In GIS development, common spatial databases include:
1. PostgreSQL/PostGIS：
PostgreSQL is an open source object-relational database management system, and PostGIS is a spatial database extension for PostgreSQL that can be used to store, query, and analyze spatial data.
2. Oracle Spatial：
Oracle Spatial is an extension of Oracle database that provides functions for storing and querying spatial data, including geometric object types, spatial indexes, and spatial query operations.
3. SQL Server Spatial：
SQL Server Spatial is an extension of the Microsoft SQL Server database. It provides functions for storing and querying spatial data, including geometric object types, spatial indexes, and spatial query operations.
4. SQLite/SpatiaLite：
SQLite is a lightweight embedded relational database management system, while SpatiaLite is a spatial database extension for SQLite that can be used to store, query and analyze spatial data.
5. MongoDB：
MongoDB is a NoSQL database that can store various types of data, including spatial data. MongoDB stores spatial data in GeoJSON format and provides some spatial query operations.
6. MySQL Spatial：
MySQL Spatial is an extension of the MySQL database. It provides functions for storing and querying spatial data, including geometric object types, spatial indexes, and spatial query operations.
Each database has its unique advantages and applicable scenarios. Developers can choose the appropriate spatial database according to specific needs.
The following are their application scenarios and cases:
1. PostgreSQL/PostGIS:
- Application scenarios: Suitable for projects that require storage and processing of spatial data in an open source environment, such as WebGIS application development, geographic data analysis, etc.
- Case: OpenStreetMap uses PostgreSQL/PostGIS as a data storage and analysis tool, supporting map editing, route planning and other functions.
2. Oracle Spatial:
- Application scenarios: Suitable for projects that process large-scale spatial data in enterprise-level environments, such as telecommunications network planning, urban planning, etc.
- Case: Oracle Spatial is widely used in network planning and optimization of telecom operators, supporting base station site selection analysis, signal coverage analysis, etc.
3. SQL Server Spatial:
- Application scenarios: Applicable to projects under the Microsoft technology ecosystem, such as .NET application development, GIS applications integrated with Microsoft SQL Server, etc.
- Case: ESRI ArcGIS uses SQL Server Spatial as the data storage and processing engine to support geographic information system applications with ArcGIS as the core.
4. SQLite/SpatiaLite:
- Application scenario: Suitable for spatial data management and query in lightweight, stand-alone environment, such as mobile applications, desktop applications, etc.
- Case: A mobile map application that uses SQLite/SpatiaLite to store and query geographic data, supporting offline map viewing and track recording functions.
5. MongoDB:
- Application scenarios: Suitable for projects that need to process large-scale spatial data, such as the Internet of Things, location services, etc.
- Case: Uber uses MongoDB to store and query spatial data, supporting real-time location tracking, trip planning and other functions.
6. MySQL Spatial:
- Application scenarios: Suitable for projects that need to process simple spatial data in the MySQL environment, such as small web applications, lightweight GIS applications, etc.
- Case: Google Earth uses MySQL Spatial to store and query geographic data, supporting three-dimensional earth visualization and geographic information display.
According to the specific project requirements, technical environment and scale, an appropriate spatial database can be selected for storage, query and analysis of spatial data. The above cases are only some examples. Actual applications may have more choices and customization requirements depending on the specific situation.