List of Top Authors of Applied Demography Series sorted by articles.

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

This study provides estimates of births, deaths and net-migration, by ethnic group, for each electoral ward (England and Wales) and local authority area (England, Wales and Scotland), for the period July 1st, 1991 –June 30th, 2001.

The study uses the eight-category classification of ethnic group: White, Caribbean, African, Indian, Pakistani, Bangladeshi and Other. Ethnic group is not included in civil registration of births and deaths in the UK. These estimates are based on estimates of fertility of each ethnic group in each locality, based on local child/woman ratios, common schedules of mortality, and estimates of ethnic group population consistent with the latest estimates of mid-year population for 1991 and 2001 by the Office for National Statistics (ONS) and the General Register Office. Net migration is estimated indirectly as the residual after births and deaths are deducted from population change during the period 1991-2001, using standard methods of applied demography described in Simpson, Finney and Lomax (2008). There are no other estimates of demographic components of change for this period.

The eight ethnic group categories are known to be more stable between the two censuses of 1991 and 2001 than other possible classifications that amalgamate the 10 ethnic group categories of 1991 with the 16 ethnic group categories of 2001. The least stable categories across this time are Caribbean, African, and Other.

Further information is available on the Ethnic Group Population Change and Integration: a Demographic Approach to Small Area Ethnic Geographies ESRC Award web page.

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).

A more recent web map on this same topic is available for ArcGIS Online subscribers here.This map shows the socioeconomic status of each census tract. Data come from the US Census Bureau's 2011-2015 American Community Survey. Neighborhood Socioeconomic Status, over and above individual socioeconomic status, is a predictor of many health outcomes. The Neighborhood Socioeconomic Status (NSES) Index is on a scale from 0 to 100 with 50 being the national average around 2010. The Index incorporates the following indicators (fields in this layer's attribute table):Median Household Income (from Table B19013)Percent of individuals with income below the Federal Poverty Line (from Table S1701)The educational attainment of adults (age 25+) (from Table B15003)Unemployment Rate (from Table S2301)Percent of households with children under the age of 18 that are "female-headed" (no male present) (from Table B11005)NSES = log(median household income) + (-1.129 * (log(percent of female-headed households))) + (-1.104 * (log(unemployment rate))) + (-1.974 * (log(percent below poverty))) + .451*((high school grads)+(2*(bachelor's degree holders)))To learn more about how the NSES Index was developed, please explore this journal articleMiles, Jeremy and Weden, Margaret; Lavery, Diana; Escarce, José; Kathleen Cagney; Shih, Regina. 2016. “Constructing a Time-Invariant Measure of the Socio-Economic Status of U.S. Census Tracts.” Journal of Urban Health, vol. 93, issue no.1, pp. 213-232. or this PPT presentation presented at the University of Texas at San Antonio's Applied Demography Conference in 2014.

This is the data without the conjoint answers attached that is used for demographic data analysis for respondents

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.

In August of 2018, FSSA’s Office of Healthy Opportunities deployed a social risk assessment survey. The 10-question survey was made available to anyone applying online through FSSA for health coverage, the Supplemental Nutritional Assistance Program or Temporary Assistance for Needy Families. The results of this survey are aggregated and presented below and can help communities better understand the social risk factors affecting the health of those applying for our services. Please read and review the following information regarding the use of this data prior to viewing the tool. This survey was made available to those individuals who applied online ONLY and does not represent anyone who applied in-person, by telephone, by mail or any other method. In 2018, online applications accounted for 79% of those who applied for SNAP, TANF or health coverage. Survey completion is voluntary and does not impact eligibility for SNAP, TANF or health coverage. Applications are filed at a household level and may represent several individuals. The application process identifies a primary contact person for the household, and that individual’s demographics are represented on the dashboard; for example, person’s gender, race and education level. An individual who completes more than one application and survey over any given time period is represented once for each instance, and the survey answers and demographic details are based on each application’s responses. For example, an applicant’s age, education level and survey answers can change over time, and the reporting reflects any such changes. All information is presented in aggregate to ensure personally identifiable information is protected. To protect the privacy of individuals, data representing 20 or less individuals in any county will not be displayed. I.e. it will show as blank

GapMaps GIS Data sourced from Applied Geographic Solutions includes over 40k Demographic variables across topics including estimates & projections on population, demographics, neighborhood segmentation, consumer spending, crime index & environmental risk available at census block level.

Survey demographics as compared to US medical school graduates who applied to EM.

Persons aged 18 to 49 living in private households at the timepoint of survey

median and interquartile range and non-parametric Mann-Whitney U statistics applied.Demographics and clinical measures.

Financial overview and grant giving statistics of Int Assoc Of Apllied Demographics

The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010. To estimate the male and female populations by age in 2010, the proportions of males and females in each 5-year age group from ages 0-4 to ages 85+ for the given census year were calculated. These proportions were then applied to the 2010 estimates of the total population to obtain 2010 estimates of male and female populations by age. In some cases, the spatial resolution of the age and sex proportions was coarser than the resolution of the total population estimates to which they were applied. The population density rasters were created by dividing the population count rasters by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

Although functional traits are defined based on their impact on demographic parameters, trait-demography relationships are often reported as weak. These weak relationships might be due to disregarding trait interactions and environmental contexts, which should modulate species trait-demography relationships. We applied different models, including boosted regression tree (BRT) models, to investigate changes in the relationship between traits and demographic rates of tropical tree species in plots along an elevational gradient and among time intervals between censuses, analyzing the effect of a strong drought event. Based on a large dataset of 18,000 tree individuals from 133 common species, distributed among twelve 1-ha plots (habitats) in the Atlantic Forest (Brazil), we evaluated how trait interactions and the environmental context influence the demographic rates (growth, mortality, and recruitment). Functional traits, trait-trait, and trait-habitat interactions predicted demography wi..., Data from forest inventories conducted in twelve 1-ha plots distributed in undisturbed areas of “Restinga†(one plot), Lowland forest (four plots), Submontane forest (four plots), and Montane forest (three plots) of the Serra do Mar. All woody stems (trees, palms, and tree ferns) with a diameter at breast height (DBH) equal to or larger than 4.8 cm were tagged, taxonomically identified, and measured for diameter and re-censused four times over 12 years (2005 – 2016). The forest inventories database represents 22,770 stems from 21,509 tree individuals belonging to 685 species from 70 botanical families. For each species, we collected data on six functional traits representing the leaf, seed, and wood economics spectra. We measured leaf area (LA, cm2), leaf dry matter content (LDMC, mg g- 1), and specific leaf area (SLA, cm2 g- 1) from ten leaves of ten individuals per species. As a measure of the species’ potential size, hereafter referred to as ‘DBH’, we calculated the 0.95 percentile ...,

Excel file showing the table and the graph for figure 1 in the manuscript: "Knowledge and attitudes among life scientists towards reproducibility within journal articles: a research survey."

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at the census tract level, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsLocalized Interventions: Facilitates the development of localized interventions to address the needs of vulnerable populations within specific census tracts.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the census tract level.Research: Provides a detailed dataset for academic and applied research in socio-economic and demographic studies at a granular census tract level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific census tract areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2014-2018 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2014-2018 ACSEP_PCIEP_PCIPer capita income estimate, 2014-2018 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2014-2018 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2014-2018 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2014-2018 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2014-2018 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

GapMaps Demographic Data for USA & Canada sourced from Applied Geographic Solutions includes over 40k variables across topics including estimates & projections on population, demographics, neighborhood segmentation, consumer spending, crime index & environmental risk available at census block level.

Resumo The demographic dividend has aroused interest among demographers and economists because it is seen as a window of oportunity for the economic development of countries that have experienced a demographic transition. There are reasons to question the sole virtuosity of the pure demographic dividend in economic growth. Crespo-Cuaresma et al. (2014) found that educational expansion has an important role in economic gains during the demographic dividend. To verify these results for the Brazilian case, we performed a decomposition exercise of economic support ratio (ESR), an alternative to demographic dependency ratio, to analyze the first demographic dividend. A simulation, applied for the period from 1970 to 2100 considering three scenarios of educational expansion, shows that educational expansion was and will be responsible for a big share of the economic gains of the Brazilian demographic dividend period, outperforming the change in age structure effect. In addition, an increase in a work-age population with post-secondary education appears to potentialize these results.