VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. 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, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

This dataset contains non-seasonally adjusted California Unemployment Rate by age groups, from the Current Population Survey (CPS). The age group ranges are as follows; 16-19 ; 20 - 24; 25 - 34; 35 - 44; 45 - 54; 55 -64; 65+. This data is based on a 12-month moving average.

This dataset is invaluable for data science applications due to its granularity and the historical depth it offers. With detailed monthly data on unemployment rates by age groups, data scientists can perform a myriad of analyses:

• Time Series Analysis: Analyzing trends over time to forecast future unemployment rates or identify cyclic patterns.
• Demographic Analysis: Understanding which age groups are most affected by unemployment and how this changes through economic conditions.
• Geographic Analysis: Although the dataset currently focuses on California, if similar data is available for other regions, comparisons can be made to understand regional economic differences.

The dataset can also be merged with other socioeconomic indicators like GDP, education levels, and industry growth metrics to examine broader economic narratives or policy impacts.

Reasons for moving and location of previous dwelling for households that moved in the past five years, and intentions to move in less than five years for all households, Canada, provinces and territories.

This annual study provides migration pattern data for the United States by State or by county and are available for inflows (the number of new residents who moved to a State or county and where they migrated from) and outflows (the number of residents who left a State or county and where they moved to). The data include the number of returns filed, number of personal exemptions claimed, total adjusted gross income, and aggregate migration flows at the State level, by the size of adjusted gross income (AGI) and by age of the primary taxpayer. Data are collected and based on year-to-year address changes reported on U.S. Individual Income Tax Returns (Form 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, U.S. Population Migration Data.

Annual number of interprovincial migrants by province of origin and destination, Canada, provinces and territories.

The IRS publishes migration data for the US population based upon the individual tax returns filed with the IRS, where they track on a year-by-year basis

• where people were coming from - the prior state of residency
• where people moving to - the new state of residency
• number of returns filed - approximate number of households that migrated
• number of exemptions - approximate number of individuals
• the adjusted gross income (AGI) - recorded in thousands of dollars

The raw data published on the IRS website clearly shows patterns of evolution - changing patterns of what is recorded, how it is record, and naming conventions used - making it a challenge to track changes in the underlying data over time. The current dataset attempts to address these shortcomings by normalizing the record layout, standardizing the conventions, and collecting the annual into a single, coherent dataset.

An individual record is laid out with 9 fields

Y1 Y1_STATE_FIPS Y1_STATE_ABBR Y1_STATE_NAME Y2 Y2_STATE_FIPS Y2_STATE_ABBR Y2_STATE_NAME NUM_RETURNS NUM_EXEMPTIONS AGI Here, Y1 refers to the first year (from where the people are migrating) while Y2 refers to the second year (to where the people are migrating). As this is annual data, Y2 should always be the next year after Y1. Associated with each year are three different ways of identifying a state - the name of the state, it's two-letter abbreviaion, and it's FIPS code. Granted, carrying around three IDs per state is redundant; however, the various IDs are useful in different contexts. One thing to note - the IRS data represents migration into and out of the country via the introduction of a fake state, identified by STATE_NAME=FOREIGN, STATE_ABBR=FR, and STATE_FIPS=57.

From any given state, the dataset records migration to 52 destinations

• either not moving, or staying in the same state
• migrating to one of the other 49 states
• migrating to Washington DC
• migrating overseas (i.e., to the FOREIGN state)

Similarly, the dataset represents the migation into any given state as being from one of 52 destinations. Typically, the numbers associated with "staying put" constitute, by far, the largest contingent of tax payers for the given state. The one exception to this description is the FOREIGN state. The dataset does not record "staying put" outside of the country; there is no record for FOREIGN-to-FOREIGN migration. As such, there are 51, not 52, destinations paired with migration to-and-from the FOREIGN state.

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Orange County, CA (DISCONTINUED) (NETMIGNACS006059) from 2009 to 2020 about Orange County, CA; migration; flow; Los Angeles; Net; CA; 5-year; and population.

Manet Routing Dataset

A MANET dataset (Mobile Ad-hoc Network dataset) is a collection of data specifically designed to simulate or record the behavior of nodes within a Mobile Ad-hoc Network. These datasets are crucial for research, development, and testing of MANET routing protocols, security mechanisms, and network performance. The "description" of a MANET dataset typically refers to its content and characteristics, which would include:

• Node ID: Unique identifier for each mobile node.
• **Initial Position: ** X, Y, and often Z (altitude) coordinates at the start of the simulation.
• Movement Model/Trace: How nodes move over time. This is a critical component of a dynamic MANET dataset. It can be:
• Synthetic: Generated by mobility models (e.g., Random Waypoint, Random Walk, Gauss-Markov, City-Section, Reference Point Group Mobility).
• Real-world: Traces recorded from actual mobile devices (e.g., GPS data from vehicles, people moving in a specific area).
• Velocity/Speed: Magnitude and direction of node movement (e.g., vx, vy, vz in the MANETNode class).
• Battery Level/Energy: The current power status of a node. In energy-aware routing, this is a key metric. It might include initial battery, energy consumption rate, and remaining energy over time.
• Capabilities: Other attributes like processing power, communication range, antenna type, specific roles (e.g., router, sensor). Deep Learning Projects for Final Year

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update. A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases. All cases: Includes cases among all San Francisco residents regardless of vaccination status. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.” On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available. B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address. We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included. C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day. D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”. Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time. The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category. New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days. New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a part

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update.

A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases.

1. All cases: Includes cases among all San Francisco residents regardless of vaccination status.

2. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated.

3. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.”

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available.

B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address.

We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included.

C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day.

D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”.

Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time.

The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category.

New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days.

New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a particular day with the prior six days. Percent of total new cases shows the percent of all cases on each day that were among a particular vaccination status.

Here is more information on how each case rate is calculated:

1. The case rate for all cases is equal to the number of new cases among all residents divided by the estimated total resident population.

2. Unvaccinated case rates are equal to the number of new cases among unvaccinated residents divided by the estimated number of unvaccinated residents. The estimated number of unvaccinated residents is calculated by subtracting the number of residents that have received at least one dose of a vaccine from the total estimated resident population.

3. Completed primary series case rates are equal to the number of new cases among completed primary series residents divided by the estimated number of completed primary series residents. The estimated number of completed primary series residents is calculated by taking the number of residents who have completed their primary series over time and adding a 14-day delay to the “date_administered” column, to align with the definition of “Completed primary series cases” above.

E. CHANGE LOG

• 6/28/2023 - data on cases by vaccination status are no longer being updated. This data is currently through 6/20/2023 (as of 6/28/2023) and will not include any new data after this date.
  
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “sf_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 7/15/2022 - references to “fully vaccinated” replaced with “completed primary series” in column “vaccination_status".
• 7/15/2022 - rows with “partially vaccinated” in column “vaccination_status” removed from dataset.

People's living arrangements change over the life cycle. In many countries, when young people move out of their parents' home, they frequently share dwellings with other young people before setting up their own home to move in with a partner and/or to start a family. At the same time, when children leave home, elderly parents often would like to downsize as household size has diminished. This indicator shows how living arrangements vary across age groups, with a particular focus on youth (aged 15 to 29) and seniors (65 and older). The data presented here are based on household survey microdata and concern population-level data. They exclude living arrangements such as group accommodation, such as nursing homes, hospitals and military quarters. Data in this indicator refer to private households. The data analysis considers living arrangements in the latest year available for different age groups.

This data collection is part of the American Housing Metropolitan Survey (AHS-MS, or "metro") which is conducted in odd-numbered years. It cycles through a set of 21 metropolitan areas, surveying each one about once every six years. The metro survey, like the national survey, is longitudinal. This particular survey provides information on the characteristics of a Seattle metropolitan sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units in 2009. The data are presented in eight separate parts: Part 1, Home Improvement Record, Part 2, Journey to Work Record, Part 3, Mortgages Recorded, Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner of Rental Units Record, Part 6, Person Record, Part 7, High Burden Unit Record, and Part 8, Recent Mover Groups Record. Part 1 data include questions about upgrades and remodeling, cost of alterations and repairs, as well as the household member who performed the alteration/repair. Part 2 data include journey to work or commuting information, such as method of transportation to work, length of trip, and miles traveled to work. Additional information collected covers number of hours worked at home, number of days worked at home, average time respondent leaves for work in the morning or evening, whether respondent drives to work alone or with others, and a few other questions pertaining to self-employment and work schedule. Part 3 data include mortgage information, such as type of mortgage obtained by respondent, amount and term of mortgages, as well as years needed to pay them off. Other items asked include monthly payment amount, reason mortgage was taken out, and who provided the mortgage. Part 4 data include household-level information, including demographic information, such as age, sex, race, marital status, income, and relationship to householder. The following topics are also included: data recodes, unit characteristics, and weighting information. Part 5 data include information pertaining to owners of rental properties and whether the owner/resident manager lives on-site. Part 6 data include individual person level information, in which respondents were queried on basic demographic information (i.e. age, sex, race, marital status, income, and relationship to householder), as well as if they worked at all last week, month and year moved into residence, and their ability to perform everyday tasks and whether they have difficulty hearing, seeing, and concentrating or remembering things. Part 7 data include verification of income to cost when the ratio of income to cost is outside of certain tolerances. Respondents were asked whether they receive help or assistance with grocery bills, clothing and transportation expenses, child care payments, medical and utility bills, as well as with rent payments. Part 8 data include recent mover information, such as how many people were living in last unit before move, whether last residence was a condo or a co-op, as well as whether this residence was outside of the United States.

This data collection is part of the American Housing Metropolitan Survey (AHS-MS, or "metro") which is conducted in odd-numbered years. It cycles through a set of 21 metropolitan areas, surveying each one about once every six years. The metro survey, like the national survey, is longitudinal. This particular survey provides information on the characteristics of a New Orleans metropolitan sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units in 2009. The data are presented in eight separate parts: Part 1, Home Improvement Record, Part 2, Journey to Work Record, Part 3, Mortgages Recorded, Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner of Rental Units Record, Part 6, Person Record, Part 7, High Burden Unit Record, and Part 8, Recent Mover Groups Record. Part 1 data include questions about upgrades and remodeling, cost of alterations and repairs, as well as the household member who performed the alteration/repair. Part 2 data include journey to work or commuting information, such as method of transportation to work, length of trip, and miles traveled to work. Additional information collected covers number of hours worked at home, number of days worked at home, average time respondent leaves for work in the morning or evening, whether respondent drives to work alone or with others, and a few other questions pertaining to self-employment and work schedule. Part 3 data include mortgage information, such as type of mortgage obtained by respondent, amount and term of mortgages, as well as years needed to pay them off. Other items asked include monthly payment amount, reason mortgage was taken out, and who provided the mortgage. Part 4 data include household-level information, including demographic information, such as age, sex, race, marital status, income, and relationship to householder. The following topics are also included: data recodes, unit characteristics, and weighting information. Part 5 data include information pertaining to owners of rental properties and whether the owner/resident manager lives on-site. Part 6 data include individual person level information, in which respondents were queried on basic demographic information (i.e. age, sex, race, marital status, income, and relationship to householder), as well as if they worked at all last week, month and year moved into residence, and their ability to perform everyday tasks and whether they have difficulty hearing, seeing, and concentrating or remembering things. Part 7 data include verification of income to cost when the ratio of income to cost is outside of certain tolerances. Respondents were asked whether they receive help or assistance with grocery bills, clothing and transportation expenses, child care payments, medical and utility bills, as well as with rent payments. Part 8 data include recent mover information, such as how many people were living in last unit before move, whether last residence was a condo or a co-op, as well as whether this residence was outside of the United States.

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.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - 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.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

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.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files

The 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. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates.

This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania.

It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Related dataset

Same authors also produced the Labeled dataset of IEEE 802.11 probe requests with same data layout and recording equipment.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device). Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.

The following information about each received PR is collected: - MAC address - Supported data rates - extended supported rates - HT capabilities - extended capabilities - data under extended tag and vendor specific tag - interworking - VHT capabilities - RSSI - SSID - timestamp when PR was received.

The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package to collect, preprocess, and transmit the data.

Data preprocessing

The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database. For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure:

PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:

{ 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.

This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored. The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval. If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended. If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key. The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png

At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the Single_PR_capture_example.json file.

Folder structure

For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period. Each folder contains four files, each containing samples from that device.

The folders are named after the start and end time (in UTC). For example, the folder 2022-09-22T22-00-00_2022-09-23T22-00-00 contains samples collected between 23th of September 2022 00:00 local time, until 24th of September 2022 00:00 local time.

Files representing their location via mapping: - 1.json -> location 1 - 2.json -> location 2 - 3.json -> location 3 - 4.json -> location 4

Environments description

The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset. As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system. Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided.

Four Raspbery Pi-s were used: - location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano) - location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo - location 3 -> nothernmost window in the building of Via Etnea near Piazza Università - location 4 -> first window top the right of the entrance of the University of Catania

Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access) Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.

Known dataset shortcomings

Due to technical and physical limitations, the dataset contains some identified deficiencies.

PRs are collected and transmitted in 10-second chunks. Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred.

Every 20 minutes the service is restarted on the recording device. This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond. For this reason, up to 20 seconds of data will not be recorded in each 20-minute period.

The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.

 Location 1 - Piazza del Duomo - Chierici

The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period. Its location is constant and is not disturbed, dataset seems to have complete coverage.

 Location 2 - Via Etnea - Piazza del Duomo

The device is located inside the building. During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed. As the device was moved back and forth, power outages and internet connection issues occurred. The last three days in the record contain no PRs from this location.

 Location 3 - Via Etnea - Piazza Università

Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building. Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall when no people are present. This device appears to have been collecting data throughout the whole dataset period.

 Location 4 - Piazza Università

This location is wirelessly connected to the access point. The device was placed statically on a windowsill overlooking the square. Due to physical limitations, the device had lost power several times during the deployment. The internet connection was also interrupted sporadically.

Recognitions

The data was collected within the scope of Resiloc project with the help of City of Catania and project partners.

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.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - 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.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

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...

This dataset includes the number of people enrolled in DSS services by town and by program from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here. About this data For privacy considerations, a count of zero is used for counts less than five. A recipient is counted in all towns where that recipient resided in that year. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. 2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year. On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units in 2009. The data are presented in eight separate parts: Part 1, Home Improvement Record, Part 2, Journey to Work Record, Part 3, Mortgages Recorded, Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner of Rental Units Record, Part 6, Person Record, Part 7, High Burden Unit Record, and Part 8, Recent Mover Groups Record. Part 1 data include questions about upgrades and remodeling, cost of alterations and repairs, as well as the household member who performed the alteration/repair. Part 2 data include journey to work or commuting information, such as method of transportation to work, length of trip, and miles traveled to work. Additional information collected covers number of hours worked at home, number of days worked at home, average time respondent leaves for work in the morning or evening, whether respondent drives to work alone or with others, and a few other questions pertaining to self-employment and work schedule. Part 3 data include mortgage information, such as type of mortgage obtained by respondent, amount and term of mortgages, as well as years needed to pay them off. Other items asked include monthly payment amount, reason mortgage was taken out, and who provided the mortgage. Part 4 data include household-level information, including demographic information, such as age, sex, race, marital status, income, and relationship to householder. The following topics are also included: data recodes, unit characteristics, and weighting information. Part 5 data include information pertaining to owners of rental properties and whether the owner/resident manager lives on-site. Part 6 data include individual person level information, in which respondents were queried on basic demographic information (i.e. age, sex, race, marital status, income, and relationship to householder), as well as if they worked at all last week, month and year moved into residence, and their ability to perform everyday tasks and whether they have difficulty hearing, seeing, and concentrating or remembering things. Part 7 data include verification of income to cost when the ratio of income to cost is outside of certain tolerances. Respondents were asked whether they receive help or assistance with grocery bills, clothing and transportation expenses, child care payments, medical and utility bills, as well as with rent payments. Part 8 data include recent mover information, such as how many people were living in last unit before move, whether last residence was a condo or a co-op, as well as whether this residence was outside of the United States.

[1] The Progress by Population Group analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included subsets of the 1,111 measurable HP2020 objectives that have data available for any of six broad population characteristics: sex, race and ethnicity, educational attainment, family income, disability status, and geographic location. Progress toward meeting HP2020 targets is presented for up to 24 population groups within these characteristics, based on objective data aggregated across HP2020 topic areas. The Progress by Population Group data are also available at the individual objective level in the downloadable data set. [2] The final value was generally based on data available on the HP2020 website as of January 2020. For objectives that are continuing into HP2030, more recent data will be included on the HP2030 website as it becomes available: https://health.gov/healthypeople. [3] For more information on the HP2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf. [4] Status for objectives included in the HP2020 Progress by Population Group analysis was determined using the baseline, final, and target value. The progress status categories used in HP2020 were: a. Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target (the percentage of targeted change achieved was equal to or greater than 100%); (ii) The baseline and most recent values were equal to or exceeded the target (the percentage of targeted change achieved was not assessed). b. Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change. c. Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point. d. Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline. NOTE: Measurable objectives had baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 Progress by Population Group database.