North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming 29 gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

This statistic shows the average monthly wireless data usage per user in the United States by age in the first two quarters of 2018. In the first half of 2018, users 25 years and younger used 4.1 GB of cellular and 16.8 GB of Wi-Fi wireless data.

In 2022, the average data used per smartphone per month worldwide amounted to 15 gigabytes (GB). The source forecasts that this will increase almost four times reaching 46 GB per smartphone per month globally in 2028.

United States rose 107.6% of Average per Capita Monthly Mobile Data Use in 2014, compared to the previous year.

In the first quarter of 2021, the average Singaporean mobile internet user consumed about 12.7 GB of data per month. With the increasing demand of online video and social media content this figure is expected to further grow over the next few years.

As of 2023, the average data consumption per user per month in India was at 24.1 gigabytes. 4G data traffic contributes to 99 percent of the overall data traffic while 5G was launched in India in October 2022. Increased online education, remote working for professionals and higher OTT viewership contributed to the data traffic growth.

As of the second quarter of 2024, the average residential high-speed broadband subscription in Canada downloaded around 460 gigabytes of data per month. This was a decrease on the previous quarter, when the average download volume reached a record 488 gigabytes per month.

In a May 2022 study, Greater London had the highest average broadband usage per household in the United Kingdom (UK) at 506 GB/month, over 11 percent above the national average of 456 GB/month. The region with the lowest broadband consumption per household was in the South West, over 12 percent lower than the national average at 400 GB/month.

This dataset provides the average (annual, winter, summer) residential metered water consumption within residential neighbourhoods provided in m3/month for the City of Edmonton.

Average monthly residential winter water consumption is the average consumption of the following months: January, February, March, April, October, November and December.

Average monthly residential summer water consumption is the average consumption of the following months: May, June, July, August and September.

Only those residential neighbourhoods with at least ten accounts are illustrated to ensure customer privacy.

Residential consumption refers to water used primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

Thematic mapping is based on the following ranges:

0-10 m3/month – orange 10-20 m3/month – green 20-30 m3/month – purple 30-35 m3/month – blue 35-60 m3/month – red 60 m3/month and up – maroon

Key information about United States Monthly Earnings

• United States Monthly Earnings stood at 4,901 USD in Feb 2025, compared with the previous figure of 4,887 USD in Jan 2025
• US Monthly Earnings data is updated monthly, available from Mar 2006 to Feb 2025, with an average number of 3,469 USD
• The data reached the an all-time high of 4,901 USD in Feb 2025 and a record low of 2,743 USD in Mar 2006

CEIC calculates Monthly Earnings from Average Weekly Earnings multiplied by 4. U.S. Bureau of Labor Statistics provides Average Weekly Earnings in USD. Monthly Earnings include Private Non Agricultural sector only.


Further information about United States Monthly Earnings

• In the latest reports, US Population reached 341 million people in Dec 2024
• Unemployment Rate of US increased to 4 % in Feb 2025
• The country's Labour Force Participation Rate remained the same rate at 62 % in Feb 2025

The average mobile data usage per capita in 2018 was significantly less for the ETNO perimeter of Europe than for Japan, South Korea, and the United States. Europeans on average used 4.3 gigabytes per month of mobile data compared to that of 6.91, 7.15, and 7.24 gigabytes per month in Japan, South Korea, and the United States, respectively. It is important to note that there is a huge variation between European countries in terms of average usage, as Europe* is a regional representation compared to the selected countries included in this study.

The average Twitter user spends 5.1 hours per month on the platform.

The average Facebook user spends about 19.6 per month on Facebook every month. This works out to be about 39 minutes per day.

This statistic shows the average price of cellular data per gigabyte in the United States from 2018 to 2023. In 2018, the average price of cellular data was estimated to amount to 4.64 U.S. dollars per GB.

TikTok has 102.3 million monthly active users in the US alone. This is forecasted to reach 121.1 million by 2027.

Average monthly count of unduplicated children in paid foster care per month by fiscal year. This dataset counts unique children regardless of payment types during the month. Calculations exclude children and young adults where cost of care was not covered by Title IV-E or state paid foster care. A young adult is any person in foster care who was 18 to 21 years of age at anytime during the fiscal year. Some children are served in more than one eligibility type in a month.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

This layer shows household size by tenure (owner or renter). This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the average household size as well as the count of all housing units. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25009, B25010, B19019Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Key information about Argentina Household Income per Capita

• Argentina Annual Household Income per Capita reached 4,522.736 USD in Dec 2023, compared with the previous value of 4,354.647 USD in Dec 2022.
• Argentina Annual Household Income per Capita data is updated yearly, available from Dec 2004 to Dec 2023, with an averaged value of 3,818.417 USD.
• The data reached an all-time high of 5,915.943 USD in Dec 2017 and a record low of 1,383.333 USD in Dec 2004.
• In the latest reports, Retail Sales of Argentina dropped 13.777 % YoY in Jan 2024.

CEIC calculcates Annual Household Income per Capita from quarterly Monthly Average Household Income per Capita multiplied by 3 and converts it into USD. The National Institute of Statistics and Censuses provides Average Household Income per Capita in local currency. The Central Bank of Argentina average market exchange rate is used for currency conversions. Household Income covers Urban area only.

China Electricity Consumption: per Capita: Average data was reported at 6,257.000 kWh in 2022. This records an increase from the previous number of 6,032.000 kWh for 2021. China Electricity Consumption: per Capita: Average data is updated yearly, averaging 1,066.997 kWh from Dec 1978 (Median) to 2022, with 45 observations. The data reached an all-time high of 6,257.000 kWh in 2022 and a record low of 261.265 kWh in 1978. China Electricity Consumption: per Capita: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCB: Electricity Summary.