Spatial refers to space. Temporal refers to time. Spatiotemporal, or spatial temporal, is used in data analysis when data is collected across both space and time. It describes a phenomenon in a certain location and time — for example, shipping movements across a geographic area over time (see above example image).
(a) Under pure spatial variation, factors vary across a spatial transect but are constant from one time period to another. (b) Under pure temporal variation, factors vary from one time to another but are constant across space.
Spatial sampling and quantization of a natural video signal digitizes the image plane into a two dimensional set of digital pixels that define a digital image. Temporal sampling of a natural video signal creates a sequence image frames typically used for motion pictures and television.
Spatial data mining refers to the process of extraction of knowledge, spatial relationships and interesting patterns that are not specifically stored in a spatial database; on the other hand, temporal data mining refers to the process of extraction of knowledge about the occurrence of an event whether they follow, ...
Typical examples of spatiotemporal data mining include discovering the evolutionary history of cities and lands, uncovering weather patterns, predicting earthquakes and hurricanes, and determining global warming trends.
Intuitively, temporal attributes are time data in the real world. But after the temporal attribute has been modeled, it represents the time data in the temporal model.
In a nutshell, spatial resolution refers to the capacity a technique has to tell you exactly which area of the brain is active, while temporal resolution describes its ability to tell you exactly when the activation happened.
Examples of this order can be divided into two categories: spatial and temporal. Examples of spatial order can be found within nature, such as how the eye is perfectly adapted to see things. Temporal order is order of the universe itself, such as how forces, such as gravity, are perfectly suited to sustaining life.
Spatial data is any type of data that directly or indirectly references a specific geographical area or location. Sometimes called geospatial data or geographic information, spatial data can also numerically represent a physical object in a geographic coordinate system.
The spatial frequency refers to how many complete periods the signal goes through for a given unit of distance (eg. cylcles/m) while the temporal frequency refers to how many complete periods the signal goes through for a given unit of time (eg.
Spatial refers to space. Temporal refers to time. Spatiotemporal, or spatial temporal, is used in data analysis when data is collected across both space and time. It describes a phenomenon in a certain location and time — for example, shipping movements across a geographic area over time (see above example image).
Signals, by definition, are varying quantities. They may vary over time (temporal) or over an x−y plane (spatial), over three dimensions, or perhaps over time and space (e.g., a video sequence). Understanding how signals change over time (or space) helps us in several key application areas.
Spatial summation occurs when several weak signals from different locations are converted into a single larger one, while temporal summation converts a rapid series of weak pulses from a single source into one large signal [Note from Ferguson: summation interval ~ 5-100 msec.)
The spatial attributes of the spatial object provide the information with respect to spatial locations, for example, latitude, elevation, longitude, and shape.
Attribute Data. Attribute data are the information linked to the geographic features (spatial data) that describe features. That is, attribute data are the “[n]on- graphic information associated with a point, line, or area elements in a GIS.”
Temporal data is simply data that represents a state in time, such as the land-use patterns of Hong Kong in 1990, or total rainfall in Honolulu on July 1, 2009. Temporal data is collected to analyze weather patterns and other environmental variables, monitor traffic conditions, study demographic trends, and so on.
Spatial–temporal reasoning is an area of artificial intelligence which draws from the fields of computer science, cognitive science, and cognitive psychology. The theoretic goal—on the cognitive side—involves representing and reasoning spatial-temporal knowledge in mind.
There are two basic types of reference locality – temporal and spatial locality. Temporal locality refers to the reuse of specific data and/or resources within a relatively small time duration. Spatial locality (also termed data locality) refers to the use of data elements within relatively close storage locations.
For a geostationary satellite, temporal resolution can be defined as the duration of time between capturing two consecutive images. For example, a geostationary satellite, METEOSAT-11, captures imagery at every 15 minute interval. As a result, its temporal resolution is 15 minutes.
Some movements have to be adapted to temporal characteristics of environmental events. An example is catching or hitting a ball. Perhaps of greatest importance is the timing of movements in playing music. These examples suggest a distinction between extrinsic and intrinsic timing.
Temporal means time. A temporal word is a word that refers to time. Temporal words are used as transitions in writing. Some examples are before, meanwhile, once, yesterday, during, while, and finally.
In practice theory, temporal dimensions incorporate the past, present, and future of temporality and objective time, while spatial dimensions involve the places and paths of spatiality and objective space.
In temporal summation, multiple neurotransmitters are released from one presynaptic terminal while in spatial summation, multiple presynaptic terminals release neurotransmitters to generate a postsynaptic action potential.
Temporal and spatial summation of synaptic input on a neuron underlies the integration of information from diverse sources. The convergence of input and comparison of this input at the neuronal level is the foundation of decision-making.
If you have a color monitor, take a magnifying glass and look close and you will see a whole bunch of red, green, and blue dots which make all that you see on the screen. The animation to the left is another example of spatial summation.