Labor Force Participation Rate in the United States decreased to 62.40 percent in May from 62.60 percent in April of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

Dataset

This dataset was created by Aditya Chhabra

Released under CC0: Public Domain

Contents

Unemployment Rate in the United States remained unchanged at 4.20 percent in May. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year

Percentage of workforce teleworking or working remotely prior to February 1, 2020, on March 31, 2020, and percentage of workforce able to carry out a majority of their duties during the COVID-19 pandemic, by North American Industry Classification System (NAICS) code, business employment size, type of business and majority ownership.

Number and percentage of adults being moderately active or active during leisure time, by age group and sex.

Drug Use By Age

This directory contains data behind the story How Baby Boomers Get High. It covers 13 drugs across 17 age groups.

Source: National Survey on Drug Use and Health from the Substance Abuse and Mental Health Data Archive.

DescriptionThese files contain data from the article "Local fitness landscape of the green fluorescent protein". Raw sequencing data for this experiment is available at SRA (http://www.ncbi.nlm.nih.gov/sra) under BioProject PRJNA282342 (http://www.ncbi.nlm.nih.gov/bioproject/PRJNA282342/). Files presented here are data sets obtained at different stages of analysis (as illustrated in the file "Data_low.png"). All files are tab-separated tables with a header at first row. Some table cells may be empty (e.g. list of mutations for wild-type).Please note the mutations notation used throughout the files. It is described in details here: http://mixcr.readthedocs.org/en/latest/appendix.html#alignment-and-mutations-encoding. Briefly, all positions are zero-based (i.e. first nucleotide has index 0) and type of mutation (substitution, deletion or insertion) is indicated as the first letter of mutation description. For example, SG101A is the substitution G>A at position 101. The reference avGFP sequence is provided as “avGFP_reference_sequence.fa” file.File names and content1. Final data sets: genotypes with corresponding log-brightness valuesnucleotide_genotypes_to_brightness.tsv – processed file “barcodes_to_brightness.tsv”, with genotypes aggregated by their nucleotide sequence, and brightness information averaged across all barcodes that share the same nucleotide genotype.Columns:nMutations – list of nucleotide mutations (see above for mutations notation); empty for wild-type,aaMutations – list of amino acid mutations; empty for wild-type or genotypes with only synonymous substitutions,uniqueBarcodes – number of unique barcodes sharing the same nucleotide genotype,medianBrightness – median of log-brightness values across barcodes that share the same nucleotide genotype,std – standard deviation of log-brightness values across barcodes that share the same nucleotide genotype; empty for genotypes represented by a single barcode.amino_acid_genotypes_to_brightness.tsv – processed file “barcodes_to_brightness.tsv”, with genotypes aggregated by their amino acid sequence, and brightness information averaged across all barcodes that share the same amino acid genotype.Columns:aaMutations – list of amino acid mutations; empty for wild-type,uniqueBarcodes – number of unique barcodes sharing the same amino acid genotype,medianBrightness – median of log-brightness values across barcodes that share the same amino acid genotype,std – standard deviation of log-brightness values across barcodes that share the same amino acid genotype; empty for genotypes represented by a single barcode.2. Intermediate data set: estimated brightness values for each barcode.For details of brightness estimation please see the protocol in the original paper.barcodes_to_brightness.tsv – final data set containing aggregated, clean and filtered data on genotypes with substitutions only (no indels).Columns:barcode – molecular barcode sequence of the genotype,nMutations – list of nucleotide mutations (see above for mutations notation),aaMutations – list of amino acid mutations,brightness – log-brightness of the barcoded sequence.3. Early data set: processed raw sequencing datapopulations.zip archive contain files with names in the following form: L{k}R{m}.tsv. The files contain aggregated read counts of barcodes for each particular sorted population, where {k} is the index of sorting gate and {m} is the index of replica. For example, file L1R2.tsv contains counts for barcodes found in brightness population L1 in experimental replica R2. (see below for median sorting gate brightness values).Files with {k} = 0 (e.g. L0R1.tsv) contain results of sequencing of bacterial population before sorting.Columns:barcode - molecular barcode sequence (see protocol in original paper),count - number of occurrence of this barcode in sequences for particular sorted population,minQuality - minimal phred quality for barcode sequence.Important: please see “Normalization” section below that describes how we translated read counts into the number of cells for each barcode.genotypes.tsv – contains processed Illumina MiSeq sequencing data of GFP genotypes for each barcode (genotype to barcode correspondence).Columns:barcode – molecular barcode sequence of the genotype,minCoverage – minimal coverage of target GFP sequence by sequencing reads (see protocol in the paper),meanCoverage – mean coverage of target GFP sequence by sequencing reads,nMutations – list of nucleotide mutations (see above for mutations notation),aaMutations – list of amino acid mutations for genotypes without indels, empty string (!) for genotypes with indels.Information on data processingThe data processing workflow is outlined in the file “Data_low.png”. We processed data from Illumina MiSeq sequencing run to reconstruct full-length sequences of GFP and relate each GFP sequence to the corresponding barcode. We then analyzed Illumina HiSeq sequencing of cell populations sorted by fluorescence-activated cell sorting, for each of the four replicas of the experiment. We counted reads that each barcoded genotype has in each brightness population. We then fitted each barcode distribution with two Gaussian distributions using the values of logarithms of sorting gates medians. When aggregating information from replicas we eliminated barcodes that displayed too broad distribution across the brightness populations or had conflicts between replicas. We saved resulting filtered data into the file “barcodes_to_brightness.tsv”.NormalizationA fixed number of cells with known barcodes (AAGTTCTAAATAACAATCCC, AATACCAGTAAGGACTTAA, TATGGTACTTAATTTACAGT, TATTTACGGGTATGACTGGG) was added to every population after sorting, about 1333 cells for each barcode. These cells passed all sample preparation procedures together with the library being a control for each sample in each replica. When analysing the sequencing data, we used these controls to translate the number of reads per barcode to the number of sorted cells. Barcodes with less than three cells across the population samples were later removed at the data filtering stage.Estimation of brightnessFor some of the barcodes a bimodal distribution of cells across the fluorescence gate populations was observed. These distributions were not reproduced across experimental replicas, indicating that they represent an artifact of the experimental procedure rather than inherent genotype properties. We fitted each barcode distribution within each replica with two Gaussian distributions using actual values of logarithms of sorting gates boundaries. Thus, the resulting distributions parameters were expressed in actual brightness logarithm values. We filtered out the cases where the log-value of fluorescence of the major Gaussian component was below 0.65, or its sigma exceeded 0.4. When aggregating information from replicas we eliminated barcodes for which less than three replicas belonged to the ±0.45-neighbourhood of the median value calculated across all replicas.The following median values of brightness within sorting gates were used to estimate the brightness of the genotypes:Replica 0 (from L1 to L8): 10751, 5970, 3190, 1372, 418, 179, 81, 20,Replica 1 (from L1 to L8): 16278, 9189, 4942, 1817, 433, 179, 72, 20,Replica 2 (from L1 to L8): 7984, 5914, 3207, 1337, 428, 160, 69, 20,Replica 3 (from L1 to L8): 12989, 6864, 3522, 1377, 414, 147, 58, 20.Please see the original paper for the description of level and structure of the noise in the final estimations of log-brightness.

The percent of persons between the ages of 16 and 64 formally employed or self-employed and earning a formal income. It is used to understand how many persons are working out of the entire population, not just those in the labor force (persons who may be looking for work or working). Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

The percent of persons between the ages of 16 and 64 not working out of all persons, not just those in the labor force (persons who may be looking for work). These persons are seeking work that pays a formal income. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

The “All Activity Trend” shows the number of positive cases that were interviewed (bar graph) and the percentage of those interviewed who reported each select high to moderate exposure activity types (i.e. personal care, dining out, social-related activities, work, travel, gym/fitness, sports, and faith-related events) during their exposure period (trend lines) on a weekly basis.Note: Data subject to change on a daily basis. Data are restricted to positive cases with a completed contact tracing interview. Possible exposure data are collected during the contact tracing interview as self-reported activities occurring within the 2-week period before the date of symptom onset for symptomatic individuals or the date of test sample collection for asymptomatic individuals. Data collection methods were altered starting the week of Dec 11 for gym/fitness and sports, so should not be compared to previous values.* High to Moderate Exposure Activity Types are not exhaustive and include travel, personal care, faith events, work, dining out, social events, gym/fitness, and sports.Data is updated on a weekly basis.

Research Hypothesis H1: There would be a lack of IT employees' knowledge about the importance of good neck posture, isometric and stretching exercises. H2: Isometric and stretching exercises would be a good effect on improving neck pain

Background: Neck pain is one of the most common musculoskeletal complaints among men and women specifically those working on a computer. Aim to evaluate the effect of an educational program about isometric and stretching exercises on neck pain among Information Technology employees at new Assuit city. Method and material: Quasi-experimental research design and a single population proportion formula to calculate sample size required for the study through using Open Epi, Version 3. Total final size 118 employee and the program was implemented on (73) employees having neck pain according exclusion criteria. Period of collecting data was from the mid of April 2021 to mid of December 2021. Three tools were used, a tool I: A structured questionnaire which consisted of three parts: 1st part: socio-demographic data, 2nd part: assessment of the nature of the work, and 3rd part: assessment of knowledge of employees. Tool (II): Neck Pain Questionnaire(NPQ) was used to evaluate the degree of neck pain and functional disability, tool (III): observational checklist. Data entry and data analysis were done using SPSS version 22 (Statistical Package for Social Science) (SPSS Inc., Chicago, II., USA). Data were presented as number, percentage, mean, standard deviation, median and range. Chi-square test was used to compare qualitative variables. In case of parametric data, Paired samples t-test was done to compare quantitative data between pre-test and post-test. Pearson correlation was done to measure correlation between quantitative variables. While in case of non-parametric data, Wilcoxon Signed Rank Test was done to compare quantitative variables between pre-test and post-test. P-value considered statistically significant when P < 0.05.

The Labour Market Indicators spreadsheet for boroughs and regions will no longer be updated from March 2015. The final version from March 2015 will still be available to download at the bottom of this page. Most of the data is available within datasets elsewhere on the Datastore.

Workforce Jobs
Unemployment
Model based Unemployment for Boroughs
Claimant Count rates for Boroughs and Wards
Employment Rate Trends
Employment rates by Gender, Age and Disability
Number of Self Employed, Full and Part Time Employed
Employment by Occupation
Employment by Industry
Employment, Unemployment, Economic Activity and Inactivity Rates by Disability
Employment by Ethnicity
Economic Inactivity by Gender and Reason
Qualifications of Economically Active, Employed and Unemployed
Qualification levels of working-age population
Apprenticeship Starts and Achievements
Young People Not in Employment, Education or Training (NEET), Borough
19 year olds Qualified to NVQ Level 3
GCE A level examination results of 16-18 year olds
GCSE Results by Pupil Characteristics
People Claiming Out-of-Work Benefits
People Claiming Incapacity Benefit
Children Living in Workless Households
Gross Value Added, and Gross Disposable Household Income
Earnings by place of residence
Earnings by place of work
Business Demographics
Employment projections by sector
Jobs Density
Population Estimates
Population Migration

Core Indicators

Number of London residents of working age in employment
Employment rate
Number of male London residents of working age in employment
Male employment rate
Number of female London residents of working age in employment
Female employment rate
Workforce jobs
Jobs density
Number of London residents of working age who are economically inactive
Economic inactivity rate
Number of London residents aged 16+ who are unemployed (model based)
Proportion of London residents aged 16+ who are unemployed (model based)
Claimant unemployment
Claimant Count as a proportion of the working age population
Incidence of skill gaps (Numbers and rates)
GCSE (5+ A*–C) attainment including English and Maths
Number of working age people in London with no qualifications
Proportion of working age people in London with no qualifications
Number of working age people in London with Level 4+ qualifications
Proportion of working age people in London with Level 4+ qualifications
Number of people of working age claiming out of work benefits
Proportion of the working age population who claim out of work benefits
Number of young people aged 16-18 who are not in

Utilising a regression analysis we created a correlation matrix utilising a number of demographic indicators from the Local Insight platform. This application is showing the distribution of the datasets that were found to have the strongest relationships, with the base comparison dataset of proprtion of residents with self-reported bad health. This app contains the following datasets: rate of households deprived in two dimensions, proportion of people receiving disability benefits, proportion of people who are out of work and receiving benefits relating to poor health, proportion of people who are out of work and receiving ESA due to nervous system diseases, proportion of people who are out of work and receiving ESA due to respiratory or circulatory diseases, proportion of residents self-reporting their health as very bad, proportion of working-age males receiving Personal Independence Payment, proportion of working-age people receiving benefits due to poor health or disability, proportion of female benefit recipients and proportion of residents with a limiting long-term illness.

San Francisco Flood Health Vulnerability

Socioeconomic, Demographic, Health, and Housing Indicators

By City of San Francisco [source]

About this dataset

This dataset provides a comprehensive composite index that captures the relative vulnerability of San Francisco communities to the health impacts of flooding and extreme storms. Predominantly sourced from local governmental health, housing, and public data sources, this index is constructed from an array of socio-economic factors, exposure indices,Health indicators and housing attributes. Used as a valuable planning tool for both health and climate adaptation initiatives throughout San Francisco, this dataset helps to identify vulnerable populations within the city such as areas with high concentrations of children or elderly individuals. Data points included in this index include: census blockgroup numbers; the percentage of population under 18 years old; percentage of population above 65; percentage non-white; poverty levels; education level; yearly precipitation estimates; diabetes prevalence rate; mental health issues reported in the area; asthma cases by geographic location;; disability rates within each block group measure as well as housing quality metrics. All these components provide a broader understanding on how best to tackle issues faced within SF arising from any form of climate change related weather event such as floods or extreme storms

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to analyze the vulnerability of the population in San Francisco to the health impacts of floods and storms. This dataset includes a number of important indicators such as poverty, education, demographic, exposure and health-related information. These indicators can be useful for developing effective strategies for health and climate adaptation in an urban area.

To get started with this dataset: First, review the data dictionary provided in the attachments section of this metadata to understand each variable that you plan on using in your analysis. Second, see if there are any null or missing values in your columns by checking out ‘Null Value’ column provided in this metadata sheet and look at how they will affect your analysis - use appropriate methods to handle those values based on your goals and objectives. Thirdly begin exploring relationships between different variables using visualizations like pandas scatter_matrix() & pandas .corr() . These tools can help you identify potential strong correlations between certain variables that you may have not seen otherwise through simple inspection of the data.

Lastly if needed use modelling techniques like regression analysis or other quantitative methods like ANOVA’s etc., for further elaboration on understanding relationships between different parameters involved as per need basis

Research Ideas

• Developing targeted public health interventions focused on high-risk areas/populations as identified in the vulnerability index.
• Establishing criteria for insurance premiums and policies within high-risk areas/populations to incentivize adaption to climate change.
• Visual mapping of individual indicators in order to identify trends and correlations between flood risk and socioeconomic indicators, resource availability, and/or healthcare provision levels at a granular level

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: san-francisco-flood-health-vulnerability-1.csv | Column name | Description | |:---------------------------|:----------------------------------------------------------------------------------------| | Census Blockgroup | Unique numerical identifier for each block in the city. (Integer) | | Children | Percentage of population under 18 years of age. (Float) | | Children_wNULLvalues | Percentage of population under 18 years of age with null values. (Float) | | Elderly | Percentage of population over 65 years of age. (Float) | | Elderly_wNULLvalues | Percentage of population over 65 years of age with null values. (Float) | | NonWhite | Percentage of non-white population. (Float) ...

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.

These data are taken from the ANNUAL datasets from the Labour Force Survey (LFS) carried out by the Office for National Statistics (ONS), providing labour market data back to 1996 for the NUTS2 areas in Wales, and back to 2001 for the local authorities in Wales. The availability of local authority data is dependent upon on an enhanced sample (around 350 per cent larger) for the annual LFS, which commenced in 2001. For years labelled 1996 to 2004 in this dataset, the actual periods covered are the 12 months running from March in the year given to February in the following year (e.g. 2001 = 1 March 2001 to 28 February 2002). Since 2004, the annual data have been produced on a rolling annual basis, updated every three months, and the dataset is now referred to as the Annual Population Survey (APS). The rolling annual averages are on a calendar basis with the first rolling annual average presented here covering the period 1 January 2004 to 31 December 2004, followed by data covering the period 1 April 2004 to 31 March 2005, with rolling quarterly updates applied thereafter. Note therefore that the consecutive rolling annual averages overlap by nine months, and there is also a two-month overlap between the last period presented on the former March to February basis, and the first period on the new basis. The population can be broken down into economically active and economically inactive populations. The economically active population is made up of persons in employment, and persons unemployed according to the International Labour Organisation (ILO) definition. This report allows the user to access these data. Although each measure is available for different population bases, there is an official standard population base used for each of the measures, as follows. Population aged 16 and over: Economic activity level, Employment level, ILO unemployment level Population aged 16-64: Economic inactivity level 16-64 population is used as the base for economic inactivity. By excluding persons of pensionable age who are generally retired and therefore economically inactive, this gives a more appropriate measure of workforce inactivity. Rates for each of the above measures are also calculated in a standard manner and are available in the dataset. With the exception of the ILO unemployment rate, each rate is defined in terms of the shares of population that fall into each category. The ILO unemployment rate is defined as ILO unemployed persons as a percentage of the economically active population. Although each rate is available for the different population bases, there is an official standard population base used for each of the rates, as follows. Percentage of population aged 16-64: Economic activity, Employment,. Economic inactivity Percentage of economically active population aged 16 and over: ILO unemployment

Unemployment numbers and rates for those aged 16 or over. The unemployed population consists of those people out of work, who are actively looking for work and are available to start immediately. Unemployed numbers and rates also shown for equalities groups, by age, sex, ethnic group, and disability. The data are taken from the Labour Force Survey and Annual Population Survey, produced by the Office for National Statistics. The data are produced monthly on a rolling quarterly basis. The month shown is the month the quarter ends on. The International Labour Organization defines unemployed people as: without a job, want a job, have actively sought work in the last 4 weeks and are available to start work in the next 2 weeks, or, out of work, have found a job and are waiting to start it in the next 2 weeks. The figures in this dataset are adjusted to compensate for seasonal variations in employment (seasonally adjusted). Data by equalities groups has a longer time lag and is only available quarterly from the Annual Population Survey, which is not seasonally adjusted. Useful links Click here for Regional labour market statistics from the Office for National Statistics. Click here for Labour market statistics from the Office for National Statistics. See here for GLA Economics' Labour Market Analysis. See here for Economic Inactivity statistics. See here for Employment rates. This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.