Our location data powers the most advanced address validation solutions for enterprise backend and frontend systems.

A global, standardized, self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries.

All geospatial data for address data validation is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Address Validation at Zip Code Level Database (Geospatial data)

• Address capture and address validation

• Address autocomplete

• Address verification

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Product Features

• Dedicated features to deliver best-in-class user experience

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

Data export methodology

Our location data packages are offered in variable formats, including .csv. All geospatial data for address validation are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why do companies choose our location databases

• Enterprise-grade service

• Full control over security, speed, and latency

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

• Seamlessly integrated into your software

Note: Custom address validation packages are available. Please submit a request via the above contact button for more details.

Data producers or those who maintain parcel data can use this tool to validate their data against the state Geospatial Advisory Committee (GAC) Parcel Data Standard. The validations within the tool were originally created as part of a MetroGIS Regional Parcel Dataset workflow.

Counties using this tool can obtain a schema geodatabase from Parcel Data Standard page hosted by MnGeo (link below). All counties, cities or those maintaining authoritative data on a local jurisdiction's behalf, are encouraged to use and modify the tool as needed to support local workflows.

Parcel Data Standard Page
http://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

Specific validation information and tool requirements can be found in the following documents included within this resource.
Readme_HowTo.pdf
Readme_Validations.pdf

This folder contains processed and derived data, and script for the manuscript, 'Detecting synthetic population bias using a spatially-oriented framework and independent validation data'.Abstract: Models of human mobility can be broadly applied to find solutions addressing diverse topics such as public health policy, transportation management, emergency management, and urban development. However, many mobility models require individual-level data that is limited in availability and accessibility. Synthetic populations are commonly used as the foundation for mobility models because they provide detailed individual-level data representing the different types and characteristics of people in a study area. Thorough evaluation of synthetic populations are required to detect data biases before the prejudices are transferred to subsequent applications. Although synthetic populations are commonly used for modeling mobility, they are conventionally validated by their sociodemographic characteristics, rather than mobility attributes. Mobility microdata provides an opportunity to independently/externally validate the mobility attributes of synthetic populations. This study demonstrates a spatially-oriented data validation framework and independent data validation to assess the mobility attributes of two synthetic populations at different spatial granularities. Validation using independent data (SafeGraph) and the validation framework replicated the spatial distribution of errors detected using source data (LODES) and total absolute error. Spatial clusters of error exposed the locations of underrepresented and overrepresented communities. This information can guide bias mitigation efforts to generate a more representative synthetic population.

This dashboard depicts the status of NG9-1-1 data submitted to Datmark VEP, a data validation software solution.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

Context: This artifact was produced within the context of the European Horizon Project Eco2Adapt, and as part of my postdoctoral research. These validation rules were designed to validate forestry datasets such as trees within plots or particular areas. However, they were generalised to address any geospatial object. Geographic shapes are a list of rules implemented using the Shapes Constraint Language (SHACL). SHACL is a W3C recommendation. These rules enable the validation of geographic entities based on the standards for geospatial data GeoSPARQL. The rules are valid for any system using the GeoSPARQL standard. Current version covers the full list of Simple Function and predicates such as sfWithin, sfIntersect, sfOverlaps.

Data producers or those who maintain address points can use this tool to validate their data against the state Geospatial Advisory Council (GAC) Address Point Standard. The validations within the tool were originally created as part of a workflow for counties under the Metropolitan Emergency Services Board (MESB) jurisdiction to pre-check their address points before being submitting data to the aggregated Metro Address Point Dataset. The primary driver behind developing the validations was to support 911 dispatch workflows administered by the MESB.

Counties using this tool can obtain a schema geodatabase from MnGeo (link below). All counties, cities or those maintaining authoritative data on a local jurisdiction’s behalf, are encouraged to use and modify the tool as needed to support local workflows.

Datasets Supported:
Address Points
Road Centerlines

Standards Page
https://www.mngeo.state.mn.us/committee/standards/standards_adopted_devel.html

Specific validation information and tool requirements can be found in the following documents included within this resource.
Readme_HowTo.pdf
Readme_Validations.pdf

BIEN data validation and standardization tools.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. A draft hard copy vegetation map at the 1:12,000 scale was printed and checked against the interpreted aerial photographs. As a final internal accuracy check, we applied photointerpretative observations and classification relevés over the vegetation map to determine if the polygon labels matched the field data. Finally, map validation occurred prior to the accuracy assessment. Staff from RSGIG conducted a field trip in conjunction with other meetings in Flagstaff, AZ in January 2001 to refine and assess the initial mapping effort. On this trip we collected additional photointerpretative observations and ground-truthed aerial photograph signatures using landmarks and GPS waypoints. Map classes were lumped or split to account for inadequacies in the final photointerpretation. Metadata are required for all spatial data produced by the federal government. RSGIG used SIMMS™ software and CPRS used ArcCatalogue software to create the FGDC-compliant metadata files attached to the spatial databases and to this report (see Appendix A). The metadata files explain the vegetation coverage and ancillary coverages created by RSGIG, the classification relevé data coverage created by CPRS, and the accuracy assessment observation data created by CPRS.

This data includes all datasets and codes for adversarial validation in geospatial machine learning prediction and corresponding experiments. Except for datasets (Brazil Amazon basion AGB dataset and synthetic species abundance dataset) and code, Reademe.txt explains each file's meaning.

This data includes all datasets and codes for adversarial validation in geospatial machine learning prediction and corresponding experiments. Except for datasets (Brazil Amazon basion AGB dataset and synthetic species abundance dataset) and code, Reademe.txt explains each file's meaning.

Data producers or those who maintain park and trail data can use this tool to validate their data against the MetroGIS Park and Trail data standard. The validations within the tool were created to support the Metro Park and Trail datasets (see associated datasets below).

MetroGIS Park and Trail Data Page
https://www.metrogis.org/projects/park-and-trail.aspx

Specific validation information and tool requirements can be found in the following documents included within this resource.
Readme_HowTo.pdf
Readme_Validations.pdf

Our North America Point of Interest (POI) data supports various location intelligence projects and facilitates the development of precise mapping and navigation tools, location analysis, address validation, and much more. Gain access to highly accurate, clean, and North America scaled POI data featuring over 28 million verified locations across 2 countries. We have been providing this data to companies worldwide for 30 years.

• Develop mapping and navigation tools and software.
• Identify new areas and locations suitable for business development.
• Analyze the presence of competitors and nearby populations.
• Optimize routes to enhance delivery efficiency.
• Evaluate property values based on nearby infrastructure.
• Support disaster management by identifying high-risk areas.
• Promote your products and services using geotargeting strategies.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas: 1. Gaining a Competitive Edge: Utilize point of interest (POI) data to analyze competitors, identify high-opportunity areas, and attract more customers. 2. Enhancing Customer Journeys: Leverage location intelligence to provide personalized, real-time recommendations that boost customer engagement. 3. Optimizing Store Expansion: Select the most profitable locations by analyzing foot traffic, demographics, and competitor insights. 4. Streamlining Deliveries: Improve fulfillment accuracy through address validation, reducing failed shipments and increasing customer satisfaction. 5. Driving Smarter Campaigns: Use geospatial insights to effectively target the right audiences, enhance outreach, and maximize campaign impact.

Links to recordings of the Integrated Services Program and 9-1-1 & Geospatial Services Bureau webinar series, including NG9-1-1 GIS topics such as: data preparation; data provisioning and maintenance; boundary best practices; and extract, transform, and load (ETL). Offerings include:Topic: Virginia Next Generation 9-1-1 Dashboard and Resources Update Description: Virginia recently updated the NG9-1-1 Dashboard with some new tabs and information sources and continues to develop new resources to assist the GIS data work. This webinar provides an overview of changes, a demonstration of new functionality, and a guide to finding and using new resources that will benefit Virginia public safety and GIS personnel with roles in their NG9-1-1 projects. Wednesday 16 June 2021. Recording available at: https://vimeo.com/566133775Topic: Emergency Service Boundary GIS Data Layers and Functions in your NG9-1-1 PSAP Description: Law, Fire, and Emergency Medical Service (EMS) Emergency Service Boundary (ESB) polygons are required elements of the NENA NG9-1-1 GIS data model stack that indicate which agency is responsible for primary response. While this requirement must be met in your Virginia NG9-1-1 deployment with AT&T and Intrado, there are quite a few ways you could choose to implement these polygons. PSAPs and their GIS support must work together to understand how this information will come into a NG9-1-1 i3 PSAP and how it will replace traditional ESN information in order to make good choices while implementing these layers. This webinar discusses:the function of ESNs in your legacy 9-1-1 environment, the role of ESBs in NG9-1-1, and how ESB information appears in your NG9-1-1 PSAP. Wednesday, 22 July 2020. Recording available at: https://vimeo.com/441073056#t=360sTopic: "The GIS Folks Handle That": What PSAP Professionals Need to Know about the GIS Project Phase of Next Generation 9-1-1 DeploymentDescription: Next Generation 9-1-1 (NG9-1-1) brings together the worlds of emergency communication and spatial data and mapping. While it may be tempting for PSAPs to outsource cares and concerns about road centerlines and GIS data provisioning to 'the GIS folks', GIS staff are crucial to the future of emergency call routing and location validation. Data required by NG9-1-1 usually builds on data that GIS staff already know and use for other purposes, so the transition requires them to learn more about PSAP operations and uses of core data. The goal of this webinar is to help the PSAP and GIS worlds come together by explaining the role of the GIS Project in the Virginia NG9-1-1 Deployment Steps, exploring how GIS professionals view NG9-1-1 deployment as a project, and fostering a mutual understanding of how GIS will drive NG9-1-1. 29 January 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225474Topic: Getting Your GIS Data from Here to There: Processes and Best Practices for Extract, Transform and Load (ETL) Description: During the fall of 2019, VITA-ISP staff delivered workshops on "Tools and Techniques for Managing the Growing Role of GIS in Enterprise Software." This session presents information from the workshops related to the process of extracting, transforming, and loading data (ETL), best practices for ETL, and methods for data schema comparison and field mapping as a webinar. These techniques and skills assist GIS staff with their growing role in Next Generation 9-1-1 but also apply to many other projects involving the integration and maintenance of GIS data. 19 February 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225007Topic: NG9-1-1 GIS Data Provisioning and MaintenanceDescription: VITA ISP pleased to announce an upcoming webinar about the NG9-1-1 GIS Data Provisioning and Maintenance document provided by Judy Doldorf, GISP with the Fairfax County Department of Information Technology and RAC member. This document was developed by members of the NG9-1-1 GIS workgroup within the VITA Regional Advisory Council (RAC) and is intended to provide guidance to local GIS and PSAP authorities on the GIS datasets and associated GIS to MSAG/ALI validation and synchronization required for NG9-1-1 services. The document also provides guidance on geospatial call routing readiness and the short- and long-term GIS data maintenance workflow procedures. In addition, some perspective and insight from the Fairfax County experience in GIS data preparation for the AT&T and West solution will be discussed in this webinar. 31 July 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224774Topic: NG9-1-1 Deployment DashboardDescription: I invite you to join us for a webinar that will provide an overview of our NG9-1-1 Deployment Dashboard and information about other online ISP resources. The ISP website has been long criticized for being difficult to use and find information. The addition of the Dashboard and other changes to the website are our attempt to address some of these concerns and provide an easier way to find information especially as we undertake NG9-1-1 deployment. The Dashboard includes a status map of all Virginia PSAPs as it relates to the deployment of NG9-1-1, including the total amount of funding requested by the localities and awards approved by the 9-1-1 Services Board. During this webinar, Lyle Hornbaker, Regional Coordinator for Region 5, will navigate through the dashboard and provide tips on how to more effectively utilize the ISP website. 12 June 2019. Recording not currently available. Please see the Virginia Next Generation 9-1-1 Dashboard and Resources Update webinar recording from 16 June 2021. Topic: PSAP Boundary Development Tools and Process RecommendationDescription: This webinar will be presented by Geospatial Program Manager Matt Gerike and VGIN Coordinator Joe Sewash. With the release of the PSAP boundary development tools and PSAP boundary segment compilation guidelines on the VGIN Clearinghouse in March, this webinar demonstrates the development tools, explains the process model, and discusses methods, tools, and resources available for you as you work to complete PSAP boundary segments with your neighbors. 15 May 2019. Recording available at: https://www.youtube.com/watch?v=kI-1DkUQF9Q&feature=youtu.beTopic: NG9-1-1 Data Preparation - Utilizing VITA's GIS Data Report Card ToolDescription: This webinar, presented by VGIN Coordinator Joe Sewash, Geospatial Program Manager Matt Gerike, and Geospatial Analyst Kenny Brevard will provide an overview of the first version of the tools that were released on March 25, 2019. These tools will allow localities to validate their GIS data against the report card rules, the MSAG and ALI checks used in previous report cards, and the analysis listed in the NG9-1-1 migration proposal document. We will also discuss the purpose of the tools, input requirements, initial configuration, how to run them, and how to make sense of your results. 10 April 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224495Topic: NG9-1-1 PSAP Boundary Best Practice WebinarDescription: During the months of November and December, VITA ISP staff hosted regional training sessions about best practices for PSAP boundaries as they relate to NG9-1-1. These sessions were well attended and very interactive, therefore we feel the need to do a recap and allow those that may have missed the training to attend a makeup session. 30 January 2019. Recording not currently available. Please see the PSAP Boundary Development Tools and Process Recommendation webinar recording from 15 May 2019.Topic: NG9-1-1 GIS Overview for ContractorsDescription: The Commonwealth of Virginia has started its migration to next generation 9-1-1 (NG9-1-1). This migration means that there will be a much greater reliance on geographic information (GIS) to locate and route 9-1-1 calls. VITA ISP has conducted an assessment of current local GIS data and provided each locality with a report. Some of the data from this report has also been included in the localities migration proposal, which identifies what data issues need to be resolved before the locality can migrate to NG9-1-1. Several localities in Virginia utilize a contractor to maintain their GIS data. This webinar is intended for those contractors to review the data in the report, what is included in the migration proposal and how they may be called on to assist the localities they serve. It will still ultimately be up to each locality to determine whether they engage a contractor for assistance, but it is important for the contractor community to understand what is happening and have an opportunity to ask questions about the intent and goals. This webinar will provide such an opportunity. 22 August 2018. Recording not currently available. Please contact us at NG911GIS@vdem.virginia.gov if you are interested in this content.

This is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation, dissimilarity quantification method, and dissimilarity-adaptive cross-validation.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Final WUPA map classes used for interpreting the aerial photographs were derived (1) from plant associations and alliances described by CPRS, (2) from the Anderson (1976) Level II land use classification system, (3) from land cover classes, and (4) from unique stands specific to WUPA. A draft hard copy vegetation map at the 1:12,000 scale was printed and checked against the interpreted aerial photographs. As a final internal accuracy check, RSGIG applied photointerpretation observations and classification relevés over the vegetation map to determine if the polygon labels matched the field data. Map validation occurred prior to the accuracy assessment. Because of the difficulties in interpreting the vegetation directly from the aerial photographs, we eventually mapped and/or validated much of the project area in the field. Metadata are required for all spatial data produced by the federal government. RSGIG used SIMMS™ software to create the three FGDC-compliant metadata files attached to the spatial databases and to this report. The metadata files explain the vegetation coverage and ancillary coverages created by RSGIG, the plot data coverage created by CPRS, and the accuracy assessment data created by CPRS.

Our Italy Point of Interest (POI) data supports various location intelligence projects and facilitates the development of precise mapping and navigation tools, location analysis, address validation, and much more. Gain access to highly accurate, clean, and country scaled POI data featuring over 4 million verified locations across Italy. We have been providing this data to companies worldwide for 30 years.

• Develop mapping and navigation tools and software.
• Identify new areas and locations suitable for business development.
• Analyze the presence of competitors and nearby populations.
• Optimize routes to enhance delivery efficiency.
• Evaluate property values based on nearby infrastructure.
• Support disaster management by identifying high-risk areas.
• Promote your products and services using geotargeting strategies.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas: 1. Gaining a Competitive Edge: Utilize point of interest (POI) data to analyze competitors, identify high-opportunity areas, and attract more customers. 2. Enhancing Customer Journeys: Leverage location intelligence to provide personalized, real-time recommendations that boost customer engagement. 3. Optimizing Store Expansion: Select the most profitable locations by analyzing foot traffic, demographics, and competitor insights. 4. Streamlining Deliveries: Improve fulfillment accuracy through address validation, reducing failed shipments and increasing customer satisfaction. 5. Driving Smarter Campaigns: Use geospatial insights to effectively target the right audiences, enhance outreach, and maximize campaign impact.

Introduction and Rationale:Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update SPAN. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in the final version of SPAN.Contents:Spatial dataNational rasters of land cover in the conterminous United States: 2012-2021Rasters of pixels mismatched between CDL and NVC: 2012-2021Resources in this dataset:Resource Title: SPAN land cover in the conterminous United States: 2012-2021 - SCINet File Name: KammererNationalRasters.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). Spatial Products for Agriculture and Nature ('SPAN') land cover in the conterminous United States from 2012-2021. This raster dataset is available in GeoTIFF format and was created by joining agricultural classes from the USDA-NASS Cropland Data Layer (CDL) to national vegetation from the LANDFIRE National Vegetation Classification v2.0 ('Remap'). Pixels of national vegetation are the same in all rasters provided here and represent land cover in 2016. Agricultural pixels were taken from the CDL in the specified year, so depict agricultural land from 2012-2021. Resource Title: Rasters of pixels mismatched between CDL and NVC: 2012-2021 - SCINet File Name: MismatchedNational.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). This dataset includes pixels that were classified as agriculture in the NVC but, in the CDL, were not agriculture (or were a conflicting agricultural class). For more details, we refer users to the linked publication describing our geospatial processing and validation workflow.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node455886/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Mapping and interpretation of JODA involved a five step process including: (1) field reconnaissance, (2) map class development, (3) image processing and interpretation, (4) draft map validation, and (5) spatial database development. Field reconnaissance was initiated by CTI and NMI staff in 2008 to quickly familiarize the mappers with the vegetation patterns and distribution at JODA. As the classification plot data were acquired later in 2008, feedback on the dominant and characteristic plant species was solicited from ORNHIC ecologists. boundary placement and labeling. Field notes were made directly on vegetation map copies and an additional 70 observation points were sampled to support the notations. Confusing sites were visited including the Picture Gorge area where shadows on the NAIP imagery prevented viewing the distribution of vegetation types. Ground data and ground photographs were collected to insure consistent mapping of confusing sites. Upon return to the office, minor updates of the draft vegetation map were completed prior to the AA task.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. In late May and June of 1997, after a preliminary vegetation map had been prepared by the photointerpreter, a map validation step was performed in which further data were collected to obtain more information on the vegetation types and to better correlate the vegetation with the signatures on the aerial photographs. Sampling was conducted at points selected by the photointerpreter based on stratified random design in which more extensive vegetation types were allocated more points. This resulted in the collection of 36 observation points. At each point, the dominant species in each vegetation stratum were recorded with an estimated cover class. These extra points gave a better understanding of the variation within vegetation types and allowed sampling of three types that had not been found in the previous field season.