The smartphone penetration ranking is led by Canada with 97 percent, while the United Arab Emirates is following with 97 percent. In contrast, Mozambique is at the bottom of the ranking with 9.48 percent, showing a difference of 87.52 percentage points to Canada. The penetration rate refers to the share of the total population.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).

Provide information of small-scale business operators that currently support mobile payment tools. Additionally, it is recommended to open the file using tools such as Notepad, UltraEdit, etc.; if the file opening tool has character set options, please select UTF-8 Chinese code for Chinese. The data link was adjusted to https://eip.fia.gov.tw/data/BGMOPEN87.csv on June 22, 2020.

Key Mobile Payments StatisticsTop Mobile Payments AppsFinance App Market LandscapeMobile Payments Transaction VolumeMobile Payments UsersMobile Payments Adoption by CountryMobile Payments TPV in...

Cryptocurrency payments are forecast to grow at a CAGR of nearly 17 percent between 2023 and 2030, although the market is relatively small. The forecast is according to a market estimate made in early 2023, based on various conditions and sources available at that time. It should be noted, however, that cryptocurrency used for payments is predicted to be a far smaller market than the predicted transaction value of CBDC, or the forecast market size of instant payments. Indeed, research from early 2023 across 40 countries suggested that the market share of cryptocurrency in e-commerce transaction was "less than one percent" in all survey countries, with predictions being this would not change in the future.

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.

2020 Administrator Carlow Cavan Charitable recreati... Charity Claims for Deferral... Clare Commercial rates Community and Local... Compliance data Compliance letters Compliance rate Cork City Cork County Credit Card DLR Debit Card Declared Deduct at Source Deferred Department of the E... Diplomatic properties Donegal Dublin City Exchequer Receipts Executor Exempt Financial Loss Fingal First Time Buyer Galway City Galway County Household Charge Insolvent Kerry Kildare Kilkenny LPT LPT Collected Laois Leitrim Limerick City and C... Local Adjustment Fa... Local Authorities Long-term illness Longford Louth Mandatory Deduction... Mayo Meath Mobile homes Monaghan Multiple Property O... Nursing homes Offaly Payment Types Properties Returned Public Body Pyrite damaged Roscommon Service Provider Severely incapacita... Sheriff Single debit authority Sligo South Dublin Tipperary Unfinished Housing ... Unsold Valuation Bands Valuations Waterford City and ... Westmeath Wexford Wicklow special needs unused

India Mobile Payments (Mobile App based): Volume: Inter-bank data was reported at 16,130.822 Unit mn in Mar 2025. This records an increase from the previous number of 14,132.804 Unit mn for Feb 2025. India Mobile Payments (Mobile App based): Volume: Inter-bank data is updated monthly, averaging 5,653.555 Unit mn from Nov 2019 (Median) to Mar 2025, with 65 observations. The data reached an all-time high of 16,130.822 Unit mn in Mar 2025 and a record low of 1,025.433 Unit mn in Apr 2020. India Mobile Payments (Mobile App based): Volume: Inter-bank data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI017: Mobile Payments.

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 “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 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 “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

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

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 “Uganda - National Panel Survey 2019-2020” and “Uganda - High-Frequency Phone Survey on COVID-19 2020-2021” documentations available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Uganda National Panel Survey 2019-2020 and Uganda High-Frequency Phone Survey on COVID-19 2020-2021 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 “Uganda - National Panel Survey 2019-2020” and “Uganda - High-Frequency Phone Survey on COVID-19 2020-2021” documentations available in the Microdata Library for details.

Analysis of ‘Strategic Measure_Number and Percentage of instances where people access court services other than in person and outside normal business hours (e.g. phone, mobile application, online, expanded hours) – Municipal Court’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/6b92f18f-a66f-4334-be31-626816fff206 on 26 January 2022.

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

The dataset supports measure S.D.4.a of SD23.

The Austin Municipal Court offers services via in person, phone, mail, email, online, in the community, in multiple locations, and during non-traditional hours to make it easier and more convenient for individuals to handle court business. This measure tracks the percentage of customers that utilize court services outside of normal business hours, defined as 8am-5pm Monday-Friday, and how many payments were made by methods other than in person. This measure helps determine how Court services are being used and enables the Court to allocate its resources to best meet the needs of the public. Historically, almost 30% of the operational hours are outside of traditional hours and the average percentage of payments made by mail and online has been over 59%.

View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/c7z3-geii

Data source: electronic case management system and manual tracking of payments received via mail. Calculation: Business hours are manually calculated annually. - A query is run from the court’s case management system to calculate how many monetary transactions were posted. S.D.4.a: Numerator: Number of payments received by mail is entered manually by the Customer Service unit that processes all incoming mail. S.D.4.a Denominator: Total number of web payments is calculated using a query to calculate a total number of payments with a payment type ‘web’ in the case management system. Measure time period: Annual (Fiscal Year) Automated: No Date of last description update: 4/10/2020

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

This dataset relates to a study exploring off-grid sanitation practices in Kenya, Peru, and South Africa, with a focus on how various user demographics access and utilize sanitation facilities. The study contrasts container-based sanitation with alternative methods. Participants, acting as citizen researchers, gathered confidential information using a specialized mobile application. The primary objective was to uncover obstacles and challenges, with the intention of sharing insights with other municipalities interested in implementing container-based sanitation solutions for off-grid regions.

Over the course of 12 months, participants received incentives for consistent involvement, following a micro-payment for micro-tasks model. Selection of participants was randomized, involving attendance at a training session and, if necessary, provision of a smartphone which they retained at the conclusion of the project. Weekly smartphone surveys were conducted in more than 300 households within informal settlements across the three countries throughout the project duration. These surveys aimed to capture daily routines, well-being, income levels, usage of infrastructure services, livelihood or environmental shocks and other socioeconomic factors on a weekly basis, contributing to more comprehensive analyses and informed decision-making processes.

The smartphone-based methodology offered an efficient and adaptable means of data collection, facilitating broad coverage across diverse geographical areas and subjects, while promoting regular engagement. Open Data Kit (ODK) tools were utilized to support data collection in resource-limited settings with unreliable connectivity.

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.

The Wireless Spectrum R&D Interagency Working Group workshop, Artificial Intelligence and Wireless Spectrum: Opportunities and Challenges, was held in August 2019 to explore the role of AI techniques to improve wireless spectrum use and management. The workshop underscored the opportunities and challenges facing next-generation wireless spectrum management in the areas of future communications networks, dynamic spectrum allocation and policy management, and spectrum sharing. AI technologies appear to have high potential for the wireless spectrum domain. However, there are fundamental challenges that need to be addressed. For example, AI-based solutions need to be well-defined, supported by the appropriate datasets, and able to be verified and validated. Continual acquisition and appropriate use of trustworthy, validated data and datasets will be critical components of both the opportunities and the challenges AI presents to wireless spectrum policy and management.

The dataset contains information about the contents of 100 Terms of Service (ToS) of online platforms. The documents were analyzed and evaluated from the point of view of the European Union consumer law. The main results have been presented in the table titled "Terms of Service Analysis and Evaluation_RESULTS." This table is accompanied by the instruction followed by the annotators, titled "Variables Definitions," allowing for the interpretation of the assigned values. In addition, we provide the raw data (analyzed ToS, in the folder "Clear ToS") and the annotated documents (in the folder "Annotated ToS," further subdivided).

SAMPLE: The sample contains 100 contracts of digital platforms operating in sixteen market sectors: Cloud storage, Communication, Dating, Finance, Food, Gaming, Health, Music, Shopping, Social, Sports, Transportation, Travel, Video, Work, and Various. The selected companies' main headquarters span four legal surroundings: the US, the EU, Poland specifically, and Other jurisdictions. The chosen platforms are both privately held and publicly listed and offer both fee-based and free services. Although the sample cannot be treated as representative of all online platforms, it nevertheless accounts for the most popular consumer services in the analyzed sectors and contains a diverse and heterogeneous set.

CONTENT: Each ToS has been assigned the following information: 1. Metadata: 1.1. the name of the service; 1.2. the URL; 1.3. the effective date; 1.4. the language of ToS; 1.5. the sector; 1.6. the number of words in ToS; 1.7–1.8. the jurisdiction of the main headquarters; 1.9. if the company is public or private; 1.10. if the service is paid or free. 2. Evaluative Variables: remedy clauses (2.1– 2.5); dispute resolution clauses (2.6–2.10); unilateral alteration clauses (2.11–2.15); rights to police the behavior of users (2.16–2.17); regulatory requirements (2.18–2.20); and various (2.21–2.25). 3. Count Variables: the number of clauses seen as unclear (3.1) and the number of other documents referred to by the ToS (3.2). 4. Pull-out Text Variables: rights and obligations of the parties (4.1) and descriptions of the service (4.2)

ACKNOWLEDGEMENT: The research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021, project no. 2020/37/K/HS5/02769, titled “Private Law of Data: Concepts, Practices, Principles & Politics.”

This is the raw pollutant data collected on August 6, 2014 in the greater Denver, Colorado area by three mobile air pollution platforms. Data was collected by Aclima, Inc. This is the raw data used to explore different statistical methods for assessing platform performance and comparability in the publication "Uncertainty in collocated mobile measurements of air quality" by Andrew R. Whitehill et al., 2020.

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...

About this dataset

Context

The dataset tabulates the median household income in Mobile. It can be utilized to understand the trend in median household income and to analyze the income distribution in Mobile by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Mobile, AL Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Mobile, AL: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Mobile, AL
• Mobile, AL households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Mobile median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the Mobile County household income by gender. The dataset can be utilized to understand the gender-based income distribution of Mobile County income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Mobile County, AL annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Mobile County, AL annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2022)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Mobile County income distribution by gender. You can refer the same here

India Mobile Payments (Mobile App based): Value: Inter-bank data was reported at 30,993,299.400 INR mn in Mar 2025. This records an increase from the previous number of 26,571,622.783 INR mn for Feb 2025. India Mobile Payments (Mobile App based): Value: Inter-bank data is updated monthly, averaging 13,755,768.395 INR mn from Nov 2019 (Median) to Mar 2025, with 65 observations. The data reached an all-time high of 30,993,299.400 INR mn in Mar 2025 and a record low of 2,916,109.638 INR mn in Apr 2020. India Mobile Payments (Mobile App based): Value: Inter-bank data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI017: Mobile Payments.

This data set provides mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. For more information please see the Ookla Github repository or the Registry of Open Data on AWS. Ookla licenses data to NGOs and educational institutions to fulfill its mission: to help make the internet better, faster and more accessible for everyone. Ookla hopes to further this mission by distributing the data to make it easier for individuals and organizations to use it for the purposes of bridging the social and economic gaps between those with and without modern Internet access. AURIN has generated a subset corresponding to the intersection with the extent of the 2016 Greater Capital City Statistical Areas (GCCSA) boundaries from the ABS Australian Statistical Geography Standard (ASGS). It was then reprojected from EPSG 4326 (WGS84) to 4283 (GDA94). Additional columns have been added corresponding to the matching boundaries of the 2016 ABS Statistical Area Levels 2, 3 and 4, and the 2020 Local Government Areas. These were spatially joined using the centroid of each polygon, and therefore, should only be used as an approximation. Furthermore, grid cells residing outside of these boundaries, such as offshore or over rivers, are assigned a null value in these columns.