How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Consumer Spending in the United States increased to 16350.20 USD Billion in the second quarter of 2025 from 16291.80 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

Analysis of ‘Parking Statistics in North America’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/terenceshin/searching-for-parking-statistics-in-north-america on 28 January 2022.

--- Dataset description provided by original source is as follows ---

ABOUT

This dataset identifies areas within a city where drivers are experiencing difficulty searching for parking. Cities can use this data to identify problem areas, adjust signage, and more. Only cities with a population of more than 100,000 are included.

Data

Some variables to highlight:

• AvgTimeToPark: The average time taken to search for parking (in minutes)
• AvgTimeToParkRatio: The ratio between the average time taken to search for parking and of those not searching for parking in the current geohash
• TotalSearching: The number of drivers searching for parking
• PercentSearching: The percentage of drivers that were searching for parking
• AvgUniqueGeohashes: The average number of unique geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking
• AvgTotalGeohashes: The average number of all geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking
• CirclingDistribution: JSON object representing the neighbouring geohashes at the 7 character level whereby vehicles searching for parking tend to spend their time. Each geohash will have the average percentage of time spent in that geohash prior to parking.
• HourlyDistribution: JSON object representing the average prevalence of searching for parking by hour of day (% distribution based on number of vehicles experiencing parking problems)
• SearchingByHour: JSON object representing the average percentage of vehicles searching for parking within the hour
• PercentCar: Percentage of vehicles with parking issues that were cars
• PercentMPV: Percentage of vehicles with parking issues that were multi purpose vehicles
• PercentLDT: Percentage of vehicles with parking issues that were light duty trucks
• PercentMDT: Percentage of vehicles with parking issues that were medium duty trucks
• PercentHDT: Percentage of vehicles with parking issues that were heavy duty trucks
• PercentOther: Percentage of vehicles with parking issues that were unknown classification

Content

This dataset is aggregated over the previous 6 months and is updated monthly. This data is publicly available from Geotab (geotab.com).

Inspiration

As some inspiration, here are some questions:

• Which cities are the hardest to find parking?
• By joining population data externally, can you determine a relationship between a region's population and the time that it takes to find parking?
• Similarly, by finding external data, is there a correlation between GDP and parking times? What about average household income?

--- Original source retains full ownership of the source dataset ---

Average Weekly Hours in the United States increased to 34.30 Hours in July from 34.20 Hours in June of 2025. This dataset provides - United States Average Weekly Hours - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 0.781 % in 2013. This records a decrease from the previous number of 0.856 % for 2012. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 0.880 % from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 1.078 % in 2000 and a record low of 0.724 % in 2008. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Weighted Average;

Release Date: 2018-08-10.[NOTE: Includes firms with payroll at any time during 2016. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2016 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status for at least one owner and were not publicly held or not classifiable by gender, ethnicity, race, and veteran status. The 2016 Annual Survey of Entrepreneurs asked for information for up to four persons owning the largest percentage(s) of the business. Percentages are for owners of respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016. ..Release Schedule. . This file was released in August 2018.. ..Key Table Information. . These data are related to all other 2016 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2016 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2016 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. For Characteristics of Business Owners (CBO) data, all estimates are of owners of firms responding to the ASE. That is, estimates are based only on firms providing gender, ethnicity, race, or veteran status; or firms not classifiable by gender, ethnicity, race, and veteran status that returned an ASE online questionnaire with at least one question answered. The ASE online questionnaire provided space for up to four owners to report their characteristics.. CBO data are not representative of all owners of all firms operating in the United States. The data do not represent all business owners in the United States.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016 contains data on:. . Number of owners of respondent firms with paid employees. Percent of number of owners of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of owners of respondent firms. . All owners of respondent firms. Female. Male. Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Nonminority. Veteran. Nonveteran. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Owner...

Release Date: 2017-07-13.[NOTE: Includes firms with payroll at any time during 2015. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2015 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status for at least one owner and were not publicly held or not classifiable by gender, ethnicity, race, and veteran status. The 2015 Annual Survey of Entrepreneurs asked for information for up to four persons owning the largest percentage(s) of the business. Percentages are for owners of respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015. ..Release Schedule. . This file was released in July 2017.. ..Key Table Information. . These data are related to all other 2015 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2015 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2015 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. For Characteristics of Business Owners (CBO) data, all estimates are of owners of firms responding to the ASE. That is, estimates are based only on firms providing gender, ethnicity, race, or veteran status; or firms not classifiable by gender, ethnicity, race, and veteran status that returned an ASE online questionnaire with at least one question answered. The ASE online questionnaire provided space for up to four owners to report their characteristics.. CBO data are not representative of all owners of all firms operating in the United States. The data do not represent all business owners in the United States.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015 contains data on:. . Number of owners of respondent firms with paid employees. Percent of number of owners of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of owners of respondent firms. . All owners of respondent firms. Female. Male. Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Nonminority. Veteran. Nonveteran. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Owner's...

One measure used to analyze roadway reliability is the Planning Time Index (PTI). It is the ratio of the 95th percentile travel time relative to the free-flow (uncongested) travel time. PTI helps in understanding the impacts of nonrecurring congestion from crashes, weather, and special events. It approximates the extent to which a traveler should add extra time to their trip to ensure on-time arrival at their destination. A value of 1.0 indicates a person can expect free-flow speeds along their route. A 2.0 index value indicates a traveler should expect that the trip could be twice as long as free-flow conditions. PTI values from 2.0 to 3.0 indicate moderate unreliability, and ones greater than 3.0 are highly unreliable.

The data comes from aggregated Global Positioning System probe data—anonymized data from mobile apps, connected vehicles, and commercial fleets—provided to the Probe Data Analytics (PDA) Suite by INRIX, a travel data technology company. The PDA Suite was created by a consortium of sponsors, including the Eastern Transportation Coalition and the University of Maryland.

PTI values by region, subregion, and county are grouped either as highway facilities or local roads. Highways are roadway segments classified as interstates, turnpikes, and expressways in the PDA Suite. Local roads are segments classified as U.S. routes, state routes, parkways, frontages, and others. The PDA Suite reports weekday hourly averages by facility type and direction. Average weekday values are reported by facility type and direction, within the following time periods:

• Morning (AM): 6:00 AM–9:59 AM;
• Midday (MD): 10:00 AM–2:59 PM;
• Evening (PM): 3:00 PM–6:59 PM;
• Nighttime (NT): 7:00 PM–5:59 AM; and
• Daily: 12:00 AM–11:59 PM.

Although INRIX data collection precedes years reported in Tracking Progress, early years of reporting are highly variable based on a lack of facility coverage. The years from 2011 onward show higher stability for highway facilities for most counties and for the region. For local facilities, 2014 and beyond is where values seem most stable due to more widespread facility coverage.

Historic data for the federal Transportation Performance Management (TPM) system performance reporting requirements is shown. These are Level of Travel Time Reliability (LOTTR), Level of Truck Travel Time Reliability (TTTR), and Annual Hours of Peak-Hour Excessive Delay (AHPHED). The entire states of Pennsylvania and New Jersey are included for LOTTR and TTTR, so the region’s figures can be compared with statewide data.

LOTTR is used to calculate the percentage of roadway segments that are considered reliable. A road segment with an LOTTR of less than 1.5 is considered reliable. Reliable segment lengths in miles are multiplied by their Annual average daily traffic (AADT) values times the average number of people in a vehicle. Then, this sum is then divided by the exact same product for all road segments, to get the resulting percentage of roadway that is reliable for the geography.

TTTR measures how consistent travel times are for trucks on interstates. This can be helpful with analyzing goods movement along the region’s interstates. TTTR is calculated by dividing the 95th percentile of travel times by the 50th percentile of travel times, using the highest value over the Morning (AM), Midday (MD), Evening (PM), Nighttime (NT), and weekend. Each interstate segment multiplies its length by the travel time ratio, the results are summed and then divided by total Interstate length in the geography to determine the area’s TTTR value.

AHPHED is the average number of hours per year spent by motorists driving in congestion during peak periods. This can be useful for analyzing the impact of congestion from an individual’s perspective, since it analyzes how many hours the average person spends stuck in congestion. The figures used are based on the 2010 urbanized area boundaries in the Census. In 2020, they were renamed to urban areas. There are only Mercer County PHED values from 2021 onward, because they only apply to the second four-year TPM performance period, when the Trenton, NJ Urban Area was required to track metrics and set performance targets. AHPHED per capita is that figure divided by the urban area’s population during that year.

Congestion is susceptible to external forces like the economy. A downturn can reduce congestion, but this reflects fewer and shorter trips for households and businesses during lean times and may not represent an improvement. Therefore, it may be useful to correlate these results with the Miles Driven indicator.

VITAL SIGNS INDICATOR Commute Time (T3)

FULL MEASURE NAME Commute time by residential location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Form B08013 (2006-2018; place of residence; overall time) Form C08136 (2006-2018; place of residence; time by mode) Form B08301 (2006-2018; place of residence) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

This paper presents new data documenting the level and evolution of public spending on non-contributory programs for 16 countries in Latin America and the Caribbean. Salaried formal workers contribute to social security and in return have access to an array of benefits -mainly old-age pensions and health services. In recent decades, informal workers – salaried and non-salaried- have gained access to similar benefits, financed through general revenues. Our calculations indicate that, on average, the region spends 1.7% of GDP in these programs. Although they were created in response to social demands, by targeting informal workers these programs may create a behavioral response -i.e. more informality. This paper does not attempt to measure behavioral effects. Its main contribution is to be the first to document this “subsidy to informality” following a common methodology across countries and years in the region.

Household Saving Rate in the United States remained unchanged at 4.50 percent in June from 4.50 percent in May of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Government spending represents the total expenditures made by a country's public sector over a year. The metric 'Government Spending (% GDP)' indicates the annual percentage of GDP that each country (El Salvador, Guatemala, Honduras, and Nicaragua) spends, compared to the Central American average. Similarly, 'Government Spending per capita in dollars ($)' compares the expenditure per person in each of these countries against the Central American average. This data is assessed based on information obtained from Datosmacro.

For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.

Detailed breakdown of average weekly household expenditure on goods and services in the UK. Data are shown by place of purchase, income group (deciles) and age of household reference person.

Average Mortgage Size in the United States decreased to 376.08 Thousand USD in June 30 from 379.21 Thousand USD in the previous week. This dataset includes a chart with historical data for the United States Average Mortgage Size.

Survey of Household Spending (SHS), average household spending, Canada, regions and provinces.

VITAL SIGNS INDICATOR Commute Time (T3)

FULL MEASURE NAME Commute time by residential location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Form B08013 (2006-2018; place of residence; overall time) Form C08136 (2006-2018; place of residence; time by mode) Form B08301 (2006-2018; place of residence) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

VITAL SIGNS INDICATOR Commute Time (T4)

FULL MEASURE NAME Commute time by employment location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Table B08536 (2018 only; by place of employment) Table B08601 (2018 only; by place of employment) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.