In this skill builder, you'll work with coordinates on the map. You'll do the following:View the coordinates as you move your mouse over the map Copy the coordinates Capture the coordinates of a location clicked on the map Convert the coordinates between different formats, including latitude and longitude (XY), decimal degrees (DD), degrees decimal minutes (DDM), degrees minutes seconds (DMS), military grid reference system (MGRS), United States National Grid (USNG), and Universal Transverse Mercator (UTM) Enter a coordinate and go to that location on the map

Utah FORGE has been established to develop, test, and improve the technologies and techniques required to develop EGS-type geothermal resources. Drilling of the first of two deep deviated wells, 16A(78)-32, will begin in the second half of 2020. This well will serve as the injection well for the injection-production well pair that will form the heart of the laboratory. This submission contains an archive of well location data within the Roosevelt Hot Springs geothermal area. An Excel spreadsheet is included containing updated GPS data for Utah FORGE wells drilled during Phase 2C (56-32, 68-32, 78-32). This data was collected over time by the Utah Geological Survey and contains all coordinates collected and the final averaged XY coordinates in both Longitude and Latitude and UTM Zone 12, NAD83. Elevation is also included. GIS shapefiles with well points are provided in the archive.

Loudoun County Parcel X,Y coordinates table. Available in Latitude and Longitude decimal degrees and Virginia State Plane North.

New Parking Citations dataset here: https://data.lacity.org/Transportation/Parking-Citations/4f5p-udkv/about_data ---Archived as of September 2023--- Parking citations with latitude / longitude (XY) in US Feet coordinates according to the California State Plane Coordinate System - Zone 5 (https://www.conservation.ca.gov/cgs/rgm/state-plane-coordinate-system). For more information on Geographic vs Projected coordinate systems, read here: https://www.esri.com/arcgis-blog/products/arcgis-pro/mapping/gcs_vs_pcs/ For information on how to change map projections, read here: https://learn.arcgis.com/en/projects/make-a-web-map-without-web-mercator/

Parking citations with latitude / longitude (XY) in US Feet coordinates according to the NAD_1983_StatePlane_California_V_FIPS_0405_Feet projection.

This data set provides a means of identifying an x-y coordinate for the approximate center (centroid) of landnet units based on the corresponding standardized PLSS description (e.g., for PLSS Section this is DTRS -- Direction, Township, Range, and Section codes). This process is sometimes referred to as "protraction". The Landnet centroid shapefile includes coordinates in WTM83/91 and latitude/longitude expressed as decimal degrees or degrees, minutes and seconds.

Parking citations with latitude / longitude (XY) in US Feet coordinates according to the NAD_1983_StatePlane_California_V_FIPS_0405_Feet projection.

Enrichissement du fichier fourni par la Poste avec les noms de régions, départements et commune en lettre minuscule ainsi que leurs codes INSEE respectifs. https://www.data.gouv.fr/fr/datasets/base-officielle-des-codes-postaux/ Fichier de correspondance entre les codes communes (INSEE) et les codes postaux au format csv. Ce fichier comprend : code_commune_INSEE nom_commune_postal code_postal libelle_acheminement ligne_5 latitude longitude code_commune article nom_commune nom_commune_complet code_departement nom_departement code_region nom_region Exemple: Code_commune_INSEE,Nom_commune,Code_postal,Libelle_acheminement,Ligne_5,longitude,latitude,code_commune,article,nom_commune,nom_commune_complet,code_departement,nom_departement,code_region,nom_region 1001 L ABERGEMENT CLEMENCIAT 1400 L ABERGEMENT CLEMENCIAT 46.1534255214 4.92611354223 1 L' Abergement-Clémenciat L'Abergement-Clémenciat 1 Ain 84 Auvergne-Rhône-Alpes 1002 L ABERGEMENT DE VAREY 1640 L ABERGEMENT DE VAREY 46.0091878776 5.42801696363 2 L' Abergement-de-Varey L'Abergement-de-Varey 1 Ain 84 Auvergne-Rhône-Alpes 1004 AMBERIEU EN BUGEY 1500 AMBERIEU EN BUGEY 45.9608475114 5.3729257777 4 Ambérieu-en-Bugey Ambérieu-en-Bugey 1 Ain 84 Auvergne-Rhône-Alpes Il correspond aux codes postaux de France (métropole et DOM), ceux des TOM, ainsi que MONACO. Note aux réutilisateurs: les contours géographiques des communes, à partir de leurs codes INSEE, sont aussi disponibles sur https://www.data.gouv.fr/fr/. Vous découvrirez des formats de fichiers supplémentaires, des outils de visualisation et des API sur https://datanova.legroupe.laposte.fr. 💓💓💓 Suport Vous pouvez soutenir mon travail https://github.com/sponsors/mohamed-badaoui Merci 🙏

Parking citations with latitude / longitude (XY) in US Feet coordinates according to the NAD_1983_StatePlane_California_V_FIPS_0405_Feet projection.

Tags survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES Summary BES Research, Applications, and Education Description XY Positions for BES telephone survey. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey. This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because of its higher spatial accuracy than other databases describin... Visit https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F337%2F610 for complete metadata about this dataset.

Parking citations with latitude / longitude (XY) in US Feet coordinates according to the NAD_1983_StatePlane_California_V_FIPS_0405_Feet projection.

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).

List of Primary Schools for academic year 2016/2017. Published by Department of Education. Available under the license cc-by (CC-BY-4.0).This dataset contains all basic primary school details including address, longitude/latitude, geo (XY) coordinates and eircode information...

These TowCam navigation files were generated during 2002 R/V Melville cruise VANC01MV along a minor rift originating at the East Pacific Rise (EPR) at 2°40'N continuing southeastward for ~65 km. Photographs were assigned the position of the ship, assuming minimal layback of the camera sled. The files are in ASCII text format and present with the latitude and longitude in decimal degrees. There is one navigation file per TowCam deployment. The files were generated as part of a project called Geochemical and Geological Investigations of the Incipient Rift at 2° 40'N, East of the East Pacific Rise. Funding was provided by NSF awards OCE00-96360 and OCE00-99154.

Abstract This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied: This data represents all the locations of NSW river gauges the Bureau of Meteorology has ingested into internal systems from NSW Department of Primary Industries at this point in time. There are plans to onboard more NSW agency data in the future. ## Purpose Data obtained under the Water Act 2008 for national water monitoring purposes ## Dataset History This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied: Data is in excel format and contains the following fields: Station number Station name Long name Name of the spatial reference Easting Northing Latitude Longitude Please note location fields (x,y, lat, long) contain different number of digits which may impact transformation to geospatial object. Please ensure all records are represented spatialy during transformation. ## Dataset Citation Bureau of Meteorology (2016) River Gauge Locations NSW BOM 20160203. Bioregional Assessment Source Dataset. Viewed 14 June 2018, http://data.bioregionalassessments.gov.au/dataset/d2292117-f3b6-4c50-8756-2d9eba1a81a1.

Geometric Mean Baseline of chlora from Sentinel-3A OLCI, data courtesy of Copernicus Program, mapping matches NOAA CoastWatch VIIRS Sectors cdm_data_type=Grid Conventions=CF-1.6, COARDS, ACDD-1.3 cw_orbit_type=descending cw_pass_type=day cw_processing_version=NRT S3A_OL_2_WFR cw_projection=Geographic cw_satellite=Sentinel-3A cw_sensor=OLCI cw_station_code=MAR cw_station_name=European Organisation for the Exploitation of Meteorological Satellites cw_swath_sync_lines=1 Easternmost_Easting=60.033750000000026 geospatial_lat_max=44.88375 geospatial_lat_min=-0.1087500000000044 geospatial_lat_resolution=0.0075000000000000015 geospatial_lat_units=degrees_north geospatial_lon_max=60.033750000000026 geospatial_lon_min=-0.03374999999997864 geospatial_lon_resolution=0.007500000000000001 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=[cwutils 4.1.0.53 20250211_095418] cwcomposite --pedantic --coherent chlor_a/latitude --optimal sat_zenith/minabs --method optimal --inputs - --match ^(wqsf0|sat_zenith|wqsf1|latitude|longitude|chlor_a)$ /home/pub/svc_cwatch/aps/aps_r/browse/lvl4/modis/coastwatch/hdf/OLCVCW_I2025191_C3_052603_070402_070702_LG00_closest_chlora.hdf [cwutils 4.1.0.53 20250211_095418] cwgraphics /home/pub/svc_cwatch/aps/aps_r/browse/lvl4/modis/coastwatch/hdf/OLCVCW_I2025191_C3_052603_070402_070702_LG00_closest_chlora.hdf [cwutils 4.1.0.53 20250211_095418] cwimport --match=^graphics$ --copy /home/pub/svc_cwatch/aps/aps/aps/share/coastwatch/icons/North750XY0045Geographic.hdf /home/pub/svc_cwatch/aps/aps_p/browse/lvl4/modis/coastwatch/maskhdf/OLCVCW_I2025191_I2025191_F1_XY00_closest_chlora.hdf id=noaacwS3AOLCIchlaSectorXYDaily infoUrl=https://coastwatch.noaa.gov/cw/satellite-data-products/ocean-color/near-real-time/olci-sentinel3-global.html institution=Copernicus Program instrument=OLCI keywords_vocabulary=GCMD Science Keywords naming_authority=gov.noaa.coastwatch Northernmost_Northing=44.88375 OBSERVED_PROPERTY=chlor_a platform=Sentinel-3A source=Sentinel-3A_OLCI_chlora sourceUrl=(local files) Southernmost_Northing=-0.1087500000000044 standard_name_vocabulary=CF Standard Name Table v29 testOutOfDate=now-2days time_coverage_end=2025-07-10T12:00:00Z time_coverage_start=2024-07-07T12:00:00Z Westernmost_Easting=-0.03374999999997864

This dataset contains the location of all active fire stations and fire related facilities within the City of Toronto. The locations are identified by XY (latitude and longitude) coordinates.

Geometric Mean Baseline of chlora from Sentinel-3B OLCI, data courtesy of Copernicus Program, mapping matches NOAA CoastWatch VIIRS Sectors cdm_data_type=Grid Conventions=CF-1.6, COARDS, ACDD-1.3 cw_orbit_type=descending cw_pass_type=day cw_processing_version=NRT S3B_OL_2_WFR cw_projection=Geographic cw_satellite=Sentinel-3B cw_sensor=OLCI cw_station_code=MAR cw_station_name=European Organization for the Exploitation of Meteorological Satellites cw_swath_sync_lines=1 Easternmost_Easting=60.033750000000026 geospatial_lat_max=44.88375 geospatial_lat_min=-0.1087500000000044 geospatial_lat_resolution=0.0075000000000000015 geospatial_lat_units=degrees_north geospatial_lon_max=60.033750000000026 geospatial_lon_min=-0.03374999999997864 geospatial_lon_resolution=0.007500000000000001 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=[cwutils 4.1.0.53 20250211_095418] cwcomposite --pedantic --coherent chlor_a/latitude --optimal sat_zenith/minabs --method optimal --inputs - --match ^(wqsf1|wqsf0|longitude|latitude|sat_zenith|chlor_a)$ /home/pub/svc_cwatch/aps/aps_t/browse/lvl4/modis/coastwatch/hdf/OLCVCW_J2025187_C3_080405_080705_081005_KH00_closest_chlora.hdf [cwutils 4.1.0.53 20250211_095418] cwgraphics /home/pub/svc_cwatch/aps/aps_t/browse/lvl4/modis/coastwatch/hdf/OLCVCW_J2025187_C3_080405_080705_081005_KH00_closest_chlora.hdf [cwutils 4.1.0.53 20250211_095418] cwimport --match=^graphics$ --copy /home/pub/svc_cwatch/aps/aps/aps/share/coastwatch/icons/North750XY0045Geographic.hdf /home/pub/svc_cwatch/aps/aps_w/browse/lvl4/modis/coastwatch/maskhdf/OLCVCW_J2025187_J2025187_F1_XY00_closest_chlora.hdf id=noaacwS3BOLCIchlaSectorXYDaily infoUrl=https://coastwatch.noaa.gov/cw/satellite-data-products/ocean-color/near-real-time/olci-sentinel3-global.html institution=Copernicus Program instrument=OLCI keywords_vocabulary=GCMD Science Keywords naming_authority=gov.noaa.coastwatch Northernmost_Northing=44.88375 OBSERVED_PROPERTY=chlor_a platform=Sentinel-3B source=Sentinel-3B_OLCI_chlora sourceUrl=(local files) Southernmost_Northing=-0.1087500000000044 standard_name_vocabulary=CF Standard Name Table v29 testOutOfDate=now-2days time_coverage_end=2025-07-06T12:00:00Z time_coverage_start=2024-07-06T12:00:00Z Westernmost_Easting=-0.03374999999997864

This dataset is gravity anomaly data of the Auckland and Whangarei volcanic fields (AVF and WVF) in the northern part of North Island. The X-Y coordinate system of this dataset is NZTM, and the Longitude-Latitude coordinate system is NZGD2000. The uploader gathers this data from a series of AVF day trip fieldworks (2021 - 2023), a week-long WVF campaign (2022), correspondences with authors, and the following theses lodged in The University of Auckland's General Library:Auckland Volcanic FieldCaleb John Gasston (2017)Hellen Anne Williams (2003)Sian Julia France (2003)Mazin Al-Salim (2000)Dev Kumar Affleck (1999)Craig Andrew Miller (1996)Shane Robert Moore (1996)David Rout (1991)Grant Willis Roberts (1980)Isla Margaret Nixon (1977)Helen Joan Anderson (partially, 1977)Whangarei Volcanic FieldPaul Johann Röllin (1991)All names and years of the respective data contributors have been included in the CSV files.

Dies sind Daten, die vom Deutschen Wetterdienst mit dem COSMO-DE-Modell erhoben und über das Pamore-Programm bezogen wurden. Es handel sich um Assimilationsanalyse-Daten. Die Assimilationsanalyse ist ein erneuter Durchlauf des COSMO-DE Modells mit Messstationsdaten als Randbedingung. Sie stellt nach Möglichkeit das tatsächlich gewesene Wetter dar. Alle Wind- und Temperaturdaten sind von den DWD-Angaben direkt übernommen und aus dem COSMO-DE-Modell. Urheber der Daten ist somit der Deutsche Wetterdienst. Es handelt sich hierbei um eine Aufbereitung, Zusammenstellung und einen Zuschnitt der Daten in ein, für die Energiesystemsimulation, handliches und niederschwellig zugängliches Format. Dateiformat: hdf5 - Version 1 (erstellt mit Matlab) Jede Datei enthält als Werte: Latitudes in °, /latitude, 2D-Gitter, single-precision (4-Byte floating point), (X Y) Longitudes in °, /longitude, 2D-Gitter, single-precision (4-Byte floating point), (X Y) Die eigentlichen Werte (s.u.), /

Lizenz: GeoNutzV