Representation of spatial and temporal data

Abstract Kenya is faced with a rising demand in electricity resulting from a rapidly growing economy and an increasing population. Being a tropical country, lying astride the equator, solar energy is one of the readily available renewable energy resource options to meet this need. Unfortunately, there is still very low adoption of solar systems in the country which could be majorly attributed to lack of adequate solar resource assessment.

Representation of spatial and temporal data

First, the domain of w3cgeo: Point is defined as a sub-class of w3cgeo: As a result, we have inconsistency in how w3cgeo: SpatialThing may be interpreted.

NetCDF Climate and Forecast (CF) Metadata Conventions

Geometry ; Because foaf: Person is defined as a sub-class of w3cgeo: SpatialThingsome other people find it natural to equate w3cgeo: Featureand that it is not the same as w3cgeo: However, in some applications it is more useful to describe the variation of property values in space and time.

Such descriptions are formalized as coverages. Users of spatial information may employ both viewpoints. So what is a coverage? As defined by [ ISO ] it is simply a data structure that maps points in space and time to property values. For example, an aerial photograph can be thought of as a coverage that maps positions on the ground to colors.

A river gauge maps points in time to flow values. A weather forecast maps points in space and time to values of temperature, wind speed, humidity and so forth. One way to think of a coverage is as a mathematical function, where data values are a function of coordinates in space and time.

You can see from the above paragraph that non-gridded data like a river gauge measurement can also be modelled as coverages. Nevertheless, you will often find a bias toward gridded data in discussions and software that concern coverages.

Although the definition above presents a coverage as a data structure, conceptually it still has spatial extent. Similarly, we might say in the hydrology example, where a river gauge measures flow values at regular sampling times, the spatial extent would be the monitoring point where the river gauge is positioned.

We say that a coverage is really just a special type of Spatial Thing with some particular properties. Spatial Things and coverages may be related in several ways: As the property value of a Spatial Thing whose value varies within the extent of that Spatial Thing; for example, the varying strength of mobile-network coverage throughout the UK.

The values of a common property for a distributed set of Spatial Things provide a discrete sampling of a coverage; for example, the measurement of soil moisture based at a set of sampling stations can be compiled to show the spatial variation of soil moisture across the region where the sampling stations are located.

A coverage can be defined using three main pieces of information: The domain of the coverage is the set of points in space and time for which we have data values. For example, in a river gauge measurement, the domain is the set of times at which the flow was measured. In a satellite image, the domain is the set of pixels.

In a weather forecast, the domain is a set of grid cells. The range of the coverage is the set of measured, simulated or observed data values. A single coverage may record values for lots of different quantities; for example, a weather forecast predicts values for many things temperature, humidity etc.

So the range of a coverage often consists of several lists of data values, one for each measured variable. Each element within each list corresponds with one of the elements of the domain e. The range metadata describes the range of the coverage, to help users to understand what the data values mean.

This may include links to definitions of variables, units of measure and other bits of useful information. Usually, the most complex piece of information in the coverage is the definition of the domain. This can vary quite widely from coverage type to coverage type, as the list above shows.temporal data models facilitate visualization and analysis of dynamic attributes and features deļ¬ned with spatial and temporal extension.

However, even with inclusion of temporal referencing or time-based. Arthur Schopenhauer was among the first 19 th century philosophers to contend that at its core, the universe is not a rational place.

Inspired by Plato and Kant, both of whom regarded the world as being more amenable to reason, Schopenhauer developed their philosophies into an instinct-recognizing and ultimately ascetic outlook, emphasizing . Temporal characterization occurs when you have a series of images taken at different time.

Correlations between the images are often used to monitor the dynamic changes of the object. The representation of spatial data, especially the representation of spatiotemporal data, is the foundation for many spatial analysis routines.

However, it is largely ignored in cyberinfrastructure research. Geographic Information Science and Technology Body of Knowledge First Edition Edited by David DiBiase, Michael DeMers, Ann Johnson, Karen Kemp.

Representation of spatial and temporal data

A spatial database is a database that is optimized for storing and querying data that represents objects defined in a geometric space. Most spatial databases allow the representation of simple geometric objects such as points, lines and polygons.

Some spatial databases handle more complex structures such as 3D objects, topological .

Spatial database - Wikipedia