Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

The demographic data displayed in this theme of Florida’s Roadmap to Living Healthy are quantitative measures that exhibit the socioeconomic state of Florida’s communities. The data sets comprising this themed map include topics such as population, race, income level, age, education, housing, and lifestyle data for all of Florida’s 67 counties, and other basic demographic characteristics. The Florida Department of Agriculture and Consumer Services has utilized the most current demographic statistical data from trusted sources such as the U.S. Census Bureau, U.S. Department of Housing and Urban Development, U.S. Department of Labor Bureau of Labor Statistics, Florida Department of Children and Families, and Esri to craft this custom visualization. Demographics provide profound perspective to your data analytics and will help you recognize the distinctive characteristics of a population based on its location. This demographic-themed mapping tool will simplify your ability to identify the specific socioeconomic needs of every community in Florida.

Interactive analysis of estimated population change for England and Wales, by geography, age and sex. Annual estimates are from mid-2011 onwards.

This website provides demographic information about laboratories, including CLIA number, facility name and address, where the laboratory testing is performed, the type of CLIA certificate, and the date the certificate expires. This list is updated monthly and represents the information in the system at the time of update. For additional information about a particular laboratory, contact the appropriate State Agency or Regional Office CLIA contact (refer to State Agency or Regional Office CLIA link found on the left-hand navigation pane).

About this dataset

Context

The dataset tabulates the Tool population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Tool.

Content

The dataset constitues the following two datasets across these two themes

• Tool, TX Population Breakdown by Gender
• Tool, TX Population Breakdown by Gender and Age

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/.

Examining disparities in social outcomes as a function of gender, age, or race has a long tradition in psychology and other social sciences. With an increasing availability of large naturalistic data sets, researchers are afforded the opportunity to study the effects of demographic characteristics with real-world data and high statistical power. However, since demographic characteristics are often determined by having participants rate images of targets, limits in participant pools can hinder researchers from analyzing large data sets. Here, we present a tutorial on how to use two face classification algorithms, Face++ and Kairos. We also test and compare their accuracy under varying conditions and provide practical recommendations for their use. Drawing on two face databases (n = 2,805 images), we find that classification accuracy is (a) relatively high, (b) similar for standardized and more variable images, and (c) dependent on various factors. Kairos outperformed Face++ on all three demographic variables; accuracy was lower for Hispanic and Asian (vs. Black and White) targets; and both algorithms tended to overestimate the age of targets. In sum, we propose that automated face classification can be a useful tool for researchers interested in studying the effects of demographic characteristics in large naturalistic data sets.

This map contains NYC administrative boundaries enriched with various demographics datasets.Learn more about Esri's Enrich Layer / Geoenrichment analysis tool.Learn more about Esri's Demographics, Psychographic, and Socioeconomic datasets.Search for a specific location or site using the search bar. Toggle layer visibility with the layer list. Click on a layer to see more information about the feature.

Interactive analysis of estimated UK population change, by geography, age and sex. Annual estimates are for mid-2011 to mid-2016.

About this dataset

Context

The dataset tabulates the Tool 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 Tool. 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 to 64 years with a poulation of 1,399 (59.38% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Tool population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Tool is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Tool 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 Tool Population by Age. You can refer the same here

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

About this dataset

Context

The dataset tabulates the Tool Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Tool, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Tool.

Key observations

Among the Hispanic population in Tool, regardless of the race, the largest group is of Mexican origin, with a population of 83 (84.69% of the total Hispanic population).

Content

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

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Tool
• Population: The population of the specific origin for Hispanic or Latino population in the Tool is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Tool total Hispanic or Latino 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 Tool Population by Race & Ethnicity. You can refer the same here

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

Analysis of ‘MHS Dashboard Adult Demographic Datasets and Report Tool’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4d9050d6-9c6e-459b-94b9-690a6e989bb4 on 26 January 2022.

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

The following datasets are based on the adult (age 21 and over) beneficiary population and consist of aggregate MHS data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets.

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

SEMCOG's Community Explorer tool is great for dynamically visualizing demographic and economic data in Southeast Michigan. Use this dataset to extend Community Explorer and make your own visualization.This tool has over 40 indicators across 4 geography types (County, Community, School Districts, Census Tracts). Not only are the data columns available, but we also include the Margin of Error (MOE) to better understand the reliability of each column.IndicatorsTotal PopulationPopulation Density (Persons/Acre)Median AgePercent Age 65+Percent Age 65+ Living AlonePercent Ages 5 to 17Ratio Youth to SeniorsPercent Bachelor's Degree or HigherPercent People in PovertyPercent AsianPercent BlackPercent HispanicPercent WhiteTotal HouseholdsAverage Household SizePercent Households with SeniorsPercent Households with ChildrenPercent Households with No CarPercent Households with Internet AccessTotal Households without Internet AccessPercent Households with Broadband Internet AccessTotal Households without Broadband Internet AccessPercentage Households with Computing DevicesTotal Households without a Desktop or LaptopPercent Seniors with Broadband Internet AccessPercent Children without Broadband Internet AccessPercent Children without Computing DevicesTotal Housing UnitsPercent VacantPercent Owner OccupiedPercent Renter OccupiedPercent Single FamilyPercent Multi-FamilyTotal JobsJob Density (Jobs/Acre)Unemployment RateLabor Force Participation RateMedian Household IncomePer Capita IncomeMedian Housing ValueAverage Commute Time (Minutes)Percent Drive Alone to WorkPercent Commute by Transit

App Data Statistics Tool Market Outlook

The global app data statistics tool market size was valued at approximately USD 5.3 billion in 2023 and is projected to reach USD 11.9 billion by 2032, growing at a CAGR of 9.2% during the forecast period. Several growth factors, including the escalating demand for data-driven decision-making and the rise in mobile app usage, are driving this market. As organizations increasingly recognize the value of data analytics in enhancing user engagement and optimizing app performance, the adoption of app data statistics tools is expected to surge significantly.

The growth of the app data statistics tool market is primarily fueled by the exponential increase in mobile app usage worldwide. With billions of smartphone users generating vast amounts of data daily, companies are leveraging app data statistics tools to gain actionable insights. These tools help in understanding user behavior, tracking app performance, and identifying areas for improvement. Furthermore, the growing emphasis on personalized user experiences has led to an increased demand for sophisticated analytics tools, thereby driving market growth.

Another critical growth factor is the rising importance of data-driven decision-making in various industries. Organizations across sectors such as BFSI, healthcare, retail, and media are increasingly relying on app data statistics tools to make informed decisions. These tools enable businesses to analyze large datasets, uncover trends, and optimize their strategies. The adoption of analytics tools is also propelled by the need to improve customer satisfaction and loyalty, as companies strive to offer tailored experiences to their users. The integration of artificial intelligence and machine learning in analytics tools further enhances their efficiency and accuracy, contributing to market growth.

Moreover, the market is benefitting from technological advancements and the increasing availability of advanced analytics tools. Innovations such as real-time analytics, predictive analytics, and big data analytics are enhancing the capabilities of app data statistics tools. These advancements enable organizations to gain deeper insights and make faster, more accurate decisions. Additionally, the proliferation of cloud-based solutions is making analytics tools more accessible and affordable for businesses of all sizes. Cloud deployment offers scalability, flexibility, and cost-efficiency, which are particularly attractive to small and medium enterprises (SMEs).

The role of Product Analytics Software is becoming increasingly significant in the realm of app data statistics tools. These software solutions are designed to help businesses understand how users interact with their products, providing insights that are crucial for enhancing user experience and driving product development. By analyzing user data, companies can identify trends and patterns that inform strategic decisions, such as feature enhancements and marketing strategies. The integration of Product Analytics Software with app data statistics tools enables businesses to gain a comprehensive view of user behavior, facilitating more informed decision-making and ultimately leading to improved product offerings.

Regionally, North America holds the largest market share, driven by the presence of numerous tech giants and a high adoption rate of advanced technologies. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period. The rapid digitization, increasing smartphone penetration, and the rising number of app developers in countries like China and India are driving the demand for app data statistics tools. Europe also presents significant growth opportunities, with increasing investments in technology and data analytics across various industries. Latin America and the Middle East & Africa are emerging markets with growing awareness and adoption of analytics tools.

Component Analysis

The app data statistics tool market is segmented by components into software and services. Software components dominate the market, driven by the demand for sophisticated analytics solutions that can process vast amounts of data. These software tools are designed to collect, analyze, and visualize data, enabling organizations to derive meaningful insights. The growing adoption of artificial intelligence and machine learning technologies in software solutions further enhances their capabilities, making them indispensable for

Although South Africa is the global epicenter of the HIV epidemic, the uptake of HIV testing and treatment among young people remains low. Concerns about confidentiality impede the utilization of HIV prevention services, which signals the need for discrete HIV prevention measures that leverage youth-friendly platforms. This paper describes the process of developing a youth-friendly internet-enabled HIV risk calculator in collaboration with young people, including young key populations aged between 18 and 24 years old. Using qualitative research, we conducted an exploratory study with 40 young people including young key population (lesbian, gay, bisexual, transgender (LGBT) individuals, men who have sex with men (MSM), and female sex workers). Eligible participants were young people aged between 18–24 years old and living in Soweto. Data was collected through two peer group discussions with young people aged 18–24 years, a once-off group discussion with the [Name of clinic removed for confidentiality] adolescent community advisory board members and once off face-to-face in-depth interviews with young key population groups: LGBT individuals, MSM, and female sex workers. LGBT individuals are identified as key populations because they face increased vulnerability to HIV/AIDS and other health risks due to societal stigma, discrimination, and obstacles in accessing healthcare and support services. The measures used to collect data included a socio-demographic questionnaire, a questionnaire on mobile phone usage, an HIV and STI risk assessment questionnaire, and a semi-structured interview guide. Framework analysis was used to analyse qualitative data through a qualitative data analysis software called NVivo. Descriptive statistics were summarized using SPSS for participant socio-demographics and mobile phone usage. Of the 40 enrolled participants, 58% were male, the median age was 20 (interquartile range 19–22.75), and 86% had access to the internet. Participants’ recommendations were considered in developing the HIV risk calculator. They indicated a preference for an easy-to-use, interactive, real-time assessment offering discrete and private means to self-assess HIV risk. In addition to providing feedback on the language and wording of the risk assessment tool, participants recommended creating a colorful, interactive and informational app. A collaborative and user-driven process is crucial for designing and developing HIV prevention tools for targeted groups. Participants emphasized that privacy, confidentiality, and ease of use contribute to the acceptability and willingness to use internet-enabled HIV prevention methods.

This map service displays demographic data used in EJSCREEN. All demographic data were derived from American Community Survey 2006-2010 estimates. EJSCREEN is an environmental justice screening tool that provides EPA with a nationally consistent approach to screening for potential areas of EJ concern that may warrant further investigation. The EJ indexes are block group level results that combine multiple demographic factors with a single environmental variable (such as proximity to traffic) that can be used to help identify communities living with the greatest potential for negative environmental and health effects. The EJSCREEN tool is currently for internal EPA use only. It is anticipated that as users become accustomed to this new tool, individual programs within the Agency will develop program use guidelines and a community of practice will develop around them within the EPA Geoplatform. Users should keep in mind that screening tools are subject to substantial uncertainty in their demographic and environmental data, particularly when looking at small geographic areas, such as Census block groups. Data on the full range of environmental impacts and demographic factors in any given location are almost certainly not available directly through this tool, and its initial results should be supplemented with additional information and local knowledge before making any judgments about potential areas of EJ concern.

This dataset tracks the updates made on the dataset "MHS Dashboard Children and Youth Demographic Datasets and Report Tool" as a repository for previous versions of the data and metadata.

This dataset contains selected projection matrices from Janovský and Herben 2020 (see Related Works) and R code for analyses (examples).

Original and revised subnational population estimates of England and Wales for mid-2012 to mid-2016. Interactive annual data are available by administrative area, age, sex and component of population change.