This API returns a geography of a specified geography type by the geography id.

Simple municipal name/GEOID lookup table. The table combines GEOID with census county names and municipal names. Stored as view in the demographics schema.

USA Census Block Groups (CBG) for Urban Search and Rescue. This layer can be used for search segment planning. Block groups generally contain between 600 and 5,000 people and the boundaries generally follow existing roads and waterways. The field segment_designation is the last 6 digits of the unique identifier and matches the field in the SARCOP Segment layer.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.* *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the block group itself.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

U.S. Census BlocksThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Census Blocks in the United States. A brief description of Census Blocks, per USCB, is that "Census blocks are statistical areas bounded by visible features such as roads, streams, and railroad tracks, and by nonvisible boundaries such as property lines, city, township, school district, county limits and short line-of-sight extensions of roads." Also, "the smallest level of geography you can get basic demographic data for, such as total population by age, sex, and race."Census Block 1007Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Census Blocks) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 69 (Series Information for 2020 Census Block State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (U.S. Census Blocks - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: What are census blocksFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Context

Dataset created to link between County - State Name, State-County FIPS, and ZIP Code.

Acknowledgements

Data Sources

• US HUD

https://www.huduser.gov/portal/datasets/usps.html

• Census Bureau

https://www2.census.gov/geo/docs/reference/codes/files/national_county.txt https://www.census.gov/geo/reference/codes/cou.html

Data cleaned by Data4Democracy and hosted originally on Data.World: https://github.com/Data4Democracy/zip-code-to-county https://data.world/niccolley/us-zipcode-to-county-state

ZCTA data from USPS 6.2017 release.

Image from Reddit.

[Metadata] Geoid 12B: Hybrid geoid model used to convert the ellipsoidal height obtained by the Global Navigation Satellite System (to the orthometric height of a specific vertical datum). The National Geodetic Survey (NGS) has been producing the hybrid geoid to convert the ellipsoidal height obtained from the Global Navigation Satellite System (GNSS) to the orthometric height of a specific vertical datum. The GEOID12B model is intended to transform between NAD 83 (2011/PA11/MA11) and the respective vertical datums for the different regions, including NAVD88, GUVD04, ASVD02, NMVD03, PRVD02 and VIVD09.

This API returns a search for the demographic information for a particular geography type and geography ID

This API returns broadband summary data by geography IDs for a specific geography type. It is designed to retrieve broadband summary data by geography and census metrics (population or households) combined as search criteria. The data includes wireline and wireless providers, different technologies and broadband speeds reported in the particular area being searched for on a scale of 0 to 1.

We present a regional geoid model for the area of Lake Vostok, Antarctica, from a combination of local airborne gravity, ice-surface and ice-thickness data and a lake bathymetry model. The topography data are used for residual terrain modelling (RTM) in a remove-compute-restore approach together with the GOCE satellite model GOCO03S. The disturbing potential at the Earth's surface, i.e. the quasigeoid, is predicted by least-squares collocation (LSC) and subsequently converted to geoid heights. Compared to GOCO03S our regional solution provides an additional short-wavelength signal of up to 1.48 m, or 0.56 m standard deviation, respectively. More details can be found in Schwabe et. al (2014).

Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number (e.g., Blocks 3001, 3002, 3003 to 3999 in census tract 1210.02 belong to block group 3). Current block groups do not always maintain these same block number to block group relationships due to boundary and feature changes that occur throughout the decade. For example, block 3001 might move due to a change in the census tract boundary. Even if the block is no longer in block group 3, the block number (3001) will not change. However, the GEOID for that block, identifying block group 3, would remain the same in the attribute information in the TIGER/Line Shapefiles because block GEOIDs are always built using the decennial geographic codes.Block groups delineated for the 2020 Census generally contain 600 to 3,000 people. Local participants delineated most block groups as part of the Census Bureau's PSAP. The Census Bureau delineated block groups only where a local or tribal government declined to participate or where the Census Bureau could not identify a potential local participant.A block group usually covers a contiguous area. Each census tract contains one or more block groups and block groups have unique numbers within census tract. Within the standard census geographic hierarchy, block groups never cross county or census tract boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and AIANNH areas.Block groups have a valid range of zero (0) through nine (9). Block groups beginning with a zero generally are in coastal and Great Lakes water and territorial seas. Rather than extending a census tract boundary into the Great Lakes or out to the 3-mile territorial sea limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore.

🇺🇸 미국

This API is designed to find the rankings by geography within the state for a specific metric (population or household) and rank (any of the metrics from provider, demographic, technology or speed). The results are the top ten and bottom ten records within the state for the particular geography type and my area rankings. Additionally we include +/- 5 rankings from the 'my' area rank.

The geoid model for Taiwan, including the Kinmen and Matzu islands, covers the area 118°E–125°E and 21°N–27°N with a grid resolution of 30" x 30". The gravimetric model is based on land, shipborne and airborne gravity data, as well as marine gravity derived from satellite altimetry. After merging data by least-squares collocation, a remove-restore procedure is applied. First height anomalies are computed by 1D FFT with Wong-Gore modification of the Stokes kernel and then they are converted into the geoid heights. The reference global gravity model is EGM2008 up to degree and order 2190. The used digital terrain model is derived from several photogrammetric surveys at a resolution of 3"×3" and 9"×9" for the inner and outer zone, respectively. The gravimetric model fits the GPS/levelling control points with a standard deviation of 7.9 cm (and a mean difference of 21.9 cm). The geoid model is provided in ISG format 2.0 (ISG Format Specifications), while the file in its original data format is available at the model ISG webpage.

Context

There is no story behind this data.

These are just supplementary datasets which I plan on using for plotting county wise data on maps.. (in particular for using with my kernel : https://www.kaggle.com/stansilas/maps-are-beautiful-unemployment-is-not/)
As that data set didn't have the info I needed for plotting an interactive map using highcharter .

Content

Since I noticed that most demographic datasets here on Kaggle, either have state code, state name, or county name + state name but not all of it i.e county name, fips code, state name + state code.

Using these two datasets one can get any combination of state county codes etc.

States.csv has State name + code
US counties.csv has county wise data.

Acknowledgements

Picture : https://unsplash.com/search/usa-states?photo=-RO2DFPl7wE
Counties : https://www.census.gov/geo/reference/codes/cou.html
State :

Inspiration

Not Applicable.

This API is designed to find the rankings by any geography ID within the nation with a specific census metric (population or household) and ranking metric (any of the metrics from provider, demographic, technology or speed). The results are the top ten and bottom ten rankings within the nation for the particular geography type and my area rankings include +/- 5 rankings from the my area rank.

We present a geoid solution for the Weddell Sea and adjacent continental Antarctic regions. There, a refined geoid is of interest, especially for oceanographic and glaciological applications. For example, to investigate the Weddell Gyre as a part of the Antarctic Circumpolar Current and, thus, of the global ocean circulation, the mean dynamic topography (MDT) is needed. These days, the marine gravity field can be inferred with high and homogeneous resolution from altimetric height profiles of the mean sea surface. However, in areas permanently covered by sea ice as well as in coastal regions, satellite altimetry features deficiencies. Focussing on the Weddell Sea, these aspects are investigated in detail. In these areas, ground-based data that have not been used for geoid computation so far provide additional information in comparison with the existing high-resolution global gravity field models such as EGM2008. The geoid computation is based on the remove–compute–restore approach making use of least-squares collocation. The residual geoid with respect to a release 4 GOCE model adds up to two meters and more in the near-coastal and continental areas of the Weddell Sea region, also in comparison with EGM2008. Consequently, the thus refined geoid serves to compute new estimates of the regional MDT and geostrophic currents.

This dataset contains annual mean geostrophic velocity calculated at depth corresponding to negative values of the marine geoid. It is calculated from two open sources from the geodetic (EIGEN-6C4) and oceanographic (NCEI) communities. The first source EIGEN-6C4 is a static global combined gravity field model developed by GFZ Potsdam and GRGS Toulouse up to degree and order 2190. The second source is the “World ocean geostrophic velocity inverted from World Ocean Atlas 2013 with the P-vector method†(NCEI accession 0121576). With the given non-positive values of the geoid, N, (i.e., for the oceans), the absolute geostrophic currents (u, v) are easily identified on N with 1 degree resolution from the second dataset except the equatorial zone (5oS – 5oN) due to the non-existence of the geostrophic balance. Altogether, the dataset contains 15,481 (u, v) data pairs. While there is no geostrophic motion at the surface if it coincided with the geoid, the opposite is not valid. This dataset shows that ocean geostrophic velocity doesn't zero at z=N. Data are in netcdf format.

Simple geospatial and administrative repository. This dataset is built from the Official Geographic Code of INSEE, available via their SparQL interface. ## Model There are two types of objects: - levels - areas ### Zones The file Zones {year} (json) is constructed from data extracted from the COG and contains, for all geographical scales, the following information: - uri: Entity URI in INSEE RDF graph (example: "http://id.insee.fr/geo/arrondissement/6eeefa75-7352-48ee-884f-64783b8ca290"), - name: name of the entity (example: "Lyon"), - INSEE code: INSEE code of the entity (example: "691"), - nameWithoutArticle: name without article of the entity (example: "Lyon"), - codeArticle: Entity item code (example: "0"), - type: type of entity (example: "Arrondissement"), - is_deleted: boolean indicating whether the entity has been administratively deleted (example: true), - level: level of scale of the entity (example: "fr:arrondissement"), - _id: full identifier used by data.gouv.fr (example: "fr:arrondissement:691") The Countries only zones {year} (json) file is a sample of the global Countries only zones {year} (json) file which contains only the countries. ### Levels/Levels The file contains the different possible scale levels, with the following information: - id: entity level of scale, which corresponds to the ‘level’ field in the Zones file (example: "en:region"), - label: naming the scale level (example: "French region"), - admin_level: Scale level code (example: 40), - parents: directly higher level(s) of scale (example: ["country"]) ## Construction This dataset is built from the INSEE COG via a python script available here. ## History - 30/04/2015: first version - 15/04/2016: addition of the URLs of the coats of arms/flags and an export using msgpack in order to reduce the size of the generated archive - 19/04/2016: correction version providing a finer cut of the shapes of the municipalities - 09/06/2016: correction version adding the parents for the municipalities of Corsica/DROM-COM and calculating the population for the districts - 15/06/2017: version including data from GeoHisto and using GeoIDs, integrates 2017 data (COG, OSM). - 28/08/2017: Added EPCI history from GeoHisto. - 08/05/2019: Switching to COG 2019, bug fixing, adding the "geonames" key, switching to Wikidata, cantons and iris are no longer exported - 30/11/2023: The data comes from the INSEE COG from their SparQL interface ## Archives ### Levels/Levels They make it possible to model the different known levels of the referential and their theoretical relationships. Their name is translatable. ### Zones A zone is the association of a unique identifier with a geographical polygon, a level and a name. It has less than one unique code for the level. It may have several known identifiers, which are not necessarily unique. The name is optionally translatable (ex: European Union, World) The following attributes are exported to the GeoJSON: - id: A unique identifier following the specification GeoID - code: The unique identifier for a given date of the zone for its level - level: The identifier of the level of attachment - name: The display name of the area in English (may be translated) - population: Approximate/estimated population (optional) - area: Estimated/approximate area in km2 (optional) - wikidata: The associated Wikidata node (optional) - wikipedia: A reference to Wikipedia (optional) - dbpedia: A reference to DBPedia (optional) - flag: A reference to the DBPedia flag (optional) - blazon: A reference to the DBPedia blazon (optional) - keys: a dictionary of the different codes known for this area - parents: an unordered list of the identifiers of the different known parents - ancestors: the list of possible ancestors - successful: the list of possible successors - validity: a period of validity (object with the attributes ‘start’/‘end’) (optional) ## Construction This dataset is built with the tool GeoZones whose code is published on Github. You can find the detail of French specificities on the repository. ## Possible future improvements ### Fields - Overall weight = f(population, area, level) ### Deliverables - Various clarifications - Localized JSON (in English only for now) - Translations in JSON (as a hard alternative to the current PO/MO format) - Level statistics (number of zones, coverage of attributes...)

The daily solution files are an analysis product that provides estimates of Earth orientation and site positions for each 24-hour session, the covariance matrix of the estimates, and decomposed normal equations. The solution files are in SINEX format. The SINEX product files are available on the same frequency as the EOP-S products: 24 hours after each new session data base is available.

This product contains a time series of clock biases for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.