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.

Geocoding services allow users to find discrete point locations from address data. The FirstMap geocoder searches multiple sources of data, including e911 and road centerline data that are provided by each county, to find the closest match.

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 May 2025 release

• Nationally, the May 2025 update of G-NAF shows an overall increase of 47,194 addresses (0.30%). The total number of addresses in G-NAF now stands at 15,753,927 of which 14,909,770 or 94.64% are principal.

• At some locations, there are unit-numbered addresses that appear to be duplicate addresses. Geoscape is working to identify these locations and include these addresses as separate addresses in G-NAF. To facilitate this process, some secondary addresses have had the word RETAIL added to their building names. In the first instance, this process is being progressively rolled out to identified locations, but it is expected that the requirement for this will become ongoing.

• There is one new locality in G-NAF: Keswick Island, QLD.

• The source data used for generating G-NAF STREET_LOCALITY_POINT data in New South Wales has an updated datum and changed from GDA94 to GDA2020. This has resulted in updates to the STREET_LOCALITY_POINT geometry for approximately 91,000 records, however, more than 95% of these have moved less than a metre.

• 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.

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.

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

Open Postcode Geo is a postcode dataset and API optimised for geocoding applications. You can use Open Postcode Geo to geocode a dataset, geocode user input, and therefore build a proximity search.

Data is derived from the ONS (Office for National Statistics) postcode database and is free to use, subject to including attributions to ONS, OS (Ordinance Survey) and Royal Mail.

Information is also provided on a range of topics, including education, health, crime, business, etc.

Postcodes can be entered at area, district, sector, and unit level - see Postcode map for the geographical relationship between these.

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.

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

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.

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.

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)

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

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.

The Property Location Service Plus- Public version (PLSplus-Public) is available to anyone and used to validate Queensland addresses and property descriptions. The service can also supply location data from a geocode. The service does not offer interstate (non Queensland) address validation information. The service is free of charge and requires authentication. The service versions available are Soap and REST. To use the PLSplus-Public soap service an application utilising a file called a "WSDL" (Web Service Description Language) is required. The WSDL is an XML-based language that is used for describing the functionality offered by a web service.The WSDL description provides a machine-readable description of how the service can be called, what parameters it expects, and what data structures it returns. The wsdl is available (with service guidelines) at the online resources link of this record. The wsdl contains the required endpoint for the service. PLSplus-Public now requires authentication to use it but the wsdl can be viewed at https://information.qld.gov.au/service/Addressing/ValidationService/PLSplusPublic/soap?wsdl

REST uri's and json examples for each function are available in the online resources zip file.

Support is only available to authenticated users. Instructions to apply for username and password are contained in the Information document found at the online resources link.PLSplus-Public provides the following operations:- ValidateAddress- ValidateLotPlan- ValidateCoordinates- ParseAddress and AutoCompleteAddressA successful validation of any of the first four operations (for Queensland locations) will also return other information, such as address, lot on plan, coordinates, address type and a matching confidence level for each request.The ValidateAddress operation validates the existence of a requested address. The address elements are in a rigid structure based around Australian Addressing Standards. If an identical address is found it returns all information about the address with a matching confidence. If no exact match is found it returns possible candidate addresses (up to 500) that best match the search criteria. A global confidence is given for all candidates. The requested elements of the address is parsed by the application.The ValidateAddress operation also geocodes the address and returns the geocoded coordinates.- The only field that is mandatory in the request is the street name or property name- Street Name can consist of a "street name" or "street name" and "street type",or "street name" and "street type" and "street suffix".- McCaul- McCaul Street- McCaul Street NorthThe ValidateLotPlan operation validates the existence of a Queensland "Lot on Plan" description and also returns the addresses and geocodes (if the parcel has them) for valid parcels. The requested Lot on Plan elements must be in a rigid structure to be parsed by the application. It should be noted that not all valid property descriptions have addresses and some parcels may have multiple addresses.The ValidateCoordinates operation provides a service to validate entered Latitude/Longitude coordinates within Queensland and return the closest addresses for the requested location. Search logic begins with a radius of of up to 250 metres. If no result is found the service locates the parcel or the coordinates and returns all addresses on that parcel. The coordinates elements is a rigid structure to be parsed by the application. Coordinates (in Degree.Decmial format ) with search box limited to - North Latitude: -9.0 West Longitude: 138.0- South Latitude: -29.5 East Longitude:155.0 - Coordinates will always be a negative Latitude and a positive Longitude.The AutoCompleteAddress operation returns up to 50 valid addresses based on the input of a text string. The longer the string typed the shorter the list of candidate addresses. Any address from returned candidates can be used as input to ParseAddress operation to return further information about the address. The ParseAddress operation provides a service to parse (into address components) and validate any single line Queensland address and return any associated addresses, lot on plans, geocode and confidence for a requested address. eg. Unit 1 25B Smith Street East Earlsville Qld

This dataset is no longer maintained. Please use https://data.cityofchicago.org/stories/s/CDPH-Environmental-Datasets-Search/gskz-7zdj instead.

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.

The MAR is a SOAP web service with multiple geocoding and address verification operations. This page includes information and examples for using the MAR to look up information about locations within DC through a code based querying interfaces.

The ArcGIS 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, or ArcGIS Location Platform Subscription, will provide you access to the ArcGIS 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 ArcGIS World Geocoding service documentation.

Instructions (with screenshots) to replicate results from Section 3 of the manuscript are available in "Step-by-step Instructions to Replicate Results.pdf".-------------------------------------------------------------------------------------------------------------------Step 1: Download the replication materialsDownload the whole replication folder on figshare containing the code, data and replication files.Step 2: Replicate Tables in Section 3All of the data is available inside the sub-folder replication/Data. To replicate Tables 1 and 2 from section 3 of the manuscript run the Python file replicate_section3_tables.py locally on your computer. This will produce two .csv files containing Tables 1 and 2 (already provided). Note that it is not necessary to run the code in order to replicate the tables. The output data needed for replication is provided.Step 3: Replicate Figures in QGISThe Figures must be replicated using QGIS, freely available at https://www.qgis.org/. Open the QGIS project replicate_figures.qgz inside the replication/Replicate Figures sub-folder. It should auto-find the layer data. The Figures are replicated as layers in the project. Step 4: Running the code from scratchThe accompanying code for the manuscript IJGIS-2024-1305, entitled "Route-based Geocoding of Traffic Congestion-Related Social Media Texts on a Complex Network" runs on Google Colab as Python notebooks. Please follow the instructions below to run the entire geocoder and network mapper from scratch. The expected running time is of the order of 10 hours on free tier Google Colab. 4a) Upload to Google DriveUpload the entire replication folder to your Google Drive. Note the path (location) to which you have uploaded it. There are two Google Colab notebooks that need to be executed in their entirety. These are Code/Geocoder/The_Geocoder.ipynb and Code/Complex_Network/Complex_network_code.ipynb. They need to be run in order (Geocoder first and Complex Network second). 4b) Set the path In each Google Colab notebook, you have to set the variable called “REPL_PATH” to the location on your Google Drive where you uploaded the replication folder. Include the replication folder in the path. For example "/content/drive/MyDrive/replication"4c) Run the codeThe code is available in two sub-folders, replication/Code/Geocoder and replication/Code/Complex_Network. You may simply open the Google Colab notebooks inside each folder, mount your Google Drive, set the path and run all cells.

The datasets presented here enable historical longitudinal studies of micro-level geographic factors in a rural setting. These types of datasets are new, as historical demography studies have generally failed to properly include the micro-level geographic factors. Our datasets describe the geography over five Swedish rural parishes and a geocoded population (at the property unit level) for this area for the time period 1813-1914. The population is a subset of the Scanian Economic Demographic Database (SEDD). The geographic information includes the following feature types: property units, wetlands, buildings, roads and railroads. The property units and wetlands are stored in object-lifeline time representations (information about creation, changes and ends of objects are recorded in time), whereas the other feature types are stored as snapshots in time. Thus, the datasets present one of the first opportunities to study historical spatio-temporal patterns at the micro-level.