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

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.

VT E911 Composite geocoder - uses ESITE, RDSNAME, and RDSRANGE. REFRESHED WEEKLY. VCGI, in collaboration with the VT E911 Board, has created a suite of geocoding services that can be used to batch geocode addresses using ArcGIS Desktop 10.x. This service can also be integrated into ESRI ArcGIS web-based mapping applications.Input Address Requirements Must use valid E911 addresses (street style addressing...no P.O. box addresses!) and E911 town names. Limitations Don't attempt to geocode more than 50000 records or so. You must have an Internet connection to use the services. A DSL, cable, or other high bandwidth connection is the best option. Addresses other than E911 addresses are not supported. ArcGIS Pro - How To:Startup ArcGIS ProUnder the "Insert" ribbon select Connections --> New ArcGIS Server. Server URL = https://maps.vcgi.vermont.gov/arcgis/servicesBrowse to the ./EGC_services folder and select GEOCODE_COMPOSITE (or GEOCODE_ESITE).Add the table you want to geocode to project, then right-click and select "Geocode Table". Choose the “Go to Tool” option at the bottom of the dialogue box.Make selections and run geocoder.ArcGIS Desktop (ArcMap) - How To: Startup ArcMap 10+ Add a table containing VT addresses to geocode. ?Click the "Add Data" button.Navigate to your table, choose to add your tableRight-click on the table in the table of contentsSelect "Geocode Addresses...".Select "Add" in the dialog box.Browse to the "GIS Servers" icon in your catalog, then double click "Add ArcGIS Server".Select "Use GIS Services", then Next.ServerURL = https://maps.vcgi.vermont.gov/arcgis/services then click finish.Browse to "arcgis on maps.vcgi.org (user)". Browse to .\EGC_services folder.Select GECODE_ESITE (or GEOCODE_COMPOSITE). Click OK.Select whatever options you want in the geocode dialog box, including output, then click ok.The output will be automatically added to your ArcMap session.

The VGIN Composite Geocoding service is a cascading locator consisting of Virginia Address Points, Virginia RCL (Road Centerlines), Virginia Community Anchor Institutions (CAI), and several other data layers that supply the end user with returned XY coordinates based on input address number or address name. The source data used in creating this REST service provides updated information from Virginia local governments based on quarterly, biannual, or annual submission scheduling. ESRI applications can use the geocoding service depending on intent of use. By default, the input data sources reside in the Virginia Lambert Conformal Conic 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 (Jurisdictions). Data is limited to the Commonwealth of Virginia and cannot guarantee results in other states. Underlying locator files within the service for Address Points and RCL are updated quarterly.General use within ArcGIS Desktop and ArcGIS Pro:https://vgin.vdem.virginia.gov/documents/VGIN::about-the-vgin-composite-geocoding-service/exploreDevelopers:https://developers.arcgis.com/rest/geocode/api-reference/geocoding-geocode-addresses.htmFrequently asked question about applications that need Spatial Reference adjustment on output. Search (control+f) web page using outSR. VGIN Base Map REST services utilize WGS Web Mercator (ID 102100) while the VGIN Composite Locator is WGS standard (ID 4269).Individual Address Locator Downloads (ArcGIS Pro 3.3):Address PointsRoad Centerlines

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

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 dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high pedestrian crash counts resulting in serious injuries 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: 231 KBNumber of Files: The dataset contains a total of 245 road intersection records (7 "high-high" clusters and 238 "high-low" outliers)Date Created: 21st 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 a pedestrian with a 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 pedestrian crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with pedestrian crashes that resulted in a severe injury 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 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 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}

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 dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high public transport (Bus, Bus-train, Combi/minibus, Midibus) crash counts observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018, 2019, and 2021. 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: 49,0 KBNumber of Files: The dataset contains a total of 40 road intersection records (28 "high-high" clusters and 12 "high-low" outliers)Date Created: 21st 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 bus, a bus/train, combi/minibus and midibuses were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which the public transport crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with public transport 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: 31/12/2021 (2020 data omitted)

The shapefile is generated using Geocode Addresses tool in ArcGIS Pro with the National Mental Health Facilities original Excel file from The SAMHSA (Substance Abuse & Mental Health Data Archive) data source.The last 13 columns in the attributes table are the original table from the excel file, including the name, street, city, state, zip, phone number, and service code info of the facility which could refer to the document: https://www.samhsa.gov/data/sites/default/files/reports/rpt35992/MH%20facilities/MH%20Directory/National_Directory_MH_facilities_final_04272022.pdfThe rest columns of the attribute table are the geocoding results of those locations.Link: https://www.samhsa.gov/data/report/national-directory-of-mental-health-treatment-facilities

The shapefile is generated using Geocode Addresses tool in ArcGIS Pro with the National Directory of Drug and Alcohol Abuse Treatment Facilities original Excel file from the SAMHSA (Substance Abuse & Mental Health Data Archive) data source.The first 5 columns except for the object_id and shape in the attributes table are the geocoding results of those locations. The last 13 columns are the original table from the excel file, including the name, street, city, state, zip, phone number, and service code info of the facility which could refer to the document: https://www.samhsa.gov/data/sites/default/files/reports/rpt35993/SA%20facilites/SU%20Directory/National_Directory_SA_facilities_final_04272022.pdfLink: https://www.samhsa.gov/data/report/national-directory-of-drug-and-alcohol-abuse-treatment-facilities

This ArcGIS StreetMap Premium Asia Pacific 2021 Release 1 map (based on HERE 2021 Q1 vintage) is designed for use in ArcGIS Pro and contains data for Australia South Australia 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).

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: Diciembre del año 2024Data mapeada: 97,220Score de precisión: 95,5%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 Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

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

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 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 Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov