The layer was compiled from the U.S. Census Bureau’s 2018 Planning Database (PDB), a database that assembles a range of housing, demographic, socioeconomic, and census operational data. The purpose of the data is for 2020 Census planning purposes.

Source: 2018 PDB, U.S. Census Bureau

Effective Date: June 2018

Last Update: January 2020

Update Cycle: Generally, annually as needed. 2018 PDB is vintage used for 2020 Census planning purposes by Nation and County.

The Census Planning Database is produced by the U.S. Census Bureau. It assembles a range of housing, demographic, socioeconomic, and census operational data that can be used for survey and census planning.

The Planning Database uses selected Census and selected 2012-2016 American Community Survey (ACS) estimates. In addition to variables extracted from the census and ACS databases, operational variables include the 2010 Census Mail Return Rate for each block group and tract.

This dataset is a subset of the 2018 Census Planning Database, filtered for the state of Connecticut, and including variables relating to hard to count populations. Other variables from the Census Planning Database relating to geography, population, households, housing units, and census operations at the tract and block level can also be found on the CT Data Portal with the tag "Census 2020."

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.

Data collected from the previous Census and the American Community Survey has allowed us to predict what areas of the South Suburbs are likely to be undercounted.The Hard-to-Count (HTC) Map provides residents and census workers with an in-depth view of southland region of Chicago.​By using this data, we can create plans to target these municipalities and ensure that each resident of the south suburbs is counted in the 2020 Census.The HTC Map is best viewed on a desktop computer. FREE OUTREACH MATERIALS​SSMMA has printed materials such as handouts, brochures, banners, and other printed materials for Southland residents. If interested in receiving 2020 Census care bags to reach residents while events are postponed, please contact us. Contact coordinator@southlandcounts.org for more information.​​Additional resources:​The U.S. Census Bureau - Outreach Materials

The Census Planning Database is produced by the U.S. Census Bureau. It assembles a range of housing, demographic, socioeconomic, and census operational data that can be used for survey and census planning.

The Planning Database uses selected Census and selected 2012-2016 American Community Survey (ACS) estimates. In addition to variables extracted from the census and ACS databases, operational variables include the 2010 Census Mail Return Rate for each block group and tract.

This dataset is a subset of the 2018 Census Planning Database, filtered for the state of Connecticut, and including variables relating to population. Variables relating to geography, households, housing units, census operations, and hard to count populations at the tract and block level can also be found on the CT Data Portal with the tag "Census 2020."

U.S. Census Bureau QuickFacts statistics for Nevada. 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.

The Census Planning Database is produced by the U.S. Census Bureau. It assembles a range of housing, demographic, socioeconomic, and census operational data that can be used for survey and census planning.

The Planning Database uses selected Census and selected 2012-2016 American Community Survey (ACS) estimates. In addition to variables extracted from the census and ACS databases, operational variables include the 2010 Census Mail Return Rate for each block group and tract.

This dataset is a subset of the 2018 Census Planning Database, filtered for the state of Connecticut, and including variables relating to population. Variables relating to geography, households, housing units, census operations, and hard to count populations at the tract and block level can also be found on the CT Data Portal with the tag "Census 2020."

More Metadata

This GIS layer contains the geographical boundaries of the 2010 census tracts for Loudoun County, Virginia. The 2010 Census tract boundaries are used for Census Bureau statistical data tabulation purposes, including the 2010 Decennial Census and American Community Surveys.

Census tracts are part of the sub-county census geography hierarchy of tracts, block groups, and blocks. The three census geographies nest to each other, forming a hierarchy of census tract, followed by block groups, and then blocks, with blocks being the smallest. A census tract consists of one or more census block groups and is a cluster of census blocks within the same census tract. Tracts are uniquely identified within a County by a six digit number. The last two digits will be zeros unless earlier divisions of the census tract occurred as a result of population growth.

Loudoun County's tracts were delineated by Loudoun County Government during the Census Bureau's Participant Statistical Areas Program for the 2010 Census. The 2010 Census tract layer has been modified from the Census Bureau's Tiger line file. Users should be aware that the Census's Tiger line data is devised from a mix of national and local GIS data sets. When the Tiger line data is overlaid with Loudoun County Government's detailed GIS layers it can be determined that the Census Bureau's Tiger line boundaries in some cases are slightly off from the actual location of the physical features, natural features, and governmental units such as town boundaries that they are designated to follow. The 2010 Loudoun census tract layer was generated by Loudoun County so that the tract boundaries would overlay with the features in Loudoun County's GIS data sets that the boundary are designated to follow.

The Area Hydrography Shapefile contains the geometry and attributes of both perennial and intermittent area hydrography features, including ponds, lakes, oceans, swamps (up to the U.S. nautical three-mile limit), glaciers, and the area covered by large rivers, streams, and/or canals that are represented as double-line drainage. Single-line drainage water features can be found in the Linear Hydrography Shapefile (LINEARWATER.shp). Linear water features includes single-line drainage water features and artificial path features, where they exist, that run through double-line drainage features such as rivers, streams, and/or canals, and serve as a linear representation of these features.

      The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the
      U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents
      a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or
      they can be combined to cover the entire nation.

Provides the only comprehensive, regularly collected source of information on selected economic and demographic characteristics for businesses and business owners by gender, ethnicity, race, and veteran status.

In 2023, there were approximately 980,000 human resources workers in the United States. This was a significant increase since 2020 as numbers have returned to pre-pandemic levels and beyond.

United States Imports: cif: Fr Ss 600Mm Ao w/ Hr Cls Un 3Mm Thck data was reported at 0.186 USD mn in Dec 2024. This records a decrease from the previous number of 0.363 USD mn for Nov 2024. United States Imports: cif: Fr Ss 600Mm Ao w/ Hr Cls Un 3Mm Thck data is updated monthly, averaging 0.185 USD mn from Jan 2002 (Median) to Dec 2024, with 266 observations. The data reached an all-time high of 9.130 USD mn in Aug 2012 and a record low of 0.002 USD mn in Jan 2023. United States Imports: cif: Fr Ss 600Mm Ao w/ Hr Cls Un 3Mm Thck data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA135: Imports: by Commodity: 6 Digit HS Code: HS 66 to 78.

In a survey conducted in the Asia-Pacific region in October 2020, 48 percent of respondents stated that they wished for HR to provide them with learning and development sessions, including virtual tool trainings, while working from home (WFH).

The School Workforce Annual Census (SWAC) is an electronic collection of individual level data on the school workforce in local authority maintained settings in Wales. The first collection was introduced in 2019 and collects information at November each year.

The SWAC is split into two parts: SWAC School return and SWAC Pay, HR and absences return. Information relating to codes can be found on the Welsh Government (see links below).

The SWAC School return is completed by all local authority maintained school settings in Wales, including Pupil Referral Units (PRUs). Schools record data on the workforce throughout the year in their Management Information System (MIS) software. This part of the return collects information on workforce characteristics (including Welsh language, ethnicity and disability), staff roles and curriculum taught.

The SWAC Pay, HR and Absences return is completed by each local authority, as well as schools which have opted-out of payroll and / or human resource (HR) service level agreements with their local authority. The data is maintained throughout the year in their HR and payroll systems. This return collects information on staff contracts, including salary and any additional payments they receive. This approach ensures that data for all relevant staff who work at local authority maintained schools is captured.

Sources: U.S. Census Bureau; American Community Survey, 2018-2022 American Community Survey 5-Year Estimates, Table B25077 and Table DP04; generated by CCRPC staff; using data.census.gov; https://data.census.gov/cedsci/; (26 February 2024).

Abstract

There is a long history to the agricultural census in the Netherlands. From 1934 onwards a census has been carried out (almost) every year. In recent years it is no longer purely a statistical project, but serves several purposes: on the one hand production of statistics by Statistics Netherlands and creating a frame for sampling, on the other hand providing data on individual holdings for administrative purposes by the Ministry of Economic Affairs, Agriculture and Innovation (the Ministry). Since the Ministry and Statistics Netherlands have a common interest in the census, it is held as a joint effort. In 1990, it was the last time special meeting days were organised to assess the data from the farmers. On these meeting days, farmers and enumerators jointly filled in the questionnaire manually. In the period 1991 – 1995, these sessions still took place, but the manual procedure was gradually replaced by filling in the information in a computer file. In 1996, the farmer could make a choice between coming to a special meeting place or filling in the survey form himself and returning it by postal mail. From 1997 on, a complete census was organised by postal mail every year. The year 2003 was a pilot year in which respondents had the opportunity to supply the census information through an internet application. In recent years the information is predominantly supplied via the internet. Since the statistical year 2002 the questionnaire of the agricultural census is combined with the application for animal, crop and arable land subsidies (in 2006 also for the single payment scheme). In 2007 data collection for the enforcement of the manure law is also combined in this questionnaire. This is done for efficiency reasons, both for farmers, and for administration and processing of data.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as a single unit, both technically and economically, which has a single management and which undertakes agricultural activities listed in Annex Ito the European Parliament and Council Regulation (EC) No. 1166/2008 within the economic territory of the EU, either as its primary or secondary activity.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Frame Statistics Netherlands has a business register of all industrial and non-industrial commercial establishments, but the agricultural holdings are not yet fully covered in this register. The agricultural census therefore relies on the administrative farm register (AFR) of the Ministry held by NSIR, an executive service of the Ministry. By law farmers have to register with NSIR. The AFR contains names, addresses and a few other characteristics of holders or holdings and a unique registration number. With the census information of several years Statistics Netherlands has built up a statistical farm register (SFR). Relevant characteristics from the AFR (a.o. identification number, addresses, legal status) are also stored in the SFR. Changes in addresses are entered into the AFR throughout the year, changes in the SFR of course only once a year. The SFR provides a magnificent basis for stratification and efficient sampling of subsequent agricultural statistics. An annual census may seem expensive (even when only half of the cost is looked upon as expenses for statistics). But the excellent quality of the sample frame allows for relative small samples in related agricultural statistics and thus reduction of costs.

Mode of data collection

Computer Assisted Web Interview (CAWI)

Research instrument

One questionnaire was used, integrating both the 2010 AC and the SAPM, and presented to respondents as a single statistical inquiry. The questionnaire covered all 16 core items recommended in the WCA 2010.

Questionnaire:

1 Work and education 2 Number of animals and housing 3 Horticulture under glass 4 Mushrooms, bulb growing, chicory growing 5 Crops on open land and land use 6 Agricultural land area 7 Subsidies 8 Farm data 9 Livestock manure 10 Excavation notification (WION) 11 Signature

Cleaning operations

a. Data collection and data entry About 85% of the questionnaires was filled in and returned using the web application, which already contained a lotof c hecks and validations. Paper forms were digitized by a data-entry firm and processed by NSIR in the same way as the online questionnaires. There were several quality controls to ensure correct digitization.

b. Data processing, estimation and analysis Data processing, estimation and analysis were performed in two successive stages:

1. Pre-processing at NSIR After data collection and data entry the input data go through an extensive error control phase. In this phase checks are made on missing values, valid values, unlikely values, range checks, checks of correlation in the data, checks of totals and so on. When necessary additional information is collected from the farmers by phone. Data that is checked and accepted by NSIR is forwarded to Statistics Netherlands.

2. Processing at Statistics Netherlands Processing at Statistics Netherlands involves additional error control, enrichment with additional information, such as total SO and typology, imputation for non-response and analysis. Analyses are made at several levels of aggregation and comprise comparison with previous results and agricultural data from other sources.

Data appraisal

Checking the information in the questionnaires took place using a special control programme. Data were checked for hard and soft errors. Hard errors are non-valid values. Soft errors are unlikely values. If necessary, the checking personnel contacted the respondent to correct for errors. Approximately 85 percent of the questionnaires were completed online. The online questionnaire application contained extensive interactive controls and edits.

Dissemination: Dissemination is done via the Statline database, which is available on the Internet (www.cbs.nl ). In this database, Internet users may select their own indicators and information topics. Short publications on specific subjects are presented in the form of newspaper or Internet articles. Safe access to census microdata is also provided.

Registration information on interstate, intrastate non-hazmat, and intrastate truck and bus companies that operate in the United States and have registered with FMCSA. Contains contact information and demographic information (number of drivers, vehicles, commodities carried, etc).

The market size of the human resources services industry worldwide increased significantly between 2012 and 2022, despite some fluctuation. In 2022, the staffing services market was worth almost 325 billion euros, up from 275 billion euros in the previous year. The staffing service line also held the majority share of the HR services industry that year – greater than 50 percent.

The state of companies leading the HR industry

In 2022, the leading companies in the human resources services industry worldwide were Randstad, Adecco, and Manpower. Randstand held the biggest market share of the three companies in 2022, and had revenues reaching almost 25 billion euros. Randstad is a Dutch human resources company headquartered in Amsterdam, the Netherlands.

What are the onboarding challenges faced by HR companies?

Onboarding is a means of getting recently-hired employees acquainted with the organizational skills, behaviors, and cultures of the current workplace. This process is typically carried out by human resources, although they face a myriad of challenges during the onboarding process itself. In 2018, the biggest challenge faced by HR departments worldwide was good monitoring of new employees.

As of a 2020 report, of the business and HR leaders surveyed worldwide who use artificial intelligence (AI) to assist workers in their organization, 58 percent of these claimed that the main use of AI in this context was to improve consistency and quality. A further 26 percent stated that AI is used to assist workers through improving productivity, with the remaining 16 percent using AI technology to improve insights.

1950 Employment Census Data for Baltimore, Maryland. Refer to the 1950 codebook (codebook_1950.pdf) for more information. 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.