Abstract

A crosswalk table from US postal ZIP codes to geo-points (latitude, longitude)

Documentation

Data source: public.opendatasoft.

The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not).

This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not). This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".

The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.

Every single contact from our geographical databased with 270 million+ companies comes directly from local sources that you can trust and are GDPR proof. These sources include chamber of commerces, market surveys, business listings, directories, magazines, public records, websites, conferences, telephone directories, publishers, social media and commercial partnerships. All our e-mail data is verified by automated processes and human eyes on a ongoing basis. Have an edge against your competition with our comprehensive B2B company database. Ask us for a quote!

Paid dataset with city names, coordinates, regions, and administrative divisions of World Cities. Available in Excel (.xlsx), CSV, JSON, XML, and SQL formats after purchase.

This dataset includes the 2018 latitude and longitude information for habitat and fish reach locations on the Santa Ana and San Gabriel Rivers.

This metadata record provides the latitude, longitude, and a unique ID value (PRISMID) for all raster cells used by the PRISM group at Oregon State University to characterize climate throughout the conterminous United States. The three column format of comma-separated data can be readily joined to the comma-separated climate and water-balance variables described in the associated metadata file "Water Balance Model Inputs and Outputs for the Conterminous United States".

Paid dataset with city names, coordinates, regions, and administrative divisions of United States Minor Outlying Islands. Available in Excel (.xlsx), CSV, JSON, XML, and SQL formats after purchase.

This dataset, which is a supplement to the main Places & Sentiment dataset from The Data Appeal Company, contains “text snippets” of individual reviews and social media posts collected for Point of Interests avilable in the "Places & Sentiment" database. These are real text extracts that clients or users leave in online platforms after their experience at the "place" (POI).

CORDEX (http://wcrp-cordex.ipsl.jussieu.fr) is a WCRP initiative to coordinate the downscaling of CMIP5 simulation results produced in support of the IPCC/AR5. About 30 institutes worldwide contributed to the CORDEX data base with more than 20 RCMs in various configurations and versions. CORDEX defined 13 domains (http://wcrp-cordex.ipsl.jussieu.fr/images/pdf/cordex_regions.pdf) with acronyms AFR(Africa), EUR(Europe), ARC(Arctic), ANT(Antarctica), MED(Mediterranean), SAM(South America), CAM(Central America), NAM(North America), WAS(South Asia), EAS(East Asia), CAS(Central Asia), AUS(Austral-Asia), ARB(Mediterranean-North Africa). The priority domain is the African continent. The data are requested to be generated on model grids with an approximate grid cell distance of 50/25/12 km (acronyms -44i/-22i/-11i). More data '- on native model grids '- are provided in project CORDEX_DDS-CMIP5_native-grid. RCM evaluation with the ERA-Interim re-analyses data (1989-2008) is mandatory. Besides, downscaling of historical experiments (1950-2005) and of climate change scenarios RCP 4.5 and 8.5 is requested. Many groups also used RCP 2.6 climate projections. CORDEX data are formatted following the CMIP5 data and metadata protocol where possible. For more details see http://cordex.dmi.dk/joomla/images/CORDEX/cordex_archive_specifications.pdf . A list of the variables requested by CORDEX can be downloaded from this project (CORDEX_variables_requirements_table.pdf). Not all variables will be provided by all RCMs though. Note that 3 and 6 hourly data are also available, however, only at the modelling centres on request.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

MCGD_Data_V2.2 contains all the data that we have collected on locations in modern China, plus a number of locations outside of China that we encounter frequently in historical sources on China. All further updates will appear under the name "MCGD_Data" with a time stamp (e.g., MCGD_Data2023-06-21)

You can also have access to this dataset and all the datasets that the ENP-China makes available on GitLab: https://gitlab.com/enpchina/IndexesEnp

Altogether there are 464,970 entries. The data include the name of locations and their variants in Chinese, pinyin, and any recorded transliteration; the name of the province in Chinese and in pinyin; Province ID; the latitude and longitude; the Name ID and Location ID, and NameID_Legacy. The Name IDs all start with H followed by seven digits. This is the internal ID system of MCGD (the NameID_Legacy column records the Name IDs in their original format depending on the source). Locations IDs that start with "DH" are data points extracted from China Historical GIS (Harvard University); those that start with "D" are locations extracted from the data points in Geonames; those that have only digits (8 digits) are data points we have added from various map sources.

One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates.

From June 2021 onward, we have adopted a different file naming system to keep track of versions. From MCGD_Data_V1 we have moved to MCGD_Data_V2. In June 2022, we introduced time stamps, which result in the following naming convention: MCGD_Data_YYYY.MM.DD.

UPDATES

MCGD_Data2025_02_28 includes a major change with the duplication of all the locations listed under Beijing, Shanghai, Tianjin, and Chongqing (北京, 上海, 天津, 重慶) and their listing under the name of the provinces to which they belonge origially before the creation of the four special municipalities after 1949. This is meant to facilitate the matching of data from historical sources. Each location has a unique NameID. Altogether there are 472,818 entries

MCGD_Data2025_02_27 inclues an update on locations extracted from Minguo zhengfu ge yuanhui keyuan yishang zhiyuanlu 國民政府各院部會科員以上職員錄 (Directory of staff members and above in the ministries and committees of the National Government). Nanjing: Guomin zhengfu wenguanchu yinzhuju 國民政府文官處印鑄局國民政府文官處印鑄局, 1944). We also made corrections in the Prov_Py and Prov_Zh columns as there were some misalignments between the pinyin name and the name in Chines characters. The file now includes 465,128 entries.

MCGD_Data2024_03_23 includes an update on locations in Taiwan from the Asia Directories. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown").

MCGD_Data2023.12.22 contains all the data that we have collected on locations in China, whatever the period. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). The dataset also includes locations outside of China for the purpose of matching such locations to the place names extracted from historical sources. For example, one may need to locate individuals born outside of China. Rather than maintaining two separate files, we made the decision to incorporate all the place names found in historical sources in the gazetteer. Such place names can easily be removed by selecting all the entries where the 'Province' data is missing.

The GeoJunxion Airports are part of the GeoJunxion Geo-Boundaries data suite. The GeoJunxion Airports are land use boundaries and are often used to define areas. Airport boundaries are shown in geographical data by making use of Polygons. Polygons form the geometry of boundary features. The GeoJunxion Airports Boundaries have attributes such as: Name, IATA name, type of airport, latitude/longitude and centre point.

With the GeoJunxion Airports, also named Area of Interest (AOI) and Points of Interest (POI), you can add relevant information to an area such as the name, latitude/longitude, road network and centre point. The GeoJunxion Airports database covers the geographic boundaries of Airports across the globe.

In select areas the detailed Runway and Taxiway boundaries are also available.

Additional attributes with more detailed information optionally available.

The prices vary depending on the application and individual requirements. Just talk to us, we'll be happy to make you an offer.

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 shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

R code used to produce Figures 1 and 2. (R)

Paid dataset with city names, coordinates, regions, and administrative divisions of Latvia. Available in Excel (.xlsx), CSV, JSON, XML, and SQL formats after purchase.

Crowdsourced georeference data for over three thousand maps, annotated online by participants in the BL Georeferencer project. http://britishlibrary.typepad.co.uk/magnificentmaps/2014/08/success.html "In just 28 days from release, 3,220 maps have been geo-located online by participants in the BL Georeferencer project. For this quantity of maps to be completed at such a speed is truly impressive, and testifies to much scrutiny of maps and online research by many people."

NB The image identifier refers to its Flickr identifier. "11000447924" refers to the image located at https://flickr.com/photos/biritishlibrary/11000447924

USGS-CMG time-series data from the GLOBEC Great South Channel Circulation Experiment project, mooring 490 and package 4901-a. A moored array program to investigate the recirculation of water and plankton around Georges Bank. _NCProperties=version=1|netcdflibversion=4.4.1.1|hdf5libversion=1.8.17 cdm_data_type=TimeSeries cdm_timeseries_variables=latitude, longitude, altitude, feature_type_instance contributor_name=R. Schlitz contributor_role=principalInvestigator Conventions=CF-1.6,ACDD-1.3, COARDS COORD_SYSTEM=GEOGRAPHICAL CREATION_DATE=28-May-2008 14:23:41 DATA_ORIGIN=USGS DATA_TYPE=TIME date_metadata_modified=2017-04-11T22:03:00Z DESCRIPT=VACM-C, GREAT SOUTH CHANNEL SITE 7, CLEAN DATA: NOT SCRUBBED Easternmost_Easting=-68.28616 featureType=TimeSeries geospatial_bounds=POINT(-68.28616333007812 40.5168342590332) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=40.51683 geospatial_lat_min=40.51683 geospatial_lat_resolution=0 geospatial_lat_units=degrees_north geospatial_lon_max=-68.28616 geospatial_lon_min=-68.28616 geospatial_lon_resolution=0 geospatial_lon_units=degrees_east geospatial_vertical_max=-5.0 geospatial_vertical_min=-5.0 geospatial_vertical_positive=up geospatial_vertical_resolution=0 geospatial_vertical_units=m grid_mapping_epsg_code=EPSG:4326 grid_mapping_inverse_flattening=298.257223563 grid_mapping_long_name=http://www.opengis.net/def/crs/EPSG/0/4326 grid_mapping_name=latitude_longitude grid_mapping_semi_major_axis=6378137.0 history=Fri Nov 1 20:17:32 2019: ncatted -a project,global,a,c,, CMG_Portal GLOBEC_GSC/4901-a.nc corrected sign of lon using fix_poslon.m: 2017-04-11T22:03:00Z - pyaxiom - File created using pyaxiom id=4901-a infoUrl=https://stellwagen.er.usgs.gov/ institution=USGS Coastal and Marine Geology Program keywords_vocabulary=GCMD Science Keywords latitude=40.516834 longitude=-68.28616 magnetic_variation=-17.0 MOORING=490 naming_authority=gov.usgs.cmgp ncei_template_version=NCEI_NetCDF_TimeSeries_Orthogonal_Template_v2.0 NCO=netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=40.51683 original_filename=4901-a.nc original_folder=GLOBEC_GSC project=U.S. Geological Survey Oceanographic Time-Series Data, CMG_Portal project_summary=A moored array program to investigate the recirculation of water and plankton around Georges Bank. project_title=GLOBEC Great South Channel Circulation Experiment sampling_interval=450 source=USGS sourceUrl=(local files) Southernmost_Northing=40.51683 standard_name_vocabulary=CF Standard Name Table v29 start_time=97- I -15 19.33.45 stop_time=97-VIII-17 09.56.15 subsetVariables=latitude, longitude, altitude, feature_type_instance time_coverage_duration=PT18454950S time_coverage_end=1997-08-17T09:56:15Z time_coverage_start=1997-01-15T19:33:45Z WATER_DEPTH=101 water_depth=101.0 Westernmost_Easting=-68.28616

Analysis of ‘Latitude and Longitude for Every Country and State’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/paultimothymooney/latitude-and-longitude-for-every-country-and-state on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

GPS coordinates for every world country and every USA state.

Columns = [country-code,latitude,longitude,country,usa-state-code,usa-state-latitude,usa-state-longitude,usa-state]

Acknowledgements

Original source of data was https://developers.google.com/public-data/docs/canonical/countries_csv and https://developers.google.com/public-data/docs/canonical/states_csv. Data was originally released under a Creative Commons 4.0 license.

Photo by Марьян Блан | @marjanblan on Unsplash

--- Original source retains full ownership of the source dataset ---

This dataset mainly provides a matching table of 3-digit postal codes and the coordinates of administrative district centers.

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.