia and the Pacific. A history of migration Australia is often referred to as an ‘immigrant nation’, alongside the United States, Canada, and New Zealand. Before the Second World War, migrants to Australia were almost exclusively from the UK, however after 1945, Australia’s immigration policy was broadened to attract economic migrants and temporary skilled migrants. These policy changes saw and increase in immigrants particularly from Greece and Italy. Today, Australia maintains its status as an ‘’Immigrant nation’’, with almost 30 percent of the population born overseas and around 50 percent of the population having both that were born overseas. Australian visas The Australian immigration program has two main categories of visa, permanent and temporary. The permanent visa category offers three primary pathways: skilled, family and humanitarian. The skilled visa category is by far the most common, with more than a million permanent migrants living in Australia on this visa category at the last Australian census in 2021. Of the temporary visa categories, the higher education visa is the most popular, exceeding 180 thousand arrivals in 2023.

Gridded Population of the World, Version 2 (GPWv2) consists of estimates of human population for the years 1995 and 1990 by 2.5 arc-minute grid cells. The data products are population counts (raw counts), population densities (per square km), and land area (actual area net of ice and water), all of which are available in two GIS-compatible data formats at the global, continent (Antarctica not included), and country levels. A proportional allocation gridding algorithm, utilizing 127,105 national and sub-national administrative units, is used to assign population values to grid cells. Advantages to GPWv2 include higher quality data from the U.S., Africa, Australia, Canada, Europe, Russia, New Zealand, and India; 8 times the number of administrative units; national population estimates that have been adjusted to match the United Nations national estimated population for each country; a proportional allocation algorithm that reduces error with multiple input polygons; and higher spatial resolution. GPWv2 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI) and the World Resources Institute (WRI). (Suggested Usage: To serve a wide user community by providing the latest data on human population distribution that can be used in interdisciplinary studies of the environment.)

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

About a fifth of Hispanic high school students in the United States were overweight between 2016 and 2017, making it the ethnic group with the highest percentage of overweight high school students. Female obesity rates were considerably higher than those of male students for the black and Hispanic groups during the measured period.

Overweight and obese U.S. adults

U.S. overweight rates in adults differed slightly from those of U.S. high school students in 2017. That year, the African American population had the highest overweight and obesity rates of any race or ethnicity, closely followed by American Indians/Alaska Natives and Hispanics. Over 73 percent of all African American adults in the country were either overweight or obese. In 2018, the highest rates of obesity among African Americans could be found in states, such as Mississippi, Arkansas, and Tennessee.

Overweight youth worldwide

Many children and adolescents in other countries, such as New Zealand, Greece, and Italy, also struggle with overweight and obesity. In New Zealand, for example, over forty percent of boys and girls, up to age 19, were overweight or obese in 2016. In the same year, less than ten percent of Indian children and teenagers were overweight.