Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

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This database, and the accompanying website called ‘SurgeWatch’ (http://surgewatch.stg.rlp.io), provides a systematic UK-wide record of high sea level and coastal flood events over the last 100 years (1915-2014). Derived using records from the National Tide Gauge Network, a dataset of exceedence probabilities from the Environment Agency and meteorological fields from the 20th Century Reanalysis, the database captures information of 96 storm events that generated the highest sea levels around the UK since 1915. For each event, the database contains information about: (1) the storm that generated that event; (2) the sea levels recorded around the UK during the event; and (3) the occurrence and severity of coastal flooding as consequence of the event. The data are presented to be easily assessable and understandable to a wide range of interested parties. The database contains 100 files; four CSV files and 96 PDF files. Two CSV files contain the meteorological and sea level data for each of the 96 events. A third file contains the list of the top 20 largest skew surges at each of the 40 study tide gauge site. In the file containing the sea level and skew surge data, the tide gauge sites are numbered 1 to 40. A fourth accompanying CSV file lists, for reference, the site name and location (longitude and latitude). A description of the parameters in each of the four CSV files is given in the table below. There are also 96 separate PDF files containing the event commentaries. For each event these contain a concise narrative of the meteorological and sea level conditions experienced during the event, and a succinct description of the evidence available in support of coastal flooding, with a brief account of the recorded consequences to people and property. In addition, these contain graphical representation of the storm track and mean sea level pressure and wind fields at the time of maximum high water, the return period and skew surge magnitudes at sites around the UK, and a table of the date and time, offset return period, water level, predicted tide and skew surge for each site where the 1 in 5 year threshold was reached or exceeded for each event. A detailed description of how the database was created is given in Haigh et al. (2015). Coastal flooding caused by extreme sea levels can be devastating, with long-lasting and diverse consequences. The UK has a long history of severe coastal flooding. The recent 2013-14 winter in particular, produced a sequence of some of the worst coastal flooding the UK has experienced in the last 100 years. At present 2.5 million properties and £150 billion of assets are potentially exposed to coastal flooding. Yet despite these concerns, there is no formal, national framework in the UK to record flood severity and consequences and thus benefit an understanding of coastal flooding mechanisms and consequences. Without a systematic record of flood events, assessment of coastal flooding around the UK coast is limited. The database was created at the School of Ocean and Earth Science, National Oceanography Centre, University of Southampton with help from the Faculty of Engineering and the Environment, University of Southampton, the National Oceanography Centre and the British Oceanographic Data Centre. Collation of the database and the development of the website was funded through a Natural Environment Research Council (NERC) impact acceleration grant. The database contributes to the objectives of UK Engineering and Physical Sciences Research Council (EPSRC) consortium project FLOOD Memory (EP/K013513/1).

About this dataset

Context

The dataset tabulates the population of California by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for California. The dataset can be utilized to understand the population distribution of California by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in California. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for California.

Key observations

Largest age group (population): Male # 30-34 years (1.53 million) | Female # 30-34 years (1.43 million). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the California population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the California is shown in the following column.
• Population (Female): The female population in the California is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in California for each age group.

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 California Population by Gender. You can refer the same here

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

50 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2050.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents. Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley. How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life? Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

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

The total population in China was estimated at 1409.7 million people in 2023, according to the latest census figures and projections from Trading Economics. This dataset provides - China Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Understanding historic patterns of land use and land cover change across large temporal and spatial scales is critical for developing effective biodiversity conservation management and policy. We quantify the extent and fragmentation of suitable habitat across the continental range of Asian elephants (Elephas maximus) based on present-day occurrence data and land-use variables between 850 and 2015 A.D. We found that following centuries of relative stability, over 64% (3.36 million km2) of suitable elephant habitat across Asia was lost since the year 1700, coincident with colonial-era land-use practices in South Asia and subsequent agricultural intensification in Southeast Asia. Average patch size dropped 83% from approximately 99,000–16,000 km2 and the area occupied by the largest patch decreased 83% from ~ 4 million km2 (45% of area) to 54,000 km2 (~ 7.5% of area). Whereas 100% of the area within 100 km of the current elephant range could have been considered suitable habitat in the year 1700, over half was unsuitable by 2015, driving potential conflict with people. These losses reflect long-term decline of non-forested ecosystems, exceeding estimates of deforestation within this century. Societies must consider ecological histories in addition to proximate threats to develop more just and sustainable land-use and conservation strategies. Methods Elephant occurrence data Elephant occurrence locations were initially compiled from the Global Biodiversity Information Facility (https://www.gbif.org/), Movebank (https://www.movebank.org/) and published literature as well as data contributed by the authors based on direct sightings, data logged via tracking devices, and camera traps (n>5000 locations). Records were first checked visually for irrelevant points (e.g., occurrences outside natural continental range, from GBIF) and then refined to include locations representing ecosystems where the species could conceivably flourish, including but not exclusively limited to protected areas. To minimize sampling bias that could result in model overfitting, we further subsampled data to cover the full distribution as widely as possible while eliminating redundant points located within any particular landscape. For instance, thousands of potential redundancies from collar-based tracking datasets were removed by using only one randomly selected data point per individual, per population or landscape. Outliers from the remaining points were removed using Cooks’ distance to eliminate locations that could represent potential errors. The final dataset consisted of 91 occurrence points spanning the years 1996-2015 which served as training data, where all data other than from GBIF and cited literature were contributed by the authors or individuals listed in acknowledgments. QGIS and Google Earth Pro were used to initially visualize and process the data. Predictor variables We used the Land-Use Harmonization 2 (LUH2) data products 25 as our environmental variables. The LUH2 datasets provide historical reconstructions of land use and land management from 850 to 2015 CE, at annual increments. The LUH2 data products were downloaded from the University of Maryland at http://luh.umd.edu/data.shtml (LUHv2h “baseline” scenario released October 14th 2016). They contain three types of variables gridded at 0.25° x 0.25° (approximately 30 km2 at the equator): state variables describing the land-use of a grid cell for a given year, transition variables describing the changes in a grid cell from one year to the next, and management variables that describe agricultural applications such as irrigation and fertilizer use, totaling 46 variables. Of these, we selected 20 variables corresponding to all 3 types which were expected to be relevant to elephant habitat use based on knowledge of the species’ ecology 21,22,32,81. Using ArcGIS 10 (ESRI 2017) we extracted each variable between 850–1700 CE at 25-year increments, and annually between 1700–2015. We separately obtained elevation from the SRTM Digital Elevation Model. Data analysis We limited the geographic extent of all analyses to the 13 range countries in which elephants are currently found. We used MAXENT, a maximum entropy algorithm 82, to model habitat suitability using the ‘dismo’ package in R (R Core Team 2017). Resulting raster files were binarized in ArcGIS into suitable and unsuitable habitat with a pixel size of approximately 20 km2 as a cutoff threshold. As there is no commonly accepted threshold type 84, to ensure that the specific choice of threshold did not affect the observed trends, we initially used three possible thresholds: 0.237, representing ‘maximum test sensitivity plus specificity,’ 0.284 corresponding to ‘maximum training sensitivity plus specificity,’ and 0.331 representing ‘10th percentile training presence’. Unless otherwise stated, for subsequent analyses we show only results using the threshold of 0.284, where everything below this threshold was classified as ‘unsuitable’ and everything above it was classified as ‘suitable’. The resulting binary maps were re-projected using the WGS84 datum and an Albers Equal Area Conic projection. Polygons representing the known elephant range were digitized from Hedges et al. 2008 from the category labelled as “active confirmed”. We refer to the areas within these polygons as “current range,” and refer to areas outside them as “potential range”. We compared the total extent of suitable habitat within and outside the current elephant range, quantifying changes over time. Country-level analyses were conducted for all countries except Indonesia and Malaysia where the Bornean and Sumatran ranges were treated separately in recognition of the distinct subspecies in these two regions. We ranked each region based on the percentage of the current range within that region as well as the proportion of the estimated elephant population found within it, and calculated the ratio of these ranks. We calculated the total change in extent of suitable habitat by subtracting the area of suitable habitat available in 2015 from the area available in 1700, as major changes were observed within this period. We also specifically quantified the percentage of suitable habitat found within a 100 km buffer of the current range polygons in both years. We then calculated fragmentation statistics using the program FRAGSTATS v.4.2 88. These metrics characterize changes to the spatial configuration of habitat in addition to their absolute extent. We used a ‘no sampling’ strategy with the search radius and threshold distance set to 61 km (approximately three-pixel lengths) based on the movement and dispersal capacity of elephants. See associated paper for references, tables and figures.

Background: The opioid epidemic has been extensively documented in the United States and Canada, but fewer data are available for Europe.Aim: To describe the trends in opioid use—volume of prescriptions, dosage and number of patients treated—in a Spanish population with more than 4.2 million inhabitants aged 18 years and older.Patients and Methods: Population-based cross-sectional analysis of opioid prescription in adults (≥18 years) from January 1, 2010 to December 31, 2018 in the region of Valencia, Spain. Outcomes were estimated on an annual basis: number of prescriptions, prescription rate per 100 inhabitants, dosage per capita (morphine mg equivalents, MME/c) and volume of patients treated (overall and by drug).Results: Over the study period, 2,107,756 unique patients were prescribed more than 35 million total treatments. The yearly number of treatments doubled, and total MME/c showed almost a threefold increase. Fentanyl MME/c more than tripled, accounting for 34.4% of the total MME/c in 2018. Oxycodone MME/c showed a 10-fold increase, while tapentadol, launched in 2011, showed the highest growth rates. The annual number of patients receiving at least one opioid prescription more than doubled, from 335,379 in 2010 to 722,838 in 2018.Conclusions: Even if proportions still seem far from epidemic, urgent research is warranted on the observed patterns of use, their appropriateness and their association with health and safety outcomes, especially for high-use and high-strength drugs.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

The number of Facebook users in the United States was forecast to continuously increase between 2024 and 2028 by in total 12.6 million users (+5.04 percent). After the ninth consecutive increasing year, the Facebook user base is estimated to reach 262.8 million users and therefore a new peak in 2028. Notably, the number of Facebook users of was continuously increasing over the past years.User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of Facebook users in Africa was forecast to continuously increase between 2024 and 2028 by in total 141.6 million users (+56.79 percent). After the ninth consecutive increasing year, the Facebook user base is estimated to reach 390.94 million users and therefore a new peak in 2028. Notably, the number of Facebook users of was continuously increasing over the past years.User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Facebook users in countries like Europe and Asia.

The number of Twitter users in Africa was forecast to continuously increase between 2024 and 2028 by in total 28.1 million users (+100.75 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 55.96 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Australia & Oceania and North America.

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

The number of WhatsApp users in Africa was forecast to continuously increase between 2024 and 2029 by in total 43.8 million users (+47.79 percent). After the ninth consecutive increasing year, the WhatsApp user base is estimated to reach 135.44 million users and therefore a new peak in 2029. Notably, the number of WhatsApp users of was continuously increasing over the past years.User figures, shown here regarding the platform whatsapp, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of WhatsApp users in countries like Asia and the Americas.

The percentage of households with internet access in Africa was forecast to continuously increase between 2024 and 2029 by in total 14.5 percentage points. After the twenty-eighth consecutive increasing year, the internet penetration is estimated to reach 68.81 percent and therefore a new peak in 2029. Notably, the percentage of households with internet access of was continuously increasing over the past years.Depicted is the share of housholds with internet access in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the percentage of households with internet access in countries like Caribbean and Asia.

The online banking penetration rate in Africa was forecast to continuously increase between 2024 and 2029 by in total 5.2 percentage points. After the fifteenth consecutive increasing year, the online banking penetration is estimated to reach 13.25 percent and therefore a new peak in 2029. Notably, the online banking penetration rate of was continuously increasing over the past years.Shown is the estimated percentage of the total population in a given region or country, which makes use of online banking.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the online banking penetration rate in countries like North America and Europe.

The population share with mobile internet access in Africa was forecast to continuously increase between 2024 and 2029 by in total 21.7 percentage points. After the eighteenth consecutive increasing year, the mobile internet penetration is estimated to reach 46.22 percent and therefore a new peak in 2029. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.