This is an exercise on the use of Postal Code Conversion Files (PCCF) with GIS. (Note: Data associated with this exercise is available on the DLI FTP site under folder 1873-299.)

Context

Upon reviewing the CMS website (https://data.cms.gov/covid-19/covid-19-nursing-home-data), it was apparent a number of nursing home providers were missing from a map plot that was intended to show COVID-19 statistics. I wanted to take a deeper look into the data and just play around with a few visualizations via the CMS provided data set, however I noticed the provided set did not contain any long/lat values for the nursing homes. It could also be seen that certain providers were not being mapped on the CMS website due to string being mixed with numbers in Provider IDs assigned to each provider. New Provider IDs were assigned in rank order, alphabetically and by State. Each nursing home, along with their address was pulled and used to obtain a set of coordinates for their facility and can be joined to the original dataset via Provider ID for use.

Content

Original dataset was sourced from the cms.gov website, with Geocodio being used to geocode the coordinates for the nursing homes. Per the CMS, "The data posted by CMS is what nursing homes submitted through the Centers of Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) system. CMS and CDC perform quality assurance checks on the data and may suppress data that appear to be erroneous. The data is not altered from what nursing homes report to the NHSN system. Data regarding numbers of new cases, suspected cases, or deaths are aggregated.". Nursing homes reported weekly COVID statistics spanning 05/24/20 - 08/05/2021, ranging from case, death, vaccination, equipment, etc. for both residents and staff. A separate table containing address information and coordinates for each individual provider is available for joining, in order to map each facility for visualization.

Acknowledgements

Original Data Source: https://data.cms.gov/covid-19/covid-19-nursing-home-data

Geocode Source: https://www.geocod.io

Geocoding API Market Outlook

According to our latest research, the global Geocoding API market size reached USD 1.45 billion in 2024, reflecting robust demand across diverse industries. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, with the total market value forecasted to reach USD 4.22 billion by 2033. This remarkable growth is primarily driven by the surging adoption of location-based services, the proliferation of IoT devices, and the increasing need for real-time geospatial analytics. As per our latest research, the Geocoding API market is witnessing transformative shifts owing to advancements in cloud computing, machine learning integration, and the expanding scope of digital transformation initiatives globally.

A primary growth factor for the Geocoding API market is the exponential rise in mobile device usage and the integration of geospatial data in everyday applications. Modern businesses, from retail to logistics, are increasingly relying on geocoding solutions to enhance operational efficiency, optimize delivery routes, and improve customer engagement through personalized location-based services. The widespread adoption of smartphones and the ubiquity of GPS-enabled devices have made geospatial data a critical asset, fueling the demand for robust and scalable Geocoding APIs. This trend is further reinforced by the growing popularity of ride-sharing, food delivery, and other on-demand services that require precise location mapping and real-time address resolution.

Another significant driver is the rapid digital transformation across industries, which necessitates the integration of advanced mapping and geospatial analytics into enterprise workflows. Organizations in sectors such as transportation, real estate, and government are leveraging Geocoding APIs to streamline asset tracking, urban planning, and emergency response systems. The ability to convert physical addresses into geographic coordinates and vice versa enables businesses to gain actionable insights, enhance resource allocation, and deliver superior customer experiences. Moreover, the proliferation of big data and IoT devices has intensified the need for real-time, accurate geospatial information, further propelling the adoption of Geocoding API solutions.

The evolution of cloud computing and advancements in artificial intelligence are also catalyzing the growth of the Geocoding API market. Cloud-based deployment models offer unparalleled scalability, cost-effectiveness, and ease of integration, making them the preferred choice for enterprises of all sizes. Additionally, the integration of AI and machine learning algorithms into geocoding platforms has significantly improved the accuracy, speed, and contextual relevance of geospatial data processing. These technological advancements are enabling organizations to unlock new use cases, such as predictive analytics, geofencing, and automated asset management, thereby expanding the addressable market for Geocoding APIs.

In the context of these technological advancements, the role of Location Verification API has become increasingly significant. This API facilitates the accurate verification of physical addresses, ensuring that businesses and services can reliably reach their intended destinations. By integrating Location Verification API into their operations, companies can enhance the precision of their geocoding processes, reducing errors and improving customer satisfaction. This is particularly crucial for industries such as logistics and delivery services, where the timely and accurate delivery of goods is paramount. The API not only supports the validation of addresses but also assists in maintaining up-to-date location databases, which is essential for real-time geospatial analytics and decision-making.

From a regional perspective, North America continues to dominate the Geocoding API market, accounting for a substantial share of global revenues in 2024. The region's leadership is underpinned by the presence of major technology vendors, high digital adoption rates, and a mature ecosystem for location-based services. Europe and Asia Pacific are also witnessing robust growth, fueled by increasing investments in smart city initiatives, expanding e-commerce sectors, and government-led digitalization programs. The Asia Pacific region, in particular, is poised for the faste

Context

Address geocoding, or simply geocoding, is the process of taking a text-based description of a location, such as an address or the name of a place, and returning geographic coordinates, frequently latitude/longitude pair, to identify a location on the Earth's surface - Wikipedia

What is meant by geocoding in GIS? Geocoding is typically preceded by the data cleaning step of preprocessing and standardizing the format of the data. It is a crucial part of developing a GIS (Geographic Information Systems)

This dataset contains a list of 234 valid complete USA addresses that can be used to fetch geocode.

This dataset will come in very handy for testing purposes. Such as - testing performances of geocoding services or API

Content

This dataset comes with three files of the same content - text, CSV, and JSON for ease of use.

Each address has 4 components - - address string - city - state - zipcode

Example - "777 Brockton Avenue, Abington MA 2351"

Starter Notebook

Address Geocoding Solutions(Coordinates From Text)

Acknowledgements

The dataset was collected from this GitHub gist : https://gist.github.com/HeroicEric/1102788

Cover image - Photo by CardMapr on Unsplash

The Address Service Vienna offers an addressee with geocoding as well as reverse geocoding. In the address search with geocoding, it is validated against the address database of the City of Vienna based on the input of an address or a region name and, if valid, address attributes such as district, address, country code, street name, road code, as well as the coordinate (in the desired destination coordinate system) are output. In reverse geocoding, the nearest address or addresses are output based on the input of a coordinate.

Obtain the Complete WRQS DatasetClick the link below to download the dataset in a geodatabase format:

WRQS (Geodatabase)

Please note, the complete dataset is too large for shapefile format due to the 2GB size limitation of shapefiles. The geodatabase format allows for larger file sizes, making it ideal for the complete WRQS dataset.

If you only need a portion of the WRQS dataset (less than 2,000 records), use the interactive map interface in DNRC's Open Data Portal to filter and select specific features and download your selected data in various available formats. This method is ideal for users who require only specific regions or feature types or prefer working with smaller, more manageable file sizes.Details

The Montana Water Right Query System Dataset contains water rights information for the state of Montana. It comprises 10 datasets. The spatial datasets include Points of Diversion (Estimated locations of water right diversion points), Places of Use (Polygons representing areas where water rights are utilized), Reservoirs (Point locations of water storage facilities). Additionally, the dataset includes 7 associated tables: Public Versions, Owners, Change Authorization Scanned Docs, Geocodes, Other Versions, Cases, and Water Right Types.

This comprehensive dataset is derived from the Department of Natural Resources and Conservation (DNRC) Water Rights Query Systems Database. It provides spatial and tabular information crucial for understanding water rights distribution and management in Montana.

For the most up-to-date version of the water rights database or detailed reference information, users should contact the DNRC Water Resources Division at https://dnrc.mt.gov/Water-Resources/Water-Rights/ or call 406-444-6610.

This geocoding service allows address matching against the Location Based Response System (LBRS) address points. Areas maintained by Franklin County including unincorporated areas are updated on a daily basis and published nightly. Edits from participating municipalities are synced on a weekly basis.

Geocoding AI Market Outlook

As per our latest research, the global geocoding AI market size reached USD 1.98 billion in 2024, reflecting significant momentum driven by the proliferation of location-based services and urban digital transformation initiatives. The market is set to expand at a robust CAGR of 15.7% from 2025 to 2033, projecting a value of USD 7.18 billion by 2033. This growth is primarily fueled by the increasing integration of artificial intelligence in geospatial data processing, the rising demand for real-time mapping solutions, and the surging adoption of geocoding AI across sectors such as transportation, urban planning, and emergency response.

One of the primary growth factors propelling the geocoding AI market is the exponential rise in demand for precise and real-time location intelligence across industries. Organizations are leveraging geocoding AI to enhance customer experiences, optimize supply chain operations, and improve decision-making processes based on accurate geospatial data. The proliferation of smartphones and IoT devices has led to a surge in data points requiring geocoding, thereby expanding the scope and necessity for advanced AI-driven geocoding solutions. Furthermore, the integration of AI with geospatial technologies enables the extraction of actionable insights from large volumes of location data, which is critical for applications such as targeted advertising, fleet management, and smart city development.

The surge in smart city initiatives worldwide is another significant driver for the geocoding AI market. Governments and urban planners are increasingly relying on geocoding AI to support infrastructure development, traffic management, and emergency response systems. The ability of AI-powered geocoding to process and analyze massive datasets in real time allows for efficient resource allocation and improved urban mobility. Moreover, the integration of geocoding AI with other emerging technologies such as 5G, edge computing, and advanced analytics further enhances its capability to deliver high-precision location services, thereby accelerating its adoption in both public and private sectors.

Additionally, the rapid advancement in AI algorithms and the availability of high-quality geospatial datasets have significantly improved the accuracy and scalability of geocoding solutions. Organizations are increasingly adopting cloud-based geocoding AI platforms to benefit from flexibility, scalability, and cost-effectiveness. The growing emphasis on data-driven decision-making in sectors like retail, BFSI, and telecommunications is also contributing to the widespread adoption of geocoding AI. These factors, coupled with the increasing awareness of the strategic value of geospatial intelligence, are expected to drive sustained growth in the global geocoding AI market over the forecast period.

From a regional perspective, North America dominates the geocoding AI market, owing to the presence of leading technology providers, high adoption of location-based services, and strong investments in AI research and development. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, the proliferation of digital infrastructure, and increasing government initiatives to develop smart cities. Europe also exhibits significant growth potential, supported by robust regulatory frameworks and a strong focus on innovation in geospatial technologies. Latin America and the Middle East & Africa are witnessing steady adoption, primarily in transportation, logistics, and government sectors, further contributing to the global market expansion.

Address Geocoding Software plays a pivotal role in the geocoding AI market by transforming textual address data into precise geographic coordinates. This software is essential for organizations looking to enhance their location-based services and improve the accuracy of mapping and navigation solutions. By leveraging advanced algorithms and machine learning techniques, address geocoding software can process large volumes of data efficiently, ensuring high precision and reliability. Its integration with cloud-based platforms further enhances scalability and accessibility, making it a valuable tool for businesses across various sectors, including retail, transportation, and urban planning. As the demand for real-time lo

This dataset provides the locations and associated information for amateur radio operators in the United States and territories as registered in the FCC's Universal Licensing System (ULS). It includes call signs, operator class, region codes, and historical data on previous call signs and operator classes, excluding personal and sensitive information.

Out of 1,612,849 total ULS entity records, 1,589,052 were succesfuly geocoded (98.5%). Of these, 5,486 were geocoded at either the zip code or city level (0.35%). The average Tiger geocoder rating was 16.35, indicating a fairly reasonable accuracy.

Credits Federal Communications Commission (FCC)

Summary The dataset includes the following fields:

• unique_system_identifier: An identifier for the record. This column is joinable to the other tables in the FCC ULS downloads you can get from https://www.fcc.gov/uls/transactions/daily-weekly#weekly-files
• call_sign: The assigned call sign of the radio operator.
• operator_class: The license class of the operator.
• region_codee: The FCC region code the operator is associated with.
• previous_call_sign: Previous call sign, if applicable.
• previous_operator_class: Previous license class, if applicable.
• license_status: The current status of the license. A denotes active, C denotes cancelled, E denotes expired, T denoted terminated.
• effective_date: The date the current license status took effect.
• rating: A geocoding accuracy rating from the PostgreSQL TIGER geocoder, where 0 is the highest accuracy and higher values indicate less accuracy. All geocoding results are included. If the rating is 5000 level, it was geocoded at the zip code level by the Nominatum service. Otherwise, it was geocoded at the address level by the PostGIS Tiger geocoder.
• city: The city of the operator's registered address.
• state: The state of the operator's registered address.
• zip_code: The postal zip code of the operator's registered address.
• is_po_box: A boolean value indicating whether the address was a post office box or not.

Usage Notes Users are advised to verify data accuracy and relevance for their specific purposes.

Spatial Reference Coordinate Reference System: WGS 84 (EPSG: 4326)

Please refer to the README file.

The Postal Code Conversion File (PCCF) is a digital file which provides the correspondence between the six character code and Statistics Canada's standard geographical areas (e.g. Census divisions, Census subdivisions, Federal Electoral Districts) for which census data and other statistics are produced. To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The current version of the PCCF links over 726,000 postal code records, created up to the end of December 1991, to the geographical areas used in the 1991 Census and to Universal Transverse Mercator System (UTM) coordinates and latitude/longitude coordinates. This new version contains a new field called the single postal code indicator. This field will be useful in cases where a given postal code is assigned to multiple standard geographic areas. It indicates which of the standard geographic units is the most representative of the postal code. The purpose of the PCCF is to provide linkage capabilities that can be used for numerous applications, such as market research, demographic studies and geocoding applications. The file allows users to cross-reference geographic coordinates, census areas, and user-defined areas. For example, one of its key strengths lied in its capacity to integrate census data with user data. For more information on this product or some of its applications, please refer to the 'Products and Services Manual' or contact the Regional Geographer at one of our Regional Reference Centres across Canada. Every five years, the postal code linkages on the PCCF are “converted” to the latest census geographic areas. The original PCCF was linked to the 1981 Census geographic areas. Since then, the PCCF has undergone two “conversions”, following the 1986 and 1991 censuses.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up the MTS. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces shapefile.

This georeferenced dataset tracks 7,068 locations of 6,805 health sector projects extracted from Uganda's Ministry of Finance, Planning and Economic Development. Geocoding is a process by which an address is assigned a single data point with a corresponding latitude and longitude. Geo-referencing, not to be mistaken for geocoding, is a process in which an internal coordinate system of a map, or a satellite/aerial image, is then spatially referenced.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up the MTS. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces shapefile.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up the MTS. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces shapefile.

Geocoding Of Places (Geolog) Of The Municipality Of Sao Paulo
This dataset falls under the category Planning & Policy Policy.
It contains the following data: Digital Planimetric Mapping of the City of Sao Paulo at 1:10,000 and 1:5,000 scales. Vector map generated from the scanning of 1:10,000 Gegran/Emplasa maps, containing features of the road system (axes of street), sectors, blocks and tax lots, railways, towers and high voltage lines.
This dataset was scouted on 2022-02-10 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: http://dados.prefeitura.sp.gov.br/dataset/geocodificacao-de-logradouros-geolog

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up the MTS. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces shapefile.

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.

These are the individual apparatus response to incidents by Lincoln Fire & Rescue . These data are from the LF&R records management system, and are drawn from a geocoded dataset. A small percentage of the runs cannot be geocoded, so are not reflected in the data. The geocoding rate is approximately 99%. Multiple units are typically dispatched to most incidents, and in this table, each record represents the response of an individual apparatus to an incident. Data about each unique incident is contained in the LFR_incidents tables. To create a list of all incidents and apparatus runs for a specific day incidents visit this site.