This dataset includes all individuals from the 1860 US census.
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This dataset was developed through a collaboration between the Minnesota Population Center and the Church of Jesus Christ of Latter-Day Saints. The data contain demographic variables, economic variables, migration variables and race variables. Unlike more recent census datasets, pre-1900 census datasets only contain individual level characteristics and no household or family characteristics, but household and family identifiers do exist.
The official enumeration day of the 1860 census was 1 June 1860. The main goal of an early census like the 1860 U.S. census was to allow Congress to determine the collection of taxes and the appropriation of seats in the House of Representatives. Each district was assigned a U.S. Marshall who organized other marshals to administer the census. These enumerators visited households and recorder names of every person, along with their age, sex, color, profession, occupation, value of real estate, place of birth, parental foreign birth, marriage, literacy, and whether deaf, dumb, blind, insane or “idiotic”.
Sources: Szucs, L.D. and Hargreaves Luebking, S. (1997). Research in Census Records, The Source: A Guidebook of American Genealogy. Ancestry Incorporated, Salt Lake City, UT Dollarhide, W.(2000). The Census Book: A Genealogist’s Guide to Federal Census Facts, Schedules and Indexes. Heritage Quest, Bountiful, UT
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Heritage Creek by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Heritage Creek across both sexes and to determine which sex constitutes the majority.
Key observations
There is a majority of female population, with 54.59% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
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
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.
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/.
This dataset is a part of the main dataset for Heritage Creek Population by Race & Ethnicity. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Heritage Creek 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 Heritage Creek. 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 620 (56.88% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age cohorts:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Heritage Creek Population by Age. You can refer the same here
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
U.S. Census Bureau QuickFacts statistics for Heritage Village CDP, Connecticut. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Spiders (Arachnida: Araneae) are a classic indicator taxon for evaluating the health of natural environments. However, studies of spiders’ responses to forest succession under natural and anthropogenic disturbance regimes are lacking. Yakushima Island in southwestern Japan has a unique forest ecosystem, and part of the island is designated as a world natural heritage site by UNESCO. Approximately 90% of Yakushima is covered by forest, including both plantations and natural forests. We made an inventory of spiders on Yakushima Island by collecting specimens in five forests (two plantations and three natural forests) with Malaise and window traps from 2006 to 2008 (a total of 637 traps). We collected 3487 specimens, representing 31 families and 165 species or morphotypes, including undescribed and unidentified species. All specimens were preserved in 70% ethanol, and all data were gathered into a Darwin Core Archives as sample event data. The data set is available from the GBIF network (http://osawa.nomaki.jp/dl/dwca-yakushima_spyder01-v1.0.zip /upload from JBIF node after acceptance). Because there have been no spider inventories based on such a systematic trapping survey in Japan, this data set provides new insight into the biodiversity on Yakushima Island.
The Vulnerable Population Index (VPI) is intended to guide location selection and stakeholder identification for public involvement and inform Title VI and Environmental Justice (EJ) performance measurement. The Baltimore Regional Transportation Board uses data from the US Census Bureau to determine the concentrations of seven sensitive populations for the region and for each census tract. A tract with a concentration of a sensitive population greater than the concentration of the Baltimore region as a whole is considered to be “vulnerable” for the sensitive population. The Vulnerable Population Index (VPI) indicated the number of vulnerable populations for each tract, and thus provides a general indication of the extent to which each tract is vulnerable. The VPI looks at the following variables:Population in Poverty (American Community Survey 2006-2010 5-Year Estimates)Age 75 and up (Census 2010) Non-Hispanic Minority (people who are non-White and non-Hispanic) (Census 2010) Hispanic or Latino Heritage (Census 2010)Limited English Proficiency (population who speaks English “not well” or “not at all.”) (American Community Survey 2006-2010 5-Year Estimates)Households with No Car (American Community Survey 2006-2010 5-Year Estimates)Disabled Population (Census 2000) This data was used in the interactive mapping application found at http://gis.baltometro.org/Application/VPI/index.html. For more information on Transportation Equity work and studies at BMC, go to http://www.baltometro.org/about-the-brtb/transportation-equity. Note that for ACS and Census 2000 data margins of error are not provided. This data has been modified by the Baltimore Metropolitan Council and should not replace data directly loaded from the Census.Source: Variables are American Community Survey 2006-2010 5-Year Estimates, the 2000 Census (SF3), and the 2010 Census. Census tracts are the 2010 Census. Main Index is calculated by BMC.Date: Index published in May 2015. Date of raw data is either 2000, 2010, or 2006-2010 depending on the variable. See the above list for more information.Update: The VPI is updated approximately every 5 years. Data will be added as a separate layer.Data fields:PCT_NotWhite_NotHisp - Percent of the population in each tract that is a non-Hispanic minority. PCT_Hispanic - Percent of the population in each tract that is Hispanic or Latino. Pct75up - Percent of the population in each tract that is age 75 or higher. PCT_LEP - Percent of the Limited English Proficiency population in each tract.PCT_People_in_Poverty - Percent of the population in each tract that is living below the Federal poverty level.PCT_NOCAR - Percent of households in each tract that do not have a car.PCT_Disabl - Percent of the population in each tract that is disabled. Reg_NotWhite_NotHisp - Regional average for the population that is a non-Hispanic minority. This is for the same time period as the tract data. Reg_Hispanic - Regional average for the population that is Hispanic or Latino. This is for the same time period as the tract data. Reg_75up - Regional average for the population that is age 75 or higher. This is for the same time period as the tract data. Reg_LEP - Regional average for the Limited English Proficiency population. This is for the same time period as the tract data. Reg_Poverty - Regional average for the population that is living below the Federal poverty level. This is for the same time period as the tract data. Reg_NOCAR - Regional average for percent of households that do not have a car. This is for the same time period as the tract data. Reg_Disabl - Regional average for the population that is disabled. This is for the same time period as the tract data. FLAG_NotWhite_NotHisp - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Hispanic - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_75up - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_LEP - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Poverty - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_NOCAR - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Disabl - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". INDEX - The sum of all the FLAG fields.
https://www.icpsr.umich.edu/web/ICPSR/studies/7966/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/7966/terms
This data collection contains extracts of the original DUALabs Special Fifth Count ED/BG Summary Tapes. They are comprised of limited demographic and socioeconomic variables for 27 states in the continental United States. Data are provided at the county, minor civil division, enumeration district, and block group levels for total population and Spanish heritage population for the following states: Minnesota, Nevada, Wyoming, Indiana, Kansas, Nebraska, Oklahoma, South Dakota, Colorado, Arizona, Utah, North Dakota, Montana, Idaho, Missouri, Washington, Iowa, Louisiana, Arkansas, Ohio, Michigan, Wisconsin, Illinois, Oregon, Texas, New Mexico, and California. Demographic variables provide information on race, age, sex, country and place of origin, income, and family status and size. The data were obtained by ICPSR from the National Chicano Research Network, Survey Research Center, Institute for Social Research, University of Michigan.
This data collection contains 132 Public Use Microdata Samples (PUMS) files from the 1970 Census of Population and Housing. Information is provided in these files on the housing unit, such as occupancy and vacancy status of house, tenure, value of property, commercial use, year structure was built, number of rooms, availability of plumbing facilities, sewage disposal, bathtub or shower, complete kitchen facilities, flush toilet, water, telephone, and air conditioning. Data are also provided on household characteristics such as the number of persons aged 18 years and younger in the household, the presence of roomers, boarders, or lodgers, the presence of other nonrelative and of relative other than wife or child of head of household, the number of persons per room, the rent paid for unit, and the number of persons with Spanish surnames. Other demographic variables provide information on age, race, marital status, place of birth, state of birth, Puerto Rican heritage, citizenship, education, occupation, employment status, size of family, farm earnings, and family income. This hierarchical data collection contains approximately 214 variables for the 15-percent sample, 227 variables for the 5-percent sample, and 117 variables for the neighborhood characteristics sample. (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/ICPSR00018.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically
Canadian Heritage is committed to sharing its internal research products as a commitment to open research, one of the pillars of its Open Government Strategy. The data was extracted by Statistics Canada and modified by Canadian Heritage.
An Excel workbook containing tables of census data for a range of indicators going back to 1961 where possible. Two versions are offered: the legacy '2011' workbook with data up to 2011, and a '2021' workbook updated to 2021. The 2021 update is a work in progress.
The percentage of persons, out of the total number of persons living in an area, self-identifying their ethnicity as Hispanic or Latino. Hispanic origin can be viewed as the heritage, nationality group, lineage, or country of birth of the person or the person’s parents or ancestors before they arrived in the United States. People who identify their origin as Hispanic, Latino, or Spanish may be of any race. Source: U.S. Census Bureau, American Community SurveyYears Available: 2010, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2020, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html
This data collection contains 132 Public Use Microdata Samples (PUMS) files from the 1970 Census of Population and Housing. Information is provided in these files on the housing unit, such as occupancy and vacancy status of house, tenure, value of property, commercial use, year structure was built, number of rooms, availability of plumbing facilities, sewage disposal, bathtub or shower, complete kitchen facilities, flush toilet, water, telephone, and air conditioning. Data are also provided on household characteristics such as the number of persons aged 18 years and younger in the household, the presence of roomers, boarders, or lodgers, the presence of other nonrelative and of relative other than wife or child of head of household, the number of persons per room, the rent paid for unit, and the number of persons with Spanish surnames. Other demographic variables provide information on age, race, marital status, place of birth, state of birth, Puerto Rican heritage, citizenship, education, occupation, employment status, size of family, farm earnings, and family income. This hierarchical data collection contains approximately 214 variables for the 15-percent sample, 227 variables for the 5-percent sample, and 117 variables for the neighborhood characteristics sample. (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/ICPSR00018.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the data for the Heritage Creek, KY population pyramid, which represents the Heritage Creek 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
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Heritage Creek Population by Age. You can refer the same here
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically
Canadian Heritage is committed to sharing its internal research products as a commitment to open research, one of the pillars of its Open Government Strategy. The data was extracted by Statistics Canada and modified by Canadian Heritage.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
An Excel workbook containing tables of census data for a range of indicators going back to 1961 where possible. Two versions are offered: the legacy '2011' workbook with data up to 2011, and a '2021' workbook updated to 2021. The 2021 update is a work in progress.
This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B03002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.
This data collection contains 132 Public Use Microdata Samples (PUMS) files from the 1970 Census of Population and Housing. Information is provided in these files on the housing unit, such as occupancy and vacancy status of house, tenure, value of property, commercial use, year structure was built, number of rooms, availability of plumbing facilities, sewage disposal, bathtub or shower, complete kitchen facilities, flush toilet, water, telephone, and air conditioning. Data are also provided on household characteristics such as the number of persons aged 18 years and younger in the household, the presence of roomers, boarders, or lodgers, the presence of other nonrelative and of relative other than wife or child of head of household, the number of persons per room, the rent paid for unit, and the number of persons with Spanish surnames. Other demographic variables provide information on age, race, marital status, place of birth, state of birth, Puerto Rican heritage, citizenship, education, occupation, employment status, size of family, farm earnings, and family income. This hierarchical data collection contains approximately 214 variables for the 15-percent sample, 227 variables for the 5-percent sample, and 117 variables for the neighborhood characteristics sample. (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/ICPSR00018.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.
The Handloom Census is a specialized survey undertaken to understand the intricacies of India's handloom sector, which boasts a rich heritage and provides livelihood to millions. This census captures data on the number of weavers, their socio-economic conditions, types of looms in use, patterns of production, and market dynamics. It also delves into the challenges faced by artisans, from technological constraints to financial hardships. The insights from the Handloom Census are invaluable for policymakers, designers, and industry stakeholders, enabling them to frame supportive policies, develop market strategies, and ensure the sustenance and growth of this traditional industry in the face of modern competition.
This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Spring included telemetry locations (n = 14,058) from mid-March to June, and is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically
Canadian Heritage is committed to sharing its internal research products as a commitment to open research, one of the pillars of its Open Government Strategy. The data was extracted by Statistics Canada and modified by Canadian Heritage.
This dataset includes all individuals from the 1860 US census.
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This dataset was developed through a collaboration between the Minnesota Population Center and the Church of Jesus Christ of Latter-Day Saints. The data contain demographic variables, economic variables, migration variables and race variables. Unlike more recent census datasets, pre-1900 census datasets only contain individual level characteristics and no household or family characteristics, but household and family identifiers do exist.
The official enumeration day of the 1860 census was 1 June 1860. The main goal of an early census like the 1860 U.S. census was to allow Congress to determine the collection of taxes and the appropriation of seats in the House of Representatives. Each district was assigned a U.S. Marshall who organized other marshals to administer the census. These enumerators visited households and recorder names of every person, along with their age, sex, color, profession, occupation, value of real estate, place of birth, parental foreign birth, marriage, literacy, and whether deaf, dumb, blind, insane or “idiotic”.
Sources: Szucs, L.D. and Hargreaves Luebking, S. (1997). Research in Census Records, The Source: A Guidebook of American Genealogy. Ancestry Incorporated, Salt Lake City, UT Dollarhide, W.(2000). The Census Book: A Genealogist’s Guide to Federal Census Facts, Schedules and Indexes. Heritage Quest, Bountiful, UT