A Geodatabase is a proprietary GIS file format developed in the late 1990s by Esri (a GIS software vendor) to represent, store, and organize spatial datasets within a geographic information system. A geodatabase is both a logical data model and the physical implementation of that logical model in several proprietary file formats released during the 2000s. The geodatabase design is based on the spatial database model for storing spatial data in relational and object-relational databases. Given the dominance of Esri in the GIS industry, the term "geodatabase" is used by some as a generic trademark for any spatial database, regardless of platform or design.
History The origin of the geodatabase was in the mid-1990s during the emergence of the first spatial databases. One early approach to integrating relational databases and GIS was the use of server middleware, a third-party program that stores the spatial data in database tables in a custom format, and translates it dynamically into a logical model that can be understood by the client software. In 1996, Esri purchased an early middleware product called Spatial DataBase Engine and rebranded it ArcSDE. Initially, ArcSDE stored and delivered simple vector datasets that looked very similar to shapefiles, but the need for a more robust data model emerged as Esri's Shapefile format became a de facto standard for vector spatial data, even as its shortcomings limited its use in enterprise applications. At the same time, the Arc/INFO coverage format was becoming obsolete after 20 years, unable to handle growing expectations of GIS users. Another motivating factor was that even though several relational database vendors were introducing their own spatial extensions (with the notable exception of Esri's preferred Microsoft SQL Server), their structures and interfaces varied and Esri wanted its users to see all spatial data in the same apparent structure regardless of how it was stored internally. At the end of 1999, Esri introduced the Geodatabase model as the native format used in its new ArcGIS software (branded Version 8.0 to maintain continuity with Arc/INFO). Initially, it could be implemented as a multiuser geodatabase in ArcSDE on a server or the personal geodatabase locally. Support for topology rules, linear referencing, and survey data were added in 2003 (with ArcGIS 8.3). Network data was added to the geodatabase in 2005 (ArcGIS 9.1), and vector terrain (TIN, LIDAR) in 2006 (ArcGIS 9.2). Also at the 9.2 release, ArcSDE was subsumed into ArcGIS Server and the multiuser database format was rebranded the enterprise geodatabase. Due to shortcomings in the personal geodatabase format (especially file size limitations in Microsoft Access), Esri developed a more robust custom file format, released in 2006 (ArcGIS 9.2) as the file geodatabase. It also released a product called the workgroup geodatabase that included the free Microsoft SQL Server Express for smaller multi-user applications, which has since been discontinued. Eventually, the middleware components for reading and writing the geodatabase spatial database structure were incorporated into ArcGIS desktop, eliminating the need for ArcSDE to be running on the server end. The most recent addition has been the mobile geodatabase format in 2020 (ArcGIS Pro 2.7), which uses SQLite as the backend to store the entire geodatabase as a single file. This replaces the personal geodatabase, which is no longer supported.
Applications Geodatabases, being a common format for GIS datasets, have applications anywhere GIS are widely employed. These applications are so basic oftentimes researchers do not mention their use in studies. There are several fields where their use is extensively documented, including public health, crime analysis, and resource management.
Epidemiology Since John Snow famously identified the source of a cholera outbreak, spatial data has been central to epidemiology and public health. In recent years, information that is relevant to public health has increased exponentially. Leveraged correctly, this data can allow for a rapid response to emerging diseases. To accomplish this, geodatabases are employed extensively to organize data and allow for the identification of space-time patterns. Examples of the use of geodatabase to manage epidemiological data include linking environmental and health data to find patterns. They were used extensively to organize data related to West Nile virus epidemics, and the COVID-19 pandemic. This use includes analyzing misinformation, and the infodemic, surrounding COVID-19.
Resources management Geospatial data around resource management plays is extremely complex. Factors such as the forest, water, and mineral resources being managed are obvious; however, governance and socioeconomic factors also play a large role. It is common practice to employ geodatabases to manage these diverse datasets. They have also been used in organized Early Detection Rapid Response (EDRR) efforts to treat invasive plant species to protect environmental resources.
TIGER files In 1995 The United States Census Bureau made the Topologically Integrated Geographic Encoding and Referencing, or TIGER, Mapping Service available to the public, facilitating desktop and Web GIS by hosting US boundary data. This data availability, facilitated through the internet, silently revolutionized cartography by providing the world with authoritative boundary files, for free. Today, these files, which contain up-to-date boundaries for the United States states, counties, and more, are provided to the public in prepackaged geodatabases.
Logical Model To the user, a geodatabase looks like a collection of datasets, including some containing geographic data and some auxiliary elements that add functionality to the data. This user view is identical, regardless of how the geodatabase is stored (although enterprise geodatabases add some functions).
Datasets Datasets contain geographic data. A geodatabase can contain spatially referenced data in vector or raster formats, or non-spatially referenced data in tabular format. Each dataset contains information about any number of individual items, but typically all of the items in a dataset are of the same theme (e.g., temperature measurements, roads in a city) and have the same set of properties.
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