A. SUMMARY This dataset contains population and demographic estimates and associated margins of error obtained and derived from the US Census. The data is presented over multiple years and geographies. The data is sourced primarily from the American Community Survey. B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived. C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here

Population size estimates of people who identify with particular race(s) in Alaskan Communities/Places and aggregation at Boroughs - CDAs and State level for recent 5-year American Community Survey (ACS) intervals. The 5-year interval data sets are published approximately 1/2 a period later than the End Year listed - for instance the interval ending in 2019 is published in mid-2021.Source: US Census Bureau, American Community SurveyThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: US Census Bureau - Why We Ask About RaceUSE CONSTRAINTS: The Alaska Department of Commerce, Community, and Economic Development (DCCED) provides the data in this application as a service to the public. DCCED makes no warranty, representation, or guarantee as to the content, accuracy, timeliness, or completeness of any of the data provided on this site. DCCED shall not be liable to the user for damages of any kind arising out of the use of data or information provided. DCCED is not the authoritative source for American Community Survey data, and any data or information provided by DCCED is provided "as is". Data or information provided by DCCED shall be used and relied upon only at the user's sole risk. For information about the American Community Survey, click here.

The data represents a one percent sample drawn from the full 1990 census. This was made possible through the Public Use Microdata Samples (PUMS). Furthermore, the few continuously-measured variables were discretized.

Task: The dataset can be used to study causal discovery methods.

Summary:

• Size of dataset: 2458285 x 68
• Task: Causal Discovery Problem
• Data Type: Discrete Data
• Dataset Scope: Standalone Dataset
• Ground Truth: Unknown Graph
• Temporal Structure: Static Data
• License: CC0 (see census.gov)
• Missing Values: No Missing Values

Missingness Statement: There are no missing values.

Features:

• See the file variable_description_US_Census_1990.txt for a detailed description.

Files:

• USCensus1990.csv: dataset
  
• variable_description_US_Census_1990.txt: feature details
  

This filtered view contains the population estimates for San Francisco geographic units from the U.S. Census Bureau’s American Community Survey that are used in the Department of Public Health’s public reporting. Details on the underlying geographic unit data from the American Community Survey are available below. The geographies included are census tracts, analysis neighborhoods, and zip codes (ZCTA). We are using 2016-2020 ACS estimates in our public reporting, but additional years are included in this view as well for historical purposes.

The COVID-19 reports which use this data are available on SF.gov by clicking here.

San Francisco Population and Demographic Census data dataset filtered on:

• "geography" =
  • 'neighborhood'
  • OR 'tract'
  • OR 'zcta'
• AND "demographic_category" = 'all'

B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived.

C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org

D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here

A database based on a random sample of the noninstitutionalized population of the United States, developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in mortality rates. It consists of data from 26 U.S. Current Population Surveys (CPS) cohorts, annual Social and Economic Supplements, and the 1980 Census cohort, combined with death certificate information to identify mortality status and cause of death covering the time interval, 1979 to 1998. The Current Population Surveys are March Supplements selected from the time period from March 1973 to March 1998. The NLMS routinely links geographical and demographic information from Census Bureau surveys and censuses to the NLMS database, and other available sources upon request. The Census Bureau and CMS have approved the linkage protocol and data acquisition is currently underway. The plan for the NLMS is to link information on mortality to the NLMS every two years from 1998 through 2006 with research on the resulting database to continue, at least, through 2009. The NLMS will continue to incorporate data from the yearly Annual Social and Economic Supplement into the study as the data become available. Based on the expected size of the Annual Social and Economic Supplements to be conducted, the expected number of deaths to be added to the NLMS through the updating process will increase the mortality content of the study to nearly 500,000 cases out of a total number of approximately 3.3 million records. This effort would also include expanding the NLMS population base by incorporating new March Supplement Current Population Survey data into the study as they become available. Linkages to the SEER and CMS datasets are also available. Data Availability: Due to the confidential nature of the data used in the NLMS, the public use dataset consists of a reduced number of CPS cohorts with a fixed follow-up period of five years. NIA does not make the data available directly. Research access to the entire NLMS database can be obtained through the NIA program contact listed. Interested investigators should email the NIA contact and send in a one page prospectus of the proposed project. NIA will approve projects based on their relevance to NIA/BSR''s areas of emphasis. Approved projects are then assigned to NLMS statisticians at the Census Bureau who work directly with the researcher to interface with the database. A modified version of the public use data files is available also through the Census restricted Data Centers. However, since the database is quite complex, many investigators have found that the most efficient way to access it is through the Census programmers. * Dates of Study: 1973-2009 * Study Features: Longitudinal * Sample Size: ~3.3 Million Link: *ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00134

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 3.0 https://doi.org/10.5066/P9Q9LQ4B. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

This filtered view contains the population estimates for San Francisco demographic groups from the U.S. Census Bureau’s American Community Survey that are used by Controller's Office - City Performance Unit for reporting on Police Stops

San Francisco Population and Demographic Census data dataset filtered on: "reporting_segment" = 'Police Reporting Demographic Categories'

A. SUMMARY This dataset contains population and demographic estimates and associated margins of error obtained and derived from the US Census. The data is presented over multiple years and geographies. The data is sourced primarily from the American Community Survey.

B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived.

C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org

D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here

In Austria a population census takes place every 10 years; this census contains a program of important statistical data on population and employment. They roughly corresponds to the information in the Mikrozensus standard survey but are more detailed (for instance with question on the connection of the place of residence and the workplace, questions on education, confession, etc.) Population and Mikrozensus are closely linked which the name already implies: Mikrozensus means a small-scale population census; this should demonstrate that what the population census reports only every 10 years, the Mikrozensus reports through the method of ongoing sampling. These ongoing sample are also collected in the years of the population census. The Mikrozensus however is far more detailed than the survey program of the population census because the Mikrozensus special surveys offer the possibility of asking questions which are fare beyond the scope of the population census. This complementary function of Mikrozensus and population census becomes especially obvious in the June-survey: certain questions that could not be posed in the population census due to the limited program were answered in the Mikrozensus via sampling. These were the topics: questions on the social stratification of the population questions on fertility and succession of birth questions on the silent Human Resources Probability: Stratified: Proportional Face-to-face interview

These data, which correspond to tables provided in the documentation, summarize information on the United States population aged 60 years and over that was collected in the 1980 Census of Population and Housing. The tables were prepared by the Bureau of the Census at the request of the National Institute on Aging. The tables are comprised of cross-tabulations of both "100 percent items" and "sample items" with age (bracketed in five year intervals from 60-64 through 90+). Race (White/Black/American Indian/Asian Pacific Islander/Spanish Origin) is a factor in all of the tables, either as race of respondent, of householder, or of family head. The file contains data for a complete set of tables for each of the 50 States, the District of Columbia and five territories, the nine Census divisions, the four Census regions, and the United States as a whole. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08658.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This filtered view contains the population estimates for San Francisco demographic groups from the U.S. Census Bureau’s American Community Survey that are used in the Department of Public Health’s public reporting. Details on the underlying demographic data from the American Community Survey are available below. The demographics included are race/ethnicity and age groups. Different age groups are used for reporting on cases reporting versus vaccinations. The specific groups used in each of these reports can be found by using the "reporting_segment" column. We are using 2016-2020 ACS estimates in our public reporting, but additional years are included in this view as well for historical purposes.

The COVID-19 reports which use this data are available on SF.gov by clicking here.

San Francisco Population and Demographic Census data dataset filtered on:

• "reporting_segment" =
  • 'COVID-19 cases/testing reporting - age brackets'
  • OR 'Seasonal vaccine reporting - age groups'
  • OR 'from census table B03002' (race/ethnicity)
• AND "geography_name" = 'San Francisco County, California'

B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived.

C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org

D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

Use cases for the Global Census Database (Consumer Demographic Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our demographic databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

Attitude of the German population and of critics of the census to the census after census day on 31 May 1987. Political attitudes. Topics: political interest; satisfaction with democracy in the Federal Republic; government orientation toward its own interests or public interest; perceived protection of rights to freedom by the political system and the current Federal Government; satisfaction with the job of the Federal Government; attitude to the census; receipt of a written request to fill out the questionnaire; intent to participate in the census before start of counting; personal willingness to participate in the census even given voluntary participation; assumed level of non-participation in the census; attitude to the census in one´s circle of friends and acquaintances; conversations about the census in social surroundings after conclusion of the survey and time of last conversation; knowledge about contents of the census survey; additionally expected questions; questions that one would not gladly answer; response or boycott behavior during the survey; attitude to government statistics; attitude to punishment of census boycotters and knowledge of cases of refusal; assumed willingness of the population to participate as well as honesty of responses given voluntary participation in the census; voluntarily providing selected personal data; preference for census or providing data already available by government offices; assumed benefit or damage from discussions about the census in the media and reasons for this assessment; attitude to earlier calls for boycott and to the time of survey; judgement on the success of the boycott movement; attitude to selected arguments for and against the census; benefit of a census; attitude to the obligation to provide information; census boycott as protest against the government; census participation as democratic duty; self-assessment on a left-right continuum; assumed position of the majority of the population on a left-right continuum; understanding of democracy and comparison of this right with reality in the Federal Republic; party preference; violation of fundamental rights by measures of authorities perceived personally or by persons from social surroundings; attitude to technology; perceived insecurity in contact with authorities and attitude to government offices; concerns regarding misuse of personal census data; trust in observance of data protection; attitude to storage of personal data; importance of data protection; assumed observance of data protection regulations; knowledge of cases of data misuse and source of information about such violations; assumed willingness to participate in a future census; attitude to opinion polls (scale); willingness to participate in a microcensus survey; willingness to provide information from one´s private sphere to friends, neighbors, census bureaus and scientific surveys; attitude to selected government statistics; willingness to respond in order to make statistics possible; fear of data misuse; concerns regarding misuse of personal data by selected institutions and government offices (scale); attitude to selected illegal actions (scale); religiousness (scale); attitude to questions of belief and the meaning of life (scale); belief in supernatural, inexplicable events as well as horoscopes and telepathy. Demography: month of birth; year of birth; sex; religious denomination; school education; employment; college in vicinity of place of residence; students in residential area; possession of a telephone. Interviewer rating: presence of third persons during interview and person desiring this presence; intervention of others in interview and person causing the intervention; attitude to the census of other persons present during interview; presence of further persons in other rooms; reliability and willingness of respondent to cooperate. Additionally encoded were: length of interview; date of interview; identification of interviewer; sex of interviewer; age of interviewer. Einstellung der bundesdeutschen Bevölkerung und von Volkszählungskritikern zur Volkszählung nach dem Stichtag am 31. Mai 1987. Politische Einstellungen. Themen: Politisches Interesse; Zufriedenheit mit der Demokratie in der Bundesrepublik; Interessen- oder Gemeinwohlorientierung der Regierung; empfundener Schutz der Freiheitsrechte durch das politische System und die gegenwärtige Bundesregierung; Zufriedenheit mit der Arbeit der Bundesregierung; Einstellung zur Volkszählung; Erhalt einer schriftlichen Aufforderung zum Ausfüllen des Fragebogens; Teilnahmeabsicht an der Volkszählung vor Beginn der Zählung; eigene Bereitschaft zur Teilnahme an der Volkszählung, auch bei freiwilliger Teilnahme; vermutete Höhe der Nichtbeteiligung an der Volkszählung; Einstellung zur Volkszählung im Freundes- und Bekanntenkreis; Gespräche über die Volkszählung im sozialen Umfeld nach Abschluß der Erhebung und Zeitpunkt des letzten Gesprächs; Kenntnisse über die Inhalte der Volkszählungsbefragung; zusätzlich erwartete Fragen; Fragen, die ungern beantwortet wurden; Antwort- bzw. Boykottverhalten bei der Erhebung; Einstellung zu staatlichen Statistiken; Einstellung zu einer Bestrafung von Volkszählungsboykotteuren und Kenntnis von Verweigerungsfällen; vermutete Teilnahmebereitschaft der Bevölkerung sowie der Antwortehrlichkeit bei Freiwilligkeit der Teilnahme an der Volkszählung; freiwillige Weitergabe ausgewählter persönlicher Daten; Präferenz für Volkszählung oder Weitergabe von bereits vorliegenden Daten durch die Ämter; vermuteter Nutzen oder Schaden der Diskussion über die Volkszählung in den Medien und Gründe für diese Einschätzung; Einstellung zu früheren Boykottaufrufen und zum Befragungszeitpunkt; Beurteilung des Erfolgs der Boykottbewegung; Einstellung zu ausgewählten Argumenten für und gegen die Volkszählung; Nutzen einer Volkszählung; Einstellung zur Auskunftspflicht; Volkszählungsboykott als Protest gegen den Staat; Volkszählungsteilnahme als demokratische Pflicht; Selbsteinschätzung auf einem Links-Rechts-Kontinuum; vermutete Position der Bevölkerungsmehrheit auf einem Links-Rechts-Kontinuum; Demokratieverständnis und Vergleich dieses Anspruchs mit der Wirklichkeit in der Bundesrepublik; Parteipräferenz; persönlich oder von Personen des sozialen Umfelds empfundene Verletzung der Grundrechte durch Behördenmaßnahmen; Einstellung zur Technik; empfundene Unsicherheiten bei Behördenkontakten und Einstellung gegenüber Ämtern; Befürchtungen hinsicht lich einer Zweckentfremdung der persönlichen Volkszählungsdaten; Vertrauen in die Einhaltung des Datenschutzes; Einstellung zur Speicherung personenbezogener Daten; Wichtigkeit des Datenschutzes vermutete Einhaltung der Datenschutzbestimmungen; Kenntnis von Fällen des Datenmißbrauchs und Informationsquelle über solche Verstöße; vermutete Teilnahmebereitschaft an einer zukünftigen Volkszählung; Einstellung zu Meinungsumfragen (Skala); Teilnahmebereitschaft an einer Mikrozensus-Erhebung; Weitergabebereitschaft von Informationen aus der Privatsphäre an Freunde, Nachbarn, statistische Ämter und in wissenschaftlichen Umfragen; Einstellung zu ausgewählten staatlichen Statistiken; Antwortbereitschaft, um Statistiken zu ermöglichen; Angst vor Datenmißbrauch; Befürchtungen hinsichtlich einer Zweckentfremdung der persönlichen Daten durch ausgewählte Institutionen und Ämter (Skala); Einstellung zu ausgewählten illegalen Handlungen (Skala); Religiosität (Skalometer); Einstellung zu Glaubensfragen und zum Sinn des Lebens (Skala); Glaube an übersinnliche, unerklärliche Ereignisse sowie an Horoskope und Telepathie. Demographie: Geburtsmonat; Geburtsjahr; Geschlecht; Konfession; Schulbildung; Berufstätigkeit; Hochschule in Wohnortnähe; Studenten in der Wohngegend; Telefonbesitz. Interviewerrating: Anwesenheit Dritter beim Interview und Person, die die Anwesenheit erwünschte; Eingriffe Dritter in das Interview und Person, die die Intervention herbeiführte; Einstellung der beim Interview zusätzlich anwesenden Person zur Volkszählung; Anwesenheit weiterer Personen in anderen Räumen; Kooperationsbereitschaft und Zuverlässigkeit des Befragten. Zusätzlich verkodet wurde: Interviewdauer; Interviewdatum; Intervieweridentifikation; Interviewergeschlecht; Intervieweralter. Re-interview of the persons interviewed in the second panel wave (ZA Study No. 1589) as well as of persons interviewed in the first panel wave (ZA Study No. 1588), but not contacted in the survey of the second panel wave. Wiederbefragung der in der 2. Panel-Welle befragten Personen (ZA-Studien-Nr. 1589) sowie von Personen, die in der 1. Panel-Well interviewt wurden (ZA-Studien-Nr. 1588), bei der Befragung der 2. Panel-Welle aber nicht angetroffen wurden.

Tracts; January 1, 2019 vintage; Generalized

Features provide a view of 2020 Census tracts for the San Francisco Bay Region. Features are a subset of the Census Tracts 500k service at https://data.bayareametro.gov/dataset/Census-Tracts-500K/dg5p-pxcu.

Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses.

Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline.

Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy.

In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

The Census Bureau uses suffixes to help identify census tract changes for comparison purposes. Local participants have an opportunity to review the existing census tracts before each census. If local participants split a census tract, the split parts usually retain the basic number, but receive different suffixes. In a few counties, local participants request major changes to, and renumbering of, the census tracts. Changes to individual census tract boundaries usually do not result in census tract numbering changes.

Relationship to Other Geographic Entities—Within the standard census geographic hierarchy, census tracts never cross state or county boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian, Alaska Native, and Native Hawaiian areas.

Qualified Census Tract geometries within Baltimore City Limits. Based on data from HUD published September 2024.Change Log2022-2-15:- Added FY2022 data- Metadata added- Columns renamed to a standard format- Agency names reformatted with Workday conventions2024-8-27:update the dataset and metadata to reflect the current data and descriptionData Dictionaryfield_namedescriptiondata_typerange_of_possible_valuesexample_valuessearchableCensus Tract 2010 The ID of the US census tract from 2010 Census results. Only Qualified Census tracts are includedTextIDs of QCT's in Baltimore city range from 24510030100 to 24510280500, but not by regular intervals since only tracts designated QCT are listed. The values are not integers, they are numerical IDs.24510070200NogeometryMulti-polygon shapes for each census tractThese are shape polygons, thus don't have a single value or expected rangeNo To leave feedback or ask a question about this dataset, please fill out the following form: Baltimore City Qualified Census Tracts feedback form.

TIGER road data for the MSA. When compared to high-resolution imagery and other transportation datasets positional inaccuracies were observed. As a result caution should be taken when using this dataset. TIGER, TIGER/Line, and Census TIGER are registered trademarks of the U.S. Census Bureau. ZCTA is a trademark of the U.S. Census Bureau. The Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States, Puerto Rico, and the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The boundary information in the TIGER/Line files are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement. The Census 2000 TIGER/Line files do NOT contain the Census 2000 urban areas which have not yet been delineated. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Harvard CGA Geotweet Census Archive is a subset of Harvard CGA Geotweet Archive v2.0 enriched with nationwide census data. It contains the tweet and user identification records along with census variables and sentiment scores for more than 2 billion geo-tagged tweets from January 2012 to July 2023. The sentiment scores are derived from the BERT sentiment scores from the Harvard CGA Geotweet Sentiment Archive. This dataset is available to the academic community at large, unlike the Harvard CGA Geotweet Archive v2.0 which is under Twitter's redistribution policy restriction for public sharing. It could serve as cross-validation data for publications that used data from Harvard CGA Geotweet Archive v2.0 . If you are interested in accessing this archive, please fill out our Geotweet Request Form. Before requesting or receiving Tweet IDs, requestors must agree to Twitter's Terms of Service, Twitter's Privacy Policy, and Twitter's Developer Policy . Geotweets IDs data provided by CGA can only be used for not-for-profit research and academic purposes. Recipients may not share CGA provided Tweet IDs or content derived from them without written permission from the CGA. Citations: If you use the Geotweet Archive in your research please reference it: "Harvard CGA Geotweet IDs Archive". ======================================================== Schema of Geotweet Census Archive Field name_TYPE_Description day----TEXT----The date of the tweet (YYYY-MM-DD) GEOID20----TEXT----Census block geoid tweet_count----INTEGER----Number of tweets in the census block user_count----INTEGER----Number of unique users in the census block avg_score----FLOAT----The average tweet sentiment score in the census block max_score----FLOAT----The maximum tweet sentiment score in the census block min_score----FLOAT----The minimum tweet sentiment score in the census block std_score----FLOAT----The standard deviation of tweet sentiment scores in the census block score_10q----FLOAT----The 10th quantile tweet sentiment score in the census block score_25q----FLOAT----The 25th quantile tweet sentiment score in the census block score_50q----FLOAT----The 50th quantile (median) tweet sentiment score in the census block score_75q----FLOAT----The 75th quantile tweet sentiment score in the census block score_90q----FLOAT----The 90th quantile tweet sentiment score in the census block

Connecticut Nurses Census 1917

The Connecticut Nurses Census is a part of State Archives https://cslarchives.ctstatelibrary.org/repositories/2/resources/443">Record Group 029: Records of the Military Census Department. The census forms may give basic details such as birthplace, age, marital status, maiden name, and current residence, as well as more specific information such as the name of the nursing school attended, medical specialty, and year of licensure. This census included the registration of both female and male nurses.

This index includes the name, birthplace, age, current residence, form number and box number. If a field is left blank, it is because the person who submitted the form did not answer that question (e.g. age, anybody!) People may request a copy of a census form by contacting us by telephone (860) 757-6580 or email. Please include the name of the individual and form number.

Abstract

This dataset includes data from the 2012 and 2017 USDA Census of Agriculture for American Indian Reservations combined into a single flatfile spreadsheet. The 2012 data was obtained through a special tabulation request from the USDA NASS and the 2017 data was tabulated by hand and double-checked for errors. The 2012 Census included data for only 76 reservations and the 2017 census includes data for only 75.

The Census for American Indian Reservations includes all farms and ranches within the boundaries of the Reservation but does not not distinguish between farmers and ranchers operating on Trust land with those operating in fee or deeded lands within Reservation boundaries. Furthermore, the published Census reports only quantified each variable for “Native” and “Reservation Total” and failed to report statistics for “Non-native” which conceals the extreme disparity that exists on Native American Reservations. While we have submitted a special tabulation request to the USDA NASS for the data on non-native operators, in the mean time, we have included a provisional calculation for “Non-native” producers, making it possible to analyze the racial disparity in agriculture on Native Lands. Additionally, the way the data is presented by the USDA it makes it difficult to aggregate the data for one or all reservations (e.g. the big picture). This data and dashboard, while only representing a fraction of tribal lands, represents the most complete source of data for agriculture on native lands. Additionally, we have added a GEOID column so each reservation's data can be joined with US Census Tiger spatial boundary for American Indian Areas.

The data on relationship to householder were derived from answers to Question 2 in the 2015 American Community Survey (ACS), which was asked of all people in housing units. The question on relationship is essential for classifying the population information on families and other groups. Information about changes in the composition of the American family, from the number of people living alone to the number of children living with only one parent, is essential for planning and carrying out a number of federal programs.

The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multi-generational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status.

Household – A household includes all the people who occupy a housing unit. (People not living in households are classified as living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and which have direct access from the outside of the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living arrangements.

Average Household Size – A measure obtained by dividing the number of people in households by the number of households. In cases where people in households are cross-classified by race or Hispanic origin, people in the household are classified by the race or Hispanic origin of the householder rather than the race or Hispanic origin of each individual.

Average household size is rounded to the nearest hundredth.

Comparability – The relationship categories for the most part can be compared to previous ACS years and to similar data collected in the decennial census, CPS, and SIPP. With the change in 2008 from “In-law” to the two categories of “Parent-in-law” and “Son-in-law or daughter-in-law,” caution should be exercised when comparing data on in-laws from previous years. “In-law” encompassed any type of in-law such as sister-in-law. Combining “Parent-in-law” and “son-in-law or daughter-in-law” does not represent all “in-laws” in 2008.

The same can be said of comparing the three categories of “biological” “step,” and “adopted” child in 2008 to “Child” in previous years. Before 2008, respondents may have considered anyone under 18 as “child” and chosen that category. The ACS includes “foster child” as a category. However, the 2010 Census did not contain this category, and “foster children” were included in the “Other nonrelative” category. Therefore, comparison of “foster child” cannot be made to the 2010 Census. Beginning in 2013, the “spouse” category includes same-sex spouses.

Abstract:

The 50-hectare plot at Barro Colorado Island, Panama, is a 1000 meter by 500 meter rectangle of forest inside of which all woody trees and shrubs with stems at least 1 cm in stem diameter have been censused. Every individual tree in the 50 hectares was permanently numbered with an aluminum tag in 1982, and every individual has been revisited six times since (in 1985, 1990, 1995, 2000, 2005, and 2010). In each census, every tree was measured, mapped and identified to species. Details of the census method are presented in Condit (Tropical forest census plots: Methods and results from Barro Colorado Island, Panama and a comparison with other plots; Springer-Verlag, 1998), and a description of the seven-census results in Condit, Chisholm, and Hubbell (Thirty years of forest census at Barro Colorado and the Importance of Immigration in maintaining diversity; PLoS ONE, 7:e49826, 2012).

Description:

CITATION TO DATABASE: Condit, R., Lao, S., Pérez, R., Dolins, S.B., Foster, R.B. Hubbell, S.P. 2012. Barro Colorado Forest Census Plot Data, 2012 Version. DOI http://dx.doi.org/10.5479/data.bci.20130603

CO-AUTHORS: Stephen Hubbell and Richard Condit have been principal investigators of the project for over 30 years. They are fully responsible for the field methods and data quality. As such, both request that data users contact them and invite them to be co-authors on publications relying on the data. More recent versions of the data, often with important updates, can be requested directly from R. Condit (conditr@gmail.com).

ACKNOWLEDGMENTS: The following should be acknowledged in publications for contributions to the 50-ha plot project: R. Foster as plot founder and the first botanist able to identify so many trees in a diverse forest; R. Pérez and S. Aguilar for species identification; S. Lao for data management; S. Dolins for database design; plus hundreds of field workers for the census work, now over 2 million tree measurements; the National Science Foundation, Smithsonian Tropical Research Institute, and MacArthur Foundation for the bulk of the financial support.

File 1. RoutputFull.pdf: Detailed documentation of the 'full' tables in Rdata format (File 5).

File 2. RoutputStem.pdf: Detailed documentation of the 'stem' tables in Rdata format (File 7).

File 3. ViewFullTable.zip: A zip archive with a single ascii text file named ViewFullTable.txt holding a table with all census data from the BCI 50-ha plot. Each row is a single measurement of a single stem, with columns indicating the census, date, species name, plus tree and stem identifiers; all seven censuses are included. A full description of all columns in the table can be found at http://dx.doi.org/10.5479/data.bci.20130604 (ViewFullTable, pp. 21-22 of the pdf).

File 4. ViewTax.txt: An ascii text table with information on all tree species recorded in the 50-ha plot. There are columns with taxonomics names (family, genus, species, and subspecies), plus the taxonomic authority. The column 'Mnemonic' gives a shortened code identifying each species, a code used in the R tables (Files 5, 7). The column 'IDLevel' indicates the depth to which the species is identified: if IDLevel='species', it is a fully identified, but if IDLevel='genus', the genus is known but not the species. IDLevel can also be 'family', or 'none' in case the species is not even known to family.

File 5. bci.full.Rdata31Aug2012.zip: A zip archive holding seven R Analytical Tables, versions of the BCI 50 ha plot census data in R format. These are designed for data analysis. There are seven files, one for each of the 7 censuses: 'bci.full1.rdata' for the first census through 'bci.full7.rdata' for the seventh census. Each of the seven files is a table having one record per individual tree, and each includes a record for every tree found over the entire seven censuses (i.e. whether or not they were observed alive in the given census, there is a record). Detailed documentation of these tables is given in RoutputFull.pdf (File 1).

File 6. bci.spptable.rdata: A list of the 1064 species found across all tree plots and inventories in Panama, in R format. This is a superset of species found in the BCI censuses: every BCI species is included, plus additional species never observed at BCI. The column 'sp' in this table is a code identifying the species in the R census tables (File 5, 7), and matching 'mnemomic' in ViewFullTable (File 3).

File 7. bci.stem.Rdata31Aug2012.zip: A zip archive holding seven R Analytical Tables, versions of the BCI 50 ha plot census data in R format. These are designed for data analysis. There are seven files, one for each of the 7 censuses: 'bci.stem1.rdata' for the first census through 'bci.stem7.rdata' for the seventh census. Each of the seven files is a table having one record per individual stem, necessary because some individual... Visit https://dataone.org/datasets/urn%3Auuid%3Ae4c356db-3351-4b58-a744-ea213b25e2a2 for complete metadata about this dataset.