About this dataset

Context

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

Key observations

The largest age group in David City, NE was for the group of age 40 to 44 years years with a population of 250 (8.30%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in David City, NE was the 80 to 84 years years with a population of 45 (1.49%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.

This 2014 Census data set represents the data collected in three different ways:

qualitative analysis of the open access repository websites

automatic validation of the metadata via OAI-PMH using the DINI-Validator [2]

online survey of repository operators

As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.

[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. D-Lib Magazine, 19. http://dx.doi.org/10.1045/november2013-vierkant

[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml

[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. figshare. http://dx.doi.org/10.6084/m9.figshare.677099

About this dataset

Context

The dataset tabulates the St. David 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 St. David. 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 342 (62.41% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age 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 St. David population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in St. David is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the St. David 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 St. David Population by Age. You can refer the same here

This is the dataset used for David Davó's Master's Thesis, available on GitHub

This dataset is based on daos-census and dao-analyzer, but including the proposals-text table.

About this dataset

Context

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

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, David City did not include 6, or 7-person households. Across the different household sizes in David City the mean income is $86,024, and the standard deviation is $42,287. The coefficient of variation (CV) is 49.16%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $23,827. It then further increased to $115,208 for 5-person households, the largest household size for which the bureau reported a median household income.

Content

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

Household Sizes:

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

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for David City median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the David City median household income by race. The dataset can be utilized to understand the racial distribution of David City income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• David City, NE median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in David City, NE (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of David City median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in David City, NE, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 14,482, while the mean income for the highest quintile (20% of households with the highest income) is 250,035. This indicates that the top earners earn 17 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 564,228, which is 225.66% higher compared to the highest quintile, and 3896.06% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 David City median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the population of St. David by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of St. David across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 55.11% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the St. David is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of St. David total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for St. David Population by Race & Ethnicity. You can refer the same here

An appreciation of historical landuse and its effects is crucial when interpreting the structure, composition, and spatial characteristics of modern forests. The Harvard Forest has compiled many different historical data sources in an ongoing effort to understand how anthropogenic disturbances have shaped our modern landscapes. Estimates of town land use and land cover were gathered from a variety of sources, including tax valuations (1801-1860) and state agricultural census records (1865-1905). Data prior to 1801 rarely cover the entire state and are excluded from these datasets. Data on forest structure are available for several time periods, including 1885 and 1895 (Agricultural Censuses) and 1916-1920s (State Forester’s reports).

Plan Description: This is an adaptation of the People’s Bloc plan, #012.The main difference is that this plan gives some diversity of representation to the North County area, which is very large, and from which the commission heard several requests for diversity of representation. The part of North County that District 3 here includes is largely Latinx and working class, in kinship with much of the population in the rest of that District.Plan Objectives:This is an adaptation of the People’s Bloc plan, #012.The main difference is that this plan gives some diversity of representation to the North County area, which is very large, and from which the commission heard several requests for diversity of representation, particularly from unincorporated areas contained within or east-south east of Palmdale. [I have a friend in the electoral reform community who lives in a different California county, in the foothills of the Sierra Nevada. A sizeable minority of residents in the area shares her political perspective. But because of where they live, and because districts are drawn to be compact, those residents do not get representation to their liking in the State Legislature or U.S. House of Representatives. My friend often complains about that and longs to have at least one district that stretches from elsewhere to include her residence or a nearby sympatico area, and that might elect a representative to her liking. Just like with North Los Angeles County, a little diversity in representation of the Sierra foothills could be a good thing.] Now that LA County CRC software users can no longer assign official U.S. Census areas (blocks, block groups, or tracts) to districts, and are offered “RDUs” (made-up ReDistricting Units) instead, this is the best I could do. RDU3096 gets in the way of better connections from North County to the southern part of the county. And we would need to use census blocks to “break through” to the unincorporated “hole” in the incorporated city of Palmdale, from which the commission heard at least one request for separate representation (rescue from the city?), without dividing the incorporated city’s population. (RDU3006 is too big to allow that.) The software/database bait-and-switch to RDUs only from the demo version also makes it necessary to take a small part of the populated area of the City of Santa Clarita to draw District 3 here.The part of North County that District 3 here includes is, as the commission heard, largely Latinx and working class, in kinship with much of the population in the rest of District 3 here, as a community of interest. At this point, it would not be practicable for this district to be geographically compact and keep that community connected.

This is the final dataset and R script used for the analysis for the paper titled All Ridership Is Local: Accessibility, Competition, and Stop-Level Determinants of Daily Bus Boardings in Portland, Oregon. The .csv and .RDS files contain the same final dataset with all the variables used in the final models.

Plan Objectives: This submission, “A3S Possibility,” gives effect to public commenters desire to have more than one Supervisor representing the north area of the county, and for unincorporated areas near Palmdale to be represented by a different Supervisor than Palmdale’s. It keeps communities of interest together throughout the county, and has three Supervisors representing areas on the Pacific Ocean’s coast.... Because the software currently limits users to Redistricting Units for drawing districts, this submission requires the following adjustments to be considered or adopted as intended:Assign Census Tract 6037930200 to District 3; andAssign Redistricting Units 3023, 3032, 3069, 3070, 3071, and 3091 to District 5 (see A3Spossheart-colored.png).

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for David City, NE, is 34.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for David City, NE, is 30.5.
• Total dependency ratio for David City, NE is 65.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for David City, NE is 3.3.

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Census/projection-disaggregated gridded population datasets for 189 countries in 2020 using Built-Settlement Growth Model (BSGM) outputs. Available at: https://www.worldpop.org/doi/10.5258/SOTON/WP00684

Replication files for David Slichter, "The Employment Effects of the Minimum Wage: A Selection Ratio Approach to Measuring Treatment Effects,” Journal of Applied Econometrics, forthcoming

Firstly, I’ve provided a .do file called sr.do which contains general code for implementing the selection ratio approach, with detailed instructions written as comments in the code.

For the minimum wage application, the main data file is mw_final.dta. A .csv version is also provided. Observations are a county in a time period. I have added self-explanatory variable labels for most variables. A few variables warrant a clearer explanation:

adj1-adj14: List of FIPS codes of all counties which are adjacent to the county in question. Each variables holds one adjacent county, and counties with fewer than 14 neighbors will have missing values for some of these variables.

change, logchange: Minimum wage this quarter - minimum wage last quarter, measured either in dollars or in logs.

time, t1-t108: The variable "time" converts years and quarters into a univariate time period, with time=1 in 1990Q1 and time=108 in 2016Q4. t1-t108 are indicators for each of these time periods.

lnemp_1418, lnearnbeg_1418, lnsep_1418, lnhira_1418, lnchurn_1418: Logs of employment, earnings, separations, hires, and churn, respectively, for 14-18 year olds.

gt1-gt6: Dummies for inclusion in each of the six comparisons used for the main (i.e., not spillover-robust) analysis. All treated counties which neighbor a control country take value 1 for each of these variables; all other treated counties take value 0. Among control counties, gt1=1 if the county neighbors a treated county and 0 otherwise, gt2=1 if the county has gt1=0 but neighbors a gt1=1 county, gt3=1 if county has gt1=gt2=0 but neighbors a gt2=1 county, etc.

h2-h6: Dummies for inclusion in each of the first spillover-robust (i.e., excluding border counties only) comparisons. Among control counties, h2-h6 are equal to gt2-gt6. Among treated counties, h2-h6 are equal to 1 if the treated county has gt1=0 but borders a gt1=1 county, and 0 otherwise.

k3-k6: Dummies for inclusion in each of the second spillover-robust (i.e., excluding two layers) comparisons. Among control counties, these variables are equal to gt3-gt6. Among treated counties, all observations take value 1 except those with gt1=1 or h2=1.

The data sources are as follows. The minimum wage law series is taken from David Neumark's website (https://www.economics.uci.edu/~dneumark/datasets.html). The economic variables are taken from the QWI, which I accessed via the Ithaca Virtual RDC. County adjacency files were downloaded from the Census Bureau (https://www.census.gov/geo/reference/county-adjacency.html).

The file main.do then runs the analyses. The resulting output file containing results is results.dta.

For the incumbency application, the main data file is incumb_final.dta. A .csv version is also provided. This file is drawn from Caughey and Sekhon's (2011) data; see their description of most variables here: https://doi.org/10.7910/DVN/8EYYA2

The key added variables are _IDistancea1-_IDistancea50, which are dummies for inclusion in the 50 comparisons used in the paper. Treated observations (i.e., Democratic wins) with margin of victory below 5 points have each of these variables equal to 1. Control observations have these variables equal to 1 if they fall within the margin of victory range, e.g., _IDistancea9=1 for control observations with Republican margin of victory between 8 and 9 points. Note that these variables are redefined by the code for the analyses of treatment effects away from the discontinuity. Lastly, there is a variable called RepWin which is the treatment variable when treatment is defined as a Republican winning.

The file sr_incumb.do then performs the analysis.

Please contact me with any questions at slichter@binghamton.edu.

Unmanned aerial vehicles (UAVs) provide an opportunity to rapidly census wildlife in remote areas while removing some of the hazards. However, wildlife may respond negatively to the UAVs, thereby skewing counts. We surveyed four species of Arctic cliff-nesting seabirds (glaucous gull Larus hyperboreus, Iceland gull Larus glaucoides, common murre Uria aalge and thick-billed murre Uria lomvia) using a UAV and compared censusing techniques to ground photography. An average of 8.5% of murres flew off in response to the UAV, but >99% of those birds were non-breeders. We were unable to detect any impact of the UAV on breeding success of murres, except at a site where aerial predators were abundant and several birds lost their eggs to predators following UAV flights. Furthermore, we found little evidence for habituation by murres to the UAV. Most gulls flew off in response to the UAV, but returned to the nest within five minutes. Counts of gull nests and adults were similar between UAV and ground photography, however the UAV detected up to 52.4% more chicks because chicks were camouflaged and invisible to ground observers. UAVs provide a less hazardous and potentially more accurate method for surveying wildlife. We provide some simple recommendations for their use.

The FORCIS (Foraminifera Response to Climatic Stress) database is a synthesis grouping datasets on living planktonic foraminifera. We assembled foraminiferal diversity and distribution data in the global oceans from 1910 until 2018, curating published and unpublished datasets. This database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump from the global ocean.

The FORCIS database version 01 is composed of 5 files (“.csv” format). All data coming from different sampling devices were put into separate “.csv” files. Only the data of the CPR from the Southern Hemisphere have been separated from the Northern Hemisphere CPR data as the data structure is not the same (species counts resolved vs. binned total counts, respectively).

Apart from the file of CPR data from the Northern Hemisphere that contains only metadata and binned total counts, all the remaining four files contain 4 blocks:

• Block 1: metadata (from column 1 to 71)

• Block 2: original counts (from column 72 to 274)

• Block 3: generated counts based on the validated taxonomy (from column 275 to 331). We added “_VT” to each species name to distinguish it from other taxonomy levels. E.g. “g_bulloides” became “g_bulloides_VT”. The number of species counted per subsample is also reported in the column “number_of_species_counted_VT”

• Block 4: generated counts based on the lumped taxonomy (from column 332 to 379). In this case, we added “_LT” to each species name. E.g. “n_dutertrei” became “n_dutertrei_VT”. We also calculated the number of species counted per subsample and reported it in the column “number_of_species_counted_LT”

Foraminifera abundance data counts are reported in different categories in the blocks 1,2 and 3 and described in the table below:

For more details about the FORCIS database column description, please check the data descriptor paper Chaabane et al. (2023) (https://doi.org/10.1038/s41597-023-02264-2).

The database is kept open for any new entries and the updated version will be released in csv format. The labels of updated versions of the released “.csv” files will contain the date of their publication and versioning number.

The main archival source of MapRom database is the “Census of the population or Statistics of the Principalities (Wallachia and Moldova) of 1838”, ANIC, Fond Catagrafii, part I, Inventory number 501, volumes numbered I/8 to I/107 from the Historical National Archives, Bucharest, mostly unpublished and now digitized and automatized for the first time, in regard to Romani population. This Census was conducted by the Interior Ministry in February 1838. It is the first preserved Romanian modern census of population and dwellings, which introduced new techniques of reviewing, such as the nominal lists and the great number (24) of demographic variables, including ethnicity. The unit of observation was the “household”, and the scope was to obtain detailed information about: 1) the settled population by age group, and sex; 2) the number of households and their structure; 3) the number of residential buildings and their distribution according to the material from which they are built; 4) population distribution according to their participation in economic activity; 5) distribution of population by skills and occupations. Based on this information, the MapRom database presents: statistics of the Roma households per village, number of Roms per village, number of different Romani ethnic sub-groups (Lăieș, Vătraș, Rudar, Zlătar, etc), average Romani household size per village, age distribution, sex distribution, number of Roms per skill, geographic distribution of the Roms (in uplands and lowlands), cultivated land area by Roms per village, etc. We found insignificant number of free Roms, rest of them were slaves. For more than 50% of them, we have reconstructed, from other (unpublished) sources the name of the owners (private noblemen, or Monasteries and churches). We searched for other statistical documents to complete our data from 1838 Census, such as the Statistics of the Turkish Gypsies (1833, manuscript ANIC, Vornicia temnitelor, file 354/1833), Statistics of Boyar Gypsy slaves (1832, manuscript ANIC, Diplomatice, dos. 147/1832) and Statistics of Monastery Gypsy slaves (1844, manuscript ANIC, Logofeția Pricinilor Bisericești, dos. 25/1844). etc. When we compared these statistics, we found major discrepancies leading to the important source critical conclusion that the 1838 census concerns mostly the permanently settled Gypsies and included few of the nomadic people. Further examination showed that the number of nomadic Gypsies was relatively small. We also found documents that indicated that Gypsies of the Muslim faith were not either registered in the 1838 census. Over time the nominal lists of the 1838 Census for four counties (Ialomiţa, Gorj, Mededinţi and Vâlcea) as well as some two rural sub-districts and two towns has been lost, but that for 14 Wallachian counties has been preserved and entered in MapRom. The five volumes of nominal material for the capital city Bucharest (with a very large and multi-ethnic population) was not researched in this project as the census there was complexly different from that of the rural provinces in form and execution, but it is hoped that it can be researched at a later date. We estimate that MapRom gathers information on between two-thirds and three-fourths of Wallachia’s Romani population.

Census/projection-disaggregated gridded population datasets, adjusted to match the corresponding UNPD 2020 estimates, for 183 countries in 2020 using Built-Settlement Growth Model (BSGM) outputs.

NOTE: For information on confidentiality protection, nonsampling.error, and definitions see .http://www.census.gov/prod/cen2000/island/GUAMprofile.pdf..U.S. Census BureauCensus 2000