The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

SKMM Annual Report - Complaint Statistics Related to Mobile Content Services No. of Views : 152

About this dataset

Context

The dataset tabulates the population of Mobile by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Mobile across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 53.3% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Mobile is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Mobile total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mobile Population by Race & Ethnicity. You can refer the same here

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

About this dataset

Context

The dataset tabulates the population of Mobile County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Mobile County. The dataset can be utilized to understand the population distribution of Mobile County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Mobile County. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Mobile County.

Key observations

Largest age group (population): Male # 10-14 years (14,553) | Female # 60-64 years (15,080). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Mobile County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Mobile County is shown in the following column.
• Population (Female): The female population in the Mobile County is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Mobile County for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mobile County Population by Gender. You can refer the same here

Switzerland is leading the ranking by population share with mobile internet access, recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

About this dataset

Context

The dataset tabulates the population of Mobile by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Mobile. The dataset can be utilized to understand the population distribution of Mobile by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Mobile. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Mobile.

Key observations

Largest age group (population): Male # 20-24 years (7,282) | Female # 30-34 years (7,886). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Mobile population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Mobile is shown in the following column.
• Population (Female): The female population in the Mobile is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Mobile for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mobile Population by Gender. You can refer the same here

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

This data release presents streamflow recession analyses and supporting statistics for 164 U.S. Geological Survey (USGS) streamgages located in or near the watersheds of Mobile and Perdido Bays in the south-central United States (see Tatum and others, 2024). Streamgages were selected based on a minimum of two complete decades of daily streamflow data since January 1, 1950, with the additional requirement that data coverage includes the entire 2010s decade. Daily streamflow data were retrieved on March 8, 2024 (U.S. Geological Survey, 2024). The primary output of this release, gfactor.txt, is a pipe-delimited text file containing 22 attributes and over 1,000 unique records summarizing decadal streamflow statistics for each site. Key identifying attributes include the USGS site identification number (site_no), streamgage name (station_nm), geographic coordinates (dec_lat_va, dec_long_va), contributing drainage area (CDA), and projected coordinates (xkm, ykm). Key statistical attributes consist of decadal counts of daily streamflow conditions (decreasing_count, increasing_count, nochange_count, total_count), as well as summary statistics that characterize the declining days distribution, including the 50th percentile (median), L-moments (L1, L2, T3, T4, T5), and streamflow recession indices associated with the 90th percentile (gfactor, gfactor_emp). Additional attributes capture adjusted statistics produced through a detrending procedure designed to assess and remove potential decadal biases in the declining day distribution. Core statistical computations were carried out using custom functions developed in the R environment for statistical computing (R Core Team, 2024).

The psychological and physiological health of undergraduates was correlated with the sleep quality, which can be improved through increasing physical activity. However, the correlations between physical activity and sleep quality are subject to various factors. In this study, we investigated the effects of self-control and mobile phone addiction on the correlations between physical activity on undergraduates’ sleep quality at the psychological and behavioral levels. Data was collected through a survey with a convenient sample of 2,274 students in China. The study utilized scales of physical activity, sleep quality, self-control, and mobile phone addiction to quantitatively evaluate the impact of physical activity on the sleep quality of undergraduates. The correlations were analyzed using SPSS 26.0, including descriptive statistics, confidence tests, common method bias tests, correlation analysis, and hypothesis tests. Pearson correlation analysis shows that physical activity was significantly correlated with sleep quality (r = -0.541, p < 0.001), and that physical activity and sleep quality were significantly correlated with self-control and mobile phone addiction. Regression analysis shows that physical activity had a significant positive regression effect on self-control (standardized regression coefficient β = 0.234, p < 0.001), a significant negative regression effect on mobile phone addiction (β = –0.286, p < 0.001), and a significant negative regression effect on sleep quality (β = –0.351, p < 0.001). Further, a chain mediation model of physical activity → self-control → mobile phone addiction → sleep quality was proposed. The findings provide basic data for college students to promote physical activity and improve sleep quality.

About this dataset

Context

The dataset tabulates the population of Mobile by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Mobile. The dataset can be utilized to understand the population distribution of Mobile by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Mobile. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Mobile.

Key observations

Largest age group (population): Male # 20-24 years (7,239) | Female # 30-34 years (7,467). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Mobile population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Mobile is shown in the following column.
• Population (Female): The female population in the Mobile is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Mobile for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mobile Population by Gender. You can refer the same here

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Nigeria General Household Survey, Panel (GHS-Panel) 2018-2019 and Nigeria COVID-19 National Longitudinal Phone Survey (COVID-19 NLPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

About this dataset

Context

The dataset tabulates the population of Mobile City by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Mobile City across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 57.28% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Mobile City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Mobile City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mobile City Population by Race & Ethnicity. You can refer the same here

The integration of mobile devices into adolescents’ daily lives is significant, making it imperative to prioritize their safety and security. With the imminent arrival of fast internet (6G), offering increased bandwidth and reduced latency compared to its predecessor (5G), real-time streaming of high-quality video and audio to mobile devices will become feasible. To effectively leverage the fast internet, accurately classifying Mobile Applications (M-APPs) is crucial to shield adolescents from inappropriate content, including violent videos, pornography, hate speech, and cyberbullying. This work introduces an innovative approach utilizing Deep Learning techniques, specifically Attentional Convolutional Neural Networks (A-CNNs), for classifying M-APPs. The goal is to secure adolescent mobile usage by predicting the potential negative impact of M-APPs on adolescents. The proposed methodology employs multiple Machine and Deep Learning (M/DL) models, but A-CNNs based on Bidirectional Encoder Representations from Transformers embeddings outperformed other models, achieving an average accuracy of 88.74% and improving the recall from 99.33% to 99.65%.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

Abstract

To monitor the socioeconomic impacts of the coronavirus disease 2019 (COVID-19) pandemic and inform policy responses and interventions, the COVID-19 High-Frequency Phone Survey (HFPS) of households was designed aspart of a World Bank global initiative. For Cambodia, a total of 5 survey rounds are planned, with households being called back every 1 to 2 months. This allows for the impact of the pandemic to be tracked as it unfolds and provides data to the government and development partners in near real-time, supporting an evidence-based response to thecrisis. Two additional rounds are conducted in 2022. Due to the higher attrition rate of LSMS+, the World Bank teamdecided to use the same sample of households that had been interviewed for the 2019/2020 Cambodia Socio-Economic Survey (CSES) implemented from July 2019 to June 2020 by the National Institute of Statistics (NIS). The CSES is representative at national and urban/rural level.

The extensive information collected in CSES 2019/20 providesa rich set of background information on which the COVID-19 High-Frequency Phone Survey of households can beleveraged to assess the differential impacts of the pandemic in the country. Data collection of the Cambodia COVID-19 HFPS based on CSES sample started in February 2022. The HFPS interviewed 1698 households from the 2019/20 CSES with a phone number. Sampling weights were adjusted to make sure that the surveyed sample remains representative at national and urban/rural.

The questionnaire covers a series of topics, such as access to food, foodinsecurity, impact of the Covid-19 on income sources and coping mechanisms, access to social assistance, and impactof Covid-19 on economic activity. The questionnaire is designed to be administered between 20 to 25 minutes. Thesurvey is implemented using Computer Assisted Telephone Interviewing.

Geographic coverage

National coverage - rural and urban.

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households (with a phone number) excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The HFPS drew its sample from the Cambodia Socio-Economic Survey implemented from July 2019 to June 2020 by the National Institute of Statistics (NIS). The phone survey was successfully completed for 1706 households in February and 1698 households in April 2023.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The Cambodia COVID-19 High Frequency Phone Survey of households questionnaire consists of the following sections:

Round 1 - Interview Information - Household Roster - Knowledge Regarding the Spread of COVID-19 - Behaviour and Social Distancing - Access to Basic Services - Employment - Income Loss - Coping/Shocks - Food Security - Aid and Support/ Social Safety Nets

Round 2 - Interview Information - Household Roster - Migration - Access to Basic Services - Employment - Income Loss - Food Security - Aid and Support/ Social Safety Nets

Round 3 - Interview Information - Household Roster - Knowledge Regarding the Spread of COVID-19 - Access to Basic Services - Employment - Income Loss - Food Security - Aid and Support/ Social Safety Nets - Payment method

Round 4 - Interview Information - Household Roster - Access to Basic Services - Employment - Income Loss - Coping/Shocks - Food Security - Aid and Support/ Social Safety Nets - Payment method

Round 5 - Interview Information - Household Roster - Access to Basic Services - Employment - Income Loss - Food Security - Aid and Support/ Social Safety Nets

Round 6 - Interview Information - Household Roster - Social Economic Status - Access to Basic Services - Employment - Income Loss - Food Security - Coping/Shocks - Aid and Support/ Social Safety Nets - Relief Transfer - Education - SWIFT

Round 7 - Interview Information - Household Roster - Social Economic Status - Disability - Access to Basic Services - Employment - Income Loss - Food Security - Coping/Shocks - Aid and Support/ Social Safety Nets - Relief Transfer - Education - SWIFT

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Only households that consented to being interviewed were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

The number of mobile broadband connections per 100 inhabitants in the United States was forecast to continuously increase between 2024 and 2029 by in total 21.1 connections (+11.49 percent). After the fifteenth consecutive increasing year, the mobile broadband penetration is estimated to reach 204.76 connections and therefore a new peak in 2029. Notably, the number of mobile broadband connections per 100 inhabitants of was continuously increasing over the past years.Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of mobile broadband connections per 100 inhabitants in countries like Canada and Mexico.