The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Brazil: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

In 202, according to the estimation, the Brazilian state of São Paulo was home to nearly 46 million people, making it the most populous state in the South American country. With less than half of São Paulo's population, Minas Gerais was the second most populous state in Brazil at that time. These two states are located in the South-East region of the country. Along with Rio de Janeiro and Espírito Santo, these states constitute Brazil's most populated region.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

These maps represent a modeled distribution of population based on the nominal censuses of the town of Curitiba and adjacent towns, providing a snapshot of the demographic situation in a specific year. Classification of data is normalized for each year to allow comparison between different categories of data for the same year.

These maps represent a modeled distribution of population based on the nominal censuses of the town of Curitiba and adjacent towns. Classification of data is normalized for each category to allow comparison between different periods of time.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

This data set contains physical, hydrologic, political, demographic, and societal maps for the Ji-Parana River Basin, in the state of Rondonia, Brazil. These data were used as base information in subsequent investigations of land use/land cover, biogeochemistry, soils, and water balance processes (Ballester et al., 2003). This data set includes a Digital Elevation Model (DEM), river networks and morphometric characteristics of the region (derived from the DEM), and a number of social and demographic vector sets (roads as of 2001, county borders, population change from 1970-2000, and settlement projects). The DEM is provided in GeoTIFF format. Other files are provided as shapefiles.

Brazil Country Boundary provides a 2021 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2021 boundaries are provided by Michael Bauer Research GmbH. They are sourced from 2013 Instituto Brasileiro de Geografia e Estatistica. These were published in October 2022. A new layer will be published in 18 months. Other administrative boundaries for this country are also available: Unidade Municipio Distrito Subdistrito Sectore

Figure 1. Origin of Phakopsora pachyrhizi populations in Brazil map. Leaves sampled collected of soybean plants infected with Asian soybean rust collected the 2017/2018 soybean seasons. Brazilian States: BA - Bahia and PR - Paraná. Municipalities: BA - Luis Eduardo Magalhães; PR - Campo Magro, Palmeira, Palotina and Ponta Grossa.

BackgroundThis study aims to describe the role of mobility in malaria transmission by discussing recent changes in population movements in the Brazilian Amazon and developing a flow map of disease transmission in this region.Methodology/Principal findingsThis study presents a descriptive analysis using an ecological approach on regional and local scales. The study location was the municipality of Porto Velho, which is the capital of Rondônia state, Brazil. Our dataset was obtained from the official health database, the population census and an environmental database. During 2000–2007 and 2007–2010, the Porto Velho municipality had an annual population growth of 1.42% and 5.07%, respectively. This population growth can be attributed to migration, which was driven by the construction of the Madeira River hydroelectric complex. From 2010 to 2012, 63,899 malaria-positive slides were reported for residents of Porto Velho municipality; 92% of the identified samples were autochthonous, and 8% were allochthonous. The flow map of patients' movements between residential areas and areas of suspected infection showed two patterns of malaria transmission: 1) commuting between residential areas and the Jirau hydropower dam reservoir, and 2) movements between urban areas and farms and resorts in rural areas. It was also observed that areas with greater occurrences of malaria were characterized by a low rate of deforestation.ConclusionsThe Porto Velho municipality exhibits high malaria endemicity and plays an important role in disseminating the parasite to other municipalities in the Amazon and even to non-endemic areas of the country. Migration remains an important factor for the occurrence of malaria. However, due to recent changes in human occupation of the Brazilian Amazon, characterized by intense expansion of transportation networks, commuting has also become an important factor in malaria transmission. The magnitude of this change necessitates a new model to explain malaria transmission in the Brazilian Amazon.

There is a considerable gap linking human dimensions and marine ecosystem services with Sustainable Development Goals, and one of these issues relate to differing perspectives and ideas around concepts of human development. There is also a lack of contemporary evaluations of coastal communities from developing nations under the lens of wellbeing and social vulnerability indexes. This study contributes to that discussion by presenting an analysis of Brazilian coastal municipalities, based on two indexes: The Social Vulnerability Index (SVI) and the Municipal Human Development Index (MHDI). These indicators intend to map some aspects of social well-being and development in the Brazilian territory under different perspectives. MHDI illustrates the average population conditions in a certain territory for humans to thrive, while the SVI points more specifically to the lack of assets necessary for wellbeing in a territory. The main aims are to map inequalities between coastal municipalities based on these two indexes and to provide a critical view reinforcing the importance of also considering natural capital as a key issue for wellbeing. Both indexes were developed with data from the Brazilian Institute of Geography and Statistics Census of 2010, the most recent one available for municipalities. Overall, 65.9 and 78% of a total of 387 Brazilian coastal municipalities assessed were ranked below SVI and MHDI country average values, respectively. Both indexes indicated higher human development conditions in Southern municipalities than in Northern ones, especially for income and education conditions, also showing large heterogeneity of discrepancies among and within regions. The importance of combined approaches for local socioeconomic wellbeing improvements, as measured by the MHDI and the SVI, and natural capital optimization seems essential for improvements in coastal communities’ quality-of-life conditions.

Usage notes

Rapidly expanding road networks have been a key driver of the fragmentation and isolation of many wildlife species, and are a source of significant mortality due to collisions with vehicles. But not all animals are affected equally by transportation infrastructure, and in most cases little is known about the population-scale consequences of roads for wildlife. Even less information is available to characterize species’ behavioral responses to roads. Although research shows that maned wolves (Chrysocyon brachyurus) in Brazil are experiencing considerable fragmentation and road mortality, the degree to which these disturbances are impacting the species’ viability is unknown. The goal of this study was to analyze the potential effects of roads on maned wolf population size and structure. We used a simulation model to evaluate the population-scale consequences of individual maned wolf interactions with roads, which can result in road crossing, avoidance, or mortality due to a collision with a vehicle. We also forecasted where in Brazil these impacts might be most significant. Our model incorporated species demographic and movement parameters, plus habitat quality and a map of the road network. We found that even moderate rates of road mortality led to severe declines in population size, and that four specific locations accounted for a disproportionate fraction of roadkill events. Our approach will be generally useful for evaluating the relative importance of road effects on species conservation in many ecological systems, for prioritizing data collection efforts, and for informing conservation policies and mitigation strategies.

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.

As of February 2025, India had a total of 413.85 million Instagram users, the largest Instagram audience in the world. The United States had 171.7 million users, and Brazil had 140.7 million. Indonesia, Turkey, and Japan ranked in fourth, fifth and sixth position, respectively. Kazakhstan is the leading country for Instagram audience reach, with 86.2 percent of the population using the social media service. Turkey came in second, with a penetration rate of 85.5 percent and Uruguay ranked third, with 87.1 percent, followed closely by the UAE, Brazil, and Bahrain. It took Instagram 11.2 years to reach the milestone of 2 billion monthly active users worldwide. WhatsApp, also owned by Meta, took 11 years, whilst Facebook took 13.3 years and YouTube took just over 14 years. Instagram’s demographics in the United States As of March 2025, Instagram was the fourth most visited social media service in the United States, after Facebook, Pinterest and X. Out of TikTok, Instagram and Snapchat, TikTok was the most used of all three platforms by Generation Z. Overall, 57 percent of Gen Z social media users used Instagram in 2021, down from 61 percent in 2020 and 64 percent in 2019. Instagram finds most popularity with those in the 25 to 34 year age group, and as of January 2025, roughly 28.3 of all users in the United States belonged to this age group. The social media app was also more likely to be used by women. Most followed accounts on Instagram Instagram’s official account had the most followers as of April 2024 with over 672 million followers. Manchester United forward Cristiano Ronaldo (@cristiano) had over 628 million followers on the platform, while the Argentinian footballer Lionel Messi (@leomessi) had over 502 million followers. The Instagram accounts of the American singer and actress Selena Gomez (@selenagomez) and the media personality and makeup mogul Kylie Jenner (@kyliejenner) had over 400 million followers each.

Primer and probe sequences for candidate gene expression analysis. (XLS 35 kb)

As of February 2025, India was the country with the largest YouTube audience by far, with approximately 491 million users engaging with the popular social video platform. The United States followed, with around 253 million YouTube viewers. Brazil came in third, with 144 million users watching content on YouTube. The United Kingdom saw around 54.8 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? In July 2024, the United Arab Emirates was the country with the highest YouTube penetration worldwide, as around 94 percent of the country's digital population engaged with the service. In 2024, YouTube counted around 100 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets In 2024, YouTube was among the most popular social media platforms worldwide. In terms of revenues, the YouTube app generated approximately 28 million U.S. dollars in revenues in the United States in January 2024, as well as 19 million U.S. dollars in Japan.

P-values of single and multiple QTLs and haplotype combinations associated with witches’ broom disease. The first tab contains the single QTL with their different parental haplotype combinations. The different columns contain the total number of trees with each haplotype and the results of the Chi-squared test for resistance and susceptibility. The last column contains the percentage of resistant trees with the particular haplotype combination. The second and third tab contain the same info but for combinations of two and three QTL, respectively. The combinations marked in grey belong to the top five of lowest P-values and indicate the highest associations between the QTL and WBD resistance/susceptibility. (XLS 81 kb)

Location of the SNP markers on the integrated linkage map. (XLS 278 kb)