This Africa Geocoding locator is a view of the World Geocoding Service constrained to search for places in the countries of Africa. The World Geocoding Service finds addresses and places in all supported countries around the world in a single geocoding service. The service can find point locations of addresses, cities, landmarks, business names, and other places. The output points can be visualized on a map, inserted as stops for a route, or loaded as input for a spatial analysis.The service is available as both a geosearch and geocoding service:Geosearch Services – The primary purpose of geosearch services is to locate a feature or point of interest and then have the map zoom to that location. The result might be displayed on the map, but the result is not stored in any way for later use. Requests of this type do not require a subscription or a credit fee. Geocoding Services – The primary purpose of geocoding services is to convert an address to an x,y coordinate and append the result to an existing record in a database. Mapping is not always involved, but placing the results on a map may be part of a workflow. Batch geocoding falls into this category. Geocoding requires a subscription. An ArcGIS Online subscription will provide you access to the World Geocoding service for batch geocoding.The service can be used to find address and places for many countries around the world. For detailed information on this service, including a data coverage map, visit the World Geocoding service documentation.

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.

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 August 2025 release

• Nationally, the August 2025 update of G-NAF shows an overall increase of 40,716 addresses (0.30%). The total number of addresses in G-NAF now stands at 15,794,643 of which 14,950,491 or 94.66% are principal.

• In the ACT, there have been minor updates to the address parsing of flat-numbered addresses aimed at: improving the address representation of flat-numbered addresses; improving address coverage; and improving address alignment between contributors. This change affects approximately 4,000 addresses.

• A small number of additional address sites have implemented the use of the BUILDING_NAME attribute as part of the merge criteria to improve address coverage for flat-numbered addresses in NSW and QLD. These changes have resulted in the creation of approximately 400 addresses in NSW and 120 in QLD.

• A focus has been applied to Tasmanian street-locality addresses to reduce the number of these addresses. For the August 2025 release, there is a reduction of some 900 street-locality addresses in Tasmania.

• 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.

This dataset serves as a lookup table to determine if environmental records exist in a Chicago Department of Public Health (CDPH) environmental dataset for a given address.
Data fields requiring description are detailed below. MAPPED LOCATION: Contains the address, city, state and latitude/longitude coordinates of the facility. In instances where the facility address is a range, the lower number (the value in the “Street Number From” column) is used. For example, for the range address 1000-1005 S Wabash Ave, the Mapped Location would be 1000 S Wabash Ave. The latitude/longitude coordinate is determined through the Chicago Open Data Portal’s geocoding process. Addresses that fail to geocode are assigned the coordinates 41.88415000022252°, -87.63241000012124°.This coordinate is located approximately just south of the intersection of W Randolph and N LaSalle. COMPLAINTS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Complaints dataset. NESHAPS & DEMOLITON NOTICES: A ‘Y’ indicates that one or more records exist in the CDPH Asbestos and Demolition Notification dataset. ENFORCEMENT: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Enforcement dataset. INSPECTIONS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Inspections dataset. PERMITS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Permits dataset. TANKS: A ‘Y’ indicates that one or more records exist in the CDPH Storage Tanks dataset. Each 'Y' is a clickable link that will download the corresponding records in CSV format.

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?

Big “p” policy changes at the state and federal level are certainly important to health equity, such as eligibility for and generosity of Medicaid benefits. Medicaid expansion has significantly expanded the number of people who are eligible for Medicaid and the creation of the health insurance exchanges (Marketplace) under the Affordable Care Act created a very visible avenue through which people can learn that they are eligible. Although many applications are now submitted online, physical access to state, county, and tribal government Medicaid offices still plays a critical role in understanding eligibility, getting help in applying, and navigating required documentation for both initial enrollment and redetermination of eligibility. However, as more government functions have moved online, in-person office locations and/or staff may have been cut to reduce costs, and gentrification has shifted where minoritized, marginalized, and/or low-income populations live, it is unclear if this key local connection point between residents and Medicaid has been maintained. Our objective was to identify and geocode all Medicaid offices in the United States for pairing with other spatial data (e.g., demographics, Medicaid participation, health care use, health outcomes) to investigate policy-relevant research questions. Three coders identified Medicaid office addresses in all 50 states and the District of Columbia by searching state government websites (e.g., Department of Health and Human Services or analogous state agency) during late 2021 and early 2022 for the appropriate Medicaid agency and its office locations, which were then reviewed for accuracy by a fourth coder. Our corpus of Medicaid office addresses was then geocoded using the Census Geocoder from the US Census Bureau (https://geocoding.geo.census.gov/geocoder/) with unresolved addresses investigated and/or manually geocoded using Google Maps. The corpus was updated in August through December 2023 following the end of the COVID-19 public health emergency by a fifth coder as several states closed and/or combined offices during the pandemic. After deduplication (e.g., where multiple counties share a single office) and removal of mailing addresses (e.g., PO Boxes), our dataset includes 3,027 Medicaid office locations. 1 (December 19, 2023) – original version 2 (January 25, 2024) – added related publication (Data in Brief), corrected two records that were missing negative signs in longitude 3 (February 6, 2024) – corrected latitude and longitude for one office (1340 State Route 9, Lake George, NY 12845) 4 (March 4, 2024) – added one office for Vermont after contacting relevant state agency (280 State Road, Waterbury, VT 05671)

Postal codes are part of nearly every administrative and research data set, and postal code conversion using PCCF or PCCF+ and related tools is now the usual way of exploiting their rather impressive potential. The resulting small area geography and/or latitude-longitude coordinates have a wide variety of possible uses, even where individual measures of SES are also available on a data set. Familiarity with the methods (tools and techniques), as well as the strengths and limitations of dealing with postal coded data, will allow data service providers to help users to more meaningfully exploit their potential.

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

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

A Lightning talk on Geocoding overview and options.

An NC OneMap experience builder web app with AddressNC points and address location search using the AddressNC Geocoder. This replaces the similar deprecated web application.

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training dataset and use this model to predict whether a location word in the test dataset accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

Context

Created for use in the Renewable and Appropriate Energy Lab at UC Berkeley and Lawrence Berkeley National Laboratory.

Content

Geography: All 58 Counties of the American State of California

Time period: 2015

Unit of analysis: Tons per year

Variables:

• CO: County ID as numbered in the County dropdown menu on the California Air Resources Board Facility Search Tool
• AB
• FACID
• DIS
• FNAME
• FSTREET
• FCITY
• FZIP
• FSIC: Facility Standard Industrial Classification Code specified by the US Department of Labor
• COID
• DISN
• CHAPIS
• CERR_CODE
• TOGT: Total organic gases consist of all hydrocarbons, i.e. compounds containing hydrogen and carbon with or without other chemical elements.
• ROGT: Reactive organic gases include all the organic gases exclude methane, ethane, acetone, methyl acetate, methylated siloxanes, and number of low molecular weight halogenated organics that have a low rate of reactivity.
• COT: The emissions of CO are for the single species, carbon monoxide.
• NOXT: The emissions of NOx gases (mostly nitric oxide and nitrogen dioxide) are reported as equivalent amounts of NO2.
• SOXT: The emissions of SOx gases (sulfur dioxide and sulfur trioxide) are reported as equivalent amounts of SO2.
• PMT: Particulate matter refers to small solid and liquid particles such as dust, sand, salt spray, metallic and mineral particles, pollen, smoke, mist and acid fumes.
• PM10T: PM10 refers to the fraction of particulate matter with an aerodynamic diameter of 10 micrometer and smaller. These particles are small enough to penetrate the lower respiratory tract.
• PM2.5T: PM2.5 refers to the fraction of particulate matter with an aerodynamic diameter of 2.5 micrometer and smaller. These particles are small enough to penetrate the lower respiratory tract.
• lat: Facility latitude geocoded by inputting FSTREET, FCITY, California FZIP into Bing’s geocoding service.
• lon: Facility longitude geocoded in the same way.

Sources: All columns except for lat and lon were scraped from the California Air Resources Board Facility Search Tool using the Request module from Python’s Urllib library. The script used is included below in scripts in case you would like to get additional columns.

The lat and lon columns were geocoded using the Geocoder library for Python with the Bing provider.

Scripts

download.py

import pandas as pd
out_dir = 'ARB/'
file_ext = '.csv'
for i in range(1, 59):
  facilities = pd.read_csv("https://www.arb.ca.gov/app/emsinv/facinfo/faccrit_output.csv?&dbyr=2015&ab_=&dis_=&co_=" + str(i) + "&fname_=&city_=&sort=FacilityNameA&fzip_=&fsic_=&facid_=&all_fac=C&chapis_only=&CERR=&dd=")
  for index, row in facilities.iterrows():
    curr_facility = pd.read_csv("https://www.arb.ca.gov/app/emsinv/facinfo/facdet_output.csv?&dbyr=2015&ab_=" + str(row['AB']) + "&dis_=" + str(row['DIS']) + "&co_=" + str(row['CO']) + "&fname_=&city_=&sort=C&fzip_=&fsic_=&facid_=" + str(row['FACID']) + "&all_fac=&chapis_only=&CERR=&dd=")
    facilities.set_value(index, 'PM2.5T', curr_facility.loc[curr_facility['POLLUTANT NAME'] == 'PM2.5'].iloc[0]['EMISSIONS_TONS_YR'])
  facilities.to_csv(out_dir + str(i) + file_ext)

geocode.py

import geocoder
import csv
directory = 'ARB/'
outdirectory = 'ARB_OUT/'
for i in range(1, 59):
  with open(directory + str(i) + ".csv", 'rb') as csvfile, open(outdirectory + str(i) + '.csv', 'a') as csvout:
    reader = csv.DictReader(csvfile)
    fieldnames = reader.fieldnames + ['lat'] + ['lon'] # Add new columns
    writer = csv.DictWriter(csvout, fieldnames)
    writer.writeheader()
    for row in reader:
      address = row['FSTREET'] + ', ' + row['FCITY'] + ', California ' + row['FZIP']
      g = geocoder.bing(address, key='API_KEY')
      newrow = dict(row)
      if g.latlng:
        newrow['lat'] = g.json['lat']
        newrow['lon'] = g.json['lng']
        writer.writerow(newrow) # Only write row if successfully geocoded

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. 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.

Analysis of ‘CDPH Environmental Records Lookup Table’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/da7d36e5-3715-4ce1-b173-8df0dcd9c651 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset serves as a lookup table to determine if environmental records exist in a Chicago Department of Public Health (CDPH) environmental dataset for a given address.
Data fields requiring description are detailed below. MAPPED LOCATION: Contains the address, city, state and latitude/longitude coordinates of the facility. In instances where the facility address is a range, the lower number (the value in the “Street Number From” column) is used. For example, for the range address 1000-1005 S Wabash Ave, the Mapped Location would be 1000 S Wabash Ave. The latitude/longitude coordinate is determined through the Chicago Open Data Portal’s geocoding process. Addresses that fail to geocode are assigned the coordinates 41.88415000022252°, -87.63241000012124°.This coordinate is located approximately just south of the intersection of W Randolph and N LaSalle. COMPLAINTS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Complaints dataset. NESHAPS & DEMOLITON NOTICES: A ‘Y’ indicates that one or more records exist in the CDPH Asbestos and Demolition Notification dataset. ENFORCEMENT: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Enforcement dataset. INSPECTIONS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Inspections dataset. PERMITS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Permits dataset. TANKS: A ‘Y’ indicates that one or more records exist in the CDPH Storage Tanks dataset. Each 'Y' is a clickable link that will download the corresponding records in CSV format.

--- Original source retains full ownership of the source dataset ---

The presentation provided an introduction to the PCCF and PCCF+, the uses of small-area data, components of a postal code, SLI geocoding versus population-weighting, pitfalls of automated geocoding, and why one would want to use PCCF+ file.

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.

Data from European cities with results of test for quality addresses data algorithm (paper ISPS IJGI).Addresses data used on this paper are available on these websites:A) OpenAddresses: https://batch.openaddresses.io/dataB) OpenStreetMap: https://wiki.openstreetmap.org/wiki/Downloading_dataC) Google Places: https://developers.google.com/maps/documentation/places/web-service/overview?D) Bing: https://learn.microsoft.com/en-us/bingmaps/rest-services/locations/E) Here: https://developer.here.com/documentation/geocoding-search-api/*NOTES: Due to rights and property reasons, we can not distribute commercial and authoritative addresses data used on this study

The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).

Abstract

A crosswalk table from US postal ZIP codes to geo-points (latitude, longitude)

Documentation

Data source: public.opendatasoft.

The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not).

This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not). This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".

The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.