Facebook and Columbia University - CIESIN provide the High Resolution Settlement Layer as the world's most accurate population datasets. More info can be found here: https://dataforgood.fb.com/tools/population-density-maps/

These maps are the distribution of human population spanning Pakistan and India. Each of the 13 TIFF files is a 10 x 10 degree tile (the lower latitude coordinate and longitude coordinates are in the file name). A VRT file is also included.

Dataset

This dataset was created by Ayushmaan03

Released under MIT

Contents

• 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.

The dataset contains Year and State wise Density of Population

Note: 1. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 2. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. 3. For working out the density of India and Jammu & Kashmir for 1991,2001, the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account.

The territories of Pakistan and India are mostly covered by the non-political blocks AS42 through AS50, going roughly from West to East. Please see the attached map of these non-political boundary blocks.

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File.

This dataset is a cross-sectional survey collected from two provinces in Pakistan (Punjab) and India (State of Gujarat). The sample included both urban and rural parts of the two countries. The dataset mainly looks at children’s learning and general life experiences in the early years of childhood (at ages 4 to 8). We assessed 1,129 children on tasks of basic numeracy, literacy, and social-emotional learning using a standardised measure of assessment, implemented at two points in time with a gap of 12 months. Also, we collected data on household characteristics, children's learning performance and a parents' survey of children's activities and learning outcomes.School enrolment of around 80% in India and Pakistan is lower than targets associated with Sustainable Development Goal 4 and functional levels of literacy and numeracy are inadequate even for many young children who are enrolled in school. This project, based on 1,500 young children from one province in Pakistan and one state in India, seeks to examine patterns of enrolment of children and school readiness by socio-economic group, family background, urban / rural locations and individual characteristics such as gender, disability and health. Attendance and progress at school will be analysed over one year, demonstrating how much of a difference school attendance can make to children’s cognitive development and health outcomes. The project aims to collect in-depth information from families and communities on their views of schools and any barriers to attendance, and conduct a systematic review of the evidence. The findings are expected to have policy implications on school enrolment, attendance and retention. The team's final project report was published on 20 September 2022, and a foreword has been authored by Ziauddin Yousafzai (Malala Yousafzai’s father). This dataset is a longitudinal design in which the same households were followed and children were assessed at the baseline and after the gap of 12 months. This study involved trained and highly experienced enumerators leading to the initial sample of 1,129 children by involving 783 households from selected districts (both urban and rural areas) in Punjab, Pakistan and Gujarat, India depending on the population dynamics of the village and enumerators' access to the households. Children aged 3 to 8 (according to parental reports of children’s age) were assessed by standardised tests. Parents were surveyed by family questionnaires regarding household socioeconomic conditions, reasons for school choice, children's general health and interest in attending school, and experience of access to their children's education during lockdown.

BackgroundMost pregnancy hypertension estimates in less-developed countries are from cross-sectional hospital surveys and are considered overestimates. We estimated population-based rates by standardised methods in 27 intervention clusters of the Community-Level Interventions for Pre-eclampsia (CLIP) cluster randomised trials.Methods and findingsCLIP-eligible pregnant women identified in their homes or local primary health centres (2013–2017). Included here are women who had delivered by trial end and received a visit from a community health worker trained to provide supplementary hypertension-oriented care, including standardised blood pressure (BP) measurement. Hypertension (BP ≥ 140/90 mm Hg) was defined as chronic (first detected at

Rising global food insecurity driven by population growth needs urgent measure for universal access to food. This research employs Comparative Performance Analysis (CPA) to evaluate the Global Food Security Index (GFSI), its components [Affordability (AF), Availability (AV), Quality & Safety (Q&S) and Sustainability & Adaptation (S&A)] in tandem with Annual Population Change (APC) for world’s five most populous countries (India, China, USA, Indonesia and Pakistan) using dataset spanning from 2012 to 2022. CPA is applied using descriptive analysis, correlation analysis, Rule of Thumb (RoT) and testing of hypothesis etc. RoT is used with a new analytical approach by applying the significance measures for correlation coefficients. The study suggests that India should enhance its GFSI rank by addressing AF and mitigating the adverse effects of APC on GFSI with a particular focus on Q&S and S&A. China needs to reduce the impact of APC on GFSI by prioritizing AV and S&A. The USA is managing its GFSI well, but focused efforts are still required to reduce APC’s impact on Q&S and S&A. Indonesia should improve across all sectors with a particular focus on APC reduction and mitigating its adverse effects on AF, AV, and S&A. Pakistan should intensify efforts to boost its rank and enhance all sectors with reducing APC. There is statistically significant and negative relation between GFSI and APC for China, Indonesia and found insignificant for others countries. This study holds promise for providing crucial policy recommendations to enhance food security by tackling its underlying factors.

The dataset contains Decade and State wise Urban, Rural, Total Population and Decadal Growth Rate

Note: 1. The Population figures exclude population of areas under unlawful occupation of Pakistan and China, where Census could not be taken. 2. In Arunachal Pradesh, the census was conducted for the first time in 1961. 3. Population data of Assam include Union Territory of Mizoram, which was carved out of Assam after the 1971. 4. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 5. The 1991 Census could not be held in Jammu & Kashmir. Total Population for 1991 has been worked out by Interpolation. 6. India and Manipur figures include estimated Population for those of the three sub-divisions viz., Mao Maram,Paomata and Purul of Senapati district of Manipur as census result of 2001 in these three sub-divisions were cancelled due to technical and administrative reasons

During the Indian Ocean Expedition of the German research vessel "Meteor" and the following cruise with the Pakistani fishing vessel "Machhera" in February and March 1965, sediments were sampled from the shelf, continental slope and the Arabian Basin off Pakistan and India. The biostratigraphic studies are based on sedimentary material from 24 sediment cores up to 480 cm long and 100 grab samples.The faunal residues of the > 160 µ fraction (chiefly foraminifera and pteropods) were determined and counted in order to get an idea of the climatic conditions during the Late Quaternary of this region. Biostratigraphic correlations of these Late Quaternary deposits are only possible if the thanatocoenosis of the surface sediments are well known. The analysis of the benthonic foraminiferal populations resulted in the definition of several foraminiferal facies. The following sequence of forarniniferal facies, named after their most characteristic members, can be distinguished from the shelf to the deep-sea:1. Ammonia-Florilus facies ; 2. Ammonia-Cancris facies; 3. Cassidulina-Cibicides facies; 4. Uvigerina-Cassidulina facies ; 5. Buliminacea facies ; 6. deepwater facies, partly with Bulimina aculeata or with Nonionidae. On the upper continental slope there is a zone extremely poor in benthonic foraminifera. In this water depth the oxygen minimum layer (0.05-0.02 ml/l) of the water column reaches the slope.Almost no connection can be observed between the living and the dead foraminiferal population of the same sample.The regional distribution of the planktonic foraminifera from plankton tows as well as from the surface sediments shows marked differences in the species composition of faunas from different regions within the area of investigation. That depends on oceanographic conditions such as upwelling, dissolution of carbonate at great depths etc.Based on the results of faunal analysis of samples from the recent sea-floor, a biostratigraphic subdivision of the sediments in the cores was established. The following biostratigraphically defined sections could be distinguished from the top of the sediment cores downwards : 1. Relatively cool climatic conditions are reflected by the foraminiferaof the uppermost core sections. 2. The next section is characterized by much warmer conditions (Holocene climatic optimum). The C-14 ages of this interval range from 4000 to 10 000 years B.P. according to different authors. C-14 dates on the material investigated do not give reliable clues. 3. Foraminiferal populations adapted to much colder conditions can be observed in the underlying core section. The boundary between the warm climate reflected by the foraminifera of section 2 and the cold climate (section 3) is relatively sharp. It can be correlated from core to core over the whole area investigated. The cold climate sediments of section 3 are underlain by different cool-, warm- and cold-climate sediments which can only be correlated over very short distances.Since it appears certain that the last really cold conditions ended earlier in the Arabian Sea and its vicinity than in Europe it is recommended not to use the European stratigraphic terms for the Quaternary.Because of the lack of reliable absolute sediment ages for the cores no exact sedimentation rates can be given. According to rough estimates, however, the rates are 1-2 cm/1000 years in the deep basin and up to 40 cm/1000 years on the upper continental slope. Sedimentation rates are always larger near the mouth of the Indus-River than off South India at stations of about the same water depth.Planktonic gastropods (mainly pteropods) cannot be used for biostratigraphic purposes in the region under consideration. All of them seem to be displaced from the shelf. Their distribution there is given in.

This dataset contains genome-wide SNPs for pink bollworm (Pectinophora gossypiella) samples from Australia, India, the U.S., and Pakistan. SNP data was generated by Diversity Arrays Technology, Canberra.

South Asia is one of the most densely populated regions in the world. This dataset comprehensively collects historical materials related to the population of South Asia and previous research results (see data description documents and references for details), carefully examines and estimates the population of South Asia (now India, Pakistan, Nepal, Bangladesh) from 640 to 1801 AD, and connects it with the population census data of British India from 1871 to 1941 (Nepal's data comes from Nepal's census data) and the United Nations World Population Prospects data from 1950 to 2020, obtaining the population of South Asia for a total of 22 periods (640, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1595, 1750, 1801, 1871, 1901, 1921, 1941, 1960, 1980, 2000, 2010, 2020) from 640 to 2020. Next, based on geographic detectors, select the dominant environmental factors that affect the spatial distribution of population, collect historical data on the distribution of residential areas (see data description document and references for details), and use a random forest regression model to spatialize the population size. On the basis of excluding uninhabited areas such as water bodies, glaciers, and bare/unused land, and determining the maximum historical population distribution range, a 1km resolution population dataset for South Asia from 640 to 2020 was developed. The leave one method was used to test the model, and the variance explained was 0.81, indicating good model accuracy. Compared with the existing HYDE historical population dataset, this study incorporates more historical materials and the latest research results in estimating the historical population; In using random forest regression for historical population spatial simulation, this study considers the changes in South Asian settlements over the past millennium, while the HYDE dataset only considers natural elements and considers them stable and unchanged. Therefore, this dataset is more reliable than the HYDE dataset and can more reasonably reveal the spatiotemporal characteristics of population changes in South Asia during historical periods. It is the basic data for the long-term evolution of human land relations, climate change attribution, and ecological protection research in South Asia.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

Abstract

The third wave of the Asian Barometer survey (ABS) conducted in 2010 and the database contains nine countries and regions in East Asia - the Philippines, Taiwan, Thailand, Mongolia, Singapore, Vietnam, Indonesia, Malaysia and South Korea. The ABS is an applied research program on public opinion on political values, democracy, and governance around the region. The regional network encompasses research teams from 13 East Asian political systems and 5 South Asian countries. Together, this regional survey network covers virtually all major political systems in the region, systems that have experienced different trajectories of regime evolution and are currently at different stages of political transition.

The mission and task of each national research team are to administer survey instruments to compile the required micro-level data under a common research framework and research methodology to ensure that the data is reliable and comparable on the issues of citizens' attitudes and values toward politics, power, reform, and democracy in Asia.

The Asian Barometer Survey is headquartered in Taipei and co-hosted by the Institute of Political Science, Academia Sinica and The Institute for the Advanced Studies of Humanities and Social Sciences, National Taiwan University.

Geographic coverage

13 East Asian political systems: Japan, Mongolia, South Koreas, Taiwan, Hong Kong, China, the Philippines, Thailand, Vietnam, Cambodia, Singapore, Indonesia, and Malaysia; 5 South Asian countries: India, Pakistan, Bangladesh, Sri Lanka, and Nepal

Analysis unit

-Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Compared with surveys carried out within a single nation, cross-nation survey involves an extra layer of difficulty and complexity in terms of survey management, research design, and database modeling for the purpose of data preservation and easy analysis. To facilitate the progress of the Asian Barometer Surveys, the survey methodology and database subproject is formed as an important protocol specifically aiming at overseeing and coordinating survey research designs, database modeling, and data release.

As a network of Global Barometer Surveys, Asian Barometer Survey requires all country teams to comply with the research protocols which Global Barometer network has developed, tested, and proved practical methods for conducting comparative survey research on public attitudes.

Research Protocols:

• National probability samples that give every citizen in each country an equal chance of being selected for interview. Whether using census household lists or a multistage area approach, the method for selecting sampling units is always randomized. The samples may be stratified, or weights applied, to ensure coverage of rural areas and minority populations in their correct proportions. As such, Asian Barometer samples represent the adult, voting-age population in each country surveyed.

A model Asian Barometer Survey has a sample size of 1,200 respondents, which allows a minimum confidence interval of plus or minus 3 percent at 95 percent probability.

Mode of data collection

Face-to-face [f2f]

Research instrument

A standard questionnaire instrument containing a core module of identical or functionally equivalent questions. Wherever possible, theoretical concepts are measured with multiple items in order to enable testing for construct validity. The wording of items is determined by balancing various criteria, including: the research themes emphasized in the survey, the comprehensibility of the item to lay respondents, and the proven effectiveness of the item when tested in previous surveys.

Survey Topics: 1.Economic Evaluations: What is the economic condition of the nation and your family: now, over the last five years, and in the next five years? 2.Trust in institutions: How trustworthy are public institutions, including government branches, the media, the military, and NGOs. 3.Social Capital: Membership in private and public groups, the frequency and degree of group participation, trust in others, and influence of guanxi. 4.Political Participatio: Voting in elections, national and local, country-specific voting patterns, and active participation in the political process as well as demonstrations and strikes. Contact with government and elected officials, political organizations, NGOs and media. 5.Electoral Mobilization: Personal connections with officials, candidates, and political parties; influence on voter choice. 6.Psychological Involvement and Partisanship: Interest in political news coverage, impact of government policies on daily life, and party allegiance. 7.Traditionalism: Importance of consensus and family, role of the elderly, face, and woman in theworkplace. 8.Democratic Legitimacy and Preference for Democracy: Democratic ranking of present and previous regime, and expected ranking in the next five years; satisfaction with how democracy works, suitability of democracy; comparisons between current and previous regimes, especially corruption; democracy and economic development, political competition, national unity, social problems, military government, and technocracy. 9.Efficacy, Citizen Empowerment, System Responsiveness: Accessibility of political system: does a political elite prevent access and reduce the ability of people to influence the government. 10.Democratic vs. Authoritarian Values: Level of education and political equality, government leadership and superiority, separation of executive and judiciary. 11.Cleavage: Ownership of state-owned enterprises, national authority over local decisions, cultural insulation, community and the individual. 12.Belief in Procedural Norms of Democracy: Respect of procedures by political leaders: compromise, tolerance of opposing and minority views. 13.Social-Economic Background Variables: Gender, age, marital status, education level, years of formal education, religion and religiosity, household, income, language and ethnicity. 14.Interview Record: Gender, age, class, and language of the interviewer, people present at the interview; did the respondent: refuse, display impatience, and cooperate; the language or dialect spoken in interview, and was an interpreter present.

Cleaning operations

Quality checks are enforced at every stage of data conversion to ensure that information from paper returns is edited, coded, and entered correctly for purposes of computer analysis. Machine readable data are generated by trained data entry operators and a minimum of 20 percent of the data is entered twice by independent teams for purposes of cross-checking. Data cleaning involves checks for illegal and logically inconsistent values.

Linearized FST distances between Mauritius and the different South Asian sub-regions (MAU  =  Mauritius; PWI  =  Pakistan and West India; SWI  =  Southwest India; NI  =  North India; SEI  =  Southeast India; BEI  =  Bangladesh and East India).

The genetic origin of ethnic groups present in South Asia is complex. Widespread factors such as complex societal caste structure, language shifts, the coexistence of tribal alongside caste populations, and a varied and vast geography augment this complexity. In face of this complexity, a holistic approach is required for the study of population histories. The isolated Dravidian population, Brahui in Pakistan, represents a remnant of complex ethnolinguistic population history. Genetic studies conducted to date have not demonstrated a close genetic link between the Brahui and other Dravidian populations of the Indian subcontinent. However, none of these studies included the Kurukh-Malto populations, which are linguistically closest to the Brahui. In this study, we included the Kurukh speaking Oraon population and their neighbours in high-resolution genetic analyses to investigate their allele and haplotype sharing with the Brahui population of Pakistan. Our intrapopulation analyses on Oraon collected from Bangladesh and India suggested a a common South Asian source for the Oraon that is genetically distinct from the extent of Indian Mundari (Austroasiatic) populations. The interpopulation comparison of Oraon showed a closer genetic affinity with the geographically more distant Mawasi (North Munda) and Gond (South Dravidian) populations, rather than their immediate neighbours. Moreover, our extensive statistical analyses found no signal of an Oraon-related ancestry inBrahui. One possible explanation to this finding is that thegenetic signature related to the Dravidian population might have been entirely lost in Brahui due toextensiveadmixture with neighboring populations.