Between 2017 and 2019, more than half of Poland's children replied that they always or often felt safe online.

The number of internet users in Japan amounted to around ***** million in 2021. This figure was projected to increase to almost ***** million internet users by 2026. Japan’s online populationJapan is among the countries with the highest number of internet users in the world. It is home to one of the most advanced IT industries and has a very high internet penetration rate. When it comes to devices, most people use smartphones to access the internet, which indicates the prevalence of mobile internet connections. Partly as a countermeasure against the rapid aging of the population in Japan, the Japanese government introduced the concept of "Society 5.0" in 2016, which aims to increase the digitalization and connectivity of the economy as well as various aspects of social life. This industry policy will further increase the importance of the internet in the future. Current usageA survey on the most common reasons why people in Japan use the internet showed that using social networking services, communicating via email, and searching for information were among the leading activities. The same survey also showed that a majority of people engaged in using video sharing websites and purchasing goods and services online. While many people in Japan are avid users of the internet, surveys show that most have concerns regarding online safety. According to one survey, a clear majority of respondents feel insecure when using the internet, mainly because of privacy and security concerns. In a different survey, the vast majority Japanese respondents stated that they wanted to do more to protect their privacy.

Dataset Description:

This dataset captures the real-world online behavior of teenagers, focusing on e-safety awareness, cybersecurity risks, and device interactions. The data was collected from network activity logs and e-safety monitoring systems across various educational institutions and households in Texas and California. Spanning from January 2017 to October 2024, this dataset includes interactions with social media platforms, educational websites, and other online services, providing an in-depth look at teenage online activities in urban and suburban settings. The dataset is anonymized to protect user privacy and contains real incidents of network threats, security breaches, and cybersecurity behavior patterns observed in teenagers.

Use Cases:

Predicting e-safety awareness and online behavior patterns. Detecting malware exposure risk and cybersecurity vulnerabilities. Analyzing online habits related to social media and internet consumption. Evaluating cybersecurity behaviors like password strength, VPN usage, and phishing attempts. Features Overview:

S.No Feature Name Description 1 Device Type The type of device used during the online session (Mobile, Laptop, Tablet, Desktop, etc.) 2 Malware Detection Whether malware was detected on the device during the session (Yes/No) 3 Phishing Attempts Number of phishing attempts experienced during online activity 4 Social Media Usage Frequency of social media usage (Low, Medium, High) 5 VPN Usage Whether a VPN was used during the session (Yes/No) 6 Cyberbullying Reports Number of reported cyberbullying incidents 7 Parental Control Alerts Number of alerts triggered by parental control software 8 Firewall Logs Number of blocked or allowed network connections by the firewall 9 Login Attempts Number of login attempts during the session 10 Download Risk Risk level associated with downloaded files (Low, Medium, High) 11 Password Strength Strength of the passwords used (Weak, Moderate, Strong) 12 Data Breach Notifications Number of alerts regarding compromised personal information 13 Online Purchase Risk Risk level of online purchases made (Low, Medium, High) 14 Education Content Usage Frequency of engagement with educational content (Low, Medium, High) 15 Age Group Age category of the teenager (Under 13, 13-16, 17-19) 16 Geolocation Location of network access (US, EU, etc.) 17 Public Network Usage Whether the online activity occurred over a public network (Yes/No) 18 Network Type Type of network connection (WiFi, Cellular, etc.) 19 Hours Online Total hours spent online during the session 20 Website Visits Number of websites visited per hour during the session 21 Peer Interactions Level of peer-to-peer interactions during online activity 22 Risky Website Visits Whether visits to risky websites occurred (Yes/No) 23 Cloud Service Usage Whether cloud services were accessed during the session (Yes/No) 24 Unencrypted Traffic Whether unencrypted network traffic was accessed during the session (Yes/No) 25 Ad Clicks Whether online advertisements were clicked during the session (Yes/No) 26 Insecure Login Attempts Number of insecure login attempts made (e.g., over unencrypted networks) Potential Research and Machine Learning Applications:

Cybersecurity and anomaly detection models. Predictive modeling for e-safety awareness and risk behaviors. Time-series analysis of internet consumption and security threat trends. Behavioral clustering and pattern recognition in teenage online activity. Data Collection Method: The data was collected through collaboration with local schools and cybersecurity monitoring agencies. Real-time network monitoring systems captured interactions across different online platforms. All personally identifiable information (PII) was anonymized to ensure privacy, making the dataset ideal for public use in research and machine learning tasks.

This dataset provides a rich foundation for studying teenage online behavior patterns and developing predictive models for cybersecurity awareness and risk mitigation. Researchers and data scientists can use this data to create models that better understand online behavior, identify security risks, and design interventions to improve e-safety for teenagers.

Tempe Census Census Tracts and internet access by household. Data source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates, table BD28011 (Internet Subscription in Household). Also includes "low response scores" from the the Census Bureau's data from the 2018 Planning Database (PDB), which was established to prepare for the upcoming 2020 Census.For more information on the low response score, see the United States Census Bureau 2018 Planning Database:https://www.census.gov/topics/research/guidance/planning-databases.htmlLayer generally supports 2020 Census story map Ensuring a Complete Count in the 2020 Census.

This statistic shows the top skills e-enabled by L&D departments worldwide in 2017. During the survey, ** percent of the respondents stated that health and safety skills were e-enabled in their organization.

According to a survey conducted in Indonesia in April 2019, **** percent of respondents believed that doing an online transaction in Indonesia was safe. Indonesia is one of the biggest online markets worldwide. As of March 2017, online penetration in the country stood at only slightly over ** percent. Popular online activities include mobile messaging and social media.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The National Child Development Study: Linked Health Administrative Datasets (Hospital Episode Statistics), England, 1997-2023: Secure Access includes data files from the NHS Digital HES database for those cohort members who provided consent to health data linkage in the Age 50 sweep. The HES database contains information about all hospital admissions in England. The following linked HES data are available:

1) Accident and Emergency (A&E)
The A&E dataset details each attendance to an Accident and Emergency care facility in England, between 01-04-2007 and 31-03-2020 (inclusive). It includes major A&E departments, single speciality A&E departments, minor injury units and walk-in centres in England.

2) Admitted Patient Care (APC)
The APC data summarises episodes of care for admitted patients, where the episode occurred between 01-04-1997 and 31-03-2023 (inclusive).

3) Critical Care (CC)
The CC dataset covers records of critical care activity between 01-04-2009 and 31-03-2023 (inclusive).

4) Out Patient (OP)
The OP dataset lists the outpatient appointments between 01-04-2003 and 31-03-2023 (inclusive).

5) Emergency Care Dataset (ECDS)
The ECDS lists the emergency care appointments between 01-04-2020 and 31-03-2023 (inclusive).

6) Consent data
The consents dataset describes consent to linkage, and is current at the time of deposit.

CLS/ NHS Digital Sub-licence agreement
NHS Digital has given CLS permission for onward sharing of the NCDS/HES dataset via the UKDS Secure Lab. In order to ensure data minimisation, NHS Digital requires that researchers only access the HES variables needed for their approved research project. Therefore, the HES linked data provided by the UKDS to approved researchers will be subject to sub-setting of variables. The researcher will need to request a specific sub-set of variables from the NCDS/HES data dictionary, which will subsequently be made available within their UKDS Secure Account. Once the researcher has finished their research, the UKDS will delete the tailored dataset for that specific project. Any party wishing to access the data deposited at the UK Data Service will be required to enter into a Licence agreement with CLS (UCL), in addition to the agreements signed with the UKDS, provided in the application pack.

CLS Hospital Episode Statistics data access update July 2025

From March 2027, HES data linked to all four CLS studies will no longer be available via the UK Data Service. For projects ending before March 2027, uses should continue to apply via UKDS. However, if access to a wider range of linked Longitudinal Population Studies data is needed, UKLLC might be more suitable. For projects ending after March 2027, users must apply via UKLLC.

Latest edition information
For the third edition (April 2025), the data have been updated to include linked data for the financial years 2017-2022. In addition, a new dataset for Emergency Care (ECDS) episodes has been added, along with a dataset detailing the consent for linkage. Furthermore, the study documentation has also been updated.

This report to Congress provides information on the performance of states on seven national outcome categories and also includes data on contextual factors and findings of analyses conducted across states. (PDF (PDF)- 4,518KB) (PDF (PDF) - 945KB) The PDF is best viewed in Chrome or Firefox. If using Internet Explorer (IE), please right click the link, save the file, and view it locally. Executive Summary Contextual Factors State Performance on Outcome Measures Conclusion and Recommendations for Further Investigation Child Welfare Outcomes Data Site Introduction to the Child Welfare Outcomes, Data, and Analysis Outcome Measures Context Data Data Sources Data Analyses in the Report The Child Welfare Outcomes Report Data Site Chapter 1: Child Welfare Outcomes Demographic Data National Child Population Children in Foster Care Foster Care Entry Rates Children Waiting for Adoption and Children Adopted Summary Chapter 2: Keeping Children Safe Child Victims and Child Fatalities Range of State Performance on Safety-Related Outcome Measures Changes Over Time in State Performance on Measures of Maltreatment Recurrence and Maltreatment of Children in Foster Care Summary of Findings Regarding Keeping Children Safe Chapter 3: Finding Permanent Homes for Children in Foster Care Range of Performance in Achieving Permanency for Children in Foster Care Changes Over Time in State Performance on Measures of Achieving Permanency Summary of Findings Regarding Achieving Permanency for Children in Foster Care Chapter 4: Achieving Timely Reunifications and Adoptions for Children in Foster Care Caseworker Visits Timeliness of Reunifications Changes Over Time in State Performance With Regard to Achieving Timely Reunifications Timeliness of Adoptions Changes Over Time in State Performance With Regard to Timeliness of Adoptions Summary of Findings Regarding Achieving Reunifications and Adoptions in a Timely Manner Chapter 5: Achieving Stable and Appropriate Placement Settings for Children in Foster Care Changes Over Time in State Performance on Measures of Achieving Stable and Appropriate Placement Settings for Children in Foster Care Summary of Findings Regarding Achieving Stable and Appropriate Placements for Children in Foster Care Chapter 6: State Comments on Performance Relevant to the Seven National Child Welfare Outcomes Appendix A: Adoption and Safe Families Act of 1997 (Pub. L. 105—89) Appendix B: Child Welfare Outcomes Report: Outcomes and Measures Appendix C: Caseworker Visits Appendix D: Child Welfare Outcomes Report: Data Sources and Elements Appendix E: Child Maltreatment 2017: Summary of Key Findings Appendix F: The AFCARS Report: FY 2017 Estimates Appendix G: Data-Quality Criteria Metadata-only record linking to the original dataset. Open original dataset below.

This layer Technology Access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total households in given census tract.Layer includes:Key demographicsTotal Households % With an Internet subscription% Dial-up with no other type of Internet subscription% Broadband of any type% Cellular data plan% Broadband such as cable, fiber optic or DSL% Satellite Internet service% Without an Internet subscriptionCurrent Vintage: 2017-2021ACS Table(s): S2801 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: Dec 8, 2022Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

The graph presents the estimated number of electric scooter (E-Scooter) and electric bike (E-Bike) accidents in the United States from 2017 to 2023. The x-axis represents the years, spanning from '17 to '23, while the y-axis indicates the annual number of accidents. Over this seven-year period, the total number of accidents rose sharply from 11,200 in 2017 to 74,600 in 2023. Specifically, E-Scooter accidents increased from 7,700 in 2017 to 40,400 in 2023, and E-Bike accidents grew from 3,500 in 2017 to 34,200 in 2023. The data shows a consistent and significant upward trend in both E-Scooter and E-Bike accidents, contributing to the overall rise in total accidents each year. This information is presented in a line graph format, effectively highlighting the rapid increase in electric scooter and bike accidents across the United States.

Abstract

The objective of the survey is to prepare and publish statistical information on the availability of computers in households, Internet access at home; frequency and purposes of Internet usage; use of e-commerce, e-government services; computer literacy; ICT safety, obstacles to ICT and Internet usage. Moreover, respondents’ demographic and social characteristics, enabling the analysis of survey results by respondents’ sex, age, educational attainment, employment status, are surveyed.

Analysis unit

Households Individual

Universe

Survey population – all residents of Lithuania aged 16–74. Statistical unit – individual aged 16–74. Individuals residing in institutional households (care homes, imprisonment institutions, monasteries, convents, seminaries, etc.) are not surveyed.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size:

Households: 7 000 Individuals: 7 000

Sampling and statistical methodology:

Data of the Population Register are used. The State Enterprise Centre of Registers is the manager of the Population Register. Data of the Population Register in on-line mode are submitted to Statistics Lithuania.

The Population Register database includes data on the residents of the Republic of Lithuania: the citizens of Lithuania, the citizens of foreign countries or persons without citizenship, declaring the place of residence in Lithuania or registering any changes of the civil state in a registry office.

The Population Register is updated regularly. All persons are obliged to declare their place of residence, i.e. to submit data on the address of the place of residence to an institution responsible for the declaration of the place of residence.

However, not all movements of the population within the country are reflected: not all persons report about changing the address to a responsible institution or the declared place of residence is not the main place of residence. Consequently, if the person included in the sample does not live at the address specified, the person actually living at that address whose birthday is the closest to the date of the interview is asked to answer the survey questionnaire.

A one-stage sampling was used, with stratification by type of residence (urban/rural) and by size (for urban area). A simple random sample of individuals aged 16- 74 from every stratum was drawn using the Population Register. Households whose members are selected are surveyed. One individual in the household was interviewed.

Mode of data collection

Other [oth]

Research instrument

Questionnaire accessible online at: https://apklausos.stat.gov.lt/en/statistines-anketos

Response rate

72.8% (IND)

Published: 14 December 2017 - This report, generated from Hospital Episode Statistics (HES) A&E data, sets out data coverage, data quality and performance information for the following five A&E indicators: • Left department before being seen for treatment rate • Re-attendance rate • Time to initial assessment • Time to treatment • Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year.

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers:

1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332

2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344

3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567

The file with the database is available in excel.

DATA SOURCES

The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas.

With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index.

To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted:

Eurostat [3]

Directorate-General for Mobility and Transport (DG MOVE). European Union [4]

The World Bank [5]

World Health Organization (WHO) [6]

European Transport Safety Council (ETSC) [7]

European Road Safety Observatory (ERSO) [8]

European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9]

EU BestPoint-Project [10]

Ministerstvo dopravy, República Checa [11]

Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12]

Ministerie van Infrastructuur en Waterstaat, Países Bajos [13]

National Statistics Office, Malta [14]

Ministério da Economia e Transição Digital, Portugal [15]

Ministerio de Fomento, España [16]

Trafikverket, Suecia [17]

Ministère de l’environnement de l’énergie et de la mer, Francia [18]

Ministero delle Infrastrutture e dei Trasporti, Italia [19–25]

Statistisk sentralbyrå, Noruega [26-29]

Instituto Nacional de Estatística, Portugal [30]

Infraestruturas de Portugal S.A., Portugal [31–35]

Road Safety Authority (RSA), Ireland [36]

DATA BASE DESCRIPTION

The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure.

Table. Database metadata

Code

Variable and unit

fatal_pc_km

Fatalities per billion passenger-km

fatal_mIn

Fatalities per million inhabitants

accid_adj_pc_km

Accidents per billion passenger-km

p_km

Billions of passenger-km

croad_inv_km

Investment in roads construction per kilometer, €/km (2015 constant prices)

croad_maint_km

Expenditure on roads maintenance per kilometer €/km (2015 constant prices)

prop_motorwa

Proportion of motorways over the total road network (%)

populat

Population, in millions of inhabitants

unemploy

Unemployment rate (%)

petro_car

Consumption of gasolina and petrol derivatives (tons), per tourism

alcohol

Alcohol consumption, in liters per capita (age > 15)

mot_index

Motorization index, in cars per 1,000 inhabitants

den_populat

Population density, inhabitants/km2

cgdp

Gross Domestic Product (GDP), in € (2015 constant prices)

cgdp_cap

GDP per capita, in € (2015 constant prices)

precipit

Average depth of rain water during a year (mm)

prop_elder

Proportion of people over 65 years (%)

dps

Demerit Point System, dummy variable (0: no; 1: yes)

freight

Freight transport, in billions of ton-km

ACKNOWLEDGEMENTS

This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges.

Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study.

REFERENCES

1. International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance.

2. United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT_40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020).

3. European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021).

4. Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021).

5. World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021).

6. World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021).

7. European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011;

8. Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021).

9. Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237.

10. Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic;

11. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2003/2004; Hamburg, Germany, 2004; ISBN 3871542946.

12. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2018/2019. In Verkehrsdynamik; Flensburg, Germany, 2018 ISBN 9783000612947.

13. Ministerie van Infrastructuur en Waterstaat Rijksjaarverslag 2018 a Infrastructuurfonds; The Hague, Netherlands, 2019; ISBN 0921-7371.

14. Ministerie van Infrastructuur en Milieu Rijksjaarverslag 2014 a Infrastructuurfonds; The Hague, Netherlands, 2015; ISBN 0921- 7371.

15. Ministério da Economia e Transição Digital Base de Dados de Infraestruturas - GEE Available online: https://www.gee.gov.pt/pt/publicacoes/indicadores-e-estatisticas/base-de-dados-de-infraestruturas (accessed on Apr 29, 2021).

16. Ministerio de Fomento. Dirección General de Programación Económica y Presupuestos. Subdirección General de Estudios Económicos y Estadísticas Serie: Anuario estadístico; NIPO 161-13-171-0; Centro de Publicaciones. Secretaría General Técnica. Ministerio de Fomento: Madrid, Spain;

17. Trafikverket The Swedish Transport Administration Annual report: 2017; 2018; ISBN 978-91-7725-272-6.

18. Ministère de l’Équipement, du T. et de la M. Mémento de statistiques des transports 2003; Ministère de l’environnement de l’énergie et de la mer, 2005;

19. Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle

Forecasts suggest that by 2019 there will be a total of *** million public Wi-Fi hotspots available worldwide. This 2019 figure would represent a near quadrupling of public Wi-Fi hotspots since 2016, showing the rapid rise in these networks around the world. This trend towards growth is expected to continue into at least the early 2020s. Public Wi-Fi Wi-fi wireless internet networks allow users to connect to the web using a range of mobile devices without the need to physically connect to ethernet ports. Public Wi-Fi offerings such as municipal wireless networks and those found in coffee shops or cafes allow users to connect without the need for a specific password. Although public Wi-Fi hotspots are a welcome service for many people, there are many concerns over the safety of information accessed over these networks. As of 2017, around ** percent of people stated that they had logged in to a personal email account over public Wi-Fi, while ** percent stated that they had logged in to their social media accounts. IT professionals tend to advise against using these public networks for tasks that require sensitive personal information as it may be accessible by other users of the network. Public opinion is relatively split about the safety of these public Wi-Fi networks: ** percent of people state that they feel safe on public Wi-Fi, while ** percent state that they feel unsafe.