Spatial data
Spatial data describes the location, shape, or spatial relationships of features. It can represent discrete objects, continuous fields, or networks.
Geographic coordinate system: A coordinate system that locates positions on Earth using latitude and longitude. It provides a common way to encode locations on the Earth's surface.
Spatial database: A database designed to store, index, and query data with spatial properties. It manages spatial features and supports location-based queries.
Geographic information system: A system for capturing, managing, analyzing, and displaying geographically referenced information. GIS brings spatial data together for analysis, editing, and visualization.
Spatial data quality: The degree to which spatial data are accurate, complete, consistent, current, and fit for use. Errors in location, scale, or attributes can distort spatial conclusions.
Map projection: A mathematical transformation that represents locations on a curved surface in a plane. It explains how Earth coordinates become usable in flat maps.
R-tree: A tree data structure that indexes spatial objects by bounding rectangles. Its bounding-box hierarchy speeds searches for nearby or intersecting features.
Remote sensing: The acquisition of information about places or objects without direct physical contact, often using satellites or aircraft. Satellite and airborne sensors produce spatial datasets of Earth's surface.
Spatial uncertainty: The lack of exactness or confidence in spatial measurements, boundaries, or representations. Coordinates and mapped boundaries often have uncertainty that affects interpretation.
Vector data: Spatial data represented as points, lines, and polygons with coordinates. It models discrete features such as buildings, roads, and administrative boundaries.
Spatial join: A database operation that combines records according to a spatial relationship. It links features based on relations such as containment, overlap, or proximity.