Terms of UseData Limitations and DisclaimerThe user’s use of and/or reliance on the information contained in the Document shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.This is an ongoing data development project. Attempts have been made to contact all PWS systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some PWS service areas included in this datalayer have not been verified by the PWS or the municipality involved, but since many of those areas are based on information published online by the municipality, the PWS, or in a publicly available report, they are included in the estimated PWS service area datalayer.Please note: All PWS service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a PWS service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the PWS – please contact the relevant PWS. Not all service areas have been confirmed by the systems.Please use the following citation to reference these data:MassDEP, Water Utility Resilience Program. 2025. Community and Non-Transient Non-Community Public Water System Service Area (PubV2025_3).IMPORTANT NOTICE: This MassDEP Estimated Water Service datalayer may not be complete, may contain errors, omissions, and other inaccuracies and the data are subject to change. This version is published through MassGIS. We want to learn about the data uses. If you use this dataset, please notify staff in the Water Utility Resilience Program (WURP@mass.gov).This GIS datalayer represents approximate service areas for Public Water Systems (PWS) in Massachusetts. In 2017, as part of its “Enhancing Resilience and Emergency Preparedness of Water Utilities through Improved Mapping” (Critical Infrastructure Mapping Project ), the MassDEP Water Utility Resilience Program (WURP) began to uniformly map drinking water service areas throughout Massachusetts using information collected from various sources. Along with confirming existing public water system (PWS) service area information, the project collected and verified estimated service area delineations for PWSs not previously delineated and will continue to update the information contained in the datalayers. As of the date of publication, WURP has delineated Community (COM) and Non-Transient Non-Community (NTNC) service areas. Transient non-community (TNCs) are not part of this mapping project.Layers and Tables:The MassDEP Estimated Public Water System Service Area data comprises two polygon feature classes and a supporting table. Some data fields are populated from the MassDEP Drinking Water Program’s Water Quality Testing System (WQTS) and Annual Statistical Reports (ASR).The Community Water Service Areas feature class (PWS_WATER_SERVICE_AREA_COMM_POLY) includes polygon features that represent the approximate service areas for PWS classified as Community systems.The NTNC Water Service Areas feature class (PWS_WATER_SERVICE_AREA_NTNC_POLY) includes polygon features that represent the approximate service areas for PWS classified as Non-Transient Non-Community systems.The Unlocated Sites List table (PWS_WATER_SERVICE_AREA_USL) contains a list of known, unmapped active Community and NTNC PWS services areas at the time of publication.ProductionData UniversePublic Water Systems in Massachusetts are permitted and regulated through the MassDEP Drinking Water Program. The WURP has mapped service areas for all active and inactive municipal and non-municipal Community PWSs in MassDEP’s Water Quality Testing Database (WQTS). Community PWS refers to a public water system that serves at least 15 service connections used by year-round residents or regularly serves at least 25 year-round residents.All active and inactive NTNC PWS were also mapped using information contained in WQTS. An NTNC or Non-transient Non-community Water System refers to a public water system that is not a community water system and that has at least 15 service connections or regularly serves at least 25 of the same persons or more approximately four or more hours per day, four or more days per week, more than six months or 180 days per year, such as a workplace providing water to its employees.These data may include declassified PWSs. Staff will work to rectify the status/water services to properties previously served by declassified PWSs and remove or incorporate these service areas as needed.Maps of service areas for these systems were collected from various online and MassDEP sources to create service areas digitally in GIS. Every PWS is assigned a unique PWSID by MassDEP that incorporates the municipal ID of the municipality it serves (or the largest municipality it serves if it serves multiple municipalities). Some municipalities contain more than one PWS, but each PWS has a unique PWSID. The Estimated PWS Service Area datalayer, therefore, contains polygons with a unique PWSID for each PWS service area.A service area for a community PWS may serve all of one municipality (e.g. Watertown Water Department), multiple municipalities (e.g. Abington-Rockland Joint Water Works), all or portions of two or more municipalities (e.g. Provincetown Water Dept which serves all of Provincetown and a portion of Truro), or a portion of a municipality (e.g. Hyannis Water System, which is one of four PWSs in the town of Barnstable).Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder. The location of these circles are estimates based on the general location of the source wells or the general estimated location of the service area - these do not represent the actual service area.Service areas were mapped initially from 2017 to 2022 and reflect varying years for which service is implemented for that service area boundary. WURP maintains the dataset quarterly with annual data updates; however, the dataset may not include all current active PWSs. A list of unmapped PWS systems is included in the USL table PWS_WATER_SERVICE_AREA_USL available for download with the dataset. Some PWSs that are not mapped may have come online after this iteration of the mapping project; these will be reconciled and mapped during the next phase of the WURP project. PWS IDs that represent regional or joint boards with (e.g. Tri Town Water Board, Randolph/Holbrook Water Board, Upper Cape Regional Water Cooperative) will not be mapped because their individual municipal service areas are included in this datalayer.PWSs that do not have corresponding sources, may be part of consecutive systems, may have been incorporated into another PWSs, reclassified as a different type of PWS, or otherwise taken offline. PWSs that have been incorporated, reclassified, or taken offline will be reconciled during the next data update.Methodologies and Data SourcesSeveral methodologies were used to create service area boundaries using various sources, including data received from the systems in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and PWS websites. When provided with water line data rather than generalized areas, 300-foot buffers were created around the water lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas.Verification ProcessSmall-scale PDF file maps with roads and other infrastructure were sent to every PWS for corrections or verifications. For small systems, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the PWS and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all or nearly all of the municipality is served by the PWS.Sources of information for estimated drinking water service areasThe following information was used to develop estimated drinking water service areas:EOEEA Water Assets Project (2005) water lines (these were buffered to create service areas)Horsely Witten Report 2008Municipal Master Plans, Open Space Plans, Facilities Plans, Water Supply System Webpages, reports and online interactive mapsGIS data received from PWSDetailed infrastructure mapping completed through the MassDEP WURP Critical Infrastructure InitiativeIn the absence of other service area information, for municipalities served by a town-wide water system serving at least 97% of the population, the municipality’s boundary was used. Determinations of which municipalities are 97% or more served by the PWS were made based on the Percent Water Service Map created in 2018 by MassDEP based on various sources of information including but not limited to:The Winter population served submitted by the PWS in the ASR submittalThe number of services from WQTS as a percent of developed parcelsTaken directly from a Master Plan, Water Department Website, Open Space Plan, etc. found onlineCalculated using information from the town on the population servedMassDEP staff estimateHorsely Witten Report 2008Calculation based on Water System Areas Mapped through MassDEP WURP Critical Infrastructure Initiative, 2017-2022Information found in publicly available PWS planning documents submitted to MassDEP or as part of infrastructure planningMaintenanceThe

This is a series-level metadata record. 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 filewith no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independentdata set, or they can be combined to cover the entire nation. New England City and Town Area (NECTA) Divisions subdivide a NECTA containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of cities and towns. NECTA Divisions are defined by the Office of Management and Budget (OMB) and consist of a main city or town that represents an employment center, plus adjacent cities and towns associated with the main cityor town through commuting ties. Each NECTA Division must contain a total population of 100,000 or more. Because NECTA Divisions represent subdivisions of larger NECTAs, it is not appropriate to rank or compare NECTA Divisions with NECTAs.Not all NECTAs with urban areas of this size will contain NECTA Divisions. The NECTA Divisions boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2017.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are special-purpose governments and administrative units designed by state and local officials to provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to develop demographic estimates and to support educational research and program administration. The NCES Common Core of Data (CCD) program is an annual collection of basic administrative characteristics for all public schools, school districts, and state education agencies in the United States. These characteristics are reported by state education officials and include directory information, number of students, number of teachers, grade span, and other conditions. The administrative attributes in this layer were developed from the 2021-2022 CCD collection. For more information about NCES school district boundaries, see: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries. For more information about CCD school district attributes, see: https://nces.ed.gov/ccd/files.asp. Notes: -1 or MIndicates that the data are missing. -2 or N Indicates that the data are not applicable. -9 Indicates that the data do not meet NCES data quality standards. All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

In an effort to properly document the sites and areas where data were collected, personnel of the BOReal Ecosystem-Atmosphere Study (BOREAS) Information System (BORIS) obtained and compiled geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas. The data are stored in two text files as American Standard Code for Information Interchange (ASCII) characters.

Mid-year (30 June) estimates of the usual resident population for 2011 Census Output Areas (OAs) in the East region of England.

Graph and download economic data for Other Financial Information: Estimated Market Value of Owned Home by Region: Residence in the Midwest Census Region (CXU800721LB1103M) from 1984 to 2023 about Midwest Census Region, owned, market value, information, financial, residents, housing, estimate, and USA.

This layer contains data defining the exterior boundaries of land under common ownership, typically within condominium properties within Fairfax County, Virginia. The common area layer was created to depict these areas of land that do not actually have parcel identification numbers. These areas of land are owned by a group of properties that lie within the common area. Information portrayed on this layer was initially derived from the ink-on-mylar property maps maintained by the County since the early 1960s. Contact: Fairfax County Department of Information Technology GIS DivisionData Accessibility: Publicly AvailableUpdate Frequency: DailyLast Revision Date: 1/1/2000Creation Date: 1/1/2000Feature Dataset Name: GISMGR.PARCELSLayer Name: GISMGR.COMMON_AREAS

The Barrow Area Information Database (BAID) data collection is comprised of geospatial data for the research hubs of Utqiaġvik (formerly known as Barrow), Atqasuk and Ivotuk on Alaska's North Slope. Over 18,000 research plots and instrument locations are included in the BAID research sites database. Updates to the database of observation sites are ongoing through field mapping of new research locations and extant sampling sites dating back to the 1940s. Ancillary data layers included with this update include boardwalks, trails, boat launches, snow fences, road centerlines and place names for major features around Utqiaġvik. These geospatial datasets serve as an update to previously published information. Information is compiled to support science planning and communication with the local communities of Utqiaġvik and Atqusuk, as well as the North Slope Borough. The distribution of previously mapped research sites and infrastructure, which are tracked in BAID, are often considered during the planning phase for new projects.

This dataset contains the custom geographies used to study the registered nurse workforce and identifies which areas are designated as registered nurse shortage areas (RNSA) as of the year labeled in the title.

"This file provides digital data for all 1990 precensus map features, the associated 1990 census initial tabulation geographic area codes, such as 1990 census block numbers, and the codes for the Jan. 1, 1990 political areas on both sides of each line segment of every mapped feature. The data contain basic information on 1990 census geographic area codes, feature names, and address ranges in the form of ten ""Record Types."" The Census Bureau added four new record types in response to some us er and vendor requests to provide point and area information contained in the Census Bureau's Precensus Map sheets that is not contained in the Precensus TIGER/Line files. The record types include: Basic data records (individual Feature Segment Records), shape coordinate points (feature shape records), additional decennial census geographic area codes, index to alternate feature names, feature name list, additional address range and zip code data, landmark features, area landmarks, area boundaries, and polygon location. Each segment record contains appropriate decennial census and FIPS geographic area codes, latitude/longitude coordinates, the name of the feature (including the relevant census feature class code identifying the segment by category), and, for areas formerly covered by the GBF/DIME-Files, the address ranges and ZIP code associated with those address ranges for each side of street segments. For other areas, the TIGER?Line files do not contain address ranges or ZIP Codes. The shape records provide coordinate values that describe the shape of those feature segments that are not straight."

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Tabulated data with information on crop types and areas in each river reach in the Hunter river system model. This dataset was derived by the Bioregional Assessment Programme from existing catchment land use data obtained from the Australian Bureau of Agricultural and Resource Economics and Sciences.

Purpose

The AWRA-R river model needs details of irrigated areas and crop types in each river reach in which irrigation is present in order to determine areal extent and crop factors of the most common crop types .

Dataset History

Areas and crop types are obtained from the Catchment scale Land Use Management (CLUM). The dataset was clipped using catchment boundaries defined by the AWRA-R modelling domain and the information summarised by reach in order to determine crop types and associated crop factors . Irrigation areas are determined using the first level classification in the CLUM dataset which describes the main land use type. Crop types were determined from the third level classification, which provides detailed information on crop types.

Dataset Citation

Bioregional Assessment Programme (2016) HUN AWRA-R Irrigation Area Extents and Crop Types v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/90013748-6247-47ad-bddb-07a9c2d857cd.

Dataset Ancestors

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From National Surface Water sites Hydstra

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From HUN AWRA-R calibration catchments v01

• Derived From Bioregional Assessment areas v05

• Derived From HUN AWRA-R calibration nodes v01

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From GEODATA TOPO 250K Series 3

• Derived From Victoria - Seamless Geology 2014

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Activity Project Area NEPA represents an area (polygon) within which one or more activities related to the National Environmental Policy Act (NEPA) are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and DownloadsThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityProjectAreas_01/MapServer/0 https://www.fs.fed.us/emc/nepa/index.htm For complete information, please visit https://data.gov.

The Nonattainment Area - 8 hr Ozone (1997) dataset was designated on June 15, 2004 by the Environmental Protection Agency (EPA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset provides detailed information about areas identified in the U.S. where ground-level ozone have not met the National Ambient Air Quality Standards (NAAQS) established in 1997 for ground-level ozone and have been designated "nonattainment areas (NAA)". This dataset also shows the nonattainment areas at the time of the 8-Hour Ozone (1997) National Ambient Air Quality Standards (NAAQS) revocation on April 6, 2015. These data are weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. For more information about this non-attainment area, please see the website https://www.epa.gov/green-book/green-book-8-hour-ozone-1997-area-information-naaqs-revoked. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529059

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

Shellfish Area Classification Set:

The Connecticut Department of Environmental Protection cooperated with the Department of Agriculture, Bureau of Aquaculture to publish the Connecticut Shellfish Classification data. More recent information may be available from the Department of Agriculture since the time this information was originally published in 2007. For information or questions on shellfish area classifications contact the Connecticut Department of Agriculture, Bureau of Aquaculture (DA/BA). Mailing address: P.O. Box 97, Milford, CT, 06460, USA. Voice: 203-874-0696. Fax: 203-783-9976. E-mail: dept.agriculture@snet.net. Final authority for the classification of any shellfish area rests with the DA/BA. One of a set of three 1:24,000-scale datalayers that represent the classifications of shellfish growing waters for the State of Connecticut shoreline towns. This datalayer is composed of polygon features. The shellfishing areas are delineated and classified by the DA/BA, which is the state shellfish control authority in Connecticut. The Connecticut Department of Environmental Protection (DEP) applied information from the DA/BA to the hydrography data to create digital data of shellfish area classifications. DA/BA reassesses pollution sources and shellfish growing areas annually. The digital data is current to that effective date or last amended date recorded on the assessment date list (see supplemental information). This data is subject to change and the DA/BA may have more recent information for some areas. DEP cooperated with the DA/BA to publish the DA/BA Shellfish Area Classifications data. More recent shellfish classification information may now be available from DA/BA since the time this information was originally published in 2007. The three classification datalayers are feature based. Waterbodies, such as rivers and lakes and ponds, that appear as area features in the hydrography datalayer are classified in the Shellfish Area polygon shapefile. Smaller water bodies, such as streams and creeks, that appear as line features in the hydrography datalayer are classified in the Shellfish Area line shapefile. A separate point shapefile contains the marinas that are classified by DA/BA. Contact DA/BA or local health departments for additional information regarding the classification of marinas and anchorage areas. Three additional datalayers add to the classification picture. Markers, such as buoys, demarcation signs and piers, are referred to in DA/BA text describing the shellfish area classifications. The town boundary lines as depicted on DA/BA oyster/shellfish ground charts extend to the Connecticut/New York mid-Long Island Sound boundary line. The jurisdiction line on the charts indicates the boundary between state and town jurisdictional control over shellfish grounds. The jurisdiction line is separate from the shellfishing area classifications

The Connecticut Department of Environmental Protection cooperated with the Department of Agriculture, Bureau of Aquaculture to publish the Connecticut Shellfish Classification data. More recent information may be available from the Department of Agriculture since the time this information was originally published in 2006. For information or questions on shellfish area classifications contact the Connecticut Department of Agriculture, Bureau of Aquaculture (DA/BA). Mailing address: P.O. Box 97, Milford, CT, 06460, USA. Voice: 203-874-0696. Fax: 203-783-9976. E-mail: dept.agriculture@snet.net. Final authority for the classification of any shellfish area rests with the DA/BA. This 1:24,000 scale layer depicts town boundary lines as shown on DA/BA oyster/shellfish ground charts. These boundary lines extend to the Connecticut/New York mid-Long Island Sound boundary line. This datalayer is composed of polygon features. See also the corresponding line feature class (Shellfish Area Town Line). This layer is intended to be used for cartographic purposes in conjunction with the 1:24,000 scale Shellfish Area Classifications data that represent the classifications of shellfish growing waters for the State of Connecticut shoreline towns. Shellfish Bed Jurisdiction is a related datalayer that depicts the jurisdiction line on the charts, i.e., the boundary between state and town jurisdictional control over shellfish grounds. It is referenced here to clarify that in some cases the town boundary lines of the Shellfish Area Town Poly layer and the Shellfish Bed Jurisdiction layer are different and are so noted. General Information Applicable to Shellfish Classifications Datalayers: The shellfishing areas are delineated and classified by the DA/BA, which is the state shellfish control authority in Connecticut. The Connecticut Department of Environmental Protection (DEP) applied information from the DA/BA to the hydrography data to create digital data of shellfish area classifications. DA/BA reassesses pollution sources and shellfish growing areas annually. The digital data is current to that effective date or last amended date recorded on the assessment date list (see supplemental information). This data is subject to change and the DA/BA may have more recent information for some areas. The shellfish datalayers that can be used together as a group include Shellfish Area Classification Line, Shellfish Area Classification Poly, Shellfish Area Marina, Shellfish Area Marker, Shellfish Area Town Poly, Shellfish Area Town Line, Shellfish Bed Jurisdiction, and Connecticut Managed Shellfish Beds.

The records listed in this file can be accessed online: http://online.akronohio.gov/apdonline/reportlookup/EULA.aspx?referrer=ReportLookup. (XLSX)

Key Table Information.Table Title.Annual Geographic Information Table.Table ID.GEOINFO2023.GEOINFO.Survey/Program.Geography.Year.2023.Dataset.GEO Geography Information.Source.U.S. Census Bureau, 2023 Geography.Release Date.August 15, 2024.Dataset Universe.Geographic information available in data.census.gov for year 2023.Methodology.Data Items and Other Identifying Records.Geographic Area Name Area (Land, in square meters) Area (Land, in square miles) Area (Water, in square meters) Area (Water, in square miles) Internal Point (Latitude) Internal Point (Longitude) For full list of all the variables including those available in the API refer to the following link: https://api.census.gov/data/2023/geoinfo/variables.html.Unit(s) of Observation.Geographic entity.Geography Coverage.For a full list defining the geographies covered go to https://api.census.gov/data/2023/geoinfo/geography.html.Technical Documentation/Methodology.https://www.census.gov/programs-surveys/geography/about/glossary.html.Table Information.API Information.https://api.census.gov/data/2023/geoinfo.html.Data-Specific Notes.The Geography Information dataset (GEOINFO) contains all the geographies that are disseminated by the U.S Census Bureau during a calendar year. The dataset combines all these disseminated geographies into one centralized location to allow for easy user access. The Geography Information dataset includes spatial attributes for the disseminated geographies, such as a point of internal latitude, a point of internal longitude, and the area of the water and land both in square meters and square miles. The geographies contained in the Geography Information dataset are the geographies disseminated for surveys and programs such as the American Community Survey, Community Resilience Estimates, Current Population Survey, Decennial Census, Economic Census, Economic Surveys, Household Pulse Survey, International Database, Population Estimates, Secondary Employment Outcomes, Public Sector, and Survey of Market Absorption. The Geography Information dataset does include island area geographies but does not contain any international geographies. The Geography Information dataset will be created annually for the calendar year prior once all of the Geographic Information Tables for the various surveys and programs are received for the year. The Geography Information dataset will be released around the early summer every year. The program will first produce a Geography Information dataset for data year 2023 and eventually produce datasets going backwards to data year 2020. The program will also produce a Geography Information dataset for every subsequent year after data year 2023. Note: The Geography Information dataset contains the geographies disseminated for the Population Estimates Program but does not currently support the release of the population estimates. Please refer to the following URL for population estimates: https://www.census.gov/programs-surveys/popest/data.html Note: The Geography Information dataset for 2023 does not include any island area geographies..Additional Information.Contact Information.census.data@census.gov.Suggested Citation.U.S. Census Bureau. "Annual Geographic Information Table" Geography, GEO Geography Information, Table GEOINFO, -1, https://data.census.gov/table/GEOINFO2023.GEOINFO?q=GEOINFO: Accessed on June 24, 2025..

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials t

Dataset contains counts and measures for households from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 2.

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The variables included in this dataset are for households in occupied private dwellings (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated):

• Count of households in occupied private dwellings
• Access to telecommunication systems (total responses)
• Household crowding index for levels 1 and 2
• Household composition
• Number of usual residents in household
• Average number of usual residents in household
• Number of motor vehicles
• Sector of landlord for households in rented occupied private dwellings
• Tenure of household
• Total household income
• Median ($) total household income
• Weekly rent paid by household for households in rented occupied private dwellings
• Median ($) weekly rent paid by household for households in rented occupied private dwellings.

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Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Household crowding

Household crowding is based on the Canadian National Occupancy Standard (CNOS). It calculates the number of bedrooms needed based on the demographic composition of the household. The household crowding index methodology for 2023 Census has been updated to use gender instead of sex. Household crowding should be used with caution for small geographical areas due to high volatility between census years as a result of population change and urban development. There may be additional volatility in areas affected by the cyclone, particularly in Gisborne and Hawke's Bay. Household crowding index – 2023 Census has details on how the methodology has changed, differences from 2018 Census, and more.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.

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Symbol

-997 Not available

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.