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A spatial data infrastructure (SDI), also called geospatial data infrastructure, [1] is a data infrastructure implementing a framework of geographic data, metadata, users and tools that are interactively connected in order to use spatial data in an efficient and flexible way.
There are also many different types of geodata, including vector files, raster files, geographic databases, web files, and multi-temporal data. Spatial data or spatial information is broader class of data whose geometry is relevant but it is not necessarily georeferenced, such as in computer-aided design (CAD), see geometric modeling.
GIS data acquisition includes several methods for gathering spatial data into a GIS database, which can be grouped into three categories: primary data capture, the direct measurement phenomena in the field (e.g., remote sensing, the global positioning system); secondary data capture, the extraction of information from existing sources that are ...
The field develops software and web services to model and analyse spatial data, serving the needs of geosciences and related scientific and engineering disciplines. The term is often used interchangeably with Geomatics , although the two have distinct focuses; Geomatics emphasizes acquiring spatial knowledge and leveraging information systems ...
Geographic information science (GIScience, GISc) or geoinformation science is a scientific discipline at the crossroads of computational science, social science, and natural science that studies geographic information, including how it represents phenomena in the real world, how it represents the way humans understand the world, and how it can be captured, organized, and analyzed.
Aerial photography, land cover data, and digital elevation models all provide coverage data. Generally, a coverage can be multi-dimensional, such as 1-D sensor timeseries, 2-D satellite images, 3-D x/y/t image time series or x/y/z geo tomograms , or 4-D x/y/z/t climate and ocean data.
A spatiotemporal database embodies spatial, temporal, and spatiotemporal database concepts, and captures spatial and temporal aspects of data and deals with: Geometry changing over time and/or Location of objects moving over invariant geometry (known variously as moving objects databases [ 1 ] or real-time locating systems ).
The transformation phase of a spatial ETL process allows a variety of functions; some of these are similar to standard ETL, but some are unique to spatial data. [3] Spatial data commonly consists of a geographic element and related attribute data; therefore spatial ETL transformations are often described as being either geometric transformations – transformation of the geographic element ...