Our Demographics package in the USA offers data pertaining to the households, education, work, and commute of residents of the United States of America at Census Block Level. Each data variable is available as a sum, or as a percentage of the total population within each selected area.

What is included?

At the Census Block level, this dataset includes some of the following key features:

Commute Insights

• Length of Commute: <5 min, 5-14 min, 15-29 min, 30-44 min, 45-59 min, 60-89 min, 90+ min
• Work Location: Home, Elsewhere

Method of Commute

• Bicycle
• Car
• Carpool
• Foot
• Motorcycle
• Other
• Public Transport
• Taxi

• Commute Insights provide marketers with valuable information to enhance their understanding of consumer behavior.

  • Length of Commute: Insights into the duration of commutes (categorized by time intervals) allow marketers to tailor their messaging based on the available time window. Short commutes may benefit from concise and immediate content, while longer commutes may provide an opportunity for more in-depth engagement.
  • Work Location: Understanding whether individuals work from home or elsewhere enables marketers to adjust their strategies accordingly. For those working from home, promotions related to home-based products or virtual services may be more effective, while those working elsewhere may respond better to location-specific offers.
  • Method of Commute: The method of commute provides marketers with insights into lifestyle choices. For instance, advertising eco-friendly products to bicycle or public transport commuters, or promoting in-car entertainment for drivers, allows for targeted and relevant marketing campaigns. This data helps tailor promotions to align with consumers' transportation preferences and habits.

• This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.

  Still looking for demographic data at the postal code level? Contact sales.

• There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:

  • Commuting
  • Educational Attainment
  • Employment
  • Families
  • Finance
  • Housing

About three million people in Italy commuted by bus, tram, or trolleybus every day in 2019. Lombardy was the Italian region with the highest number of people using this public transportation either every day or frequently during the week (over one million). Lazio, and Campania followed in the ranking, with 939 thousand and 521 thousand commuters, respectively.

The percentage of commuters that use other means to travel to work out of all commuters aged 16 and above. Source: American Community Survey Years Available: 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

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.

Number of family residents in Milan who move daily for study or work within Milan, leaving from their usual residence and returning daily by vehicle, time spent, departure time and gender. The data was collected through the 15th General Census of Population and Housing, carried out by ISTAT in 2011. In this census, for the first time, some information of a socio-economic nature was collected on a sample basis. Information on the characteristics of commuting trips is collected on a sample basis. For further information see www.istat.it

Abstract Little explored in literature, the daily home-school traveling reflects a spatial incoherence between the distribution of the school age population and basic education schools (secondary level) in Natal, Brazil. In this paper, we explore the profile of the commuters (students) from an intra-municipal political and administrative perspective. Thus, we were able to capture the daily flows of students among Natal neighborhoods. For this, we use the concept of living space as theoretical foundation and the school census database in 2012. Thus, the correlation between the place of residence and student studies showed a relationship between the traveling to school and school performance, measured by age-grade distortion in a logistic regression model. We identified that the chance for a student to have a good academic performance is not associated to the proximity of his home to school. In this sense, the dynamics of the population in terms of school distribution and students may represent important aspects of urban management and the provision of educational services, particularly in a context where demographic trends point to a significant reduction in school-age population in the next years.

VITAL SIGNS INDICATOR
Commute Time (T3)

FULL MEASURE NAME
Commute time by residential location

LAST UPDATED
January 2023

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 - https://data.census.gov/
2006-2021
Form C08136
Form C08536
Form B08301
Form B08301
Form B08301

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 (ACS) 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 times 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 metropolitan statistical areas (MSAs) for the nine other major metropolitan areas.

As of October 1, 2015, the commuting population in Japan's Greater Tokyo Area amounted to approximately 22.25 million people, representing the metropolitan area with the most commuters. Since 2000, the commuting population has successively decreased in each region.

The percentage of commuters that use other means to travel to work out of all commuters aged 16 and above. Source: American Community SurveyYears Available: 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

In 2021, about 28 percent of commuters were covered by the urban rail transit in super large-sized cities in China, while about 21 percent of them were covered in very large-sized cities. The railway coverage ratio is used as a significant indicator in evaluating the urban railway network.

MT 6.11.3 Population by commuting, educational attainment, industry, age and sex Tables Mt6 11 3 Population By CommutingTSV Dimension education (education)

Our Demographics package in the USA offers data pertaining to the households, education, work, and commute of residents of the United States of America at Census Block Level. Each data variable is available as a sum, or as a percentage of the total population within each selected area.

Percent of residents commuting to work through modes other than driving from the US Census Bureau's American Community Survey. Data is provided for the City of Everett, Snohomish County, Washington State, and US, as well as all census tracts within Everett.

When asked about "Attitudes towards mobility", most Italian respondents pick "Owning a car is important to me" as an answer. 62 percent did so in our online survey in 2024.

The rural-urban commuting area codes (RUCA) classify U.S. census tracts using measures of urbanization, population density, and daily commuting from the decennial census.

The most recent RUCA codes are based on data from the 2000 decennial census. The classification contains two levels. Whole numbers (1-10) delineate metropolitan, micropolitan, small town, and rural commuting areas based on the size and direction of the primary (largest) commuting flows. These 10 codes are further subdivided to permit stricter or looser delimitation of commuting areas, based on secondary (second largest) commuting flows. The approach errs in the direction of more codes, providing flexibility in combining levels to meet varying definitional needs and preferences.

The 1990 codes are similarly defined. However, the Census Bureau's methods of defining urban cores and clusters changed between the two censuses. And, census tracts changed in number and shapes. The 2000 rural-urban commuting codes are not directly comparable with the 1990 codes because of these differences.

An update of the Rural-Urban Commuting Area Codes is planned for late 2013.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Webpage with links to Excel files For complete information, please visit https://data.gov.

For more recent and more detailed data, see https://data.gov.lv/dati/dataset/tea.

Commuting of the employed population between administrative territories 1.3.2011. The data is compiled by processing corrected information obtained in the 2011 Population Census on the addresses of the place of residence and workplace. Lists the areas between which commuting flows comprise at least 10 inhabitants.

The census questionnaires did not always indicate the address of the local activity unit, instead of the unidentified number of questionnaires the registered address was used. This factor should be taken into account in the analysis of the data.

For example, if a company has branches throughout Latvia (for example, SJSC “Latvijas Pasts”, SIA “MAXIMA Latvija”, SIA “RIMI LATVIA”), part of the population census questionnaires may not specify the address of the unit, but its legal address. As a result, people’s workplaces will be located in the territory of the company’s registered office (e.g., Riga).

The Classification of Administrative Territories is published at https://data.gov.lv/dati/dataset/atvk.

Generalised boundaries of territorial units are published at https://data.gov.lv/dati/dataset/robezas.

This dataset uses workday commuting (auto and transit) information estimated, by traffic analysis zone (TAZ), for 2019 and projected for 2030, 2040, and 2050 by the Wasatch Front Travel Demand Model (WF TDM). WFTDM built and maintained jointly by WFRC and MAG, the metropolitan planning organizations for, respectively, the Salt Lake City - Ogden and Provo-Orem metro areas. These results take consider future population and job distributions produced with support of the WFRC/MAG Real Estate Market Model and future transportation infrastructure and services in the fiscally-constrained 2019-2050 Regional Transportation Plans (RTPs). Fields names should be intuitive and include: TAZ - Traffic Analysis Zone unqiue identifier;

IsRsdntl19 - Does the TAZ had residential population in the year 2019; Auto19Avg - Average auto commute time (one way) for residents of the TAZ for 2019; Trans19Avg - Average transit commute time (one way) for residents of the TAZ for 2019; Auto30Avg - Average auto commute time (one way) for residents of the TAZ for 2030; Trans30Avg - Average transit commute time (one way) for residents of the TAZ for 2030; Auto40Avg - Average auto commute time for (one way) residents of the TAZ for 2040; Trans40Avg - Average transit commute time (one way) for residents of the TAZ for 2040; Auto50Avg - Average auto commute time (one way) for residents of the TAZ for 2050; Trans50Avg - Average transit commute time (one way) for residents of the TAZ for 2050; DAuto1950 - Increase in auto commute time (one way) for residents of the TAZ from 2019 to 2050;DTrans1950 - Increase in auto commute time (one way) for residents of the TAZ from 2019 to 2050;CountyName - County; CityArea - Assignment of TAZ to a city or other named area. The resulting areas are approximate, not exact boundaries; LargeDist - Large district name for sumary travel analysis; MedDist - Medium district name for sumary travel analysis; SmallDist - Small district name for sumary travel analysis; Pop2019 - Estimated population of the TAZ for 2019 (calculated with assistance from the WFRC/MAG Real Estate Market Model); Pop2030 - Projected population of the TAZ in 2030 (calculated with assistance from the WFRC/MAG Real Estate Market Model); Pop2040 - Projected population of the TAZ in 2040 (calculated with assistance from the WFRC/MAG Real Estate Market Model); Pop2050 - Projected population of the TAZ in 2050 (calculated with assistance from the WFRC/MAG Real Estate Market Model); DevAcres - Estimated area within the TAZ that is developed or for which development is allowable/practical in the future

Please be advised that there are issues with the Small Area boundary dataset generalised to 20m which affect Small Area 268014010 in Ballygall D, Dublin City. The Small Area boundary dataset generalised to 20m is in the process of being revised and the updated datasets will be available as soon as the boundaries are amended.This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Small Areas national boundary data (generalised to 20m) produced by Ordnance Survey Ireland (OSi). The layer represents Census 2016 theme 11.1, population aged 5+ by means of travel to work, school or college. Attributes include a breakdown of population by means of travel to work, school or college (e.g. bicycle, car driver, on foot). Census 2016 theme 11 represents Commuting. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO.The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets.

The Arlington Profile combines countywide data sources and provides a comprehensive outlook of the most current data on population, housing, employment, development, transportation, and community services. These datasets are used to obtain an understanding of community, plan future services/needs, guide policy decisions, and secure grant funding. A PDF Version of the Arlington Profile can be accessed on the Arlington County website.