Arizona composite geocoding service using NextGen 9-1-1 address points and road centerlines datasets. The source data used in creating this web service provides updated information from Arizona local governments based on quarterly, biannual, or annual submission scheduling. ArcGIS desktop or applications can use the geocoding service depending on the intent of use. By default, the input data sources reside in the NAD83 UTM Zone 12N projection but can be translated upon output by desktop software or application settings. Each locator element uses a result hierarchy from the most granular result provided as an output first (Address Points) to the least granular last (Road Centerline). Data is limited to Arizona and cannot guarantee results in other states. For more information about the Arizona geocoder, please visit https://azgeo-data-hub-agic.hub.arcgis.com/If you are using the AZGeo Address Locator in ArcGIS Desktop or ArcGIS Pro you can connect to the AZGeo server via: https://azgeo.az.gov/arcgis/rest/services/ and then look in the 'geocoders' folder and use AZGeo_Address_Locator.

Vermont composite geocoding service built with VT E911 data. This service can be used by ArcGIS Pro 2.8.x+ to batch geocode addresses stored in a table. It also can be used as a geocoder with most ArcGIS Online apps, as well as QGIS. [How To Use The Vermont Geocoding Service]This ArcGIS Online item utilizes the ArcGIS Server geocoding service at this REST Endpoint: https://maps.vcgi.vermont.gov/arcgis/rest/services/EGC_services/GCS_E911_COMPOSITE_SP_v2/GeocodeServer

Updated July 2nd 2020 to adopt Pro 2.6 release and create Pro locators.This sample contains an ArcGIS Pro 2.6 Toolbox file containing five Spatial ETL Tools:ImportPSV2 - imports pipe separated source text files into a new (or existing, optionally to be overwritten) File Geodatabase.ImportStatePSV2 - the same as ImportPSV2 except includes a filter for a target state.MakeAllLocalityAliases - makes a city or locality alias table used in locator creation.MakeAddress2 - makes a point feature class ADDRESS with the schema similar to the ADDRESS_VIEW example in the PSMA documentation.MakeReferenceAddress - creates a point feature class REFERENCEADDRESS from the ADDRESS features, having expanded house number ranges and house number and subaddress details in suitable fields. This is the primary role data for the locator.The download also includes FME workbench FMW files (2020) for use in that product and ArcGIS Pro.You must re-source the Spatial ETL tools in the download toolbox to point to the FMW files in the download and you must re-path the data sources in each Spatial ETL tool to suit your project workspace.A model CreateGNAFLocator is in the download toolbox, use this to create your locator. A sample locator for the ACT is included.The sample locator and ones you create will support subaddress inputs, like flats and units.ImportPSV2 takes 19 hours to process 104M features on my machine. You might like to process a state at a time.If you add intermediate data to a map or leave an output geodatabase expanded in the Catalog pane you may get an error when writing output because of file locking. It is recommended you do not open an output workspace in Pro until app processing is complete.MakeAddress2 and MakeReferenceAddress take 4 hours to run for all Australia.The schema expected is as per February 2021, it may change each release, read the source documentation for change notices, this sample may not be maintained. The primary and foreign key fields according to PSMA's data model are indexed.G-NAF download site is: https://data.gov.au/dataset/geocoded-national-address-file-g-naf

The new Oregon Address Geocoder is used to find the location coordinates for street addresses in the State of Oregon. This service is:FreePublicUpdated regularlyOutputs location coordinates in Oregon Lambert, feet (SRID 2992)Uses over 2 million address points and 288,000 streets for referenceIt is an ArcGIS multirole locator with two roles:Point Address - Generally more accurate results from rooftop location points. Includes a Subaddress if a unit number is located.Street Address - Less accurate results from an estimated distance along a street centerline address range if a Point Address was not found.Instructions for using the Geocoder via ArcGIS Pro, ArcGIS Online, and REST Services are below:ArcGIS ProWeb ServicesArcGIS Online

This geocoding service provides the ability to perform tabular geocoding, reverse geocoding, and identifying results for locations that contain sub-addresses. This service and the supporting data are provided by the AddressNC program.A geocoding locator file is also available for users of ArcGIS Pro or ArcGIS Desktop in an offline/disconnected environment.

The address-matching process derives spatial data points from input tabular address data. This geocoding package is constructed using road centerline and address point data published by the NJ Office of Information Technology, Office of GIS (NJOGIS.) The geocoder is refreshed monthly using updates of those datasets.For additional support or to download a copy of the geocoding package please visit https://njgin.nj.gov/njgin/edata/geocoding.The geocoder includes a multirole locator that references comprehensive statewide address point and road centerline datasets. These datasets are maintained and published by NJOGIS in cooperation with county and municipal GIS agencies. The data have been reprocessed from the published releases to accommodate optimization of the locators. The published source data can be found here: Address Points - https://njgin.nj.gov/njgin/edata/addresses; Road Centerlines - https://njgin.nj.gov/njgin/edata/roads

This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high motorcycle (Motorcycle: Above 125cc, Motorcycle: 125cc and under, Quadru-cycle, Motor Tricycle) crash counts that resulted in injuries (slight, serious, fatalities) observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018 and 2019. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 157 KBNumber of Files: The dataset contains a total of 158 road intersection records (11 "high-high" clusters and 147 "high-low" outliers)Date Created: 22nd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents with spatial definitions.Once geocoded, road intersection crashes that involved either a motor tricycle, motorcycle above 125cc, motorcycle below 125cc and quadru-cycles and that were additionally associated with a slight, severe or fatal injury type were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which these motorcycle crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with motorcycle crashes identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

This dataset offers a detailed inventory of unsignalled road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high crash counts observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018 and 2019. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises the unsignalled road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 172 KBNumber of Files: The dataset contains a total of 176 road intersection records ( 47 "high-high" clusters and 129 "high-low" outliers)Date Created: 22nd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents that were able to be spatially defined.Once geocoded, road intersection crashes that were said to have occurred in the absence of visible road signage were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which these crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, unsignalled road intersections associated with crashes identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

This ArcGIS StreetMap Premium North America 2022 Release 4 map (based on HERE 2022 Q3 vintage) is designed for use in ArcGIS Pro and contains data for West Virginia supporting map display, geocoding and routing.

Note: Only the latest version of the map is available for download. See the 
Pro map coverage and click on the map to access details 
(including file size, updated date, and data source).

Accurate locations of people or places of interest is important to drive businesses and improve governement services. For accurate location, correctly geocoding addresses becomes important. Street addresses may sometimes be missing the country information and geocoding such incomplete addresses often results in poor accuracy. Geocoding accuracy and performance increases when the country is specified. This model categorizes incomplete addresses by automatically assigning the country they belong to.This deep learning model is trained on address dataset provided by openaddresses.io and can be used to classify addresses from 18 different countries in the world.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Text Classification Model tool available in the GeoAI toolbox in ArcGIS Pro.. Follow the guide to fine-tune this model.InputText on which country classification will be performed. Text should include street number or apartment number, street name, city or state.OutputText (classified country)Supported countriesThis model supports addresses from the following countries:AR – ArgentinaAT – AustriaAU – AustraliaBE – BelgiumCA – CanadaCH – SwitzerlandDE – GermanyDK – DenmarkES – SpainFI – FinlandFR – FranceIS – IcelandIT – ItalyKR – South KoreaLU – LuxemburgNZ – New ZealandSI – SloveniaUS – USAModel architectureThis model uses the xlm-roberta architecture implemented in Hugging Face Transformers.Accuracy metricsThe table below summarizes the precision, recall and F1-score of the model on the validation dataset.Training dataThe model has been trained on openly licensed data from openaddresses.io. Sample resultsHere are a few results from the model.

OS Code-Point® Open is an OpenData postcode-level dataset providing a point location for all geographic postal codes in Great Britain. The gazetteer service allows geocoding and postcode searching against this dataset. It is ideal for a variety of uses including planning A to B journeys, performing analysis, managing assets (such as premises) or utilising postcode lookups. Attributes: Postcode units, eastings, northings, positional quality indicator, NHS® regional health authority code, NHS health authority code, country code, administrative county code, administrative district code and administrative ward code.Data Currency: February 2022

This is a Locator for finding British National Grid references. It provides lookups on the British National Grid, which can be applied to all Ordnance Survey maps of Great Britain. You can use it to query by absolute coordinates or by tile. Both types of query return the centre point of the corresponding 10k grid square BNG tile. Enter grid coordinates as absolute XY: 123456, 654321 Enter tile queries as Grid squares: TL44; as sub tile: TQ1234 or; as quadrant SN1234SE

This is an Esri geocoding web service based on the Montana Geographic Names Framework. It was published from a "POI Role" based locator constructed in ArcGIS Pro. The POI locator was built using a point feature class with data classified as "Montana" or "Official" for the primary location table. An alternate name table was constructed of names with a designation of "Alternate" or "BGN". ("BGN" designated names indicate contrast between "MT Names" and "Official" U.S. Board on Geographic Names. The name is the official name recognized by the Board on Geographic Names, but the State of Montana believes it is incorrect.) Output location properties are based on the following for the primary table: "Place Join ID " from MT GNIS "GNIS_ID" field, "Place Name" from MT GNIS "Name" field, "Category" classification from MT GNIS "Class" field, "County" (or subregion) from MT GNIS "County" field. Output location properties are based on the following for the alternate name table: "Join ID" from AltName "GNIS_ID" field, "Place Name" from AltName "Name" field. Geolocator was constructed with "Global High" precision type. The geocoding service can be used in ArcGIS or via the REST endpoint. More information on the Montana Geographic Names Framework: https://mslservices.mt.gov/Geographic_Information/Data/DataList/datalist_MetadataDetail.aspx?did={0c57ebe2-f8e8-4d55-b159-ab3202898956}

Statewide Download (FGDB) (SHP)Users can also download smaller geographic areas of this feature service in ArcGIS Pro using the Copy Features geoprocessing tool.The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, as well as the NJ Department of Transportation, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The previous New Jersey statewide road segment data (Tran_road_centerline_NJ), which included the road name alias information, has been transformed into the NENA data model to create the street name alias table.The existing road centerlines were loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. The data subsequently have been updated and corrected.The road centerlines no longer contain any linear referencing information. The linear referencing will only be maintained by the NJ Department of Transportation as part of the NJ Roadway Network.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

Download .zipThis file contains point data used for the construction of lake maps for State of Ohio. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. The data was collected by fisheries biologists with the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived from this data by creating a raster file from the point bathymetry and boundary lake data. The Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

La data de la Población Penitenciaria originalmente se almacena en Excel el proceso metodológico del mapeo es mediante un script GIS desarrollado por la Unidad de Estadística luego se valida con el geocoding de ArcGIS PRO las direcciones que nose han podido mapear como último paso las direcciones complejas se mapean de manera manual uno a uno. Las características de la data son los siguientes: Sistema de Coordenadas Geográficas: GCS_WGS_1984Datum: D_WGS_1984Meridiano: GreenwichUnidad angular: GradosAño de mapeo: Marzo del año 2022Data mapeada: 85,448Score de precisión: 92,3%Fuente de la data: Registro Penitenciario/INPEElaborado: Unidad de Estadística/INPE

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2064 Morse Rd, Bldg G-2Columbus, OH, 43248Telephone: 614-265-6481Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2105 Morse Rd, Bldg G-2Columbus, OH, 43289Telephone: 614-265-6522Email: gis.support@dnr.ohio.gov

Download .zipThis file contains point data used for the construction of lake maps for State of Ohio. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. The data was collected by fisheries biologists with the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived from this data by creating a raster file from the point bathymetry and boundary lake data. The Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov