About this dataset

Context

The dataset tabulates the data for the Mobile, AL population pyramid, which represents the Mobile population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Mobile, AL, is 27.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Mobile, AL, is 26.2.
• Total dependency ratio for Mobile, AL is 53.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Mobile, AL is 3.8.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group for the Mobile population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Mobile for the selected age group is shown in the following column.
• Population (Female): The female population in the Mobile for the selected age group is shown in the following column.
• Total Population: The total population of the Mobile for the selected age group is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Quantitative and qualitative data sets for 24 sites across Ghana, Malawi and South Africa:
a) SPSS dataset on young people’s use of mobile phones in Ghana, Malawi and South Africa.  4626 cases (young people aged 7-25 years): 1568 Ghana; 1544 Malawi; 1514 South Africa.  719 variables (+ 11 ‘navigation facilitators’) b) 1,620 Qualitative transcripts from interviews with people of diverse ages, 8y upwards: individual interviews [using either i.theme checklist or ii call register checklist]; focus group interviews [not all sites]: 50-80 transcripts for most sites.

This research project, which commenced in August 2012, explored how the rapid expansion of mobile phone usage is impacting on young lives in sub-Saharan Africa. It builds directly on our previous research on children’s mobility within which baseline quantitative data and preliminary qualitative information was collected on mobile phone usage (2006-2010) across 24 research sites, as an adjunct to our wider study of children’s physical mobility and access to services.

In this study our focus is specifically on mobile phones and we cover a much wider range of phone-related issues, including changes in gendered and age patterns of phone use over time; phone use in building social networks (for instance to support job search); impacts on education, livelihoods, health status, safety and surveillance, physical mobility and possible connections to migration, youth identity, and questions of exploitation and empowerment associated with mobile phones.

Mixed-method, participatory youth-centred studies have been conducted in the same 24 sites as in our earlier work across Ghana, Malawi and South Africa (urban, peri-urban, rural, remote rural, in two agro-ecological zones per country). We have built on the baseline data for 9-18 year-olds gathered in 2006-2010, through repeat and extended studies, but also included additional studies with 19-25 year-olds (to capture changing usage and its impacts as our initial cohort move into their 20s).

About this dataset

Context

The dataset tabulates the Mobile population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Mobile. The dataset can be utilized to understand the population distribution of Mobile by age. For example, using this dataset, we can identify the largest age group in Mobile.

Key observations

The largest age group in Mobile, AL was for the group of age 25-29 years with a population of 14,593 (7.79%), according to the 2021 American Community Survey. At the same time, the smallest age group in Mobile, AL was the 80-84 years with a population of 3,616 (1.93%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Mobile is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Mobile total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Percentage of smartphone users by selected smartphone use habits in a typical day.

About this dataset

Context

The dataset tabulates the Mobile population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Mobile. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 115,828 (61.79% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Mobile population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Mobile is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Mobile is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6372737%2F47d4254cf709e8038a199096ce2e3b75%2Fimage_thumbnail%20(4).jpg?generation=1665091063695984&alt=media" alt="">

The dataset, acquired from WISDM Lab, consists of data collected from 36 different users performing six types of human activities (ascending and descending stairs, sitting, walking, jogging, and standing) for specific periods of time.

These data were acquired from accelerometers, which are able of detecting the orientation of the device measuring the acceleration along the three different dimensions. They were collected using a sample rate of 20 Hz (1 sample every 50 millisecond) that is equivalent to 20 samples per second.

These time-series data can be used to perform various techniques, such as human activity recognition.

Fields:

• user: the user who acquired the data (integer from 1 to 36).

• activity: the activity that the user was carrying out. It could be:

  • 1. walking
  • 2. Jogging
  • 3. Sitting
  • 4. Standing
  • 5. Upstairs
  • 6. Downstairs.

• timestamp: generally the phone's uptime in nanoseconds.

• x-axis: The acceleration in the x direction as measured by the android phone's accelerometer.
  Floating-point values between -20 and 20. A value of 10 = 1g = 9.81 m/s^2, and 0 = no acceleration. The acceleration recorded includes gravitational acceleration toward the center of the Earth, so that when the phone is at rest on a flat surface the vertical axis will register +-10.

• y-axis: same as x-axis, but along y axis.

• z-axis: same as x-axis, but along z axis.
  

Remember to upvote if you found the dataset useful :).

Inspiration

The data can be used to perform human activity prediction. I strongly suggest you to take a look to this article if you want to have a reference for performing this task, and considering that the given dataset was already cleaned. In addition, you can try to perform other feature engineering and selection techniques, and using more complex models for prediction.

Acknowledgement

Data were fetched from the WISDM dataset website, and they were cleaned, deleting missing values, replacing inconsistent strings and converting the dataset to csv.

Jeffrey W. Lockhart, Tony Pulickal, and Gary M. Weiss (2012). "Applications of Mobile Activity Recognition," Proceedings of the ACM UbiComp International Workshop on Situation, Activity, and Goal Awareness, Pittsburgh, PA.

Gary M. Weiss and Jeffrey W. Lockhart (2012). "The Impact of Personalization on Smartphone-Based Activity Recognition," Proceedings of the AAAI-12 Workshop on Activity Context Representation: Techniques and Languages, Toronto, CA.

Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). "Activity Recognition using Cell Phone Accelerometers," Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data (at KDD-10), Washington DC.

The Comprehensive Annual Modular Survey (CAMS) was conducted by the National Sample Survey Office (NSSO) during July, 2022 - June, 2023 as a part of NSS 79th round.

This dataset contains State, Age-Group and Gender-wise data on below topics.

1) Persons able to read and write short simple statements in their everyday life with understanding 2) Persons able to read and write short simple statements in their everyday life with understanding and also able to perform simple arithmetic calculations 3) Mean years of schooling in formal education 4) Persons of age 6 to 10 years currently enrolled in primary education (Class I to Class V) 5) Persons with some secondary education 6) Distribution of persons of age 6 to 18 years who never enrolled in formal education by major reasons at the time of survey 7) Persons graduated in science and technology among all graduates 8) Youth in formal and non-formal education and training in the previous 12 months 9) Youth not in education, employment, or training 10) Average medical expenditure (Rs.) per household and per person on hospitalised treatment (including institutional delivery) during last 365 days and on non-hospitalised treatment during the last 30 days 11) Average out-of-pocket medical expenditure (OOPME) per household and per person for treatment on hospitalisation (including institutional delivery) during last 365 days and non-hospitalisation during last 30 days 12) Persons who have an account individually or jointly in any bank/ other financial institution/mobile money service provider 13) Number of borrowers per 1,00,000 persons 14) Persons able to use mobile (including smartphone) 15) Persons who used mobile telephone during the last 3 months preceding the date of survey 16) Persons able to use internet 17) Persons who used internet during the last 3 months preceding the date of survey 18) Population covered by 4G or above mobile technology 19) Persons who send messages (e.g., e-mail, messaging service, SMS) with attached files (e.g., documents, pictures, video) using mobile or computer-like devices during last three months preceding the date of survey 20) Persons who performed copy and paste tools to duplicate or move data, information, documents, etc using mobile or computer-like devices during last three months preceding the date of survey 21) Persons who can search internet for information and Persons who can send or receive emails and Persons who can perform online banking transactions 22) Households possessing different assets as on date of survey 23) Urban population having convenient access to transport facilities and percentage of rural population with all- weather roads within a distance of 2 km from the place of living 24) Persons of age less than 5 years who have registered with civil authority for the birth certificate ever 25) Households using clean fuel for cooking 26) Households having access to improved principal sources of drinking water and percentage of households having access to improved latrine (among households with access to latrine)for each State/UT 27) Number of First-Stage Units (FSUs), households and persons surveyed 28) Estimated number of households and persons

About this dataset

Context

The dataset tabulates the Mobile City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Mobile City. The dataset can be utilized to understand the population distribution of Mobile City by age. For example, using this dataset, we can identify the largest age group in Mobile City.

Key observations

The largest age group in Mobile City, TX was for the group of age 25-29 years with a population of 61 (21.86%), according to the 2021 American Community Survey. At the same time, the smallest age group in Mobile City, TX was the 45-49 years with a population of 0 (0.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Mobile City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Mobile City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

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

Contex

Today, many companies have a mobile presence. They provide their services and products free in their mobile applications in an attempt to transitions their customers to a paid membership like youtube premium, pandora premium. Based on the budget the companies need how much it will pay? as marketing efforts are never free. These companies need to know exactly who to target with offers and promotions.

Market : the target audience is customers who use a company free products ( the users who installed the app with the free feature)

Products : The paid membership often provides enhanced versions of the free product.

Goal : The objective of the needed model is to predict which users will not subscribe to the paid membership, So greater marketing efforts can go into trying to convert them to paid users.

Content

The columns of the data consist of

1- user_id 2- first open 3- day_of_week 4 - hour 5- age 6 - Screen_list 6- numscreens 7- minigames 8- used_premium_feature 9- enrolled 10- enrolled_date 11 - liked

The number of Apple iPhone unit sales dramatically increased between 2007 and 2024. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around **** million smartphones. By 2024, this number reached over ***** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2025, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A19 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

About this dataset

Context

The dataset tabulates the Mobile population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Mobile across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Mobile was 183,289, a 0.93% decrease year-by-year from 2021. Previously, in 2021, Mobile population was 185,017, a decline of 0.85% compared to a population of 186,611 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Mobile decreased by 20,041. In this period, the peak population was 203,330 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Mobile is shown in this column.
• Year on Year Change: This column displays the change in Mobile population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset tabulates the Mobile population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Mobile across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Mobile was 182,595, a 0.38% decrease year-by-year from 2022. Previously, in 2022, Mobile population was 183,290, a decline of 1.02% compared to a population of 185,176 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Mobile decreased by 20,735. In this period, the peak population was 203,330 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Mobile is shown in this column.
• Year on Year Change: This column displays the change in Mobile population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

****Description :****

The University of Texas at Arlington Real-Life Drowsiness Dataset (UTA-RLDD) was created for the task of multi-stage drowsiness detection, targeting not only extreme and easily visible cases, but also subtle cases when subtle micro-expressions are the discriminative factors. Detection of these subtle cases can be important for detecting drowsiness at an early stage, so as to activate drowsiness prevention mechanisms. Subtle micro-expressions of drowsiness have physiological and instinctive sources, so it can be difficult for actors who pretend to be drowsy to realistically simulate such expressions. Our UTA-RLDD dataset is the largest to date realistic drowsiness dataset.

The RLDD dataset consists of around 30 hours of RGB videos of 60 healthy participants. For each participant we obtained one video for each of three different classes: alertness, low vigilance, and drowsiness, for a total of 180 videos. Subjects were undergraduate or graduate students and staff members who took part voluntarily or upon receiving extra credit in a course. All participants were over 18 years old. There were 51 men and 9 women, from different ethnicities (10 Caucasian, 5 non-white Hispanic, 30 IndoAryan and Dravidian, 8 Middle Eastern, and 7 East Asian) and ages (from 20 to 59 years old with a mean of 25 and standard deviation of 6). The subjects wore glasses in 21 of the 180 videos, and had considerable facial hair in 72 out of the 180 videos. Videos were taken from roughly different angles in different real-life environments and backgrounds. Each video was self-recorded by the participant, using their cell phone or web camera. The frame rate was always less than 30 fps, which is representative of the frame rate expected of typical cameras used by the general population.

Each video was self-recorded by the participant, using a cell phone or web camera of the participant. The frame rate was always less than 30 fps, which is representative of the frame rate expected of normal cameras used by the general population.

****Data Collection :****

Sixty healthy participants took part in the data collection. Subjects were instructed to take three videos of themselves by their phone/web camera (of any model or type) in three different drowsiness states, based on the KSS table(Table 1), for around 10 minutes each, and upload the videos as well as their corresponding labels on an online portal provided via a link. Subjects were given ample time (20 days) to produce the three videos. Furthermore, they were given the freedom to record the videos at home or at the university, any time they felt alert, low vigilant or drowsy while keeping the camera set up (angle and distance) roughly the same. All videos were recorded in such an angle that both eyes were visible, and the camera was placed within one arm length away from the subject. These instructions were used to make the videos similar to videos that would be obtained in a car, by phone placed in a phone holder on the dash of the car while driving. The proposed set up was to lay the phone against the display of their laptop while they are watching or reading something on their computer((Fig 1). Also, they were asked to do the same task (reading, watching or idle) in all of the three videos for consistency reasons. The three classes were explained to the participants as follows:

1) Alert : One of the first three states highlighted in the KSS table in Table 1. Subjects were told that being alert meant they were completely conscious so they could easily drive for long hours.

2) Low Vigilant : As stated in level 6 and 7 of Table 1, this state corresponds to subtle cases when some signs of sleepiness appear, or sleepiness is present but no effort to keep alert is required. While subjects could possibly drive in this state, driving would be discouraged.

3) Drowsy : This state means that the subject needs to actively try to not fall asleep (level 8 and 9 in Table 1).

![https://lh4.googleusercontent.com/_x9tO5i9vAnUef2xP3udAsRu4abi8e53U-uoy5GCRWMrIoPDKBuVkpcfId3EbcnThl-6_1bwPf4stK3Xws9HfpXW3ie8iEXA_BC0KoYq2bt30TtpJUl5E-pMUMNIw62Ekyfbf4xcX1b1qJecawFoWd2i9PWPojNqfk_V4ezsDmaDodc=w1280]

@inproceedings{ghoddoosian2019realistic,

title={A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection},

author={Ghoddoosian, Reza and Galib, Marnim and Athitsos, Vassilis},

booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},

pages={0--0},

year={2019}

} URL to home page: https://sites.google.com/view/utarldd/home

About this dataset

Context

The dataset tabulates the Mobile City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Mobile City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Mobile City was 154, a 2.67% increase year-by-year from 2021. Previously, in 2021, Mobile City population was 150, an increase of 5.63% compared to a population of 142 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Mobile City decreased by 42. In this period, the peak population was 233 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Mobile City is shown in this column.
• Year on Year Change: This column displays the change in Mobile City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Mobile County: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 6,526(4.07%) households where the householder is under 25 years old, 52,718(32.84%) households with a householder aged between 25 and 44 years, 57,192(35.63%) households with a householder aged between 45 and 64 years, and 44,071(27.46%) households where the householder is over 65 years old.
• In Mobile County, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

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:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Mobile County median household income by age. You can refer the same here

About this dataset

Context

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

Key observations

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

Content

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

Age groups:

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

Scope of gender :

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Mobile. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Mobile. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Mobile, householders within the 45 to 64 years age group have the highest median household income at $62,183, followed by those in the 25 to 44 years age group with an income of $49,441. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $48,685. Notably, householders within the under 25 years age group, had the lowest median household income at $24,490.

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.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Mobile median household income by age. You can refer the same here

About this dataset

Context

The dataset illustrates the median household income in Mobile, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Mobile decreased by $1,596 (3.19%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 4 years and declined for 7 years.

https://i.neilsberg.com/ch/mobile-al-median-household-income-trend.jpeg" alt="Mobile, AL median household income trend (2010-2021, 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. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous 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 Mobile median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Mobile City, TX population pyramid, which represents the Mobile City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Mobile City, TX, is 90.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Mobile City, TX, is 4.2.
• Total dependency ratio for Mobile City, TX is 95.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Mobile City, TX is 23.8.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group for the Mobile City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Mobile City for the selected age group is shown in the following column.
• Population (Female): The female population in the Mobile City for the selected age group is shown in the following column.
• Total Population: The total population of the Mobile City for the selected age group is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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