Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

Companion DATA

Title: Using social media and personality traits to assess software developers' emotional polarity

Authors: Leo Moreira Silva Marília Gurgel Castro Miriam Bernardino Silva Milena Santos Uirá Kulesza Margarida Lima Henrique Madeira

Journal: PeerJ Computer Science

Github: https://github.com/leosilva/peerj_computer_science_2022

The folders contain:

Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.

/analysis analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications

/dataset alldata.json: contains the dataset used in the paper

/ethics_committee committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version. committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version. committee_submission_form_english_version.pdf: the project submitted to the committee. English version. committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version. consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version. consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version. data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version. data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.

/notebooks General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period

/surveys Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version. Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version. Demographic_Survey_answers.xlsx: participants' demographic survey answers ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits. ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits. ibf_answers.xlsx: participantes' and psychologists' answers for BFI

We have removed from dataset any sensible data to protect participants' privacy and anonymity. We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.

In a matter of days we have used my customizable software package to grow O.T.I.'s Facebook page likes from 10k to almost 18k likes. From the graph presented in the updated pdf, you can clearly see the impact our training phase had on the advertising performance, showcasing how the software package's suggestions increased/decreased page engagement.

Once the training phase was completed a sharp increase in page likes, fan enagement, and shares was observed. An increase in external online traffic at the website and blog, as well as offline traffic was also observed.

We will be putting together a full report on this software once we have completed our investigation.

It is versatile, and our results suggest that it can be customized to any page producing any content. We have used similar generic engines, powered by neural networks, to locate patterns in other areas of science (reaction prediction software). The key to our work is a series of custom neural networks. A group to locate patterns, and another series of groups for additional pattern analysis once key parameters have been identified.

This software package has significant commercial value and utilizes novel concepts in computer science/probability that will not be described publicly for proprietary reasons.

The National Oceanic and Atmospheric Administration (NOAA) Coral Reef Conservation Program (Coral Program) invests approximately $5 million of its annual operating budget to support the National Coral Reef Monitoring Program (NCRMP) for biological, climate, and socioeconomic monitoring throughout the U.S. Pacific, Atlantic, Caribbean, and Gulf of Mexico coral reef areas. The monitoring program is unique for its national scale across a vast geographic area as well as its progressive inclusion of social science integrated with biophysical science. The effort provides a consistent flow of information about the status and trends of environmental conditions, natural resources, and the people and processes that interact with coral reef ecosystems. The overarching goal is to collect the scientific data needed to evaluate changing conditions of U.S. coral reef ecosystems, which are among the most biologically diverse and economically valuable ecosystems on earth, providing billions of dollars in food, jobs, recreational opportunities, coastal protection, and other important ecosystem services. The program focuses on four monitoring themes: benthic community structure, fish community structure, climate impacts, and socioeconomic condition. Within the benthic theme, the core indicators include: coral species abundance and size structure, coral diversity, coral condition, benthic percent cover, key coral and mobile invertebrate species, and reef rugosity. Data provided here include species abundance. The coral demographics protocol provides more detailed and species-specific insight (‘signal magnitude’) for coral populations.

Individual data collections: Gulf of Mexico: https://doi.org/10.7289/v5vd6wts Florida: https://doi.org/10.7289/v5xw4h4z Puerto Rico: https://doi.org/10.7289/v5pg1q23 US Virgin Islands: https://doi.org/10.7289/v5ww7fqk

Monthly Population Estimates by Universe, Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to December 1, 2016 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on these files. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2015) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

Overview This dataset re-shares cartographic and demographic data from the U.S. Census Bureau to provide an obvious supplement to Open Environments Block Group publications.These results do not reflect any proprietary or predictive model. Rather, they extract from Census Bureau results with some proportions and aggregation rules applied. For additional support or more detail, please see the Census Bureau citations below. Cartographics refer to shapefiles shared in the Census TIGER/Line publications. Block Group areas are updated annually, with major revisions accompanying the Decennial Census at the turn of each decade. These shapes are useful for visualizing estimates as a map and relating geographies based upon geo-operations like overlapping. This data is kept in a geodatabase file format and requires the geopandas package and its supporting fiona and DAL software. Demographics are taken from popular variables in the American Community Survey (ACS) including age, race, income, education and family structure. This data simply requires csv reader software or pythons pandas package. While the demographic data has many columns, the cartographic data has a very, very large column called "geometry" storing the many-point boundaries of each shape. So, this process saves the data separately, with demographics columns in a csv file and geometry in a gpd file needed an installation of geopandas, fiona and DAL software. More details on the ACS variables selected and derivation rules applied can be found in the commentary docstrings in the source code found here: https://github.com/OpenEnvironments/blockgroupdemographics. ## Files While the demographic data has many columns, the cartographic data has a very, very large column called "geometry" storing the many-point boundaries of each shape. So, this process saves the data separately, with demographics columns in a csv file named YYYYblcokgroupdemographics.csv. The cartographic column, 'geometry', is shared as file named YYYYblockgroupdemographics-geometry.pkl. This file needs an installation of geopandas, fiona and DAL software.

Annual State Resident Population Estimates for 5 Race Groups (5 Race Alone or in Combination Groups) by Age, Sex, and Hispanic Origin // Source: U.S. Census Bureau, Population Division // Note: 'In combination' means in combination with one or more other races. The sum of the five race groups adds to more than the total population because individuals may report more than one race. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see http://www.census.gov/popest/data/historical/files/MRSF-01-US1.pdf. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2013) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Abstract

The 1991 Indonesia Demographic and Health Survey (IDHS) is a nationally representative survey of ever-married women age 15-49. It was conducted between May and July 1991. The survey was designed to provide information on levels and trends of fertility, infant and child mortality, family planning and maternal and child health. The IDHS was carried out as collaboration between the Central Bureau of Statistics, the National Family Planning Coordinating Board, and the Ministry of Health. The IDHS is follow-on to the National Indonesia Contraceptive Prevalence Survey conducted in 1987.

The DHS program has four general objectives: - To provide participating countries with data and analysis useful for informed policy choices; - To expand the international population and health database; - To advance survey methodology; and - To help develop in participating countries the technical skills and resources necessary to conduct demographic and health surveys.

In 1987 the National Indonesia Contraceptive Prevalence Survey (NICPS) was conducted in 20 of the 27 provinces in Indonesia, as part of Phase I of the DHS program. This survey did not include questions related to health since the Central Bureau of Statistics (CBS) had collected that information in the 1987 National Socioeconomic Household Survey (SUSENAS). The 1991 Indonesia Demographic and Health Survey (IDHS) was conducted in all 27 provinces of Indonesia as part of Phase II of the DHS program. The IDHS received financial assistance from several sources.

The 1991 IDHS was specifically designed to meet the following objectives: - To provide data concerning fertility, family planning, and maternal and child health that can be used by program managers, policymakers, and researchers to evaluate and improve existing programs; - To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and the availability of contraception; - To measure the development and achievements of programs related to health policy, particularly those concerning the maternal and child health development program implemented through public health clinics in Indonesia.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data [ssd]

Sampling procedure

Indonesia is divided into 27 provinces. For the implementation of its family planning program, the National Family Planning Coordinating Board (BKKBN) has divided these provinces into three regions as follows:

• Java-Bali: Jakarta, West Java, Central Java, Yogyakarta, East Java, and Bali
• Outer Java-Bali I: Aceh, North Sumatra, West Sumatra, South Sumatra, Lampung, West Kalimantan, South Kalimantan, North Sulawesi, South Sulawesi, and West Nusa Tenggara
• Outer Java-Bali II: Riau, Jambi, Bengkulu, East Nusa Tenggara, East Timor, Central Kalimantan, East Kalimantan, Central Sulawesi, Southeast Sulawesi, Maluku, and Irian Jaya.

The 1990 Population Census of Indonesia shows that Java-Bali contains about 62 percent of the national population, while Outer Java-Bali I contains 27 percent and Outer Java-Bali II contains 11 percent. The sample for the Indonesia DHS survey was designed to produce reliable estimates of contraceptive prevalence and several other major survey variables for each of the 27 provinces and for urban and rural areas of the three regions.

In order to accomplish this goal, approximately 1500 to 2000 households were selected in each of the provinces in Java-Bali, 1000 households in each of the ten provinces in Outer Java-Bali I, and 500 households in each of the 11 provinces in Outer Java-Bali II for a total of 28,000 households. With an average of 0.8 eligible women (ever-married women age 15-49) per selected household, the 28,000 households were expected to yield approximately 23,000 individual interviews.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The DHS model "A" questionnaire and manuals were modified to meet the requirements of measuring family planning and health program attainment, and were translated into Bahasa Indonesia.

Cleaning operations

The first stage of data editing was done by the field editors who checked the completed questionnaires for completeness and accuracy. Field supervisors also checked the questionnaires. They were then sent to the central office in Jakarta where they were edited again and open-ended questions were coded. The data were processed using 11 microcomputers and ISSA (Integrated System for Survey Analysis).

Data entry and editing were initiated almost immediately after the beginning of fieldwork. Simple range and skip errors were corrected at the data entry stage. Secondary machine editing of the data was initiated as soon as sufficient questionnaires had been entered. The objective of the secondary editing was to detect and correct, if possible, inconsistencies in the data. All of the data were entered and edited by September 1991. A brief report containing preliminary survey results was published in November 1991.

Response rate

Of 28,141 households sampled, 27,109 were eligible to be interviewed (excluding those that were absent, vacant, or destroyed), and of these, 26,858 or 99 percent of eligible households were successfully interviewed. In the interviewed households, 23,470 eligible women were found and complete interviews were obtained with 98 percent of these women.

Note: See summarized response rates by place of residence in Table 1.2 of the survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the IDHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate analytically.

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the IDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of standard error of a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which one can reasonably be assured that, apart from non-sampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the IDHS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar year since birth - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

Abstract

The Demographic and Health Survey (DHS) Program is a USAID funded program that supports governments with their demographic and health data surveys and makes the data available on their website. Demographic and health data for 46 African countries https://www.dhsprogram.com/Countries/Country-List.cfm is available from the DHS site. Researchers can register their projects with the Program on the site to obtain access to this data https://www.dhsprogram.com/data/new-user-registration.cfm Reports for each survey round are also available from the site https://www.dhsprogram.com/publications/index.cfm

Analysis unit

Households and individuals

Kind of data

Survey data

Bible Study Software Market Outlook

The global Bible Study Software market size was valued at approximately USD 600 million in 2023 and is projected to reach around USD 1 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.5% during the forecast period. The growth of this market is primarily driven by the increasing digitization and the growing popularity of digital religious materials among various user groups.

A significant factor contributing to the growth of the Bible Study Software market is the widespread adoption of digital tools for religious education and personal study. As technology becomes more integrated into daily life, religious communities are increasingly turning to software solutions to facilitate Bible study, making these tools indispensable. Digital platforms offer a variety of features such as search functionalities, cross-referencing, and multimedia integration, which enhance the overall study experience and make the text more accessible to users of all ages and backgrounds.

Additionally, the surge in remote learning and virtual gatherings, spurred by global events such as the COVID-19 pandemic, has further accelerated the demand for Bible study software. Churches and educational institutions have had to adapt to new modes of teaching and community building, which has led to an increased reliance on digital solutions. This transition not only supports regular study but also fosters a sense of community among users who may be geographically dispersed, thus driving market growth.

Increasing smartphone penetration and internet accessibility are also crucial drivers for the Bible Study Software market. With a significant portion of the global population now owning smartphones and having consistent internet access, mobile and web-based applications for Bible study have seen a steep rise in usage. These platforms offer convenience and flexibility, allowing users to engage with religious texts anytime and anywhere, which is particularly appealing to younger demographics who are accustomed to digital media consumption.

Regionally, North America holds the largest share of the Bible Study Software market, driven by a high rate of technological adoption and a strong Christian demographic. Europe follows closely, with a growing interest in digital religious resources. Meanwhile, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period, owing to the increasing Christian population and rapid digitization in countries such as South Korea, India, and the Philippines. The Middle East & Africa and Latin America are also showing promising signs of growth, albeit at a slower pace compared to other regions.

Platform Analysis

The Bible Study Software market is segmented by platform into Windows, Mac, iOS, Android, and Web-based. The Windows segment currently dominates the market, largely due to the widespread use of personal computers running on Windows OS in homes, churches, and academic institutions. Windows-based software offers robust functionalities, including advanced search options, complex note-taking abilities, and seamless integration with other software, making it a preferred choice for serious Bible scholars and educators.

Mac users, although a smaller segment, represent a growing market share. The appeal of Mac-based Bible study software lies in its user-friendly interface and the seamless integration with other Apple products. The increasing popularity of Mac computers in academic and professional settings contributes to the growth of this segment. Developers are increasingly focusing on creating high-quality, Mac-compatible Bible study tools to cater to this niche but growing user base.

The iOS and Android segments are witnessing significant growth, driven by the proliferation of smartphones and tablets. Mobile-based Bible study applications offer unparalleled convenience, enabling users to study on the go. These apps often include features such as verse-of-the-day notifications, audio Bibles, and social sharing capabilities, which enhance user engagement and retention. Given the global trend toward mobile internet usage, the iOS and Android segments are expected to continue growing rapidly.

Web-based platforms are also gaining traction, particularly among users who prefer not to download software. These platforms offer flexibility and accessibility from any device with internet connectivity, making them an attractive option for occasional users and those who prioritize cross-device compatibility. Web-based

Annual Population Estimates for the United States; States; Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Related Statistical Areas; Counties; and Subcounty Places; and for Puerto Rico and Its Municipios: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through May. // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency in July 2015. // The 2010 Census did not ascertain the military status of the household population. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on this file. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., Vintage 2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

About this dataset

Context

The dataset tabulates the Salt Lake 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 Salt Lake 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 Salt Lake City was 204,657, a 1.74% increase year-by-year from 2021. Previously, in 2021, Salt Lake City population was 201,165, an increase of 0.25% compared to a population of 200,658 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Salt Lake City increased by 22,917. In this period, the peak population was 204,657 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Salt Lake City is shown in this column.
• Year on Year Change: This column displays the change in Salt Lake 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 Salt Lake City Population by Year. You can refer the same here

Annual Resident Population Estimates by Single Year of Age, Sex, Race, and Hispanic Origin // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2016) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

About this dataset

Context

The dataset tabulates the Little Valley 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 Little Valley 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 Little Valley was 1,090, a 0.28% increase year-by-year from 2021. Previously, in 2021, Little Valley population was 1,087, a decline of 0.46% compared to a population of 1,092 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Little Valley decreased by 31. In this period, the peak population was 1,140 in the year 2010. 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 Little Valley is shown in this column.
• Year on Year Change: This column displays the change in Little Valley 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 Little Valley Population by Year. You can refer the same here

Geodemographic Segmentation Data from Caliper Corporation contain demographic data in a way that is easy to visualize and interpret. We provide 8 segments and 32 subsegments for exploring the demographic makeup of neighborhoods across the country.

Abstract (en): This study contains teaching materials developed over a period of years for a four-week workshop, Longitudinal Analysis of Historical Demographic Data (LAHDD), offered through the ICPSR Summer Program in 2006, 2007, 2009, 2011 and 2013, with one-day alumni workshops in 2010, 2012, and 2014. Instructors in the workshops are listed below. Funding was provided by The Eunice Kennedy Shriver National Institute of Child Health and Human Development, grants R25-HD040525 and R25-HD-049479, the ICPSR Summer Program and the ICPSR Director. The course was designed to teach students the theories, methods, and practices of historical demography and to give them first-hand experience working with historical data. This training is valuable not only to those interested in the analysis historical data. The techniques of historical demography rest on methodological insights that can be applied to many problems in population studies and other social sciences. While historical demography remains a flourishing research area with publications in key journals like Demography, Population Studies, and Population, practitioners were dispersed, and training was not available at any of the population research centers in the U.S. or elsewhere. One hundred and ten participants from around the globe took part in the workshops, and have gone on to establish courses of their own or teach in other workshops. We offer these materials here in the hopes that others will find them useful in developing courses on historical demography and/or longitudinal data analysis. The workshop was organized in three tracks: A brief tour of historical demography, event-history analysis, and data management for longitudinal data using Stata and Microsoft Access. The data management track includes 13 exercises designed for hands-on learning and reinforcement. Included in this project are the syllabii and reading lists for the three tracks, datasets used in the exercises, documents setting out each exercise, a file with the expected results, and for many of the exercises, an explanation. Video tutorials helpful with the Access exercises are accessible from ICPSR's YouTube channel https://www.youtube.com/playlist?list=PLqC9lrhW1Vvb9M1QpQH23z9UlPYxHbUMF. Users are encouraged to use these materials to develop their own courses and workshops in any of the topics covered. Please acknowledge NICHD R25-HD040525 and R25-HD-049479 whenever appropriate. Historical demography instructors: Myron P. Gutmann, University of Colorado Boulder Cameron Campbell, Hong Kong University of Science and Technology J. David Hacker, University of Minnesota Satomi Kurosu, Reitaku University Katherine A. Lynch, Carnegie Mellon University Event history instructors: Cameron Campbell, Hong Kong University of Science and Technology Glenn Deane, State University of New York at Albany Ken R. Smith, Huntsman Cancer Institute and University of Utah Database management instructors: George Alter, University of Michigan Susan Hautaniemi Leonard, University of Michigan Teaching Assistants: Mathew Creighton, University of Massachusetts Boston Emily Merchant, University of Michigan Luciana Quaranta, Lund University Kristine Witkowski, University of Michigan Project Manager: Susan Hautaniemi Leonard, University of Michigan Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. Eunice Kennedy Shriver National Institute of Child Health and Human Development (R25 HD040525).

This data collection contains the Census Software Package (CENSPAC), a generalized data retrieval system that the Census Bureau developed for use with its public use statistical data files. CENSPAC primarily provides processing capabilities for summary data files, but it also has some features that are applicable to microdata files. The actual software provides sample JCL for system installation, programs for system reconfiguration, source code for CENSPAC, and machine-readable data dictionaries for STF 1, STF 2, STF 3, and STF 4.

This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data. Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set. Exceptions to this are: Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence. Data from Gateway to Research is made available under an Open Government Licence (Version 3.0). Contents Survey data & analysis: esrc_data-survey-analysis-data.zip Other data: esrc_data-other-data.zip Transcripts: esrc_data-transcripts.zip Data Management Plan: esrc_data-dmp.zip Survey data & analysis The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data. The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled. The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly. A pdf copy of the survey questions is available on GitHub. The survey data has been decoupled into: survey-results-key.csv - maps a question number and the responses to the actual question values. q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16). q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23). q17-institutions.csv - the institution/location of the respondent (Q17). q18-funding.csv - funding sources within the last 5 years (Q18). Please note the code that has been used to do the analysis will not run with the decoupled survey data. Other data files included CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered. DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021. projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence. locations.csv - latitude and longitude for the institutions in the cleaned locations. subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot. topics.csv - topic classification for the ESRC projects for the 24th February data snapshot. Interview transcripts The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts: 1269794877.md 1578450175.md 1792505583.md 2964377624.md 3270614512.md 40983347262.md 4288358080.md 4561769548.md 4938919540.md 5037840428.md 5766299900.md 5996360861.md 6422621713.md 6776362537.md 7183719943.md 7227322280.md 7336263536.md 75909371872.md 7869268779.md 8031500357.md 9253010492.md Data Management Plan The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

Retail Analytics Software Market Outlook

As of 2023, the global retail analytics software market size is valued at approximately $5 billion, and it is projected to reach around $13 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 11.2% over the forecast period. The substantial growth is driven primarily by the increasing reliance on data-driven decision-making within the retail industry. As retailers aim to enhance customer experiences, optimize inventory management, and streamline operational efficiencies, the adoption of retail analytics software is poised to expand significantly.

The growth of the retail analytics software market is fueled by the rapid digital transformation across the retail sector. As more retailers embrace e-commerce and omnichannel strategies, the need for effective analytics tools becomes critical to gain insights into consumer preferences and behavior. Retailers are leveraging these software solutions to analyze large volumes of data, enabling them to make more informed decisions about merchandising, marketing, and customer engagement. Additionally, the evolution of artificial intelligence and machine learning technologies is enhancing the capabilities of retail analytics platforms, allowing for more accurate predictions and personalized consumer experiences.

Another significant growth factor is the increasing focus on customer-centric strategies. Today’s consumers demand personalized experiences and expect retailers to anticipate their needs. Retail analytics software allows businesses to analyze customer data and segment them based on buying behavior, preferences, and demographics. This enables retailers to tailor their offerings and marketing efforts to individual customer segments, thereby enhancing customer satisfaction and loyalty. As competition in the retail space intensifies, the ability to deliver personalized experiences becomes a crucial differentiator, further propelling the demand for advanced analytics solutions.

Moreover, the need for operational efficiency and cost optimization is driving the adoption of retail analytics software. In a highly competitive market, retailers are under constant pressure to reduce costs while maintaining quality service. Analytics tools help retailers optimize inventory levels, reduce stockouts and overstock situations, and improve supply chain efficiencies. By leveraging predictive analytics, retailers can forecast demand more accurately, plan inventory purchases, and minimize waste, ultimately leading to improved profitability. The capability to streamline operations and enhance efficiency positions retail analytics software as an indispensable tool for modern retailers.

From a regional perspective, North America currently dominates the retail analytics software market, attributed to the presence of major retail players and the early adoption of advanced technologies. The region’s mature retail market and the increasing consumer shift towards online shopping are contributing to the demand for sophisticated analytics solutions. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by the rapid expansion of the retail sector in emerging economies such as China and India. Rising smartphone penetration and internet usage in these countries are paving the way for the growth of e-commerce, thereby increasing the demand for retail analytics software.

Component Analysis

The retail analytics software market is segmented by component into software and services. The software segment holds the lion’s share of the market, driven by the increasing need for comprehensive analytics tools that can process large amounts of data and provide actionable insights. Retailers are increasingly investing in advanced software solutions that offer features like predictive analytics, customer segmentation, and real-time reporting. These capabilities enable them to make informed decisions about inventory management, marketing strategies, and customer engagement. As the retail landscape becomes more complex, the demand for sophisticated software solutions is expected to grow significantly.

The services segment, although smaller than the software segment, is also experiencing notable growth. As retailers implement new analytics tools, there is a growing need for professional services such as consulting, implementation, and support. These services help retailers tailor analytics solutions to their specific needs and ensure a seamless integration with existing systems. Additionally, as retailers continue to innovate and adopt new techn

Abstract

The 1998 Ghana Demographic and Health Survey (GDHS) is the latest in a series of national-level population and health surveys conducted in Ghana and it is part of the worldwide MEASURE DHS+ Project, designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1998 GDHS is to provide current and reliable data on fertility and family planning behaviour, child mortality, children’s nutritional status, and the utilisation of maternal and child health services in Ghana. Additional data on knowledge of HIV/AIDS are also provided. This information is essential for informed policy decisions, planning and monitoring and evaluation of programmes at both the national and local government levels.

The long-term objectives of the survey include strengthening the technical capacity of the Ghana Statistical Service (GSS) to plan, conduct, process, and analyse the results of complex national sample surveys. Moreover, the 1998 GDHS provides comparable data for long-term trend analyses within Ghana, since it is the third in a series of demographic and health surveys implemented by the same organisation, using similar data collection procedures. The GDHS also contributes to the ever-growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The major focus of the 1998 GDHS was to provide updated estimates of important population and health indicators including fertility and mortality rates for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of key variables for the ten regions in the country.

The list of Enumeration Areas (EAs) with population and household information from the 1984 Population Census was used as the sampling frame for the survey. The 1998 GDHS is based on a two-stage stratified nationally representative sample of households. At the first stage of sampling, 400 EAs were selected using systematic sampling with probability proportional to size (PPS-Method). The selected EAs comprised 138 in the urban areas and 262 in the rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second stage selection of households. At the second stage of sampling, a systematic sample of 15 households per EA was selected in all regions, except in the Northern, Upper West and Upper East Regions. In order to obtain adequate numbers of households to provide reliable estimates of key demographic and health variables in these three regions, the number of households in each selected EA in the Northern, Upper West and Upper East regions was increased to 20. The sample was weighted to adjust for over sampling in the three northern regions (Northern, Upper East and Upper West), in relation to the other regions. Sample weights were used to compensate for the unequal probability of selection between geographically defined strata.

The survey was designed to obtain completed interviews of 4,500 women age 15-49. In addition, all males age 15-59 in every third selected household were interviewed, to obtain a target of 1,500 men. In order to take cognisance of non-response, a total of 6,375 households nation-wide were selected.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the GDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. These questionnaires were based on model survey instruments developed for the international MEASURE DHS+ programme and were designed to provide information needed by health and family planning programme managers and policy makers. The questionnaires were adapted to the situation in Ghana and a number of questions pertaining to on-going health and family planning programmes were added. These questionnaires were developed in English and translated into five major local languages (Akan, Ga, Ewe, Hausa, and Dagbani).

The Household Questionnaire was used to enumerate all usual members and visitors in a selected household and to collect information on the socio-economic status of the household. The first part of the Household Questionnaire collected information on the relationship to the household head, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. For this purpose, all women age 15-49, and all men age 15-59 in every third household, whether usual residents of a selected household or visitors who slept in a selected household the night before the interview, were deemed eligible and interviewed. The Household Questionnaire also provides basic demographic data for Ghanaian households. The second part of the Household Questionnaire contained questions on the dwelling unit, such as the number of rooms, the flooring material, the source of water and the type of toilet facilities, and on the ownership of a variety of consumer goods.

The Women’s Questionnaire was used to collect information on the following topics: respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunisation and health, marriage, fertility preferences and attitudes about family planning, husband’s background characteristics, women’s work, knowledge of HIV/AIDS and STDs, as well as anthropometric measurements of children and mothers.

The Men’s Questionnaire collected information on respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, as well as knowledge of HIV/AIDS and STDs.

Response rate

A total of 6,375 households were selected for the GDHS sample. Of these, 6,055 were occupied. Interviews were completed for 6,003 households, which represent 99 percent of the occupied households. A total of 4,970 eligible women from these households and 1,596 eligible men from every third household were identified for the individual interviews. Interviews were successfully completed for 4,843 women or 97 percent and 1,546 men or 97 percent. The principal reason for nonresponse among individual women and men was the failure of interviewers to find them at home despite repeated callbacks.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of shortfalls made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 1998 GDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 1998 GDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 1998 GDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 GDHS is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.