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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]
Spatiotemporal patterns are patterns that occur in a wide range of natural phenoma and are characterized by a spatial and temporal patterning. The general rules of pattern formation hold. In contrast to "static", pure spatial patterns, the full complexity of spatiotemporal patterns can only be recognized over time.
Satellites and aircraft collect digital spatial data acquired from remotely sensed images which can be used in machine learning. They are sometimes hard to analyse using basic image analysis methods and convolutional neural networks can be used to acquire an embedding of images bound to a given geographical object or a region.
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The Modified Temporal Unit Problem (MTUP) is a source of statistical bias that occurs in time series and spatial analysis when using temporal data that has been aggregated into temporal units. [ 1 ] [ 2 ] In such cases, choosing a temporal unit (e.g., days, months, years) can affect the analysis results and lead to inconsistencies or errors in ...