What We Eat in America (WWEIA) is the dietary intake interview component of the National Health and Nutrition Examination Survey (NHANES). WWEIA is conducted as a partnership between the U.S. Department of Agriculture (USDA) and the U.S. Department of Health and Human Services (DHHS). Two days of 24-hour dietary recall data are collected through an initial in-person interview, and a second interview conducted over the telephone within three to 10 days. Participants are given three-dimensional models (measuring cups and spoons, a ruler, and two household spoons) and/or USDA's Food Model Booklet (containing drawings of various sizes of glasses, mugs, bowls, mounds, circles, and other measures) to estimate food amounts. WWEIA data are collected using USDA's dietary data collection instrument, the Automated Multiple-Pass Method (AMPM). The AMPM is a fully computerized method for collecting 24-hour dietary recalls either in-person or by telephone. For each 2-year data release cycle, the following dietary intake data files are available: Individual Foods File - Contains one record per food for each survey participant. Foods are identified by USDA food codes. Each record contains information about when and where the food was consumed, whether the food was eaten in combination with other foods, amount eaten, and amounts of nutrients provided by the food. Total Nutrient Intakes File - Contains one record per day for each survey participant. Each record contains daily totals of food energy and nutrient intakes, daily intake of water, intake day of week, total number foods reported, and whether intake was usual, much more than usual or much less than usual. The Day 1 file also includes salt use in cooking and at the table; whether on a diet to lose weight or for other health-related reason and type of diet; and frequency of fish and shellfish consumption (examinees one year or older, Day 1 file only). DHHS is responsible for the sample design and data collection, and USDA is responsible for the survey’s dietary data collection methodology, maintenance of the databases used to code and process the data, and data review and processing. USDA also funds the collection and processing of Day 2 dietary intake data, which are used to develop variance estimates and calculate usual nutrient intakes. Resources in this dataset:Resource Title: What We Eat In America (WWEIA) main web page. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/wweianhanes-overview/ Contains data tables, research articles, documentation data sets and more information about the WWEIA program. (Link updated 05/13/2020)

Data Entry Service Market Outlook

The global data entry service market size is poised to experience significant growth, with the market expected to rise from USD 2.5 billion in 2023 to USD 4.8 billion by 2032, achieving a Compound Annual Growth Rate (CAGR) of 7.5% over the forecast period. This growth can be attributed to several factors including the increasing adoption of digital technologies, the rising demand for data accuracy and integrity, and the need for businesses to manage vast amounts of data efficiently.

One of the key growth factors driving the data entry service market is the rapid digital transformation across various industries. As businesses continue to digitize their operations, the volume of data generated has increased exponentially. This data needs to be accurately entered, processed, and managed to derive meaningful insights. The demand for data entry services has surged as companies seek to outsource these non-core activities, enabling them to focus on their primary business operations. Additionally, the widespread adoption of cloud-based solutions and big data analytics has further fueled the demand for efficient data management services.

Another significant driver of market growth is the increasing need for data accuracy and integrity. Inaccurate or incomplete data can lead to poor decision-making, financial losses, and a decrease in operational efficiency. Organizations are increasingly recognizing the importance of maintaining high-quality data and are investing in data entry services to ensure that their databases are accurate, up-to-date, and reliable. This is particularly crucial for industries such as healthcare, BFSI, and retail, where precise data is essential for regulatory compliance, customer relationship management, and operational efficiency.

The cost-effectiveness of outsourcing data entry services is also contributing to market growth. By outsourcing these tasks to specialized service providers, organizations can save on labor costs, reduce operational expenses, and improve productivity. Service providers often have access to advanced tools and technologies, as well as skilled professionals who can perform data entry tasks more efficiently and accurately. This not only leads to cost savings but also allows businesses to reallocate resources to more strategic activities, driving overall growth.

From a regional perspective, the Asia Pacific region is expected to witness the highest growth in the data entry service market during the forecast period. This can be attributed to the region's strong IT infrastructure, the presence of numerous outsourcing service providers, and the growing adoption of digital technologies across various industries. North America and Europe are also significant markets, driven by the high demand for data management services in sectors such as healthcare, BFSI, and retail. The Middle East & Africa and Latin America are anticipated to experience steady growth, supported by increasing investments in digital infrastructure and the rising awareness of the benefits of data entry services.

Service Type Analysis

The data entry service market can be segmented into various service types, including online data entry, offline data entry, data processing, data conversion, data cleansing, and others. Each of these service types plays a crucial role in ensuring the accuracy, integrity, and usability of data. Online data entry services involve entering data directly into an online system or database, which is essential for real-time data management and accessibility. This service type is particularly popular in industries such as e-commerce, where timely and accurate data entry is critical for inventory management and customer service.

Offline data entry services, on the other hand, involve entering data into offline systems or databases, which are later synchronized with online systems. This service type is often used in industries where internet connectivity may be unreliable or where data security is a primary concern. Offline data entry is also essential for processing historical data or data that is collected through physical forms and documents. The demand for offline data entry services is driven by the need for accurate and timely data entry in sectors such as manufacturing, government, and healthcare.

Data processing services involve the manipulation, transformation, and analysis of raw data to produce meaningful information. This includes tasks such as data validation, data sorting, data aggregation, and data analysis. Data processing is a critical componen

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in American Fork. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In American Fork, the median income for all workers aged 15 years and older, regardless of work hours, was $54,439 for males and $27,701 for females.

These income figures highlight a substantial gender-based income gap in American Fork. Women, regardless of work hours, earn 51 cents for each dollar earned by men. This significant gender pay gap, approximately 49%, underscores concerning gender-based income inequality in the city of American Fork.

- Full-time workers, aged 15 years and older: In American Fork, among full-time, year-round workers aged 15 years and older, males earned a median income of $71,091, while females earned $50,384, leading to a 29% gender pay gap among full-time workers. This illustrates that women earn 71 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in American Fork.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 American Fork median household income by race. You can refer the same here

Snapshot img

Biometrics-As-A-Service Market Size 2024-2028

The biometrics-as-a-service market size is forecast to increase by USD 3.5 billion at a CAGR of 19.46% between 2023 and 2028.

The market is experiencing significant growth due to increasing security and surveillance needs across various industries, particularly In the BFSI sector. The adoption of advanced biometric technologies such as face and voice recognition is on the rise, offering enhanced security and convenience. However, privacy concerns remain a major challenge, with consumers expressing apprehensions regarding the use of their biometric data. Addressing these concerns through robust data security measures and transparent data handling practices is essential for market growth. Additionally, the integration of biometric systems with IoT devices and the emergence of contactless biometric solutions are key trends shaping the market.Overall, the market is poised for growth, driven by the need for secure and convenient identification solutions.

What will be the Size of the Biometrics-As-A-Service Market During the Forecast Period?

Request Free Sample

The market is experiencing significant growth due to increasing demands for advanced security solutions in response to terrorist and theft activities. Biometric technologies, such as fingerprint sensors and facial recognition, are becoming crucial components of national security and e-passport programs worldwide. Government support and investment in contactless biometric solutions have fueled the adoption of these technologies in criminal identification systems and security systems. The Android platform and touch-based technologies are driving the integration of biometrics into everyday devices, making advanced authentication services more accessible. The cloud is facilitating the deployment and management of biometric solutions, enabling contact-free sensing and reducing the need for physical infrastructure.Biometric shopper analytics and loss prevention solutions are also gaining traction in retail and commercial sectors, providing valuable insights and enhancing personal data security. The coronavirus pandemic has further accelerated the need for touch-free and contact-free sensing solutions, as businesses seek to minimize physical contact and maintain social distancing. Advanced biometric technologies, including facial recognition and advanced authentication services, continue to evolve, offering more accurate and efficient solutions for border patrol agents and other security applications. Overall, the market is poised for continued growth, driven by the need for enhanced security and data protection.

How is this Biometrics-As-A-Service Industry segmented and which is the largest segment?

The biometrics-as-a-service industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2017-2022 for the following segments. ApplicationSite access controlTime recordingMobile applicationWeb and workplaceModalityUnimodalMultimodalGeographyNorth AmericaUSAPACChinaJapanEuropeGermanyUKSouth AmericaMiddle East and Africa

By Application Insights

The site access control segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, particularly In the segment of biometric site access control systems. This expansion is driven by escalating security threats, data breaches, and criminal activities, leading to increased demand for advanced security measures. Biometric site access control systems are being deployed across various industries, including healthcare, finance, transportation, and government, to bolster security and prevent unauthorized access. Government initiatives in countries like India, Australia, China, the UK, and the US, aimed at implementing biometric site access control systems for enhanced security, are also gaining traction. Contactless biometric solutions, such as iris recognition and touchless technologies, are increasingly popular due to the ongoing pandemic.Biometric-as-a-Service (BaaS) solutions offer cost-effective, cloud-based biometrics management, making them an attractive alternative to traditional methods. Biometric capture devices, integration services, and low-cost solutions are essential components of BaaS offerings. Biometric onboarding, authentication capabilities, and multi-modal systems are also key features of these solutions. Business applications, including healthcare, enterprise, automotive, imaging, financial services, legal, manufacturing, education, logistics, and retail, are benefiting from the adoption of BaaS solutions. Behavioral biometrics and multi-factor authentication solutions are also gaining popularity for added security.

Get a glance at the Biometrics-As-A-Service Industry r

About this dataset

Context

The dataset presents median household incomes for various household sizes in American Fork, UT, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in American Fork. Across the different household sizes in American Fork the mean income is $104,667, and the standard deviation is $40,961. The coefficient of variation (CV) is 39.13%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $42,736. It then further increased to $163,657 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/american-fork-ut-median-household-income-by-household-size.jpeg" alt="American Fork, UT median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 American Fork median household income. You can refer the same here

Synthetic Medical Image Data Services Market Outlook

According to our latest research, the global synthetic medical image data services market size stood at USD 452 million in 2024, reflecting robust adoption across healthcare and life sciences sectors. The market is expected to grow at a remarkable CAGR of 33.7% from 2025 to 2033, reaching a projected value of USD 5.4 billion by 2033. This exponential growth is primarily driven by the escalating demand for high-quality, diverse, and annotated medical imaging datasets to power artificial intelligence (AI) and machine learning (ML) algorithms for diagnostics, research, and training purposes. As per our comprehensive analysis, the rapid integration of synthetic data solutions is revolutionizing medical imaging workflows, enabling healthcare stakeholders to overcome data scarcity and privacy concerns while accelerating innovation.

The synthetic medical image data services market is experiencing significant growth due to the increasing need for large, annotated datasets to train and validate AI-driven diagnostic tools. Traditional approaches to medical image acquisition are often hampered by regulatory restrictions, data privacy concerns, and the inherent variability and scarcity of rare disease cases. Synthetic data generation addresses these challenges by creating realistic, customizable, and privacy-compliant datasets that enhance the performance and generalizability of AI models. Furthermore, the adoption of synthetic data accelerates the development cycle for new imaging technologies and supports the validation of medical devices, fostering a more agile and innovative healthcare ecosystem. The growing sophistication of generative adversarial networks (GANs) and other deep learning techniques has further improved the realism and utility of synthetic images, making them increasingly indispensable for modern medical imaging applications.

Another key growth factor for the synthetic medical image data services market is the rising emphasis on data privacy and compliance with regulations such as HIPAA in the United States and GDPR in Europe. These regulations impose stringent requirements on the use and sharing of patient data, often limiting the availability of real-world medical images for research and commercial purposes. Synthetic data offers a compelling solution by generating de-identified datasets that closely mimic real patient data without exposing sensitive information. This not only facilitates collaborative research and cross-institutional projects but also enables companies to scale their AI development efforts globally without the risk of data breaches or legal repercussions. As the healthcare industry continues to prioritize patient confidentiality, the demand for synthetic data services is expected to surge.

The market is further propelled by the expanding applications of synthetic medical image data in education, training, and research. Medical professionals, students, and researchers increasingly rely on diverse and complex datasets to hone their diagnostic skills, test new hypotheses, and develop innovative imaging solutions. Synthetic data bridges the gap where real-world datasets are insufficient or unavailable, providing a cost-effective and scalable alternative for simulation-based training and validation. This capability is especially valuable in regions with limited access to advanced imaging resources or rare clinical cases. As academic and research institutions intensify their focus on AI and machine learning in healthcare, synthetic data services are poised to become a cornerstone of medical education and innovation.

From a regional perspective, North America currently leads the synthetic medical image data services market, accounting for the largest share due to its advanced healthcare infrastructure, strong presence of AI technology providers, and supportive regulatory environment. Europe follows closely, driven by robust investments in digital health and a proactive stance on data privacy. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, increasing healthcare expenditure, and a burgeoning ecosystem of AI startups. Latin America and the Middle East & Africa, while still nascent, are expected to witness accelerated adoption as healthcare modernization initiatives gain momentum. Overall, the global market landscape is characterized by dynamic growth opportunities, with both developed and emerging regions contributing to the expansion of synthetic medical image da

Snapshot img

Automatic Data Capture Market Size 2024-2028

The automatic data capture (ADC) market size is forecast to increase by USD 48.69 billion at a CAGR of 13.64% between 2023 and 2028.

The market is experiencing significant growth due to the increasing adoption of advanced technologies in manufacturing industries. Key trends include the rising application of RFID and barcode scanners in smart factories, which facilitate real-time tracking and improve operational efficiency. Big data services and enterprise resource planning (ERP) software are also driving market growth by enabling seamless data exchange between various systems and devices. Furthermore, the integration of biometrics and machine-to-machine (M2M) communication in ADC solutions enhances security and streamlines processes. However, challenges such as high implementation costs and complex integration processes may hinder market growth. Overall, the ADC market is poised for strong expansion, driven by the need for accurate and efficient data capture and management in various industries.

What will be the Size of the Automatic Data Capture (ADC) Market During the Forecast Period?

Request Free Sample

The market encompasses a range of technologies, including barcodes, biometrics, voice recognition, QR codes, and transducers, used for capturing and processing data in various industries. These technologies are integral to streamlining business operations by preventing errors associated with manual data entry, such as incorrect data capture and human errors.
ADC solutions are particularly prominent in sectors like healthcare, where they are utilized for tracking medication, patient care admission, and identifying patients through biometric scanners and smart cards with magnetic strips. The market is witnessing continuous growth as technology advances, with innovations in biometric systems, barcode readers, and forensic sciences driving expansion. Enhancements in ADC technology aim to further automate identification processes and reduce operational expenses.

How is this Automatic Data Capture (ADC) Industry segmented and which is the largest segment?

The automatic data capture (ADC) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  RFID
  Barcode scanner
  Barcode printer
  Wearable scanners


Application

  Industrial
  Financial security
  Retail
  T and L
  Others


Geography

  North America

    US


  APAC

    China


  Europe

    Germany
    UK
    France


  South America



  Middle East and Africa

By Product Insights

The RFID segment is estimated to witness significant growth during the forecast period.

The market encompasses various technologies, including barcodes, biometrics, voice recognition, QR codes, transducers, scanners and readers, biometric scanners, printers and recorders, smart cards, magnetic strips, and more. These technologies are increasingly adopted in sectors like healthcare, manufacturing, transportation, and warehouse management to gather data, reduce manual data entry, manage security, and enhance operational efficiency. Technologies such as barcodes, QR codes, and RFID are particularly popular for their ability to prevent errors, facilitate identification, and enable automated counting. However, concerns around data security, misplacement, unauthorized access, and malware attacks remain significant challenges. Cloud computing and advanced technologies like image recognition, speech recognition, and thermal barcodes are helping to address these issues.

Get a glance at the market report of share of various segments Request Free Sample

The RFID segment was valued at USD 23.12 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 43% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The market is experiencing substantial growth in North America, driven by the increasing adoption of smart factories and Industry 4.0. ADC technologies, including barcodes, biometrics, voice recognition, QR codes, transducers, scanners and readers, biometric scanners, printers and recorders, smart cards, magnetic strips, and RFID, are integral to smart factories and industries. These technologies enable machine-to-machine (M2M) communication, reducing manual work and increasing inventory accuracy. The healthcare sector is also a significant contributor to the ADC market, with applications in patient admittance, medication tracking, and identification.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in American Falls. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In American Falls, the median income for all workers aged 15 years and older, regardless of work hours, was $46,155 for males and $23,058 for females.

These income figures highlight a substantial gender-based income gap in American Falls. Women, regardless of work hours, earn 50 cents for each dollar earned by men. This significant gender pay gap, approximately 50%, underscores concerning gender-based income inequality in the city of American Falls.

- Full-time workers, aged 15 years and older: In American Falls, among full-time, year-round workers aged 15 years and older, males earned a median income of $48,075, while females earned $31,436, leading to a 35% gender pay gap among full-time workers. This illustrates that women earn 65 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in American Falls, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/american-falls-id-income-by-gender.jpeg" alt="American Falls, ID gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 American Falls median household income by gender. You can refer the same here

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Healthcare Data Anonymization Services Market Outlook

According to our latest research, the global healthcare data anonymization services market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by increasing regulatory demands and heightened focus on patient privacy. The market is projected to grow at a CAGR of 15.8% from 2025 to 2033, with the total market value expected to reach USD 5.44 billion by 2033. This impressive growth trajectory is underpinned by the rising adoption of digital health solutions, stringent data protection laws, and the ongoing digitalization of healthcare records worldwide.

The primary growth factor fueling the healthcare data anonymization services market is the proliferation of electronic health records (EHRs) and the expanding use of big data analytics in healthcare. As healthcare providers and organizations increasingly leverage advanced analytics for improving patient outcomes, there is a corresponding surge in data generation. However, these vast datasets often contain sensitive patient information, making data anonymization essential to ensure compliance with regulations such as HIPAA, GDPR, and other regional privacy laws. The increasing frequency of data breaches and cyberattacks has further highlighted the importance of robust anonymization services, prompting healthcare organizations to prioritize investments in data privacy and security solutions. As a result, demand for both software and service-based anonymization solutions continues to rise, contributing significantly to market growth.

Another key driver for the healthcare data anonymization services market is the growing emphasis on research and clinical trials, which require the sharing and analysis of large volumes of patient data. Pharmaceutical and biotechnology companies, as well as research organizations, are increasingly collaborating across borders, necessitating the anonymization of datasets to protect patient identities and comply with international data protection standards. The adoption of cloud-based healthcare solutions has also facilitated the secure and efficient sharing of anonymized data, supporting advancements in personalized medicine and population health management. As organizations seek to balance innovation with compliance, the demand for advanced anonymization technologies that offer high accuracy and scalability is expected to accelerate further.

Technological advancements in artificial intelligence (AI) and machine learning (ML) are also shaping the future of the healthcare data anonymization services market. These technologies are enabling more sophisticated and automated anonymization processes, reducing the risk of re-identification while maintaining data utility for research and analytics. The integration of AI-driven tools into anonymization workflows is helping organizations streamline operations, minimize human error, and achieve greater compliance with evolving regulatory requirements. Additionally, the increasing availability of customizable and interoperable anonymization solutions is making it easier for healthcare organizations of all sizes to adopt and scale these services, thereby broadening the market’s reach and impact.

From a regional perspective, North America continues to dominate the healthcare data anonymization services market, accounting for the largest share in 2024. This leadership position is attributed to the presence of advanced healthcare infrastructure, widespread adoption of EHRs, and strict regulatory frameworks governing patient data privacy. Europe follows closely, driven by the enforcement of the General Data Protection Regulation (GDPR) and a strong culture of data protection. The Asia Pacific region is witnessing the fastest growth, propelled by increasing healthcare digitalization, government initiatives to modernize healthcare systems, and rising awareness of data privacy among patients and providers. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as healthcare organizations in these regions begin to prioritize data security and compliance.

&

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within American Fork. The dataset can be utilized to gain insights into gender-based income distribution within the American Fork population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within American Fork, among individuals aged 15 years and older with income, there were 12,169 men and 10,456 women in the workforce. Among them, 7,760 men were engaged in full-time, year-round employment, while 3,803 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.07% fell within the income range of under $24,999, while 7.18% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 34.82% of men in full-time roles earned incomes exceeding $100,000, while 11.89% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 American Fork median household income by race. You can refer the same here

Snapshot img

Backend-as-a-Service Market Size 2024-2028

The Backend-as-a-Service (BaaS) Market size is estimated to grow by USD 6.90 billion at a CAGR of 23.2% between 2023 and 2028. The market is experiencing robust growth driven by key factors. The widespread adoption of smartphones plays a significant role, as consumers increasingly rely on mobile devices for various tasks. This trend fuels the demand for BaaS solutions, which enable seamless integration and functionality across mobile applications. Moreover, the expanding applications of BaaS in E-Commerce and Customer Relationship Management (CRM) sectors further accelerate market growth. Businesses leverage BaaS to streamline operations, enhance user experiences, and gain valuable insights into customer behavior, driving efficiency and profitability.

What will be the Size of the Market During the Forecast Period?

For More Highlights About this Report, Download Free Sample in a Minute

Market Dynamics

The market is flourishing due to the proliferation of social media platforms like Facebook and Twitter, driving social media penetration rates worldwide. This surge in digital activity necessitates robust cloud infrastructure and innovative AI technology to handle the vast amounts of data generated. Leading providers such as Oracle Mobile Cloud service, FIS, and the Banking-as-a-Service (BaaS) Hub cater to diverse platforms like Android, iOS, and Windows, utilizing frameworks such as Titanium, HTML, Node, and tools like Apache Usergrid, AWS Amplify, and Backendless to build scalable and efficient solutions. Developers benefit from platforms like Firebase, Kuzzle, and Kumulos, integrating technologies like GraphQL for seamless data management and access control. Command Line Interface tools streamline development processes, while Azure Communications service and AT&T offer robust communication capabilities.

Key Market Driver

One of the key factors driving the market growth is the increasing adoption of smartphones. There is an increasing use of smartphone mobile applications as they use a wide range of mobile applications for work, play, communication, shopping, and other activities. In addition, platforms that offer backend services as a service (BaaS) give developers access to cloud-based infrastructure and tools for creating, deploying, and managing mobile applications.

Moreover, by offering features such as data storage, server-side logic, push notifications, and user authentication, these platforms free up developers to concentrate more on building new and reliable front-end experiences. In addition, the increasing number of people using smartphones is fuelling demand for mobile apps, which is driving up the use of BaaS platforms. Hence, such factors are positively impacting the market. Therefore, it is expected to drive the market growth during the forecast period.

Significant Market Trends

A key factor shaping the market growth is the increasing adoption of cloud-based infrastructure. The growing adoption of cloud-based infrastructure is indeed a primary trend in the market. In addition, developers can concentrate on frontend development and application logic by outsourcing infrastructure management and backend development responsibilities to a cloud computing service called BaaS.

Moreover, there is a growing use of cloud-based infrastructure in the BaaS market due to factors such as scalability and flexibility, cost savings, simple integration, enhanced security, global accessibility, quick development, and deployment. In addition, BaaS frees up developers' time and resources by taking care of things like patch management, system upgrades, and server maintenance. Hence, such factors are positively impacting the market trends. Hence, such factors are driving the market during the forecast period.

Major Market Restraint

Issues in data security and privacy are some of the key challenges hindering market growth. Some of the main challenges associated with the backend as a service market include data breaches, compliance with regulations, lack of control, data residency, and sovereignty, vulnerabilities in third-party libraries, insider threats, and inadequate encryption techniques. In addition, a key component of data security is encryption.

Moreover, poor encryption procedures or careless handling of encryption keys can result in data breaches and jeopardize user privacy. In addition, while utilizing a backend as a service platform, businesses may find that their visibility and control over the security measures put in place by the supplier are restricted. Furthermore, concerns concerning the security and privacy of their data may arise from this lack of control. Hence, such factors are negatively impacting the backend-as-a-service market. Therefore, it is expected to hinder the market growth during the forecast period.

Market Segmentation By Deployment

The on-premises segme

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North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

As organizations grow more data-driven, the integration of data preparation tools with Business Intelligence (BI) and advanced analytics platforms is becoming a critical driver of market growth. Clean, well-structured data is the foundation for accurate analysis, predictive modeling, and data visualization. Without proper preparation, even the most advanced BI tools may deliver misleading or incomplete insights. Businesses are now realizing that to fully capitalize on the capabilities of BI solutions such as Power BI, Qlik, or Looker, their data must first be meticulously prepared. Data preparation tools bridge this gap by transforming disparate raw data sources into harmonized, analysis-ready datasets. In the financial services sector, for example, firms use data preparation tools to consolidate customer financial records, transaction logs, and third-party market feeds to generate real-time risk assessments and portfolio analyses. The seamless integration of these tools with analytics platforms enhances organizational decision-making and contributes to the widespread adoption of such solutions. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data preparation tools has significantly improved their efficiency and functionality. These technologies automate complex tasks like anomaly detection, data profiling, semantic enrichment, and even the suggestion of optimal transformation paths based on patterns in historical data. AI-driven data preparation not only speeds up workflows but also reduces errors and human bias. In May 2022, Alteryx introduced AiDIN, a generative AI engine embedded into its analytics cloud platform. This innovation allows users to automate insights generation and produce dynamic documentation of business processes, revolutionizing how businesses interpret and share data. Similarly, platforms like DataRobot integrate ML models into the data preparation stage to improve the quality of predictions and outcomes. These innovations are positioning data preparation tools as not just utilities but as integral components of the broader AI ecosystem, thereby driving further market expansion. Data preparation tools address these needs by offering robust solutions for data cleaning, transformation, and integration, enabling telecom and IT firms to derive real-time insights. For example, Bharti Airtel, one of India’s largest telecom providers, implemented AI-based data preparation tools to streamline customer data and automate insights generation, thereby improving customer support and reducing operational costs. As major market players continue to expand and evolve their services, the demand for advanced data analytics powered by efficient data preparation tools will only intensify, propelling market growth. The exponential growth in global data generation is another major catalyst for the rise in demand for data preparation tools. As organizations adopt digital technologies and connected devices proliferate, the volume of data produced has surged beyond what traditional tools can handle. This deluge of information necessitates modern solutions capable of preparing vast and complex datasets efficiently. According to a report by the Lin...

Data Science Platform Market Outlook

The global data science platform market size was valued at approximately USD 49.3 billion in 2023 and is projected to reach USD 174.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.1% during the forecast period. This exponential growth can be attributed to the increasing demand for data-driven decision-making processes, the surge in big data technologies, and the need for more advanced analytics solutions across various industries.

One of the primary growth factors driving the data science platform market is the rapid digital transformation efforts undertaken by organizations globally. Companies are shifting towards data-centric business models to gain a competitive edge, improve operational efficiency, and enhance customer experiences. The proliferation of IoT devices and the subsequent explosion of data generated have further propelled the need for sophisticated data science platforms capable of analyzing vast datasets in real-time. This transformation is not only seen in large enterprises but also increasingly in small and medium enterprises (SMEs) that recognize the potential of data analytics in driving business growth.

Moreover, the advancements in artificial intelligence (AI) and machine learning (ML) technologies have significantly augmented the capabilities of data science platforms. These technologies enable the automation of complex data analysis processes, allowing for more accurate predictions and insights. As a result, sectors such as healthcare, finance, and retail are increasingly adopting data science solutions to leverage AI and ML for personalized services, fraud detection, and supply chain optimization. The integration of AI/ML into data science platforms is thus a critical factor contributing to market growth.

Another crucial factor is the growing regulatory and compliance requirements across various industries. Organizations are mandated to ensure data accuracy, security, and privacy, necessitating the adoption of robust data science platforms that can handle these aspects efficiently. The implementation of regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States has compelled organizations to invest in advanced data management and analytics solutions. These regulatory frameworks are not only a challenge but also an opportunity for the data science platform market to innovate and provide compliant solutions.

Regionally, North America dominates the data science platform market due to the early adoption of advanced technologies, a strong presence of key market players, and significant investments in research and development. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the increasing digitalization initiatives, a growing number of tech startups, and the rising demand for analytics solutions in countries like China, India, and Japan. The competitive landscape and economic development in these regions are creating ample opportunities for market expansion.

Component Analysis

The data science platform market, segmented by components, includes platforms and services. The platform segment encompasses software and tools designed for data integration, preparation, and analysis, while the services segment covers professional and managed services that support the implementation and maintenance of these platforms. The platform component is crucial as it provides the backbone for data science operations, enabling data scientists to perform data wrangling, model building, and deployment efficiently. The increasing demand for customized solutions tailored to specific business needs is driving the growth of the platform segment. Additionally, with the rise of open-source platforms, organizations have more flexibility and control over their data science workflows, further propelling this segment.

On the other hand, the services segment is equally vital as it ensures that organizations can effectively deploy and utilize data science platforms. Professional services include consulting, training, and support, which help organizations in the seamless integration of data science solutions into their existing IT infrastructure. Managed services provide ongoing support and maintenance, ensuring data science platforms operate optimally. The rising complexity of data ecosystems and the shortage of skilled data scientists are factors contributing to the growth of the services segment, as organizations often rely on external expert

Graph and download economic data for Household Debt Service Payments as a Percent of Disposable Personal Income (TDSP) from Q1 1980 to Q1 2025 about disposable, payments, debt, personal income, percent, personal, households, services, income, and USA.

This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for over 69 percent of all work trips in 2023. This is the same rate as 2019, and the first increase since 2017, both years being before the COVID-19 pandemic began.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. The percentage of people carpooling to work in 2023 was lower than every year except 2016 since this data first started being tracked in 2005. The percentage of people walking to work increased from 2022 to 2023, but this increase is not statistically significant.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure is still about 2.5 times higher than 2019, even with the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

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