The term ‘geospatial’ refers to interdependent resources like maps, imagery, datasets, tools, and procedures that tie every event, feature, or entity to a location, and use this information for various applications. To easily understand location, data must be represented using standard parameters such as position in a coordinate system, place name, or street address.
A geospatial database is optimized for storing and querying data that represents objects defined in a geometric space, such as vector data and raster data. With data volume growing exponentially, a geospatial database provides the best manageability and security to analyze large, complex, heterogeneous spatial data.
Geospatial database platforms provide specialized management, processing, and analysis engines required for complex geospatial data. The scalability and performance of such systems are two key factors for success, along with providing development and integration support.
For interoperability, geospatial database platforms support standards defined by the Open Geospatial Consortium (OGC), which provide a unified framework and web services—Web Feature Services (WFS) for vector data, Web Coverage Service (WCS) for raster data, and Catalog Services (CSW) used to locate, manage, maintain distributed geospatial data applications and services.
Geographical Information System (GIS) is a tool on top of a geospatial database to edit and maintain geospatial data. GIS support geospatial objects, which are organized in layers that can be overlaid both visually and logically.
Geospatial analysis is about understanding complex interactions based on geographic relationships— answering questions based on where people, assets, and resources are located. Geospatial insights enable users to provide better customer service, optimize workforce, locate retail or distribution centers, manage assets, perform situational analysis, and evaluate sales and marketing campaigns, among many examples.
‘Geospatial data’ refers to information about features, objects, and classes on Earth’s surface or even in space. Geospatial data is typically large, stored in complex data types, and require specialized indexing, querying, processing, and analysis algorithms.
Geospatial data represents:
Geospatial data is made up of geometries and their cartographic representations, called ‘attributes’. Geometries can be points, lines, polygons, and collections of these elements.
These geometries can have attributes like color, line thickness that are cartographic (for display) and other attributes like population (inside of polygons), or items that can be measured or scaled.
Both geometry and attribute data are connected through a relational database management system like Oracle’s spatial database. The database management system can power the most demanding geospatial processes with the highest performance, scalability, and security. They also provide easy integration with other GIS and nonGIS applications, resulting in lower development efforts.
Geospatial raster data is a complex set of information gathered from Landsat satellite enhanced Thematic Mapper (ETM+) sensors, which record light, infrared reflectance value, and their position in the grid. Location data such as color, height of a digital innovation model, and several variables is attached to every grid cell. Examples include thematic maps, digital elevation model/ digital surface model (DEM/DSM), remote sensing (RS) images, photogrammetric photos, scanned maps, geophysical images, and geological maps.
Raster data types are large and have a very different data structure compared to vector data types. Raster data sets can grow very quickly, resulting in huge volumes of geospatial information that require data management systems such as Oracle’s spatial database.
In addition, point clouds are a complex 3D data type created from light detection and ranging (LiDAR) applications. A point cloud refers to a type of geometry for storing large amounts of data that represents a 3D shape or feature. Each point has its own set of X, Y, and Z coordinates along with other attributes. Point clouds are often created by methods used in photogrammetry or remote sensing by LiDAR applications.
The integration of fundamentally different types of data is one of the central tasks of geospatial data analysis. A vital tool in geospatial data analysis is data visualization, through maps. Maps are usually created from remote sensing data—the fields, forests, and more become digitized attributes given to polygons, and are then colored appropriately.
Data categories may include, but are not limited to:
In today’s hyperconnected world, where every object has a digital footprint and is part of a global network, location and location-based information becomes critical for analysis, management, administration, and governance. Location intelligence helps us to know where events, activities, individuals, streets, or buildings are, enabling us to develop applications that track location of objects of interest. They have a wide application in many private and public sector organizations, for a variety of functions, such as:
Enchance customer experience with targeted marketing, site planning, indoor customer flow with location intelligence
Discover risk zones and other patterns based on customer location data analysis and customize offers based on this intelligence
Optimize workflows and reduce costs for mobile network planning, utilities facility management for cell tower placement
Improve planning care while tracking disease outbreak patterns, epicenters, exposures, and environmental impact based on location
Increase competitiveness by efficiently analyzing outages and effectively planning field services
Improve operational efficiency by processing large volumes of complex heterogenous spatial data for maintaining railway assets, airport assets, air traffic, long-haul trucking, and parcel delivery
Enhance customer experience by combining GIS and CAD systems for Building Information Modeling (BIM) and facilities management, connecting workflows, eliminating data silos, and providing location context
Enable governing entities to analyze national or local datasets for digital battlefield and surveillance, contact tracing, crime mapping, predictive policing, and emergency services
World’s first self-driving database
World's leading converged, multi-model database management system
Unmatched Oracle Database performance, scale, and availability
Data warehousing without complexity