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 most accurate and up-to-date database for point addressing, with over 270 million precise point addresses in 70 countries.

Geocoding available in 196 countries, with high-precision mapping of display or navigable positions. Input a structured or free-form address to get results ranked by relevance or proximity.

Reverse Geocoding: Get a physical address from a set of geocoordinates. Use heading information to understand direction of movement, and get addresses, landmarks or area information around a position.

Search data: Search a rich database of ~120M POIs/places, that is updated daily, and interact with Places rich attributes covering information from name and category, to price range, contact and URLs.

Autosuggest: Get better suggestions with fewer strokes for places, addresses, chain queries or category queries, as well as provide search text matches with or without spatial filters.

Context

Trying to make use of the location feature in the "Real or Not? NLP with Disaster Tweets" competition. I tried to geocode the locations, hoping that at least the difference between locations that can be geocoded (e.g. Birmingham) vs those that cannot be (e.g. "your sisters bedroom") would be a good feature. Additionally, geocoding provides longitude and latitude features that may be helpful.

Content

The dataset captures whether a location could be geocoded (that is: it is a valid location in the world).

Acknowledgements

Geocoding is done with Nominatim

Inspiration

Can you make better tweet classifications with geocoded locations?

Insurance Geocoding Solutions Market Outlook

According to our latest research, the global insurance geocoding solutions market size reached USD 1.47 billion in 2024, reflecting a robust and accelerating adoption across insurance sectors worldwide. The market is projected to expand at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 4.18 billion by 2033. This impressive growth trajectory is primarily driven by the increasing demand for precise location-based analytics, risk mitigation, and digital transformation within the insurance industry. The integration of geospatial technologies is enabling insurers to optimize underwriting, claims management, and customer engagement, fueling substantial market expansion.

The primary growth factor for the insurance geocoding solutions market is the rising necessity for risk assessment and mitigation in the face of more frequent and severe weather events, natural disasters, and urbanization. Insurers are leveraging geocoding solutions to accurately pinpoint risk-prone locations, assess exposure, and price policies accordingly. This capability not only enhances underwriting precision but also reduces the likelihood of unforeseen losses. As regulatory bodies worldwide tighten requirements around risk transparency and solvency, the demand for advanced geospatial analytics in insurance continues to surge. Furthermore, the proliferation of IoT devices and real-time data feeds is enriching the quality and granularity of geospatial datasets, further strengthening the marketÂ’s value proposition.

Another significant driver is the ongoing digital transformation across the insurance sector. Insurers are increasingly investing in automation, artificial intelligence, and cloud computing to streamline operations and improve customer experience. Geocoding solutions serve as a foundational technology in this transformation, enabling seamless integration with digital platforms and core insurance systems. Enhanced data visualization and location intelligence empower insurers to tailor products, detect fraud, and deliver personalized services. As competition intensifies and customer expectations evolve, the ability to leverage spatial data for actionable insights is becoming a critical differentiator for insurance providers, propelling the adoption of geocoding solutions.

The growing emphasis on customer analytics and targeted marketing is also fueling the expansion of the insurance geocoding solutions market. Insurers are utilizing geospatial data to segment customers, identify underserved regions, and optimize distribution strategies. This targeted approach not only improves customer acquisition and retention but also enables insurers to launch innovative, location-based products and services. The convergence of geocoding with big data analytics and machine learning is unlocking new opportunities for predictive modeling and scenario planning, further amplifying the marketÂ’s growth potential. As insurers seek to stay ahead in a rapidly evolving landscape, investment in geocoding technologies is expected to remain a top priority.

Geodemographic Segmentation is becoming an increasingly vital tool for insurers looking to refine their marketing strategies and enhance customer engagement. By leveraging geodemographic data, insurance companies can gain deeper insights into the socio-economic and demographic profiles of their customer base. This allows them to tailor their products and services to meet the specific needs and preferences of different customer segments. The integration of geodemographic segmentation with geocoding solutions enables insurers to identify potential markets, optimize distribution channels, and deliver personalized marketing campaigns. As the demand for more targeted and effective customer interactions grows, geodemographic segmentation is set to play a pivotal role in shaping the future of insurance marketing strategies.

From a regional perspective, North America currently dominates the insurance geocoding solutions market, accounting for the largest share owing to the early adoption of advanced analytics, well-established insurance infrastructure, and stringent regulatory standards. Europe follows closely, driven by increasing regulatory compliance and the need for sophisticated risk management tools. The Asia Pacific region is emerging as a high

It is an ArcGIS multirole locator with two roles:

1. Point Address - Generally more accurate results from rooftop location points. Includes a Subaddress if a unit number is located.
   
2. 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 Pro
Web Services
ArcGIS Online

Geocoding AI Market Outlook

According to our latest research, the global Geocoding AI market size reached USD 2.18 billion in 2024 and is projected to grow at a robust CAGR of 15.7% through the forecast period, reaching USD 7.21 billion by 2033. This significant expansion is fueled by the rapid digitization of spatial data, the proliferation of location-based services, and the increasing adoption of artificial intelligence to enhance real-time geospatial analytics and decision-making. The demand for precise geocoding solutions is being driven by industries such as transportation, urban planning, and retail, where accurate location intelligence is paramount for operational efficiency and strategic growth.

One of the primary growth factors for the Geocoding AI market is the surge in demand for location-based services across various industries. With the rise of connected devices and the Internet of Things (IoT), enterprises are leveraging geocoding AI to provide real-time, context-aware services to their customers. For instance, in the retail and e-commerce sector, businesses use geocoding AI to optimize last-mile delivery, personalize marketing campaigns, and enhance customer experiences by offering location-specific recommendations. Additionally, the increasing integration of geocoding solutions into mobile applications and smart city frameworks is driving further adoption, as organizations seek to harness accurate geospatial data for better planning and resource allocation.

Another significant driver of market growth is the advancement in artificial intelligence and machine learning algorithms, which have substantially improved the accuracy, scalability, and efficiency of geocoding processes. Modern Geocoding AI solutions can process vast volumes of unstructured address data, correct errors, and provide precise latitude and longitude coordinates in real time. This technological progress is particularly beneficial for sectors like transportation and logistics, where route optimization and fleet management rely heavily on accurate geospatial information. Furthermore, the growing need for emergency management and disaster response has highlighted the importance of reliable geocoding AI to ensure rapid and effective deployment of resources in critical situations.

The third key factor propelling the Geocoding AI market is the increasing adoption of cloud-based deployment models. Cloud platforms offer scalability, flexibility, and cost-effectiveness, enabling organizations of all sizes to implement advanced geocoding solutions without significant upfront investments in infrastructure. The shift towards cloud-based geocoding AI is also facilitating seamless integration with other enterprise applications, enhancing data interoperability, and supporting remote and distributed workforces. As a result, small and medium enterprises (SMEs), which previously faced barriers to entry due to high costs and technical complexities, are now able to leverage sophisticated geocoding capabilities to compete more effectively in the digital economy.

From a regional perspective, North America currently dominates the Geocoding AI market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of major technology providers, high adoption rates of advanced analytics, and well-established digital infrastructure are key contributors to North America’s leadership. Meanwhile, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, expanding e-commerce, and significant investments in smart city initiatives. Latin America and the Middle East & Africa are also showing promising growth potential, supported by increasing government initiatives to improve urban planning and disaster management capabilities.

Component Analysis

The Component segment of the Geocoding AI market is bifurcated into Software and Services, each playing a pivotal role in the overall ecosystem. Geocoding AI software encompasses the core platforms and applications that process, standardize, and translate address data into geographic coordinates. These software solutions are continually evolving, integrating advanced AI and machine learning capabilities to enhance accuracy, speed, and user experience. The software segment is witnessing substantial investment from both established technology giants and innovative startups, resulting in a dynamic and competitive landscape. Customization, scalability, and ease of inte

Spatially analyze healthcare data and extract fresh insights by transforming readable addresses to geographic coordinates with Spatialitics HIPAA-Compliant Health Geocoder._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Street centerline network for the City of Portland, Multnomah County, Clackamas County and Washington County developed for geocoding purposes. This dataset has a character (textual) street number field to better allow for locating addresses that have leading zeros (e.g., 0680 SW Bancroft St.). This dataset does not yet include the data fields and structure necessary for performing routing operations.--Additional Information: Category: Transportation - Streets Purpose: Provides a dataset including all address segments to be used for geocoding (locating) addresses in the Portland Region. Includes the capability to search those addresses whose street number begins with a zero.-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=52067

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data.

From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Changes in the November 2025 release

• Nationally, the November 2025 update of G-NAF shows an increase of 32,773 addresses overall (0.21%). The total number of addresses in G-NAF now stands at 15,827,416 of which 14,983,358 or 94.67% are principal.

• There is one new locality for the November 2025 Release of G-NAF, the locality of Southwark in South Australia.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.

Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added.

The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.

End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

What to Expect When You Download G-NAF

G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.

Licensed Cannabis Store Locations in Canada (Geocoded)

This dataset provides a comprehensive list of all publicly licensed and operating retail cannabis store locations across Canada. It aims to offer a single, unified source for analyzing the evolving landscape of cannabis retail, supporting research in geographic analysis, market penetration, and regulatory studies. Each entry includes essential retail details like Store Name, City, Province, Full Address, Postal Code, and, critically, geocoded Latitude and Longitude coordinates for immediate mapping and spatial analysis.

Data Collection and Provenance

The data was systematically compiled from official public listings provided by provincial and territorial regulatory bodies, which are primarily accessible through the Health Canada portal:

Primary Source Portal: https://www.canada.ca/en/health-canada/services/drugs-medication/cannabis/laws-regulations/provinces-territories.html

The compilation methodology varied based on the structure of the provincial data releases:

-> Structured Sources (e.g., Alberta, Ontario, Saskatchewan): Data for these provinces were available in a structured, easily consumable format (e.g., downloadable files or APIs). They were merged directly into the master dataset.

-> Manual Sourcing: For provinces and territories where official data was only available as lists on government websites, the information was manually copied and structured to ensure completeness across the dataset.

Methodology and Data Enhancement

The raw source data often lacks precise geographic coordinates, limiting spatial analysis. Therefore, a critical step in creating this dataset was geocoding:

-> Coordinate Addition: Using the available Postal Code and Full Address fields, geographical coordinates (Latitude and Longitude) were obtained for each store location.

-> API Utilization: The geocoding process was conducted using the Google Geocoding API to ensure high accuracy and map-friendly visual presentation. Locations that lacked a complete postal code were prioritized using the full address for the best possible coordinate match.

This enhancement makes the dataset immediately usable for mapping projects, GIS analysis, and geospatial research.- -

Context

This is a supplementary dataset to the Data Science for Good: Kiva Crowdfunding challenge. In the Kiva challenge, the kiva_loans.csv file contains a large record of loans with the borrower's locations. This dataset provides the latitude and longitude of these locations.

The original dataset also includes another file loan_themes_by_region.csv, which provides some additional information on geographical locations of the loan themes offered. However, there are significantly more borrower locations than loan theme locations, and these two locations are not always the same. This dataset tries to solve this problem by directly obtaining the geocode of all borrower locations via Google Maps Geocoding API.

Content

There are four columns in the CSV. "Region" and "country" match the corresponding fields in kiva_loans.csv. "Latitude" and "longitude" are self-explanatory. Queries without valid results from the Google Maps API are indicated by latitude=-999, longitude=-999.

The geocodes are not manually validated and should be used with caution. Bad query results may happen due to mistakes in the original dataset or Google Maps' autocorrection.

Acknowledgements

The building of this dataset uses the following API: https://developers.google.com/maps/documentation/geocoding/intro

Inspiration

This dataset can help participants in the Kiva challenge by allowing them to compare location proximity and visualise data on a world/regional map when analysing Kiva's loans. The original purpose of the dataset is for me to visualise loan type clustering results on a world map and find similarities in borrower needs between remote, disjoint regions, however I hope the community will find better, more creative uses for this tiny dataset.

This dataset represents commercial business locations that are California Department of Fish and Wildlife authorized Fishing License Sales Agents. These are locations where the public can purchase fishing licenses. The spatial locations are created by geocoding the address information provided by the business, using best available geocoding methods. Rural locations are potentially less accurate in their location assignment than urban locations. If a location is in doubt, contact the business first to confirm.

The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. Getting started guide To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. The geographic coordinates, which represent the standard geostatistical areas linked to each postal codeOM on the PCCF, are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for marketing, planning, or research purposes. In April 1983, the Statistical Registers and Geography Division released the first version of the PCCF, which linked postal codesOM to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codesOM are directly geocoded to 2016 Census geography while others are linked via various conversion processes. A quality indicator for the confidence of this linkage is available in the PCCF.

The dataset contains locations and attributes for above ground permits applied for and approved by the District Department of Transportation. They are existing occupied constructions and events. Examples include: moving trucks, roll off debris container, moving storage container, construction staging area, mobile crane work zone, other reserved parking. The public space permit process is described on the DDOT website https://ddot.dc.gov.These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.

The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. Getting started guide To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. In April 1983, the Geography Division released the first version of the PCCF, which linked postal codes to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codes are directly geocoded to 2006 Census geography. This improves precision of the file over the previous conversion process used to align postal code linkages to new geographic areas after each census. About 94% of the postal codes were linked to geographic areas using the new automated process. A quality indicator for the confidence of this linkage is available in the PCCF.

The dataset contains locations and attributes of above ground permits applied for and approved by the District Department of Transportation. They are newly occupied constructions and events. Examples include: moving truck, roll off debris container, moving storage container, construction staging area, mobile crane work zone, other reserved parking. The public space permit process is described on the DDOT website https://ddot.dc.gov.These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.

The dataset contains locations and attributes of above ground permits applied for and approved by the District Department of Transportation. They are newly occupied constructions and events. Examples include: moving truck, roll off debris container, moving storage container, construction staging area, mobile crane work zone, other reserved parking. The public space permit process is described on the DDOT website https://ddot.dc.gov.These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.

This dataset contains address points from the Countywide Address Management System, a collaborative program between the County’s Registrar/Recorder, Chief Information Officer, Public Works Department, Department of Regional Planning, and many local cities to manage addresses and street centerlines for the purposes of geocoding and cartography. More information about this layer can be found on the https://cams-lacounty.hub.arcgis.com/ What this data is (and isn’t)This dataset contains the best available information, with close to 3 million primary and secondary addresses in the County of Los Angeles. It does NOT include information about every unit, suite, building, and sub-address. With probably over 7 million addresses, we have a ways to go.DescriptionThis dataset includes over 2.9 million individual points for addresses in the County. Data has been compiled from best available sources, including city databases, LA County Assessor parcels, and the County’s House Numbering maps. Please see the Source field for information.Street Name information has been split into multiple fields to support the County’s specifically designed geocoders – please see the entry on LA County Specific Locators and Matching rules for more information.Multi-address ParcelsSome of our data sources (LA City, LA County, for example) have mapped each individual address in their city. These may also show unit information for an address point. A property with multiple addresses will show a point for each address. For some cities where this has not happened, the data source is the Assessor, where the primary address of the property may be the only address shown. We invite cities and sources with more detailed information to join the CAMS consortium to continue to improve the data.Legal vs. Postal CitiesMany users confuse the name the Post Office delivers main to (e.g. Van Nuys, Hollywood) as a legal city (in this case Los Angeles), when they are a postal city. The County contains 88 legal cities, and over 400 postal names that are tied to the zipcodes. To support useability and geocoding, we have attached the first 3 postal cities to each address, based upon its zipcocode.