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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 ).
Geospatial metadata (also geographic metadata) is a type of metadata applicable to geographic data and information.Such objects may be stored in a geographic information system (GIS) or may simply be documents, data-sets, images or other objects, services, or related items that exist in some other native environment but whose features may be appropriate to describe in a (geographic) metadata ...
Full integration of spatial data analysis tools with the R: classes for spatial data; handling spatial data; reading and writing spatial data; point pattern analysis; geostatistics; disease mapping and areal data analysis; spatial regression and ecological analysis. R GPL-2: Google Earth: yes Linux, MAC OS, Windows: Google: Google Earth: 3D ...
Time geography or time-space geography is an evolving transdisciplinary perspective on spatial and temporal processes and events such as social interaction, ecological interaction, social and environmental change, and biographies of individuals. [1]
Qualified Dublin Core is often used with a "dot syntax", with a period separating the element and the qualifier(s). This is shown in this excerpted example provided by Chan and Hodges: [11] Title: D-Lib Magazine Title.alternative: Digital Library Magazine Identifier.ISSN: 1082-9873 Publisher: Corporation for National Research Initiatives
SaTScan can also be adapted and applied to certain astronomical studies, particularly those that involve analyzing spatial and temporal patterns in astronomical data. [ 2 ] [ 14 ] For example, SaTScan could identify clustering patterns in the distribution of galaxies or other astronomical objects, such as stars.
Because the world is much more complex than can be represented in a computer, all geospatial data are incomplete approximations of the world. [9] Thus, most geospatial data models encode some form of strategy for collecting a finite sample of an often infinite domain, and a structure to organize the sample in such a way as to enable interpolation of the nature of the unsampled portion.
There are five types of resolution when discussing satellite imagery in remote sensing: spatial, spectral, temporal, radiometric and geometric. Campbell (2002) [ 6 ] defines these as follows: Spatial resolution is defined as the pixel size of an image representing the size of the surface area (i.e. m 2 ) being measured on the ground, determined ...