Before the coronavirus (COVID-19) pandemic, 17 percent of U.S. employees worked from home 5 days or more per week, a share that increased to 44 percent during the pandemic. The outbreak of the COVID-19 pandemic accelerated the remote working trend, as quarantines and lockdowns made commuting and working in an office close to impossible for millions around the world. Remote work, also called telework or working from home (WFH), provided a solution, with employees performing their roles away from the office supported by specialized technology, eliminating the commute to an office to remain connected with colleagues and clients. What enables working from home?

To enable remote work, employees rely on a remote work arrangements that enable hybrid work and make it safe during the COVID-19 pandemic. Technology supporting remote work including laptops saw a surge in demand, video conferencing companies such as Zoom jumped in value, and employers had to consider new communication techniques and resources. Is remote work the future of work?

The response to COVID-19 has demonstrated that hybrid work models are not necessarily an impediment to productivity. For this reason, there is a general consensus that different remote work models will persist post-COVID-19. Many employers see benefits to flexible working arrangements, including positive results on employee wellness surveys, and potentially reducing office space. Many employees also plan on working from home more often, with 25 percent of respondents to a recent survey expecting remote work as a benefit of employment. As a result, it is of utmost importance to acknowledge any issues that may arise in this context to empower a hybrid workforce and ensure a smooth transition to more flexible work models.

The trend of working remotely has been slowly increasing globally since 2015, with a *** to ***** percent annual increase rate. However, the COVID-19 pandemic in 2020 upended the world economy and global markets. Employment trends were no exception to this, with the share of employees working remotely increasing to some ** percent in 2022 from just ** percent two years prior. The industry with the highest share of remote workers globally in 2023 was by far the technology sector, with over ** percent of tech employees worldwide working fully or mostly remotely. How are employers dealing with remote work? Many employers around the world have already adopted some remote work policies. According to IT industry leaders, reasons for remote work adoption ranged from a desire to broaden a company’s talent pool, increase productivity, and reduce costs from office equipment or real estate investments. Nonetheless, employers worldwide grappled with various concerns related to hybrid work. Among tech leaders, leading concerns included enabling effective collaboration and preserving organizational culture in hybrid work environments. Consequently, it’s unsurprising that maintaining organizational culture, fostering collaboration, and real estate investments emerged as key drivers for return-to-office mandates globally. However, these efforts were not without challenges. Notably, ** percent of employers faced employee resistance to returning to the office, prompting a review of their remote work policies.

Remote Work Statistics: The future is here we say, as technology made sure to let employees spread around the globe to work remotely. Just before the pandemic people commuting to offices daily shifted to completely mobile work opportunities. Market reports of distance work state that the future of remote work will be adopted by many companies soon as employees focus on such job opportunities only. These Remote Work Statistics are written from various aspects that need to be taken into consideration while setting policies for mobile work. Editor’s Choice Mobile workers with communicative employers are 5X more productive and 3X less feel burned out. 25% of remote employees are planning to change their locations for a better lifestyle. Around 55% of Americans believe their work can be performed remotely in their industry. Remote work statistics say that, in May 2021, remote work job postings on LinkedIn increased by 350%. Remote work Statistics state that in the year 2022, the remote workplace market was valued at $20.1 billion, and it is projected to reach 58.5 billion by the year 2027 at a CAGR of 23.8%. 59% of distance employees said, their office is functional in 2 to 5 various times zones. For every mobile work employee companies save around $22K every month, on the other hand, employees save on average $4000 every year due to a reduction in commute. In the upcoming years, employers are planning to spend more on remote work tools as well as virtual manager training. 16% of people say that they are worried about their company not allowing mobile work once the pandemic ends. On average, women are more like to work remotely than men as stated by Remote Work Statistics.

The Corona crisis (COVID-19) affects a large proportion of companies and freelancers in Germany. Against this background, the study examines the personal situation and working conditions of employees in Germany in times of corona. The analysis mainly refers to the situation in May 2020 and can only make limited statements about the further situation of the employed persons in the course of the corona pandemic. Personal situation: change in working times during the corona crisis; current work situation (local focus of one´s own work); preference for home office; preference for future home office; financial losses due to the corona crisis; concerns about the financial and economic consequences of the corona crisis in Germany; concerns about the corona crisis in personal areas (job security, current working conditions, financial situation, career opportunities, family situation, health, psychological well-being, housing situation); support from the employer in the corona crisis. 2. Economy and welfare state: political interest; assessment of the economic situation in Germany; preferred form of government (strong vs. liberal state); agreement on various statements on the weighing of values in the Corona crisis (the restrictions on public life to protect the population from Corona are not in proportion to the economic crisis caused by it, the money now being made available for economic aid will later be lacking in other important areas such as education, infrastructure or climate protection, for politicians, the health of the population is the top priority, the interests of the economy influence them less strongly with regard to the corona crisis, the worst part of the crisis is now behind us, as a result of the economic effects of the corona crisis the contrast between rich and poor in Germany will become even more pronounced, the corona crisis affects the low earners more than the middle class, the corona crisis significantly advances the digitalisation of the world of work); perception of state action in the corona crisis on the basis of pairs of opposites (e.g. bureaucratic - unbureaucratic, passive - active, etc.); responsibility of the state to provide financial support to companies in the corona crisis; responsibility of the state to provide financial support to private individuals in the corona crisis over and above basic provision; recipients of state financial aid in the corona crisis (companies, directly to needy private individuals, companies and private individuals alike); assessment of the bureaucracy involved in state financial aid (speed vs. exact examination). 3. Measures: awareness of current measures to support business and individuals in the corona crisis; assessment of current measures to support business and individuals in the corona crisis; reliance on assistance in the corona crisis; nature of assistance used in the corona crisis; barriers to use of assistance in the corona crisis; assessment of the effectiveness of the state measures to cope with the corona crisis; appropriate additional measures to mitigate the economic consequences; concerns about the consequences of the planned state measures (increasing tax burden, rising social contributions, rising inflation, stagnating pension levels, rising retirement age, reduction of other state transfers, safeguarding savings). 4. Information: active search for information on financial assistance offers by the Federal Government in the corona crisis; self-assessment of the level of information on measures to support business and private individuals in the corona crisis; request for detailed information on state assistance measures in the corona crisis (e.g. application process, sources of funding, conditions for receiving assistance, etc.) sources of information used about state aid measures in the Corona crisis; contact with institutions offering economic and financial aid during the Corona crisis (development bank/ municipal development agency, employment agency, tax office, none of them); experience with institutions offering economic and financial aid during the Corona crisis (appropriate treatment). 5. Outlook: assessment of the future economic situation in Germany; assessment of Germany´s future as a strong business location; assessment of its own future economic situation; assessment of the duration of the economic impairment caused by the Corona crisis. Demography: age; sex; education; employment; self-localization social class; net household income; current household income; household income before the crisis; occupational activity; belonging to systemically important occupations; number of persons in the household; number of children under 18 in the household; size of town; party sympathy; migration background. Additionally coded: current number; federal state; education (low, medium, high); weighting factor.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

Abstract copyright UK Data Service and data collection copyright owner. Background The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation. Secure Access QLFS data Secure Access datasets for the QLFS are available from the April-June 1992 quarter, and include additional, detailed variables not included in the standard 'End User Licence' (EUL) versions (see under GN 33246). Extra variables that typically can be found in the Secure Access versions but not in the EUL relate to:geography (see 'Spatial Units' below)date of birth, including dayeducation and training: including type of 'other qualifications', more detail regarding the number of O'levels/GCSE passes, type of qualification gained in last 12 months, class of first degree, type of degree held, UK country of highest degree, type of current educational institution, level of Welsh baccalaureate, activities to improve knowledge or skills in last 12 months, attendance at adult learning taught courses, attendance at leisure or educational classes, self-teaching, payment of job-related training feeshousehold and family characteristics: including number of family units (and extended family units) with dependent children only, and with non-dependent children only, total number of family units with more than one person, total number of eligible people, type of household, type of family unit, number of bedroomsemployment: including industry code of main job, whether working full-time or part-time, reason job is temporary, payment of own National Insurance and tax, when started working at previous job, whether paid or self-employed in previous job, contracts with employment agencyunemployment and job hunting: including main reason for not being employed prior to current job, reasons for leaving job (provision of care or other personal/family reasons), use of internet for job hunting, if and when will work in the futuretemporary leave from work: including proportion of salary received and duration of leaveaccidents at work and work-related health problemsnationality, national identity and country of birth: including whether lived continuously in UK, month of most recent arrival to UK, frequency of Welsh speakingoccurrence of learning difficulty or disabilitybenefits, including additional variables on type of benefits claimed and tax credit paymentsSecure Access versions of QLFS household datasets are available from 2009 under SN 7674. Prospective users of a Secure Access version of the QLFS will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version. Well-Being variables are not included in the LFS Users should note that subjective well-being variables (Satis, Worth, Happy, Anxious and Sad) are not available on the LFS, despite being referenced in the questionnaire. Users who wish to analyse well-being variables should apply for the Annual Population Survey instead (see SNs 6721 and 7961). LFS Documentation The documentation available from the Archive to accompany LFS datasets largely consists of the relevant versions of each volume of the user guide. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.The study documentation presented in the Documentation section includes the most recent documentation for the LFS only, due to available space. Documentation for previous years is provided alongside the data for access and is also available upon request. Variables DISEA and LNGLST Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.Latest Edition InformationFor the thirty-eighth edition (October 2023), a new data file for April-June 2023 and a new 2023 variable catalogue have been added to the study. Main Topics: The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter. The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health focussed on problems that affect the respondent's work. Since then, the questions have covered all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details.

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Personal Well-Being, April 2011 - March 2014 The ONS have released a combined three-year Personal Well-Being dataset covering April 2011 to March 2014 in response to user demand. The enhanced sample size of this dataset allows for more robust analysis of sub groups in the population and for local areas. Further information may be found in the documentation. The SL access version of the APS April 2011 - March 2014 Personal Well-Being dataset is held under SN 7593. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2011 2014 ACADEMIC ACHIEVEMENT ADULT EDUCATION AGE ANXIETY APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS CHRONIC ILLNESS COHABITATION CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DEGREES DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES EMPLOYMENT SERVICES ETHNIC GROUPS FAMILY BENEFITS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER HAPPINESS HEADS OF HOUSEHOLD HEALTH HEALTH STATUS HIGHER EDUCATION HOME BASED WORK HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LONGTERM UNEMPLOYMENT Labour and employment MANAGERS MARITAL STATUS MATERNITY BENEFITS NATIONAL IDENTITY NATIONALITY OCCUPATIONAL TRAINING OCCUPATIONS OLD AGE BENEFITS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR QUALIFICATIONS RECREATIONAL EDUCATION RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY SELF EMPLOYED SICK LEAVE SICK PAY SICKNESS AND DISABI... SMOKING SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS STATE RETIREMENT PE... SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WELL BEING SOCIETY WORKING CONDITIONS WORKPLACE vital statistics an...

New Home Sales in the United States increased to 627 Thousand units in June from 623 Thousand units in May of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This is the multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. Our dataset not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is suitable for several research fields, such as work psychology, user modeling and context aware systems.The collection of this dataset was supported by the Dutch national program COMMIT (project P7 SWELL). SWELL is an acronym of Smart Reasoning Systems for Well-being at Work and at Home. Notes on the content of the dataset:- The uLog XML files refer to documents in the dataset. Most extensions of these files have changed due to file conversions. The original extension is now included in the file names at the end.- Due to copyrights not all original documents and images are included in the dataset.- Variable C in 'D - Physiology features (HR_HRV_SCL - final).csv' refers to the type of block, 1, 2 or 3.

Home Ownership Rate in the United States decreased to 65.10 percent in the first quarter of 2025 from 65.70 percent in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This longitudinal survey concentrates on the socio-economic and socio-cultural position of the four largest ethnic minority groups in the Netherlands: Turks, Moroccans, Surinamese and Antillians/Arubans. It can also give insight in the differences between those groups, and a comparison in time. This study covers a wide range of topics. Education: in Netherlands or abroad, how many years, level and type / now following education / education of parents Work: kind of work, how long with this company, how got this job / training, promotion / supervising position / work with Dutch people or own ethnic group / ever been unemployed / if not working, why, looking for work. Income: net income, does r. get any benefit (unemployment, old age, study, WAO) / income enough to live off / send money or goods to home country, how many people dependent. Housing: type of housing, how old, how many rooms, owner-occupier or renting, rental subsidies / looking for different house, how long, why. Health: how is health, how often visit GP. Language: speak Dutch with spouse, children / trouble with Dutch language in conversation, reading newspapers, letters Nationality: which / kind of papers allow r. to live in the Netherlands / what group do you consider yourself part of, do you think the position of this group has changed for the better or worse in the recent years, own position. Social: is r. member of a (sports) club, many or few members of same ethnic group / does r. have Dutch visitors, spend more time with Dutch people or own ethnic group / would r. like to move back to home country, why (not). Religion: which, same religion as brought up in / importance / how often visit church. Neighbourhood: contact with neighbours / feel at home in neighbourhood / foreigners in neighbourhood. Opinions on: Importance of education for boys and girls / illegal work / divorce / respect for authorities, parents / euthanasia / death penalty / punishment / sex / relations / best age for woman to marry, get first child, what size of family is best / role of mother and father in providing, care, decisions. Born in which country, mother, father, r., spouse / age / how many people in household, married, living together / how many children, living at home / what age left parent's house / what year came to live in the Netherlands, why / have lived outside the Netherlands after moving here, how long, why.

Analysis of ‘COVID-19 Vaccinations by ZIP Code’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/73e760fb-93e8-49cb-8d11-3e81c4e41c82 on 13 February 2022.

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

NOTE, 11/4/2021: With the authorization of vaccine for children age 5-11, we have added three columns to this dataset. For each grouping of columns (Total, 1st Dose, and Series Completed), there is now a 5+ column. Care should be taken when summing values to avoid accidental double-counting.

COVID-19 vaccinations administered to Chicago residents based on the home ZIP Code of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). The ZIP Code where a person lives is not necessarily the same ZIP Code where the vaccine was administered.

Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a COVID- 19 vaccine series. Requirements vary depending on the vaccine received. Pfizer and Moderna vaccines require two doses for a completed series. Johnson & Johnson is a single-dose vaccine. ·Total doses administered: Number of all COVID-19 vaccine doses administered.

Daily counts are shown for the total number of doses administered, number of people with at least one vaccine dose, and number of people who have a completed vaccine series. Cumulative totals for each measure as of that date are also provided. Vaccinations are counted based on the day the vaccine was administered.

Coverage percentages are calculated based on cumulative number of people who have received at least one vaccine dose and cumulative number of people who have a completed vaccine series in each ZIP Code.

Population counts are from the U.S. Census Bureau American Community Survey 2015-2019 5-year estimates and can be seen in the ZIP Code, 2019 rows of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa).

Actual counts may exceed population estimates and lead to >100% coverage, especially in areas with small population sizes. Additionally, the medical provider may report a work address or incorrect home address for the person receiving the vaccination which may lead to over or under estimates of vaccination coverage by geography. 

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

Data Source: Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau American Community Survey

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

Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

This longitudinal survey concentrates on the socio-economic and socio-cultural position of the four largest ethnic minority groups in the Netherlands: Turks, Moroccans, Surinamese and Antillians/Arubans. It can also give insight in the differences between those groups, and a comparison in time. The survey is accomplished in cooperation with the Netherlands Kinship Panel Study (NKPS). This study covers a wide range of topics. Education: in Netherlands or abroad, how many years, level and type / now following education / education of parents Work: kind of work, how long with this company, how got this job / training, promotion / supervising position / work with Dutch people or own ethnic group / ever been unemployed / if not working, why, looking for work. Income: net income, does r. get any benefit (unemployment, old age, study, WAO) / income enough to live off / send money or goods to home country, how many people dependent. Health: how is health, how often visit GP. Language: speak Dutch with spouse, children / trouble with Dutch language in conversation, reading newspapers, letters Nationality: which / kind of papers allow r. to live in the Netherlands / what group do you consider yourself part of, do you think the position of this group has changed for the better or worse in the recent years, own position. Social: is r. member of a (sports) club, many or few members of same ethnic group / does r. have Dutch visitors, spend more time with Dutch people or own ethnic group / would r. like to move back to home country, why (not). Religion: which, same religion as brought up in / importance / how often visit church. Neighbourhood: contact with neighbours / feel at home in neighbourhood / foreigners in neighbourhood. Opinions on: Importance of education for boys and girls / illegal work / divorce / respect for authorities, parents / euthanasia / death penalty / punishment / sex / relations / best age for woman to marry, get first child, what size of family is best / role of mother and father in providing, care, decisions. Born in which country, mother, father, r., spouse / age / how many people in household, married, living together / how many children, living at home / what age left parent's house / what year came to live in the Netherlands, why / have lived outside the Netherlands after moving here, how long, why. Op verzoek van deposant zijn de databestanden vanaf 20dec2022 Open Access beschikbaar.

The number of employed persons in Canada decreased to 21020.40 Thousand in July of 2025 from 21061.20 Thousand in June of 2025. This dataset provides - Canada Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Direct link: Short-Term Rental Eligibility Dataset

DATASET CONTEXT

Boston's ordinance on short-term rentals is designed to incorporate the growth of the home-share industry into the City's work to create affordable housing for all residents. We want to preserve housing for residents while allowing Bostonians to benefit from this new industry. Starting on on January 1, 2019, short-term rentals in Boston will need to register with the City of Boston.

Eligibility for every unit in the City of Boston is dependant on the following six criteria:

• No affordability covenant restrictions
• Compliance with housing laws and codes
• No violations of laws regarding short-term rental use
• Owner occupied
• Two- or three-family dwelling
• Residential use classification

The Short-Term Rental Eligibility Dataset leverages information, wherever possible, about these criteria. For additional details and information about these criteria, please visit https://www.boston.gov/short-term-rentals.

ABOUT THIS DATASET

ATTENTION: The Short-Term Rental Eligibility Dataset is now available for residents and landlords to determine their registration eligibility.

NOTE: These data are refreshed on a nightly basis.

In June 2018, a citywide ordinance established new guidelines and regulations for short-term rentals in Boston. Registration opened January 1, 2019. The Short-Term Rental Eligibility Dataset was created to help residents, landlords, and City officials determine whether a property is eligible to be registered as a short-term rental.

The Short-Term Rental Eligibility Dataset currently joins data from the following datasets:

• Live Street Address Management (SAM) Addresses
• Assessing Online
• Code Enforcement - Building and Property
• Problem Properties
• Income-Restricted Properties
• STR Registration Data

HOW TO DETERMINE ELIGIBILITY FOR SHORT-TERM RENTAL REGISTRATION

1. ** Open** the Short-Term Rental Eligibility Dataset. In the dataset's search bar, enter the address of the property you are seeking to register.

2. Find the row containing the correct address and unit of the property you are seeking. This is the information we have for your unit.

3. Look at the columns marked as “Home-Share Eligible,” “Limited-Share Eligible,” and “Owner-Adjacent Eligible.”

   A “yes” under any of these columns means your unit IS eligible for registration under that short-term rental type. Click here for a description of short-term rental types.

   A “no” under any of these columns means your unit is NOT eligible for registration under that short-term rental type. Click here for a description of short-term rental types.

4. If your unit has a “yes” under “Home-Share Eligible,” “Limited-Share Eligible,” or “Owner-Adjacent Eligible,” you can register your unit here.

WHY IS MY UNIT LISTED AS “NOT ELIGIBLE”?

If you find that your unit is listed as NOT eligible, and you would like to understand more about why, you can use the Short-Term Rental Eligibility Dataset to learn more. The following columns measure each of the six eligibility criteria in the following ways:

1. No affordability covenant restrictions

   • A “yes” in the “Income Restricted” column tells you that the unit is marked as income restricted and is NOT eligible.

The “Income Restricted” column measures whether the unit is subject to an affordability covenant, as reported by the Department of Neighborhood Development and/or the Boston Planning and Development Agency.
For questions about affordability covenants, contact the Department of Neighborhood Development.

1. Compliance with housing laws and codes

   • A “yes” in the “Problem Properties” column tells you that this unit is considered a “Problem Property” by the Problem Properties Task Force and is NOT eligible.

Learn more about how “Problem Properties” are defined here.

* A **“yes”** in the **“Problem Property Owner”** column tells you that the owner of this unit also owns a “Problem Property,” as reported by the Problem Properties Task Force. 

Owners with any properties designated as a Problem Property are NOT eligible.

No unit owned by the owner of a “Problem Property” may register a short-term rental.
Learn more about how “Problem Properties” are defined here.

* The **“Open Violation Count”** column tells you how many open violations the unit has. Units with **any open** violations are NOT eligible. Violations counted include: violations of the sanitary, building, zoning, and fire code; stop work orders; and abatement orders. 

NOTE: Violations written before 1/1/19 that are still open will make a unit NOT eligible until these violations are resolved.
If your unit has an open violation, visit these links to appeal your violation(s) or pay your code violation fine(s).

* The **“Violations in the Last 6 Months”** column tells you how many violations the unit has received in the last six months. Units with **three or more** violations, whether open or closed, are NOT eligible. 

NOTE: Only violations written on or after 1/1/19 will count against this criteria.
If your unit has an open violation, visit these links to appeal your violation(s) or pay your code violation fine(s).

How to comply with housing laws and codes:
Have an open violation? Visit these links to appeal your violation(s) or pay your code violation fine(s).
Have questions about problem properties? Visit Neighborhood Service’s Problem Properties site.
a legal restriction that prohibits the use of the unit as a Short-Term Rental under condominium bylaws.
Units with legal restrictions found upon investigation are NOT eligible.

If the investigation of a complaint against the unit yields restrictions of the nature detailed above, we will mark the unit with a “yes” in this column. Until such complaint-based investigations begin, all units are marked with “no.”
NOTE: Currently no units have a “legally restricted” designation.
Limited-Share
If you are the owner-occupant of a unit and you have not filed for Residential Tax Exemption, you can still register your unit by proving owner-occupancy. It is recommended that you submit proof of residency in your short-term rental registration application to expedite the process of proving owner-occupancy (see “Primary Residence Evidence” section).

* **“Building Owner-Occupied”** measures whether the building has a single owner AND is owner occupied. A “no” in this column indicates that the unit is NOT eligible for an owner-adjacent short-term rental. 

If you believe your building occupancy data is incorrect, please contact the Assessing Department.

1. Two- or three-family dwelling

   • The “Units in Building” column tells you how many units are in the building. Owner-Adjacent units are only allowed in two- to three-family buildings; therefore, four or more units in this column will mark the unit as NOT eligible for an Owner-Adjacent Short-Term Rental.

   • A “no” in the “Building Single Owner” column tells you that the owner of this unit does not own the entire building and is NOT eligible for an Owner-Adjacent Short-Term Rental.

   If you believe your building occupancy data is incorrect, please contact the Assessing Department.
   R4

   If you believe your building occupancy data is incorrect, please contact the Assessing Department.

Visit this site for more information on unit eligibility criteria.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

City boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_CITY: CDTFA incorporated city name
• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to